Details About this Research
Land Core Co-founder and Executive Director, Aria McLauchlan, along with a cross-disciplinary research team including agroecologist Dr. Tim Bowles from U.C. Berkeley, statistician Dr. Frederi Viens from Rice University and agricultural economist Dr. Lawson Connor from the University of Arkansas System Division of Agriculture, is examining and correlating over 17 years of data on corn and soybean fields in the U.S. Midwest, including: 1) the adoption of important soil health-building practices, specifically cover cropping, no till/conservation tillage and crop rotations, via remote sensing; 2) estimated corn and soybean yields, via remote sensing and modeling; 3) key environmental variables using publicly-available climate, weather, soil and geological data, and 4) county-level economic data such as input use and crop insurance indemnities.
Utilizing causal inference methods and Bayesian statistics, the researchers are translating this data into a model capable of predicting the likelihood of reduced financial risk at field, farm, county and state levels for corn and soybeans, the dominant crops in the U.S.
The model framework the researchers are developing will answer questions like:
- How does soil-health management, like increased cover cropping, for example, affect resilience to water stress?
- How do yields, yield variability and yield trends over time compare in fields with a 5/10/15 year history of soil-health management vs. those with standard practices?
- What is the likelihood of corn yields dropping 5+ percent below 10-year average under dry conditions on a given field? How does the reduction in management time and input costs improve the overall profitability of a corn yield?
- How do the yield risk benefits of soil-health management compare to the level of risk implied by commodities market volatility?