RESUMEN
Globally, the degradation of soil, water, and forests has had a significant impact on both livelihoods and the environment. This issue is particularly severe in developing countries, including Ethiopia. Despite extensive efforts to implement conservation measures for soil, water, and forests in the highlands of Ethiopia, there has been a lack of thorough evaluation and documentation regarding the adoption of these practices by rural households. It is crucial to have scientific and up-to-date information at various spatial scales in order to effectively monitor existing practices, scale up successful initiatives, and promote sustainable regional development. Therefore, this paper focuses on analyzing the adoption of soil, water, and forest conservation activities by households in the upper Gelana watershed, South Wollo zone, Amhara Regional State of Ethiopia. The field data collection for this study took place from January to March 2022, from 150 rural household heads. Data analysis was carried out using SPSS software version 23. Descriptive statistics, Pearson bivariate correlation, and multinomial logistic regression were used. The survey findings revealed that 69 % of the respondents had implemented various soil, water, and forest conservation measures at different stages. The Pearson correlation results indicated a positive relationship between the adoption of soil, water and forest conservation practices. The multinomial logistic regression analysis has revealed that age, gender, access to credit, and access to extension services, significantly influenced the households' decision behaviour to adopt soil conservation practices. Age, access to extension service, and access to water resource were significant predictors of adoption of water conservation practices; whereas age, educational status, and access to extension service were significant predictors of adoption of forest conservation practices. This study underscores the significance of institutional factors in driving the adoption of technology in the research area. It further recommends policies that prioritize the dissemination of information on effective strategies, improvement of access to extension services, water resources, and credit facilities to promote sustainable watershed management. This study is exceptional in its innovative approach, which explores the convergence of these vital conservation domains within the distinct setting of the upper Gelana watershed. Studying the adoption of these technologies is crucial for informing policy-making and designing effective interventions that promote sustainable watershed practices. In this case, the Ministry of Agriculture, and development agents should scale up the adoption of these practices and take remedial actions for those not yet adopted.
RESUMEN
Land use and land cover change (LULCC) without appropriate management practices has been identified as a major factor contributing to land degradation, with significant impacts on ecosystem services and climate change and hence on human livelihoods. Therefore, up-to-date and accurate LULCC data and maps at different spatial scales are significant for regular monitoring of existing ecosystems, proper planning of natural resource management, and promotion of sustainable regional development. This study investigates the temporal and spatial dynamics of land use land cover (LULC) changes over 31 years (1990-2021) in the upper Tekeze River basin, Ethiopia, utilizing advanced remote sensing techniques such as Google Earth Engine (GEE) and the Random Forest (RF) algorithm. Landsat surface reflectance images from Landsat Thematic Mapper (TM) (1990, 2000, and 2010) and Landsat 8 Operational land imager (OLI) sensors (2021) were used. Besides, auxiliary data were utilized to improve the classification of LULC classes. LULC was classified using the Random Forest (RF) classification algorithm in the Google Earth Engine (GEE). The OpenLand R package was used to map the LULC transition and intensity of changes across the study period. Despite the complexity of the topographic and climatic features of the study area, the RF algorithm achieved high accuracy with 0.83 and 0.75 overall accuracy and Kappa values, respectively. The LULC change results from 1990 to 2021 showed that forest, bushland, shrubland, and bareland decreased by 12.2, 24.8, 1.2, and 15.4%, respectively. Bareland has changed to farmland, settlement, and dry riverbed and stream channels. Expansion of dry stream channels and sandy land surfaces has been observed from 1990 to 2021. Bushland has shown an increment by 17.2% from 1900 to 2010 but decreased by 19.5% from 2010 to 2021. Throughout the study period, water, farmland, dry stream channels and riverbeds, and urban settlements showed positive net gains of 484, 8.7, 82, and 26778.5%, respectively. However, forest, bush, shrub, and bareland experienced 12.17, 24.8, 1.2, and 15.37% losses. The observed changes showed the existing land degradation and the future vulnerability of the basin which would serve as an evidence to mitigate land degradation by avoiding the future conversion of forest, bushland, and shrubland to farmland, on the one hand, and by scaling up sustainable farmland management, and afforestation practices on degraded and vulnerable areas, on the other hand.
RESUMEN
Soil erosion is a major environmental problem in Ethiopia, reducing topsoil and agricultural land productivity. Soil loss estimation is a critical component of sustainable land management practices because it provides important information about soil erosion hotspot areas and prioritizes areas that require immediate management interventions. This study integrates the Revised Universal Soil Loss Equation (RUSLE) with Google Earth Engine (GEE) to estimate soil erosion rates and map soil erosion in the Upper Tekeze Basin, Northern Ethiopia. SoilGrids250 m, CHIRPS-V2, SRTM-V3, MERIT Hydrograph, NDVI from sentinel collections and land use land cover (LULC) data were accessed and processed in the GEE Platform. LULC was classified using Random forest (RF) classification algorithm in the GEE platform. Landsat surface reflectance images from Landsat 8 Operational land imager (OLI) sensors (2021) was used for LULC classification. Besides, different auxiliary data were utilized to improve the classification accuracy. Using the RUSLE-GEE framework, we analyzed the soil loss rate in different agroecologies and LULC types in the upper Tekeze basin in Waghimra zone. The results showed that the average soil loss rate in the Upper Tekeze basin is 25.5 t ha-1 yr-1. About 63 % of the basin is experiencing soil erosion above the maximum tolerable rate, which should be targeted for land management interventions. Specifically, 55 % of the study area, which is covered by unprotected shrubland is experiencing mean annual soil loss of 34.75 t ha-1 yr-1 indicating the need for immediate soil conservation intervention. The study also revealed evidence that this high mean soil loss rate of the basin can be reduced to a tolerable rate by implementing integrative watershed management and exclosures. Furthermore, this study demonstrated that GEE could be a good source of datasets and a computing platform for RUSLE, in particular for data scarce semi-arid and arid environments. The results from this study are reliable for decision-making for rapid soil erosion assessment and intervention prioritization.
RESUMEN
The sustainable land management program (SLMP) of Ethiopia aims to improve livelihoods and create resilient communities and landscape to climate change. Soil organic carbon (SOC) sequestration is one of the key co-benefits of the SLMP. The objective of this study was to estimate the spatial dynamics of SOC in 2010 and 2018 (before and after SLMP) and identify the SOC sequestration hotspots at landscape scale in four selected SLMP watersheds in the Ethiopian highlands. The specific objectives were to: 1) comparatively evaluate SOC sequestration estimation model building strategies using either a single watershed, a combined dataset from all watersheds, and leave-one-watershed-out using Random Forest (RF) model; 2) map SOC stock of 2010 and 2018 to estimate amount of SOC sequestration and potential; 3) evaluate the impacts of SLM practices on SOC in four SLMP watersheds. A total of 397 auger composite samples from the topsoil (0-20 cm depth) were collected in 2010, and the same number of samples were collected from the same locations in 2018. We used simple statistics to assess the SOC change between the two periods, and machine learning models to predict SOC stock spatially. The study showed that statistically significant variation (P < 0.05) of SOC was observed between the two years in two watersheds (Gafera and Adi Tsegora) whereas the differences were not significant in the other two watersheds (Yesir and Azugashuba). Comparative analysis of model-setups shows that a combined dataset from all the four watersheds to train and test RF outperform the other two strategies (a single watershed alone and a leave-one-watershed-out to train and test RF) during the testing dataset. Thus, this approach was used to predict SOC stock before (2010) and after (2018) land management interventions and to derive the SOC sequestration maps. We estimated the sequestrated, achievable and target level of SOC stock spatially in the four watersheds. We assessed the impact of SLM practices, specifically bunds, terraces, biological and various forms of tillage practices on SOC using partial dependency algorithms of prediction models. No tillage (NT) increased SOC in all watersheds. The combination of physical and biological interventions ("bunds + vegetations" or "terraces + vegetations") resulted in the highest SOC stock, followed by the biological intervention. The achievable SOC stock analysis showed that further SOC stock sequestration of up to 13.7 Mg C ha--1 may be possible in the Adi Tsegora, 15.8 Mg C ha-1 in Gafera, 33.2 Mg C ha-1 in Azuga suba and 34.7 Mg C ha-1 in Yesir watersheds.