Echinococcosis - a rare spinal disorder

No Abstract. South African Medical Journal Vol. 96(8) 2006: 680-68

Livelihood strategies in the rural Kenyan highlands

The concept of a livelihood strategy has become central to development practice in recent years. Nonetheless, precise identification of livelihoods in quantitative data has remained methodologically elusive. This paper uses cluster analysis methods to operationalize the concept of livelihood strategies in household data and then uses the resulting strategy-specific income distributions to test whether the hypothesized outcome differences between livelihoods indeed exist. Using data from Kenya’s central and western highlands, we identify five distinct livelihood strategies that exhibit statistically significant differences in mean per capita incomes and stochastic dominance orderings that establish clear welfare rankings among livelihood strategies. Multinomial regression analysis identifies geographic, demographic and financial determinants of livelihood choice. The results should facilitate targeting of interventions designed to improve household livelihoods.Livelihood strategy, Kenya, Smallholder agriculture, Cluster analysis, Community/Rural/Urban Development,

Feeding dairy cattle: a manual for smallholder dairy farmers and extension workers in East Africa

This booklet is designed to guide extension workers and possibly smallholder dairy farmers through the basics of feeding dairy animals. It includes essential background information as well as practical advice and suggestions. By better understanding how a cow digests its food, the importance of providing a balanced diet, how nutritional needs vary at different stages of the animal's life and how different types of feed can meet these needs, dairy farmers will be able to get the most benefit from their investment and keep their valuable animals healthy and productive

The Maize Green Revolution in Kenya Revisited

The maize green revolution, which increased maize yields through the use of improved varieties and fertilizer, has stalled since the mid-eighties in Kenya. This paper examines whether the stagnation of yields continued in the 1990s in spite of the implementation of the maize liberalization policies by the Kenya Government. Analysis of farm level surveys from 1992 and 2002 indicates slight increases in the use of improved maize varieties and fertilizer, but a substantial decrease in the intensity of fertilizer use. The econometric analysis suggests that the intensity of fertilizer use has a major effect on yield. The use of improved maize varieties, however, did not affect yield, suggesting that there are local varieties for some areas that do as well as improved varieties. Research is needed to develop improved varieties for some areas, and also needed for the development of alternative affordable soil fertility measures.green revolution, maize, adoption, soil fertility, Kenya, Crop Production/Industries, International Development,

CCAFS East Africa Regional Science Workshop Report

The East Africa regional program of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) hosted a three-day workshop to engage with partners from agricultural research, agricultural extension, climate services and products, food security, and early warning systems in East Africa. The overall objective of the workshop was to plan and develop thematic research to support climate risk management, adaptation and mitigation options in East Africa, resulting in four to five project concepts that can be supported as seed participatory action research (PAR) activities. From these seed activities, CCAFS intends to build longer term projects from those with the greatest potential to deliver bigger outcomes and impact. The workshop built on the regional needs for research and priorities identified from previous national and regional workshops in 2011

Smart Armyworm Surveillance: Project Technical Report

This project was designed to test three different technologies for monitoring the fall armyworm, a highly migratory moth pest of maize in Kenya. This insect is invasive and is estimated to have caused between US$200 and US$600 million dollars’ worth of crop damage in Africa since it was first observed in western Africa in 2016. The three technologies we piloted were entomological radar, digital pheromone traps and an image identification app. The hypothesis we set out to test was that high-altitude moth migrations are linked to pest incidence at ground level and the later impacts of feeding damage. Our objective was to install and launch all three technologies in the space of a year, with the aim of developing an integrated dataset that would provide an overview of near-ground and upper-atmospheric movements of fall armyworm. This data would be shared with multiple different stakeholders in real-time. Over a 12-month period between March 2019 and February 2020, we installed the entomological radar, 20 digital pheromone traps and launched the Nondo app to test this hypothesis. Although there were several challenges, the outcomes after one year include: 1. Preliminary radar data. We were able to detect insects at heights up to 800m and the preliminary data would suggest these are targets of the appropriate mass to be classed as noctuid moths 2. A network of digital pheromone traps. We observed fall armyworm in almost all of the locations where the traps were installed and received daily automated updates, despite the traps being hosted in very rural parts of Kenya. 3. An image detection algorithm for the fall armyworm and other maize pests. This provided a very high level of accuracy for species where we were able to provide more than 100 images (>90% in some cases). This project has profound potential for impact as it demonstrates the successful implementation of an “internet of things” approach to biological monitoring in very rural parts of Kenya. Indeed, one of the highlights of this project is the deployment of the digital trap network beyond the end of the project. This is the first time such an ambitious, multi-layered monitoring network comprising radar, pheromone traps, machine learning and decision support apps has been established in Sub-Saharan Africa. This project is a proof-of-concept that clearly demonstrates the potential of digital monitoring to deliver major impacts for farmers in Sub-Saharan Africa