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 data science in agriculture 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 agriculture.
What are predictive analytics tools?
Predictive data analytics 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 data science and agriculture work together?
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.
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 science and agriculture driven decision-making can lead to improvements in crop yields, better ROI, more sustainable production, and higher quality of produce.
The missing link of a fully aggregated data – the breaking of data silos
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 agricultural data science, 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 agricultural data science, 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 to agriculture data analytics, 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.
