Impacts of Climate Variability and Weather Forecasts on Livestock Production and Marketing in Baringo County, Kenya

Abstract

The study focused on the impacts of climate variability and weather forecasts on livestock production and marketing in Mogotio and Marigat sub-counties in Baringo County, Kenya. The pastoralists have continued practicing livestock production and marketing without reliable climate/ weather forecasts in the face of increasingly variable climate. The specific objectives of the study were to: examine the impacts of seasonal climate variability on livestock production and marketing; determine the performance of indigenous and conventional climate forecasts over the study period; establish the impact of indigenous and conventional seasonal weather forecasts on livestock production and marketing in a changing climate; and recommend interventions to enhance efficiency and effectiveness in livestock production and marketing in the context of climate change. Diverse methodologies were applied in the study. Station average and Theissen polygon methods were used to estimate the mean areal precipitation of the study area over a 30 year period (1974-2003). A cross-sectional household survey, two focus group discussions, 10 key informant interviews (KII) and one stakeholder engagement forum were undertaken. Using a simple random sampling technique, 454 households were sampled for the survey. Both descriptive and inferential statistics were performed for the precipitation and temperature trends using Microsoft Excel, while Statistical Package for the Social Sciences (SPSS) Version 20 was used to analyze the household survey data. The focus group discussions were transcribed and developed into themes. Based on the results, the range of the estimated mean areal precipitation was 803.20 mm to 939.05 mm. Linear regression line showed a decline in rainfall of 2.4 mm and 4.72mm per year by use of the station average and Theissen polygon methods, respectively. This was because the four stations where the former method was used covered 2757 km2 and were located in the lowlands while the six stations for the latter method covered a wider area of 3905.76 km2 and within the highlands. Both were over pointed by high variability. A majority (68.2%) of the pastoralists and agro-pastoralists were not aware that they could access a blend or a mix of indigenous traditional knowledge (ITK) and conventional weather forecasting advisory services in the study area. Also, a significant portion (29%) of the pastoralists and agro-pastoralists revealed that the popularity of ITK weather forecast among the Arid and Semi-Arid Lands communities was as a result of being easily interpreted by the local communities. Findings from focus group discussions (FGDs) and key informants interviews (KIIs) revealed that pastoralists and agro-pastoralist generally believed more in conventional methods as reliable weather predictor for extreme climatic events such El Niño and droughts. A majority of the pastoralists interviewed expressed the view that the indigenous knowledge practiced in weather forecasting had a positive impact on the calving rates (65.1%), calving interval (62.4%), fertility (52.4%), growth rate (50.3%) as well as the milk production (46.4%) of their livestock. A majority (31%) of the interviewed key informants reported that the level of farmer preparedness before extreme weather or climate risks had a positive impact on the performance of livestock productivity and marketing. The majority (32.84%) of the respondents reported that they controlled breeding of their livestock as a means of averting adverse impacts of seasonal climate variability. Climate change and variability impact in the area were averse to livestock production and marketing. Based on the findings of this study it was concluded that seasonal weather forecast-based planning can be an appropriate climate change adaptation strategy informed by a reliable blend or mixture of ITK and conventional weather forecasting advisory services. The study recommends an up-scaling of existing seasonal rainfall advisories based on participatory scenario planning and knowledge integration.