Data Analytics at the Center of Next Generation Agriculture Lobby - Agmatix

Data Analytics at the Center of Next-Generation Agriculture

Every aspect of agricultural processes can be captured into a data point. From factors like soil temperature and moisture at planting to variable rates of nutrients applied to pest and disease pressure, key data can be captured throughout the growing season. At harvest and beyond, yield and quality data, climate data, and marketing data can all shed light on the crop and business. 

That data becomes meaningful and impactful for future agricultural decisions through its applications in data analytics. When data is transformed into insights that can be used to better understand agronomic, economic, and environmental conditions, users can drive innovation and improve agriculture’s sustainability every season. 

Big data analytics and digital transformation are the essential bridge between agriculture today and new-generation agriculture. Agriculture data analytics companies like Agmatix are leading the way in creating tools to enable new-generation capabilities.

How Is Data Analytics Used in Agriculture?

Data analytics is critical for agriculture. In modern agriculture, as more data is collected and stored automatically, the opportunity for data to make an impact is massively increasing. Both qualitative and quantitative data are collected on farms and in agriculture. 

For example, yield data is qualitative data that provides a report card on crop productivity and is often used to make decisions about crop varieties for the following year. Drone imagery of a growing crop provides a quantitative dataset that can be used to make decisions about nutrient or fungicide applications. 

All four categories of data analytics – descriptive, diagnostic, predictive, and prescriptive –  have the opportunity to inform innovation in agriculture. From describing the impact of a weather event on a crop to predicting yield outcomes relative to a new production practice, agricultural data analysis can shed light. Diagnostic analytics can precisely point to possible reasons for a specific response in a crop, and clarity about how to adjust nutrient application rates for a specific soil type comes from prescriptive data analytics. These four categories are commonly used in precision agriculture applications

A variety of sectors within the industry rely on agricultural data analysis. Crop yields, specifically, can be improved through data analytics for precision agriculture. Maximizing active crop time through strategic crop rotation can maximize yields. Determining this manually would be challenging; but through data analytics, machine learning can analyze datasets including weather patterns, soil types, temperature readings, and satellite imagery to pinpoint the crops best suited to that environment. 

Resource allocation on the farm can be a very challenging balance between maximizing profitability, environmental sustainability, and crop productivity. Water, nutrients, and pesticides are all critical inputs that are consumed during the growing season. Using machine learning to analyze soil conditions – like nutrient and water levels – can help farmers make irrigation and fertilizer adjustment decisions in real-time to ensure plants have what they need to maximize production without wasting valuable inputs.

Lastly, farmers are impacted by a changing climate. Adjusting to these changes means historical knowledge and tried-and-true schedules are no longer accurate. Weather patterns can change from year to year, making planting and harvesting timeline decisions difficult. 

Analytics models can compare historical weather trends with recent weather trends to predict upcoming conditions. Armed with this information, farmers can adjust planting schedules and harvesting timelines. 

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Data Analytics at the Center of Next Generation Agriculture Inside - Agmatix

How Is Agmatix Contributing to Next-Generation Agriculture?

Agmatix is harnessing agriculture data science to drive innovative technologies that move agriculture towards a more sustainable and productive future. It’s an agriculture data analytics company focused on creating a world where high-quality, standardized agronomic data is put to use to tackle the toughest challenges facing agriculture. That work has already taken the shape of several different Agmatix platforms that utilize agriculture big data analytics

Digital Crop Advisor is designed to provide optimized, data-driven nutrition plans to optimize crop yield and environmental sustainability. Agronomists can run simulations and allow the algorithm to adjust for crop nutritional needs. This enables users to compare multiple recommendations for key outcomes, including understanding tradeoffs between yield and environmental impacts. 

The Agronomic Trial Manager unlocks next-generation field trials. Through an intuitive, cloud-based system, end-to-end management of field trials is easy. Collaboration and reporting are straightforward, supporting fast decision-making and speed to market. 

Insights and models, supported by agriculture data analytics, reveal new value from data. Through analyzing aggregated and standardized data, such as field trial results or legacy trial data, the Agmatix platform can convert it into agronomic insights and crop models. This approach will take agronomic data further than it’s ever been. 

Agmatix values open data and powers open databases in collaboration with the International Fertilizer Association, Wageningen University & Research, the African Plant Nutrition Institute, and Innovative Solutions for Decision Agriculture. 

The Global Crop Nutrient Removal Database is designed to create a holistic database highlighting the relationships between nutrient inputs and outputs in a variety of production and environmental conditions. Through understanding crop nutrient removal, agronomists can adjust for specific nutrient amounts required for the next crop on that land. 

The Nutrient Omission Database is intended to provide data that leads to site-specific recommendations and optimized nutrient management. Legacy nutrients that have accumulated in the soil over multiple years of fertilization can contribute to crop nutrition; having data around these levels allows this information to be an input into advanced fertilization tools and nutrient management plans. 

There is a myriad of ways that Agmatix is harnessing the power of data analytics to transform agriculture. The next generation of agriculture will be powered by data analytics that predicts, diagnoses, describes and prescribes actions and results that ultimately make each season better than the next. The impact of next-generation agriculture will be immense as the industry grapples with feeding a growing population and caring for the environment. Agmatix will be leading the way with data analytics for precision agriculture and a better world.