Machine learning detects importance of land stewardship in conservation policy
A machine learning algorithm finds success in cooperative forest management policies that allow greater autonomy by smallholder farmers.
At the southern tip of the Himalayas, farmers in the Kangra region of India’s Himachal Pradesh graze cattle among rolling hills and forests. The forests, under management by the state or farmer cooperatives, are thriving. But a new University of Illinois study shows, unlike state-managed forests, farmer cooperatives directly benefit both forest health and farmers.
The finding itself may not be new — previous research and social-ecological theory suggest that land ownership leads to enhanced stewardship and improved environmental outcomes — but the study confirmed the conclusion in a new way, using machine learning.
“This is the first application of machine learning algorithms in natural resources policy and governance, evaluating how policies actually work on the ground,” says Pushpendra Rana, postdoctoral research associate in the Department of Natural Resources and Environmental Sciences at U of I and lead author on the study published in Environmental Research Letters.
Machine learning harnesses modern computing power to explore patterns in large datasets, an advantage over traditional policy impact evaluations. The efficacy of environmental policy is often tested empirically, with experimental “treatments” (areas with new policies in place) and “controls” (business as usual). Researchers physically measure outcomes like tree growth or soil health and make comparisons between treatments and controls. The work can yield accurate estimates of impact but is time consuming and provides only a single snapshot in time.
This article originally appeared on sciencedaily.com. To read the full article, click here.
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