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Intensification of chicken production : farm distribution modelling to inform disease transmission models
Population and income growth, urbanisation and technological advances have led to intensification of livestock production systems over the past decades. This has significant health and environmental impacts (e.g. soil pollution and spread of epidemics). A trend in intensification is observable globally, but the level of intensification varies between countries. As the characteristics and the distribution of farms appear to change as a result of intensification, the impacts of livestock production systems are expected to evolve. This creates a need for a better understanding of the spatial factors influencing intensive farming. We also need high-resolution maps of livestock production systems, which are crucial to help assess the impacts of livestock intensification process. This dissertation explores the spatial features of chicken production and elaborates methods to predict chicken farm locations. First, we characterise the spatial constraints of chicken farmers in their early stages of intensification, using survey data from western Kenya. We found a diversity of semi-intensive farms in this region and show that local conditions affect production management types. Second, we build farm distribution models (FDMs) to predict chicken farms’ location and size in four countries spread along a gradient of intensification; Nigeria, Thailand, Argentina and Belgium. We also test the ability of the FDMs to simulate the spread of highly pathogenic avian influenza H5N1 in Bangladesh. Using point pattern analysis method, we developed FDMs that account for farm clustering. This allows for a more realistic spatial representation of livestock systems. Upon further validation, this method could represent an interesting tool to model the transmission of epidemic diseases in livestock systems.
Affiliations
UCLouvainSST/ELI/ELIC - Earth & Climate
Citations
APA
Chicago
FWB
Chaiban, C. (2019). Intensification of chicken production : farm distribution modelling to inform disease transmission models. https://hdl.handle.net/2078.5/123994