Steppes on the Mongolian Plateau, mainly within the Republic of Mongolia and the Inner Mongolia Autonomous Region (IMAR) of China, have been subjected to widespread degradation as a result of climate change and human utilization. Field experiments and long-term observations suggest that the productivity of degraded grassland ecosystems might show greater instability, i.e. stronger interannual variation in vegetation activities, when driven by climate change. However, it remains unknown whether this hypothesized destabilization of steppe vegetation activity has occurred in the past three decades and how this destabilization has fed back to livestock production on the plateau. Herein, we define temporal instability of vegetation activity using three indicators, the start and end of the growing season as indicated by the normalized difference vegetation index (NDVI) and the mean growing-season NDVI, and examine their trends between 1983 and 2015. Our results show a significant destabilization of vegetation activity over a large proportion of the total steppe area. Compared with the IMAR, vegetation destabilization has occurred to a significantly higher extent in Mongolia. Climate warming, drying and interannual climate variability accounted for approximately 60%–80% of the vegetation destabilization. The destabilization of steppe productivity was significantly associated with the interannual variability of livestock production in Mongolia, while the interannual variability of steppe productivity and livestock production were decoupled in the IMAR. Our findings highlight the need to improve livestock production systems and conserve degraded grasslands for sustainable development in view of the destabilization of steppe productivity on the Mongolian Plateau.

Grasslands provide essential forage sources for global livestock production. Remote sensing approaches have been widely used to estimate the biomass production of grasslands from regional to global scales, but simultaneously mapping the forage biomass and quality metrics (e.g., crude fiber and crude protein) is still relatively lacking despite an increasing need for better livestock management. We conducted novel gradient grass-cutting experiments and measured hyperspectral reflectance, forage biomass, crude fiber per area (CF_area), and crude protein per area (CP_area) across 19 temperate grassland sites in the Xilingol region, Inner Mongolia, China. Based on these measurements, we identified sensitive spectral bands, calculated nine potential spectral indices (Normalized Difference Vegetation Index, Enhanced Vegetation Index, Red Edge Normalized Difference Vegetation Index, Red-Edge Inflection Point, Inverted Red-Edge Chlorophyll Index algorithm, Normalized Difference Red Edge Index, Nitrogen Reflectance Index, Normalized Greenness Index, Land Surface Water Index) and established Random Forest (RF) models that well predicted forage biomass (R² = 0.67, NRMSE = 12%), CF_area (R² = 0.59, NRMSE = 14%), and CP_area (R² = 0.77, NRMSE = 10%). Among these nine indices, Land Surface Water Index (LSWI, calculated by R_785-900 and R_2100-2280) was identified to be the most important predictor and was then used to establish empirical power law models, showing comparable prediction accuracies (forage biomass, R² = 0.53; NRMSE = 14%; CF_area, R² = 0.40, NRMSE = 17%; CP_area, R² = 0.72, NRMSE = 11%) in comparison to Random Forest models. Combining the empirical power law models with the LSWI calculated from Sentinel-2 observations, we further mapped the forage biomass and quality and estimated the livestock carrying capacity. The predicted forage biomass, CF_area, and CP_area all showed a significant increase with higher mean annual precipitation, but showed no significant correlations with mean annual temperature. Compared with the estimates based on crude protein, the conventional approach solely based on forage biomass consistently overestimated livestock carrying capacity, especially in wetter areas. Our work provides an approach to simultaneously map the forage biomass and quality metrics and recommends a LSWI-based power law model for rapid and low-cost assessment of regional forage status to guide better livestock management.