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

Area of Science:

• Ecology
• Remote Sensing
• Animal Science

Background:

• Adaptive management of large herbivores relies on understanding forage dynamics and animal performance.
• Remote sensing advances provide spatial-temporal forage biomass data, informing rangeland management.
• Limited knowledge exists on spatial-temporal diet quality patterns and their impact on herbivore performance.

Purpose of the Study:

• To model daily mass gains of free-ranging yearling cattle using combined field and satellite data.
• To assess the influence of diet quality, derived from satellite phenology, on cattle performance.
• To provide a tool for adaptive management of large herbivores in rangelands.

Main Methods:

• Combined weekly diet quality and monthly mass gain field observations with pseudo-daily satellite-derived phenological metrics.
• Developed a model to predict daily mass gains of cattle in shortgrass steppe.
• Validated predictions across 40 paddocks over a 10-year period.

Main Results:

• Strong relationships were found between diet quality and satellite-derived phenological metrics (green-up, senescence).
• Satellite-derived diet quality strongly predicted monthly mass gains (R² = 0.68).
• Senescing vegetation negatively impacted mass gains, irrespective of forage availability.

Conclusions:

• Cattle performance in rangelands is significantly affected by diet quality, linked to vegetation phenology.
• The satellite-based pseudo-daily approach enables improved adaptive management strategies for large herbivores.
• This method can inform livestock movement, predict herd health, and optimize grazing seasons under changing environmental conditions.