Estimation of the genetic coefficients of the ceres-maize model for the simulation of yield by non-destrictuve methods
The Crop Environment Resource Synthesis-Maize (CERES-Maize) mechanistic model, included in the Decision Support System for Agrotechnology Transfer (DSSAT), is a useful and powerful tool that simulates the growth and grain yield of maize in different environments. The qualitative and quantitative information provided to the CERES-Maize model guarantees reliability in the simulations obtained. However, it requires a lot of information, including soil characteristics, daily climate, crop characteristics and management, and six genetic coefficients. The objective of this research was to assess a non-destructive methodology for estimating the six genetic coefficients P1, P2, P5, G2, G3 (associated with plant maturity stages) and Phyllochron interval (PHINT), based on the maize physiology and measured by the Growing Degree Days (GDD), base 10. Two experiments were established at the International Maize and Wheat Improvement Center (CIMMYT) experimental station in Tlaltizapan, Morelos, Mexico, where 27 white and 14 yellow maize hybrids were manually sown in an irrigation conservation tillage system. Once the maize growth and grain yield simulations were obtained with CERES-Maize model, the genetic coefficients were calibrated using the Generalized Likelihood Uncertainty Estimation (GLUE). After calibration of the six genetic coefficients for all hybrids, average values of P1, G2, and G3 were within the typical range, while P2 and P5 were greater than the typical range and PHINT was below typical range. However, the simulation model showed good performance after calibration, with an average R2 of 0.9809 and 0.9730 between measured and simulated grain yields for white and yellow hybrids, respectively. The coefficients estimated in this study can be used in the CERES-Maize model to simulate grain yields for the hybrids used in different regions of the country.