Evaluation of two maize models for nine US locations

Crop models can be evaluated based on accuracy in simulating several Sears' yields for one location or on accuracy in simulating long-term mean yields for several locations. Our objective was to see how the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) model and a new version of CERES-Maize (Crop Environment Resource Synthesis) simulate grain yield of rainfed maize (Zea mays L.). We tested the models at one county in each of nine states: Minnesota, New York, Iowa, Illinois, Nebraska, Missouri, Kansas, Louisiana, and Texas (MN, NY, IA, IL, NE, MO, KS, LA, and TX). Simulated gain yields were compared with grain yields reported by the National Agricultural Statistical Service (NASS) for 1983 to 1992. In each county we those a soil commonly used in maize production, and we used measured weather data. Mean simulated grain yield for each county was always within 5% of the mean measured gain yield for the location. Within locations, measured grain yield was regressed on simulated grain yields and tested to see if the slope was significantly different from 1.0 and if the y-intercept was significantly different from 0.0, both at the 95% confidence level. Only at MN, NY, and NE for ALMANAC and at MN, NY, and TX for CERES was slope significantly different from 1.0 or intercept significantly different from 0.0. The CVs of simulated grain yields were similar to the those of measured yields at most sites. Also, both models were appropriate for predicting an individual year's yield for most counties. Values for plant parameters, such as heat units for development and the harvest index, and values for soil parameters describing soil water-holding rapacity offer users reasonable inputs for simulating maize grain yield over a wide range of locations.