Predicting Grazing Conflicts Based on Limited Resources in Northern Kenya

Abstract

This study aimed at determining causes of grazing conflicts in Northern Kenya which were used to develop a conflicts predicting model. It specifically intended to evaluate seasonality of pasture resources, establishing how availability of grazing resources was related to grazing conflicts and predicting how communities were likely to cope with them. It was anchored on the theory that competition for limited forage triggers intra and inter-conservancy livestock movements, causing conflicts over grazing resources. The study used mixed methods of ecological, remote sensing and social survey designs. Purposive sampling was used to select four conservancies out of a population of fifteen, where three of them were community-managed while the fourth was privately owned which acted as a control. Two plots each measuring 50mx50m were set up in each of them using handheld Global Positioning System (GPS). Clip-dry-and-weigh method was used to assess grass biomass during dry and wet seasons. Five samples of clippings were obtained per plot using 0.5mx0.5m wire quadrant randomly in both seasons. Visual estimates were used to assess ground cover percentages, species variability and diversity along transects between the plots in both seasons and recorded in Range Condition Checklists and tables of quantities. A population of 106 respondents was picked through systematic random sampling from the lists of conservancy grazing committees and data collected using self-administered structured questionnaires, focused group discussions and content analysis of literature. The data was analyzed using Statistical Package for Social Sciences (SPSS) version 26. Frequency counts, means and percentages were computed for all quantitative data and results presented using frequency distribution tables and graphs. Qualitative data on status of the bio-physical, land-use and rainfall patterns were tracked using remote sensing techniques. Temporal and spatial variability of forage, land-use and land-cover changes were tracked using MODIS 250m resolution and Landsat-8 sensor, which were analysed using Quantum Geographical Information System (QGIS) to produce Normalized Difference Vegetation Indices (NDVI). The results established that forage and water availability and livestock numbers were responsible for the largest variability of grazing conflicts. It was found that seasonality of rainfall and the communities grazing regimes trigger livestock movements to unknown areas, sparking a trail of conflicts on their way. The research also found out that in the largest period of the year, community conservancies bore the greatest effects of environmental externalities due to lack of adherence to grazing plans leading to overgrazing and pasture degradation. It was further found that pastoral communities have different methods of copping with grazing conflicts in the study area. The study synthesized results on dependent and independent variables and came up with a new model for predicting grazing conflicts in Northern Kenya. The study recommended further investigations on the effects of other factors contributing to grazing conflicts that were not accounted for. It also recommended further research on methodology to establish the levels of competition for resources by different browsers. On practice, it recommended inclusion of structured dialogue in conflicts mitigation and diversification of social-economic activities by the pastoralists to cushion them from the effects of grazing conflicts. On policy, it recommended inclusion of local administration, national agencies and relevant stakeholders on conflicts mitigation processes to make them more authentic and resultant agreements enforceable.