Predicting spatial‐temporal patterns of diet quality and large herbivore performance using satellite time series

Abstract

Adaptive management of large herbivores requires an understanding of how spatial‐temporal fluctuations in forage biomass and quality influence animal performance. Advances in remote sensing have yielded information about the spatial‐temporal dynamics of forage biomass, which in turn have informed rangeland management decisions such as stocking rate and paddock selection for free‐ranging cattle. However, less is known about the spatial‐temporal patterns of diet quality and their influence on large herbivore performance. This is due to infrequent concurrent ground observations of forage conditions with performance (e.g., mass gain), and previously limited satellite data at fine spatial and temporal scales. We combined multi‐temporal field observations of diet quality (weekly) and mass gain (monthly) with satellite‐derived phenological metrics (pseudo‐daily, using data fusion and interpolation) to model daily mass gains of free‐ranging yearling cattle in shortgrass steppe. We used this model to predict grazing season (mid‐May to October) mass gains, a key management indicator, across 40 different paddocks grazed over a 10‐year period (n = 138). We found strong relationships between diet quality and the satellite‐derived phenological metrics, especially metrics related to the timing and rate of green‐up and senescence. Satellite‐derived diet quality estimates were strong predictors of monthly mass gains (R2 = 0.68) across a wide range of aboveground net herbaceous production. Season‐long predictions of average daily gain and cattle off‐mass had mean absolute errors of 8.9% and 2.9%, respectively. The model performed better temporally (across repeated observations in the same paddock) than spatially (across all paddocks within a given year), highlighting the need for accurate vegetation maps and robust field data collection across both space and time. This study demonstrates that free‐ranging cattle performance in rangelands is strongly affected by diet quality, which is related to the timing of vegetation green‐up and senescence. Senescing vegetation suppressed mass gains, even if adequate forage was available. The satellite‐based pseudo‐daily approach presented here offers new opportunities for adaptive management of large herbivores, such as identifying within‐season triggers to move livestock among paddocks, predicting wildlife herd health, or timing the grazing season to better match earlier spring green‐up caused by climate change and plant species invasion.

Remotely Sensed Spatiotemporal Variation in Crude Protein of Shortgrass Steppe Forage

“…Our calibrated model provides new insight as previous works focused on the association between NDVI and CP content [38,39,59]. However, the phenology derived from NDVI also explained a high variation in CP content [57]. Three key aspects from this work should advance additional efforts.…”

“…To date, we lacked a full picture of the association between CP content and portions of the spectrum independent of NDVI for long-term remote sensing data. Moreover, NDVI has shown limited sensitivity to moderate to high chlorophyll content or high quantities of leaf layers under erectophile canopies because, under such a canopy arrangement, more radiation is scattered to the bottom leaves, thus decreasing NIR reflectance [57]. Under low vegetation cover, soil reflectance also contributes to NIR reflectance.…”

“…Second, we found a positive association between CP content and the green portion of the spectrum. This represents a seasonal and independent estimation of CP content from either growth rate of biomass [20,57] or the total amount of biomass [62]. These latter efforts use strong associations with the red and different wavebands within the infrared portion of spectrum for their remote sensing approaches.…”

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

“…For example, the rapid advancement of predictive forecast tools like Grass-Cast that translate seasonal climate outlooks (Hartman et al, 2020;Peck et al, 2019) can assist grazing land managers with proactively using guides for flexible stocking rates (Raynor et al, 2020). Further, advances in remote sensing (Kearney et al, 2022a(Kearney et al, , 2022b provide managers with the current vegetation conditions to alter the timing of grazing on landscape components, and use targeted grazing to reduce invasive plants and manage vegetation (Bailey et al, 2019;Marchetto et al, 2021). Managers can proactively plan for extreme precipitation events by incorporating soil health management practices that enhance infiltration and prevent runoff during deluges, as well as retaining soil water during drought periods (Bagnall et al, 2022;Derner et al, 2018b).…”

“…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).…”