Rethinking Impact: Understanding the complexity of poverty and change

Summary of workshop on Rethinking Impact: Understanding the complexity of poverty and change. March 26–28, 2008, Cali, ColombiaImpact, CGIAR, Workshop, Agricultural and Food Policy, Food Security and Poverty, Research and Development/Tech Change/Emerging Technologies,

Options de stockage des données

English version available in IDRC Digital Library: Data storage optionsBill and Melinda Gates FoundationGlobal Affairs Canada (GAC

Livestock Vaccine Innovation Fund : guidelines for preparing Final Technical Reports

Bill and Melinda Gates FoundationGlobal Affairs Canada (GAC

Data storage options

French version available in IDRC Digital Library: Options de stockage des donnéesBill and Melinda Gates FoundationGlobal Affairs Canada (GAC

Risk mapping of Rinderpest sero-prevalence in Central and Southern Somalia based on spatial and network risk factors

<p>Abstract</p> <p>Background</p> <p>In contrast to most pastoral systems, the Somali livestock production system is oriented towards domestic trade and export with seasonal movement patterns of herds/flocks in search of water and pasture and towards export points. Data from a rinderpest survey and other data sources have been integrated to explore the topology of a contact network of cattle herds based on a spatial proximity criterion and other attributes related to cattle herd dynamics. The objective of the study is to integrate spatial mobility and other attributes with GIS and network approaches in order to develop a predictive spatial model of presence of rinderpest.</p> <p>Results</p> <p>A spatial logistic regression model was fitted using data for 562 point locations. It includes three statistically significant continuous-scale variables that increase the risk of rinderpest: home range radius, herd density and clustering coefficient of the node of the network whose link was established if the sum of the home ranges of every pair of nodes was equal or greater than the shortest distance between the points. The sensitivity of the model is 85.1% and the specificity 84.6%, correctly classifying 84.7% of the observations. The spatial autocorrelation not accounted for by the model is negligible and visual assessment of a semivariogram of the residuals indicated that there was no undue amount of spatial autocorrelation. The predictive model was applied to a set of 6176 point locations covering the study area. Areas at high risk of having serological evidence of rinderpest are located mainly in the coastal districts of Lower and Middle Juba, the coastal area of Lower Shabele and in the regions of Middle Shabele and Bay. There are also isolated spots of high risk along the border with Kenya and the southern area of the border with Ethiopia.</p> <p>Conclusions</p> <p>The identification of point locations and areas with high risk of presence of rinderpest and their spatial visualization as a risk map will be useful for informing the prioritization of disease surveillance and control activities for rinderpest in Somalia. The methodology applied here, involving spatial and network parameters, could also be applied to other diseases and/or species as part of a standardized approach for the design of risk-based surveillance activities in nomadic pastoral settings.</p

Spatial and Temporal Pattern of Rift Valley Fever Outbreaks in Tanzania; 1930 to 2007

Rift Valley fever (RVF)-like disease was first reported in Tanzania more than eight decades ago and the last large outbreak of the disease occurred in 2006–07. This study investigates the spatial and temporal pattern of RVF outbreaks in Tanzania over the past 80 years in order to guide prevention and control strategies. A retrospective study was carried out based on disease reporting data from Tanzania at district or village level. The data were sourced from the Ministries responsible for livestock and human health, Tanzania Meteorological Agency and research institutions involved in RVF surveillance and diagnosis. The spatial distribution of outbreaks was mapped using ArcGIS 10. The space-time permutation model was applied to identify clusters of cases, and a multivariable logistic regression model was used to identify risk factors associated with the occurrence of outbreaks in the district. RVF outbreaks were reported between December and June in 1930, 1947, 1957, 1960, 1963, 1968, 1977– 79, 1989, 1997–98 and 2006–07 in 39.2% of the districts in Tanzania. There was statistically significant spatio-temporal clustering of outbreaks. RVF occurrence was associated with the eastern Rift Valley ecosystem (OR = 6.14, CI: 1.96, 19.28), total amount of rainfall of .405.4 mm (OR = 12.36, CI: 3.06, 49.88), soil texture (clay [OR = 8.76, CI: 2.52, 30.50], and loam [OR = 8.79, CI: 2.04, 37.82]). RVF outbreaks were found to be distributed heterogeneously and transmission dynamics appeared to vary between areas. The sequence of outbreak waves, continuously cover more parts of the country. Whenever infection has been introduced into an area, it is likely to be involved in future outbreaks. The cases were more likely to be reported from the eastern Rift Valley than from the western Rift Valley ecosystem and from areas with clay and loam rather than sandy soil texture

Improving dietary and health data for decision making in agriculture and nutrition actions in Africa

The report covers activities and conference sessions regarding questions and strategies for implementation of the project which aims to develop and build capacity of an information and communications technology-based (ICT-based) platform. This will collect low-cost, high-frequency, high-resolution dietary and health data, and will combine accuracy and frequency through recording events in near-real time. The workshop focused on the following issues: i) What data should the tool/platform collect? ii) Tool-functionality, and methods for testing it, along with comparison to traditional data collection methods iii) Potential uses and scalability of the tool

Improving adoption of technologies and interventions for increasing supply of quality feed in low- and middle-income countries

The global increase in the demand for and production of animal-source foods (four-to five-fold increase between 1960 and 2015), which has been mostly concentrated in low- and middle-income countries (LMIC), provides smallholder livestock producers with an opportunity for improving their livelihoods and food and nutrition security. However, across livestock production systems in many LMIC, limited supplies and high cost of good quality feed severely constrains exploitation of this opportunity. In many of such countries, feeds and feeding-related issues are often ranked as the primary constraint to livestock production and increased consumption of animal-source foods. Here we review the complex biophysical, socio-economic and technological challenges related to improving quality feed supply and the reasons for generally low adoption of apparently proven feed enhancement technologies. We describe also successful interventions and conclude by recommending strategies for improving quality feed supply in LMIC that account for and overcome the prevailing challenges