Adoption dynamics of climate smart livestock technologies in smallholder farmers (a case of Chegutu district)

Abstract

Climate change is negatively impacting food security and livelihoods of smallholder farmers. The effects of climate change have been evidenced in Zimbabwe in the past decade Zimbabwe has adopted sustainable development goals as part of its national development agenda. SDG1, SDG2 and SDG 13 are vital towards eradicating poverty and food insecurity in the face of a changing climate. Climate-smart agriculture concept offer an opportunity for farmers to improve productivity, resilience and reduce greenhouse gases. Considering Zimbabwe is an agro based economy, the adoption of this concept is key transforming the agriculture sector towards improving productivity, resilience and sustainability. This study focused on smallholder farmers in Chegutu district, Zimbabwe. The main aim was to assess the adoption of climate smart livestock technologies in smallholder farmers. The study employed a mixed methods approach using a household questionnaire and key informant interviews to gather data. The data was collected from 150 randomly selected households. The study used Binary Logistic and Poisson regression models to account for the factors that influenced the adoption of climate smart innovations in the district. Based on descriptive analysis the majority of the farmers were males (58.7%), average age was 51 years and average household size was 7. Most of the farmers lived in old resettlement areas and attained at least secondary education (34, 7%). However 98 % had no access to credits, about 35% of the farmers had received training on CSLT and 62% had not encountered a government veterinary officer in the past 12 months. The study found that adoption rates of climate smart livestock technologies was very low with only 20,5 % of farmers adopting homemade feed and 11% adopting crossed cattle. Education level, farm type, household size and land size were positively significant to the adoption intensity of the technologies. On the other hand, it was noted that age, education level, access to credit, farm size, farm type and household size influenced the probability of a farmer adopting a certain technology. It was also revealed from the study that livestock diseases and lack of extension services were the highlights that are de-incentivizing farmer’s decisions to adopt smart technologies. To address these challenges, recommendations included prioritizing trainings and education on CSLT, providing credit facilities as well as increasing farmer contact with extension services. Triggering extension services will improve access of farmers to reliable information about the technologies and their benefits. Furthermore the government need to increase awareness of climate-smart technologies through a multi-disciplinary and inter-sectorial approach to foster behaviour change in farmers towards the uptake of climate smart practices.