Browsing by Author "Kadigi, Michael Lucas"

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Comparing ARFIMA and ARIMA models in forecasting under five mortality rate in Tanzania
(Asian Journal of Probability and Statistics, 2025-01-15) Mwijalilege, Sadock Aron; Kadigi, Michael Lucas; Kibiki, Castory
Tanzania has been taking various measures to drop the Under-Five Mortality Rate (UFMR), but the pace to meet national and global UFMR targets has been slow. Nevertheless, the decline for the past years has continued to be low as compared to the Sustainable Development Goals (SDGs) target which is set at 25 deaths/1000 live births by 2030. The lack of statistical modeling-based forecast values of UFMR results into setting targets that are not SMART towards the realization of national and international goals of the health sector. Thus, the current study uses both ARFIMA and ARIMA to make forecasts of UFMR in Tanzania from 2021 to 2030 by using data extracted from the World Databank - World Development Indicators (WDI). Also, an accuracy comparison between the ARFIMA and ARIMA best-fit models in forecasting UFMR was conducted. The forecasts from the best ARFIMA (1, 0.284243, 2) model indicate that by June 2026 the rate will on average be 41 deaths/1,000 live births as compared to the Tanzanian Five Year Development Plan Phase III (TFYDP-III) target of 40 deaths/1,000 live births; whereas the best fit ARIMA (1, 2, 0) model forecasts depict that the rate will be 40.1 deaths/1,000 live births as compared to the TFYDP-III target. In relation to the UN SDGs target of 25 deaths/1,000 live births by 2030, the ARFIMA (1, 0.284243, 2) model forecast values indicate that by 2030, Tanzania will experience a decrease in UFMR to 35.2 deaths/1,000 live births. The ARIMA (1, 2, 0) forecast values indicate that by 2030, Tanzania will experience a decrease in UFMR to 32.9 deaths/1,000 live births. The results of using RMSE and MAPE forecasting model accuracy measures reveal that the ARFIMA (1, 0.284243, 2) model performs better than ARIMA (1, 2, 0) in forecasting UFMR.
Factors influencing choice of milk outlets among smallholder dairy farmers in Iringa municipality and Tanga city
(2013) Kadigi, Michael Lucas
The global markets are increasingly being integrated due to globalization and liberalisation. Drastic changes prompted by technological change are daily transpiring in the agricultural produce marketing which put smallholder farmers’market survival at stake. The notable changes are manifested in terms of value addition and product differentiation. This study was undertaken to identify factors that determine milk value chain choice amongst smallholder dairy in Iringa and Tanga urban. The specific objectives were: to assess and compare profitability between informal and formal milk value chain participants; examining factors that influence choice of milk marketing channels/outlets among smallholder dairy farmers. Purposive and random sampling techniques were employed in selecting 160 smallholder dairy farmers and 62 middlemen. Both descriptive and quantitave techniques (Gross Margin and Multinomial Logistic Regression) were used in data analysis. The enterprises’ profitability between dairy farmers selling milk through the informal and formal milk channels was statistically different (P< 0.05), implying that informal milk channel is shown significantly being more profitable than the formal channel, with a mean difference of 385.00 TZSper litre. The Multinomial Logistic results show that, the highly statistically significant variables at 1% (P<0.01) level of significance are the price offered per litre of milk, family size of household, education level of household head, sex of household head, volume of milk produced, and access to credits. These findings suggest that an adjustment in each one of the significant variables can significantly influence the probability of participation in either formal or informal marketing channels. In view of research findings, several policy proposals are suggested. These include offering reasonable prices price per litre of milk, propelling collective actions, provision of non-price incentives, re-structuring existing dairy institutional arrangements,establishing milk collection centers,encouraging value addition (adoption of best upgrading practices) and investment in dairy processing (empowering SMEs).