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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/85293
Ver los metadatos del registro completo
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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 |
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1842268660602765312 |
score |
13.13397 |