Faculty of Natural Resourceshttps://hdl.handle.net/13049/72024-03-29T11:32:33Z2024-03-29T11:32:33ZLivelihood benefits and challenges of Acacia decurrens-based agroforestry system in Awi Zone highlands, Northwest Ethiopia.Afework, AmeneSewnet Minale, AmareDemel, Teketayhttps://hdl.handle.net/13049/7262024-03-19T09:02:46Z2024-01-18T00:00:00ZLivelihood benefits and challenges of Acacia decurrens-based agroforestry system in Awi Zone highlands, Northwest Ethiopia.
Afework, Amene; Sewnet Minale, Amare; Demel, Teketay
Acacia decurrens (hereafter Acacia) agroforestry system has been expanding rapidly in the northwestern highlands of Ethiopia. The agroforestry system provides multiple eco-environmental services; however, there is inadequate quantitative evidence on its livelihood benefits. This study, therefore, investigated the livelihood benefits and challenges of Acacia-based agroforestry system in the Awi area, Northwest Ethiopia. Data was collected through household survey quetionnaires (296 randomly selected Acacia growers), focused-group discussions, interviews, and observations. A combination of quantitative and qualitative methods was used for the data analysis. The findings showed that crop production, charcoal making, animal rearing, and fuelwood selling were the major sources of livelihood. Notwithstanding the complex challenges (Acacia pests/diseases, traditional charcoal-making, limited road access and market opportunities, negative human-health impacts, and high production cost), Acacia-based agroforestry positively affected farmers livelihoods. Comparatively, the natural, physical, financial, human and social capital indices of farmers were higher by 0.25, 0.24, 0.43, 0.25, and 0.06, respectively, in the post-than pre-Acacia periods. The overall livelihood index of farmers increased from 0.47 (pre-Acacia) to 0.71 in the post-Acacia period. The study concluded that this agroforestry practice has immense livelihood benefits, although diverse challenges question its sustainability. Therefore, short and long-term strategies should be designed to strengthen the opportunities and address the challenges.
The article was published under CC BY-NC-ND licence.
2024-01-18T00:00:00ZUrban land-use classification using machine learning classifiers: comparative evaluation and post-classification multi-feature fusion approach.Ouma, Yashon OKeitsile, AmantleNkwae, BoipusoOdirile, PhillimonMoalafhi, DitiroQi, Jiaguohttps://hdl.handle.net/13049/7072024-03-19T09:02:42Z2023-02-22T00:00:00ZUrban land-use classification using machine learning classifiers: comparative evaluation and post-classification multi-feature fusion approach.
Ouma, Yashon O; Keitsile, Amantle; Nkwae, Boipuso; Odirile, Phillimon; Moalafhi, Ditiro; Qi, Jiaguo
Accurate spatial-temporal mapping of urban land-use and land-cover (LULC) provides critical information for planning and management of urban environments. While several studies have investigated the significance of machine learning classifiers for urban land-use mapping, the determination of the optimal classifiers for the extraction of specific urban LULC classes in time and space is still a challenge especially for multitemporal and multisensor data sets. This study presents the results of urban LULC classification using decision tree-based classifiers comprising of gradient tree boosting (GTB), random forest (RF), in comparison with support vector machine (SVM) and multilayer perceptron neural networks (MLP-ANN). Using Landsat data from 1984 to 2020 at 5-year intervals for the Greater Gaborone Planning Area (GGPA) in Botswana, RF was the best classifier with overall average accuracy of 92.8%, MLP-ANN (91.2%), SVM (90.9%) and GTB (87.8%). To improve on the urban LULC mapping, the study presents a post-classification multiclass fusion of the best classifier results based on the principle of feature in-feature out (FEI-FEO) under mutual exclusivity boundary conditions. Through classifier ensemble, the FEI-FEO approach improved the overall LULC classification accuracy by more than 2% demonstrating the advantage of post-classification fusion in urban land-use mapping.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2023-02-22T00:00:00ZSpatiotemporal trend analysis of groundwater level changes, rainfall, and runoff generated over the Notwane Catchment in Botswana between 2009 and 2019.Tafila, O.Moalafhi, D. B.Ranganai, R. T.Moreri, K. K.https://hdl.handle.net/13049/7062023-08-07T09:18:00Z2023-03-11T00:00:00ZSpatiotemporal trend analysis of groundwater level changes, rainfall, and runoff generated over the Notwane Catchment in Botswana between 2009 and 2019.
Tafila, O.; Moalafhi, D. B.; Ranganai, R. T.; Moreri, K. K.
The semi-arid south-eastern part of Botswana has recently been experiencing severe water shortages, and the demand currently surpasses the supply in the greater Gaborone area. Within the context of increased stormwater runoff generated over the area and the potential for groundwater recharge, this study aims to investigate the relationships between groundwater depths and rainfall amounts and identify their patterns and significance or lack thereof over Botswana’s largest water demand centre that falls within the data-scarce Notwane catchment area (NCA). Trend analysis of monthly rainfall and groundwater levels between 2012 and 2019 and their homogeneity were undertaken using the Mann-Kendal test, followed by the application of the water balance method to estimate runoff over the catchment between 2009 and 2019. Runoff and precipitation between the two periods were compared using paired t-tests. Investigations revealed that rainfall increased insignificantly, whereas groundwater depth generally increased significantly. The homogeneity test revealed a general insignificant increase in rainfall over NCA. No catchment-wide conclusions were obtained regarding groundwater depth increases. Water-balance computed runoff in 2019 was an increase of 13.7% from that computed in 2009, despite the conservative 3% increase in rainfall between the two periods. Increase in runoff could even be higher if land use changes were incorporated. This study revealed that there is groundwater recharge over the catchment, particularly after heavy rainfall events. The results of this study offer insights for identifying groundwater recharge potential zones, which could inform decision making with regard to strategies for induced groundwater recharge to replenish groundwater resources that can conjunctively be used with surface water resources.
2023-03-11T00:00:00ZEstimation of Likely Impact of Climate Variability on Runoff Coefficients fromLimpopo Basin using Artificial Neural Nefwork (ANN)Moalafhi, Ditiro BensonParida, Bhagabat, PrasadDube, Opha, Paulinehttps://hdl.handle.net/13049/7052024-03-19T09:05:37Z2005-01-12T00:00:00ZEstimation of Likely Impact of Climate Variability on Runoff Coefficients fromLimpopo Basin using Artificial Neural Nefwork (ANN)
Moalafhi, Ditiro Benson; Parida, Bhagabat, Prasad; Dube, Opha, Pauline
Forecasting future response behaviour of a semi-arid catchment in terms of runoff coefficient being trivial, an attempt has been made io apply an Artificial Neural Network (ANN) model to-forecast the run off coefficients (ROC) for the Limpopo catchment system in Botswana. ROCs computed from l97l to lfl00' h-v the water balance technique have been used to develop the optimal network architecture with appropriate choice of the size of input vectors, number of hidden layers and number of neurons in the hidden layers, training algorithms and transfer functions for the network. Based on its performance in terms of reproducibility of the water balance run off coefficients, the network was used to forecast the runoff coefficients up to 20 1 6. For the decades between I 971 - I 980, 1981-1990 and 1991-2000 the average runoff coefficients were found to be 0.40 to 0.4.l and0.47 respectively' The average forecast runoff coefficients for the decade 2001-2010 and the period 2011 - 2016 were found to marginally increase to likely values of 0.48 and 0.50 respectively. This may therefore need an appropriate watershed management strategy to conserve soils and run off from the basin.
2005-01-12T00:00:00Z