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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
28/01/1999 |
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Data da última atualização: |
28/03/2019 |
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Autoria: |
COLSON, J.; WALLACH, D.; BOUNIOLS, A.; DENIS, J.-B.; JONES, J. W. |
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Afiliação: |
JOSIANNE COLSON, INRA; DANIEL WALLACH, INRA; ANDRÉE BOUNIOLS, INRA; JEAN-BAPTISTE DENIS, INRA; JAMES W. JONES, University of Florida. |
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Título: |
Mean squared error of yield prediction by SOYGRO. |
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Ano de publicação: |
1995 |
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Fonte/Imprenta: |
Agronomy Journal, v. 87, n. 3, p. 397-402, May-June 1995. |
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DOI: |
10.2134/agronj1995.00021962008700030002x |
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Idioma: |
Inglês |
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Conteúdo: |
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 I(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. |
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Palavras-Chave: |
Crop modeling; Modelagem; SOYGRO model; Yield prediction. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01814naa a2200229 a 4500
001 1006984
005 2019-03-28
008 1995 bl uuuu u00u1 u #d
024 7 $a10.2134/agronj1995.00021962008700030002x$2DOI
100 1 $aCOLSON, J.
245 $aMean squared error of yield prediction by SOYGRO.$h[electronic resource]
260 $c1995
520 $aYield 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 I(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.
653 $aCrop modeling
653 $aModelagem
653 $aSOYGRO model
653 $aYield prediction
700 1 $aWALLACH, D.
700 1 $aBOUNIOLS, A.
700 1 $aDENIS, J.-B.
700 1 $aJONES, J. W.
773 $tAgronomy Journal$gv. 87, n. 3, p. 397-402, May-June 1995.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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