Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage

Autores
Irisarri, Jorge Gonzalo Nicolás; Durante, Martin; Derner, Justin D.; Oesterheld, Martin; Augustine, David J.
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In the Great Plains of central North America, sustainable livestock production is dependent on matching the timing of forage availability and quality with animal intake demands. Advances in remote sensing technology provide accurate information for forage quantity. However, similar efforts for forage quality are lacking. Crude protein (CP) content is one of the most relevant forage quality determinants of individual animal intake, especially below an 8% threshold for growing animals. In a set of shortgrass steppe paddocks with contrasting botanical composition, we (1) modeled the spatiotemporal variation in field estimates of CP content against seven spectral MODIS bands, and (2) used the model to assess the risk of reaching the 8% CP content threshold during the grazing season for paddocks with light, moderate, or heavy grazing intensities for the last 22 years (2000–2021). Our calibrated model explained up to 69% of the spatiotemporal variation in CP content. Different from previous investigations, our model was partially independent of NDVI, as it included the green and red portions of the spectrum as direct predictors of CP content. From 2000 to 2021, the model predicted that CP content was a limiting factor for growth of yearling cattle in 80% of the years for about 60% of the mid-May to October grazing season. The risk of forage quality being below the CP content threshold increases as the grazing season progresses, suggesting that ranchers across this rangeland region could benefit from remotely sensed CP content to proactively remove yearling cattle earlier than the traditional October date or to strategically provide supplemental protein sources to grazing cattle.
EEA Concepción del Uruguay
Fil: Irisarri, Jorge Gonzalo Nicolás. Rothamsted Research. Sustainable Agriculture Sciences; Reino Unido
Fil: Durante, Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concepción del Uruguay; Argentina
Fil: Durante, Martin. Instituto Nacional de Investigación Agropecuaria (INIA). Estación Experimental INIA Tacuarembó. Programa Pasturas y Forrajes; Uruguay
Fil: Derner, Justin D. United States Department of Agriculture-Agricultural Research Service. Rangeland Resources Research Unit; Estados Unidos
Fil: Oesterheld, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina
Fil: Oesterheld, Martin. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina
Fil: Augustine, David J.. United States Department of Agriculture–Agricultural Research Service. Rangeland Resources and Systems Research Unit; Estados Unidos
Fuente
Remote Sensing 14 (4) : 854 (February 2022)
Materia
Forrajes
Teledetección
Proteina Bruta
Evaluación de Riesgos
Pastoreo
Ganado Bovino
Forage
Remote Sensing
Crude Protein
Risk Assessment
Grazing
Cattle
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/11480
Acceder
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oai_identifier_str oai:localhost:20.500.12123/11480
network_acronym_str INTADig
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network_name_str INTA Digital (INTA)
spelling Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe ForageIrisarri, Jorge Gonzalo NicolásDurante, MartinDerner, Justin D.Oesterheld, MartinAugustine, David J.ForrajesTeledetecciónProteina BrutaEvaluación de RiesgosPastoreoGanado BovinoForageRemote SensingCrude ProteinRisk AssessmentGrazingCattleIn the Great Plains of central North America, sustainable livestock production is dependent on matching the timing of forage availability and quality with animal intake demands. Advances in remote sensing technology provide accurate information for forage quantity. However, similar efforts for forage quality are lacking. Crude protein (CP) content is one of the most relevant forage quality determinants of individual animal intake, especially below an 8% threshold for growing animals. In a set of shortgrass steppe paddocks with contrasting botanical composition, we (1) modeled the spatiotemporal variation in field estimates of CP content against seven spectral MODIS bands, and (2) used the model to assess the risk of reaching the 8% CP content threshold during the grazing season for paddocks with light, moderate, or heavy grazing intensities for the last 22 years (2000–2021). Our calibrated model explained up to 69% of the spatiotemporal variation in CP content. Different from previous investigations, our model was partially independent of NDVI, as it included the green and red portions of the spectrum as direct predictors of CP content. From 2000 to 2021, the model predicted that CP content was a limiting factor for growth of yearling cattle in 80% of the years for about 60% of the mid-May to October grazing season. The risk of forage quality being below the CP content threshold increases as the grazing season progresses, suggesting that ranchers across this rangeland region could benefit from remotely sensed CP content to proactively remove yearling cattle earlier than the traditional October date or to strategically provide supplemental protein sources to grazing cattle.EEA Concepción del UruguayFil: Irisarri, Jorge Gonzalo Nicolás. Rothamsted Research. Sustainable Agriculture Sciences; Reino UnidoFil: Durante, Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concepción del Uruguay; ArgentinaFil: Durante, Martin. Instituto Nacional de Investigación Agropecuaria (INIA). Estación Experimental INIA Tacuarembó. Programa Pasturas y Forrajes; UruguayFil: Derner, Justin D. United States Department of Agriculture-Agricultural Research Service. Rangeland Resources Research Unit; Estados UnidosFil: Oesterheld, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Oesterheld, Martin. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Augustine, David J.. United States Department of Agriculture–Agricultural Research Service. Rangeland Resources and Systems Research Unit; Estados UnidosMDPI2022-03-23T14:42:39Z2022-03-23T14:42:39Z2022-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/11480https://www.mdpi.com/2072-4292/14/4/8542072-4292https://doi.org/10.3390/rs14040854Remote Sensing 14 (4) : 854 (February 2022)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-10-23T11:17:54Zoai:localhost:20.500.12123/11480instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-23 11:17:54.925INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage
title Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage
spellingShingle Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage
Irisarri, Jorge Gonzalo Nicolás
Forrajes
Teledetección
Proteina Bruta
Evaluación de Riesgos
Pastoreo
Ganado Bovino
Forage
Remote Sensing
Crude Protein
Risk Assessment
Grazing
Cattle
title_short Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage
title_full Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage
title_fullStr Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage
title_full_unstemmed Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage
title_sort Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage
dc.creator.none.fl_str_mv Irisarri, Jorge Gonzalo Nicolás
Durante, Martin
Derner, Justin D.
Oesterheld, Martin
Augustine, David J.
author Irisarri, Jorge Gonzalo Nicolás
author_facet Irisarri, Jorge Gonzalo Nicolás
Durante, Martin
Derner, Justin D.
Oesterheld, Martin
Augustine, David J.
author_role author
author2 Durante, Martin
Derner, Justin D.
Oesterheld, Martin
Augustine, David J.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Forrajes
Teledetección
Proteina Bruta
Evaluación de Riesgos
Pastoreo
Ganado Bovino
Forage
Remote Sensing
Crude Protein
Risk Assessment
Grazing
Cattle
topic Forrajes
Teledetección
Proteina Bruta
Evaluación de Riesgos
Pastoreo
Ganado Bovino
Forage
Remote Sensing
Crude Protein
Risk Assessment
Grazing
Cattle
dc.description.none.fl_txt_mv In the Great Plains of central North America, sustainable livestock production is dependent on matching the timing of forage availability and quality with animal intake demands. Advances in remote sensing technology provide accurate information for forage quantity. However, similar efforts for forage quality are lacking. Crude protein (CP) content is one of the most relevant forage quality determinants of individual animal intake, especially below an 8% threshold for growing animals. In a set of shortgrass steppe paddocks with contrasting botanical composition, we (1) modeled the spatiotemporal variation in field estimates of CP content against seven spectral MODIS bands, and (2) used the model to assess the risk of reaching the 8% CP content threshold during the grazing season for paddocks with light, moderate, or heavy grazing intensities for the last 22 years (2000–2021). Our calibrated model explained up to 69% of the spatiotemporal variation in CP content. Different from previous investigations, our model was partially independent of NDVI, as it included the green and red portions of the spectrum as direct predictors of CP content. From 2000 to 2021, the model predicted that CP content was a limiting factor for growth of yearling cattle in 80% of the years for about 60% of the mid-May to October grazing season. The risk of forage quality being below the CP content threshold increases as the grazing season progresses, suggesting that ranchers across this rangeland region could benefit from remotely sensed CP content to proactively remove yearling cattle earlier than the traditional October date or to strategically provide supplemental protein sources to grazing cattle.
EEA Concepción del Uruguay
Fil: Irisarri, Jorge Gonzalo Nicolás. Rothamsted Research. Sustainable Agriculture Sciences; Reino Unido
Fil: Durante, Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concepción del Uruguay; Argentina
Fil: Durante, Martin. Instituto Nacional de Investigación Agropecuaria (INIA). Estación Experimental INIA Tacuarembó. Programa Pasturas y Forrajes; Uruguay
Fil: Derner, Justin D. United States Department of Agriculture-Agricultural Research Service. Rangeland Resources Research Unit; Estados Unidos
Fil: Oesterheld, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina
Fil: Oesterheld, Martin. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina
Fil: Augustine, David J.. United States Department of Agriculture–Agricultural Research Service. Rangeland Resources and Systems Research Unit; Estados Unidos
description In the Great Plains of central North America, sustainable livestock production is dependent on matching the timing of forage availability and quality with animal intake demands. Advances in remote sensing technology provide accurate information for forage quantity. However, similar efforts for forage quality are lacking. Crude protein (CP) content is one of the most relevant forage quality determinants of individual animal intake, especially below an 8% threshold for growing animals. In a set of shortgrass steppe paddocks with contrasting botanical composition, we (1) modeled the spatiotemporal variation in field estimates of CP content against seven spectral MODIS bands, and (2) used the model to assess the risk of reaching the 8% CP content threshold during the grazing season for paddocks with light, moderate, or heavy grazing intensities for the last 22 years (2000–2021). Our calibrated model explained up to 69% of the spatiotemporal variation in CP content. Different from previous investigations, our model was partially independent of NDVI, as it included the green and red portions of the spectrum as direct predictors of CP content. From 2000 to 2021, the model predicted that CP content was a limiting factor for growth of yearling cattle in 80% of the years for about 60% of the mid-May to October grazing season. The risk of forage quality being below the CP content threshold increases as the grazing season progresses, suggesting that ranchers across this rangeland region could benefit from remotely sensed CP content to proactively remove yearling cattle earlier than the traditional October date or to strategically provide supplemental protein sources to grazing cattle.
publishDate 2022
dc.date.none.fl_str_mv 2022-03-23T14:42:39Z
2022-03-23T14:42:39Z
2022-02
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/20.500.12123/11480
https://www.mdpi.com/2072-4292/14/4/854
2072-4292
https://doi.org/10.3390/rs14040854
url http://hdl.handle.net/20.500.12123/11480
https://www.mdpi.com/2072-4292/14/4/854
https://doi.org/10.3390/rs14040854
identifier_str_mv 2072-4292
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Remote Sensing 14 (4) : 854 (February 2022)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 12.982451

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