Comparing genetic coefficient estimation methods using the CERES-Maize model

Abstract Many crop simulation models use genetic coefficients to characterize varieties or hybrids. Two methods now used with CERES-Maize to obtain genetic coefficients are: (1) direct experimental measurement; and (2) estimation using the Genetic Coefficient Calculator (GENCALC), an iterative computerized procedure. The objective of this research was to compare an adaptation of the Uniform Covering by Probabilistic Region (UCPR) method with these two approaches. UCPR delineates a joint confidence region for the parameters corresponding to a goodness-of-fit threshold level. The study focuses on two genetic coefficients, duration of the juvenile phase (P1) and photoperiod sensitivity (P2), for five maize hybrids. Field experiments were conducted at Rossville, KS, during 1995 in which genetic coefficients of four of the hybrids were determined. Silking date data for the same hybrids were obtained from the Kansas Corn Performance Tests for use in estimating coefficients with UCPR and GENCALC. UCPR was better than GENCALC at minimizing squared error but at the cost of much longer run times. Both estimation procedures underestimated P1 relative to the field data. This may have resulted from the model's propensity to overestimate leaf number. An independent set of silking date data for B73×Mo17 from the Kansas Corn Performance Tests was used for comparing methods. Simulated silking dates using P1 and P2 values obtained by UCPR and GENCALC accounted for only 26 and 47%, respectively, of the variability in actual dates. Both underestimated longer durations to silking. Use of published values for P1 and P2 accounted for 45% of variability but underestimated all data (bias − 9.5 days).