Big Data Is Precision Agriculture’s Best Tool to Feed the World
Not all use cases are created equal, however, and developers need to focus on top opportunities like input reduction and machinery maintenance, Lux Research says
BOSTON, MA – June 23, 2015 – Big data can be the most flexible tool for increasing the efficiency of food production through precision agriculture – a quantified approach to cultivation that uses sensing, input modulation, and data analytics to enhance the efficiency of agriculture. Using it correctly will be essential to feeding the world’s growing population while also improving the environment, according to Lux Research.
Today, farmers struggle to find value in big data – tech developers need to focus on meeting strategic and tactical goals, and identify use and business cases to lay the groundwork for adoption and growth.
“There’s no dearth of information or computing power – today’s challenge is integrating the deluge of data in a way that farmers buy into,” said Mark Bünger, Lux Research Director and one of the lead authors of the report titled, “Big Data in Precision Agriculture.”
“While many farmers are still reluctant despite increasing availability of information and technology, there are clear ways to use big data in agriculture now,” he added.
Lux Research analysts assessed the various ways in which big data can help precision agriculture, and their economic viability. Among their findings:
- Focus should be on farm-to-fork economics. Big data strategies in agriculture must start with overall farm-to-fork economics, focusing on data-driven tactical goals like increasing the profitability of crops, and strategic goals like minimizing the environmental impact of agriculture while ensuring food security.
- Top use cases organize into five major categories. Based on an analysis of 120 use cases for big data in agriculture, Lux Research identified five categories: Reduce input and environmental impact while maintaining output; manage and maintain machinery; finance and administration; obtain best possible price at market; and comply with regulations.
- Investment is tied to paybacks. For big data in agriculture, even the most apparently beneficial use case needs a business case. The payback should be based on measurable increases in revenue or reductions in cost, complemented by soft benefits like improved customer satisfaction or competitive dynamics.