corn

Ground-based data on crop production in the USA is provided through surveys conducted by the National Agricultural Statistics Service (NASS) and the Census of Agriculture (AgCensus). Statistics from these surveys are widely used in economic analyses, policy design, and for other purposes. However, missing data in the surveys presents limitations for research that requires comprehensive data for spatial analyses.We created comprehensive county-level databases for nine major crops of the USA for a 16-yr period, by filling the gaps in existing data reported by NASS and AgCensus. We used a combination of regression analyses with data reported by NASS and the AgCensus and linear mixed-effect models incorporating county-level environmental, management, and economic variables pertaining to different agroecozones. Predicted yield and crop area were very close to the data reported by NASS, within 10% relative error. The linear mixed-effect model approach gave the best results in filling 84% of the total gaps in yields and 83% of the gaps in crop areas of all the crops. Regression analyses with AgCensus data filled 16% of the gaps in yields and crop areas of the major crops reported by NASS.

This model was developed at Idaho National Laboratory and focuses on crop production. This model is an agricultural cultivation and production model, but can be used to estimate biomass crop yields.

The Decision Support System for Agriculture (DSS4Ag) is an expert system being developed by the Site-Specific Technologies for Agriculture (SST4Ag) precision farming research project at the INEEL. DSS4Ag uses state-of-the-art artificial intelligence and computer science technologies to make spatially variable, site-specific,
economically optimum decisions on fertilizer use. The DSS4Ag has an open architecture that allows for external input and addition of new requirements and integrates its results with existing agricultural systems’ infrastructures. The DSS4Ag reflects a paradigm shift in the information revolution in agriculture that is precision farming.

A methodology was developed to estimate quantities of crop residues that can be removed while maintaining rain or wind erosion at less than or equal to the tolerable soil-loss level. Six corn and wheat rotations in the 10 largest corn-producing states were analyzed. Residue removal rates for each rotation were evaluated for conventional, mulch/reduced, and no-till field operations. The analyses indicated that potential removable maximum quantities range from nearly 5.5 million dry metric t/yr for a continuous corn rotation using conventional till in Kansas to more than 97 million dry metric t/yr for a corn-wheat rotation using no-till in Illinois.