| Registro completo |
| Provedor de dados: |
Agronomy
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| País: |
Brazil
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| Título: |
Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil
Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil
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| Autores: |
Moreto, Victor Brunini
Rolim, Glauco de Souza
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| Data: |
2015-10-01
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| Ano: |
2015
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| Palavras-chave: |
Agrometeorologia 5.01.05.00-0 crop model
Water balance
Prediction
Production. agrometeorologia
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| Resumo: |
Forecast is the act of estimating a future event based on current data. Ten-day period (TDP) meteorological data were used for modeling: mean air temperature, precipitation and water balance components (water deficit (DEF) and surplus (EXC) and soil water storage (SWS)). Meteorological and yield data from 1990-2004 were used for calibration, and 2005-2010 were used for testing. First step was the selection of variables via correlation analysis to determine which TDP and climatic variables have more influence on the crop yield. The selected variables were used to construct models by multiple linear regression, using a stepwise backwards process. Among all analyzed models, the following was notable: Yield = - 4.964 x [SWS of 2° TDP of December of the previous year (OPY)] – 1.123 x [SWS of 2° TDP of November OPY] + 0.949 x [EXC of 1° TDP of February of the productive year (PY)] + 2.5 x [SWS of 2° TDP of February OPY] + 19.125 x [EXC of 1° TDP of May OPY] – 3.113 x [EXC of 3° TDP of January OPY] + 1.469 x [EXC of 3 TDP of January of PY] + 3920.526, with MAPE = 5.22%, R2 = 0.58 and RMSEs = 111.03 kg ha-1.
Forecast is the act of estimating a future event based on current data. Ten-day period (TDP) meteorological data were used for modeling: mean air temperature, precipitation and water balance components (water deficit (DEF) and surplus (EXC) and soil water storage (SWS)). Meteorological and yield data from 1990-2004 were used for calibration, and 2005-2010 were used for testing. First step was the selection of variables via correlation analysis to determine which TDP and climatic variables have more influence on the crop yield. The selected variables were used to construct models by multiple linear regression, using a stepwise backwards process. Among all analyzed models, the following was notable: Yield = - 4.964 x [SWS of 2° TDP of December of the previous year (OPY)] – 1.123 x [SWS of 2° TDP of November OPY] + 0.949 x [EXC of 1° TDP of February of the productive year (PY)] + 2.5 x [SWS of 2° TDP of February OPY] + 19.125 x [EXC of 1° TDP of May OPY] – 3.113 x [EXC of 3° TDP of January OPY] + 1.469 x [EXC of 3 TDP of January of PY] + 3920.526, with MAPE = 5.22%, R2 = 0.58 and RMSEs = 111.03 kg ha-1.
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| Tipo: |
Info:eu-repo/semantics/article
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| Idioma: |
Inglês
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| Identificador: |
http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/19766
10.4025/actasciagron.v37i4.19766
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| Editor: |
Universidade Estadual de Maringá
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| Relação: |
http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/19766/pdf_91
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| Formato: |
application/pdf
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| Fonte: |
Acta Scientiarum. Agronomy; v. 37, n. 4 (2015); 403-410
Acta Scientiarum. Agronomy; v. 37, n. 4 (2015); 403-410
1807-8621
1679-9275
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