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MEDIA WORKSHOP REPORT - ON AGRICULTURAL COMMERCIALISATION IN TANZANIA
The following applicants have been selected in the first round to join degree programmes at School of Agricultural Economics and Business studies 2020/2021 academic year which commences on the 23rd November, 2020.
For more detail please ( click here)
Congratulation to Dr. Ibrahim L. Kadigi for successfully defended his PhD thesis titled: “Risk Inclusion in Forecasting and Economic Feasibility Analyses of Staple Food Cereals in Tanzania: The Case of Maize, Sorghum and Rice” pursued at the School of Agricultural Economics and Business Studies SAEBS, of Sokoine University of Agriculture (SUA).
Dr. Ibrahim L. Kadigi holds (MSc. Agricultural Economics) is currently a freelance researcher/consultant based at Soil-Water Management Research Programme, Department of Engineering Sciences and Technology.
He was supervised by Prof. Khamaldin D. Mutabazi,Dr. D. Philip Damas Dean of School and Dr. Sixbert K. Mourice. The defence was held on Tuesday, 6th October 2020 with five panellists; Prof. A. Isinika (Chairperson), Dr. J. Makindara (Internal Examiner)(Head of Department of Busines Management), Dr. D. Philip DamasDean of School (Supervisor), Dr. Roselyne Alphonce (Appointee of the Dean) and Dr. N. Seluhinga (Appointee of the Head).
Summary of the Thesis
Maize (Zea mays L.), rice (Oryza sativa), and sorghum (Sorghum bicolor L. Moench) are major staple food crops to the most population in Tanzania. The three crops provide the primary source of livelihood for the majority of rural farming households. Unfortunately, like for any other crops, some risks and uncertainties remain about the future productivity and profitability of these essential food crops. These uncertainties hinder the implementation of different strategies, agricultural policies, and plans set to achieve an agriculture revolution, hence impacting the decision of investment in agricultural technologies. The general object of this study was to apply a Monte Carlo Simulation Model (MCSM) which incorporates the randomness of the prices, yields, interest & rates, production costs and other stochastic variables to estimate the economic feasibility of the staple food crops for 7-years through 2025. Following the MCSM protocol, the Maize, Sorghum and Rice simulation model (MASORISIM) was developed for analysis. The study also evaluated the economic viability of proposed interventions aimed to minimize the risks and uncertainties in agricultural production. These interventions include the application of 40kg N/ha, an adjustment in plant population to 3.3 plants/m2, use of improved seeds and system of rice intensification (SRI). Two regions, namely Dodoma (semi-arid) and Morogoro (sub-humid), were included in the analysis.
The results on economic feasibility in terms of Net Present Value (NPV) revealed a probability of positive NPV for all the crops in Dodoma despite a higher relative risk for rice. The results in Morogoro presented a high probability of success for rice and sorghum with maize, indicating the highest relative risk and a 2.9% probability of negative NPV. On the proposed interventions for maize, the study finds that the use of improved plant population had the lowest annual net return with the application of 40kg N/ha fetching the highest return. Also, the study demonstrated a zero probability of negative net returns for farms using recommended rates of fertilizer for both sub-humid and semi-arid areas. The optimized plant population presented 16.4% to 26.6% probability of negatives net returns for semi-arid and 14.6% to 30.2% probability of negative net returns for sub-humid zones. The results for rice interventions show a 2% and zero probability of net cash income (NCI) being negative for partial and full SRI adopters, respectively. Meanwhile, farmers using local and improved seeds have 66% and 60% probability of NCI being negative, correspondingly. Rice farms which applied fertilizers in addition to improved seeds have a 21% probability of negative returns.
Although the results show a probability of positive NPV for the crops, the income generated by these crops is still low with high variabilities. These results help to inform policymakers and agencies promoting food security and eradication of poverty on the benefits of encouraging improved maize and rice farming practices in the country. Similar studies are needed to explore the potential of different interventions highlighted in the Agriculture Climate Resilient Plan (ACRP 2014 – 2019) for better decision-making. This study was conducted only in two regions, but similar works can be undertaken following the MCSM protocol to include other regions and more crops.
A training course on Data Analysis Using R software was conducted for ten days starting from on 7th to 18th of September, 2020 at the School of Agricultural Economics and Agribusiness (SAEBS) of Sokoine University of Agriculture (SUA). The training was conducted as part of internal capacity building activities for the Trade, Development and Environment (TRADE) Hub researchers and postgraduate students who will soon be undertaking their fieldwork, data analysis and thesis/report writing.
TRADE Hub is a five-year project running from 13th February 2019 to 12th February 2024. The project is implemented by Sokoine University of Agriculture (SUA) in collaboration with other partners from 15 different countries in Africa, Asia, the UK, and Brazil. It is financed through the Global Challenges Research Fund (GCRF), the UK Research and Innovation (UKRI) Collective Fund. The hub operates based on the hypothesis that trade in wildlife and agricultural commodities could become an engine for inclusive economic growth and poverty reduction. The role of the hub is therefore to address the intractable challenge of how to eliminate the negative impacts on people and ecosystems from trade.
The Tanzania TRADE hub team is led by Prof. R.M.J. Kadigi of the Department of Food and Resource Economics, SAEBS at SUA. Other members of the team include Prof. Pantaleo K.T Munishi of the Department of Ecosystem and Conservation, SUA; Prof. Japhet J. Kashaigili of the Department of Forest Resources Assessment and Management, SUA; Dr. Charles P. Mgeni, of the Department of Agricultural Economics and Agribusiness, SUA; Mr. Joseph Rajabu Kangile of the School of Agricultural Economics and Business Studies, SUA; and Dr. Paulo Wilfred of the Open University of Tanzania (OUT).
Specifically, the Country TRADE Hub team undertakes the following:
- Mapping of relevant trade policies in wildlife- and commodity-exporting countries, value chain structures, strategies, agreements, protocols, demand and supply balance sheets for modeling of economic impacts;
- Analysis of interactions of policy frameworks between importing and exporting countries, as well as, the role of historical evolution of policies and norms in shaping current situations for selected wildlife products and agricultural commodities, and identification of best practices;
- Analysis of international trade governance and performance, valuation of trade flows and evaluation of factors that influence the trade (both the supply and demand driven factors).
In its initial assessment of training needs among its researchers and partners, the hub has identified and prioritized several training activities including the training on how to use R software to analyse data. The R software is increasingly used by many researchers to analyse their research data for its many powerful benefits, such as, easiness to wrangle data, reproduce research, and its advanced visualisations as well as its ability to swiftly implement new theoretical approaches.
The training on data analysis using R software at SAEBS commenced with an opening remark from the TRADE Hub Country Lead, Prof. Reuben M.J. Kadigi who introduced the hub and its brief profile before introducing the training facilitators.
Prof. Reuben M.J. Kadigi, the TRADE Hub Country Lead, opening the training course
The facilitator, Mr. Michael demonstrating to one the participants of the training how to undertake a multivariate modelling using R software
Participants during one of the practical sessions on data analysis using R software
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