This project is investigating innovative approaches to support sustainable supplies of food, energy, and water in intensively cultivated regions. We are focused on specific, high-impact innovations along two related frontiers: using new crops and technologies and inducing behavioral change. Our work is divided into four areas, each with a specific goal. The first is Biophysical Research, where our goal is to understand the biophysical processes affecting innovations in corn and soybean cropping systems in a humid and temperate environment. The second is Data Science, where our goal is to distinguish different crop types at varying spatial and temporal resolutions using machine learning and data from earth-observing satellites. The third is Socioeconomic Research, where our goal is to quantify the factors influencing the adoption of cropping systems and to develop spatially distributed predictive models of landholder behavior. The final area is the intersection of the previous three -- Integrative Modeling, where our goal to quantify systems-level behavior, accounting for the causal relationships and feedbacks within and between socioeconomic and biophysical systems.
Findings from field research are expected to advance the understanding of the potential of diversified cropping practices in northern climates. Data science approaches developed in this project will find use in many other applications that have data available at multiple spatial and temporal resolutions. Survey research will advance understanding of the drivers of and constraints to the adoption of cover crops. Qualitative findings will document farmer perspectives about cover crop adoption. Finally, the modeling work in this project will develop techniques for spatial scaling in economics, watershed modeling, and life cycle analysis.
Products and outreach
Jia, Xiaowei and Wang, Mengdie and Khandelwal, Ankush and Karpatne, Anuj and Kumar, Vipin "Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring" Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring, 2019
Jia, Xiaowei, Ankush Khandelwal, David J Mulla, Philip G Pardey, Vipin Kumar. “Bringing automated, remote‐sensed, machine learning methods to monitoring crop landscapes at scale”. Agricultural Economics, Volume 50, Issue S1, October 2019
Peterson, Jeffrey M. "Innovation as a policy strategy for natural resource protection" Natural Resource Modeling, 2019
News and related links
- Growing Prospects for Winter Annual Crops in the Upper Midwest. Minnesota Water Resources Conference Special Session. Tuesday, October 18, 2022. Track D (Meeting Rooms 7-8-9).
- NSF news release
- NSF project description
- University of Minnesota news article
- CFANS Video/”Cover” story