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CROP BREEDING, GENETICS & CYTOLOGY

A Gene-Based Model to Simulate Soybean Development and Yield Responses to Environment

^a Agric.& Biol. Eng. Dep.
^b Dep. of Agronomy
^c Horticultural Sci. Dep., Univ. of Florida, Gainesville, FL 32611

^* Corresponding author (Charlie.Messina{at}pioneer.com)

Realizing the potential of agricultural genomics into practical applications requires quantitative predictions for complex traits and different genotypes and environmental conditions. The objective of this study was to develop and test a procedure for quantitative prediction of phenotypes as a function of environment and specific genetic loci in soybean [Glycine max (L.) Merrill]. We combined the ecophysiological model CROPGRO-Soybean with linear models that predict cultivar-specific parameters as functions of E loci. The procedure involved three steps: (i) a field experiment was conducted in Florida in 2001 to obtain phenotypic data for a set of near-isogenic lines (NILs) with known genotypes at six E loci; (ii) we used these data to estimate cultivar-specific parameters for CROPGRO-Soybean, minimizing root mean square error (RMSE) between observed and simulated values; (iii) these parameters were then expressed as linear functions of the (known) E loci. CROPGRO-Soybean predicted various phenological stages for the same NILs grown in 2002 in Florida with a RMSE of about 5 d using the E loci–derived parameters. A second evaluation of the approach used phenotypic data from cultivar trials conducted in Illinois. Cultivars were genotyped at the E loci using microsatellites. The model predicted time to maturity in the Illinois variety trials with RMSE around 7.5 d; it also explained 75% of the time-to-maturity variance and 54% of the yield variance. Our results suggest that gene-based approaches can effectively use agricultural genomics data for cultivar performance prediction. This technology may have multiple uses in plant breeding.

Abbreviations: cM, centimorgan • DOY: day of year • GC, genetic coefficients • ME, mean error • MEP, mean error of the prediction • NILs, near-isogenic lines • PCR, polymerase chain reaction • PTD, photothermal days • RMSE, root mean square error • RMSEP, root mean square error of prediction • SSR, simple sequence repeat • TD, thermal days

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