Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage

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.

Practical considerations for adaptive strategies by US grazing land managers with a changing climate

“…These climatic determinants influence livestock production as well (Derner et al., 2019; Raynor et al., 2020). Further, advances in remote sensing technology and big data analyses improve our ability to predict future variability in grazing land productivity (Gaffney et al., 2018; Kearney et al., 2022a; Podebradska et al., 2022) and quality (Irisarri et al., 2022). Satellite time series data have been used to predict spatial and temporal patterns of forage production and diet quality, as well as the resultant livestock performance in the Great Plains (Kearney et al., 2022b).…”

Perspectives on the Special Issue for Applications of Remote Sensing for Livestock and Grazingland Management

“…Pearson et al [103] coupled remotely sensed vegetation indices with electronic identification of cattle via automatic weighing stations at watering points to show that cattle live weights, and live weight change can be modeled from a combination of vegetation indices, Julian day, and rainfall data. Finally, Irisarri et al [104] present a novel way to estimate crude protein content of forages using the MODIS platform. What distinguishes this work from previous attempts is that they focus mostly on the red and green portions of the electromagnetic spectrum, rather than relying solely upon NDVI.…”