Submitted to: Wageningen Journal of Life Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/17/2009
Publication Date: 1/15/2010
Citation: White, J.W., 2010. Combining ecophysical models and genomics to decipher the gem-to-p problem: recent advances in crop growth modelling. NJAS - Wageningen Journal of Life Sciences, 57:53-58. Interpretive Summary: Accurate prediction of how crop yields vary with variety would allow farmers to make better decisions both to increase yields and protect the environment. Computer-based models can combine information on genotypes (G), environment (E) and management (M) and predict useful traits such as economic yield. Geneticists term such traits as phenotypes (P), so such models can be viewed as deciphering the “GEM-to-P” problem. This paper reviews use of genetics and genomics to improve the predictive power of models with emphasis on wheat, sorghum and common bean. Data on the actual genotypes of varieties should be more reliable than data based on field measurements. Using the Cropping System Model (CSM) for bread wheat, sorghum and common bean, model coefficients were estimated from genetic data. For all three crops, the simulations using gene-based coefficients were as accurate as those determined from field studies. The main constraint to wider use of this approach is the limited number of genes that have been characterized, but progress in application of plant genomics now allows rapid, robust characterization of known genes and data limitations are diminishing. Genomics can also improve how processes are modeled, including those affecting time of flowering, branching or tillering, and plant residue composition. Realizing the potential benefits of incorporating crop genetics and genomics in models, however, will not happen spontaneously. Modelers must broaden their understanding of genomics and related fields, while developing effective collaborations with the plant biology community. This paper suggests novel lines of research that will improve our ability to predict traits from genetic, environmental and management, which ultimately should make our farm enterprises more efficient and reduce potential adverse effects on soil and water resources.
Technical Abstract: 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 arguably, representations of genetic effects are overly 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), sorghum (Sorghum vulgare) and common bean (Phaseolus vulgaris). 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. Genotypic data should be more reliable than phenotypic data, since environmental effects are minimized. Using the Cropping System Model (CSM) for bread wheat, sorghum and common bean, coefficients were estimated through linear functions of gene effects. For all three crops, simulations using 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 modeled traits. However, data limitations are diminishing as genomic tools allow 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 modeled. Examples include determining the end of the juvenile phase, characterizing interactive effects of temperature on photoperiod sensitivity, improving how tiller development is modeled, and estimating carbon costs of low-lignin traits for bioenergy crops. The merger of ecophysiological models with genomics, however, will not happen spontaneously. Modelers must broaden their understanding of genomics and related fields, while developing effective collaborations with the plant biology community.