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SYMPOSIUM PAPERS

Genetic Coefficients in the CROPGRO–Soybean Model

Links to Field Performance and Genomics

^a Dep. of Agronomy, P.O. Box 110500, Univ. of Florida, Gainesville, FL 32611-0500
^b Dep. of Agric. and Biol. Eng., Univ. of Florida, Gainesville, FL 32611
^c Dep. of Agric. Eng., Iowa State Univ., Ames, IA 50011
^d Dep. of Agronomy, Univ. of Illinois, Urbana, IL 61801
^e Dep. of Plant, Soil and General Agriculture, Southern Illinois Univ., Carbondale, IL 62901

* Corresponding author (kjb{at}mail.ifas.ufl.edu)

Received for publication August 24, 2001. 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 CO₂. 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.

Abbreviations: G x E, genotype by environment • HI, harvest index • LAI, leaf area index • MG, maturity group • PTD, photothermal day • QTL, quantitative trait loci • SLN, specific leaf nitrogen • SLW, specific leaf weight, SWFAC, ratio of root water uptake to crop transpirational demand • WUE, water use efficiency

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