The sustainability of food-energy-water systems (FEWs) depends critically on whether agriculture can reduce its environmental and ecological footprints while continuing to increase crop yields. Conservation tillage (CT) has been advocated as having advantages of reducing soil erosion, improving soil quality and agricultural productivity, saving energy and water, and improving water quality. However, the adoption of CT has stalled in the U.S. and remains low in many other countries including China, due partly to insufficient understanding of CT’s economic and environmental impacts and to farmers’ behavioral gaps and deviations from the optimum. This project aims to take an integrated systems approach to link process-based models that can simulate complex biophysical and biogeochemical interactions of tillage effects on FEWs, with observational data-based econometric models that incorporate farmer behaviors and decisions. The linked models will be calibrated with experimental field data and decades of observational data in the U.S. Corn Belt, and rich experimental field data and newly available observational data in Northeastern China, taking advantage of the vast heterogeneity between the two countries. Simulation of the linked models will generate and aggregate spatially and temporally explicit knowledge and information for farmers to better understand the best practices associated with CT in relation to FEWs, for governments to design more effective targeting policies to achieve a social optimum that accounts for environmental externalities, and ultimately for optimal adoption and diffusion of CT. The project, involving international and multidisciplinary collaboration between teams from U.S. and China, will advance a set of interdisciplinary models of CT in FEWs contexts that are grounded on frontier science and behavioral studies, integrate data from vastly different regions, and are applicable to a wide range of biophysical and social economic settings.
This project will fill important knowledge and model gaps about the impacts of CT in FEWs settings. Impact analyses of CT have mostly been conducted through controlled experiment studies and process based simulation models, without taking full account of farmer behavior. Econometric models using observational data can reveal farmer behaviors, but cannot make “out-of-sample†predictions. More importantly, process-based models and econometric models on CT have not been linked to incorporate both biophysical and behavioral processes. The proposed research will link econometric models and process-based simulation models in novel ways that allow better understanding of farmers’ suboptimal behavior and decisions. The linked models will enable policy scenario analysis and simulation of dynamic impacts of CT on FEWs, for each type of field-weather-policy combinations, to understand CT adoption and impacts. The field level analysis can be scaled up and aggregated to greater scales such as counties and regions. The linked models will provide more trustworthy and reliable outputs, better inform farmers about CT with spatially explicit adoption and implementation recommendations, and provide more effective policy recommendations for socially optimal CT adoption and implementation. The project will generate practical recommendations and policies on optimal CT practice, which will be disseminated to stakeholders including farmers and policy makers in the U.S. and China and elsewhere in the world. The research will be incorporated into various undergraduate and graduate programs and courses at the institutions in the U.S. and China.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
