6 ways data analytics leads to better decisions for farmers
Many of us are lucky to have food on the table and grocery stores stocked with the food and beverages we want or need, and this is in a large part, due to our farmers. If you didn’t grow up on a family farm or a rural area where corn and beans grow in either direction as far as you can see, it may be hard to feel connected to a farmer. On average, one US farm feeds 166 people annually in the US and abroad. With the global population expected to be over 10 billion by 2050, this means the world’s farmers will have to grow about 70% more food than what is now produced.
Feeding more mouths with less arable land sounds like an overwhelming task at times. Luckily for us, farmers have continuously evolved their industry and have adapted more suitable methods and technology to achieve more efficient and higher yielding agriculture. This is in part due to the digital transformation that agribusiness leaders continuously go through with the use of data and analytics to drive better, faster decisions and production abilities.
Farmers are business leaders, family members, citizen data scientists, agronomists, innovators and the backbone of our food supply. Here are six ways data analytics leads to better decisions on farming and crop research and development (R&D) which help our farmers feed the world.
Farmers need innovative research to drive new trait discoveries in seed varieties. These discoveries can lead to plants that are drought tolerant so they can grow in conditions where that crop may not have been able to grow before, or be resistance to pests and weeds and produce a higher yield with less resource inputs. Crop science R&D is typically a long and expensive process. On average, companies screen tens of thousands of traits in order to find the next new innovation. On top of that, it takes an average of 10-15 years of research to validate the new discoveries efficacy and safety.
With analytics, this tedious process can be automated to increase the speed and reduce resources needed to qualify potential candidates, without risking the quality of the research. The use of analytics for accelerating R&D work to bring new innovations to market faster is not a new concept, but it is one that is constantly evolving and becoming more precise to meet the needs of life science companies. In fact, the top global life science companies use data analytics for their R&D work for this very reason.
Farmers rely not only on new seed varieties — they also need proven field trials that back up the efficacy and safety of that new trait. The seasonal nature of most crops slows the adoption of the newest field informatics-based approaches. Trial programs must collect, transfer and validate harvest results quickly to prepare next season’s protocols and generate test materials before planting new trials. This creates season-bound analytical due dates unheard of in most other industries.
To make it even more challenging, researchers in global organizations may have integrated product development efforts across hemispheres and need to make decisions in a matter of weeks, or just a few harried days. Agriculture analytics facilitates reliable statistical analysis, even among related hierarchical groups. This allows field development experts to uncover hidden patterns in large data sets to determine treatment effects that help with rating product performance. Data analysis can help R&D teams decide how big a field trial needs to be, how to plan resource consumption and help with field screening.
