Browsing by Author "Nimusiima, Alex"
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Item An Appraisal of Adaptation Measures to Climate Variability by Smallholder Irish Potato Farmers in South Western Uganda(American Journal of Climate Change, 2020) Mugagga, Frank; Nimusiima, Alex; Elepu, JuliusClimate change and variability are a reality and have had marked effects on both human and ecological systems. Adaptation to such effects either directly or indirectly is viewed as a novel way of reducing the spread of the associated risks. The study was conducted in Rubanda District with a general aim of evaluating the effectiveness of adaptation measures to climate variability used by smallholder Irish potatoes farmers. Specifically, we sought to appraise smallholder Irish potato farmers’ perceptions about the effectiveness of climate variability adaptation measures and implications on Irish potato productivity. We undertook a cross-sectional study to collect data from total of 197 farmers using a structured questionnaire. Other participatory methods such as focus group discussions and key informant interview were also used to complement the household survey. Data were analyzed using SPSS Version 23 to generate descriptive statistics as well as relationships between the adaptation measures and the productivity of the Irish potatoes using a Cobb-Douglas production function. The Cobb-Douglas model revealed that the use of fertilizers was significantly and positively associated with productivity of Irish potato (P < 0.001). Furthermore, Smallholder Irish potato farmers perceived the use of technology, terracing, early planting and application of fertilizers as the most effective climate variability adaptation measures. The study recommends that measures that are cost-effective, efficient and coherent should be promoted to offset the impacts of climate variability which would include the adoption of improved potato varieties and use of fertilizers with proper management of erosion. Off-season planting of Irish potatoes in the wetlands should be discouraged by the local administration and the National Environment Management Authority. This calls for a collective action involving the agricultural practitioners and inline civil society organizations to ensure that farmers have access to such inputs.Item The damage caused by landslides in socio-economic spheres within the Kigezi highlands of South Western Uganda(Environmental & Socio-economic Studies, 2021) Nseka, Denis; Mugagga, Frank; Opedes, Hosea; Ayesiga, Patience; Wasswa, Hannington; Mugume, Isaac; Nimusiima, Alex; Nalwanga, FaridahAn assessment of the socio-economic implications of landslide occurrence in the Kigezi highlands of South Western Uganda was conducted. Landslide occurrence is on the increase and threatens community livelihoods in these highlands. Detailed field investigations were undertaken with the help of local communities between June 2018 and May 2020 to identify and map recent and visible landslide scars in Rukiga uplands of Kigezi highlands. In the course of field inventories, 85 visible landslide scars were identified and mapped using handheld GPS receivers to produce a landslide distribution map for the study area. A socio-economic analysis was conducted to establish the effects of landslide damage on people’s livelihoods as well as their existing coping and adaptation mechanisms. The assessment was administered through field observations and surveying, focus group discussions, key informants and household interviews as well as the use of Local Government Environmental Reports. The study established an increase in the spatial-temporal distribution of landslides over the Kigezi highlands in the past 40 years. The landslides have resulted in a reduction in the quality of land, loss of lives, destruction of transport infrastructures, settlements, farmlands, crops and other socio-economic infrastructures. Therefore, it is important to look for reliable and sustainable measures to prevent landslide hazards. Total landscape reforestation with deep-rooted trees can possibly reduce the landslide risk. It is also important to undertake policy implementation for preparedness and mitigation plans against landslides in this region and in the country at large. Proper soil and water conservation measures could help in enhancing soil strength against landslide hazards.Item Empirical Relationships between Banana Yields and Climate Variability over Uganda(J. Environ. Agric. Sci, 2016) Sabiiti, Geoffrey; Ininda, Joseph Mwalichi; Ogallo, Laban; Opijah, Franklin; Nimusiima, Alex; Otieno, George; Ddumba, Saul Daniel; Nanteza, Jamiat; Basalirwa, CharlesVariations in weather and climate have a significant impact on rain-fed banana yields in East Africa. This study examined empirical linkages between banana yields and variations in rainfalland temperature over Uganda for the historical period (1971-2009) using time series moments,correlation and regression analysis. The Food and Agriculture Organization (FAO) Crop Water Assessment Tool (CROPWAT) was used to estimate banana crop water requirements, soil moisturedeficits and their effects onbanana yield levels under rain-fed conditions for different regions. Thestudy observed high comparability in moment indices with some significant differences reflected in thevalues of the banana yields and rainfall and temperature moment indices. The cumulative effect ofrainfall and temperature variations on banana yields was discernible from strong correlationcoefficients of up to 78%. The CROPWAT simulations indicated up to 46% reductions in optimalbanana yields due to soil moisture deficits within banana plantations. In conclusion, the study observedstronger linkages between banana yields and temperature variations than rainfall. In addition,temperature manifests both direct and indirect effects on banana growth while rainfall exhibitscomparatively high intra-seasonal and intra-annual variability with lag effects on banana yields. Thestudy provides a strong scientific basis for the development of coping, adaptation and mitigationstrategies in the banana farming subsector in the region due to the anticipated shifts in rainfall and temperature extremes and changes across Uganda and neighbouring regions.Item Evaluation of WRF‑chem simulations of NO2 and CO from biomass burning over East Africa and its surrounding regions(Atmospheric and Oceanic Sciences, 2022) Opio, Ronald; Mugume, Isaac; Nakatumba‑Nabende, Joyce; Nanteza, Jamiat; Nimusiima, Alex; Mbogga, Michael; Mugagga, FrankIn East Africa, biomass burning in the savanna region emits nitrogen dioxide ( NO2), carbon monoxide (CO), and aerosols among other species. These emissions are dangerous air pollutants which pose a health risk to the population. They also affect the radiation budget. Currently, limited academic research has been done to study their spatial and temporal distribution over this region by means of numerical modeling. This study therefore used the Weather Research and Forecasting model coupled with chemistry (WRF-chem) to simulate, for the first time, the distribution of NO2 during the year 2012 and CO during the period June 2015 to May 2016 over this region. These periods had the highest atmospheric abundances of these species. The model’s performance was evaluated against satellite observations from the Ozone Monitoring Instrument (OMI) and the Measurement of Pollution in the Troposphere (MOPITT). Three evaluation metrics were used, these were, the normalized mean bias (NMB), the root mean square error (RMSE) and Pearson’s correlation coefficient (R). Further, an attempt was made to reduce the bias shown by WRF-chem by applying a deep convolutional autoencoder (WRF-DCA) algorithm and linear scaling (WRF-LS). The results showed that WRF-chem simulated the seasonality of the gases but made below adequate estimates of the gas abundances. It overestimated NO2 and underestimated CO throughout all the seasons. Overall, for NO2, WRF-chem had an average NMB of 3.51, RMSE of 2 × 1015 molecules/cm2 and R of 0.44 while for CO, it had an average NMB of − 0.063, RMSE of 0.65 × 1018 molecules/cm2 and R of 0.13. Furthermore, even though both WRF-DCA and WRF-LS successfully reduced the bias in WRF-chem’s NO2 estimates, WRF-DCA had a superior performance compared to WRF-LS. It reduced the NMB by an average of 3.2 (90.2%). Finally, this study has shown that deep learning has a strong ability to improve the estimates of numerical models, and this can be a cue to incorporate this approach along other stages of the numerical modeling process.Item Institutional Determinants to Climate Variability Adaptation by Smallholder Irish Potato Farmers in Rubanda District, South Western Uganda(American Journal of Climate Change, 2019) Mugagga, Frank; Elepu, Julius; Nimusiima, Alex; Bamutaze, YazidhiClimate variability and change pose greater challenge not only to human life but to the environment at large. This study sought to evaluate the significance of institutional factors in climate variability adaptation of smallholder Irish potato farmers in Rubanda District, South Western Uganda with the objective of assessing the adaptation measures adopted by smallholder Irish potato farmers, determining the institutional factors that influence adoption of climate variability adaptation measures; and evaluating the institutional challenges that affect the adapting Irish potato farmers. A cross-sectional survey was undertaken to collect data from 197 systematically sampled smallholder farmers from two purposively selected sub counties (Muko and Bubaare) in Rubanda District, using structured questionnaires; whilst key informant interviews were used to elicit data from purposively selected personnel from the local government as well as private and civil society organizations. Multiple linear regression was used to determine the relative influence of selected variables on adaptation measures against climate variability. Results indicate that smallholder Irish potato farmers are adapting to climate variability through agronomic measures such as terracing, mulching, contour ploughing, changing planting dates, early planting, crop-rotation, and technology related measures such as rain water harvesting technologies, adaptive varieties and fertilizers among others. Results from multiple linear regression analysis show that several institutional factors are influencing adoption of climate variability adaptive measures with the most significant ones being access to agricultural extension services, cultivated area and size of land owned. Despite the interventions undertaken, adaptation to climate variability is constrained by the limited access to financial/credit resources and in- adequate technical capacity as well as limited access to information and irregularity of extension services. The study recommends that public and private institutions and personnel, both technical and political, at the various levels of local government, work together to improve extension services, communication as well as enhancing access to credit facilities among smallholder farmers, who will also need to further strengthen existing social groups to enhance their bargaining power.Item Nature and Dynamics of Climate Variability in the Uganda Cattle Corridor(African Journal of Environmental Science and Technology, 2013) Nimusiima, Alex; Basalirwa, C. P. K.; Majaliwa, J.G.M.; Otim-Nape, W.; Okello-Onen, J.; Rubaire-Akiiki, C.; Konde-Lule, J.; Ogwal-Byenek, S.The study was conducted in the districts of Nakaseke and Nakasongola stratified into four farming systems of crop dominancy, pastoralists, mixed crop and livestock and fishing. The study was guided by two research questions: (1) how do community residents perceive climate change/variability? (2) What is the trend and nature of climate variability and how does it compare with people’s perceptions? Ninety eight percent (98%) of the respondents reported that the routine patterns of weather and climate had changed in the last 5 to 10 years and it has become less predictable with sunshine hours being extended and rainfall amounts being reduced. This compared well with the analyzed secondary data. Over 78% respondents perceived climate change and variability to be caused by tree cutting other than the known scientific reasons like increase in industrial fumes or increased fossil fuel use. Climate data showed that over the period 1961 to 2010 the number of dry spells within a rainfall season had increased with the most significant increase observed in the first rainfall season of March to May as compared to the season of September to November. The first dry season of June/July to August is short while the second dry season of December to February is long during the study period. The two rainfall seasons of March to May and September to November seem to be merging into one major season from May to November. Temperature data shows a significant increasing trend in mean annual temperatures with the most increase observed in the mean annual minimum temperatures than the maximum temperatures.Item WRF Simulations of Extreme Rainfall over Uganda’s Lake Victoria Basin: Sensitivity to Parameterization, Model Resolution and Domain Size(Journal of Geoscience and Environment Protection, 2020) Opio, Ronald; Sabiiti, Geoffrey; Nimusiima, Alex; Mugume, Isaac; Sansa-Otim, JulianneRainfall extremes have strong connotations to socio-economic activities and human well-being in Uganda’s Lake Victoria Basin (LVB). Reliable prediction and dissemination of extreme rainfall events are therefore of paramount im-portance to the region’s development agenda. The main objective of this study was to contribute to the prediction of rainfall extremes over this region using a numerical modelling approach. The Weather Research and Forecasting (WRF) model was used to simulate a 20-day period of extremely heavy rainfall that was observed in the March to May season of 2008. The underlying interest was to investigate the performance of different combinations of cumulus and mi-crophysical parameterization along with the model grid resolution and do-main size. The model output was validated against rainfall observations from the Tropical Rainfall Measuring Mission (TRMM) using 5 metrics; the rain-fall distribution, root mean square error, mean error, probability of detection and false alarm ratio. The results showed that the model was able to simulate extreme rainfall and the most satisfactory skill was obtained with a model se-tup using the Grell 3D cumulus scheme combined with the SBU_YLin micro-physical scheme. This study concludes that the WRF model can be used for simulating extreme rainfall over western LVB. In the other 2 regions, central and eastern LVB, its performance is limited by failure to simulate nocturnal rainfall. Furthermore, increasing the model grid resolution showed good po-tential for improving the model simulation especially when a large domain is used.