Using crop simulation to generate genotype by environment interaction effects for sorghum in water-limited environments

By Scott C. Chapman, Mark Cooper, Graeme L. Hammer. 2002

96 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6c720a25fbb0e95170695bab5e79017b9d339ce7

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In Genetics

In Plant Science

Citations in the corpus

Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials (2008, methodology): “In years like 1919 and 1939 where the rank changes were greater, the trials had poor correlations with over-all performance, associated with a high GEI variance component (Chapman et al. 2002a).”
The role of root architectural traits in adaptation of wheat to water-limited environments. (2006, background): “1996; Sinclair and Muchow 2001; Chapman et al. 2002; Sinclair et al. 2005). In only a few simulation studies has the impact of modified root traits on water uptake and crop yield been quantified. Sinclair and Muchow (2001) demonstrated that simulated maize yields were enhanced by an increased depth ”
Improving drought tolerance in maize: a view from industry (2004, background): “…and bottom-up (gene-to-phenotype) discovery approaches has been suggested as a modeling framework to integrate information across the levels of organization involved in studying and understanding gene-to-phenotype relationships (Hammer et al., 2002; Chapman et al., 2002; Peccoud et al., 2004).”
Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems (2005, background): “Chapman et al. (2000) illustrated how the sequence of environment types would confound the expression of 2 genotypes.”
PAPER PRESENTED AT INTERNATIONAL WORKSHOP ON INCREASING WHEAT YIELD POTENTIAL, CIMMYT, OBREGON, MEXICO, 20–24 MARCH 2006 Understanding the physiological basis of yield potential in wheat (2007, methodology): “In addition, simulation modelling can incorporate plausible genetic effects on traits and provide a new approach to exploring GrE (e.g. Chapman et al. 2002).”
Statistical models for genotype by environment data: from conventional ANOVA models to eco-physiological QTL models (2005, background): “The inadequacy of earlier generations of crop growth models for addressing GEI was already acknowledged in the 1990s by a number of crop physiologists whose writings are included in the book by Cooper and Hammer (1996). These crop physiologists performed a general evaluation ofmultiple models and fo”