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Abstract Rangelands, covering half of the global land area, are critically degraded by unsustainable use and climate change. Despite their extensive presence, global assessments of rangeland condition and sustainability are limited. Here we introduce a novel analytical approach that combines satellite big data and statistical modeling to quantify the likelihood of changes in rangeland conditions. These probabilities are then used to assess the effectiveness of management interventions targeting rangeland sustainability. This approach holds global potential, as demonstrated in Mongolia, where the shift to a capitalist economy has led to increased livestock numbers and grazing intensity. From 1986 to 2020, heavy grazing caused a marked decline in Mongolia’s rangeland condition. Our evaluation of diverse management strategies, corroborated by local ground observations, further substantiates our approach. Leveraging globally available yet locally detailed satellite data, our proposed condition tracking approach provides a rapid, cost-effective tool for sustainable rangeland management.

It is expected that tropical deforestation and related increases in heat exposure have negative impacts on labour productivity, but the size of the effect is not well known. Here, the authors show that deforestation reduces productivity by 8.22% in rural Indonesia and causes behavioural adaptation responses like more work breaks.

The management of fire season has been proposed as a climate mitigation tool but the scope and scale of this action is unclear. Here the authors use global emissions datasets to assess emissions mitigation opportunities for savanna fires, highlighting significant reduction potential in 37 countries.