168 research outputs found
CGIAR Capacity Development Framework : Engaging with Partners
No full textPresented by Iddo Dror, Head of Capacity Development, International Livestock Research Institute (ILRI) Chair, CGIAR Capacity Development Community of Practice at Celebrate FARA, Johannesburg, 27 Nov 201
Index-Based Livestock Insurance case study: Highlights
No full textIddo Dror, head of capacity development at ILRI and visiting professor at the University of Geneva, Switzerland shares key highlights of the Index-Based Livestock Insurance (IBLI) Case Study and its application, with clippings from a session taught at the Graduate School of Business, University of Cape Town, South Africa
Overview of Capacity Development at ILRI
No full textAs part of an internal ‘capacity development’ week at the International Livestock Research Institute (ILRI) in December 2015, the institute presented different aspects of this work. In this video, Iddo Dror, head of the Capacity Development Unit, outlines the range of capacity development activities taking place throughout ILRI
Using satellite data to insure camels, cows, sheep and goats: IBLI and the development of the world’s first insurance for African pastoralists
No full textThis sample size was distributed across the 16 sub-locations in Marsabit on the basis proportional allocation using
the 1999 household population census statistics. There were only two exceptions to this rule: a minimum sample
size of 30 households and maximum of 100 households per sub-location; to participate in a panel survey. The next
step was to draw the actual sample. In all survey sites, a list of all inhabitants was developed with chiefs and local
elders. The IBLI research team with the local enumerators went to each and every sub-location and in a painstaking
process accompanied by local elders and assistant chiefs and wrote down names of all household heads. Other lists,
such as food aid distribution, were also included or used to augment what was collected. In addition, key informants
were asked to classify households into three wealth groups based on livestock holdings; 1. Low (less than 10 TLU9),
2. Medium (between 10 and 20 TLU), and 3. High (more than 20 TLU). For each sub-location, random sampling was
stratified by wealth class. However, because in several instances there were too few among the high wealth category,
all such households were included.
The data available includes household-level information collected annually (beginning in 2009) for 924 households.
Each household has been revisited annually to track the dynamics of change across a range of key variables. Household
attrition-rate has been less than 5% annually. In each round of the survey, households were asked to answer
questions about health, education, livestock holdings, herd migration, livelihood activities, income, consumption, risk
preferences, market interactions, assets, and access to credit and many more aspects
Can our research benefit from ‘tech, fun and games’? Leveraging alternative learning approaches and technologies to enhance R4D outcomes
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