Governments across Africa are looking to protect pastoralists from the impacts of extreme weather with livestock insurance programs. But what works?
A recent study by the Crowdsourcing for Rangeland Conditions project—implemented through a collaboration between ILRI, Cornell University and the University of Sydney—applied a crowdsourcing approach to collect detailed information on forage conditions in northern Kenya.
Originally posted on ILRI news:
From left to right: Jimmy Smith, director general of the International Livestock Research Institute (ILRI); Andrew Tuimur, principal secretary in Kenya’s State Department of Livestock; and Willy Bett, cabinet secretary for the Kenya Ministry of Agriculture, Livestock and Fisheries during a press conference held on 20 Feb 2017 announcing payments…
Food security and resilience-building have become central in the international development community’s efforts to help developing countries and vulnerable populations manage climate change.
Developed in partnership with International Livestock Research Institute, Cornell University and University of California Davis, IBLI uses data gathered by satellite to create a vegetation index that can be used to track the density of vegetation available to pastoralists.
Andrew Mude, a Kenyan economist, has a way of explaining satellites. When he’s talking to pastoral in his country’s north — people who roam the earth with a dozen head of cattle and very little else — he talks about the stars that don’t act like other stars. “They’re actually taking pictures of the ground,” Mude says. Herders, a stargazing people, understand.
Pastoral populations of Sub-Saharan Africa are particularly vulnerable to environmental shocks, which contribute to livestock mortality and therefore losses in both wealth and productive assets. Although conventional insurance mechanisms covering individual losses are generally not cost effective (page 2) in low-income pastoral communities that engage in extensive grazing, index insurance for livestock offers a promising …