Modelling emerging intensive poultry sector at farm-level in western Kenya

Intensification of livestock production occurs in each part of the world, although at different speeds. However, it comes with many environmental, health and societal impacts. Assessing these impacts requires to know the geographical distribution of livestock. Therefore, understanding underlying spatial patterns of livestock distribution and its evolution with intensification is essential to further explore how these distributions may be associated with zoonotic disease risk, for example. The spatial distribution of farms and the factors influencing it, tend to vary between production systems (extensive backyard household vs commercial larger intensive farms). Extensive systems are generally homogeneously distributed and correlated to rural populations, especially in developing and transition economies. Intensive farms tend to cluster and to be more closely associated to accessibility to inputs and markets than to land resources. However, factors influencing the distribution of intensive farming are poorly known, especially at fine scales, and their assessment suffers from a lack of spatially explicit data sets with precise farm locations and characteristics. This is particularly true in developing countries, where farm locations are not recorded formally. Although extensive backyard systems still dominate in Kenya, an intensive poultry sector is emerging. We aimed to characterize the different farm types of this emerging commercial chicken sector and to determine their spatial distribution. For that purpose, we collected data on all commercial farms (50-1000 birds) in western Kenya, in an area of 40km diameter at the intersection of Busia, Bungoma and Kakamega counties. A cluster analysis will be used to characterize the different types of farmers of the early stages of intensification process using both individual characteristics (chicken breeds raised, feed types and destination of output types) and contextual variables (access to market and access to city). We will also test if the numbers of animals well represent intensification level of a farm, a hypothesis assumed in previous works at broader scales. Finally, spatial determinants of farm types in western Kenya will be investigated to identify which characteristics make some areas more suitable for the establishment of the different types of farm. This study will allow the understanding of the factors encouraging farmers to start a commercial activity. This will in turn help further modelling of chicken populations at farm-level, i.e. predicting farm location and size