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Data Science Powers Innovation

The Disruptive Nature of Data Analytics

Disruptive innovation in an industry – where a company with fewer resources takes on established businesses – has the potential to change the landscape and bring new opportunities forward.

Data science presents context and insights to drive disruptive innovation. In the medical industry, understanding data around percentages of medical conditions treated in medical clinics led to the disruptive rise of retail medical clinics to address low-end market needs. Similar opportunities for low-end disruption and new market disruption exist in spades in agriculture, too!

Data science is a space where various scientific, technological, and socioeconomic factors interconnect. These factors can include data and data source availability, high performance analytical processing, open-source analytics, applications that are business and market-ready, and societal global challenge focus. Data science can also be described as the space where analytics, statistical modeling, data mining, artificial intelligence, and monitoring systems intersect. 

Data science isn’t a new concept. It’s been around since the 1960s when a mathematician predicted an empirical science would be born from modern-day electronic computing. Once the personal computer hit the market and households around the world, data science began to take shape, though data was mostly just collected without conversion into insights. Rapid growth in the data science space occurred in the early 2000s with the rise of AI, deep learning, and machine learning applications. 

This 50-year evolution has led to changes in almost every industry worldwide. From personalized healthcare recommendations to route-optimization models that minimize traffic and optimize shipping routes to digital ad targeting, data has created opportunities to see things differently. This has also resulted in innovation in agriculture. 

Innovation in Agriculture

Innovation in agriculture through data science opens the door for new products, services, technology, and even business models. Innovation can take shape in two different ways: identifying an improved solution to address an existing problem, or redefining a known existing problem to make it more meaningful, and then finding a method to solve it. Both types of innovation are powerful, though usually start from different places. 

Innovation around a known problem usually mandates strong expertise and a focused view of an area. Reframing an existing problem requires a holistic view of related spaces to the known challenge. Opportunities for both innovation types exist in agriculture. 

Agriculture has always been an innovative industry. From solving the problems of sticky, midwestern soil with the self-scouring plow to a unique barbed wire that finally found a way to keep cattle contained, agriculturalists have been known to think outside the box to solve well-known problems in new ways. 

In recent years, agriculture innovation has been fueled by data science. For example, GPS system integration into farm machinery has made tillage, planting, spraying, and harvesting more accurate. This technology has taken over the industry, saving inputs by reducing pass-to-pass overlap and allowing effective, environmentally-friendly production practices that require precision – like strip-till or side dressing – to take hold in mainstream row-crop agriculture. 

When farmers have data-based prescriptions for seeding population or nutrient applications and GPS systems that place machinery in just the right place, it’s possible to adjust input rates at a sub-field level. This reduces inputs overall and increases their effectiveness. 

Satellite imagery is another example of innovation in agriculture. Field characteristics can be understood from satellite images, and fields can be broken into unique management zones for more precise management. Seeding population and nutrient application strategies can be set for sub-field areas defined by satellite image views of the bare soil. This increased precision through data science helps farmers to understand their land and adjust management to only use what inputs are truly needed – a win-win for the business and the environment.  

Farmers and agronomists have even taken to the skies with innovation in agriculture. Drones have become another tool for monitoring crop health and disease pressure across many acres. Data collected from drones can be used to make decisions on fungicide or nutrient applications for growing crops to maximize yield and profit. This new view and new data help farmers to make decisions with important economic and environmental sustainability impact. 

These innovations are just the start of using data science in agriculture for increased productivity and sustainability. Artificial intelligence, machine learning, and deep learning are all part of the technology suite that’s driving agriculture forward. 

This aligns with Food and Agriculture Organization’s efforts to increase farming sustainability. Their 2030 Agenda for Sustainable Development calls for enhanced cooperation and knowledge sharing to improve access to technology and innovation. Ultimately, this supports the objective of ending poverty and building economic growth while responding to climate change. 

Specifically for agriculture, protecting the environment and natural resources is key. The FAO is focused on innovation with a systems approach and addressing smallholder farmer needs. Scaling innovation in agriculture requires coordination across the industry, sectors, and partners, including both public and private sector stakeholders. 

Agmatix Fuels Data Science and Agriculture Innovation

Agmatix, a global agro informatics company, is creating a world where high-quality and standardized agronomic data is freely available. This supports ag professionals globally to overcome obstacles in improving sustainable food production and quality in alignment with the FAO Agenda for Sustainable Development. 

Agmatix’s work is data-driven and cutting-edge. We have a constant state of mind to improve, grow, and learn. We strive to change the world for the better with the best solutions for agriculture. That starts with open-source, high-quality, standardized data.

Agmatix’s data science and agriculture innovations include Axiom technology, Agricultural Trial Management, and the Digital Crop Advisor solutions. These data-driven tools unlock impactful insights to empower decisions on the farm. 

Axiom is a disruptive technology that standardizes and harmonizes data from various sources. The GUARDS (Growing Universal Agronomic Data Standard) protocol is based on the FAIR data principles with a specific focus on data interoperability and reusability. This includes standardized definitions with a unique bottom-up approach, leveraging ML capabilities, and unit conversion. Automatic agricultural data ingestion allows Axiom to digest data at scale, even from multiple sources, with a low cycle time and high integrity. 

Agricultural Trial Management is designed to improve productivity and efficiency while increasing the quality of field trial data. End-to-end capabilities give visibility and control throughout the entire field trial. The mobile application enables seamless in field data collection and management for instant data validation and analysis. Collaboration across all stakeholders – from trial operators and researchers to farmers – is easy, especially with field trial data that’s easily accessed, shared, and understood. Quick, data-driven decisions are possible when field trials and field trial data is well-managed. 

Digital Crop Advisor is an agriculture data science application that optimizes crop production and creates nutrient prescription plans in an easy-to-use dashboard. Big data insights help quantify and compare sustainability KPIs such as carbon footprint and nitrogen leaching. It’s even possible to compare tradeoffs between productivity and environmental impact to make educated decisions about crop nutrition. This innovative tool supports agronomy teams in addressing sustainable agriculture at any scale. 

Agmatix’s data science-based solutions support agronomists, researchers, companies, and farmers in making a difference in agriculture and in the world. Supported by open source, high-quality data, Agmatix is determined to support sustainability. 

Moving Forward 

Data science will prove to be a key part of innovation related to the future of agriculture. When it comes to food production and sustainability, data science will fuel new approaches to balancing productivity and environmental friendliness. Some of that innovation is taking place now – and Agmatix is one example of how data science applications in agriculture illuminate a better future. 

Agmatix works to leverage past and current data to help you make the best decisions  – whether that’s tackling a known problem in a new way or redefining a problem to make it more meaningful when solved. Opportunity will be unlocked through data science in agriculture. Take the next step in your innovation with Agmatix. 

The importance of digital tools in modern agriculture to successfully implement the 4R approach

Agriculture has a huge job: providing the food, fuel, and fiber needed to sustain the world’s population. Few industries have such a tremendous influence on the day-to-day lives of every person. Today, that means feeding just over 8 billion people. By 2050, farmers around the world will be feeding nearly 10 billion people while managing limited arable land and declining natural resource availability.  

In order to achieve this, minimizing environmental impacts from agriculture is critical. Increasing the productivity of agricultural land will also be required. These two concepts don’t always go hand-in-hand; but through proper management and agriculture technologies, it will be possible for farmers to both care for the Earth through sustainable production and increase production to meet growing demand. 

One clear way to achieve optimal crop production while simultaneously minimizing environmental impact is through the 4R nutrient management strategy

4R Nutrient Strategy 

The 4R nutrient strategy, which stands for the right source, right rate, right time, and right place, is a framework for optimizing the use of fertilizers in modern agriculture. The 4R approach aims to maximize the efficiency of fertilizers by ensuring that the right type of fertilizer is applied at the right rate, at the right time, and in the right place. Oftentimes, these factors are interconnected and may need to be addressed together. 

Right Source

The right source of fertilizers means selecting the appropriate type of fertilizer for the specific needs of the crops being grown. Considerations may include: 

  • Nutrient composition of the soil
  • Specific nutrient requirements of the crops
  • Fertilizer nutrient availability for delayed or immediate uptake
  • Combinations of fertilizers 
  • Economic and environmental constraints 

For example, a field with high levels of phosphorus may not need additional phosphorus. A legume crop that biologically fixes nitrogen may need less supplemental nitrogen throughout the growing season.

Right Rate

The right rate of fertilizers refers to applying the appropriate amount of fertilizers based on the needs of the crops and the characteristics of the soil. Considering the right rate means thinking through: 

  • Crop nutrient demand
  • Soil nutrient analysis 
  • Application equipment capabilities and precision 
  • Crop yield goals 

This is important because: 

  • The inappropriate application of fertilizers can result in nutrient runoff and pollution
  • Under-applying fertilizers can lead to reduced crop yields
  • Carefully following recommended application rates for fertilizers ensures that they are being used efficiently and effectively

Right Time

The right time to apply fertilizers is also critical. Application at the wrong time can result in nutrient losses or reduced crop yields. The right time means understanding:

  • Soil structure and ability to retain nutrients 
  • The possibility for climate-driven nutrient loss
  • Crop type and nutrient needs throughout the growth cycle 

The right timing strikes the balance of best nutrient uptake and minimal nutrient loss. For example, applying fertilizers when the soil is too dry can cause them to be ineffective. On the flip side, applying fertilizers when the soil is too wet can result in nutrient runoff. 

Right Place

The right place means considering the appropriate location for applying fertilizers based on the needs of the crops and the characteristics of the soil. This may include: 

  • Applying fertilizers directly to the root zone of the crops
  • Incorporating nutrients into the soil through tillage or other means

The appropriate placement will differ based on crop type, growth stage, and growth rate. It’s important to carefully consider the location of fertilizer application to ensure that the nutrients are being delivered to the areas where they will be most effective.

4R Nutrient Management in Agriculture

The 4R nutrient strategy is an important framework for optimizing the use of fertilizers in modern agriculture. By following this approach, farmers can ensure that they are using fertilizers in the most efficient and effective way possible, resulting in improved crop yields and reduced environmental impacts.

Using the 4Rs in an on-farm nutrient management strategy is a science-based way to put plant nutrients to work while being economically and environmentally sustainable. Over time, proper nutrient management through the 4R strategy can support improved soil health and enhanced soil organic matter. 

The 4Rs and AgTech

In order to successfully implement the 4R approach, it is important to use advanced decision support systems and digital platforms. These tools can help farmers and growers to accurately assess the nutrient needs of their crops and the characteristics of their soil, and to determine the most appropriate type, rate, time, and place for applying fertilizer.

For example, precision agriculture technologies such as satellite imagery, drone-based mapping, and sensors can be used to gather real-time data on soil nutrient levels, crop health, and other factors. This data can be used to generate detailed maps of the fields, which can be used to identify areas that are in need of specific nutrients, supporting the right placement of fertilizer. 

Precision agriculture technology can also support the 4Rs at the point of application. GPS and robotics technologies help deliver nutrients to the precise place they are needed at the correct rate. Capturing application data to compare with yield data helps inform future decision-making. 

In addition, digital platforms such as software applications and online tools can be used to track and record the application of fertilizers, as well as to monitor the performance of crops and soil over time. This information can be used to fine-tune fertilizer application strategies and optimize the use of fertilizers to achieve the best possible crop yields and soil health.

Agmatix’s Digital Crop Advisor platform is a digital decision support system that enables easy application of the 4Rs on-farm. Agronomists and field technicians can help farmers make data-driven decisions around the rate and placement of nutrients. 

Crop nutrition plans created with Digital Crop Advisor harness the power of data on over 150 crops to determine the right timing and source of nutrients based on specific crop needs. Plans can even include controlled-release fertilizers and a view of nutritional distribution across phenological crop stages as part of 4R nutrient management.

Digital Crop Advisor provides a view of the impact of 4R nutrient management on sustainability KPIs, too. It’s possible to run multiple simulations to compare the environmental and yield impacts of different approaches. Sustainable nutrient recommendations take carbon footprint into account as well. 

4Rs for the Future

The 4Rs provide a clear path to better use of fertilizer in growing food, fuel, and fiber for the world. Employing this strategy addresses food security for the world’s growing population through optimally using applied nutrients to balance crop production, soil health, and natural resources. 

The use of advanced decision support systems and digital platforms can greatly enhance the effectiveness of the 4R nutrient management approach. Automated fertilizer plans created with the Digital Crop Advisor crop nutrition recommendation tool make considering all aspects of the 4Rs and implementing the approach simple. 

Ultimately, the combination of the 4Rs and enabling technologies help farmers and growers to use fertilizers more efficiently and sustainably, and to achieve better crop yields and soil health for the future of the growing world. 

This blog was written by 

Liran Shmuel, Lead Agronomist of Agmatix has over 10 years of experience in startups and consulting. Liran has a proven track record of developing innovative solutions in AgTech that improve crop yield and sustainability. He is passionate about helping Ag professionals overcome obstacles in sustainable food production.

Top 5 Ways Crop Nutrient Planning can Reduce Agriculture’s Carbon Footprint

“Growers, agronomists, researchers and ag industry experts are tackling today’s biggest challenge – providing food security for the world’s growing population,” according to Ron Baruchi, CEO of Agmatix. 

Food security in the 21st century goes beyond just growing more crops. Growers, agronomists, and industry experts must unlock a path to increased productivity and quality while at the same time reducing the environmental impacts of agriculture in order to sustain production for the future. 

Crop nutrient planning, supported by digital tools, is one way in which agronomists and industry experts can help growers to increase yields while reducing agriculture’s carbon footprint. 

What are Crop Nutrient Planning and the Carbon Footprint of Agriculture?

Crop nutrient planning is a holistic approach to optimizing the economic, agronomic, and environmental requirements related to nutrients used for crop production. Historically, crop nutrient plans may have been informal or focused solely on agronomic or economic requirements. Now, crop nutrient planning supports maximizing nutrients’ economic benefits while minimizing environmental impact. 

Carbon footprint is defined as the total amount of greenhouse gases generated by a person, organization, action, or product. Carbon dioxide, methane, and nitrous oxide are all examples of common greenhouse gases. High carbon footprints increase the global temperature. Agriculture has a measurable carbon footprint, and it’s sizable. An estimated 31% of human-caused greenhouse gas emissions are tied to the global agri-food system. 

Crop nutrient planning can be a blueprint for how to reduce carbon footprint in agriculture. By setting a crop nutrient planning objective of increasing nutrient efficiency and effectiveness and reducing environmental impact, agricultural greenhouse gas emissions can be reduced. 

And because synthetic nitrogen fertilizer manufacturing is a significant source of greenhouse gas emissions, planning for this crop-essential nutrient with the environment in mind is especially important. 

5 Ways Nutrient Planning Can Reduce Agriculture’s Carbon Footprint

  1. Maximizing arable land use: through increasing efficiency, growing more food on the same amount of land is possible. Nutrient planning to increase yield can maximize production per acre while minimizing environmental impact. 
  2. Implementing a 4Rs approach: precision is an important part of crop nutrient planning. The 4 Rs refer to the right place, right rate, right time, and right source. Considering the combination of the 4 Rs to inform management decisions can minimize the carbon footprint of agriculture. For example, the right source can reduce the use of synthetic fertilizers whose manufacturing contributes to greenhouse gas emissions. The right rate, right time, and right place can reduce nutrient leaching
  3. Practicing good land stewardship: crop nutrient planning promotes sustainable practices that respect the land. Using fewer chemicals and intentionally protecting natural resources increase biodiversity in the environment. 
  4. Reducing waste: over-application or wasted nutrients are not only unprofitable for farmers – they have detrimental impacts on the environment, too. Wasted nutrients are nutrients that plants are unable to use and potentially become environmental pollutants that degrade water quality. 
  5. Using a data-driven support system: using high-quality agronomic data to support management decisions can help farmers reduce their carbon footprint. Data-driven support systems can be used to optimize crop nutrition. Running simulations to compare nutritional recommendations and understand tradeoffs between yield and environmental impact is one example of how data-driven decisions play out at the agronomist and farm level. 

Mission Critical: Reducing Agriculture’s Carbon Footprint

Agriculture is both a contributor to and at risk from a changing climate. Today, agriculture is estimated to be responsible for roughly 18% of greenhouse gas emissions. Agriculture’s emissions contributions have grown 30% in the last forty years. 

This runs contrary to the U.N.’s goal to reduce emissions by 45% by 2030 and reach net-zero emissions by 2050. Reaching net zero will prevent the Earth from warming past 1.5 degrees Celsius above pre-industrial levels. Right now, commitments and actions fall short of what’s needed to reach net zero. Reducing the carbon footprint of agriculture will help reach these emissions goals. 

Agriculture is also particularly vulnerable to the impacts of a changing climate. Corn, soybean, rice, cotton, and oat yields are already expected to be reduced due to global warming. And climate impacts could cause an uptick in irrigation needs at the same time water availability is limited. 

Improving on-farm efficiency through technology could reduce emissions from agriculture by roughly 20% by 2050. Among other examples, this could include steps to reduce nitrogen over-application in China and India, reducing or stopping tillage, improving rice straw management, and improving the fertilization of rice. Many of the opportunities to increase efficiency and decrease the carbon footprint of agriculture tie directly to nutrient planning and management. 

Digital Crop Advisor Can Help 

Determining how to reduce carbon footprint in agriculture can be challenging without the right tools. Using agronomic field trial data to support crop nutrition planning helps farmers apply the precise nutrients plants need with the lowest possible environmental footprint. 

Agmatix’ Digital Crop Advisor solution makes it easy for sales agronomists and other advisors to calculate fertilizer carbon footprint. It’s simple to compare sustainability KPIs – like nitrogen leaching, for example – against historical nutrient plans or current nutrient plans in other fields. 

With this tool in hand, nutrient planning can take into account the field characteristics, fertilizer and crop types, and more. Science-backed nutrition plans containing all crop-essential nutrients help maximize yields while minimizing agriculture’s carbon footprint. 

12 scientifically-proven crop nutrient data profiles enhance agriculture crop protocol management with unbiased product catalog-based recommendations. Nutrition plans are available for over 150 crops and can include carbon footprint-reducing options like controlled release fertilizers. Various simulations are available to deeply understand potential tradeoffs between yield growth and environmental impacts. 

Digital Crop Advisor is a key tool to address the five ways nutrient planning can reduce agriculture’s carbon footprint. As a data-driven support system intended to optimize nutrient management, Digital Crop Advisor supports land stewardship, maximizing productivity, and reducing waste. The 4 R’s are the bedrock of the recommendations. Digital Crop Advisor is a way to seamlessly support the reduction of agriculture’s carbon footprint. 

The Power of Big and FAIR Data Collaboration

Isaac Newton, one of the most influential scientists in history, once said “If I have seen further, it is by standing on the shoulders of giants.” 

This holds true in agriculture and agritech analytics. Through collaboration and open-access data, scientists are able to continue building on the achievements made before them to better our world. In agriculture, building on these achievements allows scientists and researchers to address critical problems with data – including food scarcity, feeding a growing population, reducing agricultural greenhouse gasses, and adapting to a changing climate. 

Big data has a lot of power in agriculture. Big data can be viewed as the combination of technology and analytics that can collect and compile novel data and then process it to support decision-making. Big data can provide farmers with information on factors that impact crop yields, such as soil moisture or weather changes. However, big data requires standardization, harmonization, and analysis to be valuable – and it takes a lot of resources to accomplish this due to the massive size and complexity of big data. 

Alongside big data comes the need for FAIR data. The FAIR Guiding Principles were published in 2016 to address scientific data management and stewardship. FAIR stands for Findability, Accessibility, Interoperability, and Reuse of digital assets, according to the FAIR principles. They call out that data and metadata should be easy to find for humans and computers, and the finder should know how to access the data. Then, the data needs to be integrated with other data, including workflows and applications for storage and analysis. And ultimately, FAIR promotes the reuse of data, requiring data to be well-described for replication or combination in alternate settings. 

For agriculture research, big data is the fuel that can be successfully used when the FAIR data principles are in play. Ag data analytics is a powerful tool to solve major challenges in the world, but only when data is interoperable and reusable. This can be a challenge given the size and complexity of agricultural big data. Luckily, Agmatix, an agro-informatics company, is focused on turning big data into powerful insights. 

How Agmatix’s Axiom Platform Can Help

When using unfamiliar data for modeling efforts or meta-analysis, it is all about findability, accessibility, and interoperability – the F, A, and I of the FAIR principles.  For researchers embarking on the agritech analytics journey, it is essential to get acquainted with the data producers, time frames, relevant metadata, and relevant methods and protocols. This information is the basis for finding and accessing the right data. 


Agmatix’s Growing Universal Agronomic Data Standard (GUARDS) protocol is based on the FAIR data principles. It uses ontologies and hierarchy definitions to classify data components. This reflects findability and interoperability. Datasets are automatically screened and categorized to create direct links between metadata, as well as independent and dependent parameters. 

By using the Insights module, data scientists access a user-friendly interface to visualize big datasets, descriptive statistics, and graphic content visualization. They can access standardized datasets without the time consumption and effort of individual dataset standardization. It’s even possible to share notions and conclusions between users. There are several statistical tests available for data analysis, and users can create a knowledge graph based on the ontologies and analyzed data. Ultimately, the Insights module supports data scientists in accessing diverse datasets. 

Agmatix’s Axiom platform is different from other ag data analytics platforms and agronomic analysis tools. Agmatix is the first company to apply data standardization according to the FAIR principles over agronomic legacy data and ongoing trial data collection processes. The Axiom database is the first multi-disciplinary agronomic database that allows meta-analysis alongside modeling and data sharing. Sophisticated data validations and integrity tests are performed to ensure the highest data quality according to the FAIR standards. In the heart of Axiom, an ontology codebook is used to understand and define relations between data points. 

Case Study: Corn Prediction Model Using Axiom Platform

Big and FAIR data through the Axiom platform was the basis for a recent corn yield prediction model. The model unified data from many sources across various production environments in the U.S. 

Data included farm management and experimental data from collaborators and open repositories of publicly available data. Common parameters were identified between these datasets. They included total Nitrogen applied, previous crop, soil texture, soil organic matter, and irrigation data. The base data was enriched with weather data, including precipitation and temperature at specific corn stages. The key factors in any model will change as inputs change. 

In this corn prediction model example, using unified data from multiple data sources, corn yield can be estimated at an accuracy of 16 bushels per acre – and higher if looking specifically at a single region. 

Agmatix Enables Collaboration

Agmatix drives agronomic innovation by moving research and experimental data into real-life action. The power of data is core to Agmatix’s beliefs, driving innovation in data access and decision support through agronomic analysis tools. Agmatix encourages and supports the implementation of the FAIR principles in a few different ways. 

The GUARDS protocol which governs data standardization is based on the FAIR data principles. Of particular focus are the F and I in FAIR: the representation of the metadata (Findability) and ontology allocation (Interoperability).

The INSIGHTS and MODELS platform completes the FAIR acronym. It allows access to diverse datasets (Accessibility) and sharing between projects and users (Reusability).

Agmatix’s TMP platform for planning and executing new and ongoing trials preserves the data according to the GUARDS protocol on the go. This platform unlocks the power of agronomic trials and big data. 

Agmatix is dedicated to growing data for impact – a fertile ground, rife with possibilities, when with big data and the FAIR principles. 

Unlock True Value from Agricultural Data with Agmatix

Raising a high-quality, high-yielding crop depends on healthy soils and healthy plants that perform well in their environment. Understanding how to support plant and soil needs for maximum productivity can be complex. 

Plants are affected by a multitude of factors that can enhance or limit their yield, or overall success. Generally speaking, these factors can be categorized as technological, biological, and environmental factors. 

Technological factors include agricultural practices and managerial decisions, such as tillage intensity or nutrient management. Crop diseases, pests, and weeds are all biological factors while environmental factors include climate, soil, water, and input nutrients. Understanding and managing these factors is critical for delivering a high-quality, maximum-yielding crop. 

Agmatix’s cutting-edge technology helps you analyze these factors and associated data within your crop dataset. Ultimately, this provides you with powerful agronomic insights and possible crop models to unlock true value from your data!

Data on the Farm?

Data is an increasingly important part of decision-making on the farm and in the agricultural industry. Data illuminates historical trends, clarifies hidden values, and provides an additional perspective to traditional observations and contextual information. 

Agriculture produces massive amounts of big data related to climate, weather, yield, and price. There is a multitude of uses for big data in agriculture from production efficiency to sustainability for the future. Data can be used for overcoming challenges in agriculture related to climate change, quality, and understanding markets. 

Big data in agriculture rarely comes from a single source or collection method. Rather, there are many agricultural data collection methods. These include collection by hand or manually, with a drone, with farm machinery or an IoT sensor. This data can cover all three plant-impacting factors: biological, technological, and environmental. Having a 360-degree view of factors influencing plant growth from differing agricultural data collection methods further fuels informed decision-making for the future. 

Technology and data in agriculture have the potential to yield higher returns. According to McKinsey research, the connectivity that drives data collection, collaboration, and utilization can add $500 billion incremental value to global GDP by 2030. 

Crop monitoring is one of the key use cases that will drive this value through higher yields, lower costs, and increased sustainability and resilience. That value will be derived from fruit, vegetable, cereal, and grain crops grown around the world. Other use cases for digital growth include animal health, chemical usage, carbon footprint management, water quality, and even exports. 

Armed with connectivity, agricultural data can increase precision and tracking on the farm. Precise and efficient autonomous machinery can be powered by reliable and responsive connectivity. And agriculture data can expand its reach – into even the most remote, rural areas where food is grown and farmers can adapt their decisions for the future. 

As of 2020, roughly one-fourth of U.S. farms are using connected devices for data collection and management. The cost of connectivity hardware is decreasing to a point that a return on investment through the collection and use of data can be seen in the first year. New connectivity technologies are expected to evolve throughout the next decade, unlocking additional opportunities and value for data in agriculture. 

More Data, More Opportunities

Data can address current and future pressures on the agriculture industry, such as a growing world population and mouths to feed while land availability is limited. Farmers face a projected 70% increase in needed calories for consumption by 2050, all while environmental pressures increase. 

A changing climate and catastrophic weather events add increasing risk to the world’s food supply. By 2030, the world’s water supply will drop to 40% short of meeting global needs. And one-fourth of the world’s arable land is too degraded to grow crops at scale without major restoration. Challenges – and thus, opportunities – abound for agriculture in the next few decades. 

Through collecting and analyzing data on various aspects of the growing season, it’s possible to approach complex challenges – like climate change. Data and the Internet of Things are already being used to optimize production cycles as traditional calendars and practices are no longer sufficient. 

Soil data science is deepening scientists’ understanding of soil’s contributions to climate change through releasing and sequestering greenhouse gasses. Scientists, agronomists, and growers use the better understanding of this relationship to take proactive and productive steps to reduce carbon footprint in agriculture. 

Agronomic data analytics also allow growers of any size to be hands-on with the health of their fields during the season – without actually being hands-on. It’s possible to monitor crops at scale and capture the data to use for future decision-making. From managing crop diseases and pests to making yield predictions in support of marketing decisions, agricultural data unlocks a whole lot for farmers and the agriculture industry. 

Agmatix Can Help!

It’s difficult to make decisions without data, but it’s also difficult to sift through mounds of data to make a decision. Agmatix can help you easily unlock the value of your data!

The Agmatix platform provides a view of all of your data in one unified place. Develop a deeper understanding through agronomic data standardization of information from field trials and agronomic databases. 

Actionable reports and recommendations are paired with a user-friendly interface for statistical analyses of the dataset with no code required. Pre-built analytical widgets allow anyone to find use of data science capabilities and then share custom reports – that can even be adjusted on the fly. These statistical tools increase the integrity of on-farm experiments and double down on data quality in agriculture. 

Multi-location trials over several years often lead to data floating around and even getting lost. With Agmatix, agriculture analytics for all of your trials can be viewed in a single workspace. That trial data can be shared easily so you can work with your collaborators directly for cross-trial analysis. This collaboration will improve decision-making and decrease the time from data to insights to direct action. 

Agmatix believes in the power of open data in agriculture and has worked collaboratively across the industry to increase the availability and impact of data. Collaboration across researchers and agriculture professionals built on a foundation of standardized agronomic data empowers innovation around the globe. The world’s best minds can leverage enriched agriculture datasets to provide the most accurate information to farmers and support their decision-making. Ultimately, this leads to improved yields, profits, soil health, and environmental outcomes. 

For example, the Global Crop Nutrient Removal Database is an agronomic database created in collaboration with the International Fertilizer Association, Wageningen University & Research, the African Plant Nutrition Institute, and Innovative Solutions for Decision Agriculture. 

The basis of the project is to understand the relationship between nutrient inputs and outputs in crops under differing production and environmental conditions. Knowing how crop nutrient removal from one crop can influence the next crop on that land enables agronomists to determine the most precise rates of specific nutrients needed to sustain healthy soils and crops. 

Data analytics in agriculture, powered by the right tools, unlocks high-yielding crops and better management of technological, biological, and environmental factors. Agmatix is dedicated to growing data and the power of data use to drive impact in agriculture. Field-level decisions with focus on the future can be fueled by data science technology and agriculture analytics to address today’s agriculture challenges and tomorrow’s opportunities. 

How Precision Farming Tools Can Help Agriculture Become More Resilient

Resilient: defined by Merriam-Webster as “capable of withstanding shock” and the ability to “recover from or adjust easily to change.” Resilience enables people to adapt to setbacks and bounce back from changes. In business, resilience protects continuous business operations, people, and assets. It separates those that can innovate and thrive from those that freeze and fail. 

Resilient is what agriculture must become to withstand changes in climate, rising costs, supply chain challenges, and a growing population demanding more production on less land. And while building resilience is not easy, having the proper tools at hand makes withstanding the storm much easier. 

The development and implementation of agriculture technology in recent decades has paved the way for farmers to improve their productivity and sustainability like never before. This wave of precision farming tools – often referred to as precision ag or precision agriculture – is what enables agriculture to become more resilient to changes. 

What Is Precision Agriculture?

The International Society of Precision Agriculture defines precision agriculture as “a management strategy that gathers, processes, and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.”

Precision agriculture technology leverages new technologies to increase crop yields and profitability while reducing the amounts of inputs needed for crop production. Reducing inputs can be crop-dependent, but often means minimizing the land, water, fertilizer or crop protectants needed to maintain or increase yields. Oftentimes, this requires resources to be used more effectively or precisely.

There are many examples of precision agriculture technology in use today. GPS systems on farm equipment can help farmers plant in efficient patterns to save time and fuel. Documenting where the tractor or sprayer has been through the GPS system can reduce overlapped seeds during planting or reduce overapplication of fertilizer and crop protectants. 

Unoccupied aerial vehicles can be used to fly over fields and measure disease on crops. Farmers can monitor plant health of the entire area without needing to walk through every pass on foot. With a birds-eye view, farmers can make the most sustainable and profitable crop protection decision. 

Water management can be part of precision agriculture, too. Soil moisture sensors can be used to measure current soil moisture and assist in irrigation system scheduling to ensure that water is distributed only when needed to reduce inefficient or excess application. 

In many of these examples and across the industry, precision agriculture and sustainability often go hand-in-hand. Reducing or precisely placing inputs, reducing fuel use, and improving decision-making is a win-win for farmers and the environment. 

Data Is What Makes Precision Agriculture Possible

Precision ag encompasses many different types of technologies. The collection of data for these technologies – or by these technologies – is the fuel that makes precision farming so powerful. 

Armed with data that spans multiple years, numerous crops, all applications, and different climatic events, models and machine learning can improve accuracy in outputs for farmers. Correctly identifying diseased plants from images or understanding what plants in a field are weeds versus crop lets a farmer address crop needs efficiently and effectively – with minimal input use. 

Predicting fertilizer needs based on crop developmental stage allows farmers to provide the nutrients needed at the right time and right rate for maximum effectiveness. These are all data-based precision agronomy solutions.

Data can be quantitative or qualitative, but are typically composed of single observations or numbers reflecting an important variable in the production process. Data has been used for decision-making on the farm since the 1990s, but recent advancements in communication and sensing technologies have made on-farm data collection cost-effective and increasingly popular. 

Today, big data analytics in precision agriculture improve efficiency and productivity, increase cost control, improve execution, and automate farming activities. Data management goes hand-in-hand with these benefits of precision agriculture crop management, though tools are becoming increasingly optimized for data management on the farm. What’s most critical is creating and utilizing accurate data because it is the bedrock that makes precision agriculture possible. 

Agmatix’s Tools For You

Big data analytics in precision agriculture have so much potential to improve sustainability and profitability. Agmatix develops technologies that convert data into field-level actionable insights through agronomy data science and artificial intelligence.

Agmatix’s Digital Crop Advisor is a precision agronomy crop nutritional decision support tool. Digital Crop Advisor creates customized nutrition optimization plans and standardizes field measurements. Multiple parameters are considered in the creation of the nutrition optimization plans, including crop type, field location, pH, previous crops, plant uptake, and laboratory analyses. 

Users can monitor sustainability KPIs such as carbon footprint and nitrogen leaching. WIth this precision agriculture tool, it’s even possible to understand the tradeoffs between driving high yields and minimizing environmental impact. 

Digital Crop Advisor also provides insight into crop production at any scale. Deep insights into yield, quality, and carbon emissions are possible, even across worldwide operations and a diverse team. This easy access to data enables efficient and effective decision-making. 

Agmatix is a proponent of open data to enable precision agriculture crop management. Agmatix offers an open data platform for agriculture professionals and researchers around the world. Users can easily share and access standardized agronomic data to support data analytics in precision agriculture. By breaking down data silos, the full value of precision farming is unlocked. 

Agmatix also supports open data access through partnerships with the International Fertilizer Association, Innovative Solutions for Decision Agriculture, the African Plant Nutrition Institute, and Wageningen University & Research. The Global Crop Nutrient Removal database is an example of open-source agriculture data that can be used to make informed, precise crop management decisions. 

Digital Crop Advisor transforms open data from all sources: live inputs from growers, existing databases, and data silos. This data combined with advanced algorithms takes precision agriculture crop management to the next level. 

Precision Ag for the Future 

Precision agriculture benefits farmers and sustainability through reduced input costs, increased productivity and profits, more sustainable cropping systems, and lower carbon footprints. Precision farming can help farmers at any level be more resilient to the ever-changing world. With precision agriculture technology, a successful cropping system is possible, regardless of external factors requiring adaption. 

That’s why Agmatix is focused on providing precision agronomy solutions that use the power of big data analytics for field-level insights. With the right tools, fueled by data, agriculture can position to be successful despite an uncertain future. 

10 Steps for Easy On-Farm Trials Execution

On-farm research is a powerful tool for farmers. For one Brandon, Manitoba farmer, conducting on-farm trials provided the data needed to confidently cut canola seeding rates without cutting yield potential or extending maturity. One Illinois farmer points to $100-$150 per acre increased profitability through on-farm trials he’s been running since 1985. 

For many farmers, on-farm experimentations are an excellent way to test out a new product or production practice on their own land. Farmers can generate their own science-backed, relevant information that supports decision-making to improve yield and crop quality. 

With precision agriculture technologies and big data, it’s easier than ever to successfully conduct agricultural field trials. Farmers can precisely plan and execute on-farm trials using GPS technology, and collecting data becomes simple with mobile applications and IoT sensors. Farmers and agronomists can use agronomic databases and agtech tools to standardize data or enrich their own data with multiple source datasets. 

There has never been a better time to start using on-farm research as a management tool to improve profitability and sustainability. Using proper on-farm trials methodology is critical to ensure results are accurate and reliable. The steps below outline how to conduct successful on-farm trials – from design to execution to data analysis!

1. Keep a clear direction in mind

Starting off an on-farm trial with a clear direction is a critical first step! Identify what specific practice or treatment you want to evaluate or test with on-farm research. Ensure that all participants and stakeholders understand what’s being evaluated and why it’s important. For example, a trial could be vital due to its connection to profitability or sustainability.

2. Ask the right scientific question

On-farm trials should start with a specific, scientific research question with a yes or no answer. The research question is often a comparison between two agronomic practices, such as: will yield be reduced when strip-tillage is used? Once the scientific question is identified, plan for the relevant variables to be measured in order to answer that question.

3. Determine the data collection process

To minimize confusion and ensure accuracy in data collection, determine standard operating procedures (SOPs) to gather data. These should be pre-determined approaches that are always used, regardless of who is gathering the data. For example, ahead of collection, data record sheets should be prepared, sample bags should be labeled, and harvesting procedures should be determined.

4. Plan for replication

Replication is a best practice for answering the research question when conducting on-farm trials. It involves repeating plots of the control AND the treatment across the farm, which provides a large dataset. Replication helps take into statistical consideration the variability that exists on the farm, such as soil variations or weed pressure differences, that could bias the results of the trial.

5. Be ready to randomize

Randomizing plots of the treatment and control is another way to control for field variability. Randomization refers to the placement of treatments and controls in blocks, which are randomly assigned in the trial area. This helps remove any bias for one practice or treatment over the other. Randomization and replication can be used together.

6. Know the site 

Take an inventory of the field history, variability, and other factors that might impact the results. Fully understanding the farm where the on-farm trial is taking place is critical for the results to be meaningful to other farmers. Documenting factors like geographic slope, soil type, drainage class, crop rotation history, tillage practices, chemical application practices, and other similar information is important for context.

7. Capture data and observations

On-farm research is time-sensitive. Collecting necessary data at the property times for the research question is critical because once that opportunity has passed, it’s not possible to get it back. Taking note of other observed factors that could influence the on-farm trial is important, too – even if those changes weren’t anticipated. 

8. Analyze results

Once data is collected, it’s time to dig in and answer that research question. There are various statistical approaches to determine if a treatment or practice had a significant impact on performance. Using statistics in data analysis is recommended because it creates confidence in the outcomes.  

9. Share the outcomes

Discuss findings and observations with farmers and researchers to wrap up the on-farm trial. This is also a good time to determine any next steps, which might include repeating the trial in a different area or deciding to put the production practice or change it into practice across a larger part of the farm.

10. Put findings to work!

The last step in conducting on-farm trials is turning the results into actions. Apply findings to future research on the same farm – or broaden the research opportunity by gaining traction with others.

With these steps outlining how to conduct successful on-farm trials, it’s possible to take farm profitability and sustainability to the next level. 

At Agmatix, we support on-farm experimentation with the user-friendly, flexible mobile app as an extension to our  Agronomic Trial Management solution. Farmers running trials can easily plan, manage, and collect data from trials. 

Agronomic trial management is critical to ensure the results of on-farm trials maintain scientific integrity and can be counted on to support decision-making. On-farm trial methodology can seem complex. But with the Agmatix mobile application, it’s easy to stay connected and follow the 10 key steps to on-farm research!

Why Collaboration is the Key to Innovation

Collaboration – or working together with someone else to produce or create something – is key to innovation. Working jointly in intellectual endeavors means connecting the world’s best minds, all working from the same page, to solve problems and overcome barriers. 

At Agmatix, we’re firm believers in the power of collaboration, built on a foundation of open-source agriculture data. Better agronomic data and strong connections between researchers and agriculture industry professionals lead to better yields and profits, sustainable production and improved soil health, and ultimately positive outcomes for the environment. 

Collaboration Keeps Science Moving

Dr. Kenneth Wilson, 1982 Nobel Prize in Physics recipient, said “the hardest problems of pure and applied science can only be solved by the open collaboration of the worldwide scientific community.” 

This is certainly evident in agriculture science. Collaboration has been key throughout agriculture’s history. For example, Dr. Evangelina Villegas and Dr. Surinder Vasal, with their teams at the International Maize and Wheat Improvement Center (CIMMYT), improved the productivity and nutritional content of maize through the development of quality protein maize in the 1980s. This collaboration supported the national security of communities that depend on maize. 

Maize protein is an example of collaboration in agriculture science leading the way for innovation. In the future, new challenges in agriculture will have to be addressed – and collaboration can help to overcome them. Feeding a growing population and protecting the environment come to mind as key challenges that agriculture will face in the near term. 

The United Nations projects a global population increase from 8 billion people to 10.4 billion people by the century’s end. No additional land is available to feed that growing population. Collaboration will be required to create solutions that feed more people on the same or less land.  

While feeding a growing population, agriculture will also need to protect the environment. Agriculture currently contributes around 11% of global greenhouse gas emissions. Deforestation, livestock manure, and household consumption are leading sources of greenhouse gas emissions in agriculture

Sustainable agriculture production will need to lead the way in reducing agriculture’s carbon footprint and protecting natural resources. 

Agmatix’s Tools Enable Collaboration

Collaboration is fueled by shared knowledge and information. It takes the right tools to connect people and data. Agmatix supports scientific collaboration in agriculture through our Axiom technology, Agronomic Trial Management tool, and Insights capabilities. 

Axiom Technology converts big data into actionable insights. The Growing Universal Agronomic Standard (GUARDS) protocol is an ontology hierarchy system for agriculture data harmonization that aggregates and standardizes all research data, so it can be used for collaboration. 

One component of the GUARDS protocol is to convert different measurement units into a single unit and standardize the data values accordingly. For international agriculture research teams, working in metric or imperial measurements is no longer a concern – everyone can be on the same page. Through Axiom Technology and the GUARDS protocol, all data is speaking the same language and is apples to apples. 

Agmatix’s Agronomic Trial Management is a tool for trial coordinators, research scientists, CROs, and field technicians collaborating on on-farm experiments. An easy-to-use interface enables quick communication with others on the team. Users can track task assignments across team members and receive updates related to ongoing field trials. Sharing data with team members and partners is simple, allowing for real-time visibility and control. 

For trial coordinators and research scientists, the Insights platform is a powerful tool for agricultural data collaboration. Data from all field trials are housed in a singular, unified platform. It’s easy to visualize and share all of an organization’s trial data with the team or CROs in just a few clicks. 

Field technicians, agronomists, marketers, and executive managers can use the Digital Crop Advisor platform to drive a proactive business and deeply understand their crop production markets. Management, sales agronomists, and marketing have visibility to where products are selling and what crops they’re being used in. Digital Crop Advisor allows the whole team to collaborate on proactive product supply planning driven by customer needs and real-time data. 

Agmatix’s Collaboration Culture

Agmatix doesn’t just provide tools for agriculture professionals to effectively collaborate – we’re actively working with others in the industry to enable open-source agriculture data that spurs innovation. Through partnerships with other organizations, two databases have been developed to support ag data analytics

The Global Crop Nutrient Removal Database was created through collaboration between Agmatix, the International Fertilizer Association, Innovative Solutions for Decision Agriculture, African Plant Nutrition Institute, and Wageningen University & Research. By looking at production and environmental factors that affect nutrient concentrations, it’s possible to determine the total amount of nutrients removed from the field in the harvested portion of the crop. 

This source of open agriculture data highlights the connections between inputs and outputs of crop nutrients in varying production environments. Armed with this data, crop nutrition work and management can be optimized for sustainability and crop productivity. 

Another example of open agriculture data is the Nutrient Omission Trial Database. Its goal is to provide data that supports site-specific agricultural recommendations and optimized nutrient management. The database was created in collaboration with the International Fertilizer Association, the African Plant Nutrition Institute, Innovative Solutions for Decision Agriculture, and Agmatix. 

The database enables agriculture researchers to compare crop nutrient requirements and plans to support site-specific recommendations. The database notes variations in soil fertility and other farm-specific environmental conditions. A single, open, standardized data set was created through the GUARDS protocol consolidating legacy nutrient omission research data from various sources. This streamlines integration into advanced fertilization tools and improves collaboration between Consortium for Precision Crop Nutrition members. 

Collaboration Leads the Way 

To continue making progress in providing food sustainably for the world, collaboration is essential, and Agmatix’s tools can help. Agmatix believes in a collaborative future of agriculture supported by open agriculture data for researchers and agriculture professionals around the globe. 

Through connecting people and data, Agmatix enables the type of collaboration that created quality protein maize. Agmatix takes data analytics in precision agriculture to new heights. 

With tools like Axiom Technology, Agronomic Trial Management, and Digital Crop Advisors, industry professionals can collaborate in new ways. Key sustainability and crop production decisions are supported by open-source agriculture data from the Global Crop Nutrient Database and Nutrient Omission Trial Database. With a foundation of collaboration tools and agriculture open data, the future is bright for innovation in agriculture. 

Integrated Nutrient Management and its Role in Sustainable Agriculture

Sustainability in agriculture is of increasing importance in the 21st century. As agriculture works to provide food, fuel, and fiber for a growing population, managing limited natural resources and caring for the environment are critical considerations. 

Sustainable agriculture practices can protect the environment, increase farm profitability, and increase crop production on critical food and fiber needs. In the long term, sustainable agriculture practices protect crop production, the environment, and economic viability. 

After many years of soil compaction, sustainable practices are needed to address nutrient deficiencies to apply adequate amounts of fertilizers. Increased nutrient use efficiency is needed. Integrated nutrient management for sustainable agriculture addresses these challenges by optimizing fertilizer inputs without reducing crop yield. 

What is Integrated Nutrient Management?

Integrated Nutrient Management, or INM, is a sustainable agriculture method for achieving production needs and protecting the environment for future generations. INM is the application of soil fertility management practices that maximize fertilizer and organic resource use efficiency to enhance crop production. 

Agriculture is a soil-based industry. Soil is the foundation of crop production, as it provides plants with the necessary environment, nutrients, and water for survival. Soil provides anchorage for plant roots and provides ecosystem services that sustain life on Earth. 

While soil does naturally contain a certain amount of nutrients and minerals, specific needs for nutrients and their quantities vary across crops. Even healthy, naturally nutrient-rich soils may need to be supplemented with additional fertilizers.

Integrated nutrient management for sustainable agriculture helps meet soil nutrient needs in an environmentally-conscious way. The FAO notes that INM uses the benefits from all possible sources of plant nutrients to maintain or adjust the plant nutrient supply needed to achieve the desired crop production level. 

This happens through a few different mechanisms, including improving the stock of plant nutrients in the soil, maintaining or improving soil fertility, and increasing plant nutrients. 

These approaches should include a balanced use of mineral fertilizers along with organic and biological plant nutrient sources. By addressing plant nutrient efficiency, nutrient loss to the environment – and the subsequent environmental concerns – are limited. 

The UN Climate Technology Centre and Network indicates another component of INM is participatory, farmer-led INM technology experimentation and development. This on-farm experimentation helps test locally appropriate technologies and empowers farmers by increasing their technical expertise. This supports optimal nutrient source use on a crop system or rotation basis, rather than looking singularly at a single crop. 

The Challenge 

The world’s future food needs will be hefty: feeding nearly 10 billion people by 2050, according to the UN, will require production to grow substantially. But growth alone is not enough; minimizing negative impacts on the environment is what will secure food, fuel, and fiber for generations to come.

Limited arable land and declining natural resource availability underscore this challenge. The International Food Policy Research Institute indicates that the land currently in use will need to be more productive and strategies to grow productivity will need to be linked to using nutrient resources more efficiently than in the past. 

When farmers overuse certain inputs, they can deteriorate the sensitive soil-plant-microbe environmental systems that are vital for productive, sustainable ecosystems. INM helps create balance in the input source and rate to protect the environmental systems that are so critical for plant and microbial life. 

Managing Nutrients and Sustainability with Agmatix

Agmatix, a global agro-informatics company driven by turning agronomic big data into insights, can help make INM a reality on the farm. With crop management software, an automated fertilization plan can meet both sustainability and crop production goals while adhering to the principles of INM. 

Agmatix’s Digital Crop Advisor platform is a crop nutrition recommendation tool that uses data to optimize crop nutrition, increase profit, and lower environmental footprint. Expert knowledge, cutting-edge technology, and local data insights collide in a single plant-integrated nutrient management software, making fully-customizable nutrition management fast and easy. 

With the Digital Crop Advisor plant nutrient management tool, it’s easy to access crop nutrition management plans for a field. The software has 150 crop options and 12 scientifically-proven crop nutrient data profiles for plant-integrated nutrient management. It’s even possible to view nutritional distribution across crops’ different phenological stages. 

With smart fertilizer software, farmers can monitor sustainability KPIs, such as carbon footprint and nitrogen leaching, with recommended crop fertilization plans. It’s also possible to compare various simulations and recommendations to understand options. This plant nutrient management tool makes it seamless to see the tradeoff between maximizing yields and minimizing detrimental environmental effects.

And connecting INM across a global team and directly with growers is seamless with the Digital Crop Advisor management software. Multiple devices, including a mobile app, are supported. Different languages are available. Viewing the real-time status of worldwide operations is easy, meaning INM is possible at whatever scale is necessary. 

Agronomic big data plays a big part in enabling INM on the farm. Open and comprehensive data is available through the Global Crop Nutrient Removal Database, created through collaboration between Agmatix, the International Fertilizer Association, Wageningen University & Research, African Plant Nutrition Institute (APNI), and Innovative Solutions for Decision Agriculture. Using this dataset to understand crop nutrient removal rates in the short- and long-term supports improving crop nutrient management and optimizing for sustainability. 

Conclusion

At Agmatix, we understand the drive to optimize crop yield and nutrition while being good stewards of the land. We strive to empower agriculture professionals with the most accurate, science-based tools to help make the best decisions for businesses, their growers, and the Earth. 

We know that it’s not just the soil or the crop that you make a decision based on – it’s the complex relationship between your crops, the environment, nutrients, and more. That’s why we help you harmonize and standardize all of your agronomic data to turn it into actionable insights. 

That’s the core of INM. It is balancing crop needs, the environment, nutrients, productivity, and profitability to deliver on the needs of today and tomorrow. Integrated nutrient management for sustainable agriculture and Agmatix are a winning combination for building lasting relationships with growers, caring for the Earth, and tackling the challenges of 21st-century agriculture. 

Compelling Reasons Why Farmers do On-Farm Experiments 

For over two centuries and around the world, farmers have been conducting on-farm experiments. This has allowed the translation of experimental research into real-world agronomic practices at the individual farm level. It has also connected farmers, researchers, and other agriculture industry professionals in mutually beneficial collaborations. 

On-farm experiments, while not formalized, are taking place on an estimated 30,000 farms around the world. That means on-farm experimentation today is driven by a diverse set of goals, approaches, locations, and sociotechnical ecosystems.  

The on-farm experiment process involves close collaboration with farmers to change a management value, observe the outcome, and discuss that outcome with the major goal of driving evidence-based learning and decision-making. Farmers bring deep knowledge of local production practices and experimental mentality. But effective integration between science-based and farmer-based knowledge can be a challenge. 

Farmers say what they need to improve their business is information that’s specific to their field and location, not just within their state or region . Agriculture research today is often reflective of best practices that are generalized across a wide range of farms rather than the specific challenges and considerations of one individual farm. This has been driven by reduced funding for local research

So, many farmers have turned to on-farm experimentation to gather information on their own fields to supplement academic or industry-sponsored research. Digital agriculture has the opportunity to unlock more value from these on-farm experiments. Standardized data collection and easy data analysis can increase the ease of conducting on-farm experiments and using the outcomes to make decisions. Field research software can support this. 

In fact, involving an agricultural research management software or experiment monitoring tool from the beginning of the trial has major advantages. Setting up the trial for replication and randomization enables sound decisions from on-farm experimentation. This is because it allows the data to be statistically verified. Digital tools can help farmers design the trial, and GPS guidance, yield maps, and aerial imagery can help pinpoint and execute the trial. 

The ROI with On-Farm Experiments

The reasons for doing on-farm experimentation may be specific to each farmer. For example, one grower might use the trials to understand yield gain or loss on a given production practice. Another points to the learnings from both successes and failures. And yet another uses trials to determine what inputs are absolutely necessary – and which ones can be cut. Amongst all of the farmers interviewed on their reasons for doing on-farm experiments, profitability was a common theme. Because of this, we’ve highlighted some of the main return on investment opportunities associated with on farm experiments. 

Understanding locally-relevant knowledge

Variability is common within a single field – imagine how varied growing conditions and climate can be across a county or even an entire state. For farmers, understanding the impact of a production practice in their specific growing conditions and climate is critical. 

Farmer-led on-farm experiments typically have more replications than those of agricultural scientists. Coupled with the specific soil types and weather conditions that are of interest, there’s a particular power in the vast, local data collected from on-farm experimentation. 

While these experiments may be less precise, they are an excellent complement to scientist-led research. A farmer’s local knowledge combined with a researcher’s product-specific knowledge work well together to conduct these experiments and ensure the outcomes will be relevant and impactful. 

Test research questions for the broader benefit 

Understanding and evaluating new products or practices on a small scale can help minimize the initial risk. Farmers can assess early, local, and small-scale results before going all-in and adopting something new. 

Farmers can use the outcomes of on-farm experimentation to fuel profitability-focused decisions and understand yield impact on their farms. These outcomes can also apply to other local farms – and have the potential to improve profitability and crop yield for others, too. 

Test questions also open the door to technology adoption. When new products and technologies arrive on the farm, growers have to explore how they work in their environment to determine the return on investment. New technology is also available to aid farmers in their decision-making, such as agricultural research management software. Not only is this technology being tested through on farm experimentation, but it also unlocks additional potential for on-farm experiments to be doubly powerful. 

Financial gains 

On-farm experimentation directly provides data to help farmers make decisions that impact the bottom line. Whether it’s improving crop yields and maximizing production, reducing the number of inputs like fertilizer that are needed to cut costs, or reviewing overall profitability, financial gains are possible from on-farm experimentation. 

On-farm experiments also reduce the risk of wasted time and money on new products or production practices. By understanding how that specific product or practice performs on a farmer’s own land, the chances of wasted time and money are reduced – even if the product or practice doesn’t work out.

On-Farm Experiments with Agmatix

At Agmatix, we support agriculture professionals globally to overcome obstacles in the journey to grow sustainable food production and quality. One of the ways we walk beside producers in this journey is through the tools we provide to assist with experimental collaboration. 

Agmatix mobile is an easy data collector for agronomic trials to address one of the most critical aspects of any experiment. This field research software enables timely reporting, minimized errors, and secure processes for storing and aggregating the data for analysis. Farmers can see the true value of the data they collect from field trials, and work collaboratively with others through easy sharing to maximize its impact. 

The Agmatix field research app also allows farmers to leverage legacy data from published research or from local trials conducted near them. Data standardization helps unearth insights from multiple data sets. And pre-built analytics speed up the process of transforming big data into impactful insights. 

Tools like the Agmatix mobile application are designed to make OFEs easy. Experiment monitoring tools with intuitive user interfaces allow farmers to check in on their trials and manage them throughout the growing season. Experiencing results in real-time is possible, too! 

Planning on-farm experiments can be challenging, too. Trials must be designed with treatment combinations that are flexible but keep trial outcome integrity at the forefront. Agmatix’s Agronomic Trial Management mobile app has customizable treatment combinations and trial layouts. With the app, it’s easy to visualize the trial design and layout and create data collection forms based on the parameters needed for each trial.

On-farm experiment tools have incredible power to support farmers in conducting on-farm experiments. From supporting data collection and standardization to enabling fast and easy analysis, farmers can truly experience the return on investment of their on-farm experimentation through digital tools. Agmatix is proud to offer on-farm experiment platform that helps farmers and their collaborators with these important trials. 

Unearthing Potential in Your Soil with Agmatix

There’s more to soil than what meets the eye. As the basis for life on Earth, soil is a big deal! 

Soil health is part of the bedrock of agriculture. In the last decade, interest in soil health has increased as a focus on sustainability and the benefits of healthy soils have become more mainstream. In honor of Soil Health Day, we’re breaking down the facts about soil, soil health, taking care of soil, and making sure it’s healthy for generations to come. 

What is soil made of? 

As every farmer knows, what covers the Earth and grows crops isn’t ‘just dirt.’ But what is soil made up of? While it may look like a single, dusty brown substance, soil is a combination of minerals, organic matter, living organisms, gas, and water. These give soil unique, location-dependent characteristics. 

Soil has been studied since the 1800s when soil scientists began looking into what soil is made of. Scientists have classified soil horizons, or layers within the soil, as well as soil age, soil minerals, and more! Soils are diverse due to differing parent materials and formation conditions. 

Major contributions were made by Hans Jenny introducing the CLORPT concept: soil formation is affected by Climate (CL), Organisms (O), Relief (R), Parent material (P), and Time (T). This conceptual model for understanding soil geography and geomorphology has had many impacts on soil science. It’s been a global foundation for understanding soils as a function of combined environmental factors.

Around the world, soil reflects various factors, including geologic events, organisms, climate, topography, and time. These five factors impact soil from the surface to its lowest depths. A change in one of the five soil forming factors results in “soil genesis”, creating a new soil. 

Geologic Events

Soil parent material is a result of geologic events, such as volcanic eruptions or glaciers. Volcanic ash is a lightweight parent material with high water-holding capacity and compaction susceptibility. Glaciers have ground up rocks and pushed glacial till to the soil surface, generating a different parent material entirely. And in swampy areas, peat is a common soil parent material generated from decaying plants and animals. 

Organisms

All living plants and animals can impact soil formation. Soil horizons often reflect the types of plants that grow on a site, with residue like needles, twigs, and roots incorporated into the soil. Organisms in the soil break these plant residues down. Microorganisms are present in all soils, though the types of microorganisms will depend on the plants that are present on the site. Other organisms, like worms, voles, and moles, impact the soil by creating channels in the soil or mixing it, which helps move water and air throughout the soil. 

Climate and soil

Climate, including temperature and annual precipitation, can have massive impacts on soil formation. Wetter climate can support richer vegetation, which decomposes and in time affects soil formation and its characteristics. Too much water in soil can reduce soil air, and impact what types of plants can grow on that site. Documenting annual precipitation and average soil temperature is important for understanding the climate of a given site. 

Topography

Topography refers to the type of landscape the soil is in. Landscape location includes elevation, shape, and compass direction. All of these factors change soil drainage, runoff, deposition, and erosion. For example, south-facing slopes dry out faster and are warmer from high solar heat, which speeds up chemical reactions and water evaporation. This influences soil genesis. 

Time

Soil landscapes are continuously building and degrading throughout time. Soils can be young or old. Climate can impact how quickly soils form; in hot, wet climates, soil horizons form more quickly than in cold, dry environments. Young soils have little horizon development, while older soils have many well-defined soil horizons. 

Understanding soil characteristics and what it is made of is important because of all the ways soil contributes to life on Earth. Soil plays a part in regulating water, sustaining plant life, and cycling nutrients. Soils provide nutrients and water for most of the plant life on Earth. 

They also filter pollutants and transform nutrients into biologically available forms that are prevented from leaching. All in all, soil contributes ecosystem services that microorganisms, plants, animals, and the environment all depend on. 

Is Your Soil Healthy? 

Because soil is a living natural resource, it’s possible for it to be in different states of health. Soil health is the “continued capacity of soil to function as a vital living ecosystem that sustains plants, animals, and humans,” according to the U.S. Department of Agriculture’s Natural Resources Conservation Service

In agriculture, soil health is of particular importance. Agriculture fundamentals require plant and animal life to be sustained. Soil’s ability to regulate water, filter pollutants, and stabilize and support plant roots are critical puzzle pieces to growing plants and raising animals for agricultural purposes. 

And for agriculture to be productive, plants need nutrients from the soil to grow and produce high-quality food. Because of this, soil fertility and soil productivity are of high importance. Nutrients like carbon, nitrogen, and phosphorus are cycled, stored, and transformed in the soil, fueling plant growth and productivity. 

When soil health is degraded, it can’t complete these critical functions. In short, there are many benefits of soil health that have importance in agriculture!

It’s possible for stewards of the land to protect and restore soil health, guided by soil health principles. Soil health principles include maximizing the presence of living roots, minimizing disturbance, maximizing soil cover, and maximizing biodiversity. 

Farmers can use soil health principles to focus on improving soil health. Some ways to improve soil productivity include composting, cover crops, crop rotation, and soil assessments. 

Composting

Composting organic material can drive soil health improvement. It can supply organic nutrients which can substitute synthetic fertilizer and it can build up the soil organic mater, which helps stabilize aggregate stability and help the soil withstand soil erosion and gullying. Composting can promote a slow release of nitrogen, too. 

Cover Crops

Cover crops, like rye or crimson clover legumes, are grown to protect the soil during times it is normally fallow. Cover crops are one of many ways to improve soil productivity and restore soil health. There are many benefits to cover cropping, including increasing organic matter, suppressing weeds, reducing erosion, managing compaction and soil structure, and increasing water infiltration. Cover crops do a great job of addressing all four soil health principles!

Crop Rotation

Rotating planted crops has many benefits for yield, drought tolerance, and plant disease. Crops with different rooting depths can be used in crop rotation to improve soil structure. Cover crops can be used in crop rotation to increase diversity, improve organic matter, and prevent erosion. 

Soil Assessments

Collecting data to understand the current status of soil is a critical step to determining the best course of action to protect or restore it. Soil testing and field observations can help determine the health of the soil, including physical and biological properties. 

Automate Plant Nutrition 

Because of the importance of soil health in agriculture, taking active steps towards soil health improvement is important. Beyond production practices like cover crops and composting, farmers can find ways to improve soil productivity by streamlining plant nutrition with soil health and sustainability in mind.

Soil may not naturally have the right composition or rate of nutrients for a given crop. Or, the nutrients may not be in a plant-available form. In these situations, farmers must balance applying the right type and rate of fertilizers, because over-applying can impact soil health and negatively impact the environment. 

Soil health decreases when you underapply nutrients, and soil organic matter gets depleted. Using an adaptive approach to nutrient management and regular soil testing enables tracking of nutrient levels. Armed with this knowledge, it’s possible to re-supply adequate nutrients as needed, either through manure or synthetic fertilizers. 

Adaptive approaches to nutrient management take into consideration the current state of the field and its variability. Within a field, variability in soil texture, organic matter, nutrients, fertility, compaction, soil depth, and drainage is present. Planning for all of these factors is difficult, if not impossible, with a one-size-fits-all solution. A decision support system tool, like Agmatix’s Digital Crop Advisor, makes it easy to adopt an adaptive approach to nutrient management.

Agmatix’s Digital Crop Advisor

Digital Crop Advisor is a data-driven decision support system that automates plans and makes the best of crop nutrients for a specific site. Because of the importance of soil productivity and soil fertility, a tool like Digital Crop Advisor can help assess and meet a field’s nutritional demands with environmental sustainability and soil in mind. With Digital Crop Advisor, it’s easy to meet crop nutrition needs. 

The Digital Crop Advisor map interface uses two types of satellite imagery – Sentinel-2 and Landsat 8 – with a 5-day revisit time frame to ensure accuracy. Site-specific recommendations are created using various parameters that can be customized. Yield uptake, previous crop performance, removal rate, soil characteristics, and environmental impact are just a few of the possible parameters that can build a precise, customized, optimized plan. 

Digital Crop Advisor also connects to soil analyses to enable integrated crop nutrient management. This allows adjustments to the nutrition plan to be made as needed. Lab analysis results can be used in-season and pre-season to adjust for seasonal nutritional demand. 

In a specific sustainability section, a detailed carbon footprint assessment helps farmers understand the impact of a specific nutritional plan recommendation on the environment. The sustainability section also provides a benchmark for future recommendations. Farmers can continue to improve production practices and enhance inputs with sustainability in mind. Ultimately, meeting nutritional needs with a low environmental impact leads to high yields and productive crops that enable continued growth well into the future. 

Ensuring Your Soil’s Future

The future of the world’s food production depends upon the health and productivity of agricultural soils. Farmers have a big job in protecting and restoring soil health while feeding a growing, hungry world. 

With a global population projected to reach close to 10 billion by 2050 and the challenges of a changing climate, food security relies on the capability of current soils under production to continue to produce well into the future. 

At Agmatix, we support agriculture professionals in improving sustainable food production. Food production is both impacted by and impacts soil health, and Agmatix is dedicated to providing data and decision support for farmers and industry professionals as they navigate sustainable food production and crop nutrition. With tools like Digital Crop Advisor, Agmatix is helping pave the way to a sustainable, healthy soil future. 

If You Ate Today, Thank a Farmer

Thanksgiving originated as a day to give thanks for the harvest and the preceding year. It’s celebrated in many countries around the world, including the United States, Canada, Libera, and Germany. 

When it comes to gratitude, there is one group that stands out as particularly deserving of thanks: farmers. At Agmatix, we want to extend a special thanks to farmers this Thanksgiving for all they do, and share some of the tools we’ve developed as an agro-informatics company to help them do what they do best. 

Brought to you by Farmers

Roughly 2 million farms, of an average 445 acres, exist in the United States today. That number was once over triple at its peak in 1935 when agriculture was a labor-intensive endeavor that required many small, diverse farms. Between 1935 and 1970, the number of U.S. farms dropped rapidly as non-farm employment options and on-farm productivity both increased. Today, only 1.4% of the U.S. population is actively involved in farming. 

In more recent years, the number of farms has continued to decline, though more gradually. The amount of land and labor used in farming has also decreased. However, total farm output has nearly tripled from 1948 to 2019. Thanks to technological developments, a U.S farm feeds 166 people annually – both domestically and abroad. Those developments have included genetic improvements, chemical and equipment innovations, and farm management. 

It’s not just large farms that meaningfully contribute to the world’s food supply. A 2021 FAO report noted that smallholder farmers who farm less than 2 hectares produced an estimated 35% of the world’s food on 12% of all agricultural land. Smallholder farmer impact varies based on country, with some countries like China having smallholder producers responsible for 80% of contributions to the food supply. 

Around the world, there are an estimated 570 million farms – 9 out of 10 of which are family farms. Those family farms, of all sizes, produced around 80% of the world’s food. 

Regardless of farm size, location, or crop production, there’s one thing that farmers all have in common: they’ve got many, diverse responsibilities. It takes a lot to nurture a crop from seed to harvest, and farmers have to be experts in crop production. And despite technological advancements, much of crop production is still dependent on some degree of manual labor. 

Farmers have to deeply understand inputs like seeds, fertilizers, herbicides, fungicides, and insecticide options. They have to operate, maintain, and buy equipment. They work with landlords and banks and government agencies. Day-to-day operation requires logistical prowess and management skills. And understanding the crop and commodity markets to make important contracting and sales decisions requires expertise in that area, too. It takes a lot of knowledge to be a farmer! 

With everything on farmers’ plates as they try to put food on America’s plate, there’s definitely an opportunity to lighten their load. Agmatix has designed tools with farmers in mind – to make the tough decisions a little easier. 

Agmatix Tools for the farmer

Farmers have a lot of production aspects to manage and decisions to make. There are a few Agmatix tools that can make handling agronomic trials, decision-making, and data management easier. 

Agronomic Trial Management 

Many farmers use on-farm experiments to explore how different products and production practices impact yield or product quality on their land. The data and insights from these on-farm experiments can be powerful tools to support decision-making toward increased yield, profitability, and sustainability. But managing the trials and subsequent data can be a challenge. 

Agmatix makes planning on-farm experiments simple with intuitive design methodologies and multiple treatment combinations. With the drag-and-drop your trial layout directly on a map, it’s even easier to configure an on-farm experiment for success. While the on-farm experiment is ongoing, the extended mobile app capabilities keep farmers connected to the trial – even providing status updates to keep the experiment on track. Collecting data is easy, and can be standardized with legacy data for maximum impact. And if you’re interested in trials completed or ongoing near you, we can connect you with local trial coordinators to expand the available research knowledge of your “virtual toolbox”.

Digital Crop Advisor

Making crop input decisions can be challenging, even with the help of an agronomist. With Digital Crop Advisor, farmers can easily identify the best nutrient protocol to maximize crop yield and quality, all while minimizing environmental impact. Farmers can create plans for up to 12 nutrients across 150 different crops to unleash their full potential. And all of these capabilities exist in a user-friendly interface that allows farmers to dig into performance and analyze results in real time. 

Open Databases

The Global Crop Nutrient Open Database was created in collaboration with the Consortium of Precision Crop Nutrition (CPCN), the International Fertilizer Association, and other partners. The database provides important information to improve plant nutrition plans. It contains data on nutrient content, residues, crop yields, and other related data. It allows farmers to look at production and environmental factors that impact nutrient concentration to understand the total nutrients removed from the field when the crop is harvested.  

The Global Crop Nutrient Removal Database can fuel crop management decisions as farmers assess crop nutrient removal rates and trends of nutrient demand. They can make educated decisions about nutrient application to supplement soil nutrients. And the database is accessible to both farmers and researchers to ensure open science and collaboration. 

The Nutrient Omission Trial Database supports site-specific recommendations that optimize nutrient management. Using this data, farmers can compare crop nutrient requirements and plans. The database is sensitive to soil fertility variations and takes site-specific conditions into consideration, so farmers can confidently understand trends in nutrient use efficiency in a given geography. 

The database is a collaboration between Agmatix, the African Plant Nutrition Institute, the International Fertilizer Association, and Innovative Solutions for Design Agriculture. It brings legacy nutrient omission research data into a single, open dataset. And the data is standardized and harmonized to improve collaboration. The data is even enriched with geospatial information on soils, crops, weather, and more. 

Hats off to Farmers

As U.S. President Dwight Eisenhower once said, “Farming looks mighty easy when your plow is a pencil and you’re a thousand miles from a cornfield.” Farming can be risky, as the markets and weather are outside a farmer’s control. What works on paper might fail in the field, and Mother Nature might throw a curveball at any time.

At Agmatix, we know farming isn’t a 9 to 5 job for 40 hours a week. Farmers work in acres, not hours – and work until the job is done. They wear a lot of hats on the farm and need to be experts in many areas. Regardless of rain, snow, or sunshine, holidays or weekends, farmers can be found working hard to reap what they sow. That’s why we’re proud to serve farmers and support the time-honored work they do. 

Farmers produce the food, fuel, and fiber needed to keep the world running. So this Thanksgiving, don’t forget to pause and thank the farmer that helped your holiday dinner make it to the table. 

Top 5 2023 Agriculture Trends to Watch

The agricultural industry is always growing and progressing. Growing food, fuel, and fiber look much different today than it did hundreds of years ago. Mechanization changed agriculture’s reliance on human labor and horsepower. The Green Revolution harnessed selective breeding allowing more grain to be produced per acre. And today, technology like big data and artificial intelligence is driving another wave of change to improve the way we farm.

In 2023, keep an eye out for trends in agriculture that represent familiar challenges and new technologies. With USDA agricultural projections, the adoption and implementation of artificial intelligence (AI) for furthering crop optimization, and capabilities for remote big data analysis, we’ll outline some of the biggest upcoming agroinformatics trends. Then, we’ll share how Agmatix – a cutting-edge agroinformatics company – can help make sure you’re on top of the changing industry. 

1. Feeding a Growing World 

By 2050, the Food and Agriculture Organization estimates we will need to produce 60% more food to feed a world population reaching nearly 10 billion. Even if we hit that mark, 300 million people will still be grappling with food scarcity.

Developing countries will have a growing role in the global economy and food demand. Today, countries like the Philippines and Colombia are setting records for U.S. exports. In 2023, developing countries will continue to account for most of the growth in U.S. agricultural exports. 

Global crop production and trade continue to be variable, driven by factors like technological environments and the geopolitical climate. The USDA Foreign Agriculture Service estimates that by 2023, global rice consumption will be up by 7M tons to surpass 518M tons. Due to inflation, food prices are expected to rise between 3 and 4 percent.

The US Farm Service Agency also indicated that for 2023, there would be fewer acres dedicated to corn production in the United States – a decrease of 1.2M acres. This means that there will be a greater need for farmers to produce more within the land that they have to meet the needs for ethanol, animal feed, food, and fiber production with the growing global population.

The agriculture industry has been severely impacted by extreme weather events, COVID-19 supply chain disruptions, and worldwide economic concerns, as reported by the World Agricultural Supply and Demand Estimates in 2022. All of these factors, and more, have the potential to shift the reality of 2023 from these projected outlooks.  

2. Artificial Intelligence  

As technology adoption grows, the digital age in croplands continues to thrive! From measuring soil nutrient levels to monitoring irrigation amounts and using droney imagery to map crop fields and estimate disease presence, artificial intelligence (AI) will become a constant presence in agriculture productions of all sizes. 

According to BI Intelligence Research, global spending on smart technology and connected systems in the ag space is projected to triple in revenue by 2050. That includes AI and machine learning. AI spending alone is predicted to grow at a compound annual growth rate of 25.5% between 2020 and 2026, eventually reaching $4 billion. 

Synthetic data is often used to validate AI models. Synthetic data is based on real-world data and created by a model that uses the parameters of real-world datasets. A “digital twin” with synthetic data in a system that emulates real life can be particularly helpful in agriculture, where variables like soil types and weather conditions must be understood for real-world applications. Synthetic data is a powerful tool – so much so that Gartner predicts synthetic data will outpace real data in AI models by 2030. Synthetic data and AI will be trends that can be operationalized in agriculture in 2023 and beyond!

3.Precision Agriculture 

Precision agriculture harnesses smart, connected technology systems to improve grower outcomes. Growers can save time, money, and resources – which are all at a premium in today’s world – with tools that support crop monitoring and targeted crop nutrition plans. 2023 trends in digital agriculture will include exciting updates in the precision ag space. 

Agmatix’s Digital Crop Advisor is a data-driven decision support system that connects fields, farmers, and agronomists. Armed with seamless crop nutrition optimization plans, sustainability KPI monitoring, and data from over 150 different crops, farmers can make the best decisions for their fields and the environment. 

And in 2023, using precision agtech tools for precise fertilizer application will be more important than ever. At the start of the 2023 season, farmers will face far higher fertilizer costs. Nitrogen prices, driven by corn prices, natural gas prices, and geopolitical events, are expected to be higher than just a year earlier. Fertilizer costs have increased the most of any input in 2022. 

4. Big Data

The Internet of Things (IoT) and the copious amounts of data associated with sensors and equipment are driving forces in the agricultural revolution. While agriculture might not be what immediately comes to mind when you think of the Internet of Things (IoT), it’s a driving force in the agricultural revolution. Generally speaking, IoT represents billions of smart devices with chips and sensors that are connected to the internet. 

Smart agriculture systems that use IoT collect massive amounts of data. For example, networks of connected sensors, devices, and infrastructure create digital twins of the crop and field. This develops machine learning to further applications of technology in agriculture. 

Remote data analysis is becoming more common, too. In combination with AI, IoT-driven data analysis can provide insight into complex things like crop performance and climate patterns. Because crop performance is dependent on so many factors, data analysis can help farmers, agronomists and industry professionals better understand the impacts of technologies, production practices, or even the changing climate. 

In order to support big data in agriculture, larger compute-and-store architecture is needed for real-time data analyses. One example of this need is field trials – which create a lot of data. The potential for field trials to drive decision-making is only unlocked when the various datasets collected can be analyzed and turned into actionable insights.

Big data analytics can be the key to supporting big data in agriculture, especially in real-time. For data-driven decisions, Agmatix’s Agronomic Trial Management solution allows collected data to be standardized. This allows data to be evaluated, compared, and protected from potential loss. This powerful capability is housed within a user-friendly interface to support farmers and researchers in their analytics. 

Another aspect of big data is the databases in which data is stored. Open databases with standardized data that can be shared across different experts and stakeholders allow for maximum collaboration. Innovation and new ideas in agriculture are possible when open science is supported and researchers around the world can access high-quality data to support their work. Agmatix is leading the way in this area with the Nutrient Omission Trial Database and the Global Crop Nutrient Removal Database. 

5. Sustainability

Sustainability isn’t a new trend or topic in 2023, but it’s a safe bet that companies will double down on their efforts and they’ll need to quantify their impact on sustainability. The agriculture industry will have similar opportunities to engage in sustainability efforts with renewed vigor. 

Agmatix is committed to helping agriculture professionals improve sustainable practices in food production. Managing both crop yield and quality while addressing sustainable farming will be key. Digital Crop Advisor monitors sustainability KPIs in recommended crop nutrition and fertilization plans to estimate the carbon footprint and nitrogen leaching. This provides farmers and agronomy professionals with the information they need to choose the most sustainable crop nutrition plans. 

Along with positively impacting the environment, financial benefits of environmental sustainability also exist. 

For US producers, the USDA’s Natural Resources Conservation Service offers a variety of financial resources to support conservation and resource sustainability. Both technical assistance and financial support are available for improving air quality, conserved ground and surface water, soil health, improved or created wildlife habitat, and more. 

Carbon markets are one example of a new financial consideration for crop producers. While the U.S. carbon market is still developing, there is increasing interest in pricing carbon and evaluating offsets to drive farmer behavior. 

Agmatix Helps You Stay On Top of Trends in Agriculture 2023

Staying abreast of all the changes in the agriculture industry can be a challenge. But with pressures from a changing climate and growing population, adapting is necessary. Luckily, Agmatix has a hand in all of 2023’s major digital agriculture trends. As a single source for sustainability based on crop models and big data, Agmatix makes it easy to stay on top of agtech trends in 2023.  

As agriculture changes and trends influence priorities, quickly understanding the impact of new technologies or production practices is critical. But it’s difficult to isolate the impact of a given factor on crop yield or quality. Agmatix has solutions available to create crop models that predict plant growth and development based on crop management practices and the environment in which the crop is grown. These models can be used to assess future impacts of climate change, too!

Agmatix also connects precision ag, big data, and sustainability in the nutrient management space. With tools like Digital Crop Advisor, Agmatix makes it easy to make data-based, site-specific management and optimization decisions for over 150 different crops. And sustainability stays at the forefront with KPI monitoring. An agriculture field trial data tool has a lot of power to support profitable, sustainable decision-making. 

All of these tools are based on big data collection and agriculture data enrichment. Agmatix helps turn agronomic big data into powerful insights that can change the way we farm. Our platform ingests and harmonizes datasets – including legacy trial data. Turning that data into actionable insights is simple with statistical analysis that requires no code. And collaboration with big data is easy with standardized data through the GUARDS ontologies

Agmatix remains forward-looking when it comes to data and technology. With over 670 million data points and 53 million values of professional observation, it’s possible for Agmatix to generate synthetic data to support digital twins. Digital twins – where a computer uses real-world data to maintain statistic correlations – have enormous potential to emulate real life in agriculture. 

It would be possible to create a digital twin of a field trial to understand which variables are needed for the trial to be successful in the real world. Knowing the necessary soil type or weather conditions can drive meaningful results quickly, and at scale. Digital twins could also be used to fill in gaps in real-world datasets. Synthetic data based on statistical models can complete the picture if a remote sensor fails or equipment errors leave data missing. 

Agmatix is staying on top of agroinformatics trends by building on a foundation of big data and precision agriculture. Feeding a growing world while caring for the soil, water, and climate that sustains agriculture will be a challenge. In 2023 and beyond, Agmatix is making cutting-edge technology available to grow data for impact across the industry. 

5 Tips for Designing a Successful On-Farm Field Trial

Field trials are woven into the history and future of agriculture. They’ve been heavily used in formal plant breeding for the advancement of agricultural practices, products, and ideas.

Equipped with the tools to gather and interpret field trial data, you can now use agricultural field trials as a powerful way to determine what inputs and agronomic practices work best on your land. And you don’t need to be a research and development crop researcher or data scientist to glean insights from these trials. 

There are five key steps to use this scientific practice as an effective on-farm, boots-on-the-ground practice: identify the why, design the trial, gather data, interpret results, and draw conclusions. With these five steps, you can be better equipped to conduct a well-designed on-field experiment. 

1. Identify the “why”

Beginning with the end in mind is critical for successful agricultural field trial design. You need to identify the purpose of your on-field experiment and define the research question you want to answer. What do you want to learn from your agricultural experiment?

Good field research questions can be answered with a “yes” or “no” and will directly impact your farming operation or bottom line. Answering these questions will give you the information you need to make decisions about your agronomic practices, which inputs your crop needs, and how you can increase yield, reduce cost, and improve sustainability. 

A few examples of good research questions could be: 

“Would applying fungicide in-furrow in corn at planting increase yield?” 

“Does corn variety X yield higher than corn variety Y in well-drained soils?” 

“Does the use of a bio-stimulant increase corn yield?” 

The research question will then help you determine what controls or treatments are needed. 

The treatment group receives the change that you want to see the results of – such as a drought-resistant variety, an earlier planting date, or a new insecticide. 

The control or check group is identical in all ways to the treatment group except they don’t receive the treatment. The control is used to compare the results of the treatment. Control plots and treatment plots together are referred to as a block. 

The research question you select should provide a clear line of sight to the equipment and resources needed to complete the trial. As you plan out the research question, think through how the results can be measured, if the available test site is appropriate for the research question, and how the field history might impact the outcome of the study. 

Keep in mind that field trials can be conducted on a single farm or across many locations. 

2. Design the trial 

Any on-farm experiment requires a good field research design. While few fields out there fit the “flat and square” description, even slight variations in slope, fertility, and soil type can impact the integrity of field research. 

To control for field variability, field trial design should account for variation. Fields should be sectioned according to field characteristics. Using replication and randomization in the on-farm field trial design can reduce result bias and account for field variability as well. 

Replication means repeating field trial treatment blocks of the control or check and the treatment, often four to six times, across the field. This provides a large quantity of data. But, it may not minimize result bias, depending on the field characteristics. For example, a field whose northern end consistently yields higher means the northernmost treatment in each block would always yield higher. 

Randomization can be used in combination with replication to overcome field variability. The treatment blocks are placed randomly throughout the field as opposed to in a pattern, which removes any preference for one treatment over another. 

Research designs set the on-farm field trial up for success, both in execution and in data analysis. Research designs should include both replication and repeatability. Conditions within blocks should be similar but can differ from block to block. 

Some common agricultural field trial designs include paired comparisons, randomized complete block designs, and split-plot designs. 

A paired comparison is a field trial design for comparing any pair of treatments, such as two different fertilizer rates or crop varieties. Blocks include one plot of each treatment, placed randomly within the block. The block is replicated across the field, typically four to six times. A paired comparison is a type of randomized block design, but because it only involves one treatment and one control, statistical analysis of the results is more simple. 

For a comparison of three or more treatments, a randomized complete block design is a good choice. It involves a block that includes all treatments and an untreated check in a randomized order within the block. The block is repeated at least four times across the field. 

Interactions between treatments can be studied through a split-plot design, where main treatments have sub-treatments applied to them. Split-plot design can also be used when one of the treatments requires additional replication. This field study research design requires a larger area and additional management due to its complexity.

3. Gather data

Data collection is an essential part of your on-farm experiment. Take note of what data you need to collect before you begin your on-farm research.

You may already record things like planting, application, harvest dates, varieties and population(s) planted, and moisture.r Collecting your normal data in a consistent manner as well as capturing additional crop condition and growth data is critical. Data collected could include notes or photographs. Information such as node or pod counts, pest pressure, or storm damage will help you better interpret the study results after the crop is harvested. 

Yield data is a vital dataset to capture for your on-field experiment. Ensure weight is measured from a calibrated scale, and moisture and test weight are also captured, if applicable. Consider capturing data from the center rows of each plot to minimize the impact of potential treatment drift. 

Your on-farm research plan should include data management and storage processes. Cloud-based technologies and telematics make data collection and storage automatic, reducing the opportunity for error and creating efficiency. 

4. Analyze and Interpret Experiment Results 

Interpreting data collected during field trials can feel overwhelming. But analysis is what makes data actionable, and it requires going beyond a simple comparison of treatment averages.

Statistical analysis helps determine if there’s a significant difference between treatments – meaning the results aren’t due to chance or variability within the field. 

When an experimental is designed for statistical analysis – such as one of the agricultural field trial designs above – the Least Significant Difference (LSD) can be used to determine whether the results are likely to occur again in the future because they are due to the treatment. The LSD is based on a probability level that indicates how certain you can be that you’re correct. If the averages of the two different treatments differ by more than the LSD value, you can be confident that the result will likely occur again in the future.

You don’t have to be a statistician to have confidence in your on-field experiment design and results. Today, there are powerful agronomic tools and software available at your fingertips to streamline this process. Using technology to manage data, including storing it and sharing it with farm advisors, can also make the interpretation of field trial results simple. 

5. Draw Conclusions and Determine What’s Next 

Think back on the initial research question. What conclusions can you draw from your agricultural experiment? Based on your findings, you might decide to change production practices or that it just didn’t pencil out. Either way, the results are valuable to understand field performance and management outcomes. 

You might be ready to make a decision for next year based on what you found. Or, you might still have questions or doubts. To build confidence, you can conduct the same on-farm field study over multiple seasons or in different fields to ensure the results weren’t tied to that year or that location. 

It’s also worth thinking about how to enrich your dataset. Is there a neighbor you could partner with to expand the trial? What about an aggregated, legacy dataset that you could compare your results to? Knowing if the results are repeatable both on your farm and across a broader geography can be helpful when deciding to implement a new practice as a result of an on-field experiment. 

Agronomic Field Trial Design and Management Made Easy with Agmatix

The five steps for field trial success are building blocks for better decision-making on the farm. A well-conducted agriculture experiment can unlock yield potential and build confidence in the return on investment of new production practices. 

But, the process doesn’t have to be difficult. Agmatix’s holistic field trial software is a user-friendly platform designed to help you scientifically plan and manage on-field experiments. You can design your trial on-map with hundreds of treatment combinations. You can set up status updates for your field trial and easily communicate with those involved in the trial. 

Once your trial is complete, data collection for agronomic trials makes it easy to use big data to create value for your farm through operationalizing insights. You can standardize legacy data or combine agronomic data to dig deeper into information that was once difficult to piece together. With the whole picture in mind, field data management can take your on-farm field trials to the next level. You can be confident in the next best step to farm for the future. 

Agmatix’s Carbon Footprint Optimizer

Every choice and every action has an impact on carbon footprint. Unlike footprints in the sand, the results of our actions don’t just fade away with the next big wave to wash ashore. 

While that sounds like a big responsibility, it’s also a great opportunity to be more aware of carbon-intensive activities and actively work to reduce greenhouse gas emissions. 

What is carbon footprint?

Carbon footprint is defined as the total amount of greenhouse gases generated by a person, organization, action, or product. Common greenhouse gases include carbon dioxide, methane, and nitrous oxide. 

Globally, the average person’s carbon footprint is 4 tons. A person’s carbon footprint is impacted by how they travel, the electricity and water they use in their home, and even what they eat. 

The consequences of high carbon footprints have already taken their toll on the global climate. The Earth is now over a degree celsius warmer than it was in the 1800s. 

Limiting temperature rise to less than one and a half degrees celsius would limit the most challenging climate impacts, but globally, this is out of reach based on current emissions levels. Carbon dioxide emissions, if left unmitigated, could increase the global temperature by as much as 4.4 degrees Celsius by the end of this century. 

Carbon Footprint in Agriculture

People aren’t the only ones who have measurable carbon footprints. Products, businesses, and even industries can measure their carbon footprints – and agriculture is no different. 

It’s estimated that US agriculture emitted 669.5 million metric tons of carbon dioxide in 2020, which equates to 11.2% of the total GHG emissions in the US (compared to industrial, transportation, commercial, and residential categories). That’s the equivalent carbon footprint of nearly 167.4 million global citizens. 

Globally, agriculture, forestry, and land use change contribute nearly a quarter of all Greenhouse Gas Emissions. 

From production to food supply chains, an estimated 31% of human-caused global greenhouse gas emissions come from the global agri-food system. That’s approximately 16.5 billion tonnes of greenhouse gas emissions, primarily stemming from deforestation and livestock manure. 

Five footprint-influencing crop production processes include crop production products, fertilizer production, fertilizer application, energy and fuels, and other inputs such as seed or water. To better understand the impact of these processes, CropLife International compared them across their main production regions to determine their overall carbon footprint. 

The results show that different crops have vastly different ratios of carbon footprint by the crop production process. For example, energy and fuels contribute the most to cotton’s carbon footprint, but fertilizer production has the largest impact on the carbon footprint of maize and wheat. 

Across cotton, maize, rice, soybeans, and wheat, crop production products had the lowest percentage contribution to the crop’s carbon footprint. These products increase the efficiency of other inputs and production practices, such as fertilizer or tillage, because they protect yield. 

They also reduce the need for in-field activities that have high greenhouse gas emissions. In some cases, crop production products have a net positive contribution to carbon footprint. 

By contrast, fertilizer production and application processes combined make up the majority of every crop’s carbon footprint. Variances in fertilizer inputs across global geographies and production regions have a large impact on the carbon footprint of the same crop if grown in different places with different fertilizer sources and production practices. Reducing fertilizer carbon footprint is crucial for lowering agriculture’s overall carbon footprint. 

Different production practices also impact the carbon footprint of a given crop or farming operation. In fact, changes to how farmers farm could reach up to 20% of the required GHG reduction for agriculture by 2050. Some of the most impactful improvements come from production practices like variable rate fertilization, low or no-tillage, and controlled-release fertilizers.  

One specific, significant source of greenhouse gas emissions is the manufacturing of synthetic nitrogen fertilizers used to increase crop productivity. Nitrogen is needed for plants to grow, but it can’t be obtained through the air like oxygen or carbon dioxide. Ammonia is a mass-produced compound that contains nitrogen in a form that plants can uptake through the soil. Today, ammonia is commonly used in the form of urea, diammonium phosphate (DAP), compound fertilizers, and ammonium bicarbonate. 

Synthetic nitrogen is noted as the single most important element impacting nitrous oxide emissions from farmland and agricultural soils. It’s also the second-most commonly produced chemical in the world, and its manufacturing contributes 1-2% of worldwide carbon dioxide emissions. 

The carbon footprint of nitrogen fertilizer includes ammonia synthesis and conversion of ammonia to N fertilizer products as well as transportation and fossil fuel mining. 

While the invention of ammonia as a source of synthetic nitrogen fertilizer dramatically increased the number of people that a single acre of land could feed, it also had some downsides. On average, crops only absorb roughly half the nitrogen in applied fertilizers. The remaining applied fertilizer leeches off of fields into waterways or is broken down in the soil, releasing nitrous oxide into the atmosphere. 

Synthetic N has been a strong contributor to food safety as well as grain production. Farmers may be applying excessive synthetic nitrogen to croplands due to a lack of scientific guidance and the influence of traditional ideas. Proper timing, placement, and rates of synthetic nitrogen are critical to minimize nitrogen leaching and reduce the carbon footprint of nitrogen fertilizer. 

Increasing carbon footprints and a warming planet will have a negative impact on agriculture. Climate stress on crops, such as increased salinity, more extreme drought, and weed-favorable environments are expected. 

Shifting precipitation patterns will impact crop yield, by as much as 50% for wheat crops in South Asia. The combination of more challenging environments for agriculture and the increasing world population gives rise to concerns about global hunger.  

How Agmatix Can Help Reduce Your Fertilizer Carbon Footprint

Agmatix is a global agro informatics company that aims to solve data standardization challenges to increase crop yield and quality while promoting sustainable agriculture. Agmatix’s crop management software platform combines agronomy data and advanced AI into one revolutionary platform that converts data into actionable insights. 

Agmatix’s Digital Crop Advisor solution allows growers and their agronomic advisors to keep an eye on sustainability key performance indexes (KPIs). Digital Crop Advisor provides a calculation of the carbon footprint and allows users to customize fertilizer, crop protection, and nutrient plans, including controlled release fertilizers, with a planning tool. 

It’s difficult to manage what’s unseen or unmeasured.  While Agmatix can’t calculate the carbon emission of fertilizer production, we can calculate it when the fertilizer is used – providing visibility to previously difficult-to-measure information. Farmers and agronomists can easily factor fertilizer, nutrient, and pesticide carbon footprint into their management decisions, and actively work to reduce fertilizer carbon footprint while improving yields. 

Agmatix’s crop nutrition optimization solution develops unique nutrient recommendations with sustainability in mind. With these recommendations in hand, it’s possible to quantify and compare sustainability KPIs like carbon footprint and nitrogen leaching for customers. This technology makes it possible to refine previous plans or build sustainable practices in new fields.  

And because the carbon footprint information is available prior to work being done in the field, it’s never too late to make adjustments with environmental sustainability in mind. 

Our Digital Crop Advisor helps farmers reduce the fertilizer and crop protection carbon footprint of their crop production by optimizing crop nutrition management. This ensures just the right amount of fertilizer is added to the field. 

Producers won’t unknowingly apply excess amounts of fertilizer and can ensure the product is applied at the right rate, at the right time, and in the exact right place. The Digital Crop Advisor makes it simple and streamlined to optimize carbon footprint

Using Digital Crop Advisor can also help sync up application times to reduce passes through the field. With fewer fertilizer applications delivering an optimized rate, fuel use is reduced – a benefit for the farmer’s wallet that reduces the fertilizer carbon footprint. 

For the future 

The United Nations (UN) has set global goals for net-zero greenhouse gasses. This means reducing greenhouse gasses to nearly zero and planning for remaining emissions to be reabsorbed from the atmosphere. Oceans, forests, and even pastureland can be part of the reabsorption of greenhouse gasses. 

UN’s Net Zero Commitments

The UN has identified a global need to reduce emissions by 45% by 2030 and reach net zero by 2050 in order to keep global warming below the 1.5 degree Celsius warming limit set by the Paris Agreement. Staying below that warming threshold will keep the planet liveable and avoid the greatest risks of climate change. Despite global efforts from a coalition of institutions to reach net zero, current greenhouse gas emissions are not on track to reach the UN’s goals. 

Reducing both individual and agricultural carbon footprints directly ties to the UN’s net zero goals. Efforts to reduce fertilizer carbon footprint, among other efforts in agriculture, can have a broad impact on the overall carbon footprint and greenhouse gas emissions. 

Our Commitment

Agmatix is committed to improving sustainable food production and quality. With tools like the Digital Crop Advisor calculation of carbon footprint, farmers gain an understanding of the environmental impacts of fertilizer production and application. 

Farmers can take action to reduce fertilizer carbon footprint by customizing fertilizer plans to optimize both crop nutrition and environmental sustainability. Agmatix makes it easy to connect everyday actions with environmental sustainability and carbon footprint reduction. 

Digital Crop Advisor, Your Automated Nutrient Optimization Planning Tool

Crop fertilizer supplements the needed nutrients that plants aren’t already getting from the soil they’re grown in. In order to grow and develop properly, and in turn, have a high yield, different plants require different ratios of nutrients. Different crop areas have inherently different soil nutrient levels – even within the same field. 

With the aid of precision fertilization tools, precise nutrient management customizes fertilizer inputs to what a specific crop in a specific area needs. This approach is tailored to meet crop needs as efficiently as possible while improving yield. By utilizing a precision fertilization tool, like the Digital Crop Advisor, you get decision support that ensures the right nutrient inputs are used at the correct rate and time.

Nutrient management is a hot topic for many in the agriculture industry. Recently, the United States Department of Agriculture announced additional funding for sustainable agriculture and nutrient management. With the passage of the Inflation Reduction Act on August 15, 2022, $19.5 million will go to conservation funding to support sustainable agriculture. Crop nutrition management will be incentivized through conversation programs. 

Part of that funding will go to a new outreach effort highlighting nutrient management’s economic benefits. The campaign will highlight the potential net savings to farmers who adopt a nutrient management plan – estimated at an average of $30 per acre for cropland.

If all of the 89 million acres of cropland that exceed the nitrogen loss threshold implemented a nutrient management plan, the average net savings would reach $2.6 billion. 

Beyond the financial impacts, minimizing nutrient runoff through crop nutrition optimization is key to reducing global emissions from agriculture. The Food and Agriculture Organization of the United Nations estimated that in 2019, synthetic nitrogen alone made up over eight percent of farm emissions. Integrated nutrient management is a critical aspect of sustainable agriculture.

Site-specific management of crop-essential nutrients provides field-specific optimization. In a meta-analysis of site-specific nutrient management, grain yield across all crops increased by 12%, and profitability increased by 15% while 10% less nitrogen was applied. 

Optimization of crop-essential nutrients doesn’t just provide yield and profit benefits; more precise applications and fewer fertilizer inputs can reduce leaching. Leaching results in excess soluble fertilizer escaping the crop that needs it and harming the surrounding environment. 

Precise application of fertilizer through precision agriculture and fertilization planning is a win-win for farmers, the environment, and the global population. However, implementation requires having the right data and expertise. Crop nutrition optimization planning tools can streamline the data collection and planning process, all while making it simple to connect with experts and advisors. 

Data-driven support with Digital Crop Advisor

Agmatix’s Digital Crop Advisor solution is a data-driven, AI-fueled crop nutrition decision support system (DSS) tool for automatically creating and optimizing site-specific, customized nutrition plans. Digital Crop Advisor helps you maximize yields sustainably from every crop and acre with cutting-edge technology that translates agronomic data into actionable insights. 

Our Digital Crop Advisor customized fertilization planning tool creates crop nutrition plans at the field level for applications of up to 12 nutrients. These plans are bespoke based on needs such as crops, geography, seasonality, and production goals. Both pre-season and in-season crop nutrition plans are possible. With the capability to plan for over 150 crops, even the most unique crop rotation can be managed with the crop nutrition optimization planning tool. 

The Digital Crop Advisor includes an executive dashboard that provides user-friendly access and control of the organization’s activity. With a view of the entire organization’s crop nutrition assets in a single location, users can identify issues proactively, manage crop protocols, and analyze data to extract actionable insights. 

Integrated nutrient management in sustainable agriculture is also supported by the crop nutrition decision support system by quantifying the carbon footprint of nutritional plan recommendations. Sustainability is at the forefront of the work Agmatix is doing with Digital Crop Advisor. 

Dive into the Digital Crop Advisor Features 

Global Customer Management

For farmers’ trusted advisors such as agronomists, global customer management is made easy with a main screen map-based view that shows activity for multiple growers at one time. This view serves as an agronomic data hub, making it easy to manage the availability, integrity, and security of data. 

View key grower information such as name and contact information while also seeing map-based field locations. Legacy and current agronomic data at the field level are viewable for benchmarking purposes. Global customer management streamlines the process of growing customers and growing customer success season after season. 

Precision Agriculture Capabilities

Our mapping does even more than geolocate fields! Satellite imagery with a 5-day revisit time cycle allows users to view both soil-adjusted biomass and vegetation biomass indexes. This biomass imagery allows for zone creation and precise scouting. Soil characteristic data is available, as well as crop moisture. Users get a unique bird’s-eye view of crop progress throughout the growing season, making it easy to determine field performance and assess nutritional needs. 

Nutritional Requirements

Once current field and crop conditions are well understood, crop nutrition management and optimization are simple. Users can build recommendations for up to 12 crop-essential nutrients and ensure those recommendations are specific to the crop type, variety, and expected yield. 

Our software is an integrated solution that helps field agronomists create tailored, customized crop nutrition plans which may include controlled-release fertilizers. Site-specific recommendations are generated using inputs such as yield uptake, historical crop performance, removal rate, soil characteristics, environmental impact, and more. Users can even segment nutritional needs based on phenological stages to ensure fertilizers are as efficient as possible.  

Laboratory Analysis

Soil, water, and tissue analyses are inputs to fertilization adjustments based on the crop’s specific needs. Information from these scientific sources further specifies the specific nutritional needs for crops before they’re planted and as they grow. This dataset adds increasing precision to the recommendations for nutrition management.

Nutritional Fulfillment

Agronomic data from several sources is used to create the automated fertilization plan – an actionable recommendation with measurable benefits to crop productivity and sustainability. Users can create an automated fertilization plan to meet crop nutritional requirements. The approach can be customized and adjusted based on a grower’s wants, needs, and local availability. Recommendations are compared to the specific crop’s nutrient surplus or deficiency at each phenological stage within the growing season.  

Nutritional Plan Overview

The automated fertilization plan has instant reporting functionality that can be shared with the click of a button through email, pdf, text, or WhatsApp. The plan overview includes all agronomic information, including the crop type, seasonal graphs, crop requirement tables, recommended products and application rates, and the sustainability rating of that specific crop nutrient recommendation plan. 

The report gives a comprehensive view of the most critical crop protocol management information for growers to put into action. The Nutritional Plan Overview is a great reporting feature for advisors, farmers, and applicators depending on the depth of information included. 

Sustainability

The nutritional plan overview includes a sustainability section that shares a detailed carbon footprint assessment of the nutrition recommendations for that crop. This feature allows benchmarking for future recommendations to continue refining the nutrition approach for crop productivity and environmental sustainability. 

Use it now!

How can the Digital Crop Advisor crop nutrition decision support system fit in an agronomic practice or on a farm? Easy-to-use functionality is available now, including pre-season management adjustments to customize nutritional recommendations based on a variety of crop protocols.  

Use pre-season and mid-season lab analysis integration (soil, water, and tissue) to make crop nutrition optimization dynamic. Global customer management, nutritional requirements and fulfillment, lab analysis, and sustainability information can all easily be at the fingertips of agronomists and growers. 

Integrated Nutrient Management In Sustainable Agriculture

More targeted crop nutrition management means more effective use of fertilizers and fewer resources wasted through leaching or ineffective application. The Sustainability Section takes the guesswork out of determining the environmental impact of crop-essential nutrients through big data. 

Benchmarking for future recommendations ensures sustainability continues to improve with additional seasons and additional data. Meeting crop nutrient needs to maximize yields while reducing environmental impact becomes easier and more precise over time. 

Environmental sustainability is an opportunity for all agriculturalists. The United Nations 2030 Agenda for Sustainable Development is aligned with five aspects of the Zero Hunger Challenge; the first pillar is Sustainable Food Systems. Supporting soil health means naturally sequestering carbon and building climate resilience. 

Among other things, principles for sustainable soil management include protecting the soil from chemical degradation, restoring degraded lands, maintaining water quality, and enhancing soil productivity based on its natural capacity. Integrated nutrient management in sustainable agriculture addresses these pillars. 

Precision agriculture tools are a data-driven way to implement crop protocol and integrated nutrition management. Agmatix’s Digital Crop Advisor customized fertilization planning tool enables farmers and their advisors to easily take action towards improving or protecting soil health while maximizing productivity and yields.

Benefits of Sharing Agronomic Data and Using Data Standardization

Agronomic data can be a powerful tool for agriculturalists. More and more, data is being generated on the farm and used across the agriculture research industry. Data can be useful in many different ways, just like a utility tool on a farm. But without standardization, data is siloed and difficult to share or use.

Standardization is Vital For Data Utility

Standardization of data, or converting it into a common format for processing, analyzing, and sharing, allows teams to collaborate efficiently. Agronomic data sharing widens the team’s effort to address agronomic knowledge gaps because everyone has access to the critical data. 

Agronomic data collaboration also helps validate agronomic practices to promote more efficient production. With supporting data, sharing the value and potential yield impact of a production practice is possible – and increases the likelihood of adoption of that practice on-farm. Ultimately, this leads to more efficient and sustainable crop production. 

Challenges Resulting from Data Separation

When data is collected asynchronously or comes from varied sources, it easily becomes fragmented and siloed. By nature, these silos prevent collaboration that enhances the value of the data. When two experts have collected data using different protocols and disagree on methodology, comparing datasets is difficult and the data inherently becomes less valuable. 

When data is fragmented, users spend valuable time trying to unite datasets from different sources into a singular structure. In a time and cost-sensitive industry, this process can waste valuable time and resources, diluting the potential for the data to make an impact. 

It’s easy for data to wind up in a separate state. Without agronomic data standardization, each researcher may use a different naming system, set of protocols, or data collection method. When it comes to validating the agronomic data, fellow researchers and peer reviewers may struggle to validate the data because of the lack of standardization. 

Benefits of Sharing Agronomic Data 

An agronomic database can assist in the sharing of agronomic data. Overarchingly, agronomic data standardization drives innovation, visibility, and market penetration for agronomic data companies. 

Innovation

An agronomic database can be used to foster innovation across the industry – from farmers to researchers to agronomists. Combining the unique perspectives of professionals in the industry with the data provides the critical context and expertise to use data for insights. 

Digital precision ag technologies are one example of innovation spurred by data. Digital technologies can improve production quantity and quality while reducing labor requirements and environmental impact. 

These technologies can only succeed if agronomic data companies have access to the foundational data required to expose trends, correlation, and causation. But just having access isn’t enough – these agronomic data companies must be able to ingest and use the data, which requires it to be in a singular, standard format. 

Data can also be used to train and calibrate models. Agronomic modeling is a useful tool for understanding the complexities of crops, environments, changing climates, and interactions with production practices. Through modeling, tools can be developed to make recommendations for farmers to improve pest and disease control, protect yield from the planting date, and even recommend the best variety for a specific site. 

Visibility

A perspective change is often what’s needed to draw conclusions and make decisions. In agriculture, data can provide this different perspective. Agronomic data sharing can be a tool for teaching and learning about agronomy and crop production. It can also be used to advocate leadership entities to support decision-making at high levels. 

As topics like food security, soil health, and carbon become increasingly important to global leaders, data can help experts and farmers alike determine how to produce crops sustainably, even when conditions are changing. 

Agronomic data standardization can also make data visible and useful to farmers that may not otherwise have access. Smallholder farmers may be able to adapt production practices such as fertilizer application as an example of uses of an agronomic database. 

One example of an agronomic database that increases the visibility of agronomic data is the Crop Nutrient database. Formed by the Consortium for Precision Crop Nutrition, the database provides access to global field trial data. Agriculture professionals, armed with this data, can develop crop nutrition advisory solutions to address yield, profit, and human, soil, and environmental health. 

Market penetration

Farmers may feel that the data collected on their farms adds value – but not for them. Agronomic data collaboration with companies that ensure farmers understand their rights and have access to relevant data is step one in ensuring insights from agronomic data have good market penetration. 

Consultants may also partner with farmers for agronomic data collaboration, supporting clients that are developing new products like fertilizers and bio-stimulants. 

Simplify Your Agronomic Data Standardization with Agmatix

Agmatix’s Axiom technology can be used by in-house ontologies with multiple sources to standardize data. This agronomic data harmonization platform ingests and integrates data for predictive modeling, research, and field trials. 

The GUARDS (Growing Universal Agronomic Data Standard) protocol enables agronomic data standardization in the Axiom platform by saving data into a language that’s usable by any researcher. The GUARDS protocol informs three different engines: 

  1. The Ontology engine was developed by experts to create standard definitions. This engine facilitates the definition of relations between data points and was developed with cues from the data itself. 
  2. The anomaly detection & integrity engine is powered by a technology that proactively monitors data integrity and quality using ML capabilities. The system understands and alerts for abnormal items in the data, as compared to other, similar data sets.
  3. The unit converter engine translates different measurements into a standardized unit. This helps connect and customize databases and values. 

The Axiom platform enables agronomic data insights and models through standardization. One example of a strong use for this agronomic database is comparing on-farm trial data with worldwide research data. Whether comparing dry weight vs. leaf weight, yield vs. fertilizer applied, or understanding sustainability KPIs, the Axiom platform makes it easy for farmers and researchers to collaborate and support on-farm decision-making. 

Our Axiom platform is also a helpful tool for small and large teams or teams that work with external collaborators to share and interpret data together. When the data in the agronomic database is all in the same language, researchers are able to compare apples to apples. 

This speeds up the research process while also increasing the reliability and trust in the research. This also creates alignment and increases perceived validity within the research community, rather than allowing study results to work against each other due to differing methodology viewpoints. 

Developed and trusted by professionals

Who better to create tools to support research than the researchers themselves? Through understanding the intricacies and challenges related to agronomic data standardization, professionals were uniquely positioned to develop a platform that would address common challenges and unlock additional value. 

Across the agriculture industry, opportunities abound to use data as part of problem-solving. There are big challenges ahead, with a changing climate, increasing population, and the need for high-quality food to prevent food scarcity. Unlocking data across agronomic data companies, researchers, and farmers allows the data to inform decisions and answer important agricultural questions. 

From yield and quality to production practices to breeding and bio-stimulants, agronomic data standardization is the key to many doors which open to a better world. 

How On-Farm Experiments Can Improve Your Bottom Line

Since 1985, Illinois farmer Marion Calmer has been doing his own on-farm experimentation. And as a result of those decades of research, he’s made every corn and soybean acre $100-$150 more profitable.

He’s not the only farmer who can thank on-farm studies for improving his bottom line. Mississippi farmer Thomas Hairston shared with Farm Journal that his on-farm research proved lower cotton populations were the most profitable. Participating in a multi-year study with the Practical Farmers of Iowa revealed to Jack Boyer he can reduce his nitrogen rate in corn. And Farm Progress reports that farmers participating in the Nebraska On-Farm Research Network gained an average $31.25 per acre.

But even if an on-farm experiment doesn’t result in a change that benefits the bottom line, it’s still valuable information.

“The purpose of doing on-farm research is not only showing me the profit potential, but it’s where I gained the confidence to make a change in my operation,” Calmer says in his YouTube video. “You can’t go out overnight and say, ‘I’m going to do something new.’ You’ve got to gain the confidence from some place, that’s what the on-farm research has done for me.”

It’s clear that putting in the effort to do your own on-farm experimentation can pay off. And with the help of technology, it’s easier than ever to set up and run your own trials.

Defining On-Farm Experimentation

As defined in a Nature Food article, on-farm experimentation is “an innovation process that brings agricultural stakeholders together around mutually beneficial experimentation to support farmers’ own management decisions.” The research occurs in farmers’ fields, at scales meaningful to them. 

There are six principles of on-farm experimentation

  1. Farmer centric: Farmers fuel the research process
  2. Real systems: Uses farmers’ own management and scales
  3. Evidence-driven: Insights are anchored in data
  4. Expert-enabled: Specialists add value
  5. Co-learning: Emphasis on engaging by sharing
  6. Scalable: socializing mechanisms

While there are benefits to collaborating with ag extension personnel, scientists and other farmers in research, growers are not limited to doing meaningful research with others. As long as they understand the basic principles of designing and executing a trial, growers can succeed with their own independent studies. Especially if they can tap into the power of precision ag technologies and digital tools.

Technology Key to On-Farm Experiments

Precision ag has made it easier than ever for farmers to conduct their own experiments and farmers are seizing the opportunity. John Fulton, an ag engineer at The Ohio State University, told CropLife that results from a recent survey found that nearly 85% of growers using precision ag technology are doing on-farm experiments.

GPS guidance can pinpoint trial boundaries, input application technologies can make sure a product is applied at the right rate in the right place, and yield monitors make for easier data collection. 

Growers can also use precision ag to identify potential research areas. Yield maps can identify yield inconsistencies throughout a field, while aerial imagery can detect potential pest problems, wet spots, or other issues worth exploring.

But just because these technologies make it easier to execute on-farm experiments, they don’t guarantee that a farmer is setting up their trials properly or interpreting the data correctly. 

To make sound decisions based on the results of a research study, the trial must be set up for replication and randomization. It’s not uncommon for a grower to do a simple side-by-side trial, with half the field under treatment and the other half the control plot. But as NC-ANR Academy explains, replicating and randomizing treatments is what allows the data to be statistically verified, so farmers can be confident the results of the trial are due to the treatment and not by chance or other factors. Without the assistance of digital tools to design these trials, growers are at risk of investing time and resources into a study that may not produce usable information.

Kansas State University Research and Extension precision ag economist Terry Griffin, who is a member of the Frontiers in On-Farm Experimentation, also points out that farmers are still missing a standardized way to collect data and easily use it to benefit their operations. He calls for the need of “automated, rigorous computer algorithms,” to prevent human bias, saying that “even the best scientists sometimes have a bias to them.”

Agmatix App Simplifies Trial Setup, Data Collection and Interpretation

Agmatix recognizes the need to better design on-farm experiments, collect data easily and have confidence in the results. Which is why we’ve launched the free Agmatix app for on-farm experimentation for growers. 

Whether a grower is online or offline, the app will help them conduct an experiment without any assistance. In a matter of seconds, they can set up basic trial information, set the treatments so they’re both replicated and randomized, set the desired measurements, and have a standardized way to collect research data that is preserved and secure. The app can also statistically analyze the data so they can have confidence in the decisions they make based on the results.

The app we’re launching will also help connect interested farmers with ongoing trials near them, as well as past trial data on relevant crops and products they may be interested in. 

The Agmatix app is free and supported on both Apple and Android devices.

The app is available in the and , so you can start planning your on-farm experiments for next season. 


And if you need help taking your on-farm experiments to the next level, our Agronomic Trial Management software can help you plan out a statistically verified trial, guide you with end-to-end trial management, standardize collected data, and provide valuable insights based on the results.

Ensuring Productive Food Systems with Agmatix

October 16th is World Food Day. It commemorates the founding of the United Nations Food and Agriculture Organization (FAO) in 1945. It reminds us to reflect on food production, world hunger, and sustainability. 

It’s a critical time to be considering these factors. The COVID-19 pandemic, climate warming, inflation, and international tensions in recent years have all impacted the global struggle for food security. Nearly 10 percent of the global population suffered from hunger in 2020, a sharp rise compared to 8.4% in 2019. 

A dramatic increase in prices for critical foods like wheat, barley, rice, rapeseed, and sunflower oils is moving vulnerable countries into a food crisis. Increasing costs for critical inputs are partly to blame. And the changing climate is increasing pests and diseases, all while changing the nutrient makeup of staple crops. 

By the numbers, ⅔ of those experiencing acute food insecurity are rural food producers. 3.1 billion people – representing nearly 40% of the world’s population – cannot afford a healthy diet. Women are 15% more likely than men to be moderately or severely food insecure. 

In 2021, 193 million people experienced high acute food insecurity. High acute food insecurity necessitates humanitarian assistance to survive. Over half a million endured catastrophic conditions, resulting in starvation and death. 

This environment is not so different from the one in which the FAO was founded. In the later years of the Second World War, global leaders recognized the importance of nutrition and the criticality of addressing agricultural challenges using science and technology. 

The FAO was established with the goals of raising nutrition and living standards globally, improving food production and distribution efficiency, and improving the condition of rural populations. As the FAO has evolved, it has benefitted the expanding world economy and increased global freedom from hunger.

Production By the Numbers

Agriculture is a growing contributor to the world economy. Between 2000 and 2019, global value added from agriculture, forestry, and fishing grew by 73%. Agriculture was also an employer of 27% of the global workforce. 

In the same time period, the production of primary crops hit a record high and grew 53%. Sugar cane, maize, wheat, and rice make up half of the global primary crop production. 

Both vegetable oil and meat production increased between 2000 and 2019. Vegetable oil production, driven by demand for palm oil, has more than doubled. Meat production has grown by 44%. 

China, India, the United States, and Brazil are the world’s biggest food-producing countries. They have a sizable land mass, proper climate zones, and a large population supporting their agricultural production. 

China is the world’s largest agricultural producer, pumping out $1.5 trillion in annual output. China is also the world’s most populous country, but only has 10% of the world’s arable land. China’s eastern and southern regions are highly productive, leading the country to produce one-fourth of global grains. China also leads the world in cereal grain, cotton, fruit, vegetable, meat, poultry, egg, and fishery production. 

Despite the high production, China has moved away from full self-sufficiency in 2000. In 2020, the country imported nearly 23% of its food needs due to declining soybean output and loss of farmland. In 2019, China became the world’s leading importer of agricultural products. 

India has both the second-largest population and second-highest agricultural output in 2020. India leads the world in the production of milk, jute, and pulses. It’s second in the world in the production of rice, wheat, sugarcane, fruit, vegetables, cotton, and groundnuts. 

India is the world’s largest exporter of refined sugar and milled rice. India was the 9th biggest exporter of agricultural products in 2019. 

The United States follows China and India in agricultural production. However, the U.S. has a fraction of the agricultural workforce of China or India. Top commodities for the country include corn, soybeans, dairy, wheat, and sugar cane. 

The U.S. led the world in agriculture exports in 2020. China, Canada, Mexico, and Japan are leading importers of U.S. agriculture products. California made up 13.5% of the U.S.’s ag production in 2020. 

Fourth-ranked for agricultural production is Brazil. The Brazilian economy has long focused on agriculture. Brazil’s exports ranked third, behind the U.S. and the Netherlands. The country is the top exporter of soybeans, raw sugar, and poultry. China imported nearly $30 billion of Brazil’s agricultural exports in 2020.  

Between 2009 and 2050, the global population is expected to increase by a third. Feeding close to 10 billion people in 2050 will require a 70% increase in food production. In developing countries, agricultural production will need to almost double. Annual cereal production will have to grow by almost one billion tonnes and meat production by over 200 million tonnes. High-quality and high-volume agricultural products will be required to prevent increasing food insecurity.    

How Can We Continue to Produce Better Food? Agmatix Can Help

Agmatix is dedicated to equipping farmers and their advisors with tools for using high-quality, standardized data to improve food production with a lower environmental impact. As food production volume and quality become more critical with population growth and changes in climate, increased precision agriculture and data-based decision-making will be required to help advance food security. 

Agmatix’s agro informatics tools support global agriculture production. From corn to coffee, over 150 different crops and crop-essential nutrients are represented in our data. Agronomic Trial Management, the Open Data Crop Nutrient Platform, and the Digital Crop Advisor empower field-level decisions with high-level data and powerful insights. 

Agronomic Trial Management

On-farm experiments help to understand the localized response to a specific production approach. Using the outcomes of an on-farm field trial to inform decision-making can be a powerful tool to improve productivity, but the process of executing the trial and analyzing the outcome can be burdensome. Agronomic field trial management can be a challenge for farmers. 

Agmatix makes it easy to plan, monitor, collect data, and analyze field trial outcomes.  Advances in agricultural tools and technology, such as our Agronomic Trial Management tool, can help improve crop health, yield, and quality. Ultimately, harnessing agronomic trial data can lead to helping advance food security. 

Planning the field trial is straightforward with Agmatix’s holistic, on-map planning process. A layout planner allows users to select the design methodology that works best for their on-farm research. Hundreds of treatment combinations are available. 

Once the agronomic trial is ongoing, an end-to-end project management module simplifies visibility and control. Researchers and field trial operators can communicate directly. Users can assign tasks and monitor task status. 

With Agmatix, standardizing collected data is easy, allowing users to efficiently evaluate trials. A user-friendly data collection tool unlocks data for comparison and prevents data loss. And instant reports and analysis allow for quicker decision-making. 

With Agmatix, farmers can harness the power of using agricultural technology to streamline field trials and do their part to advance food security. 

Global Crop Nutrient Removal Database

Understanding the sustainability of crop nutrient programs goes hand-in-hand with long-term food security. Agmatix has partnered with the Consortium for Precision Crop Nutrition, whose Global Crop Nutrient Removal Database is a resource to scientifically estimate nutrient removal and efficiency in the agriculture production environment. This database helps create site-specific recommendations for the most sustainable nutrient management. 

Crop yield and residue and their respective nutrient concentrations are the factors that influence crop nutrient removal. Farmers can improve crop management by understanding crop nutrient removal rates and long-term trends of nutrient removal rates. This data will help make agriculture more sustainable and help fight for food security worldwide.  

Digital Crop Advisor

Digital Crop Advisor is a precision agriculture tool that uses technology, data, and a decision support system to optimize crop nutrition. Crop-specific data supports sustainable nutrition management to create high-yielding, high-quality crops that help feed the world and help advance food security. 

Digital Crop Advisor allows farmers to get specific with sustainability. They can monitor sustainability key performance indicators – like carbon footprint and nitrogen leaching – to make the best decisions for their farm and the environment. 

Seamless crop nutrition optimization recommendations and organic manure calculations help farmers easily implement sustainable practices. And the Digital Crop Advisor even integrates with lab analysis data from tissue or soil samples, so farmers can use all the data they collect on the farm to fuel decision-making. 

World Food Day and Agmatix

This World Food Day is a perfect time to consider taking your farm or business to the next level of sustainability with Agmatix. Precision agriculture technology that uses standardized data to inform decision-making at the field level will help farmers produce more high-quality food while minimizing environmental impact. And that’s what it will take in the continuous quest for food security. 

Agmatix makes using data and agricultural technology on the farm easy. With Agronomic Trial Management tools, the Global Crop Nutrient Removal Database, and Digital Crop Advisor, farmers and their advisors can make data-based decisions at the field level. On-farm experiments, crop nutrient removal data, and optimized nutrition plans all fuel sustainable agriculture practices. 

On this World Food Day, we consider those around the world that are struggling with hunger, those that work hard to produce food, and those that work every day to enhance the sustainability of world food production. There’s a great opportunity to harness the best agricultural technology to make a difference in the lives of millions by addressing food security and building a better world of tomorrow. 

Save Time, Increase Sales and Gain Farmer Trust with Digital Crop Nutrition Support

Crop nutrition management has never been a one-size-fits-all approach. Several variables factor into a farmer’s fertilizer plans. And now they’re facing unique circumstances that make developing an effective and efficient strategy even more difficult.

Field agronomists need to develop customized crop nutrient plans that navigate these challenges while still producing a high-yielding, quality crop. That means making scientifically-backed recommendations based on high-quality data. With the right crop nutrition digital support system, agronomists can be confident that their plans will deliver exactly what the farmer needs. 

Farmers’ Challenges Affect Fertilizer Plans

The importance of developing an efficient, effective fertilizer plan has never been more important than we’ve seen in the last year.

Farmers faced record high fertilizer costs in 2022, and though prices are cooling off, they’re still looking at tight margins in 2023—especially since, as farmdoc daily points out, many farmers were able to lock in fertilizer prices for the 2022 season while they were lower in 2021. That opportunity for 2023 does not exist.

As a result of these prices, and in some cases fertilizer shortages, president and CEO of The Fertilizer Institute Corey Rosenbusch told AgWeb that farmers should lean on their retailers and other agronomic advisors to help them be as efficient with their products as possible. 

Fertilizer prices aren’t the only factor driving the need for efficiency. As Reuters reports, consumers are pressuring food producers to support farms using sustainable farming practices. So companies like PepsiCo are now sourcing sustainably grown ingredients. 

Environmental concerns from nutrient runoff are also pushing farmers to adopt sustainable practices. In some places, like the Western Lake Erie Basin in Ohio, there are restrictions around fertilizer application. While other states like Iowa have voluntary regulations that growers may be trying to abide by, especially with the growing demand for enforceable restrictions.

Complexities in Meeting Crop Nutrient Needs

On top of trying to keep costs low and ensuring a fertilizer program is the most sustainable it can be, agronomists still need to create a plan that provides crop-essential nutrients needs so that growers can still have healthy, high-yielding fields.

That’s no simple task given that they need to account for:

  • Crop type and its specific macro and micronutrient needs
  • Crop rotation
  • Soil type and conditions
  • Other management factors, such as tillage practices
  • Weather conditions
  • Available input options
  • Labor, equipment and technology available

All of those factor into designing a crop nutrient plan and determining which fertilizer products to recommend. 

Data-Driven Digital Tools Remove Guesswork

Given the complexity of creating a crop nutrient plan, agronomists need data-driven digital solutions to ensure they’re accounting for all of the different variables and designing a fertilizer program that best meets their farmers’ needs.

When built on a wealth of high-quality data, a crop nutrition decision support system can take all of the specific variables, from weather to crop prices to the impact on the environment, and provide insight into what would be the best fertilizer plan based on a grower’s budget and goals.  

Ultimately, it can help build trust between agronomists and growers. Because data removes any bias an agronomist or grower may have, it helps avoid decision-making that’s based on emotions or gut feelings. Growers can trust that their agronomists aren’t just trying to sell them a product. All parties can feel confident that what’s being recommended is the best plan because the science and facts back it up.

Optimize Crop Nutrient Plans for 150+ Crops

Agmatix’s Digital Crop Advisor delivers exactly what agronomists need—a scientific-based crop nutrition decision support system that provides customized guidance for their farmer customers.

Digital Crop Advisor is built on 12 scientifically proven crop nutrient data profiles, which allows agronomists to create unique nutrient plans for over 150 different crops, using data integration of crop-essential nutrients.

It’s also the first crop nutrition optimization planning tool that can account for controlled release fertilizers. Field agronomists can include these fertilizers in their crop nutrition management plans and see the nutritional distribution along the different phenological stages of the crop. This includes the expected depletion date of the controlled release fertilizer, so they can schedule the next fertilizer application at the optimum time.

Not only does Digital Crop Advisor tailor nutrient plans to the specific crop, it also taps into data from previous customers’ plans or other fields using our technology to provide sustainable nutrient recommendations. Agronomists can quantify and compare sustainability KPIs, such as the program’s carbon footprint or if there’s susceptibility for nitrogen leaching. Each plan will show the total carbon emissions and a percentage of how each nutrient contributes to that total.

Digital Crop Advisor is also adaptable. Suspect crop prices will take a dip or wonder how the plan would change if you got above average precipitation? Run different simulations and the customized fertilization planning tool will correctly adjust crop nutrient needs so you can compare recommendations.

And agronomists can create a customized nutrient plan instantly. Our software tool rapidly integrates field boundaries, creating an automated fertilization plan quickly, accurately, and efficiently.

Farmers are under a lot of pressure to grow crops that will still benefit their bottom line, meet their buyers’ yield and quality expectations, and do so in a way that doesn’t harm the environment. The right nutrient plan is crucial in this, and with the right crop nutrition decision support system, agronomists can become their trusted partners in making that a reality. See how Agmatix’s Digital Crop Advisor can help your agronomists become that trusted partner and optimize their crop nutrition management plans. Contact us today to schedule a demo.

Coffee: Balancing the Complexity of Production and Providing Caffeine

A cup of coffee is a revered part of many people’s morning rituals. Whether it’s a cup with friends or work day savior, over 2 billion cups of coffee are consumed daily and the global coffee market was valued at a staggering USD 465.9 billion in the year 2020! 

Since October 1 marks International Coffee Day, we wanted to share some background on the process of coffee production, where coffee is produced in the world and the unexpected but important role of agronomics in coffee quality and production. 

Understanding the Coffee Production Process

While a delicious cup of joe might magically appear out of a coffee pot each morning, there’s an interesting backstory on the beans and how they make it into our cups. There’s a ten-step process from plant to bean to brew. 

  1. Planting: Coffea trees are shrub-like plants that were domesticated in Ethiopia. They grow from a coffee bean – an unroasted one, of course! Coffee plant cultivation involves planting in beds, protecting seedlings from direct sunlight, and watering frequently. 
  1. Harvesting: Coffee trees can take three to four years to bear fruit. Called coffee cherries, the fruit of coffee trees are bright red when ripe and often require hand-picking due to ripening time variability. In some places, particularly where the plantations are flat and large, harvesting has been mechanized. Cherries can either be picked by stripping all the fruit off an entire branch at once, or individually based on their ripeness. Typically, there is one main harvest of coffee cherries per year. 
  1. Processing: Cherries are processed quickly after harvesting to avoid spoilage. Processing can be done using a dry method or a wet method, depending on local resources. The dry method involves spreading freshly picked cherries on large surfaces to dry in the sun and raking them throughout the day. 

The wet method removes the coffee cherry pulp to dry the cherries with only the parchment skin. The cherries travel through a pulping machine to separate the skin from the pulp, and then the beans are separated by weight as they sink or float in water channels. Heavy, flavorful fruits will sink. After being separated by rotating drums, the beans are fermented in water. 

  1. Drying: After 12 to 48 hours, wet method processed beans must be rinsed and dried. Beans are sun-dried or machine-dried in large tumblers until they reach 11% moisture. 
  1. Milling: The milling of coffee cherries is a multi-step, pre-export process. Wet or dry processed coffee is hulled to remove the parchment layer or the entire dried husk from the cherries. Polishing may be done to remove any remaining silver skin. Then, beans are sorted and graded by size, weight, color flaws, and imperfections. Beans can be sorted using air jets to separate beans by weight. Defective beans are removed. 
  1. Exporting: The beans are ready for international travel. They’re loaded onto ships in shipping or plastic-lined containers. 
  1. Tasting: Coffee tasting, or “cupping”, is done by professionals looking for bean visual quality, aroma, and taste. Beans are roasted in a small roaster, then ground and infused for the copper to smell and taste. Coffees are evaluated for the purpose of blending different beans or building the right roast. 
  1. Roasting: Beans are roasted to convert them from green coffee into aromatic beans that are ground and used to make that cup of joe. Beans are roasted at about 550 degrees Fahrenheit. Once the beans reach 400 degrees, the oil inside the beans begins to emerge and creates the well-known coffee flavor and aroma. Beans are cooled by air or water. Once roasted, beans are on the clock for reaching the consumer. Once roasted, beans are on the clock for reaching the consumer. 
  1. Grinding: Beans are then ground to the appropriate coarseness for the intended brewing method. The finer the grind, the more quickly coffee should be prepared. 
  1. Brewing: The last step in the coffee process is extracting the coffee through brewing. Coffee can be brewed in many different ways, depending on the grind and drinker’s preference. For example, espresso machines use fine coffee and high pressure to extract coffee and make the perfect latte or macchiato! 

Coffee Around the World

Coffee has as diverse growing locations as it does drinking locations. Over 50 countries around the world produce coffee, though the ideal location for coffee cultivation is along the Equatorial zone, between latitudes 25 degrees North and 30 degrees South. This area is referred to as “The Bean Belt”. Different varieties of coffee thrive at different altitudes, in different soils, and at different temperatures. 

Guatemala, Costa Rica, Columbia, Brazil, Ethiopia, Kenya, the Ivory Coast, Yemen, Indonesia, Hawaii, Mexico, Puerto Rice, and Vietnam all have notable coffee production. Coffee from different areas produces different flavors and aromas. 

Hawaiian coffee takes advantage of good rains, black volcanic soils, and tropical clouds to provide a rich, aromatic, medium-bodied coffee. In Puerto Rico, two major growing regions produce coffee with a balanced body and acidity and a fruity aroma. Indonesia is known for fine-aged coffees with a deeper body and less acidity. Vietnamese coffee plant cultivation happens on small plantations but the country is quickly becoming a major coffee production powerhouse. Much Vietnamese coffee is used for blending due to its mild body and light acidity. 

The quality and flavor of coffee are dependent on many variables. Plant variety and genetics, soil, climate, rainfall, sunlight or shade, altitude, and nutrient inputs all change the characteristics of coffee. For example, Guatemalan coffee grown at altitudes of 4500 feet is described as “strictly hard beans” and has a unique spicy or chocolatey complexity. Yemeni coffee cultivation happens in areas where water is sparse and the beans tend to be small and irregularly shaped, with a deep, rich, distinctive flavor. 

The Agronomics Behind the “Cup of Joe”

Coffee plant cultivation requires agronomic expertise. Soil, rainfall, shade, and wind all answer the question of what affects coffee production. 

Soil requirements for growing coffee include deep, well-drained soils. Oftentimes, volcanic-origin soils are good for growing coffee. Leached topsoil, poor drainage, or solid rock near the surface of the soil will not meet the coffee trees’ requirements for growing. 

Coffee will perform well in soils that handle high rainfall by distributing the moisture in the soil. However, fertilizer leaching is a risk in these areas, requiring intentional management for coffee plant cultivation sustainability. 

Rainfall distribution is important for coffee production. Too much moisture can increase vegetative growth and reduce fruiting, while a short dry period can help synchronize the cropping cycle. Plantations can irrigate to manage the moisture needs of the coffee plant.

Shade can be used intentionally by coffee growers to address climates that are too warm for coffee. Restricting light can also prevent trees from overbearing in areas where fertilizer supplies are limited. 

Coffee trees are susceptible to wind and require a windbreak to prevent tearing, cupping, and removal of leaves or even ripe cherries. 

What affects coffee production?

Outside of Mother Nature, coffee characteristics can be heavily influenced by cultivation practices. Coffee plantations will take fertilizer and irrigation requirements into consideration as well as pesticide application needs. 


The first five years of a coffee plant’s life are the most critical. During this time, coffee plant nutrition needs must be met to encourage vigorous root and leaf growth. Young trees have high phosphorus requirements to promote root production. 

Once trees are bearing fruit, the coffee plant nutrition needs change to sustain leaves, stems, roots, and fruit. In the plantation orchard system, pruned wood and coffee leaf litter return nutrients to the soil. But, nutrients may be lost by leaching, erosion, or volatilization. Farmers may apply fertilizer for coffee nutrient management through an irrigation system or broadcast it by hand. 

Second-year and older trees can have leaf tissue analyzed to identify any nutrient imbalances. Tissue tests can identify nutrient deficiencies before they become visible symptoms.  Fertilizers for coffee plants can be customized based on the results of the tissue analysis. 

Irrigation is an important topic in coffee cultivation. In areas with less than 60 inches of rainfall a year, irrigation is recommended, though water needs are often referred to in terms of “crop coefficient,” or the crop’s water demand in relation to the evaporation occurring in an open pan of water in the orchard. Water needs also differ for young, nonbearing trees and two-year and older trees.  

Drip or micro-emitter systems are often used on plantations. Applying water is a balance between cost efficiency and plant health. Overirrigation is a risk for coffee plant cultivation and can cause poor root development and even death. Overirrigation is more likely if there is a solid rock pan below the topsoil that catches water and allows it to stand. 

Weeds, diseases, and insects can be other challenges to growing coffee. Coffee plantations can control what major weeds they experience through manual or mechanized means. The major vines of concern are morning glory, ivy gourd, and bitter melon. Weed control can cost up to 10% of annual growing costs. Groundcovers can be used preventatively for weed control. 

Green scale is one of the most impactful pests for coffee as it sucks sap from the coffee plant and covers the leaves in black sooty mold that reduces photosynthesis. Green scale can be biologically controlled with white halo fungus. Growers can also use soil or foliar applied imidacloprid to control green scale. 

Black twig borer is another damaging insect in coffee plant cultivation. It causes wilting and death of leaves and wood, leaving bark black. The best control for these beetles is keeping trees healthy and pruning infested laterals. 

Coffee is also susceptible to nematodes and disease. Root-knot nematodes enter and feed on roots, disrupting growth. Cercospora leaf spot is a common fungus in coffee-growing areas but can be controlled by managing growing conditions and coffee plant nutrition needs. 

Other funguses to which coffee is susceptible include coffee leaf rust and coffee berry disease. Coffee leaf rust causes yellow-orange lesions on the leaves, and the fungus is spread by wind, rain, or even the clothing and coffee bags around the coffee trees. Copper fungicides and resistant varieties are the main methods of control. 

Coffee berry disease causes brown, sunken lesions on green cherries and eventually will destroy the bean. Quarantine is the best control for the fungus. The most aggressive strain of coffee berry disease is found in Africa, though other strains are seen worldwide. 

What are the challenges of growing coffee?

In recent years, coffee production has been troubled by a variety of challenges. Around the world, June 2022 production estimates have been lowered from the December 2021 projections. Too much rainfall and cloud cover has cut back Columbian crop production, while Honduras production estimates have dropped 1.4 million bags due to leaf rust slashing yields. Brazil’s drought and cold cultivation season in 2021 caused farmers to cut down coffee trees.  

With harvest 2022 in sight, Brazil’s coffee crop is projected to be at its lowest since 2014. This year’s arabica cherries have smaller than usual beans leading to disappointing yields. Smaller beans mean more are required to fill weight-based bags, and overall output is reduced. 

Sustainable Production and Improved Quality with Agmatix

Coffee crop cultivation can be a challenge! Between pests, disease, climate sensitivity, and meeting plant nutrition needs, attaining high yields and good quality cherries can feel very difficult. The good news is that crop management software can help collect data and support decision-making for the most productive, most sustainable crop possible. 

Agmatix is an agro informatics company dedicated to transforming data into insights. For coffee growers, the Digital Crop Advisor platform can help optimize crop nutrition management, customize fertilization planning, and monitor sustainability KPIs of nutrition plans and agronomic practices. 

Our Digital Crop Advisor is a decision support system with scientifically-proven data to inform specific crop protocol management. Insights are based on over 150 crops and crop-essential nutrients, and multi-device support makes it easy to optimize crop nutrition with the planning tool. 

The Digital Crop Advisor also simplifies coffee nutrient management with seamless crop nutrition optimization plans through a customized fertilization planning tool. It’s key to meet coffee plant nutrition needs, but those needs may differ based on plant age, growth stage, climate, and more. Because fertilizer for coffee plants has a risk of leaching, especially in certain soil types, precise and well-timed applications are key. Digital Crop Advisor makes balancing meeting coffee plant nutrition needs with protecting the environment easier than ever before. 

Agronomic data insights are available through the analysis of aggregated and standardized data. Both legacy and ongoing research data can be visualized and analyzed to support decision-making. Agmatix also makes it easy to harness the expertise of advisors and researchers through a collaboration wizard.  

Understanding the environmental impact of decision-making in coffee cultivation can be challenging. The Digital Crop Advisor allows users to monitor sustainability KPIs such as carbon footprint and nitrogen leaching. Integrated support of lab analysis is supported, and the customized fertilization planning tool helps producers understand the environmental impact of nutrient recommendations before they are used on the plantation. 

While “sustainability” is a buzzword in many areas of agriculture, coffee production can have a  big environmental impact, and coffee trees are particularly sensitive to changes in climate, making changes in climate of particular concern for coffee plant cultivation. Coffee quality can be negatively impacted by changing light exposure, altitude, water stress, temperature, carbon dioxide, and nutrient management. Using tools like Agmatix’s Digital Crop Advisor is a win-win for higher production, better quality, and sustainable production for the future of a good brew.  

Agmatix’s Commitment to a Zero Emissions Future

Zero Emissions Day, September 21st, is a day to pause and think about greenhouse gas emissions and the future we envision for the planet. It is a reminder that preserving the Earth in a liveable state requires the global temperature increase to be limited to 1.5 degrees Celsius. The Earth is already roughly 1 degree Celsius warmer than pre-industrial levels

Net zero is the state in which any greenhouse gas emissions are either eliminated or re-absorbed from the atmosphere. Reaching net zero will require all hands on deck, across countries and industries. It provides an opportunity for agriculture to reduce its carbon footprint, too. 

Agriculture plays a big role in global emissions. According to the U.S. Environmental Protection Agency (EPA), agriculture is responsible for roughly 18% of global greenhouse gas emissions. And that number is increasing; over the last forty years, agricultural emissions stemming from human activity have increased by 30%. Recent activity has impacted agriculture’s emissions. 

A majority of the 669.5 million metric tons of carbon dioxide equivalent emissions from agriculture came from direct nitrous oxide, and direct methane was also a big contributor. The good news is that agriculture emissions were roughly four percent lower in 2018 compared to 2000. However, nitrous oxide emissions from synthetic fertilizers and crop residue incorporation were more than 35% higher in the same period. 

Agriculture is also at risk from emissions. Climate change is expected to reduce corn, soybean, rice, cotton, and oat yields starting even as early as right now! This could increase the need for irrigation in crop production, all while regional water availability continues to be a challenge. 

Weather patterns are expected to change. Farmers are expected to have the flexibility to adjust production practices to changing weather and resources, but flexibility could be harnessed now to reduce the carbon footprint of agriculture. 

There’s a global effort to reach net zero. Agriculture is a key to reaching this goal. More than 70 countries have set a target to reach net-zero emissions, and over 1,200 companies have set targets as well. 

Innovative solutions are essential to meet the challenges the world is facing today in agricultural production. The more information farmers, researchers, and agronomists have, the better the chances to leverage agricultural big data to make informed decisions in the field that will result in higher yields, better crop quality, and lower environmental impact. 

Agmatix is one such company providing innovative solutions for agriculture because sustainability is a foundational part of who we are. Agmatix helps agronomists, researchers, and farmers customize crop nutrition recommendations based on a verified, scientific-based crop nutrient decision-support system that not only improves yield but also provides a sustainability scoring index for each crop’s nutrient management plan.

Agmatix’s Commitment to Sustainability

Agmatix is committed to helping reach the zero-emission goal by 2050. Our unique data science and AI agricultural platform transforms agricultural big data into actionable insights for farmers, researchers, and agronomists to make informed decisions to improve crop yields, and nutritional quality, while ultimately helping reduce greenhouse gas emissions and the agricultural carbon footprint.

Plant nutrition carbon footprint optimizer

The Digital Crop Advisor solution uses agronomic field trial data to allow farmers to optimize crop nutrition – applying just the right amount to give plants what they need, without an unnecessary runoff, waste of resources, and lower environmental footprint.

With Agmatix, farmers can calculate the carbon footprint of their fertilizer plans based on field characteristics, agronomic practices, crop and fertilizer type, and more. Farmers can even run simulations of different recommendations to compare environmental and yield tradeoffs to learn how to reduce their agriculture carbon footprint. 

Personal environmental responsibility and employee volunteering

Agmatix is also committed to a culture of sustainability. Employees are encouraged to take personal environmental responsibility and volunteer in ways that positively impact the world we live in. 

You Can Lead this Zero Emissions Day

Opportunities abound to be a part of Zero Emissions Day. At home, select energy-efficient products. Even small changes such as unplugging your phone as soon as it’s charged and turning the heat down a single degree can make a big difference! 

On the go, find opportunities to cycle or hop on the bus. Carpool when you can, or try taking the train.

When it comes to food and agriculture, don’t forget about local and seasonal products. Give alternative proteins a try. Avoid food waste by buying what you need and using it all. Compost any food waste you do have. 

On the farm, the USDA suggests that changes in production practices could both cut greenhouse gas emissions and pull carbon from the atmosphere through carbon sequestration. 

This is a great opportunity for agriculture to take the lead in carbon footprint reduction! How might this happen? Through things like cover crops that, in the right conditions, can reduce erosion and nutrient runoff. Or producing biofuels that replace fossil fuels and can impact emissions across many industries. Reducing or eliminating tillage can impact soil health and carbon content as well as energy use on the farm. 

For a Better Tomorrow 

Agriculture has the added challenge of reducing carbon footprint while feeding a growing population. Ron Baruchi, CEO of Agmatix noted that “Growers, agronomists, researchers and ag industry experts are tackling today’s biggest challenge – providing food security for the world’s growing population.” With improved crop management decision-making, better crop quality, higher yields, and lower carbon footprints are within reach. 

A swift change is needed in agriculture to reduce the carbon footprint of agriculture. With a big goal of a 45% reduction in emissions by 2030, farmers and the agriculture industry can help get sustainability trending in the right direction. 

Together, Agmatix and farmers can better understand the carbon footprint of individual farms using. Farmers can make informed decisions about nutrient programs and their impact on the farm’s carbon footprint. By growing data for impact, Agmatix and farmers are growing a better, more sustainable tomorrow.

Continued Innovation at Agmatix

We, at Agmatix, are committed to helping our customers make field-level decisions and gain actionable insights from their agronomic data. The agriculture industry is facing a number of challenges, such as the rapidly growing world population, climate change, supply chain disruption, and limited resources. This is why we continue to innovate and bring the latest solutions to address these challenges!

As an agro informatics company, our goal is to support agriculture professionals worldwide in using big data to make informed decisions. We do this through three core solutions: Insights & Models, Agronomic Trial Management, and the Digital Crop Advisor. Learn more about these solutions below.

Agronomic Trial Management

With our Agronomic Trial Management solution, it’s never been easier or more efficient to plan, run, and analyze your field trials. The solution’s interface has been redesigned for greater ease of use and improved navigation. In addition, users can drag and drop their trial layouts on a map, and create repetitive protocols and customizable data collection forms. 

The Agronomic Trial Management platform allows for flexible data collection using mobile devices, tablets, or a desktop, as well as field-level reporting. This feature allows users to create trial plans and perform analysis on the go. 

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Get the visibility needed to control your field trials in real-time  

Our solution helps you streamline your workflow and better collaborate with researchers and field workers by providing automatic status updates and task assignments. With full visibility of your research trials, you have the added capabilities for governance over trial protocols and task forms to help you streamline collaboration and analysis. This tool for managing field experiments enables you to “be in the field” from wherever you are.

  

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Standardize collected data for comparison, evaluation, and data loss prevention

Our user-friendly agronomic collection tool automatically standardizes field data as it’s entered and preserves it using our GUARDS protocol. For data entry, a wide range of agronomic domains and parameters are possible and we support both iOS and Android devices.

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Insights & Models

We can help you unlock true value from your agronomic data. Our Insights & Models solution analyzes and standardizes data from your field trials or experiments and converts it into power agronomic actionable insights & models. 

The Insights tool is equipped with additional statistical capabilities most frequently used in field trial analysis (paired t-test, independent t-test, one/two-way ANOVA, and Tukey-Kramer) to bring data science capabilities and agriculture predictive analysis to your fingertips. Graphical user interface, application

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Access and Analyze all of your agronomic data in one unified place

Our user-friendly interface provides you with access and viewing capabilities of all your agronomic data in one unified place. We can ingest and harmonize your legacy trial data so you can easily compare that to your ongoing field trial data (using the Agronomic Trial Management) and perform cross-trial analysis.

With our Insights solution, you can visualize and analyze your field trials in a single location and access your data at any time. Our pre-built advanced statistical analysis widgets help you easily create customized on-demand reports to analyze your trial data, enabling you to make data-driven decisions and optimize your R&D efforts.

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Collaborate with researchers and extract exactly what you need

We understand that no two research data sets are the same. That’s why our tools allow you to standardize data from multiple experiments, no matter where your research collaborations take you. 

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Calibrate and verify statistical and machine learning models

With our platform, you can create data-driven predictive models in a single workspace. Our insights and agronomic modeling solution combine data science with agronomic trial data to help you apply agro informatics best practices.

Start Analyzing Data with Advance ML Models

Digital Crop Advisor

Build stronger relationships with your customers and optimize growers yields sustainably. With our state-of-the-art technology and data insights as a decision-support system for crop nutrition management, you can gain complete operational overview capabilities within our management dashboard. 

Managers can get an overview of all operations, and see how specific products are performing in sales and crop yields across the globe. This dashboard also allows you to oversee the environmental footprint of the recommended nutrition plans, ensuring continuous sustainability improvements. 

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Operational control and visibility of your organization’s crop yield and nutrition, product performance, and sales

From a single location you can track and monitor your organization’s nutrition recommendations across all agronomy and sales teams. Get real agronomic actionable insights into the number of fields and coverage of each operation regionally and globally!

Our digital agronomist sales support tool also includes an easy-to-use dashboard that allows you to filter by time, crop, and more to help with data-driven decisions.

Empower field-level agronomists and agriculture professionals with verified, scientific-based crop nutrient decision-support engine

With Digital Crop Advisor, you can optimize your crop nutrition management using our 12 scientifically-proven crop nutrient recommendation data profiles. With over 150 different crops in our database, we can help you create unique nutrition plans for whatever you’re growing. This digital solution for fertilization planning will help you maximize crop productivity and sustainably use your resources.

It’s also the first crop nutrition optimization planning tool that accounts for controlled-release fertilizers. Field agronomists can include these fertilizers in their crop nutrition management plans and see the nutritional distribution along the different phenological stages of the crop. This includes the expected depletion date of the controlled release fertilizer, so they can schedule the next fertilizer application at the optimum time.

The standardization of field-level agronomic practices helps with operational control, visibility, and unification across the growing season.

Implement and verify crop protocols throughout your organization

Utilize our fully customizable fertilization planning tool for your grower needs and get unbiased, product catalog-based recommendations. This means that you can get crop performance analyses with rapid geospatial implementations (based on your local practices) and additional support with your lab analyses Our crop data management systems allow for protocols to be shared across your organization to improve the quality, consistency, and reproducibility of practices and data collection.

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Leverage big data insights for sustainable crop nutrition

Sustainability is a foundational part of Agmatix’s company and toolkit. With our Digital Crop Advisor, you can get customized nutrient recommendations based on carbon emissions analyses. By monitoring sustainability KPIs, our tools help you reduce nitrogen leaching and optimize nutrient use efficiency so you’re able to quantify your sustainability efforts. Our decision support system allows you to easily create crop nutrition plans to optimize your crop production while keeping sustainability in mind. 

Start Optimizing Your Crop Nutritional Plans

Agmatix makes it easier to manage your agronomic field trials, increase yields, and ensure sustainability with our trial management platform and digital crop advisor solution. With a mission to help you turn agronomic data into actionable insights, we offer various solutions to facilitate that process.

Agmatix Powers Open Databases that will Revolutionize Crop Nutrition

Farming is more than just planting seeds at the right time in hopes of rain, and harvesting after a few months. It requires a deeper understanding of the crops’ specific needs throughout their life cycle. Crop nutrient management relies on many aspects – the importance of the right nutrient source, applied at the right rate, administered at the right time, and in the right place.

With proper nutrient management, farmers can realize a number of benefits, including increased yield, increased returns, maximized nutrient use efficiency, and improved sustainability.

Farmers today are faced with many challenges. Not only do they need to manage their land and crops, but they also have to deal with the ever-changing climate and its impact on farming. One of the ways that farmers can adapt to these challenges is by using advanced fertilization tools. 

These tools allow farmers to harness the data they have available for their own field and determine the exact amount of nutrients that are needed to meet crop needs.

Agmatix understands the importance of crop nutrition and the need for data to help make the most informed decisions in the field. That’s why they’ve teamed up with other global groups to make two new databases available to the public to help revolutionize crop nutrition – the Global Crop Nutrient Removal Database and the Nutrient Omission Trial Database.

Global Crop Nutrient Removal Database

The goal of this project is to create a global, open, and comprehensive database to highlight the relationships between inputs and outputs of nutrients in crops under an array of production and environmental conditions. 

With the help of the International Fertilizer Association (IFA) and Wageningen University & Research (WUR), the Global Crop Nutrient Removal Database allows users to look at production and environmental factors affecting nutrient concentration and determine the total amount of nutrients removed from the field when the crop is harvested. This database is focused on nutritionally and industrially important crops and is equipped with data on nutrient content, residues, crop yields, and other associated data.

Crop nutrient removal is defined as the total amount of nutrients removed from the field in the harvested portion of the crop. Knowing how crop nutrient removal can impact the next planted crop allows for advanced fertilization tools to help determine the specific nutrient amounts needed to be added back into the soil if needed.

The Global Crop Nutrient Removal Database has the capability of positively impacting numerous aspects of your crop nutrition work:

  • Improved crop management through better assessment of crop nutrient removal rates, long-term trends of nutrient demand, and economical budget planning.
  • Optimized nutrient application, sustainable agriculture, food security, and environmental quality in arable crop production worldwide.
  • Promoting open science and collaboration by making the database accessible for researchers looking to improve knowledge on crop nutrient uptake and removal.

As a data contributor to the Global Crop Nutrient Database, there are numerous benefits to you right away and long-term.

  • Cost and time savings in analyzing data – Our team of professional data scientists will combine all of your data into one standardized format cost-free.
  • New insights from your data – Perform a meta-analysis to get better insights into your products, and analyze your agriculture data across various trials, locations, and fertilizer types.
  • Advertise your organization and products – Data contributors will be recognized on the database’s public website.
  • Strengthen your company’s environmental credentials – Share your organization’s contribution to a global initiative that has far-reaching environmental and social impacts.
  • Intensify your fertilizer support tools – The database will be available to you, allowing your organization to use worldwide data to strengthen its fertilizer support tools. 

Nutrient Omission Trial Database

The aim of the Nutrient Omission Trial Database project is to support site-specific recommendations and optimize nutrient management. This is a collaboration between the African Plant Nutrition Institute (APNI), IFA, Innovative Solutions for Decision Agriculture (ISDA), and Agmatix to consolidate legacy nutrient omission research data from multiple sources into a single, standardized, and open data set.

Legacy nutrients, or nutrients that have accumulated in the soil after decades of fertilizer and nutrient application, can be at sufficient levels to produce high yields for crops but need to be measured and considered in management plans. Having data related to these accumulations in a single database will allow for a streamlined integration into advanced fertilization tools and management plans.

The Nutrient Omission Trial Database offers several benefits to users to optimize nutrient management:

  • A unified database of Nutrient Omission Trials, covering different crop production environments and different nutrients. This database enables communication, comparison, queries, analysis, and references for easy access and high-quality data.
  • Improve site-specific fertilizer recommendations, as the database is sensitive to variations in soil fertility and other conditions between farms. Better assessment of the status and trends in nutrient use efficiency in different regions or countries.
  • Different data sets from multiple sources, standardized and harmonized with Agmatix’s technology platform (aligned with the GUARDS protocol). This improves knowledge and collaboration between Consortium for Precision Crop Nutrition (CPNC) members. 
  • This database has been enriched with other site-specific data, such as weather and geospatial information on soils, crops, and other relevant attributes.

As a data contributor to the Global Crop Nutrient Database, there are numerous benefits to your work and the research community at large.

  • Global use – Your data will become widely available and used for new scientific purposes, directly contributing toward multiple UN Sustainable Development Goals (include link), including reducing hunger and poverty in a sustainable manner. 
  • Acknowledgment – Data providers will be acknowledged in publications emerging from this work for their contribution, or may become collaborators on future work.
  • Advertise your organization – Data contributors will be recognized on the database’s public website.
  • A resource for you – The database will be available to you, allowing your organization to use worldwide data to benefit from this resource.

These CPNC databases are powered by the cutting-edge Agmatix platform, designed to aggregate, standardize, and harmonize agronomic data, Agmatix has also used its complex algorithms to enrich the datasets with site-specific data, such as weather and geospatial information on soils and crops. Agmatix’s agronomic data solutions continue to revolutionize agro informatics and improve sustainable food production worldwide. 

To learn more about the project, collaborations, and how the databases can be used, please visit https://cropnutrientdata.net or contact us via [email protected]

Big Data Analytics in Agriculture: the Key to Unlocking the Potential of Field Trials

In the last decade, “big data” has taken over many facets of our life – from financial decisions to social media sites, music applications, R&D, and even agriculture. These large, complex sets of information and measurements allow agriculturists to improve decision-making on when to harvest crops, fertilizer application amounts and timing, and so much more. 

With the world population expected to rise close to 10 billion by 2050, the FAO estimates that overall food production will need to increase by 70%. big data analytics in agriculture is one way to help unlock the potential of agriculture by improving yields and profitability for farmers. 

The entire field of agriculture, from sowing the seeds to harvesting and then marketing conditions, has shown potential to be positively impacted by insights from big data analytics. 

Big data analytics in agriculture

While farming has always been driven by data, the scale of it has increased exponentially over the last 200 years. The range of big data applications in agriculture is vast, as just about every aspect of agriculture has been touched by technology and data science. 

Big data not only requires access to large datasets, but powerful enough systems to process these data, and the capacity to extract valuable insights. Big data can be characterized by the following 4 V’s: volume, velocity, variety, and veracity.

  • Volume: The amount of data that is available is extremely large and continues to grow. Farmers can collect data manually on their cell phones or tablets using applications or automatically through machines and sensors on their farms, for example.
  • Velocity: The speed at which data is collected, sorted, processed, and stored has increased immensely and can occur in real-time. This has only increased over the years and lends itself to be very useful for in-the-field evaluations.
  • Variety: The types of data that are being collected range just as deep as the volume as it is. For example, farmers can use drones to image fields and estimate growth rates or disease, and sensors in the soil can help estimate the saturation level and specific fertilizer needs. Data can be captured for weather patterns, crop growth and productivity, economic trends, and soil measurements, among other things. 
  • Veracity: The accuracy of the data is very important to make meaningful conclusions from large datasets. Without high-quality data, the trustworthiness and value for scientific meaning are minimal. 

Big datasets can be generated independently, or combined from multiple data streams. Being able to standardize these datasets is extremely important to ensure that each data point has the same format, is consistent (with what makes sense for what is being measured), and is labeled correctly. 

This standardization, combined with the 4 V’s mentioned above, is what gives big data value. Focusing on efficient and streamlined processes for the generation and management of these datasets is a good investment now and in the future. 

Unlocking the potential of big data from agronomic field trials

Big data applications in agriculture are especially helpful for stakeholders who are involved with agronomic field trials. Agronomic field trials are a way for farmers and researchers to evaluate how practices, products, and equipment will work in the desired cropping systems. 

big data analytics in agriculture can provide a range of insights from field trials through the acquisition of data on crop growth, weather, topography, etc. These data are then used in statistical models or machine learning to help inform best practice decisions to increase yield or minimize risk or loss. 

Different members of the agriculture ecosystem use the data for various evaluations, depending on the area that they’re interested in. The agriculture ecosystem is composed of agronomy, economics, natural resources, food science, and systems and technology to name a few. 

Through collaboration, data can be collected once and shared with other members to be used for different evaluations. Some of the stakeholders that benefit from big data are agriculture input companies, food and beverage companies, farmers, and researchers.  

  • Agriculture Input Companies: Companies that focus on the development and production of seed, fertilizer, crop protection, and irrigation tools utilize agronomic field trials in the commercialization pipeline to evaluate the efficacy, safety, and marketability of new products. 
  • Food and Beverage Companies: These companies focus on increasing the quality and yield of their raw ingredients through crop nutrient planning and ensuring the crops are produced sustainably.
  • Farmers for On-farm experiments: Farmers rely on agriculture big data analytics to increase their crop production and improve the sustainability of their production for long-term success. 
  • Researchers in Universities, NGOs, and Government Research Centers: Researchers across the board rely on agronomic field trials for hypothesis testing and experimenting with potential solutions to problems that farmers face on a local and global scale. 

The research questions and experiments for these stakeholder groups focus on a variety of topics from drought-tolerant crops to pesticide application timing to crop rotations, but all require large amounts of data for statistical analyses. 

With an increase in quality data comes an increase in “power,” or probability of a true effect (i.e., it’s not just random pure luck). The power of agriculture big data analytics is what allows people to parse out meaningful results to inform better agricultural decisions. 

Agmatix and agriculture big data analytics solutions

For data-driven ag solutions, Agmatix has tools to assist with field-level decisions using a revolutionary platform that turns big data into powerful crop models and meaningful insights. 

The Agronomic Trial Management system allows you to standardize collected data for comparison, evaluation, and data loss prevention. With a user-friendly interface that supports multiple devices, all of the agronomic field data is standardized through a set protocol. To match whatever observations or sampling you’re conducting, customized forms can be created and used for data preservation. This tool from Agmatix can assist farmers and researchers in agriculture big data analytics.

With the need for bigger yields in coming years, it’s essential that farmers harness bigger data. The Agronomic Trial Management system provides agronomy big data standardization to make field trial management easier and more accessible. 

Driving Innovation in Agriculture with Synthetic Data

By Ron Baruchi, CEO of Agmatix

Data is the cornerstone of agricultural product innovation. It’s necessary to develop products, meet regulatory requirements, discover new mechanisms, and educate the market on how to optimize the use of ag inputs and practices.

But generating quality, real-world data is an expensive, time-consuming process. According to a 2018 Phillips McDougall crop protection industry report, it typically takes more than 150 studies to register a new active ingredient. From 2010-14, it cost around $286 million to discover and develop a new crop protection product, with $47 million (approximately 16%) going toward field trials. It also takes just over 11 years from the first synthesis to the first sale of a crop protection product.

The advent of synthetic data may soon be changing that. 

Synthetic data is based on real-world data that has been generated by a model that keeps the same statistical properties and connections between the different parameters of real-world data sets. Datasets can be fully synthetic, or partially synthetic, where synthetic data helps fill in any gaps in real-world data. 

Synthetic data is not a replacement for original data, but a secondary source—one that can significantly reduce the time, cost, and effort in obtaining original data. Which offers great potential in reducing the time and investment of bringing new agricultural products to market.

Training AI Models

Fully synthetic data is often used to validate AI models. Instead of conducting real-world experiments to train AI, we can use synthetic data to look for early signs of correlations and model validity prior to investing in large, real-world data collection. Once the AI is performing as expected, then you move forward with validating it in real-world trials.

As Fortune reported last year, John Deere was training its AI on synthetic images of weed species under different conditions so its tractors spray the right plants and not a farmer’s crops. The farm equipment manufacturer said it would eventually test how well the synthetic data training compared to AI that was trained on real data.

Using Digital Twins for Virtual Trials

Synthetic data can also be used for R&D purposes. Scientists can create a “digital twin,” in which a computer takes real-world data to maintain its statistical correlations, and generates synthetic data to create a system that emulates real life. According to Forbes, digital twins have already been created for transportation infrastructure and sports stadiums. 

In agriculture, you could create a digital twin of a field trial to test which variables, such as soil types and weather conditions, are necessary for a successful real-world field trial. This can have huge implications for agricultural input suppliers like crop protection companies, who are required to manage large field trials to receive regulatory approval, or seed companies that rely heavily on experimentation to improve their seed genetics. 

Digital twins can also be used to fill in data gaps from real-world sets. If an equipment error or a remote sensor fails and data is missing, you can generate synthetic data based on statistical models to fill in those holes and provide a complete picture of the study. Or if data is missing in certain geographic locations due to a lack of research facilities, synthetic data can help fill in those absent areas.

Preserving Privacy When Collaborating

Privacy and security have always been major barriers to obtaining real-world data, especially on-farm data. And with more agribusinesses collaborating with one another, that concern has only grown. 

But synthetic data allows companies to strip the personal and confidential information from a dataset, while keeping the data correlations and relations of the original real-world data. This opens the door for greater collaboration and confidence in data sharing.

How Can We Trust Synthetic Data?

While the potential for synthetic data is great, naturally there’s concern around its efficacy. How can we trust synthetic data and be sure it’s an accurate representation of real-world data?

Perhaps the strongest evidence is in the pharmaceutical industry, where we’re seeing increasing regulatory trust in synthetic data.

In 2020, the FDA approved a synthetic control arm for use in a Medidata cancer trial. As Jennifer Goldsack explains in an article for STAT, synthetic control arms use what’s called real-world data—data collected from external sources such as electronic health records, historical clinical trial data, and even consumer fitness trackers—instead of gathering data from patients recruited for a trial. 

The Medidata synthetic control arm was built from an archive of more than 22,000 clinical studies, and Goldsack points out there have been other examples of their use in receiving FDA and EU approval, setting regulatory precedence.

Now is the Time to Start 

It’s not inconceivable that someday regulatory bodies like the EPA could accept synthetic data in their approval process. In fact, Gartner predicts that by 2030 synthetic data will completely overshadow real data in AI models. Which means now is the time for agribusinesses to learn how to work with synthetic data.

While ag companies can create synthetic data themselves, it’s a significant investment, given the time and resources it takes to develop a scientifically sound model, and gather original data to create the synthetic data. It’s much more efficient for an R&D department to externally source synthetic data.

As a data company, Agmatix has more than 670 million data points and 53 million values of professional observation that we can use to generate synthetic data. But we can also help companies pull the data they already have and standardize it in one central repository, which can be used as both real-world data or generated into synthetic data. Once that data is generated, we develop models to convert the data into actionable insights.

The possibilities with data are unlimited. But to tap into that potential, agribusinesses need to build their data, connect it, standardize it, create and validate a model for interpreting it, and then use synthetic data to supplement it.

Applying Precision Fertilization Tools for Crop Production Optimization

Dr. Norman Borlaug was an American agronomist and 1970 Nobel Peace Prize recipient who developed high-yielding varieties of wheat that were credited with preventing the starvation of millions of people in Pakistan and India in the 1960s. 

Dr. Borlaug, often referred to as “the Father of the Green Revolution” once said, “Civilization, as it is known today, could not have evolved, nor can it survive, without an adequate food supply.” Globally, agricultural land represents 36% of the global land surface and only one-third of this is used as cropland.

The United Nations estimates that the global population in 2020 was just under 8 billion and is expected to rise to close to 10 billion by 2050. The continuous population growth has been met with a fixed, if not decreasing, area of cropland, creating a bottleneck. This bottleneck is one that will determine the trajectory of civilization, as crops are produced for food, fuel, and fiber, all of which are vital. 

To ensure the success of civilization now and long term, precision agriculture will be essential to maximize productivity. 

This population and available cropland bottleneck has also had a drastic effect on global ecosystems. Globally, agriculture emissions have increased by 30% over the last four decades, signifying that we are not only in a critical need of increased productivity but also increased sustainability. One area of agriculture that can begin to address these concerns is precision fertilization. 

What are precision fertilization tools?

Precision fertilization dates back to the 1990s and originally focused on fertilization methods, species, and application rates that were tailored to the specific environment of the crop. This required growers to take into account the soil type, weather conditions, crop needs, etc., and determine a customized fertilization approach, compared to the previously used single recommendation for an entire field. 

Nowadays, growers have access to even more detailed information regarding the soil conditions (physical, chemical, and biological), climatic information (precipitation, temperature, etc.), and crop needs (plant nutrients, such as N, P, and K), as well as more advanced technology and machinery. 

Given the available information today, precision fertilization tools can be used to help growers apply not only the right nutrient types needed but also the right amount at the right time.

With the abundance of data that can be measured in an environment, the diversity of crops that are grown globally, and the wide range of climates that growers deal with, advanced fertilization tools can be used to optimize crop production. By customizing fertilizer application precisely, input costs and carbon footprint are reduced, productivity and profits are increased, and it ultimately protects and preserves our ecosystem. 

Benefits of Precision Fertilization

While precision fertilization approaches will vary due to environment and crop needs, there are numerous benefits that have been tied to its application. Growers can anticipate lower carbon footprints and reduced input costs, increased productivity and profits, and overall more sustainable cropping systems, both locally and globally.  

Lower carbon footprint 

According to an UN-backed study, over a third of global greenhouse gas emissions caused by human activity can be attributed to the food system, starting with how food is produced.

A large contributor to these emissions is nitrogen fertilizer. By only applying the amount of nitrogen that is needed for a particular crop in a given field, growers can apply fertilizers more efficiently and possibly even reduce the amount overall. While agriculture is not the only industry that plays a role in emission reduction, data-driven approaches could minimize future negative impact. 

Reduced input costs

One of the largest input costs for growers is the fertilization of their crops, and 2020 and 2021 have brought a dramatic increase in fertilizer prices, resulting in an even larger financial burden for them. 

Understanding the spatial variability of a field, its soil properties, and the essential nutrients for the crop being grown can help farmers identify where less fertilizer may be needed, or if there are specific times it should be applied. By being strategic with application to minimize unused or leached nutrients, less fertilizer will need to be purchased.  

Increased productivity

The purpose of fertilizing crops is to provide them with nutrients that are lacking in the soil, but necessary for the plant to grow and thrive. Each crop has a different ratio of nutrients that are needed for growth and each area of cropland has a different soil composition. 

A cropland’s soil nutrient levels are affected by the cropping history, topography, soil type, weather, etc. By customizing fertilization, you’re able to meet crop requirements more efficiently and increase productivity, or yields.

Higher profits

As technology and management techniques continue to improve, so do the economics of precision farming. Increased yield and productivity coupled with reduced input costs can result in greater profits for growers. 

Sustainability

Two key performance indicators, or KPIs, to assess the sustainability of a cropping system are nitrogen leaching and carbon footprint. Nitrogen is one of the essential macronutrients for plant function, making it a commonly added fertilizer to many crop fields. 

Nitrogen leaching occurs when the amount of soil water exceeds the soil water holding capacity, causing excess water, and any soluble nitrogen, to drain through the soil, rendering it nearly useless to the crop, and harmful to the environment.

Nitrogen leaching can contaminate drinking water and other bodies of water that affect fish populations and overall water quality. By reducing nitrogen leaching and the carbon footprint of agricultural practices, we can minimize pollution and help the ecosystem remain productive over time. 

Best Uses for Precision Fertilization Tools 

Precision fertilization tools allow for variable rates of fertilizer application based on the crop nutrient needs, climatic stress, soil fertility, and topography, among other factors. With precision technology, the decisions come down to the 4 Rs: right place, right rate, right time, and right source. 

Utilizing GPS-guided application equipment to be able to geo-reference fertilization application points will help avoid overlaps and mistakes. With the proper equipment and advanced fertilization tools, variable rates of fertilizer can be easily administered at specific times, depending on the nutrient source. 

These management decisions are important individually, but the combination of the 4 Rs is crucial. The collection and analysis of agriculture field-specific data will help growers figure out how to manage their fields, but this is often timely and requires specific expertise in agronomics, agroecology, and soil science. To assist with these vital, but sometimes intimidating decisions, Agmatix, a global agro informatics company has developed smart fertilizer software that will provide you with the necessary information. 

Agmatix’s digital solutions for fertilization planning 

Agmatix’s Digital Crop Advisor is a smart fertilizer software that equips agriculture professionals with scientific-based results focusing on crop nutrition management. This easy-to-use customized fertilization planning tool helps generate crop nutrition optimization plans, monitor sustainability KPIs, offers support for any lab analysis, and more. 

Our Digital Crop Advisor is able to standardize field measurements and has multi-language support available, making this an essential tool for any global research team. With access to a rich database of different crops, crop essential nutrients, and soil data, Agmatix can help generate the best fertilization plan for your field to maximize productivity now and moving forward.

Crop Modeling Definition, Use Cases and Advantages


As the world population continues to rise, food production will have to be increased to meet and sustain the demands of our rapidly growing population. Not only will food production need to increase, but yields will need to be able to withstand climate changes which include increased temperatures and decreased rainfall patterns. 

By understanding and being able to predict crop production outcomes under various climatic situations and management approaches, farmers will be better equipped with adaptation strategies to maximize crop growth as sustainably as possible. Crop modeling tools offer a way to evaluate trade-offs of potential adaptations in climate and can help form the basis of decision-support systems for farmers.

What is Crop Modeling?

Crop modeling in agriculture uses quantitative measurements of ecophysiological processes to predict plant growth and development based on environmental conditions and crop management inputs. 

These models simulate a crop’s response (growth or yield, for example) to the environment, management, water, weather, and soil parameters, as they interact over the course of a growing season. These tools mimic the growth and development of crops to mathematically represent the various components within the cropping system. 

The concept of crop modeling dates back to the 1960s when researchers modeled agricultural systems by combining both physical and biological principles. Crop models rely on measurable inputs (by sensors, machines, or hand measurement) to determine whatever output is of interest (plant growth, crop yield, soil nitrogen, crop staging, etc.).

Data Needed for Crop Modeling

Crop modeling tools require a minimum set of data as inputs in order for the model to accurately complete a prediction. Some types and examples of input include management (planting date, planting density, crop variety, fertilization, and irrigation), soil (drainage class, pH, organic matter content, and sand/silt/clay content), physiology (leaf area index, total biomass above ground, plant height, and stand count), and climate (rainfall, air temperature, wind speed, photoperiod). 

In order to create a crop model, a variety of the inputs mentioned above need to be collected as well as the measurement of the factor that you’re wanting to predict. This then allows you to calibrate your model after determining what factors were the most influential or important in the model. 

Then the model needs to be validated with an independent data set to determine its accuracy and fine-tune the number of inputs that are necessary. Typically a parsimonious model, or model with the fewest variables but greatest accuracy, is desired.

There are a variety of public and privately-used crop modeling tools focusing on specific crops or regions. Some of the limitations that currently exist with these simulators are that: there is no single program or set of model parameters that have been identified for all global regions and crops, there’s limited precision to quantify crop responses to micronutrient stress, and there is a limited amount of validation data available to improve models across crops. 

Crop modeling in agriculture has more recently been a key tool in assessing the impact of future climate change. By collecting large amounts of input data for model calibration and validation, researchers can evaluate possible adaptation strategies and management decisions under varied climate conditions.  

graphs over crops

Benefits of Crop Modeling

There are numerous benefits to crop modeling, such as increased efficiency, increased yields, and lower environmental footprint.  

Drive Efficiency

Crop yield modeling can help drive efficiency in agricultural production systems by allowing farmers to manage their inputs more efficiently. These models work to achieve maximum crop performance while minimizing inputs, such as fertilizers, irrigation, or other applications. This allows farmers to focus on management practices that best serve their production systems. 

Increase Yields

One of the main goals in precision agriculture is to achieve the maximum crop yield while minimizing inputs and losses from cropping systems. By understanding what inputs are most important to increase yield, farmers can identify the key timepoints for management practices to optimize their yield and ROI.

Lower Environmental Footprint

Crop yield prediction can help lower the environmental footprint by demonstrating the benefit of specific cropping practices (such as crop rotation or organic fertilizer application) to positively increase yield and lower carbon footprint. Similar to efficiency, by managing and minimizing the number of inputs, there is a reduction in unnecessary or excess application of fertilizers or pesticides that can negatively impact the environment.

Crop Modeling with Agmatix

Agmatix’s technology facilitates predictive agronomic modeling using automatic data ingestion and statistical analysis software. With standardized and analyzed data, machine learning-based models assist with crop modeling for a variety of crops.

While crop yield prediction models will differ across crops and regions, there is usually a core set of model inputs that are needed once the modeling objective has been decided. 

Our crop yield modeling capabilities use multiple data sources across a wide range of production environments and nutrient parameters. Once a model is developed, it is possible to inversely map management decisions and nutrient inputs that lead to optimal yield in a production region. 

The Future of Agronomics Using Crop Modeling

Crop modeling in agriculture has the potential to provide valuable insights and solutions for ag professionals. With improved agronomic data collection, predictive modeling using multiple datasets will allow researchers and farmers to better understand the parameters and management practices that are most influential on crop growth. 

Being able to explore potential outcomes over time, given changes in climate or other inputs, opens up a whole new perspective as we work to improve efficiency and reduce environmental footprints. 

produce supply chain

The Role of Data Standardization in Driving Efficiency Across the Agriculture Supply Chain

The COVID-19 pandemic has drastically disrupted global supply chains – from consumer products to the automotive industry, food to agriculture products, and technology companies, it felt like no area went untouched. Thel pandemic and the need for globalization combined with higher-yielding crops have put even more pressure on the agriculture supply chain

It’s important to remember that while growers and crop researchers can work on improving crop growth and yield, there is a need for business people, economists, and marketing specialists to get the harvested crop to consumers through the supply chain. 

Food shortages, increased prices, harvest losses, and other events can damage the supply chain that connects growers with consumers. With more open data sharing and improved data standardization, the agricultural supply chain has the ability to become more efficient and resilient to these events. 

This is where big data in agriculture comes in. There are over 150 million on-farm experiments conducted each year, yet only 2 million field trials are analyzed each year. It’s essential that we harness the power of big data in precision agriculture to drive innovation and improve agronomic production.

Big Data in Agriculture

The term “big data” in agriculture has become the norm for researchers, growers, and policy analysts. These data are generated by farmers, agronomists, government, NGOs, research centers, academic researchers, food and beverage companies, and more, relating to every aspect of the agricultural process. 

In agriculture, there are millions of potential data points that can be collected from the time that a seed is purchased, sown into a field, grown, harvested, and then is marketed and sold to a customer. These data points can help inform future decisions for any part of this process to increase quality, yield, profit, and sustainability. 

Over the last decade, digital technologies have evolved immensely with more experience, statistical algorithms, and computational power. As the infrastructure and computing capabilities continue to improve, it’s essential that we harness the power of big data in agriculture to solve bigger issues such as sustainability, reducing carbon emissions, food security issues, and crop nutrition.

The Challenges with Big Data

There are many challenges with big data that growers and researchers encounter, two of the main ones being: underutilized resources and data, and the inability to connect data insights to data action.

Oftentimes, researchers do not use collected data to its fullest extent, missing the potential gains. Many research groups and farmers conduct the same trials and experiments repeatedly but are unaware. Whether it’s poor data management or forgotten data, many data points simply go unused. This means that all of the effort, time, and resources that went into collecting the data point were essentially wasted.

The ultimate goal of most agronomic research is to aid farmers in their productions – whether that’s through increased yield, new tools, or management practices. But a common disconnect in the pipeline is between researchers and farmers, specifically when it comes to farmer adoption or having farmers leverage data insights. 

A researcher’s inability to translate research findings into actionable protocols and a farmer’s limited application of digital technology can drastically limit agricultural progress. 

The barriers that are commonly encountered with the extensive amount of big data need to be focused on immediately. Solutions to these challenges have the potential to help address the need for increased food supply with the world population expected to rise close to 10 billion by 2050. More effective operations, reduced uncertainties, and real-time decision-making could revolutionize agriculture, all of which relate to big data.  

The Benefits of Data Standardization in Agriculture

Many agri big data challenges can initially be addressed, or at least minimized, through data standardization. Data standardization, whether that’s creating a single standard of data collection/measurement or transforming data so that they’re all in the same units or scale, allows you to actually compare data across experiments. 

Data standardization in agriculture not only helps with the quality of data but its usability. In a field that has the ability to generate data on every single practice and application within the production (think daily rainfall, fertilizer application, planting date, wind speed, etc.), these data help farmers monitor their health in real-time and can help with future management decisions to optimize yield. 

Some of the benefits of data standardization in agriculture include: relating data points and harmonizing them, improving data quality through identifying anomalies and erroneous entries, and converting measurement units into one. All of these benefits, and more, are available with Agmatix’s Axiom technology, specifically geared towards big data in agriculture.

How Agmatix Can Help

Agmatix’s platform is able to ingest and standardize your agronomic data from multiple sources, regardless of format. Our agricultural data management focuses on the interoperability and reusability of agri big data. The automation pipelines allow you to analyze your data for immediate solutions, empower your production to be more sustainable and efficient, be resilient to the supply chain, and ultimately drive innovative decisions.

  • More Sustainable: Agmatix can help you reduce farmer adoption gaps and negative environmental impacts. Farmers are able to use digital tools to collect and analyze data on their own or utilize our decision support systems to help them make the best crop nutrient and sustainability decisions. Our tools quantify your production’s carbon footprint and drive more effective reporting.
  • More Efficient: Our platform helps coordinate multiple agronomic field trials locally, regionally, or globally, giving you a more comprehensive view for budget planning and oversight purposes. With this structured approach, your speed from trial to insights from agri big data is increased. Instead of having data sitting in silos of Excel files for years, untouched, our digitized approach allows for immediate use and analysis.
  • Resilient Supply Chain: By transferring knowledge and training to farmers and connecting them through a unified platform, Agmatix helps bridge a common gap in the pipeline. There are also localized plant nutrition protocols and other insights with organizations or individuals that you collaborate with to help streamline the supply chain process across groups.
  • Drive Innovation: Utilizing a centralized data hub for the analysis of trials accelerates the pipeline development of new technologies. This data standardization in agriculture helps synthesize data from numerous sources and unlock robust insights, driving new innovation. 

Big Picture Impact

In a recent study by Ernst & Young, LLP, over 200 supply chain executives cited increased efficiency as their company’s top priority over the next year. These top executives also mentioned the need for increased implementation of AI and machine learning technologies to tackle problems that can pop up with big data. Traditional data management techniques and platforms are ill-equipped and less efficient when it comes to real-time data sharing and analysis across several organizations. 

Agmatix’s innovative ag-solutions help you manage and extract real value from big data. The platform and tools through Agmatix specifically assist with the data standardization of big data in precision agriculture to drive efficiency across the agriculture supply chain. With the versatility and scalability to assist with a single farmer’s field or dozens of global field trials, Agmatix’s technology is one that will help you unlock the true potential of your data.    

patterns of crops

Agronomic Field Trials Reimagined

Now is the time to reimagine agronomic field trials due to the constraints of climate change, shrinking arable land, and the need to feed a growing world population. 

Agronomic field trials are conducted to mimic real-life growing conditions and are critical for hypothesis testing, validating the success of a new crop variety, generating new management practices, and establishing the efficacy and safety of new commercial products. 

With the use of digital tools, researchers can reimagine the approach and methods used for field trials from early conception and planning all the way to the interpretation and analysis of results.  

Trial planning and design

In regard to the design and specifications of field trials, many components must be thought out to limit confounding variables and allow for meaningful conclusions to be made, such as treatment and variety replication, plot randomization, trial layout, and control treatments. 

The first step in conducting agronomic field trials is to establish the design and plan requirements with the end goal of the trial is to be able to attribute variability in crop performance to the treatment that’s applied, the climatic condition, or the crop genetics. 

Variability in the field characteristics will affect the crop performance, so it’s important to either choose a uniform area or denote the variability so it can be considered in the analysis. Another important planning component is the overall field trial layout, including replication and randomization. These aid in avoiding results that are simply based on coincidence, and not statistical significance.

Data collection

One of the most critical parts of any experiment is data collection. It’s important that data is collected in real-time, as it is impossible to recollect accurate data once the time has passed, especially when dealing with changing weather and agronomic conditions. 

When it comes to collecting data on agronomic field trials, it’s essential to conduct quality control to minimize errors. Reducing the amount of missing data and increasing the amount of quality data is an extremely important part of field trials that are often overlooked. 

Deploying digital tools for data collection ensures timely reporting, reduced errors, and secure processes to store and aggregate the data for analysis.   

Harnessing big data with agro-informatics

With the vast amount of big data that is generated from agronomic field trials, it’s important to unlock the potential of that data. You can unlock the true potential of big data through agro-informatics which deploys data science to drive new innovations, techniques, and scientific knowledge. In previous centuries, agriculture has been revolutionized mechanically, chemically, and biologically

By leveraging agronomic big data researchers can validate new products and accelerate pipeline development of new technologies that help farmers meet higher yields, better manage pests and diseases, and use resources more efficiently to reduce environmental impact.

Data matrix over a field

Turning data into actionable insights

The last and perhaps my valuable step to reimagine agronomic field trials is turning research data into actionable insights. This is most effectively done by deploying  field trial software, such as Agmatix’s Agronomic Trial Management solution, which allows you to easily plan, orchestrate and analyze field trials.

With digital solutions like this, you can accurately scale and design trials, visualize fields and staff activities, enter measurements effortlessly, manage staff, and analyze results in one intuitive platform. At Agmatix, we can standardize your legacy data via our GUARDS (Growers Universal Agronomic Data Standard) protocol, which enables you to unlock true value from that legacy data and to do comparative analysis against your ongoing research. 

Reimagine field trials with Agmatix

The Agmatix digital solutions help you reimagine field trials and propel your progress by unlocking true value from your big data to maximize your resources, extend your research capabilities, and empower you with actionable insights. By reimagining your approach to field trials with Agmatix, you can: 

  • Create value: Accelerate your research, and discover new innovations from existing data to extend the true value of your big data generated from field trials. 
  • Maximize resources: Leverage legacy study data and optimize data management processes. Deploy streamlined tools and pre-built analytics, to manage your operations at scale and speed time to trial insights. We are big proponents of collaboration and through our agronomic trial management solution, you can share and access data with stakeholders easily due to data standardization and strict data privacy protocols.
  • Extend capabilities: We believe analytic capabilities should be for anyone, anywhere! Our intuitive platform equips your team with tools that inspire innovation and easy collaboration, without having to worry about backend computing. With Agmatix, your group is equipped to analyze research insights and reduce the overhead of data collection, aggregation, and synchronization.
  • Operationalize insights: Our solutions speed your time from trial planning to insights and increase analysis productivity. This enables you to make data-driven decisions and accelerate new product development.

Today more than ever, adopting digital tools is essential for the next agricultural revolution. By reimagining agronomic experiments and looking outside of “how things have always been done,” your research operations and scope can reach new levels!

image of IoT and plant

The internet of things in agriculture

The internet of things (IoT) is a key driving force in the agricultural revolution. The proliferation of affordable, smart devices that are connected to the internet is transforming the efficiency of farming at field level. Smart agriculture systems using IoT  enable the collection of mass amounts of cloud-stored agricultural data. 

AI-driven big data analytics is allowing scientists, researchers, agronomists, and even individual farmers to extrapolate relevant data and develop innovative solutions to the challenges of 21st-century farming. Data-driven agriculture solutions combined with IoT also have wider applications in food production and food distribution. Benefits from IoT in agriculture are felt across the food supply value chain from smallholder farms to broadacre crop production and to consumers in the supermarkets.

What is the IoT?

The internet of things (IoT) is a general term for the billions of smart devices that contain small chips and sensors, and are connected to the internet. IoT was conceptualized at least a couple of decades ago and British developer Kevin Ashton coined the phrase the internet of things in 1999. A combination of (almost) simultaneous technical advances made theoretical concepts commercially viable, to the extent that IoT is part of our daily lives.

  • Mass production of tiny, low-cost RFID tag computer chips
  • IPv6 internet protocol which can assign unlimited unique ISP addresses
  • Rolling out powerful new broadband and wireless coverage.

IoT is growing exponentially and already encompasses many household items. If you have a sensor or timer in your home or vehicle that can be accessed and controlled via an app, you’re connected to the IoT. Our workplaces, schools, and cities are also intrinsically connected to the internet of things. IoT is now making inroads into agriculture and transforming the world’s most vital industry. 

Internet of things applications in agriculture 

The internet of things is already an intramural part of farming. Irrigation has been controlled by field-level computers for decades and farmers have relied on weather satellites for almost a generation. Now with readily available sensors researchers, agronomists and growers can collect a variety of data points to better understand what is happening at field-level.

The game-changer for agriculture is the availability and affordability of sensors and other devices that are small, robust, and connected. Smart agriculture systems using IoT include remote sensors, robots, drones, and computer imaging. IoT and agriculture are evolving into a global smart agriculture market that is expected to be worth almost $16 billion by 2025.

One of the key benefits of IoT in agriculture is the ability to apply analytical tools to transform collected data into actionable insights. Researchers and agronomists can use these insights to make an informed analysis of crop health and performance based on the parameters they are collecting data for. Farmers can also use these insights to make informed decisions about farming practices. IoT applications in agriculture can lead to better crop and seed selection, optimal crop nutrition management, improved yields and crop quality, reduced environmental damage and waste, lower production costs, and greater all-round sustainability. 

agriculture data transfer

The internet of things is transforming farming on two fundamental levels. It is making farming more efficient at field level and it is empowering farmers, agronomists, and researchers with a clear picture of what is happening at field level, by leveraging big data and smart data analytics. 

Farmers are no longer restricted by a paucity of information or gut decisions at the operational planning level. They can now make informed decisions based on empirical data, and lower their production risks. 

When cloud storage and algorithms break down traditional data silos, and information is disseminated, farmers, researchers, and agronomists are potentially empowered. They have convenient access to data (or data-driven insights and solutions) at a macro and micro level. 

Greenhouses and controlled microclimates

One of the most promising IoT applications in agriculture is the automation of smart greenhouses. A greenhouse is essentially an artificial microclimate that requires precise regulation. To create an optimal growing environment, growers need to control temperature, humidity, artificial lighting, UV light levels, soil moisture and irrigation, PH levels, and every aspect of the environment. They also need to optimize the application of inputs for crop health and nutrition. Researchers using greenhouses to mirror growing climates for regions they are testing crop varieties can also optimize their data collection efforts with IoT sensors.

The larger IoT benefits in agriculture

Benefits of IoT in agriculture go far beyond basic improvements in efficiency at field level and production level. Smart agriculture systems using IoT will provide the key to feeding a growing world population in the first half of the 21st century. The United Nations currently predicts that the global population will reach 10 billion by 2050. To meet the needs for food security, we will need to increase agricultural production by almost 70% over the next three decades. 

In addition to the need for increasing food production globally, the need to protect the environment is just as pressing. Deforestation and desertification are urgent environmental issues that need to be addressed. As stewardship practices continue to evolve, such as no-till in the fields, growers and researchers work together for better options that protect the environment and meet food production needs.

Consumers are increasingly making ethical choices when they buy food products. They also expect to see a greater diversity of high-quality, nutrient-dense, food products on their supermarket shelves. IoT and agriculture can help meet the multiple challenges of protecting the environment and implementing sustainable agriculture, reducing waste and production costs, and delivering a worldwide abundance of diverse and healthy food products. 

Agmatix data and analytical solutions

Agmatix is at the heart of big data analytics in the field of agriculture and food production. 

With the introduction of IoT in agriculture, data collection has skyrocketed. The challenge isn’t necessarily the quantity of data collected, but rather the ability to gain insights from quality data. Agmatix standardizes agricultural data and makes it readily accessible to researchers, agronomists, and agriculture professionals, both on a strategic planning level and at field-level. 

Agmatix offers practical data-driven solutions to increase crop management efficiency and boost yields. Our agronomic solutions include field trial management, digital crop advisor tools, and carbon footprint analysis. We are also invested in open agriculture data sharing, giving researchers and agronomists the ability to devise their own solutions and strategies based on empiricism. 
With the tools and technology available today, we can achieve the goals of zero hunger and great sustainability. Agmatix is determined to drive the new agricultural revolution with its innovative agro-informatics tools available for researchers, agronomists, and agriculture professionals. Together with IoT, Agmatix can help leverage your data for deeper insights into research, crop management and nutrition needs, and carbon footprint analysis.

The impact of big data analytics in transforming the ag industry

Big data is often viewed as a combination of technology and analytics that can collect and compile novel data and process it to assist in decision making. 

The data can be collected from a myriad of applications including mobile applications, sensors, social networks, websites, drones, questionnaires, and product purchases. Upon collection, big data analytics is then used to process large amounts of data generated, transforming the information into actionable plans. 

This technology has pioneered many digital transformations across several sectors, including agriculture, where big data analytics has proven to be a key component in creating more services that lead to a focused customer experience and ultimately more profit.

Big data and precision agriculture provide farmers with information on changes in weather, rainfall, soil moisture, and other factors that affect crop yield. With all this data, the growers can make accurate and reliable decisions, such as what crops to plant for better profitability and when to harvest, ultimately

There is a lot of buzz about big data and its impact on industries that have not traditionally been as digitally mature as other sectors, including agriculture. Let’s take a closer look at how big data analytics can transform agriculture and farming.increasing the yields of existing farmlands.

What is big data?

Big data is data that is so large in size and complexity that some of the more traditional data management tools cannot store it or process it efficiently. Many organizations have data silos and cannot leverage their big data internally.

Big data without standardization, harmonization, and analysis does not create value. Making big data usable takes time to find the best approach for each organization. In fact, Gartner estimates that 85% of most companies’ data management projects fail. And less than one-third of companies investing in big data feel they can effectively drive R&D value. Given these alarming figures, companies are often overwhelmed with finding the best approach.

What is big data analytics?

Big data analytics involves harvesting, cleansing, and analyzing large datasets to help users properly put their big data to work. Having the right data-based information enables data-driven decision-making. 

The scope for big data applications is large and for the agricultural industry includes the ability to track physical items, collect real-time data, forecast scenarios, and accelerate field trial research that will help improve crop management, mitigate risk, and ensure sustainability.

Transforming agriculture with big data analytics

Agriculture practices have conventionally been treated as an intuitive space with knowledge transfer from one generation to another. 

However, the pressing challenges of today such as climate change coupled with less arable land have forced a shift in how farming practices are evaluated and executed. Rapid urbanization and climate change have claimed a major share of farmlands. In the United States alone, there has been a dip in the total area of farmlands from 913 million acres in 2014 to 895 million acres in 2021.

We are at an unprecedented time in agriculture where we can no longer rely on gut feelings or intuition but rather on data-driven analysis from big data analytics. To counter the pressures of increasing food demand and climate change, policymakers and industry leaders are seeking assistance from big data analytics in agriculture.

Thanks to GPS and the internet, many farmers have been collecting data from their farms for over 20 years. Only in the last decade has the term “big data” taken on a new meaning in agriculture, given the massive amounts of data now readily collected by new sensors and tools. 

Today data points can be collected in real-time around various parameters such as water usage, rainfall patterns, fertilizer requirements, crop inputs, crop health, and yield. With all this new information available today, researchers, agronomists, and farmers can uncover a more holistic view of what is happening at field level that drives better decisions for improvements.

With the Earth’s population approaching 10 billion, the UN estimates that global food production will need to increase by at least 60% to feed the world by 2050 – not a small task. How do the agriculture and food industries respond to the challenge of sustainably increasing output in an uncertain world where natural resources are increasingly limited? By turning to big data analytics for the answer. 

In order to reach the UN’s 60% food target, it is necessary to make the most of the arable land that is available by improving crop yields, reducing waste, and optimizing supply chains. This is where big data analytics has a pivotal role to play, and it’s already disrupting the agriculture and food industries and pointing the way forward.

Disruptive digital tools continue to emerge and evolve, reshaping the post-modern agricultural landscape with smart farming methods. Data-driven decision support for smart farming provides groundbreaking opportunities to make farming more efficient by minimizing the resources used. 

With the increasing pressure to feed our growing world population sustainably, climate change and other environmental challenges present an opportunity to adopt big data analytics in agriculture as a way to maximize output and minimize the resources needed.

The adoption of data-driven agriculture solutions has been steadily increasing. Its market size is expected to hit $1.4 billion USD in 2023. That’s a Compound Annual Growth Rate (CAGR) of 12.2% since 2020.

Better risk assessment

Using weather patterns to mitigate risk 

The world is getting more unpredictable. But when it comes to the weather, a system of well-known historical complexity, that trend is going in reverse. 

Agriculture is already benefiting from detailed insights on everyday weather trends. Better data tools are unlocking new potential, improving farmers’ resilience to weather-related risk, and at the same time, opening new opportunities. Utilizing big data in agriculture provides a better understanding of the scale of the risk, which can help reduce the impact of climate-related disasters through improved planning. For instance, there are agricultural regions around the world where small changes in climate can have serious economic consequences. 

The cost of farming is also dependent on the weather. Factors like unpredictable rainfall and crop diseases can lead to unexpected yield losses and price spikes. Predictive big data analysis in agriculture allows farmers to mitigate risk associated with changing weather patterns (drought, heavy rain, flooding) and adjust accordingly, enabling more accurate forecasting of yields and production. 

Additionally, emerging data analytics tools allow insurers to better analyze the weather risk exposure of croplands, so farmers can take out insurance policies that can help them overcome weather-related liabilities where previously they may have gone bankrupt.

What do big data and measurement have to do with sustainability?

At the same time, the world’s population is increasing, and the amount of arable land is decreasing. Up to 25% of the global landmass is now unfit for agriculture due to soil degradation, salinization, and water scarcity. Freshwater sources are becoming depleted, especially in the developing world. Global demand for fresh water will exceed supply by 40% by 2030.

While the vast majority of farmers have done a great job maintaining or increasing soil health by using good stewardship and conservation practices. Additional data-driven recommendations can be instrumental in ensuring a sustainable farming future. 

According to the United Nations Food and Agriculture Organization (FAO), as much a third of the food produced in the world is wasted. This is not only a waste of resources, but it also has a major impact on climate change

The use of big data can help us reduce food waste by providing insights into where and why food is wasted, saving as much as $155–405 billion a year by 2030.

Big data and precision agriculture are essential to meet today’s challenges in agricultural production. The more information researchers, agronomists, and farmers have, the better the opportunities to leverage big data analytics at a watershed scale to make informed decisions on the best conservation practices.

Agmatix and big data analytics  

Behind the scenes, big data analysis in agriculture is driving a digital revolution that aims to provide researchers and agronomists deeper insights at the field level and give farmers more control over their field output and nutritional requirements, all while increasing efficiency and reducing costs.

Disruptive technology continues to emerge and evolve, reshaping the postmodern agricultural landscape with precision agriculture methods. Agmatix offers precision agriculture solutions that enable data-driven decision-making across research and development, agronomic practices, and sustainability challenges. 

If you’re looking to unlock true value from your big data, contact Agmatix today for a demo of our solutions.

data over field

The power of digital agriculture

Digital innovation in agriculture is driving the agricultural revolution of the 21st century just as steam power technology drove the industrial revolution. The internet of things (IoT) and cloud data storage combined with artificial intelligence, and improvements in satellite and weather technology are transforming farming practices at field level, as well as optimizing food production and distribution. This combination of new technologies is facilitating the rapid digitalization of agriculture. 

At the core of digital agriculture is data and IoT which enables harvesting more data on a massive scale. Together, these data points create a digital framework of information that can help uncover more answers around sustainability, crop management, and field testing. 

The applications of digital agriculture today are endless and scalable in both smallholder and broad-acre farms. Cloud storage and AI are breaking down data silos, potentially making previously inaccessible information available to anyone, anywhere, and maximizing the scope of agronomic field trial research, crop nutrient management and sustainability impact assessments

Digital agriculture and data management tools,  provide scientists, agricultural professionals, and farmers with the raw data they need to transform the agricultural sector into a digital-first industry that will help feed the estimated 800 million human beings who currently lack basic food security.

The second driving force of digital agriculture is public pressure and regulatory demands for environmentally sustainable agricultural practices. Consumers increasingly want higher quality, sustainably sourced food products with greater nutritional value. With this, digital agriculture and sustainability go hand in hand.

Digital agriculture to farmers

Digital agriculture offers many innovations to farmers. IoT connected devices can, for example, optimize irrigation and fertilizer applications, implement equipment automation and robotic processes, and reduce growing costs.

Digital agriculture provides a farmer with a field-level view for greater insight and control of inputs for crop health and yields. Insights from digital agriculture can reduce business costs and increase profits for farmers while also creating a  neutral – or even positive – environmental impact. 

Digital agriculture in the supply chain

A digitally connected supply chain is more efficient, resilient, and sustainable. According to Logistics Insider, only about 1% of digital technology is applied in the supply chain but that has the potential to increase to 23% by 2025. McKinsey & Company estimates the potential impact of a digitized supply chain can reduce losses in sales by 75%, lower transportation and warehousing costs up to 30%, cut administration costs, and reduce inventories up to 75%. 

Digital agriculture for consumers

Readily available high-quality foods at affordable prices are at the height of consumer demand. With the use of digital agriculture, data-driven decisions on the farm and accurate forecasting in the supply chain can help meet these demands. 

digital image of tree

Why is digital agriculture important?

An adequate supply of basic staple foods is essential for human survival. An abundance of affordable, nutritious foods is vital for the future development of human progress. Digitalization of agriculture is key to the rapid evolution from the inefficient and even detrimental farming practices of the 20th Century. Global food demand is estimated to double by 2050 and it is vital that digital innovations in agriculture keep pace to help meet the global food demands. Today we have the resources and technology to feed every human on the planet if used properly. The challenge is to channel these technologies into effective processes and apply them at a strategic, holistic level.

Digital agriculture and sustainability are intimately connected to the issue of climate change. Opinions remain divided about the causes of climate change, but it’s clear that the Earth’s climate is unstable and has fluctuated historically. These conditions require the adoption of better ways to monitor climate and weather patterns and adapt – at niche local and global levels – to the challenges of temperature, drought, and flooding will improve food security for the entire human race. 

Digital technologies in agriculture

Digital technologies in agriculture are broadly similar to digital technologies in other industries. It’s possibly our cultural perceptions of what constitutes farming and food production that makes their application seem remarkable. We take it for granted that a highly technological manufacturing plant utilizes similar automation, 24-hour electronic monitoring, precise climate control, and artificial intelligence to build space stations and satellites that can also be used to automate operations on the farm, and apply precise applications for crop health and monitor crop yield. These efficiency and productivity gains not only maximize outputs but also allocate resources for greater sustainability.   

We’re often surprised to learn that an acre of a family-owned small farming operation uses similar technology and processes to grow a bumper year-round tomato crop as would a broad-acre farm. The last decade saw a massive decrease in the cost (and size) of smart electronic devices with microprocessors, sensors, and an internet connection that assist. A line of vegetable crops in a bed of sand can be computer-controlled as easily – perhaps more easily – than an assembly line in a manufacturing plant. 

Digital technologies at field level

At the field level digital technologies such as IoT sensors can alert farmers to variations in the microclimate, PH levels, and the presence of disease or other crop pests. Automation of drip-feed irrigation can be optimized with the use of a basic weatherproof computer and crop growth can be monitored against existing statistics. The availability of big data on cloud storage, combined with new sensors and hardware applications, gives farmers, agronomists, and field technicians precise solutions tailored to local needs. 

Today with a few smartphone apps, farmers can access globally harvested data and extrapolate the information they need to make better decisions for farm management. At the same time it’s possible to analyze commodity markets, identify consumer preferences, select the ideal crop varieties and seed selection for local conditions, purchase the optimal slow-release organic fertilizers and eco-friendly pesticides, and finally connect to the best buyers and distributors.

Digital technologies and weather management

The single constant that has dominated agriculture since our Neolithic ancestors began farming is weather and climate. The ability to anticipate and adapt to volatile weather patterns and climate fluctuation is the foundation of agriculture. Weather satellites are increasingly effective and commercially viable. We’re able to predict the weather with greater accuracy over varying timescales. 

Digital agriculture gives farmers and agronomists adaptability and better risk management. It also allows them to identify opportunities in circumstances that previously counted as adverse or financially unviable. 

Over the next decade, it’s likely that the single most useful farming tool will be a smartphone. As governments expand fiber internet infrastructures and 5G wireless coverage, more remote areas will have reliable internet connections and every farmer should be able to connect directly to the global agricultural community.

Benefits of digital agriculture

Farming is not just a business that is subject to market forces and commercial realities. For hundreds of millions of people worldwide, subsistence farming is a struggle for survival. 

Access to reliable water supplies, the right fertilizers, pesticides, benign weather conditions, and a fair, competitive, and efficient supply chain is only half the battle for farmers. It is access to big data and the ability to make informed decisions that will define the digital agriculture revolution of the 21st century.. 

Food security

Digital agriculture can be a catalyst in achieving  Zero Hunger on a global level. Digital innovations in agriculture can provide greater access and affordability of IoT connected devices to smallholder farmers in underserved regions. 

The 5G revolution combined with digital agriculture enables smart irrigation, precise fertilizer strategies, and seed selections that can transform impoverished communities. As agriculture practices improve globally, communities will prosper and safeguard formerly marginal lands.

Greater equity

A potential benefit of digital agriculture is greater equity and inclusivity in agriculture. Up to now, the main concern has rightly been to improve conditions for subsistence farmers and family units farming marginal lands. Closing the digital equity gap brings unexpected benefits in the undeveloped world as we move away from obsolete farming practices and build greater support for minority and women farmers.

Agricultural entrepreneurs

Food production is already opening up to a new generation of agricultural entrepreneurs. Digital agriculture is making niche urban farming, smallholding, and homesteading an easier and more financially viable option. The days when you had to live in a rural area to be a farmer, and either inherit or buy enough land to be viable, are almost gone. 

Other benefits

We’re likely to see a transformation of our cities and suburbs as more and more people realize the dream of either becoming farmers or food producers. This will generate new employment opportunities, increase urban biodiversity, reduce our environmental impact, lower food costs, and transform the quality of life on a wide scale. 

Agmatix digital agriculture solutions

Big data is at the heart of the digital agriculture revolution. Agmatix is pioneering how we harvest data and transform it into practical field-level insights used for increasing crop yields, improving the quality of those yields, disrupting obsolete markets, and improving our shared environments and overall quality of life. 

Agmatix focuses specifically on providing innovative and comprehensive digital solutions for agronomic research and field trials. Our insights and models enable the effective analysis of existing and operational research data through advanced analytics and ML models. Through our proven decision support systems (DSS) we enable researchers, agronomists, and farmers to digitally plan, orchestrate and analyze field trial research and optimize crop nutrient recommendations. 

Agmatix is committed to creating open access to data and insights that will drive innovation, overcome agronomic challenges, and create an environmentally sustainable agricultural model for the 21st century. 

internet connection over field

The role of industry 4.0 in agriculture

Industry 4.0 is essentially the industrial revolution of the 21st century. New technologies such as cloud computing and analytics, AI, IoT, and other advanced tools are being used to increase automation, boost productivity, cut costs, and streamline operations. In addition, sensors on the factory floor are utilized for real-time visibility of the manufacturing stages, enabling predictive maintenance and decreased downtime. 

Agriculture in the era of industry 4.0

Modern-day agriculture faces multiple challenges such as reduced resources, increasing demand, and rising costs. According to a report published by FAO, the world was already off track to meet Sustainable Development Goals by 2030 and the COVID-19 pandemic has made it even harder to achieve these set goals and to monitor actual progress.

For several decades, farmers have been seeking improved methods to increase productivity, prevent waste and reduce environmental impact. Following intensive research and testing, it became clear that the same tools used to optimize manufacturing can be applied in agriculture to boost growth and ensure sustainability. 

Agriculture has been significantly impacted by Industry 4.0. In fact, Industry 4.0 and agriculture are closely intertwined. We are currently witnessing the adoption of advanced Industry 4.0 applications in agriculture, leading to a new concept – Agriculture 4.0.is a farming management system that applies new technologies and gleans applicable data to increase production and boost efficiency. 

The technology driving agriculture 4.0

Using cutting-edge tools and automated management systems, farmers can monitor crops, tweak planting, and cut production costs, offering advanced technologies that help the farmer drill down and understand the number of resources (such as irrigation, fertilizer, and pesticides) required in each area of the farm. In order to understand the impact of Industry 4.0 in agriculture, let’s examine what Industry 4.0 applications are used in agriculture. 

  • Artificial intelligence – Artificial Intelligence (AI) in the industry enables machines to accumulate data, assess situations, and offer real-time insights. On the farm, AI helps to improve harvest quality, detect diseases and pests in plants, and determine what herbicide should be used in every region at any given time. It also enables innovative farming practices like vertical agriculture which is a technologically advanced farming system that maximizes resources and increases food production in a smaller area than traditional row crops. 
  • Drones – In many industries, drones are used for surveillance, mapping, traffic control, surveying, and even delivery. On farms, there is a lot of ground to cover, but thanks to drones, many jobs no longer need to be performed manually. With applications borrowed from Industry 4.0 in agriculture, drones are able to provide useful data regarding soil conditions, disease, plant maturity, and more, often in real-time. Current data captured by drones can be compared to historical data to improve the decision-making process.
  • Sensors – Sensors are basically your extended range of sight and data collection for what is happening both above and below ground. Sensors can detect when crops need irrigation, determine if more downforce is required, and instruct what chemicals should be applied and where. Their ability to measure wind speed, spray pressure and flow, terrain changes, and more, provide the farmer with a precise picture of each and every corner of his farm. Farmers acquire the ability to monitor in-field variability and make informed decisions accordingly.
  • IoT – IoT refers to a group of connected devices (such as sensors, probes, drones, and more) as well as software, networks, and other technologies. These elements communicate with an internet network to receive and transmit data. The use of IoT in Agriculture 4.0 enables field management systems to connect data gleaned in real-time from GPS-equipped drones, satellites, sensors, and other advanced tools. Systems based on IoT can automatically adapt to weather changes and plan irrigation accordingly.   
  • Big data – The ability to collect and analyze big data has played a revolutionary role in almost every field. Agriculture is no exception. The key is turning that vast amount of big data into actionable data that can be gleaned from advanced analytics systems to assist farmers in making more informed decisions. Due to the fact that datasets may originate from different sources, it is key for data to be properly ingested, cleansed, and harmonized using AI and advanced analytics to obtain the insights for practical solutions to a farmer’s specific challenges.   
  • Automation – Automation in agriculture has significantly increased production and reduced the costs of manual labor. According to Forbes, labor costs can range from 25% to 75% of a crop’s value. The use of advanced technologies to upgrade and automate previously manual processes in farming is known as farm automation. This technique decreases man-hours and time-intensive processes of agriculture that have always served as a huge challenge to farmers around the world. Robotic technology can plant seeds, monitor and analyze the condition of crops, and even harvest crops.
illustration of industry 4.0

The benefits of agriculture 4.0

In the past farmers lacked the data-driven insights to combat dwindling resources, pests, and changing weather conditions, thanks to agriculture 4.0 this isn’t the case anymore with the unique benefits it brings to farmers

  • Increased productivity, healthier crops, and higher yields – A farmer’s ability to monitor and analyze crops in real-time leads to data-driven decision making with informed conclusions. The precise knowledge and understanding of when and where to use irrigation, fertilizers, and herbicides lead to healthier crops and higher yields.
  • Better cost management – Actionable data provides better control over expenditure and costs. It can, for example, enable a farmer to plan all the stages of cultivation, sowing, and harvesting as well as fertilization and irrigation needs far more precisely with data-driven analysis, which not only increases productivity but also cuts costs and saves on labor. 
  • Smaller carbon footprint  – The CGIAR notes that reducing agriculture’s carbon footprint is central to limiting climate change. Prior to precision agriculture, much of the fertilizer used in farms ran off into waterways or was broken down by microbes in the soil, releasing potent greenhouse gas nitrous oxide into the atmosphere. Using the newest technologies for precise application and understanding the amount of fertilizer needed in each location reduces our carbon footprint. Efficiencies gained in autonomous operations and equipment also reduce the amount of fossil fuels used to power these items. Using the newest fertilization technologies for precise application and understanding the amount of fertilizer needed in each location reduces our carbon footprint. Efficiencies gained in autonomous operations and equipment also reduce the amount of fossil fuels used to power these items. When it comes to modern-day farming and protecting our vital resources, every action can be evaluated based on informative data on both profitability and efficiency. 

Agmatix and industry 4.0

Agmatix is an agroinformatics company that develops innovative data-driven solutions for agriculture professionals worldwide. Our platform standardizes agronomic data and provides a comprehensive agronomic research and field trials platform with statistical analysis at your fingertips. Our proprietary software-as-a-service offerings provide the standardization of agronomic data and generate ML-based agronomic predictive models for data-driven decisions.
Our solutions optimize agricultural data management, enabling you to increase yield, improve crop management, boost productivity, and operate more sustainably. Even more important is the fact that our smart solution offers a user-friendly, yet intuitive dashboard that provides you with all the information you need in real-time to make the right decisions.

digital agriculture

How precision agriculture helps feed our growing world

Agriculturists and professionals face a wide range of challenges including the shrinking availability of arable land and water, consumer expectations, preserving sustainability, and a steady innovation pipeline. 

On a global level, there are even greater problems at stake: According to the FAO, moderate or severe food insecurity affects one-quarter of the world population. The UN warns that there are increasing challenges surrounding soil biodiversity and productivity leading to a rapid decline in soil health, water filtration, CO2 sequestration, and other ecosystem functions.’ 

Solutions to these pressing problems must be found, sooner rather than later. While farming was once a trial-and-error undertaking, today farmers must find more guaranteed methods to compensate for diminishing resources and meet an ever-growing demand. 

In recent years, farmers have turned to precision agriculture to help solve the many challenges connected to modern-day cultivation. 

What is precision agriculture?

Precision agriculture refers to the use of new technologies and data analysis in order to create more efficient and less costly cultivation methods. One can trace back its origin to the 1990s when PDAs and laptops enabled farmers to record soil sample locations, trace boundaries, and make notations while on the move.  

Subsequently, as both software and hardware technologies became available, new opportunities were explored. Companies, consultants, and retailers began developing proprietary programs using a variety of visual interfaces, sensors, and devices. But it soon became clear that hardware and software weren’t really “speaking the same language.” There wasn’t enough compatibility between the various elements involved in field testing to obtain a complete picture for optimal results analysis.

The development of advanced technologies such as artificial intelligence (AI) and the internet of things (IoT), in addition to drones and newer mapping tools, presents opportunities to merge hardware and software and provide customized solutions for farmers and agriculture professionals seeking customized solutions for data-driven and predictive insights to increase yield, accelerate new product development, decrease costs and increase sustainability efforts. 

What tools are used in precision agriculture technology?

In the last decade, we have witnessed the development of end-to-end precision agriculture technologies and smart data inputs based on advanced precision agriculture technology and smart data input. These platforms include decision support systems for best management practices such as  

  • Data-driven analysis best practices – Information organization is vital when seeking to maximize production and reduce costs. There is no limit to the amount of data available in agriculture today. What is often missing is the ability to properly ingest, cleanse, and govern data so that real insights can be made. Such as maximizing production and reducing costs based on better analysis and data-driven decisions.
  • Yield monitoring – Increasing crop productivity is one of the main advances precision agriculture provides today. For example, grain yield monitors constantly record the flow of grain to the combine. When linked to a GPS receiver, they can also provide crucial data for yield mapping.
  • Grid soil sampling –  Grid soil sampling is an intensified form of regular soil sampling. With advanced soil sampling, GPS mapping aids in proper tracing and tracking of inputs such as fertilizers.
  • Remote sensing –  Acquiring data from locations that are typically manual in collection such as crop and field information is where remote sensing shines.  Sensors can, for example, evaluate crop health, identify pests or infestations, estimate yield, map a field, assess crop condition, manage irrigation, and identify plant stress. With advanced data analytics, information from these sensors can be layered together to provide deeper insights versus singular data points.
  • Autonomous machinery– Whether planting, applying crop protection inputs, or fertilizers, improved precision in machinery application results in more efficient use of resources and a lower environmental impact. Tractors have come a long way from just mapping the field to now being fully autonomous and even controllable by a farmer’s smartphone. They can also collect additional data such as images from the field for better analysis of crop conditions.
image of digital elements across a field

Why is precision agriculture important?

Precision agriculture offers several key benefits such as enabling better crop health, yield management, and meeting sustainability targets. 

  • Crop nutrition optimization – Precision agriculture  can enable the maximum utilization of nutrients. These are most notably known as the “the 4 Rs”:
  • The right source – As with most things in agriculture, “one size does not fit all.” Precision agriculture helps uncover which balance of nutrients is best for every section of a field. The proper balance ensures effective plant uptake and growth.
  • The right place – It’s not enough to have the right balance of nutrients, because the location is crucial too. Precision agriculture tools such as a GPS ensure that nutrients reach exactly the right spot where they are needed, preventing loss and damage. 
  • The right rate – Advanced soil analysis technology is crucial to determine an accurate balance between adequate nutrient quantities and necessary supplementation. The use of this tool cuts costs of nutrients and diminishes waste.
  • The right time – Applying the 3 Rs above is obviously important, but timing is no less crucial. By analyzing all of the actionable data acquired around crop uptake and field operation systems, you can accurately determine when the nutrients will achieve the most benefit for the crops, thereby maximizing outcomes.
  • Better decision-making –  Farmers and agriculture professionals have first-hand knowledge of their land’s strengths and weaknesses. Precision agriculture technology provides accurate scientific tools to better understand why certain areas of land are performing better than others. When a farmer is armed with actionable data about every aspect of their farm, along with their historical expertise, they can make well-informed decisions to improve the quality and quantity of their crops. 
  • Higher productivity – Advanced precision agriculture technology enables one to drill down and reach a deeper understanding regarding soil types and nutrient levels on their farm. The soil water-holding capability and the amounts of nitrogen mineralization may vary from one area to the next, necessitating precise settings according to localized nutritional needs. For example, the use of VR desiccation enables easier harvesting. The bottom line is that healthier crops equal higher yields and greater economic profit. 
  • Efficient use of resources –  The use of precision agriculture enables farmers to reduce consumption and waste. Thanks to actionable data, you can make the best use of nutrients in each specific area, and cut the costs of herbicides, reduce emissions, and soil compaction. Automated systems can also provide operators with more accurate recommendations on how to maximize their workforce for higher productivity and crop output.
  • Lower environmental impact – The ability to pinpoint the exact amounts of pesticides and fertilizers required for specific sites enhances sustainability and reduces the carbon footprint. The use of advanced irrigation systems supports less waste of resources and decreases runoff and contamination. These technologies help growers achieve better yields and reduce the environmental impacts of their operations. 

Agmatix solutions: taking precision farming to new levels

Precision agriculture is increasingly becoming the norm for efficient farming. Farmers today have many new and amazing technologies available at their fingertips. However, in order to achieve maximum efficiency, profitability, and sustainability, precision agriculture must be used in conjunction with big data and analytics.

Agmatix is an agroinformatics company that uses data science and advanced AI technology to transform agronomic data into actionable insights at field-level. The platform gathers, harmonizes, and standardizes agronomic data and allows researchers, agronomists, and farmers a comprehensive trial management solution from trial design to orchestration, data collection, and statistical analysis. Thus transforming how agronomic data is analyzed today. 

The Agamtix platform can also calculate a nutritional plan for a specific crop, and estimate the carbon footprint based on the inputs and practices used at individual field levels. Its immediate impact is based on field parameters, environmental conditions, agronomic practices, crop type, fertilizer type, and timing of application. 

Our innovative data-driven ag solutions resolve issues around data standardization and remove gut decisions often taking place in agriculture today. We dramatically increase the ability of our customers to make better decisions that impact higher crop yields, and crop health, and promote sustainable agriculture practices.

digital agriculture

Field trial management in the age of agtech

Field trial management in the age of agtech- making order from oceans of data

Proper management of farmland is crucial to keep the business profitable.  Modern farming is no longer just about producing crops.

Today’s farmers and landowners must address myriad issues including farm planning, weather forecasting, research, soil management, seed production, storage, supply chain operations, and many other points.

In order to improve the farmer’s production systems, increase productivity, and profitability, field trials, and experiments are needed. Field trial experiments have been part of farming for centuries, as farmers sought new ways to optimize crop yields and use of resources by trying different methods. In the past, farmers relied on intuition and historical knowledge of their land to make major decisions.

Today, cutting-edge agriculture field trial management tools enable farmers to test new practices, equipment, and materials to maximize farm productivity and validate the effectiveness of innovations on their own land, where climate and soil conditions may be subject to unexpected shifts. 

What are field trials and why are they so important? 

Field trials are tests conducted in laboratories or onsite to test new products and processes. They serve as a crucial tool for documentation of product development and testing new technology in agriculture.

They enable farmers to gauge new products and follow their performance according to preset parameters. These trials, which can be carried out onsite or at controlled facilities, have the ability to yield vast amounts of data from various sources and in many configurations. 

That having been said, agricultural data silos have traditionally been used to separately store financial, crop, and inventory information drawn from field trials. Even the very best researchers can be challenged by the volume, diverse formats, and difficulties analyzing, integrating, interpreting, and making use of non-standardized agricultural Big Data. Due to this lack of interconnectivity and standardization, farmers are unable to synthesize data into coherent information, derive insights from other trials, and understand what works and where.    

Additionally, while on-farm field trials and experiments produce key data, if performed incorrectly they can introduce errors leading to incorrect results. They must be standardized and validated in order to provide agronomic actionable insights.

Challenges facing field trials

Aside from the technological challenges mentioned above, field trials currently face other major constraints and difficulties such as

  1. Data preservation during Field Trials including the systematic collection of data in a digital form.
  2. Difficulty setting up Field Trials in a way that leads to conclusive insights.
  3. The challenging complexity involved in coordinating Field Trials’ “moving parts” – technicians, personnel, equipment, and budget.
  4. The lack of standardized methods and procedures – personnel might use different notations and names. 
  5. Dissemination of the protocols needed in each trial.
  6. Planning and implementing statistical experimental designs.
  7. Analysis of data from multiple trials- the complexity of gathering data in one location followed by analysis on another platform.
  8. Collection, integration, and preservation of data layers from disparate sources including manual observations, IoT sensors, weather stations, etc.

What makes field trial management crucial?

Field trials provide you with huge amounts of data. But how can you extract actionable data that will help you improve your farm’s performance? This is where the use of Field Trial Management tools helps you solve this problem.

While field trials provide crucial information, many questions arise. How do you sift through all the data collected in field trials and draw the information relevant to your specific project? How can you be sure that the data is even relevant to your needs? How do you know if data drawn from a field trial is reliable?

For a generic, external field trial to provide you with accurate projected outcomes, you must make sure that it was conducted in soil and a climate similar to your own. In order to achieve accurate results, uniformity must be maintained and variations avoided. While some field trials can provide you with relevant data, you must first examine validity and replication. 

For many farmers, the ultimate key to successful field trials lies in independent testing and scientific field trial management systems. Initiating reliable, scientific field trials may sound intimidating. 

Admittedly, you need to set up the testing process, eliminate the element of uncertainty, sift through large amounts of data, and analyze it effectively. You must also reveal anomalies, get actionable insights, and draw practical conclusions that will improve your yield and test product quality. 

But do you require professional researchers to manage your scientific trials? The answer is no! Today, the On-Farm Experimentation (OFE) movement strongly encourages farmers to conduct self-experimentation in their own fields. Using advanced automated tools farmers can independently turn mountains of data into practical steps that optimize yields, decrease waste and improve food quality.

young plants

AgTech solutions for field trial management

AgTech plays a central role in modern field trial management. Oceans of aggregated legacy data can be sifted and analyzed to provide sustainable and practical models. 

Using cutting-edge Ag-tech tools, automatic data ingestion leverages advanced crop data technology, engineering, and science. Large amounts of raw agronomic data can be preserved in a standardized, understandable language. 

This type of breakthrough technology provides farmers with previously unavailable opportunities. Using advanced Agtech solutions for field trial management, they can independently plan and administer experiments on their own land while digitally managing field trials from start to finish.

They can also consult agronomic databases and enrich their own agriculture data through multi-source integrations.

The agmatix platform for agronomic field trial management

The Agmatix technology platform provides you with an end-to-end project management system that enables you to scientifically plan and manage field trials. 

Agmatix places digital, user-friendly field trial management at your fingertips:

  • Create a holistic planning process.
  • Gather data easily with our agriculture field trial management data tool.
  • Run your own trials using experimental design methodologies, multiple treatment combinations, and on-map layout placing.
  • Receive status updates to keep your trial on track.
  • Get direct contact between researcher and field trial operator.
  • Acquire and standardize legacy data to enrich your information sources.
  • Aggregate and harmonize agronomic data from your trials to unlock previously unavailable actionable insights.

In this age of Agriculture 4.0, you can’t afford to rely on trial and error or guesswork. In the past, independent field trial management was almost unachievable for the average farmer. 

Today, the smart, AI-based technology incorporated in Agmatix’s platform provides you with easy and effective tools to manage customized scientific field trials to meet your particular needs.  

digital agriculture

The AI agricultural revolution – a fertile ground for growth

Agriculture and farming are some of the oldest and most important professions in the world. Humanity has come a long way over the millennia in how we farm and grow crops with the introduction of various agricultural technologies. 

Food security and agricultural challenges

As the world population continues to grow and land becomes scarcer, people have needed to get creative and become more efficient about how we farm, using less land to produce more crops and increasing the productivity and yield of those farmed acres. 

Global trends are influencing food security, poverty, and the overall sustainability of food and agricultural systems.

The bottom line – agricultural outputs must improve 

According to the World Government Summit Report called Agriculture 4.0 – The Future Of Farming Technology,  four determinants of challenges for agriculture in the coming years were highlighted:

  • Demographics
  • Scarcity of natural resources
  • Climate change
  • Food waste

The report states that, due to continuously growing demand, by 2050 we will need to produce 70 percent more food. 

About 800 million people worldwide suffer from hunger and  8 percent of the world’s population (or 650 million) will still be undernourished by 2030. 

The world’s population is assumed to be nearly 10 billion by 2050, boosting agricultural requirements. At present, about 37.7% of the total land surface is used for crop production. 

The challenges of water and fertilizer supply, and the loss of land, against the backdrop of global warming, at an age of population growth, are bringing mankind to a point where it must adapt and innovate to survive. 

Just as in the era leading up to the first agricultural revolution, our way of living and consumption does not allow the status quo to continue as is.

Agriculture 4.0

Agriculture 4.0

Agriculture 4.0, which is akin to Industry 4.0 is a term for the next big trends facing the industry, including a greater focus on tools for precision agriculture, and Agri Tech, the internet of things (IoT), and the use of big data to drive greater business efficiencies in the face of rising populations and climate change.

Global spending on smart, connected agricultural technologies and systems, including AI use in agriculture and machine learning, is projected to triple in revenue by 2025, reaching $15.3 billion.

IoT-enabled Agricultural (IoT Ag) monitoring is agriculture’s fastest-growing technology segment projected to reach $4.5 billion by 2025.

What is artificial intelligence?

At its simplest form, artificial intelligence is the science and engineering of making intelligent machines. Advanced algorithms are used to create expert systems that make predictions or classifications based on input data.

Using AI in agriculture

Agriculture 4.0 will no longer depend on solely applying water, fertilizers, and pesticides uniformly across entire fields. 

Instead, by using AI in agriculture, as part of Agriculture 4.0, farmers will use the minimum quantities required for production and target specific areas to produce better yields more efficiently and profitably

Farms and agricultural operations will have to run very differently, primarily due to advancements in agricultural technology such as sensors, devices, machines, and information technology. 

AI has several applications in today’s agriculture:

  • Improving crop yield prediction – using real-time sensor data and visual analytics data from drones.
  • Optimizing pesticide application – Optimize the right mix of biodegradable pesticides and limit their application to only the field areas that need treatment to reduce costs while increasing yields.
  • Price forecasting for crops based on yield rates – predict total volumes produced are invaluable in defining pricing strategies for a given crop
  • Finding irrigation leaks, optimizing irrigation systems – measuring how effective frequent crop irrigation improves yield rates are all areas AI in agriculture contributes to improving farming efficiencies.
  • Yield mapping to find patterns – using large-scale data sets for crop planning
  • Improving pest management – pioneering drone data combined with in-ground sensors to improve pest management.
  • Identifying animal or human breaches – Monitoring every crop field’s real-time video feed identifies animal or human breaches, sending an alert immediately.
  • Solving labor shortages – businesses cannot find enough employees and turn to robotics for hundreds of acres of crops while also providing an element of security around the perimeter of remote locations
  • Improving the track-and-traceability  – assisting agricultural supply chains by removing roadblocks for getting fresher safer crops to market on time and with a traceable history.
  • Monitoring livestock’s health – By monitoring vital signs, daily activity levels, and food intake, ensuring their health is one of the fastest-growing aspects of AI and machine learning in agriculture. 

Precision agricultural tools

Robotic systems and AI will allow farms to be more profitable, efficient, safe, and environmentally friendly.

Types of agricultural technology and precision agricultural tools

Future agricultural technology will use precision agricultural tools and sophisticated technologies including AI, robots, drones, the Internet of Things (IoT), temperature sensors, aerial site images, ground/soil sensors for pH and moisture levels, GPS for geo-mapping, and location.

Advanced management of these devices which all have different outputs, formats, and data types require sophisticated tools and software to integrate with vast amounts of Big Data and volumes of research information to provide useful live information to the farmer.

This is where Agmatix comes into the picture. Agmatix’s mission is to harmonize and standardize all agronomic data and turn it into agronomic actionable insights, making it universally accessible.

Agmatix digital crop advisor 2.0

Agmatix Digital Crop Advisor 2.0 is a data-driven decision support system using large amounts of available data to optimize day-to-day crop management.

As the world’s first single engine that drives the agronomic innovation cycle from research and experimental data into meaningful real-life action, the Agmatix technology creates a new data language that can read and interpret thousands of the different data points commonly used across the agricultural industry.

The unique system then provides agronomists and farmers with the vital information needed to make better crop management decisions to increase yields and crop quality. 

Our vision is a world where high-quality and standardized agronomic data is available, supporting Ag professionals globally to overcome obstacles in improving sustainable food production and quality through crop nutrition optimization.

Graphs on ipad

Disruptive technology

Agmatix’s disruptive technology collects, defines, and categorizes data from multiple sources to standardize, harmonize, and facilitate agronomic research, field trials, and a plethora of relevant data, providing the agronomist or farmer with a smart tool to facilitate precise digital agricultural growing practices.

Our Field Trial Management technology digitally manages field trials including data collection and analysis from start to finish. Agmatix’s technology platform has now digitized above 25 million agronomic measurements from over 50,000 different field trials, covering 70 different crop types with multiple geolocations (North America, south America, Europe, AIPAC) and multiple ag domains.

Agmatix is revolutionizing agricultural practices

A.I. can help mankind tackle the many challenges that await it in providing produce for an ever-growing population, in an age of natural resource shortages and global warming. 

Our innovative technology platform uses agronomy data science and advanced AI technology to convert agronomic data into actionable insights at the field level.

Agmatix’s revolutionary approach aims to solve the lack of agronomic data standardization resulting in dramatically improved agricultural practices, crop yields, and nutritional quality while promoting sustainable agriculture in an ever-hungrier world which requires 50% more food by 2050.

digital symbols over field

The importance of predictive analytics in agriculture – making sound future decisions based on statistical science and big data!

Agriculture is a risky business. There is almost no industry that involves more risk than agriculture. The adage “you reap what you sow” is not always applicable to agriculture.

It’s extremely difficult for farmers to focus on all the required daily challenges of weather, crop disease, commodity prices, fertilization schedules when they have so much going on 

the farm.

In addition, with the world’s population and food consumption increasing, farmers need to produce more with limited water and land resources to meet these growing demands.

In thirty years’ time, there will be many more people to feed and there has to be a move beyond current farming practices to meet demand.

Predictive agriculture has been around for a long time

Experience and the bare human eye have played a key role in farming for over 12,000 years. Only in the last century has science developed statistical routines, measurements, and mathematical descriptions for drivers such as weather, soil, wind, genetics, types, and physiology of crops.

A farmer must make predictions before planting crops.

Forbes notes that “farming has always been a data-driven activity. Weather, crop health, and farm economics are all abundant agriculture data sources. The Farmer’s Almanac has been in publication since 1818 and contains long-term weather predictions, calendars, and information related to full moon dates, natural remedies, and more. It is one of the oldest examples of reference agriculture data in America.”

With the world population expected to reach more than nine billion by the year 2050, The UN’s Food and Agriculture Organization (FAO) predicts a 70-percent growth in agricultural output will be needed to serve the projected demand. 

Technological advances along with these drivers have greatly increased the attention to and implementation of data analytics in agribusiness.

What are predictive analytics tools?

Predictive analytics tools in agriculture are tools that use a variety of statistical methods including data mining, predictive modeling, and machine learning that analyze an array of current and historical agricultural, biological, climate, and hydrological data from various sources to make predictions about future outcomes on the farm.

These predictions provide farmers with actionable insights that can help develop models to improve agronomic performance, manage inputs, optimize resource use, predict market conditions, lower carbon footprint, and plan for production and challenges both at present and way into the future.

How do they work in agriculture?

Predictive analytics in agriculture seems like magic, but it stems from statistical science. At its heart, these tools use strong, reliable data to help farmers predict the likeliness of events taking place in the future.  

agriculture elements connected digitally

Predictive analytics is a real game changer

Agricultural predictive analytics is not just a buzzword in agriculture anymore, but a reality as farmers can use actionable insights to make better decisions based on scientific data and information to improve agronomic opportunities.

By using AI and Predictive Analytics, farmers can process and act upon the vast amounts of data they collect more rapidly and efficiently than ever before. 

Preparing for rainfall variability, optimizing fertilizers applications, and deciding the optimal time for sowing and harvesting are just some of the key challenges farmers can solve with predictive modeling. 

Properly integrated, predictive data analytics in agriculture enables the farmer to not only conduct better practices but also to be able to make predictions and extemporaneous adjustments due to factors such as weather, as well as more accurate calculations regarding product and fertilizer type, amounts, and application rates.

This data-driven decision-making can lead to improvements in crop yields, better ROI, more sustainable production, and higher quality of produce.

Just collecting and analyzing your farm’s data to solve the need at hand is not going to cut it.

In computing, the term “silos” has also become a great visual analogy of grain silos for many of the problems with IT and software development that are collected in individual databases or silos and not linked to anything else and cannot maximize the benefits of 21st-century advances in agricultural technology. 

Real value begins in moving to a more proactive approach, supplying the ability to fully benefit from your data, which is what breaking down silos is all about. 

To use predictive analytics tools beneficially, data in silos must be standardized and interconnected on a common platform with common data types.

Benefits of predictive analytics for agriculture 

Predictive analytics can be used in many steps of the agricultural cycle, from crop selection to harvesting. The use of predictive modeling and analytics can:

  • Select the best crop for your field: By using soil analysis data, historical weather, and other parameters farmers can make the best crop selection for any given condition.
  • Optimize irrigation – analytics can aid in predicting crop stress periods, as well as optimal amounts of irrigation needed according to crop growth stages.  
  • Optimize land preparation: GPS-enabled field management maps can be correlated with productivity maps to optimize field operations. 
  • Optimize crop protection: Predictive analytics can help predict outbreaks of pests and crop disease using factors such as soil parameters and ongoing weather conditions. 
  • Increase productivity and yields: Using predictive analytics can build management zones, help optimize crop growth, track season progress, and take measures when needed.
  • Evade lower ROI – Predictive analytics can Identify fields and subfields where ROI is repeatedly lower, and suggest if these fields should be let out of production.  
  • Mitigate supply chain uncertainty: Unpredictable weather, severe storms, drought, and changing insect behaviors due to weather are all environmental factors that impact the agribusiness supply chain. Using data can assist farmers to prepare farmers for these challenges and making decisions based on sound data.
  • Reduce detrimental environmental effects: predictive analytics can help understand conditions where environmental pollution risks are high, relate actions to environmental footprint, and help evade them. 

Agmatix and predictive data analytics

Agmatix is an agro informatics company that develops data-driven solutions for Ag professionals worldwide. Our cutting-edge platform uses agronomy data science and advanced AI technology to convert agronomic data into actionable insights at the field level. With our revolutionary approach, we aim to solve the lack of data standardization to dramatically increase crop yield, quality, and promote sustainable agriculture.

Agmatix solutions

Using precision agriculture data analytics, our advanced algorithms generate on-demand automated nutrition plans by considering multiple parameters including, among others, crop type, field location, pH, previous crops, plant uptake, and laboratory analyses. 

This technology can provide timely warnings that alert the farmer to make changes to the schedule in the case of sudden changes in environmental conditions.

The Agmatix customer/field agent interface allows the field agent agronomist to view all the farmer’s properties and operations as part of the customer card. This generates operational visibility and the ability to review all the recommendations created by field agents, including regional trends, and insights from the field.

Agmatix provides the tools and information that’s required to make better crop management decisions to improve crop yields and maximize profits.

plant and digital symbols

Precision agriculture and big data analytics: breaking down data silos

Today’s farmers are faced with many challenges including feeding a growing population, providing a livelihood for themselves, and protecting the environment. 

Farmers make everyday decisions related to farming such as how much fertilizer to be applied, time of application, the specific area to be applied, which resources are needed for plant protection, and related aspects based on limited information, traditional knowledge, and hearsay.

This can lead to wastage of resources, greater costs, and unsustainable farming in the longer run.

Precision agriculture involves the application of technologies and agronomic principles to manage all aspects of agricultural production to improve crop performance and minimize environmental impact.

What is precision agriculture?

The International Society of Precision Agriculture defines it as “a management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.

Precision agriculture uses information and knowledge via physical inputs and benefits the environment by the targeted use of inputs that reduce fertilizer losses from excess applications and the reduction of losses due to nutrient imbalances, weed escapes, insect damage while also reducing environmental loading.

The concepts of precision agriculture and sustainability are inextricably linked. From the first time, a global positioning system was used in agriculture, the potential for environmental benefits became apparent.

Intuitively, applying fertilizers and pesticides only where and when they are needed, helps reduce carbon footprint.

Why precision agriculture is not enough? 

Precision agriculture was a major innovation when it came about in the 1990s but on its own today,  it’s simply not enough!

In the world of modern applications, stand-alone data in silos is of little use to application developers and operators and cannot enable evidence-based decision-making.

These silos and the manual processing required, limit the benefits of technology for the farmer.

The future of agriculture is in precision agriculture big data analytics. These combined with interconnected systems provide for maximum efficiency, profitability, and sustainability.

data matrix

Silos are for grain, not data! 

When we think silos, we usually imagine the large metal or concrete structures that dot the landscapes of farming areas and are usually filled with commodity crops like corn or wheat.

Grain silos make good sense for farmers because grain from hundreds of miles around can be pooled and because it is all the same crop, it can be sold as a uniform commodity.

In computing, the term “silos” has also become a great visual analogy for many of the problems with IT and software development. Data silos occur naturally as each department in an organization collects and stores its own data for its own purposes. 

This type of disconnect is evident in agricultural data silos too.

Related types of data are dumped into purpose-built databases with monitoring data in one place, financial in another, and crop information and inventory in a third.

Vast quantities of data are stored in these so-called stand-alone agricultural data silos and because of their lack of interconnectivity, cannot maximize the benefits of 21st-century advances in agricultural technology. 

The importance of breaking data silos

One of the greatest challenges that crop growers and researchers face today is the fragmentation of data sources with valuable information being stored in these agricultural data silos. 

Legacy systems are often difficult to connect and manage and inherited systems often don’t speak to your current ones. 

The ability to connect these agricultural data silos is the way of the future. It can lead to innovation, collaboration, and cross-functional operations.

But just collecting and analyzing your data to solve the immediate need is not going to cut it. Real value begins in moving to a more proactive approach, providing the ability to fully benefit from your data, which is what breaking down silos is all about.

Maximizing transformation of big data into actionable insights

Having access to big data is great, but to benefit from it, this data needs to be transformed into something actionable.

Several parameters influence agricultural growing practices including changes in the weather, temperature, rain, and soil pH which can all be accurately measured.

When these are properly integrated, they enable the farmer to not only carry out sound precise agriculture but also to be able to make extemporaneous adjustments based on changes in weather and wind and make accurate calculations regarding fertilizer type and application rates.

smart agriculture

Agriculture 4.0

Smart Agriculture or Agriculture 4.0 is a combination of big data and precision agriculture in conjunction with smart devices, drones, sensors, and GPS which have revolutionized farming. 

These inputs combined with the correct software can assist decision-making based on solid evidence.

This evidence-based decision-making leads to improvements in the accuracy of fertilizer application, reduced costs, and better quality of produce.

Thanks to the introduction of the internet of things (IoT), precision agriculture big data analytics, and artificial intelligence, it is now possible to manage a larger amount of information and decision-making more accurately – and to apply the data to everything the farmer does.

Agriculture 4.0 plays a determining role concerning sustainability, by allowing agricultural practices to adapt to climate change, reduce greenhouse gas emissions, and use inputs more efficiently while preserving biodiversity and coping with water stress.

Agmatix innovative solutions

Agmatix has launched a revolutionary crop nutrition management platform that drives the agronomic innovation cycle from research and experimental data into meaningful real-life actions.

The Agmatix Digital Crop Advisor is a data-driven ag solution that uses, standardizes, and transforms available information from existing databases and data silos combined with live inputs from growers into actionable, verified scientific-based insights and recommendations needed to optimize day-to-day crop management.

By using precision agriculture big data analytics, our advanced algorithms generate on-demand automated nutrition plans by considering multiple parameters including, among others, crop type, field location, pH, previous crops, plant uptake, and laboratory analyses.

This technology can provide timely warnings that alert the farmer to make changes to the schedule in the case of sudden winds or rain.

Another important feature of our Digital Crop Advisor is the customer/field agent interface which allows the field agent agronomist to view all the farmer’s properties and operations as part of the customer card. 

This generates operational visibility and the ability to review all the recommendations created by field agents, including regional trends, and insights from the field.

Impact on sustainability

Synchronizing these data sources optimizes fertilizer application, and so reduces the risk of groundwater and greenhouse gas (GHG) pollution.

The platform can calculate a nutritional plan for a specific field, and it can estimate the carbon footprint and its immediate impact based on field parameters, environmental conditions, agronomic practices, crop type, fertilizer type, and timing of application. 

All these actions can be employed to reduce the many sources of greenhouse gas emissions from agriculture.

Enhancing yields, decreasing waste, and improving food security

Using the Agmatix Crop Advisor, in turn, leads to an improvement of the growers’ productivity, more accurate and scientific management of data inputs by agronomists, and a significant advance in crop management.

Our system provides the vital information that’s required to make better crop management decisions, resulting in better yields, decreasing food waste, less environmental pollution,  and ultimately improving food security.

tree in draught

Keeping agriculture green: joining the fight in climate change

Changes in surface temperatures, the timing of seasons, and the frequency and severity of weather events, such as droughts, floods, heatwaves, and runaway fires have become commonplace.

These climatic disturbances and their consequences have all been attributed to global warming created by an excess of greenhouse gases in the atmosphere.

Agriculture without which the planet would starve contributes approximately ten percent to total global CO2 emissions.  

Agricultural activities using chemical fertilizers, pesticides, and animal gases and wastes contribute to greenhouse gas emissions. 

The stronger demand for dairy and meat products and the intensification of agricultural practices are bound to increase because of a growing global population.

Greenhouse gases trap heat in the atmosphere, which makes the Earth warmer. By trapping heat from the sun, greenhouse gases have kept Earth’s climate habitable for humans and millions of other species. Without them, as a buffer, the earth would be uninhabitable. This trapping of heat is known as the greenhouse effect.

greenhouse effect illustration
Source: US EPA

The greenhouse effect is a good thing. It warms the planet to its comfortable average of 59 degrees Fahrenheit (15 degrees Celsius) and keeps life on earth livable. 

Without it, the world would be a frozen, uninhabitable place, more like Mars. 

Our problem is that mankind’s voracious burning of fossil fuels for energy is artificially amping up the natural greenhouse effect. The result? An increase in global warming is altering the planet’s climate systems in countless ways. 

Global warming needs to be restricted to a commonly agreed 2 degrees oC above preindustrial levels as failure to do so will result in an unpredictable and dangerous impact on humanity and the earth’s ecosystem. Human activities are responsible for almost all of the increase.

The abundance of heat-trapping greenhouse gases in the atmosphere once again reached a new record last year, with the annual rate of increase above the 2011-2020 average according to the World Meteorological Organization (WMO) Greenhouse Gas Bulletin.

The largest source of greenhouse gas emissions from human activities is burning fossil fuels for electricity, heat, and transportation with agriculture on its own contributing approximately 10 %.  

Atmospheric levels of carbon dioxide—the most dangerous and prevalent greenhouse gas—are at the highest levels ever recorded. 

Climate change 

Today, climate change is the term scientists use to describe the complex shifts, driven by greenhouse gas concentrations, that are now affecting our planet’s weather and climate systems. 

Climate change encompasses not only the rising average temperatures we refer to as global warming but also extreme weather events, shifting wildlife populations and habitats, rising seas, and a range of other impacts.

What are the major greenhouse gases?

Greenhouse gases
  • Carbon dioxide (CO2) – 65% from burning organic materials like coal, oil, gas, wood, and solid waste.
  • Methane (CH4) –  16% from landfills, natural gas and petroleum industries, and agriculture, especially from the digestive systems of grazing animals, and fermenting manure.
  • Nitrous Oxide (N2O)- 6% from agriculture and livestock, including fertilizer, manure, and the burning of agricultural residues, along with burning fuel.
  • Fluorinated gases- 2% such as hydrofluorocarbons are used as refrigerants, solvents, and in manufacturing.

It’s important to note that different greenhouse gases persist in the atmosphere for different periods, and they also absorb different amounts of heat. The “global warming potential” (or “GWP”) of a GHG indicates the amount of warming a greenhouse gas causes over a given period (normally 100 years) by persisting in the atmosphere.

Greenhouse gases can also be compared to each other on a level playing field by using the CO2e or carbon dioxide equivalent. This is the number of metric tons of CO2 emissions with the same global warming potential as one metric ton of another greenhouse gas.

Over this time frame, according to the standard data, methane scores 25 (meaning that one ton of methane will cause the same amount of warming as 25 tons of CO2), nitrous oxide comes in at 298 and some of the super-potent F-gases score more than 10,000.

greenhouse gases table

Reducing agricultural emissions

In order to reduce greenhouse gas and carbon emissions from agriculture, farming methods must become more sustainable and produce food more efficiently. 

With the use of proper GHG-efficient farming practices– some of which are alredy being used– a reduction of emissions in the sector of about 20 percent could be achieved by 2050.

Some carbon-friendly agricultural practices include efficient irrigation management like drip irrigation, dry farming where the only source of water is rainfall, dew, and existing soil moisture, cover crops, using renewable energy, and improving soil health.

Efficient irrigation management

Conserving water use is vital to any farm, particularly in times of drought. Methods like drip irrigation and carefully scheduled irrigation ensure water is used wisely. 

Drip irrigation systems deliver water directly to a plant’s roots, reducing the evaporation that happens with spray watering systems. Timers can be used to schedule watering for the cooler parts of the day, further reducing water loss. 

Using renewable energy

Maximizing energy efficiency and shifting away from fossil fuels are important steps that farms can take to reduce their climate footprint.

This can be done by using solar panels, wind turbines and minimizing the use of petroleum-based fertilizers and pesticides for farming, storage, and transportation.

Increasing soil health

Reducing carbon emissions in agriculture can be achieved by practicing carbon farming to try to mitigate the effects of climate change.

By planting trees and cover crops as CO2 absorbers via photosynthesis, farmers can compensate for some of their agricultural emissions.

About 40% of that carbon then gets deposited into the soil, where it feeds microorganisms like bacteria, fungi, protozoa, and nematodes. 

Those creatures, in return, provide mineral nutrients to the plants as a natural fertilizer. 

Other healthy practices include compost application planting cover crops and reduced or no-till cultivation.

Keeping agriculture green

Farmers can introduce land management practices such as reforesting rangelands, restoring riparian zones, and planting hedgerows and other perennial plants. These not only store carbon in their biomass but also protect the soil from erosion and conserve water.

Added benefits are that they provide shelter for wildlife, beautify farms, attract beneficial insects for pollination, and is also a natural pest control components of greening our planet. 

Reducing livestock methane emissions

It is vital to reduce methane emissions from beef and dairy livestock. Methane reducing additives can be included in feedstock which inhibits the formation of methane. Methane-reducing feed additives and supplements are most effective when grain, hay, or silage is added to the diet, especially in beef feedlots and dairies.

Through anaerobic decomposition, manure ponds on industrial dairy and cattle farms create harmful methane emissions. If phosphates and nitrogen from manure ponds reach bodies of water, it can cause eutrophication due to an oversupply of nutrients and resultant choking of water reservoirs.

Protecting farmland

Farmland is being lost due to development pressures for housing and industry. In the face of increased demand for food supply, production becomes intensified with negative implications for global warming. Sustainable intensification, an effort to increase crop yields with fewer inputs and without expanding land use, seeks to balance these priorities. 

Supporting farmers markets and local food

Did you know that food in the U.S. travels an average of 1,500 miles to get to the consumers? Most of this shipping uses fossil fuels and other natural resources and generates GHG emissions. 

Supporting local farmers at the farmer’s market keeps farming viable so that farmers can stay on their land and be successful in growing food that sustains us while caring for the earth.

All these actions can be employed to reduce the many sources of greenhouse gas emissions from agriculture.

Using technology

Agmatix, in its quest for sustainable, environmentally friendly agricultural practices has launched a revolutionary technology platform that drives the agronomic innovation cycle from research and experimental data into meaningful real-life actions.

Our system provides information that’s required to make better crop management decisions, resulting in better yields, decreasing food waste, and ultimately improving food security and reducing global hunger. 

By using AI, big data, and machine learning, our Crop Advisor decision support system calculates, manages, and reduces excess CO2 emissions from the atmosphere by evaluating and changing agronomical field practices by suggesting decisions that will reduce the overall carbon emissions in agriculture. 

The system can calculate a nutritional plan for a specific field. The system predicts its carbon footprint and its immediate impact based on field parameters, environmental conditions, agronomic practices, crop type, fertilizer type, and timing of application. 

Agmatix system screenshot

Ron Baruchi, CEO of Agmatix comments, “Growers, agronomists, researchers and ag industry experts are tackling today’s biggest challenge – providing food security for the world’s growing population “.

As a technologically disruptive and environmentally responsible company, Agmatix has adopted a revolutionary approach to agricultural practices to promote improved crop yields, nutritional quality, and sustainable agriculture,  ultimately assisting the agricultural industry to reduce greenhouse gas emissions and carbon footprints.

This has positive consequences for greenhouse gas reduction, stabilization of climate change, better environmental management, and an overall reduction of carbon footprints.

While achieving all the foregoing environmental benefits, the Agmatix Digital Crop Advisor will improve crop management decision-making.

This in turn will pave the way to better crop yields, improve grower profits, decrease food waste, and ultimately reduce global poverty and hunger.

Agricultural data’s tower of babel

The Biblical story of the Tower of Babel is well known. In their eagerness to reach the heavens, a sophisticated civilization speaking one language, have their efforts thwarted by the very deity they seek to vanquish.

Consequently, the deity makes each community speak a different language, thus creating great confusion and dispersing them across the globe.

One of the greatest challenges that crop growers face today is the fragmentation of data sources; the profusion of different file types, and the lack of tools and platforms that provide us with the ability to turn Big Data into usable insights.

This in turn has created an agricultural Tower of Babel.

Even the very best researchers and agronomists can be challenged by the sheer volume, diverse formats and difficulties interpreting, integrating, and making use of non-standardized Big Data provided by the global agricultural industry. This includes data from field trials and agronomic experimentation, weather, soil conditions, planting times, and fertilizers etc..

The rise of big data and AI in agriculture

plants digitally connected

The use of Big Data and AI in agriculture has the potential to disrupt the way farming and agronomy operate.

Current crop management systems are based on experimental data from multiple sources and standards, making it difficult to compare apples with apples and to generate insights applicable to broad production conditions. Decades-old legacy data is still stored in Excel and other tabular data formats, creating fragmented data silos. Researchers and different stakeholders need to invest significant time and resources to try and unify them. By integrating these data silos and fragmented files into a single coherent database it enables researchers to address critical research gaps, such as increasing the sustainability of production environments, reduce carbon footprints, and boost global food security. Our vision at Agmatix is to create a world where high-quality and standardized agronomic data is readily available, supporting Agro-professionals globally to address these critical research gaps.

Large volumes of data composed of different file types

person planting

The lack of uniformity and standardization has turned the available data into a veritable Tower of Babel, with unique file types, data headers and formats which when coupled together with huge data volumes makes it almost impossible to gain insights from all the available information.

The use of AI in agriculture enables agronomists and crop managers to streamline their work with greater efficiency and productivity thus leading to an improved quantity and quality of crop yields, while increasing the sustainability of production.

Enter the Agmatix 2.0 digital crop advisor era: from crop advice to crop management

using ipad in field

This week, Version 2.0 of the Agmatix Digital Crop Advisor was launched. Our Digital Crop Advisor is a data-driven decision support system that uses and transforms available information from existing databases and live inputs from growers into actionable insights and recommendations needed to optimize day-to-day crop management.

Disruptive technology

Multiple parameters that influence agricultural growing practices, including weather and soil pH, can be measured and accounted for. A farmer who wants to make the most efficient use of water resources and fertilizers must adjust nutrient quantities based on weather dynamics and considerations of crop development stages.

Agmatix’s innovative and disruptive technology collects, defines, and categorizes data from multiple sources to standardize, harmonize, and facilitate a unified database of relevant data to assist agronomic research. Our advanced algorithms generate on-demand automated nutrition plans by leveraging multiple parameters including, among others, crop type, field location, pH, previous crops, plant uptake and laboratory analyses. By synchronizing these multiple data sources, our technology optimizes fertilizer application, and so reduces the risk of groundwater and greenhouse gas(GHG) pollution.

In addition, this innovative Agro-solution helps to maximize the utilization of agricultural practices. For example, farmers often have to deal with unexpected challenges such as sudden winds that can negate spray irrigation and fertilization. Our technology can provide timely warnings that alerts the farmer or his trusted advisor to make changes to the schedule.

Precision agricultural tools enabled

digital ag

Integrating in real time all the factors that affect crop management decisions is highly challenging for farmers, agronomists and researchers.

Agmatix’s precision agriculture tool – the Digital Crop Advisor – improves the recommendations provided to the farmer for optimal crop management. Our algorithms are constantly at work, considering different dynamic parameters and accounting for the ever changing field conditions.

The Agmatix platform enables the development of statistically and scientifically relevant and valid agricultural models, which in addition to improving crop yields and profits, helps to minimize food waste and to support the pressing issues of reducing world food poverty and feeding a growing global population.

Customer / field agent interface

The customer/field agent interface allows the field agent agronomist to view all the farmer’s properties and operations as part of the customer card. This generates operational visibility and the ability to review all the recommendations created by field agents, including regional trends, and insights from the field.

Adoption of the Agmatix Digital Crop Advisor will create the opportunity to synthesize usable concrete analyses and insights out of the endless amounts and types of available data. This in turn, will lead to an improvement of the growers’ productivity, more accurate and scientific management of data inputs by agronomists, and a significant advance in crop management.

Building a global agricultural database

digital green planet

The company has partnered together with leading global research institutes, universities, NGOs, and agricultural companies who are now using Agmatix to standardize their data. Agmatix’s solutions enable them to perform statistical and modeling analysis of their data.

Agmatix is building a global database poised to unlock the value of big data in agriculture – shortening timelines, reducing waste, and increasing crop yields for agricultural professionals and farmers. This permits Agmatix to build the largest and highest quality database of standardized data in the world.

The database will equip these institutions and agribusinesses with the tools to develop machine-based models that can predict the environment’s impact on plant nutrition, enabling the short and long-term planning of crop production.

Open and accessible collaborative data

Addressing current global challenges in food security will require collaboration between different stakeholders. Collaboration enables us to harness the power of many to cover needed parameter space, sharing insights and results. The fragmentation of data hampers our ability to collaborate – unifying multiple files into a central coherent database is time consuming and labor intensive. Agmatix streamlines the standardization of data coming from multiple sources and stakeholders, thus eliminating the bottleneck in team collaboration.
Using the system, teams can statistically analyze the data, discuss results, and build publication-ready figures.

Revolutionizing agricultural practices

Agmatix’s revolutionary approach aims to solve the lack of data standardization, and provides farmers and researchers with a solution for the analysis of large volumes of data and varying data types. Better analysis based on more data, leads to better insights and management decisions. Better decisions in turn, lead to increased yields, better food quality, less environmental pollution and the promotion of a more sustainable food system.

connectivity with field background

Let’s take this offline

The key to building and implementing your offline support is choosing the correct use cases and understanding the technical challenges and solutions ahead of time.

Cloud computing is transforming the way we do business.  Software can be deployed, administered, and updated remotely while continuing to reduce complexity and improve ease of access. Although cloud solutions are readily available for urban businesses, they can be more problematic for rural agricultural enterprises.

Farmers and agronomists working in remote areas, without good 5G coverage, will continue to be disproportionately affected by a lack of stable communication channels.

This is a particular challenge for anybody who wants to implement modern high-tech agricultural methods. Farmers working in remote fields need to use farm management software and analytics systems. Agronomists developing fertilization plans for agricultural land may need access to data and programs. Individual farmers using IoT devices need to send sensor data to backend services.

There is a wide range of solutions for the lack of reliable internet connection in remote areas.

1. Low-level communication protocols such as LoRa can enable long-distance IoT communication.

2. In-field edge devices for local processing can reduce the amount of information that must be submitted remotely, and can function as an offline cache.

3. An offline support system that works without a reliable network connection. We wanted to make our crop nutrition platform readily available at field level. We took a different approach and developed Offline First.

connected phone in field

Offline development is a potentially powerful differentiator, but successful implementation requires careful planning and a clear understanding of your development goals. These include deciding what you want to make available to your users offline, to more technical challenges such as developing offline applications.

Offline first

Offline First allows for a process of progressive enhancement. The basic idea of progressive enhancement is to start with an assumption of base-level capabilities for an individual device. It’s then possible to take advantage of more device capabilities as they become available. As a starting point, we assume that your app does not have a working network connection.

As network connectivity becomes available, your app can be progressively enhanced to take advantage of the greater connectivity. This approach requires a shift in mindset to understand that lack of connectivity is no longer an error condition.

Choose the right technology platform

It’s important to plan strategically when selecting your technology stack. This initial decision will directly affect your ability to solve offline challenges. You can take a minimal approach with PWA, or consider hybrid and native solutions.

Progressive web apps

If you’re a web developer your first priority is to evaluate progressive web apps. The basic idea of progressive web apps is to combine the easy discoverability of web apps with the power of native mobile apps. As an end-user, you browse to a progressive web app just like you would browse any other website. If no web connection is available, a built-in mechanism loads the necessary files to allow offline viewing.

The fundamental difference between progressive web apps and normal online content is that progressive web apps allow for comprehensive offline support.

Ionic

Ionic is a tool that allows you to create hybrid mobile apps using SPA applications. Ionic can deploy to native platforms (including iOS and Android) using Apache Cordova, or you can deploy an Ionic app to the browser as a progressive web app. You can also extend the offline-first capabilities of your Ionic app by adding PouchDB to the mix.

Shifting business logic

Using web and cloud technology, it is possible to transfer elements of secured business logic from cloud storage to mobile devices to support offline capabilities.

This approach utilizes an existing codebase and is much harder to achieve. API changes to other assets also need to be migrated and shifted to the frontend.

Application assets

Any application must include all the necessary resources to run an offline service without any connectivity. Techniques such as hydration, cache the application’s assets on the device the first time the app is used. The next time the application is running online, it will be automatically updated.

Another approach requires the user to notify the system before going offline. The system can then prepare itself for offline mode and fetch all relevant assets.

This method can be even more complex if we also need to handle 3rd party providers such as Google Maps API and any vendor-based API calls. Additional solutions can include Mapbox Maps SDK for Android or iOS. This downloads maps of selected regions for use when a device lacks network connectivity.

Local database/data persistence

Data on the device is stored in a local database. If you have the right technology stack, the solution will vary from SQLite (which runs together with your native app) or your hybrid app using the Ionic plugin.

Security considerations

Most users will have two main security concerns: How to achieve offline user authentication, and how to store data securely on the device.

If you have sensitive information, SQLCipher can enable you to secure your data with 256-bit AES encryption.

As the device was previously online, and user authentication must have previously succeeded, we can shift to a frontend authentication mode, while ensuring that the authentication key is well protected.

N.B. If you are operating in a highly regulated environment, this solution may require approval by your IT security department.

Offline synchronization

Offline synchronization is the most challenging part of the wider process. You have to carefully analyze the individual use cases for your application and know which solutions are best for each case. Are your mobile users going to read data only, or do they need to modify existing data?

A few common patterns can ease the pain of data conflict handling. Requirements include Read/Write data last write wins, to conflict detection and resolution techniques. Some database solutions such as WatermelonDB and Firebase natively support offline persistence and database synchronization.

DNA chain next to plant

Crop digital nutrition launching in india

Our corporate digital platform has been successfully launched in the Indian market. It is a user-friendly platform that will help ICL agronomists and field agents to recommend, and optimize nutritional requirements for crops. The goal is to fulfill each field’s potential and reduce fertilizer waste. The platform will not only improve the engagement of the local ICL agronomists with their customers but will leverage the ICL brand as a tech leader among its competitors.

One of the digital platform’s biggest advantages is its ability to implement and to easily and rapidly outsource data. This is due to its agile database structure. The localization process includes agronomical and common practices. Our digital platform, and all consultations, use locally recognized units of measurement e.g. Kg/ac. We also support local languages such as Marathi.

 How we do It

The implementation process methodology is simple and straightforward, it includes the following steps:

1. It begins with an introductory meeting with the local management team and defines the scope of the work and the local point of contact for the localization process.

2. The localization kickoff involves collecting the required data on the most commonly grown crops in the particular market.

3. We quickly establish average yield ranges, growing practices, specific phenology, and the nutritional demands for each crop. We can then consider which fertilizer products to use, based on the local fertilizer catalog.

We have an ongoing emphasis on supporting local users and collecting feedback and additional requests.

Maharashtra training workshop

Our local training workshop provides full onboarding for 20 regional managers in Maharashtra, India. The program includes an introduction to the digital platform and a full demo. There is also a realistic simulation and a question and answer session.

Implementation during the COVID -19 pandemic was challenging as all contact was channeled via the web. Nevertheless, the training was conducted effectively. Our students learned to use the platform and gained the necessary skills and knowledge to achieve their farming goals.

The implementation in the Indian market was highly successful. It has proven to be a valuable tool to increase engagement with ICL customers in India and will provide the company with a competitive edge in the market.