The former have shown a credible capacity to predict phenotypic variation in the specific traits of interest for various genotypes and environments. Studies targeting specific component processes, such as aspects of phenology ( Leon et al., 2001 Yin et al., 2005 Messina et al., 2006 Uptmoor et al., 2008) or leaf growth ( Reymond et al., 2003 Tardieu et al., 2005 Sadok et al., 2007), have been more successful in this regard than those focused at the entire crop scale ( Hoogenboom et al., 1997 Yin et al., 2003). While crop models of this type have been applied successfully in support of agronomic practice ( Robertson et al., 2000 Nelson et al., 2002), the response coefficients have no clear biological interpretation, such that their linkage to genetic variability is tenuous.įor robust G-to-P prediction the structure and coefficients underpinning the explanatory capability of the crop model must link effectively to the genomic regions associated with variability in the complex trait ( Hammer et al., 2006 Chenu et al., 2009 Messina et al., 2009). This contrasts with the descriptive functions of level of tissue N concentration through the crop life cycle used to model crop N dynamics in agronomic models ( Jones and Kiniry, 1986). Recent studies on nitrogen (N) dynamics in field crops ( Jeuffroy et al., 2002 Martre et al., 2006 Bertheloot et al., 2008 van Oosterom et al., 2010, a, b) exemplify this link between experimentation and modelling in several species by quantifying N accumulation, allocation, and transfer patterns among plant organs based on their growth, composition, and activity. This requires an iterative process of targeted experimentation and analyses based on process biology operating in concert with model development ( Cooper and Hammer, 1996 Cooper et al., 2002 Messina et al., 2009). That is, crop models should explain complex phenotypic responses rather than relying on algorithms that simply describe them ( Tardieu, 2003 Hammer et al., 2005 Chenu et al., 2008 Yin and Struik, 2008). Recent studies suggest that while using crop models to tackle the G-to-P prediction problem for application in plant breeding has considerable potential, the adequacy of existing crop models for this task remains questionable ( Chapman et al., 2002 Tardieu, 2003 Yin et al., 2004 Hammer et al., 2006 Messina et al., 2009).Įnhancing the crop modelling capability for G-to-P prediction requires algorithms that represent underlying processes and generate the phenotype of the plant as an emergent consequence of model dynamics. Sinclair and Seligman, 1996), the use of such modelling approaches for G-to-P prediction is in its infancy ( Hammer et al., 2002 Hammer and Jordan, 2007). While there has been a long history of development and application of crop growth and development models for prediction in crop management (e.g. Progress in crop improvement and particularly in molecular approaches to plant breeding are limited by our ability to predict plant phenotype (P) based on its genotype (G), especially for complex traits like water productivity ( Cooper et al., 2002, 2005). The relevance to plant breeding of this capability in complex trait dissection and simulation is discussed.ĪPSIM, crop model, emergent property, gene-to-phenotype, sorghum, height, nitrogen, plant breeding, RUE, senescence Introduction Introducing these genetic effects associated with plant height into the model generated emergent simulated phenotypic differences in green leaf area retention during grain filling via effects associated with nitrogen dynamics. Genotypes differing in height were found to differ in biomass partitioning among organs and a tall hybrid had significantly increased radiation use efficiency: a novel finding in sorghum. Experiments on diverse genotypes of sorghum that underpin the development and testing of the adapted crop model are detailed.
APSIM SORGHUM SOFTWARE
The model builds on existing approaches within the APSIM software platform. The approach quantifies capture and use of radiation, water, and nitrogen within a framework that predicts the realized growth of major organs based on their potential and whether the supply of carbohydrate and nitrogen can satisfy that potential. It is designed to exhibit reliable predictive skill at the crop level while also introducing sufficient physiological rigour for complex phenotypic responses to become emergent properties of the model dynamics. A generic cereal crop growth and development model is outlined here. Suitably constructed crop growth and development models have the potential to bridge this predictability gap. Progress in molecular plant breeding is limited by the ability to predict plant phenotype based on its genotype, especially for complex adaptive traits.
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