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Agronomy Journal Abstract - SOYBEAN

Evaluating Methods for Simulating Soybean Cultivar Responses Using Cross Validation


This article in AJ

  1. Vol. 92 No. 6, p. 1140-1149
    Received: Aug 20, 1999

    * Corresponding author(s): airmak@agen.ufl.edu
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  1. Ayse Irmak *a,
  2. James W. Jonesa,
  3. Theodoros Mavromatisa,
  4. Stephen M. Welchc,
  5. Kenneth J. Booteb and
  6. Gail G. Wilkersond
  1. a Dep. of Agric. and Biol. Eng., Univ. of Florida, Gainesville, FL 32611-0570 USA
    c Dep. of Agron., Kansas State Univ., Manhattan, KS 66506 USA
    b Dep. of Agron., Univ. of Florida, Gainesville, FL 32611 USA
    d Dep. of Crop and Soil Sci., North Carolina State Univ., Raleigh, NC 27695 USA


Crop simulation models are used in research worldwide, and efforts are now being made to incorporate them into decision-support systems for farmers and their advisors. However, their on-farm acceptance will be limited unless methods can be found to determine model coefficients for new cultivars that are released by public and private breeders. The availability of data to determine coefficients is usually limited; however, soybean breeders routinely collect data for new cultivars from variety trials. Objectives of this research were to (i) estimate soybean crop-model prediction errors for anthesis, maturity, and yield using variety trial data; (ii) determine the effectiveness of cross validation for estimating prediction errors of the soybean model; and (iii) compare these errors with those based on regression equations relating specific cultivar yields to simulated maturity group (MG) yields. Root mean squared errors of prediction (RMSEP) were used for comparisons. Georgia variety trial data from 1987 through 1996 for six MG VII cultivars were divided into sets for fitting model coefficients and independent validation. The RMSEP using cross validation were similar to fitting errors when all ( n = 40) or only half of the data were used to fit cultivar coefficients. These errors were similar to those computed using independent data. The RMSEP for yield using linear regression were better than using generic MG coefficients but not as good as that found by fitting model coefficients. We conclude that soybean yield can be simulated for specific cultivars using either crop model or regression approaches, but the latter was not adequate for predicting cultivar anthesis and maturity dates.

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