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.
1. Early trait discovery
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.
2. Field trial design
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. And applying automated machine learning in the computation of the data helps to design field trial requirements quickly and at scale.
3. Precision agriculture
With drone image processing and GPS mapping, farmers can get precise and accurate information on their field conditions and crop yield. Monitoring sensors can be done in fields or indoor growing environments, autonomous grow systems or harvesters. Collecting data from all these sources can create an enormous data set, one that would overload the typical user. But with streaming data capabilities, the collection, integration and filtering of data can be done in real time and at scale to give the farmer a clear view of how their farm is performing and when preventative measures such as weed or insect management should be implemented. This aspect of precision agriculture is not often fully operationalized and that’s where turning analytics into actionable insights comes to life.
4. Animal wellness monitoring
Farmers not only grow crops, but they also raise livestock. Monitoring animal health and wellness is vital to ensuring livestock thrive in grow operations. Streaming data in real time can notify farmers when a potential health issue, such as an infection, has occurred so that measures can be taken to treat the affected animals safely and efficiently -- and prevent disease spread to the entire herd or flock.
Feed inputs are one of the highest overhead cost for livestock farmers. They must ensure the right feed is being selected to optimize the growth and health of each cattle, poultry or hog operation they are running. With data driven decisions, you can predict health and production outcomes so that success is repeatable.
5. Farm-to-fork transparency
With fewer people connected to farms today, the understanding of how food is grown, produced or packaged has become more of an unknown. Consumers want healthy and safe food options, and the integration of transparency throughout the food supply chain helps ensure the highest quality and valued foods are produced.
When the supply chain is linked from farm-to-factory-to-fork, the data connected at each of those touch points helps create an analysis of how each individual tomato, milk carton or apple was developed, harvested, produced and packaged. Without the use of analytics, it would be impossible to integrate and make sense of the data sets that are linked like small puzzle pieces. Agriculture analytics helps farmers and food manufacturers connect the pieces and create transparency for consumers.
6. Sustainable farming
Farmers have evolved their methods to reduce the unintentional negative impact of farming on the environment. Not only are sensors used to monitor crop and animal health, but they're also used to monitor sustainability efforts.
Energy and water use can be fully understood to help preserve those valuable resources for human, animal and plant health. Measuring and improving sustainability performance standards requires data and analytics to bring insights into how impactful sustainability is. Cover crops, pollinator forage, irrigation systems and plastic waste reduction are all current sustainable measures in operation today, but we must full understand the impact of those measures to help farmers and food manufacturers make decisions that provide both positive environmental and economic outcomes.
Although this is not an exhaustive list of ways farmers rely on data and analytics, it's a highlight of some of the most powerful direct and indirect ways data and analytics drive agriculture today.
Learn how SAS helps our farmers and check out this white paper: Using Agriculture Analytics to Improve Field Trials. And let’s not forget to #ThankAFarmer, today October 12 on #NationalFarmersDay and every day after that!
