Combining ecophysiological models and genomics to decipher the GEM-to-P problem

Much of agricultural research has the ultimate goal of enhancing our ability to predict phenotypes (P) based upon knowledge of genotypes (G), environment (E) and management (M) in order to quantitatively predict phenotypes (P), also known as the GEM-to-P problem. Ecophysiological models are powerful tools for quantitatively predicting phenotypes in terms of environment and management, but their representations of genetic effects are very simplistic. Genomics offers promising avenues to reduce model uncertainty by improving descriptions of the genetic differences among cultivars. This paper reviews use of genetics and genomics with emphasis on wheat (Triticum aestivum L.), sorghum (Sorghum bicolor [L.] Moench) and common bean (Phaseolus vulgaris L.). Cultivar-specific parameters, such as for photoperiod sensitivity or grain size, are often problematic because their values are estimated empirically from field studies and because the assumed physiology is inaccurate. Estimates based on genotypic data should be more reliable than estimates from phenotypic data since environmental variation is eliminated. Using ecophysiological models for wheat, sorghum and common bean, cultivar coefficients were estimated using linear functions for gene effects. For all three crops, simulations with gene-based coefficients were similar to those from conventional coefficients. Wider use of this approach has been limited by the number of loci that have been characterized for readily modelled traits. However, data limitations are diminishing as genomic tools provide robust characterization of genes such as the Vrn and Ppd series in wheat. Genomics also can contribute to understanding of how processes should be represented in models. Examples include determining the end of the juvenile phase, characterizing interactive effects of temperature on photoperiod sensitivity, improving how tiller development is modelled, and estimating carbon costs of low-lignin traits for bioenergy crops. The merger of ecophysiological models with genomics, however, will not happen spontaneously. Modellers must broaden their understanding of genomics and related fields, while developing effective collaborations with the plant biology community.