Multivariate grazing pressure indices improve prediction of semi-arid rangeland dynamics

Summary

This study developed multivariate grazing pressure indices for semi-arid rangelands in Namibia, combining cattle-related variables (distance from water, grazing offtake, vegetation damage indicators) using Principal Component Regression. Quantile regression models showed that composite indices, particularly those capturing both long- and short-term grazing dynamics, improved prediction of rangeland outcomes such as plant species diversity compared to the conventional single metric of distance from water. The findings suggest that spatially-explicit composite indices can better characterise semi-arid rangeland functionality across different land tenure systems, though absolute model fit remained variable.

Regional applicability

The methodology is directly applicable to United Kingdom upland and marginal grazing systems, particularly where understanding fine-scale grazing pressure on vegetation dynamics and biodiversity is critical. However, the indices were calibrated for semi-arid conditions; UK temperate grasslands may require index recalibration and validation before operational adoption.

Key measures

Composite grazing pressure indices (incorporating distance measures, grazing offtake, moribund plant material, cattle activity signs); bare soil percentage; perennial grass cover; plant species richness; plant species diversity; model fit statistics

Outcomes reported

The study developed and evaluated three composite grazing indices of increasing complexity to predict rangeland functionality metrics (bare soil, perennial grass cover, plant species richness and diversity) across communal and freehold tenure systems in semi-arid Namibia. Composite indices showed improved predictive power compared to the single metric of distance from water source.

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