Precision Modeling: Case Study in Agriculture
The U.S. Environmental Protection Agency (EPA) describes agriculture as “very sensitive to weather and climate.”
According to the EPA, “Response to the effects of climate change on agriculture—an industry heavily driven by land use, water and other natural resources—depends on the ability of farmers and ranchers to adapt.”
Ramsey Masri, the CEO of Ceres Imaging, a company that specializes in agriculture and sustainability based in Oakland, California, notes that agriculture is a $2.7 trillion global industry. As the global population is expected to exceed 10 billion by 2050, Masri says, “We need efficient, cost effective and precise ways to farm, while simultaneously using less water, chemicals and fertilizer in the process.”
Ceres offers imaging and analytics tools that measure crop health and sustainability and detect water, disease and pest threats; automate damage reporting and improve insurance claim accuracy after a catastrophic event; and optimize return on investment (ROI).
“Our ROI models typically show between 8% and 15% annual savings on inputs,” Masri says. “If you are a larger agribusiness with an annual yield in the $40 million to $200 million range, that adds up to serious dollars. Precise application ensures agribusiness realizes the best yield at the lowest cost.
“The input optimization also checks off the sustainability requirements. Using the precise amount of water or chemicals ensures the minimum required treatment without overuse or waste. Additionally, if the grower can demonstrate to its lender and insurer it is optimizing growing practices to ensure best outcomes, this helps the financial services industry with loss ratios.”
The World Bank Group, however, calls agriculture “a major part of the climate problem,” generating 19% to 29% of GHG emissions. Without action, the Washington-based group says, that figure could rise substantially.
“The use of data and analytics to better manage crops and fields couldn’t be more important right now,” Masri says. “With rapidly changing climate conditions, using AI to understand optimal growing conditions by region, crop type and weather patterns helps farmers know which crop to plant where and when.”