Climate-smart Agriculture Options for Enhanced Resilience and Food Security: a Case Study of Yatta, Machakos County, Kenya

Abstract

The Kenya’s agriculture sector is key for rural livelihoods though highly impacted by climate change. The agriculture is mostly rainfed and dominated by smallholder farmers which increases vulnerability to climate variability and change impacts. There is need to enhance farmers’ resilience to climate change as well as strengthen their adaptive capacity through transition to sustainable farming practices. The overall objective of this study was to investigate selected climate-smart agriculture (CSA) options that can be integrated in smallholder farming systems for enhanced resilience and food security in Yatta sub-County, Machakos County. The study sought to establish rainfall and temperature trends during the analysis period (1983-2014) and relate the trends with crop yield to determine their relationship. The study investigated farmers’ perceptions to climate change and variability and the on-farm adaptation strategies they had adopted. Based on the climate trends and farmers’ perceptions of climate change the study sought to integrate selected climate-smart agriculture (CSA) models (conservation agriculture and Zai pits) into farmers’ practices to evaluate the impact of the models on crop yield in comparison to conventional farming practice. The study adopted a mixed methodology approach which integrated qualitative and quantitative research methods. Both primary and secondary data was used. Primary data was obtained using structured questionnaire, focus group discussions and experiments while secondary data (climate and crop) was obtained from existing databases. Climatic data were analyzed using descriptive and trend analysis. Detection of statistically significant climate trends was done using parametric (linear regression) and non-parametric (Mann-Kendall test), standard precipitation index and moving averages. Multiple regression model was used to analyze relationship between crop yield and climate variables. The data from the questionnaire was analyzed using descriptive and chi-square statistics. Quantitative statistics were used to analyze the experimental data using analysis of variance. The Mann-Kendall test revealed statistically significant (P<0.05) trends for the annual and seasonal rainfall. The linear regression showed increasing trends in annual temperature which supported farmers’ perceptions with majority farmers reporting increasing daytime temperatures (79%) and number of hot days (65%) over the last five years. However, the regression analysis showed increasing rainfall trends contrary to farmers’ perceptions of decreasing seasonal and annual rainfall trends Annual-monthly rainfall variation showed a bimodal rainfall with two distinct rainfall seasons in a year, however the monthly rainfall trends were not statistically significant (P>0.05. There was a significant direct correlation between crop yield and rainfall and temperature trends. Although farmers had adopted several on-farm adaptation strategies, the adoption levels remained low. Water management strategies (water conservation and water harvesting) recorded higher adoption rates of 62.71% and 53.95% respectively. The tested CSA options proved their potential towards increasing crop yield in comparison to the conventional practices. The occurrence of climate change events in the study area has affected agriculture productivity, food security and socio-economic status of the households. Integration of the CSA into smallholder farming systems is a viable option towards attaining food security and increased resilience to CC impacts.