Fecha/hora: 01/06/2024 10:22:47

Biblioteca(s): 	INIA Las Brujas.	
Fecha corriente: 	14/03/2022	
Fecha de la última actualización: 	14/03/2022	
Autoria: 	IRISARRI, J.G.N.; DURANTE, M.; DERNER, J.D.; OESTERHELD, M.; AUGUSTINE, D.J.	
Afiliación: 	JORGE GONZALO N. IRISARRI, Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, Okehampton EX20 2SB, UK; MARTÍN DURANTE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Estación Experimental Agropecuaria Concepción del Uruguay, Instituto Nacional de Tecnología Agropecuaria (INTA), Concepcion del Uruguay 3260, Argentina; JUSTIN D. DERNER, USDA-Agricultural Research Service (ARS), Rangeland Resources and Systems Research Unit, Cheyenne, WY 82009, USA; MARTIN OESTERHELD, Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura, Buenos Aires 1417, Argentina; DAVID J. AUGUSTINE, USDA-Agricultural Research Service (ARS), Rangeland Resources and Systems Research Unit, Fort Collins, CO 80526, USA.	
Título: 	Remotely sensed spatiotemporal variation in crude protein of shortgrass steppe forage.	
Año de publicación: 	2022	
Fuente/Imprenta: 	Remote Sensing, 2022, Volume 14, Issue 4, Article number 854. OPEN ACCESS. doi: https://doi.org/10.3390/rs14040854	
ISSN: 	2072-4292	
DOI: 	10.3390/rs14040854	
Idioma: 	Inglés	
Notas: 	Article history: Received: 4 January 2022; Revised: 28 January 2022; Accepted: 9 February 2022; Published: 11 February 2022.
Academic Editors: Matthew Reeves, Humberto L. Perotto-Baldivieso, Luciano A. Gonzalez and Edward C. Rhodes.
Corresponding author: Irisarri, J.G.N.; Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, Okehampton, United Kingdom; email:gonzalo.irisarri@rothamsted.ac.uk  --  This article belongs to the Special Issue Applications of Remote Sensing for Livestock and Grazing Land Management: https://www.mdpi.com/journal/remotesensing/special_issues/Grazing_Land_Management  -- 
Supplementary Materials: https://www.mdpi.com/article/10.3390/rs14040854/s1	
Contenido: 	ABSTRACT.- 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.	
Palabras-Clave: 	Crude protein threshold; Forage quality; MOD09A1; Remote sensing; Risk assessment; Semi-arid environment; Shortgrass rangeland.	
Categoría del tema: 	--	
URL: 	https://www.mdpi.com/2072-4292/14/4/854/pdf	
Registro original: 	INIA Las Brujas (LB)	
Ejemplares: 	LB - (103016-1) - PXI - AP - DD - PP/Remote Sensing/2022 -  -  - DIS