In this study, challenges experienced by dairy cattle enterprises in Southwestern Uganda and the factors influencing their profitability were respectively analyzed using exploratory factor analysis and multiple regression in STATA 15.0 statistical software. Eighteen questions relating to the challenges experienced by dairy producers in the study area were factor analyzed using principal components analysis with varimax rotation. Kaiser-Meyer-Olkin’s measure of sampling adequacy was 0.643, above the commonly recommended value of 0.6, and Bartlett’s test of sphericity was significant (ꭓ² (153) = 1670.13, P<0.001). Using both the scree plot and eigenvalues greater than 1 to determine the underlying components, the analysis yielded five factors explaining a total of 67.42% of the variance in the data. These factors were investment constraints, productivity constraints, climatic and environmental conditions, veterinary and social security services, and marketing constraints, which explained 21.32%, 13.01%, 11.97%, 11.03%, and 10.097% of the variance after rotation, respectively. The factors hypothesized to influence the profitability of dairy enterprises were; daily milk yield per lactating cow, the prevalence rate of diseases, percentage of lactating cows to those raised on the enterprise, attendance of animal production training, the unit production cost of milk, and enterprise size according to the number of animals raised. Regression analysis results of these factors revealed that the unit production cost of milk, enterprise size, and daily milk yield per lactating cow were statistically significant. The estimated model had an R-squared value of 0.92. The recommendations emphasized in this study were reducing milk production costs, rational use of production resources, adopting improved cattle breeds, improving feeding by supplementing animal diets with concentrate feeds to increase milk yield, and general improvement in dairy herd management practices, including disease control strategies.

Abstract. Waiswa D, Günlü A. 2022. Economic analysis of dairy production in Uganda, a case study on the performance of dairy cattle enterprises in Southwestern Uganda. Asian J Agric 6: 61-67. The economic performance of dairy cattle enterprises in Southwestern Uganda was analyzed in this study. A survey was conducted on 100 dairy cattle enterprises in Mbarara, Kiruhura, Lyantonde, Ibanda, and Isingiro Districts using data compilation forms covering the 2019/20 production year. The unit production cost of milk was determined as US$0.19L-1. Veterinary expenses had the largest share of the production costs at 24.94%, followed by labor costs, depreciation of the inventory value, other expenses, and feed costs, which contributed 14.11%, 12.46%, 11.96%, and 11.41%, respectively. Additional costs included the depreciation of animals, electricity and water, buildings, equipment and machinery, maintenance-repair, and general administrative expenses, which contributed 9.95%, 7.86%, 2.54%, 2.29%, and 2.48% to the total production costs, respectively. As a result, while the net profit of the enterprises was determined as US$1435.29, their financial profitability was 0.59, the profitability factor was 12.20, and the output-input ratio was 1.06. The overall profitability of the enterprises was affected mainly by the high veterinary expenses due to the high prevalence rates of tick-borne infections and the irrational distribution of capital elements. Therefore, measures to reduce the occurrence of tick-borne diseases are considered vital in lowering milk production costs, thereby increasing the profitability of enterprises.

This study was conducted to examine the impact of changes in annual mean temperature, annual mean precipitation, carbon footprint, ecological footprint, and area harvested on cereal crop production in East Africa. The study was conducted in a panel cointegration framework using annual time series from 1980 to 2018 for five East African countries i.e., Ethiopia, Kenya, Rwanda, Tanzania, and Uganda. Unit root tests were performed using LLC, IPS, ADF-Fisher, and PP-Fisher tests, while panel co-integration tests were performed using Pedroni residual, Kao residual, and Johansen Fisher panel co-integration tests. Long-run coefficients were estimated using the Pooled Mean Group/Autoregressive Distributed Lag, Panel Fully-modified OLS, and Panel Dynamic OLS models. Empirical findings from the three models revealed that increases in annual mean temperature have adverse effects on cereal crop production, while increases in annual mean precipitation, carbon footprint, ecological footprint, and area harvested have positive effects on cereal crop production in East Africa. Based on these findings, it can be suggested that prioritization of climate change adaptation strategies in the region such as the development of drought and heat-resistant crop varieties, changing in planting dates, and investment in irrigation technologies to boost cereal crops productivity could play a role in minimizing the adverse effects of changes in climate factors.

Despite the commercial links that exist among Tanzania, Kenya, and Uganda, with maize as the most heavily traded agricultural commodity, there is a deficiency in the empirical literature on the price transmission of maize or any other traded agricultural commodity among these countries. This study attempts to fill this gap in the literature by examining the spatial price transmission of wholesale maize grain prices among these countries using the Nonlinear ARDL model. The empirical results indicate that there is no statistically significant relationship between wholesale maize prices in Uganda and those in Tanzania. However, a 1% increase (decrease) in wholesale maize prices in Kenya leads to a 0.8943% (0.7363%) increase (decrease) in wholesale maize prices in Uganda. Similarly, a 1% increase (decrease) in wholesale maize prices in Kenya leads to a 0.6079% (1.1752%) increase (decrease) in wholesale maize prices in Tanzania. On the other hand, a 1% increase (decrease) in wholesale maize prices in Uganda leads to a 0.5652% (0.6487%) increase (decrease) in wholesale maize prices in Kenya, while a 1% increase in wholesale maize prices in Tanzania leads to a 0.3635% increase in wholesale maize prices in Kenya. These findings are relevant for the development of strategies to improve market conditions and enhance growth in trade among the three countries.

Beef and cattle milk production play a significant role in reducing hunger, malnutrition, and rural poverty, improving rural livelihoods, creating employment opportunities, and supporting the overall development of Uganda's economy. This study was conducted to find a suitable ARIMA model for forecasting Uganda’s beef and cattle milk production using annual time series data from 1961 to 2020, extracted from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT). Following patterns of the Autocorrelation Function and Partial Autocorrelation Function plots of the differenced series, 4 tentative ARIMA models were identified for milk production, i.e., ARIMA (0,1,0), ARIMA (1,1,0), ARIMA (0,1,1), and ARIMA (1,1,1). While 3 tentative ARIMA models were identified for beef production, i.e., ARIMA (1,1,1), ARIMA (1,1,0), and ARIMA (0,1,1). ARIMA (0,1,0) model was selected to be the most suitable for forecasting cattle milk production because it had the smallest MAPE and Normalized BIC values. On the other hand, ARIMA (1,1,0) was selected to be the best model for forecasting beef production because it had the smallest normalized BIC value and a significant coefficient of the autoregressive component. Forecasts show that milk production will increase at an annual average rate of 1.63%, while beef production will increase at an annual average rate of 0.39% in the five-year forecast period (2021-2025). These findings are important in designing strategies to improve the beef and dairy livestock sub-sectors in Uganda.