The relative productivity index: Mapping human impacts on rangeland vegetation productivity with quantile regression forests

• The Relative Productivity Index (RPI) is a new metric of rangeland degradation. • Potential primary productivity is estimated with quantile regression forests. • RPI is the ratio of observed to potential productivity in each year. • RPI quantifies management impacts by controlling for biophysical conditions. • RPI performs well against existing methods in East African rangelands. Rangeland degradation is a persistent decline in the productivity of vegetation that supports wild or domestic herbivores. However, rangelands often exhibit highly variable climates and dynamic, resilient vegetation, making it difficult to discriminate between lasting degradation and natural variability. Here, we introduce a new and flexible index to distinguish management-driven rangeland degradation from the influence of environmental variability using freely available remote sensing-derived datasets. We use quantile regression forests to estimate the potential gross primary productivity as a continuous function of biophysical conditions including climatic, soil and topographic variables. The relative productivity index (RPI) is then defined as the ratio between observed and potential productivity in each grid cell and year. This provides a dynamic reference point against which variation in rangeland condition across space and through time can be quantified consistently within a single framework. We test the method in the arid and semi-arid rangelands of East Africa and compare its performance to three other widely used methods to distinguish management impacts from natural variability: rain use efficiency, local gross primary production scaling, and residual trend analysis. We show that our method outperforms existing approaches both in explaining spatial differences in productivity and in controlling for climatic variability through time. In combination with other remote sensing and field-derived metrics, the rangeland productivity index has the potential to become a valuable indicator for quantifying rangeland condition and supporting sustainable management and utilisation of these landscapes.

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Publication Details

Journal
Ecological Indicators
Published
2025-02-01
DOI
https://doi.org/10.1016/j.ecolind.2025.113208
Citations
1
Primary Topic
Rangeland and Wildlife Management
Type
article
Field-Weighted Citation Impact
1.72

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article

The relative productivity index: Mapping human impacts on rangeland vegetation productivity with quantile regression forests

Andrew M. Cunliffe, Guy Lomax, Tom Powell, Timothy M. Lenton
1 citations
Ecological Indicators
Rangeland and Wildlife Management
1.72
article

The relative productivity index: Mapping human impacts on rangeland vegetation productivity with quantile regression forests

Andrew M. Cunliffe, Guy Lomax, Tom Powell, Timothy M. Lenton
article en
1 citations

Abstract

• The Relative Productivity Index (RPI) is a new metric of rangeland degradation. • Potential primary productivity is estimated with quantile regression forests. • RPI is the ratio of observed to potential productivity in each year. • RPI quantifies management impacts by controlling for biophysical conditions. • RPI performs well against existing methods in East African rangelands. Rangeland degradation is a persistent decline in the productivity of vegetation that supports wild or domestic herbivores. However, rangelands often exhibit highly variable climates and dynamic, resilient vegetation, making it difficult to discriminate between lasting degradation and natural variability. Here, we introduce a new and flexible index to distinguish management-driven rangeland degradation from the influence of environmental variability using freely available remote sensing-derived datasets. We use quantile regression forests to estimate the potential gross primary productivity as a continuous function of biophysical conditions including climatic, soil and topographic variables. The relative productivity index (RPI) is then defined as the ratio between observed and potential productivity in each grid cell and year. This provides a dynamic reference point against which variation in rangeland condition across space and through time can be quantified consistently within a single framework. We test the method in the arid and semi-arid rangelands of East Africa and compare its performance to three other widely used methods to distinguish management impacts from natural variability: rain use efficiency, local gross primary production scaling, and residual trend analysis. We show that our method outperforms existing approaches both in explaining spatial differences in productivity and in controlling for climatic variability through time. In combination with other remote sensing and field-derived metrics, the rangeland productivity index has the potential to become a valuable indicator for quantifying rangeland condition and supporting sustainable management and utilisation of these landscapes.

Ecological IndicatorsVol. 171
University of Exeter (GB)
UK Research and Innovation, A.G. Leventis Foundation
Openalex Percentile: Top 19%
Rangeland and Wildlife Management
1.72
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