Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study

Autores
Monzon, Juan Pablo; Calviño, P. A.; Sadras, Victor Oscar; Zubiaurre, J. B.; Andrade, Fernando Héctor
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Precision agriculture has under delivered partially because it has been based on technologies focused on increasing the resolution of spatial variation in soil and yield and more recently automation, with less effort in incorporating the physiological principles of crop responses to environmental variation. Here we show how a whole-farm precision agriculture approach accounting for the physiological processes underlying the relationship between environment and crop development, growth and yield (“zone management”), bridge yield gaps, increased farmer profit and reduced risk, on San Lorenzo, a 5000 ha dryland farm in the southern Pampas. The farm grows wheat and barley in winter, and soybean, maize, and sunflower in summer; winter grain cereal/double-cropped soybean is a main activity. Four management zones were defined: i) Zone 1, shallow soils (< 0.8 m) with low frost risk and deep water table (> 3 m below surface); ii) Zone 2, intermediate soil depth (0.8 to 1.8 m) with low frost risk and deep water table; iii) Zone 3, deep soils (> 1.8 m) with low frost risk and deep water table; and iv) Zone 4, deep soils (> 1.8 m) with high frost risk and water table < 3 m from surface. Crop choice and practices were tailored to each zone based on ecophysiological principles including the relative sensitivity of crop growth and yield to soil depth, frost and water supply during the species-specific critical window for yield determination; for example, maize is the most sensitive crop to stress during its critical window, therefore it was excluded from Zone 1 and 2, with a substantial reduction of risk and improvement of farm output (amount of grains that can be produced in a hectare) and profit. In comparison with neighboring farms, San Lorenzo had a 54% higher farm output, and 46% higher gross margin (or 112 US$ ha−1 year−1); this was driven by a higher net income (244 US$ ha−1) despite increased total costs (132 US$ ha−1).
Fil: Monzon, Juan Pablo. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina
Fil: Calviño, P. A.. Asociación Argentina de Consorcios Regionales de Experimentación Agrícola; Argentina
Fil: Sadras, Victor Oscar. Universidad Nacional de Mar del Plata; Argentina. South Australian Research And Development Institute; Australia
Fil: Zubiaurre, J. B.. San Lorenzo Farmer; Argentina
Fil: Andrade, Fernando Héctor. Unidad Integrada Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Universidad Nacional de Mar del Plata; Argentina
Materia
Crop Model
Farm Output
Topography
Yield Gap
Zone Management
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:11336/85293

id CONICETDig_b7f082242ad116b5deb8296252f269dc
oai_identifier_str oai:ri.conicet.gov.ar:11336/85293
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case studyMonzon, Juan PabloCalviño, P. A.Sadras, Victor OscarZubiaurre, J. B.Andrade, Fernando HéctorCrop ModelFarm OutputTopographyYield GapZone Managementhttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4Precision agriculture has under delivered partially because it has been based on technologies focused on increasing the resolution of spatial variation in soil and yield and more recently automation, with less effort in incorporating the physiological principles of crop responses to environmental variation. Here we show how a whole-farm precision agriculture approach accounting for the physiological processes underlying the relationship between environment and crop development, growth and yield (“zone management”), bridge yield gaps, increased farmer profit and reduced risk, on San Lorenzo, a 5000 ha dryland farm in the southern Pampas. The farm grows wheat and barley in winter, and soybean, maize, and sunflower in summer; winter grain cereal/double-cropped soybean is a main activity. Four management zones were defined: i) Zone 1, shallow soils (< 0.8 m) with low frost risk and deep water table (> 3 m below surface); ii) Zone 2, intermediate soil depth (0.8 to 1.8 m) with low frost risk and deep water table; iii) Zone 3, deep soils (> 1.8 m) with low frost risk and deep water table; and iv) Zone 4, deep soils (> 1.8 m) with high frost risk and water table < 3 m from surface. Crop choice and practices were tailored to each zone based on ecophysiological principles including the relative sensitivity of crop growth and yield to soil depth, frost and water supply during the species-specific critical window for yield determination; for example, maize is the most sensitive crop to stress during its critical window, therefore it was excluded from Zone 1 and 2, with a substantial reduction of risk and improvement of farm output (amount of grains that can be produced in a hectare) and profit. In comparison with neighboring farms, San Lorenzo had a 54% higher farm output, and 46% higher gross margin (or 112 US$ ha−1 year−1); this was driven by a higher net income (244 US$ ha−1) despite increased total costs (132 US$ ha−1).Fil: Monzon, Juan Pablo. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Calviño, P. A.. Asociación Argentina de Consorcios Regionales de Experimentación Agrícola; ArgentinaFil: Sadras, Victor Oscar. Universidad Nacional de Mar del Plata; Argentina. South Australian Research And Development Institute; AustraliaFil: Zubiaurre, J. B.. San Lorenzo Farmer; ArgentinaFil: Andrade, Fernando Héctor. Unidad Integrada Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Universidad Nacional de Mar del Plata; ArgentinaElsevier Science2018-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/85293Monzon, Juan Pablo; Calviño, P. A.; Sadras, Victor Oscar; Zubiaurre, J. B.; Andrade, Fernando Héctor; Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study; Elsevier Science; European Journal of Agronomy; 99; 9-2018; 62-711161-0301CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.eja.2018.06.011info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1161030118302533?via%3Dihubinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:44:21Zoai:ri.conicet.gov.ar:11336/85293instacron: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:34982025-09-03 09:44:21.432CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study
title Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study
spellingShingle Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study
Monzon, Juan Pablo
Crop Model
Farm Output
Topography
Yield Gap
Zone Management
title_short Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study
title_full Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study
title_fullStr Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study
title_full_unstemmed Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study
title_sort Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study
dc.creator.none.fl_str_mv Monzon, Juan Pablo
Calviño, P. A.
Sadras, Victor Oscar
Zubiaurre, J. B.
Andrade, Fernando Héctor
author Monzon, Juan Pablo
author_facet Monzon, Juan Pablo
Calviño, P. A.
Sadras, Victor Oscar
Zubiaurre, J. B.
Andrade, Fernando Héctor
author_role author
author2 Calviño, P. A.
Sadras, Victor Oscar
Zubiaurre, J. B.
Andrade, Fernando Héctor
author2_role author
author
author
author
dc.subject.none.fl_str_mv Crop Model
Farm Output
Topography
Yield Gap
Zone Management
topic Crop Model
Farm Output
Topography
Yield Gap
Zone Management
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Precision agriculture has under delivered partially because it has been based on technologies focused on increasing the resolution of spatial variation in soil and yield and more recently automation, with less effort in incorporating the physiological principles of crop responses to environmental variation. Here we show how a whole-farm precision agriculture approach accounting for the physiological processes underlying the relationship between environment and crop development, growth and yield (“zone management”), bridge yield gaps, increased farmer profit and reduced risk, on San Lorenzo, a 5000 ha dryland farm in the southern Pampas. The farm grows wheat and barley in winter, and soybean, maize, and sunflower in summer; winter grain cereal/double-cropped soybean is a main activity. Four management zones were defined: i) Zone 1, shallow soils (< 0.8 m) with low frost risk and deep water table (> 3 m below surface); ii) Zone 2, intermediate soil depth (0.8 to 1.8 m) with low frost risk and deep water table; iii) Zone 3, deep soils (> 1.8 m) with low frost risk and deep water table; and iv) Zone 4, deep soils (> 1.8 m) with high frost risk and water table < 3 m from surface. Crop choice and practices were tailored to each zone based on ecophysiological principles including the relative sensitivity of crop growth and yield to soil depth, frost and water supply during the species-specific critical window for yield determination; for example, maize is the most sensitive crop to stress during its critical window, therefore it was excluded from Zone 1 and 2, with a substantial reduction of risk and improvement of farm output (amount of grains that can be produced in a hectare) and profit. In comparison with neighboring farms, San Lorenzo had a 54% higher farm output, and 46% higher gross margin (or 112 US$ ha−1 year−1); this was driven by a higher net income (244 US$ ha−1) despite increased total costs (132 US$ ha−1).
Fil: Monzon, Juan Pablo. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina
Fil: Calviño, P. A.. Asociación Argentina de Consorcios Regionales de Experimentación Agrícola; Argentina
Fil: Sadras, Victor Oscar. Universidad Nacional de Mar del Plata; Argentina. South Australian Research And Development Institute; Australia
Fil: Zubiaurre, J. B.. San Lorenzo Farmer; Argentina
Fil: Andrade, Fernando Héctor. Unidad Integrada Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Universidad Nacional de Mar del Plata; Argentina
description Precision agriculture has under delivered partially because it has been based on technologies focused on increasing the resolution of spatial variation in soil and yield and more recently automation, with less effort in incorporating the physiological principles of crop responses to environmental variation. Here we show how a whole-farm precision agriculture approach accounting for the physiological processes underlying the relationship between environment and crop development, growth and yield (“zone management”), bridge yield gaps, increased farmer profit and reduced risk, on San Lorenzo, a 5000 ha dryland farm in the southern Pampas. The farm grows wheat and barley in winter, and soybean, maize, and sunflower in summer; winter grain cereal/double-cropped soybean is a main activity. Four management zones were defined: i) Zone 1, shallow soils (< 0.8 m) with low frost risk and deep water table (> 3 m below surface); ii) Zone 2, intermediate soil depth (0.8 to 1.8 m) with low frost risk and deep water table; iii) Zone 3, deep soils (> 1.8 m) with low frost risk and deep water table; and iv) Zone 4, deep soils (> 1.8 m) with high frost risk and water table < 3 m from surface. Crop choice and practices were tailored to each zone based on ecophysiological principles including the relative sensitivity of crop growth and yield to soil depth, frost and water supply during the species-specific critical window for yield determination; for example, maize is the most sensitive crop to stress during its critical window, therefore it was excluded from Zone 1 and 2, with a substantial reduction of risk and improvement of farm output (amount of grains that can be produced in a hectare) and profit. In comparison with neighboring farms, San Lorenzo had a 54% higher farm output, and 46% higher gross margin (or 112 US$ ha−1 year−1); this was driven by a higher net income (244 US$ ha−1) despite increased total costs (132 US$ ha−1).
publishDate 2018
dc.date.none.fl_str_mv 2018-09
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/11336/85293
Monzon, Juan Pablo; Calviño, P. A.; Sadras, Victor Oscar; Zubiaurre, J. B.; Andrade, Fernando Héctor; Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study; Elsevier Science; European Journal of Agronomy; 99; 9-2018; 62-71
1161-0301
CONICET Digital
CONICET
url http://hdl.handle.net/11336/85293
identifier_str_mv Monzon, Juan Pablo; Calviño, P. A.; Sadras, Victor Oscar; Zubiaurre, J. B.; Andrade, Fernando Héctor; Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study; Elsevier Science; European Journal of Agronomy; 99; 9-2018; 62-71
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/doi/10.1016/j.eja.2018.06.011
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1161030118302533?via%3Dihub
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
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
_version_ 1842268660602765312
score 13.13397