AsianScientist (Feb. 26, 2019) – Using machine learning, scientists in the US have found that farmer cooperatives benefit both forest health and the farmers of the Kangra region in India’s Himachal Pradesh state. Their work is published in Environmental Research Letters.
At the southern tip of the Himalayas, farmers in Himachal Pradesh graze cattle among rolling hills and forests. While policies to manage the region’s forest have been but in place by the Indian government, the impact that these policies have had remains unclear.
In this study, scientists led by Dr. Pushpendra Rana at the University of Illinois applied machine learning algorithms to examine natural resources policy and governance, evaluating how policies actually work on the ground. Using satellite images from NASA, Rana’s machine learning algorithm was able to simultaneously evaluate policy effectiveness in over 200 forest management regions in Kangra, covering a 14-year period.
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. Unlike traditional policy impact evaluations, the algorithm was able to take a long view.
The researchers applied their algorithm in the assessment of two forest revegetation policies, implemented in Kangra starting in 2002. Forest parcels were either planted and managed by farmer cooperatives, in which farmers had long-term rights to property and could decide where to plant trees, or by the state, with less input from farmers.
When the machine-learning algorithm evaluated the entire region as a whole, it failed to identify differences between the two policies in terms of vegetation growth. Rana noted that traditional evaluation methods might have looked at that result and concluded the policies were interchangeable or unsuccessful.
“Traditional approaches usually look at the average treatment effect only, and they can’t explain any variation around the average,” he said. “Machine learning, along with social-ecological-systems theory, gives us the ability to unpack the context—in what contexts does this policy perform well or not as well?”
The researchers found that in the case of cooperative forest management, an increase in vegetation growth was associated with support for farmers’ existing livelihoods. Hence, the study suggests that farmer cooperatives may see greater success in ensuring the longevity of forests while preserving the farmers’ way of life.
“Current impact evaluation approaches tend to look at results just once—at the conclusion of a project. We measured long-term vegetation growth trajectories, allowing us to understand on-the-ground change after different policies were implemented,” said Professor Daniel Miller of the University of Illinois, a co-author on the study. “It’s important to evaluate over the long term, especially in forestry because trees take a long time to grow.”
The article can be found at: Rana & Miller (2019) Machine Learning to Analyze the Social-ecological Impacts of Natural Resource Policy: Insights From Community Forest Management in the Indian Himalaya.
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Source: University of Illinois; Photo: Pexels.
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