Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach

Autores
Alvarez, Roberto
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A regional analysis of the effects of soil and climate factors on wheat yield was performed in the Argentine Pampas in order to obtain models suitable for yield estimation and regional grain production prediction. Soil data from soil surveys and climate data from meteorological records were employed. Grain production information from statistics at county level was integrated at a geomorphological level. The Pampas was divided into 10 geographical units and data from 10 growing season were used (1995-2004). Surface regression and artificial neural networks (ANN) methodologies were tested for analyzing the data. Wheat yield was correlated to soil available water holding capacity (SAWHC) in the upper 100 cm of the profiles (r2 = 0.39) and soil organic carbon (SOC) content (r2 = 0.26). The climate factor with stronger effect on yield was the rainfall/crop potential evapotranspiration ratio (R/CPET) during the fallow and vegetative crop growing cycle periods summed (r2 = 0.31). The phototermal quotient (PQ) during the pre-anthesis period had also a significant effect on yield (r2 = 0.05). A surface regression response model was developed that account for 64% of spatial and interannual yield variance, but this model could not perform a better yield prediction than the blind guess technique. An ANN was fitted to the data that accounted for 76% of yield variability. Comparing predicted versus observed yield a lower RMSE (P = 0.05) was obtained using the ANN than using the regression or the blind guess methods. Regional production estimations performed by the ANN showed a good agreement with observed data with a RMSE equivalent to 7% of the whole surveyed area production. As variables used for the ANN development may be available around 40-60 days before wheat harvest, the methodology may be used for wheat production forecasting in the Pampas.
Fil: Alvarez, Roberto. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario; Argentina
Materia
ARGENTINE PAMPAS
WHEAT
YIELD ESTIMATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/112424

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spelling Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approachAlvarez, RobertoARGENTINE PAMPASWHEATYIELD ESTIMATIONhttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4A regional analysis of the effects of soil and climate factors on wheat yield was performed in the Argentine Pampas in order to obtain models suitable for yield estimation and regional grain production prediction. Soil data from soil surveys and climate data from meteorological records were employed. Grain production information from statistics at county level was integrated at a geomorphological level. The Pampas was divided into 10 geographical units and data from 10 growing season were used (1995-2004). Surface regression and artificial neural networks (ANN) methodologies were tested for analyzing the data. Wheat yield was correlated to soil available water holding capacity (SAWHC) in the upper 100 cm of the profiles (r2 = 0.39) and soil organic carbon (SOC) content (r2 = 0.26). The climate factor with stronger effect on yield was the rainfall/crop potential evapotranspiration ratio (R/CPET) during the fallow and vegetative crop growing cycle periods summed (r2 = 0.31). The phototermal quotient (PQ) during the pre-anthesis period had also a significant effect on yield (r2 = 0.05). A surface regression response model was developed that account for 64% of spatial and interannual yield variance, but this model could not perform a better yield prediction than the blind guess technique. An ANN was fitted to the data that accounted for 76% of yield variability. Comparing predicted versus observed yield a lower RMSE (P = 0.05) was obtained using the ANN than using the regression or the blind guess methods. Regional production estimations performed by the ANN showed a good agreement with observed data with a RMSE equivalent to 7% of the whole surveyed area production. As variables used for the ANN development may be available around 40-60 days before wheat harvest, the methodology may be used for wheat production forecasting in the Pampas.Fil: Alvarez, Roberto. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario; ArgentinaElsevier Science2009-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/112424Alvarez, Roberto; Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach; Elsevier Science; European Journal of Agronomy; 30; 2; 2-2009; 70-771161-0301CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S1161030108000865info:eu-repo/semantics/altIdentifier/doi/10.1016/j.eja.2008.07.005info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:37:39Zoai:ri.conicet.gov.ar:11336/112424instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:37:39.382CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach
title Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach
spellingShingle Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach
Alvarez, Roberto
ARGENTINE PAMPAS
WHEAT
YIELD ESTIMATION
title_short Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach
title_full Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach
title_fullStr Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach
title_full_unstemmed Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach
title_sort Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach
dc.creator.none.fl_str_mv Alvarez, Roberto
author Alvarez, Roberto
author_facet Alvarez, Roberto
author_role author
dc.subject.none.fl_str_mv ARGENTINE PAMPAS
WHEAT
YIELD ESTIMATION
topic ARGENTINE PAMPAS
WHEAT
YIELD ESTIMATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv A regional analysis of the effects of soil and climate factors on wheat yield was performed in the Argentine Pampas in order to obtain models suitable for yield estimation and regional grain production prediction. Soil data from soil surveys and climate data from meteorological records were employed. Grain production information from statistics at county level was integrated at a geomorphological level. The Pampas was divided into 10 geographical units and data from 10 growing season were used (1995-2004). Surface regression and artificial neural networks (ANN) methodologies were tested for analyzing the data. Wheat yield was correlated to soil available water holding capacity (SAWHC) in the upper 100 cm of the profiles (r2 = 0.39) and soil organic carbon (SOC) content (r2 = 0.26). The climate factor with stronger effect on yield was the rainfall/crop potential evapotranspiration ratio (R/CPET) during the fallow and vegetative crop growing cycle periods summed (r2 = 0.31). The phototermal quotient (PQ) during the pre-anthesis period had also a significant effect on yield (r2 = 0.05). A surface regression response model was developed that account for 64% of spatial and interannual yield variance, but this model could not perform a better yield prediction than the blind guess technique. An ANN was fitted to the data that accounted for 76% of yield variability. Comparing predicted versus observed yield a lower RMSE (P = 0.05) was obtained using the ANN than using the regression or the blind guess methods. Regional production estimations performed by the ANN showed a good agreement with observed data with a RMSE equivalent to 7% of the whole surveyed area production. As variables used for the ANN development may be available around 40-60 days before wheat harvest, the methodology may be used for wheat production forecasting in the Pampas.
Fil: Alvarez, Roberto. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario; Argentina
description A regional analysis of the effects of soil and climate factors on wheat yield was performed in the Argentine Pampas in order to obtain models suitable for yield estimation and regional grain production prediction. Soil data from soil surveys and climate data from meteorological records were employed. Grain production information from statistics at county level was integrated at a geomorphological level. The Pampas was divided into 10 geographical units and data from 10 growing season were used (1995-2004). Surface regression and artificial neural networks (ANN) methodologies were tested for analyzing the data. Wheat yield was correlated to soil available water holding capacity (SAWHC) in the upper 100 cm of the profiles (r2 = 0.39) and soil organic carbon (SOC) content (r2 = 0.26). The climate factor with stronger effect on yield was the rainfall/crop potential evapotranspiration ratio (R/CPET) during the fallow and vegetative crop growing cycle periods summed (r2 = 0.31). The phototermal quotient (PQ) during the pre-anthesis period had also a significant effect on yield (r2 = 0.05). A surface regression response model was developed that account for 64% of spatial and interannual yield variance, but this model could not perform a better yield prediction than the blind guess technique. An ANN was fitted to the data that accounted for 76% of yield variability. Comparing predicted versus observed yield a lower RMSE (P = 0.05) was obtained using the ANN than using the regression or the blind guess methods. Regional production estimations performed by the ANN showed a good agreement with observed data with a RMSE equivalent to 7% of the whole surveyed area production. As variables used for the ANN development may be available around 40-60 days before wheat harvest, the methodology may be used for wheat production forecasting in the Pampas.
publishDate 2009
dc.date.none.fl_str_mv 2009-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/112424
Alvarez, Roberto; Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach; Elsevier Science; European Journal of Agronomy; 30; 2; 2-2009; 70-77
1161-0301
CONICET Digital
CONICET
url http://hdl.handle.net/11336/112424
identifier_str_mv Alvarez, Roberto; Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach; Elsevier Science; European Journal of Agronomy; 30; 2; 2-2009; 70-77
1161-0301
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S1161030108000865
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.eja.2008.07.005
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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