Risk management and the potential of cattle insurance in Tigray, Northern Ethiopia

This study explores the role of livestock insurance to complement existing risk management strategies adopted by smallholder farmers. Using survey data, first, it provides insights into farmers’ risk perception of livestock farming, in terms of likelihood and severity of risk, attitude to risk and their determinants. Second, it examines farmers’ risk management strategies and their determinants. Third, it investigates farmers’ potential engagement with a hypothetical cattle insurance decision and their intensity of participation. Factor analysis is used to analyse risk sources and risk management, multiple regressions are used to identify the determinants; a Heckman model was used to investigate cattle insurance participation and intensity of participation. The findings show different groups of farmers display different risk attitude in their decision-making related to livestock farming. Production risk (especially livestock diseases) was perceived as the most likely and severe source of risk. Disease control was perceived as the best strategy to manage risk overall. Disease control and feed management were important strategies to mitigate the production risks. Disease control and participation on safety net program were found to be important to counter households’ financial risks. With regard to the hypothetical cattle insurance scheme, 94.38% of households were interested to participate in cattle insurance. Of those households that accepted cattle insurance, 77.38% of the households were willing to pay the benchmark annual premium of 4% of the animal value while for the remaining households this was not affordable. The average number of cattle that farmers were willing to insure was 2.71 at this benchmark. Results revealed that income (log income) and education levels influenced positively and significantly farmers’ participation in cattle insurance and the number of cattle to insure. The findings prompt policy makers to consider livestock insurance as a complement to existing risk management strategies to reduce poverty in the long-run.

Studies relating to the population dynamics of the European badger (Meles meles) in Ireland

European badgers (Meles meles) are an important part of the Irish ecosystem; they are a component of Ireland’s native fauna and are afforded protection by national and international laws. The species is also a reservoir host for bovine tuberculosis (bTB) and implicated in the epidemiology of bTB in cattle. Due to this latter point, badgers have been culled in the Republic of Ireland (ROI) in areas where persistent cattle bTB outbreaks exist. The population dynamics of badgers are therefore of great pure and applied interest. The studies within this thesis used large datasets and a number of analytical approaches to uncover essential elements of badger populations in the ROI. Furthermore, a review and meta-analysis of all available data on Irish badgers was completed to give a framework from which key knowledge gaps and future directions could be identified (Chapter 1). One main finding suggested that badger densities are significantly reduced in areas of repeated culling, as revealed through declining trends in signs of activity (Chapter 2) and capture numbers (Chapter 2 and Chapter 3). Despite this, the trappability of badgers was shown to be lower than previously thought. This indicates that management programmes would require repeated long-term efforts to be effective (Chapter 4). Mark-recapture modelling of a population (sample area: 755km2) suggested that mean badger density was typical of continental European populations, but substantially lower than British populations (Chapter 4). Badger movement patterns indicated that most of the population exhibited site fidelity. Long-distance movements were also recorded, the longest of which (20.1km) was the greatest displacement of an Irish badger currently known (Chapter 5). The studies presented in this thesis allows for the development of more robust models of the badger population at national scales (see Future Directions). Through the use of large-scale datasets future models will facilitate informed sustainable planning for disease control.