Cassava is one of the most important crops in global food security. It is the second most important staple crop in Africa. Its significance is enhanced by the fact that it very well tolerates droughts, and therefore it may be a prospective response to climate change in hot and dry areas. The potentials of cassava are under-utilized in Eastern Africa, and there is a lack of research studies regarding climate impacts on cassava yields in this region. The present research focuses on cassava production in Eastern Africa, analyzing the relationship of cassava yields, harvested areas, temperature, and precipitation from 1961 to 2023. The statistical analysis applies panel regression for the 63 years of time series, for the 15 most important cassava producing countries of Eastern Africa. Findings show that while the impacts of rainfall are insignificant on yields, the effects of temperature are significantly positive, indicating yield and area increases with warming climate. An expansion of the cassava growing area and the expanding rural population contributed to decreasing yields, probably because of the expansion of smallholder subsistence farming, suffering from to limitations in other farming resources. Therefore, even if climate change may benefit cassava production, other factors create severe limitations on improving yields. However, the positive response of the crop to rising temperatures is a clear sign that it is a useful choice for food security under climate change and would deserve more support from agricultural policymakers in Eastern Africa.

Climate volatility poses a critical threat to global food security, yet the differential impacts of seasonal weather anomalies on diverse staple crops remain poorly understood. This study analyses a nearly balanced panel dataset spanning 64 years (1961–2024) across 20 major cassava- and wheat-producing countries to evaluate the impacts of quarterly temperature and precipitation anomalies on yields. Methodologically, the research integrates machine learning (random forest) to capture non-linear thresholds with Mundlak-type Linear Mixed Models (LMMs) to effectively isolate long-term regional climate features from short-term annual weather shocks. The empirical results reveal a stark contrast in climate vulnerability between the two staples. Wheat is highly sensitive to weather. Sudden heat waves between June and August disrupt its growth, while warmer temperatures from September to November lead to lower long-term yields. In contrast, cassava handles yearly weather changes much better and shows no major damage from single-year shocks, though warmer temperatures from March to May historically reduce its yield. However, cassava’s structural productivity is heavily constrained by long-term regional climate characteristics and indicates that permanent shifts in regional climate regimes alter its vegetative dynamics. The formal Mundlak test robustly rejects standard random effects specifications, confirming that ignoring unobserved country heterogeneity introduces substantial statistical bias. In conclusion, while cassava serves as a resilient buffer crop against immediate seasonal weather volatility, permanent shifts in long-term climate baselines threaten the future yield security of both crops, necessitating target-oriented, climate-smart agronomic interventions rather than extensive land expansion.

The main purpose of this study was to identify the factors determining loan repayment performance of smallholder farmers; and to evaluate the based performance of the Microfinance Institutions operating in Toke Kutaye district. Primary data were collected using structured questionnaires from 122 randomly selected smallholder farmer clients. Secondary data were obtained from the annual reports and financial statements of the Microfinance Institutions. The data were analyzed using descriptive statistics and binary logit regression models. The results of the descriptive analysis revealed a significant mean difference between the defaulter and non-defaulter households in terms of total annual farm income, expense on social ceremonies, livestock units, and distance from credit sources. Although the repayment status in the study area was insignificant, there was a variation among institutions where the numbers of non-defaulters decline from Oromia Credit and Saving Share Company, Vision Fund Microfinance and Eshet Microfinance successively along with ratio statistics result. The logit model revealed that age, education level, tropical livestock unit, participation in off-farm activities, repayment period suitability, and sex found to be have positive effects whereas expense on social ceremony, family size, loan diversion found to have negative effects on loan repayment performance of the smallholder farmers. Our findings suggest, in order minimizing loan defaults in the study area, microfinance institutions, non-governmental organizations and policy makers have to effectively work on improving the constraining factors. Keywords: Binary Logit; Customers: Defaulters, Loan repayment; Smallholder farmers DOI: 10.7176/JESD/13-3-04 Publication date: February 28^th 2022