There is a need to integrate knowledge-intensive sustainable agricultural practices into a precision framework to maximize farm productivity, profitability, and sustainability.This is accomplished through the use of an information ecology that connects farms, data, tools, and people for optimal, real-time decisions. The modeling team is one of the linchpins of the PSA effort that takes data collected by the field research teams, combines it with data and insights from lab and greenhouse studies and the scientific literature, and uses the results to create and refine decision support tools and management recommendations.
We are developing adaptive decision support tools that integrate climate, soil, and landscape spatial dynamics into recommendations. This involves harmonizing data collected at different scales, from in-field to remote-sensed, using a diverse set of modeling and machine learning techniques, and deploying our tools as open-source modules for use by the wider community.
Data for developing and improving the model for water potential of surface residues include studies of the impact of rainfall and dew at different stages of residue decomposition. Data for development of the rye model are being collected from experiments in outdoor growth chambers with temperature and CO2 as treatments. (see photos below). The photos show rye at the end of the experiment as it reaches heading stage.