Influence of Climate Variability and Socio-economic Factors on Adoption of Pasture Production Technologies in Drylands of Isiolo County, Kenya.

Pastoralism is the primary livelihood in Isiolo County's arid and semi-arid regions, Kenya. Climate change, especially severe droughts, has disrupted this livelihood, leading to shortages for vulnerable communities. The study aimed to establish trends in rainfall and temperature; assess vegetation's response to rainfall variations; determine the influence of climatic factors on the adaptation of Technological, Innovation, and Management Practices (TIMPs) for pasture production; and identify the socio-economic determinants influencing TIMP uptake. It was guided by the Sustainable Approach Livelihood (SAL) Theory, which emphasizes household participation in decision-making, and the Diffusion of Innovation Theory, which focuses on the adoption process of new technological innovations. A descriptive survey research design was used, involving a population of 48,514 households, from which 382 household heads and field extension officers were sampled. Data on TIMP adoption was collected through household questionnaires, interviews with key informants, and focus group discussions (FGDs). A pilot test was conducted with 20 household heads and one extension officer not included in the main study, confirming reliability with a Cronbach Alpha coefficient of 0.86. Long-term climate data was obtained from the Kenya Meteorological Services (KMS). Rainfall and temperature trends were analyzed using time series graphs and Pearson correlation coefficients. The Normalized Difference Vegetation Index (NDVI) data, covering the period from 1985 to 2022, was derived from Landsat satellite imagery and analyzed to assess vegetation cover trends and patterns using ArcGIS and QGIS software. Hypotheses were tested using Pearson correlation and multiple linear regression, increasing drought frequency and severity, a growing population, sedentary lifestyles, and urbanization have limited livestock movement for pasture, leading to higher food insecurity and greater vulnerability among the pastoral population. The study used multiple linear regression to test the correlation between independent and dependent variables. Results showed a temperature increase of 0.019°C per year over the 30-year period. OND rainfall showed a positive trend, while MAM rainfall showed a negative trend. Pearson correlation testing resulted in a p-value of 0.557, indicating no significant correlation between rainfall and temperature. Regarding vegetation cover's response to rainfall anomalies, the test showed a p-value of 0.0075, indicating a significant correlation. Rainfall, whether surplus or deficit, directly influences vegetation greenness, though it is not the only factor. For TIMP adoption, the regression model was significant (F-statistic p-value < 0.000). Temperature had a strong positive correlation (0.791), suggesting higher temperatures increase the likelihood of adopting technologies. Rainfall's correlation with TIMP adoption was positive but statistically insignificant (p-value 0.472). Socio-economic factors positively influencing technology adoption included gender, education level, income, primary livelihood, group membership, and financial assistance, while larger land size negatively impacted adoption. Policy recommendations include implementing integrated climate adaptation strategies, improving water management systems, promoting technology adoption by enhancing education and income, and providing targeted support for farmers with larger land holdings through training, financial incentives, or technology subsidies.