Systems Analysis and Predictive Modeling

A key objective of our project is to utilize the field data we collect in local, regional and national scale models. The models examine current and predicted implications of particular crop management practices on carbon, nitrogen and water footprints, under different climate conditions and economic scenarios. These practices include no-till, extended crop rotations, drainage water management, cover crops and canopy nitrogen sensing.


Climate scientists agree that global changes are present now and will continue, but there is great uncertainty regarding the impact these global changes have on local and regional cropping systems. Scientists also agree that there is no single best climate model either for the globe or for a region (such as the Midwest). Therefore, it is necessary to consider a suite of climate scenarios to represent the expected range of plausible future climates.

Researchers are developing an analysis infrastructure to provide insight into the impacts and outcomes that will likely affect the sustainability and economic vitality of corn-based cropping systems. This infrastructure includes a database system to manage experimental data and a variety of physical and economic models capable of analyzing impacts at multiple scales...


Research Questions - Analysis and Modeling

Scientists and Staff Working on Analysis and Modeling








We're scientists and farmers working together to create a suite of practices for corn-based systems that:

  • are resilient in times of drought
  • reduce soil and nutrient losses under saturated soil conditions
  • reduce farm field nitrogen losses
  • retain carbon in the soil
  • ensure crop and soil productivity

Sustainable Corn BLOG

Farmers and scientists in the Corn Belt discussing cover crops, weather, tillage, drainage water managment and much more.


Sustainable Corn YouTube Channel

SUSTAINABLECORN.ORG | Website Administrator
This material is based upon work that is supported by the National Institute of Food and
Agriculture, U.S. Department of Agriculture, under award number 2011-68002-30190
Any opinions, findings, conclusions, or recommendations expressed on this website are those of the author(s)
and do not necessarily reflect the view of the U.S. Department of Agriculture.