The Role of Big Data in Agriculture

The world population is growing rapidly. The United Nations estimates that by 2050 the population will reach nearly 10 million. Agriculture is the backbone of any country’s economy, but with the constant rise in population, the industry must undergo significant growth to meet the demand – increasing production by almost 70%. As we move into the future, agriculture faces the urgent challenge of feeding the world. That challenge will be met, in part with the power of big data in agriculture.   

Big data technology presents farmers, researchers, and agricultural professionals with an ideal opportunity to manage resources more efficiently, maximize yield, and eventually contribute to reducing hunger and poverty worldwide.  

Precision Agriculture

Precision agriculture or precision farming is a management style focusing on accurate and controlled farming. The fundamental tenant of this approach is using information technology and on-farm data to make informed decisions. The data is collected from GPS guidance, control systems, sensors, robotics, drones, autonomous vehicles, variable rate technology, GPS-based soil sampling, automated hardware, telematics, and software – to name a few. 

Grouping data sets collected from these systems help farmers optimize inputs like water and fertilizers. Data analytics can aid farmers in monitoring crops and animals and detecting issues like pests and diseases at an early stage. Precision agriculture technology has enabled farmers to reduce waste, improve efficiency, and promote sustainability – essentially growing more with less.

Resource Management

Precision agriculture and the on-farm data collection systems are improving resource management. Big data in agriculture has been instrumental in managing resources – like monitoring irrigation water, reducing erosion, saving fuel, managing employees, and even treating specific crop or livestock needs. By monitoring the irrigation process and controlling water quantities, farmers reduce water usage and limit the potential of plant diseases. And with sensor technology, they are monitoring energy efficiencies on a crop and cow side level – armed with the on-farm data; they are reducing fertilizer, herbicide, pesticide, and antibiotic use. 

Market Analysis

The agriculture sector is dynamic, and the market factors are ever-changing. Historically, economists have always considered agricultural markets volatile but generally predictable – particularly in animal agriculture. However, in the last decade, weather events, elevated protein production, high-interest rates, and demographic changes have softened that predictability. To manage the market, analysts now rely on big data analytics to get critical insights into market trends. This information helps farmers and ranchers decide what crops to plant, when to sell when to contract, and when to store. Farmers can better track production costs against market prices to ensure maximum profits. In the long term, this will keep more farmers profitable and able to produce year after year. 

Weather Forecasting

Since the days of Benjamin Franklin’s Farmer's Almanac and long before, farmers have meticulously tracked the weather. Planting and harvesting schedules were based on the historical weather reports tracked in weather journals passed down from generation to generation. This isn’t much different than how the weather is predicted now – we still use on-farm data, just a whole lot more of it. 

Meteorologists make hourly, daily, and weekly predictions based on historical data collected during similar climate conditions. Modern data collection systems and software can process that amount of data, and the accuracy of the weather report is improving. And every year, we are getting better at computing historical data. Accurate weather forecasting can make all the difference in farming. With big data analytics in agriculture, farmers can access real-time weather data to predict weather patterns and then use that information to decide when to plant, irrigate, or harvest. 

Agricultural Lending 

Big data analytics in agriculture for credit risk assessment has been on the rise in personal finance industries over the last several years. The data is processed in algorithms, generating a predictive score of the likelihood of a person repaying a loan. This approach is beginning to be used in agricultural lending. The current ag lending model is clunky and overly conservative, making it hard for farmers to grow their operations or for new farmers to begin farming. 

We are living in the agricultural data revolution, and we are beginning to harness the power of that data to better finance farmers. The amount, quality, and functionality of on-farm data, supply chain data, macro data, personal data, and financial data are getting exponentially better by the day. Fifty percent of all US farms use farm management software to run their business. Using the same approach as in personal lending, digital lenders – like Bankbarn are using these data points to create a holistic picture of the health and risk assessment of that farm and lending to farmers based on that picture. 

Learn how digital ag lending revolutionizes the industry, check out this article on The Future of Ag Finance.

Trust and Transparency

The agriculture sector is driven by trust between consumers and farmers. Big data in agriculture infrastructure enables farmers to maximize transparency in the production process. Traceability from precision agriculture practices enables production practices, like fertilizer and pesticide application, antibiotic use, and animal husbandry, to be traced from the farm or ranch to the grocery store. With big data in analytics in agriculture, consumers can feel confident in their food purchases. 

 AI will be part of the Future of Agriculture

You cannot over-emphasize the role of big data in agriculture. Farmers can leverage technology to grow or start their operations and provide food security. Adopting "data-driven" farming incorporates a systems-based approach that considers every aspect of production, from soil management to the consumer, and leads to success for everyone along the supply chain. The benefits of big data analytics in agriculture include improved efficiency, higher crop yield, and reduced costs. With the ever-growing market, continued technology growth will lead to more innovation in agriculture and secure our food future for generations to come.