Wildlife-friendly farming recouples grazing regimes to stimulate recovery in semi-arid rangelands
| Autor: | Daniel Ramp, Bool Smuts, Jeannine McManus, Esty Yanco, Chris D. Hasselerharm |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Conservation of Natural Resources
Environmental Engineering Farms 010504 meteorology & atmospheric sciences Land management Wildlife Animals Wild Land cover 010501 environmental sciences 01 natural sciences South Africa Rangeland management Grazing Environmental Chemistry Animals Overgrazing Waste Management and Disposal Ecosystem 0105 earth and related environmental sciences Agroforestry Agriculture Vegetation Pollution Geography Rangeland Environmental Sciences |
| Popis: | While rangeland ecosystems are globally important for livestock production, they also support diverse wildlife assemblages and are crucial for biodiversity conservation. As rangelands around the world have become increasingly degraded and fragmented, rethinking farming practice in these landscapes is vital for achieving conservation goals, rangeland recovery, and food security. An example is reinstating livestock shepherding, which aims to recouple grazing regimes to vegetation conditioned to semi-arid climates and improve productivity by reducing overgrazing and rewiring past ecological functions. Tracking the large-scale ecosystem responses to shifts in land management in such sparsely vegetated environments have so far proven elusive. Therefore, our goal was to develop a remote tracking method capable of detecting vegetation changes and environmental responses on rangeland farms engaging in contrasting farming practices in South Africa: wildlife friendly farming (WFF) implementing livestock shepherding with wildlife protection, or rotational grazing livestock farming with wildlife removal. To do so, we ground-truthed Sentinel-2 satellite imagery using drone imagery and machine learning methods to trace historical vegetation change on four farms over a four-year period. First, we successfully classified land cover maps cover using drone footage and modelled vegetation cover using satellite vegetation indices, achieving 93.4% accuracy (к = 0.901) and an r-squared of 0.862 (RMSE = 0.058) respectively. We then used this model to compare the WFF farm to three neighbouring rotational grazing farms, finding that satellite-derived vegetation productivity was greater and responded more strongly to rainfall events on the WFF farm. Furthermore, vegetation cover and grass cover, patch size, and aggregation were greater on the WFF farm when classified using drone data. Overall, we found that remotely assessing regional environmental benefits from contrasting farming practices in rangeland ecosystems could aid further adoption of wildlife-friendly practices and help to assess the generality of this case study. |
| Databáze: | OpenAIRE |
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