Remotely sensed spatiotemporal variation in crude protein of shortgrass steppe forage

Authors: IRISARRI, J - Rothamsted Research; DURANTE, M - Instituto Nacional Tecnologia Agropecuaria; Derner, Justin; OESTERHELD, M - Ifibyne (UBA-CONICET); Augustine, David

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/8/2022
Publication Date: 2/11/2022
Citation: Irisarri, J.G., Durante, M., Derner, J.D., Oesterheld, M., Augustine, D.J. 2022. Remotely sensed spatiotemporal variation in crude protein of shortgrass steppe forage. Remote Sensing. 14. 854. https://doi.org/10.3390/rs14040854.
DOI: https://doi.org/10.3390/rs14040854

Interpretive Summary: Ranchers need to match the available forage and quality of this forage to demands of livestock. Remote sensing has advanced to provide accurate amounts forage, but the quality aspects are lacking. We developed a model from contemporary remote sensing technology that accurately predicted crude protein content for pastures with contrasting vegetation in a shortgrass prairie rangeland. Using this model, threshold values of crude protein can be provided to ranchers for decision-making related to strategic supplementation of protein to livestock, moving of livestock to higher quality pastures, or moving livestock from rangeland to feeding operations.

Technical 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) modelled the spatiotemporal variation of 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 2000-2021, 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.