Browsing by Author "Mourice, Sixbert K."

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The effect of nitrogen-fertilizer and optimal plant population on the profitability of maize plots in the Wami river sub-basin, Tanzania: a bio economic simulation approach
(Elsevier, 2021) Kadigi, Ibrahim L.; Richardson, James W.; Mutabazi, Khamaldin D.; Philip, Damas; Mourice, Sixbert K.; Mbungu, Winfred; Bizimana, Jean-Claude; Sieber, Stefan
Maize (Zea mays L.) is the essential staple in sub-Saharan Africa (SSA) and Tanzania in particular; the crop accounts for over 30% of the food production, 20% of the agricultural gross domestic product (GDP) and over 75% of the cereal consumption. Maize is grown under a higher risk of failure due to the over-dependence rain fed farming system resulting in low income and food insecurity among maize-based farmers. However, many practices, including conservation agriculture, soil and water conservation, resilient crop varieties, and soil fer tility management, are suggested to increase cereal productivity in Tanzania. Improving planting density, and the use of fertilizers are the immediate options recommended by Tanzania's government. In this paper, we evaluate the economic feasibility of the improved planting density (optimized plant population) and N-fertilizer crop management practices on maize net returns in semi-arid and sub-humid agro-ecological zones in the Wami River sub-Basin, Tanzania. We introduce a bio-economic simulation model using Monte Carlo simulation pro cedures to evaluate the economic viability of risky crop management practices so that the decision-maker can make better management decisions. The study utilizes maize yield data sets from two biophysical cropping system models, namely the APSIM and DSSAT. A total of 83 plots for the semi-arid and 85 plots for the sub humid agro-ecological zones consisted of this analysis. The crop management practices under study comprise the application of 40 kg N-fertilizer/ha and plant population of 3.3 plants/m2 . The study finds that the use of im proved plant population had the lowest annual net return with fertilizer application fetching the highest return. The two crop models demonstrated a zero probability of negative net returns for farms using fertilizer rates of 40 kg N/ha except for DSSAT, which observed a small probability (0.4%) in the sub-humid area. 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 suggest that the application of fer tilizer practices reduces the risks associated with the mean returns, but increasing the plant population has a high probability of economic failure, particularly in the sub-humid zone. Maize sub-sector in Tanzania is pro jected to continue experiencing a significant decrease in yields and net returns, but there is a high chance that it will be better-off if proper alternatives are employed. Similar studies are needed to explore the potential of interventions highlighted in the ACRP for better decision-making.
The impacts of current climate variability on coffee production in the northern and southern highlands of Tanzania
(Canadian Center of Science and Education, 2021) Mbwambo, Suzana G.; Mourice, Sixbert K.; Tarimo, Akwilin J. P.
Coffee is the most traded commodity in the world. In Tanzania, Coffee is the second largest traditional commodity. However, several climate change studies have predicted that coffee production will be reduced as a result of climate change. Therefore, the study aimed to assess the impact of current climate change on Tanzania’s Arabica coffee production and determine the most significant climatic variables, which influence coffee production in the respective regions. Global interpolated climatic database (Worldclim dataset) and official historical coffee production data from Tanzania Coffee Board for a period of 40 years (1970-2018) were used. Climatic parameters and coffee production were compared through descriptive statistics, correlation analysis, and multiple regressions. The Mann-Kendall method was used to detect significant trends in climatic data. The minimum temperature has been increasing at a higher rate than the maximum temperature in the Northern and Southern Highlands zones. A 1 °C increase in minimum temperature (Tmin) during short rains and annual mean temperature (Tmean) resulted in a significant coffee production decrease (-6,041 and -4,450 tons) in Kilimanjaro and Arusha regions respectively. In the Southern Highlands zone coffee production positively correlated with temperature. A significant reduction in coffee production due to a decline in long rains was also observed in the Kilimanjaro region. The warming and drought trends are likely to continue with significant implications on coffee production and this, calls for the development of suitable adaptation strategies to sustain production. Such strategies may include, re-adapting the coffee agronomic practices to climate change, improving water and nutrient use efficiency in coffee trees, and developing genetically improved coffee cultivars that will tolerate the impact of climate change
A simple convolutional neural network architecture for monitoring Tuta absoluta (Gelechiidae) infestation in tomato plants.
(Sokoine University of Agriculture, 2021-05) Mourice, Sixbert K.; Mlebus, Festo Joseph; Fue, Kadeghe G.
Tomato leaf miner (Tuta absoluta (Gelechiidae)) is a serious tomato insect pest in Tanzania, and its management or control still posess significant challenge. If left uncontrolled, the loss inflicted by the miner can be as high as 100%. Successful management of the pest may leverage on an integrated pest management (IPM) approach which, requires high throughput data on damage signs over space and time. This needs, in turn, a robust technique for pest monitoring. This study uses a deep learning technique to detect infestation symptoms of T. absoluta on tomato plants. The technique is rapid, automated and doesn’t require trained or experienced personnel. An experiment was carried out at Sokoine University of Agriculture (SUA), where two sets of tomato plants (cv. Asila F1) were planted in a screen house and in an open field. High-quality images of the tomato leaves were captured from both sets at seven days intervals for 70 days following transplanting. More images were collected from tomato gardens around Morogoro town. Collected images were labeled as being infested or non-infested. A simple convolution neural network (CNN) architecture with four convolution layers, three pooling layers, one flat layer and one dense layer, powered by Keras library and python’s Tensorflow backend, was developed in R-Software. The model accuracy was 90% on training and 82% on test data sets. This study suggests that the model can accurately identify T. absoluta infestation in tomato plants to a considerable extent. An in-depth discussion of the technique is provided in the paper.