Calibration of a crop growth model in APSIM for 15 publicly available corn hybrids in North America

Abstract

AbstractApplication of crop growth models (CGMs) in plant breeding is limited by the large number of candidate cultivars that breeders work with and the large number of CGM parameters that affect cultivar performance. The objectives of this study were to (1) calibrate 15 publicly available maize hybrids in Agricultural Production Systems sIMulator and quantify prediction accuracy in modeling physiological trait differences (yield, biomass, phenology, etc.) among genotypes; (2) better understand minimum phenotypic data requirements for CGM cultivar calibration to inform breeding efforts; and (3) quantify simulated genotype by environment interactions (G × E) across years for five traits. We calibrated hybrids with two years of multi‐trait, temporal field measurements. The R2 of simulated versus observed phenotypes was 0.89 for grain yield and over 0.80 for half of all other simulated traits. Phenology parameters accounted for nearly half of the variability in grain yield. Average (across traits) normalized root mean square error was reduced from 35% to 30% with calibration based on phenological measurements and was reduced to 20% with inclusion of physiological and nitrogen‐related measurements such as radiation use efficiency and grain nitrogen. Long‐term simulations demonstrated distinct G × E among the hybrids which accounted for 2%–29% of the total genetic variation across traits. Parameter values derived in this work will provide insight regarding important physiological traits for further phenotyping, selection, and understanding of G × E. These calibrations are for publicly available hybrids, which are currently lacking.