Genetic Coefficients in the CROPGRO–Soybean Model

By Kenneth J. Boote, James W. Jones, W. Batchelor, Emerson D. Nafziger, O. Myers. Agronomy Journal, 2003

163 citations recorded by the source corpus.

Crop growth models are tools with valuable uses in research synthesis and crop management. This paper discusses genetic coefficients in the CROPGRO-Soybean model in terms of definitions, implications for genetic improvement, relationships to field performance, and linkage to genomics. As used in crop models, genetic coefficients are mathematical constructs designed to mimic the phenotypic phenotypic outcome of genes under different environments to influence: (i) life cycle including fractional allocation to different phases, (ii) photosynthetic, (iii) vegetative, (iv) rooting, and (v) reproductive processes. Model sensitivity analyses was used to hypothesize genetic coefficients of soybean [Glycine max (L.) Merr.] and impact on field performance. Yield improvement from increased leaf photosynthesis was shown to be small if coupled to specific leaf weight. Yield improvement with longer seed filling duration was enhanced by traits such as slower N mobilization to sustain leaf photosynthesis or by genetic traits and management factors allowing adequate leaf area index before seed fill. Yield improvement under water-deficit appeared feasible from rate of root-depth increase, shift in root profile, and a slow senesce trait. Modeled genetic coefficients showed mostly additive effects on yield when evaluated in combinations; and combinations of minor changes gave yield increases of 13 to 17%, comparable to recent genetic improvement. More than additive effects occurred under good crop management or under projected rise in global CO 2 . Information from genomics, physiology, and yield performance trials can be used to derive genetic coefficients for crop models. Interaction of molecular geneticists, physiologists, and crop modelers is needed to facilitate the translation of genetic knowledge to modes of action, and finally to integrated field performance under multiple stress environments.

Subfields: Agronomy and Crop Science, Plant Science

View Paper: https://www.semanticscholar.org/paper/961aa8146c431308d5906262e504c2ff52afbf02

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Citations in the corpus

Potential benefits of drought and heat tolerance in groundnut for adaptation to climate change in India and West Africa (2014, methodology): “Plant growth simulation models can be used to assess crop growth and yield advantages due to new technologies in different environments by using environment-specific weather, soil and agronomic management data (Boote et al. 2001, 2003).”
Model-assisted phenotyping and ideotype design (2015, background): “The foremost expectation of ecophysiological models is the simulation of genotypic variation in different environments and to capture G × e × m interactions (Box 14.1) (asseng et al., 2002; Boote et al., 2003; Chapman et al., 2003; Hoogenboom and White, 2003).”
Inter-comparison of performance of soybean crop simulation models and their ensemble in southern Brazil (2017, methodology): “…models consider more details in the description of 61 all crop processes, which increases their possible uses to evaluate crop development and 62 yield, as CROPGRO model, that integrates carbon, nitrogen and water balances on growth 63 processes for estimating soybean yield (BOOTE et al., 2003).”
Predicting on-farm soybean yields in the pampas using CROPGRO-soybean (2007, methodology): “We calibrated cultivar parameters using ‘‘ad hoc’’ trials exploring a wide range of sowing dates (October‐January) in three localities: Marcos Jua´rez (32.688 latitude, 62.108 longitude), Pergamino (33.908, 60.588) and 25 de Mayo (35.438,60.178).Cultivargenetic coefficients were adjusted to reported”
Soybean Sowing Date: The Vegetative, Reproductive, and Agronomic Impacts (2008, background): “Modern soybean crop models typically undergo continual updating to make the prediction of reproductive development less empirical and more mechanistic ( Boote et al., 2003; Setiyono et al., 2007).”