Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage
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 forag...
| Autores principales: | , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
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MDPI
2022
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/11480 https://www.mdpi.com/2072-4292/14/4/854 https://doi.org/10.3390/rs14040854 |
| _version_ | 1855484789756264448 |
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| author | Irisarri, Jorge Gonzalo Nicolás Durante, Martin Derner, Justin D. Oesterheld, Martin Augustine, David J. |
| author_browse | Augustine, David J. Derner, Justin D. Durante, Martin Irisarri, Jorge Gonzalo Nicolás Oesterheld, Martin |
| author_facet | Irisarri, Jorge Gonzalo Nicolás Durante, Martin Derner, Justin D. Oesterheld, Martin Augustine, David J. |
| author_sort | Irisarri, Jorge Gonzalo Nicolás |
| collection | INTA Digital |
| 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. |
| format | Artículo |
| id | INTA11480 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INTA114802022-03-23T14:44:40Z Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage Irisarri, Jorge Gonzalo Nicolás Durante, Martin Derner, Justin D. Oesterheld, Martin Augustine, David J. Forrajes Teledetección Proteina Bruta Evaluación de Riesgos Pastoreo Ganado Bovino Forage Remote Sensing Crude Protein Risk Assessment Grazing Cattle 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 2022-03-23T14:42:39Z 2022-03-23T14:42:39Z 2022-02 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng 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) application/pdf MDPI Remote Sensing 14 (4) : 854 (February 2022) |
| spellingShingle | Forrajes Teledetección Proteina Bruta Evaluación de Riesgos Pastoreo Ganado Bovino Forage Remote Sensing Crude Protein Risk Assessment Grazing Cattle Irisarri, Jorge Gonzalo Nicolás Durante, Martin Derner, Justin D. Oesterheld, Martin Augustine, David J. Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage |
| title | 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_short | Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage |
| title_sort | remotely sensed spatiotemporal variation in crude protein of shortgrass steppe forage |
| topic | Forrajes Teledetección Proteina Bruta Evaluación de Riesgos Pastoreo Ganado Bovino Forage Remote Sensing Crude Protein Risk Assessment Grazing Cattle |
| url | http://hdl.handle.net/20.500.12123/11480 https://www.mdpi.com/2072-4292/14/4/854 https://doi.org/10.3390/rs14040854 |
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