About Us | Help Videos | Contact Us | Subscriptions

Members of ASA, CSSA, and SSSA: Due to system upgrades, your subscriptions in the digital library will be unavailable from May 15th to May 22nd. We apologize for any inconvenience this may cause, and thank you for your patience. If you have any questions, please call our membership department at 608-273-8080.


Institutional Subscribers: Institutional subscription access will not be interrupted for existing subscribers who have access via IP authentication, though new subscriptions or changes will not be available during the upgrade period. For questions, please email us at: queries@dl.sciencesocieties.org or call Danielle Lynch: 608-268-4976.



This article in AJ

  1. Vol. 87 No. 3, p. 397-402
    Received: Jan 24, 1993

    * Corresponding author(s):
Request Permissions


Mean Squared Error of Yield Prediction by SOYGRO

  1. Josianne Colson,
  2. Daniel Wallach ,
  3. Andrée Bouniols,
  4. Jean-Baptiste Denis and
  5. James W. Jones
  1. S tation d'Agronomie
    S tation de Biométrie et d'Intelligence Artificielle Institut National de la Recherche Agronomique (INRA), BP 27, 31326 Castanet-Tolosan Cédex, France
    L aboratoire de Biométrie, INRA, Route de Saint-Cyr, 78026 Versailles Cédex, France
    A gricultural Engineering Dep., Univ. of Florida, ,Gainesville FL 32611



Yield prediction is often one of the major intended uses of a crop simulation model. It is therefore important to evaluate how well a model performs as a predictor. The purpose of this study was to evaluate and analyze how well the SOYGRO model predicts soybean yield, using as a criterion the mean squared error of prediction (MSEP). The four target populations for prediction were irrigated or unirrigated plots at one location in France, for each of two varieties. The model parameters are estimated from the irrigated plots. The estimated MSEP values are on the order of 1(t ha−1)2 for all the target populations. For comparison, we defined an AVERAGE model. This model uses the average observed irrigated yield for each cultivar as the predictor of unobserved yields. AVERAGE was a better predictor than SOYGRO for the irrigated populations, while SOYGRO was better for the unirrigated populations. It seems that SOYGRO has sufficient built-in biological realism to extrapolate more reasonably than the AVERAGE model from irrigated to unirrigated conditions; however SOYGRO does not make as effective use of the data used for parameter estimation as does AVERAGE.

Contribution from INRA with the support of the Centre Technique Interprofessionnel des Oleagineux Metropolitain and in cooperation with the Univ. of Florida

  Please view the pdf by using the Full Text (PDF) link under 'View' to the left.

Copyright © .