More than 400 million people in the developing world depend on dryland agriculture for their livelihoods. Dryland agriculture involves a complex combination of productive components: staple crops, vegetables, livestock, trees and fish interacting principally with rangeland, cultivated areas and watercourses. Managing risk and enhancing productivity through diversification and sustainable intensification is critical to securing and improving rural livelihoods.

The contemporary concern about anthropogenic release of greenhouse gas (GHG) into the

environment and the contribution of livestock to this phenomenon have sparked animal

scientists’ interest in predicting methane (CH4) emissions by ruminants. Focusing on milk

production, we address six basic nutrition models or feeding standards (mostly empirical

systems) and five complex nutrition models (mostly mechanistic systems), describe their key

characteristics, and highlight their similarities and differences. Four models were selected to

Summarises the work that ILCA is doing to assemble what information is available on livestock breeds and breeding in sub-Saharan Africa.

The Niger River Basin covers 7.5% of the African continent, and is shared between nine riparian

countries. The basin countries can be categorized into water resources producers, consumers, both

producers and consumers, and minimum contributors and consumers. As in the case for most

transboundary rivers, upstream and downstream conflicts emanating from the development and

utilization of the Niger River are inevitable and are expected to be intense, particularly given the

Rangeland scientists and quantitative ecologists have developed numerous methods and monitoring techniques that can be used for vegetation sampling (Barbour et al. 1987). The methods used to position samples (transects, quadrats, lines, and points) vary and can be classed as selective, capricious, systematic, or random. One of the prerequisites for valid statistical inference is that samples are taken randomly. A random sampling procedure implies that all elements or units of the population being studied have an equal chance of being represented in the sample.