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In recent years, global attention to carbon emissions has increased, becoming one of the main drivers of global climate change. Accurate prediction of carbon emission trends in small and medium-sized countries and scientific regulation of carbon emissions can provide theoretical support and policy references for the effective and rational use of energy and the promotion of the coordinated development of energy, environment, and economy. This paper establishes a grey prediction model using the classical Logistic mathematical model in a determined environment to investigate the carbon emission system. At the same time, we use the basic principle of fractional-order accumulation to establish a grey prediction model with fractional-order Logistic and obtain the parameter estimation and time-response equation of the new model by solving the model through the theory related to fractional-order operators. The particle swarm optimization algorithm is used to complete the optimization process of the order of the fractional order grey prediction model and obtain the optimal model order. Then, the new model is applied to predict carbon emissions in five medium-emission countries: Ethiopia, Djibouti, Ghana, Belgium, and Austria. The new model shows better advantages in the validity analysis process, and the simulation results indicate that the new model proposed in this paper has stronger stability and better simulation and prediction accuracy than other comparative models, proving the model's validity. Finally, the model is used to forecast the carbon emissions of these five countries for the five years of 2021–2025, and the results are analyzed, and relevant policy recommendations are made. [ABSTRACT FROM AUTHOR]

In Ethiopia, large-scale agricultural investment (LAI) is promoted to foster adaptation to climate change among smallholder farmers by improving farming conditions and productivity. However, little has been known about this condition so far. Therefore, the aim of this paper is to study the implications of LAI for the capacity for climate change adaptation among smallholder farmers, based on a study conducted in Merti district in the Central Rift Valley of Ethiopia. An asset-based approach was used to measure the adaptive capacity of smallholders, via data collected from a household survey, undertaken among smallholder households who had, or had not, benefited, from LAI, and supported by key informant interviews and focus group discussions. Descriptive and inferential statistics and qualitative thematic analysis methods were used to analyze the data. The study found that the adaptive capacity of smallholder households was improved under the influence of LAI. LAI motivated and prompted farmers to use technologies that fostered climate change adaption, including the use of irrigation, improved crop varieties, chemical fertilizer, and engagement in alternative sources of income, which improved their food security status. But this technology and knowledge transfer mainly occurred through observation (learning by doing), not by capacity-building. However, both group of smallholders (within and without contact with the LAI) received similar benefits through government rural extension services. Therefore, the paper concludes that scaling up these technologies and alternative income sources could enhance the adaptive capacity of smallholders, but that LAIs should contribute actual technical and financial support as part of benefit-sharing or corporate responsibility. [ABSTRACT FROM AUTHOR]

We develop a new systems modeling tool that integrates knowledge from hydrology, agriculture, and economics to understand the effect of small-scale irrigation on food security and groundwater sustainability in Ethiopia. Irrigation is an effective tool to mitigate climate impacts and improve agricultural yields. Small-scale irrigation, such as decentralized groundwater irrigation, is well suited for developing countries where small-holder farming communities are widely dispersed and can only afford small infrastructure investment. We study the underlying interdependencies between food and water systems in Ethiopia, where small-holder agriculture is the foundation of the nation's economy and climate variability has led to great challenges to its food security. Our coupled market and crop model with groundwater module captures the interdependencies of climate, water availability, irrigation, crop yield, farmland allocation, crop production, transport, and consumption based on a system approach across multiple spatial scales. We study the implication of small-scale irrigation to Ethiopia's food security and water resource conditions as a "what-if" question by comparing an irrigation scenario to the calibrated baseline in 2015, a year of significant drought and crop failure over a large portion of Ethiopia. Our model offers fresh insights into geographic disparities in outcomes that are driven by baseline climate variability, soil fertility, and market conditions. In general, we find that small-scale irrigation can potentially improve food security through increases in food consumption, but it requires policy support to direct the increases of production to domestic consumption while maintaining a sustainable groundwater condition. By using Ethiopia as an example, we show the strength of our model to study how water infrastructure resources support critical functions and service in water and food systems. [ABSTRACT FROM AUTHOR]

Sesame is a major annual oil crop that is grown practically everywhere in tropical and subtropical Asia, as well as Africa, for its very nutritious and tasty seeds. Rising temperatures, droughts, floods, desertification, and weather all have a significant impact on agricultural production, particularly in developing countries like Ethiopia. Therefore, the main objective of this study is to examine the influence of climate change on the sesame yield in North Gondar, North Ethiopia, by using the autoregressive distributed Lag (ARDL) time series model. This study employed climate data from the Bahirdar Agrometeorological Center and secondary data on sesame production from the Ethiopian Statistical Service, spanning 36 years, from 1987 to 2023. Autoregressive Distributed LAG (ARDL) includes diagnostic tests for both short- and long-term autoregressive models. The results for the long-run and short-run elastic coefficients show a significant positive association between temperatures and sesame yield. Sesame yield and rainfall have a significant negative long-run and short-run relationship in North Gondar, North Ethiopia. ARDL results confirm that temperature and rainfall have significant effects on sesame productivity. Temperature had a considerable favorable effect on sesamen production, but rainfall had a negative effect in North Gondar, Ethiopia. Based on the evidence acquired from our study, we made several policy recommendations and suggestions to government officials, policymakers, new technologies, researchers, policy development planners, and other stakeholders in order to develop or implement new technology to halt its production and direct adaptation measures in light of the certainty of global warming and the characteristics of climate-dependent agricultural production. [ABSTRACT FROM AUTHOR]

Background: Aloeankoberensis M.G. Gilbert & Sebsebe and A.debrana Christian are Ethiopian endemic species currently classified as endangered and least concern, respectively under International Union for Conservation of Nature (IUCN) categories. Recent studies indicate that climate change is anticipated to significantly influence the distribution of plant species. Therefore, this study aimed to model the distribution of A.ankoberensis and A.debrana under different climate change scenarios in the North Shewa Zone, Amhara National Regional State of Ethiopia. Thirty-six and 397 georeferenced presence points for A.ankoberensis and A.debrana, respectively, and 12 environmental variables were used to simulate their current and future distributions. The ensemble model approach was used to examine the current and future (2050 and 2070) climatic suitability for both species under three shared socio-economic pathway (SSP) climate scenarios (SSP 2.6, 4.5 and 8.5). Results: The performance of ensemble model was excellent for A.ankoberensis with score of area under curve (AUC) 0.96 and true skill statistics (TSS) 0.88, and good for A.debrana with score of AUC 0.87 and TSS 0.63. The main variables that affected the species' distributions were mean diurnal range of temperature, annual precipitation, and elevation. According to the model, under the current climate conditions, 98.32%, 1.01%, 0.52%, and 0.15% were not suitable, lowly, moderately, and highly suitable areas, respectively for A.ankoberensis, and 63.89%, 23.35%, 12.54%, and 0.21% were not suitable, lowly, moderately and highly suitable areas, respectively for A.debrana. Under future climate scenarios, suitable habitats of these species could shrink. In addition, under all climate change scenarios, it is anticipated that highly suitable areas for both species and moderately suitable areas for A.ankoberensis will be lost completely in the future unless crucial interventions are done on time. Conclusions: The results indicate that the future may witness a decline in suitable habitat for A.ankoberensis and A.debrana, which leads to increasing threat of extinction. Therefore, it is crucial to develop a conservation plan and enhance climate change adaptation strategies to mitigate the loss of suitable habitats for these highland and sub-Afroalpine endemic Aloe species. [ABSTRACT FROM AUTHOR]

This study aimed to identify the status, determining factors, and challenges in adopting climate smart livestock production practices by farmers. Three-staged sampling techniques were used to select the research sites and 233 sample farmer household respondents. Data were collected mainly using a pre-tested structured questionnaire. Key informant interviews and focus group discussions were also conducted to complement the household survey data. Descriptive statistics and an ordered logistic regression model were applied to analyze the quantitative data. The result revealed that the most adopted practices were composting (85.41%) and manure management (70.39%) while the least adopted technologies were biogas generation (3.86%) and rotation grazing (22.32%). The adoption status of the sampled farmers was also categorized into low (19.74%), medium (67.81%), and high adopter (12.45%). The high cost of improved breed, use of manure for fuel, free grazing, lack of information and awareness were the major constraints to adopting the climate smart livestock production technologies. The result also revealed that education, grazing land, total livestock holding, and extension agent contact contributed significantly and positively to the adoption of smart livestock production technology, while the distance from the water source had an insignificant and negative effect on the adoption status of climate smart livestock production practices. The study suggests the relevance of the cooperation of stakeholders and strengthening extension services for the maximum benefits of climate smart livestock production. [ABSTRACT FROM AUTHOR]

We analyzed hydrological responses to changing climate and land use/land cover (LULC) for the past (1985–2020) and future (2021–2080) in the Chemoga watershed of the Upper Blue Nile Basin. The watershed comprises four agroecological environments: Moist Kolla, Moist Weyna Dega, Moist Dega, and Wet Wurch. Past and projected LULC changes under business-as-usual (BAU) and land conservation (LC) scenarios were utilized. Climate projections from 2021 to 2080, under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), were downscaled from Global Climate Models. Utilizing the Soil and Water Analysis Tool, we assessed impacts on mean annual surface runoff (SR) and evapotranspiration (ET). Maximum and minimum temperatures increased significantly in the past and future climate scenarios, with a significant rainfall increase observed under SSP5-8.5. Historical trends revealed a 16.6% increase in SR and 7% in ET from 1983–2002 to 2003–2020. Under BAU LULC with the SSP2-4.5 (SSP5-8.5) climate scenario, SR increased by 24% (26.1%) and ET by 3.1% (4.4%) from 2003–2020 to 2021–2050, followed by a subsequent SR rise of 13.7% (14.0%) and ET increase of 6.0% (5.7%) from 2021–2050 to 2051–2080. Conversely, the LC LULC with SSP2-4.5 (SSP5-8.5) resulted in a 5.3% (4.2%) SR decrease and ET increase of 9.7% (11.3%) from 2003–2020 to 2021–2050 and a further SR decrease of 1% (0.7%) and 6.1% (6.9%) ET increase from 2021–2050 to 2051–2080. The Moist Kolla agroecology experienced the highest SR increase due to vegetation clearances for commercial farming. Meanwhile, the LC scenario indicated substantial decreases in SR and marginal increases in ET in the Moist Weyna Dega agroecology due to forest restoration on steep slopes. Overall, SR showed greater sensitivity to LULC changes, while ET was more responsive to climate changes. The results emphasize considering diverse agroecological contexts for effective water resource management under changing climate and LULC scenarios. [ABSTRACT FROM AUTHOR]

There is an urgent need for interventions in addressing the rapid and disproportionate impact of antimicrobial resistance (AMR) and climate change (CC) on low- and middle-income countries. Within this context, it is important to understand indigenous knowledge in rural communities, which are highly affected. This study examined knowledge, attitude, and practices (KAP) regarding AMR and CC in the Adadle district, Somali region, Ethiopia, utilizing mixed methods, including 362 surveys and 12 focus group discussions among rural communities. Findings showed that 39% and 63% of participants were familiar with AMR and CC, respectively. Of those surveyed, 57% attributed AMR to inappropriate antimicrobial use in animals and humans, while CC was often associated with Allah/God. Multivariable analysis indicated that males exhibited superior knowledge and a positive attitude towards AMR and CC. Additionally, individuals aged 26–35 and 36–45 years showed heightened awareness of AMR and CC, respectively. Moreover, participants who were government employees, pastoralists, and business owners showed better knowledge on CC compared to family caretaker. Religious education and households with more than six members were linked to lower AMR knowledge. This study underlines a greater awareness of CC than AMR and highlights gender-based disparities, recommending integrated educational AMR programs targeting different demographics through a One Health lens, actively involving females, and incorporating local beliefs and practices. [ABSTRACT FROM AUTHOR]

8Background: Agroforestry is a kind of land use system that has been practiced since long in many parts of the world. Even if agroforestry is contributing a lot for the farmers, no study has been conducted which acknowledges the socio-economic importance of agroforestry practices in Harari regional state. Therefore, this may create a problem to expand and adapt the practices to another area for the cope and mitigate climate changes and achieve food security. Methods: The study used descriptive statistics to analyze the data. It ascertained the role of agroforestry practices in Harari regional state. A sample of 350 farmers was interviewed and selected using two-stage random sampling techniques. Result: The results showed that 100% of the sample respondents practiced agroforestry as a land use for income source, shade, soil improvement, fodder, firewood, construction material, medicinal purposes etc. The components of the agroforestry system adopted by the local community were pulse, root, cereal crops and vegetables. Khat, Eritia, Acacia, Cordial, Zeziphus were the fodder species used. 78.57% of the respondents used a stallfeeding technique to feed their animals. The respondents obtained annual income of 32,199.16 Ethiopian birrs on average. In general, the agroforestry system helps the local communities to diversify their income, fulfill animal feed and cope with and mitigate climate change. To sustain the local people with the climate change, decision-makers and researchers should give more attention to the preferred agroforestry species. [ABSTRACT FROM AUTHOR]

The combined effects of climate and land cover changes influence hydrologic responses of a basin in an offsetting or synergistic manner depending on the nature and severity of the changes. As such, estimating the impacts of these environmental changes on hydrologic responses is crucial for planning water resources management. However, such a comprehensive study is missing in most basins of Ethiopia, particularly in the Dhidhessa River basin (DRB). The aim of this study is, therefore, to quantify the combined and separate impacts of land cover and climate changes on multiple hydrologic variables for the DRB. The Calibrated Soil and Water Analysis Tool (SWAT) model and statistical techniques were integrated for this study. Quantifying the separate and combined effects of land cover and climate changes on multiple hydrologic responses at a local scale, and determining the relative contribution of the changes are the strength of this study. The result indicated better performance of the SWAT model in simulating water balance components for the DRB. Significant changes in hydrologic responses were observed in response to the land cover changes, and the increasing trends of temperature and rainfall observed during the last 30 years in DRB. The result showed increasing actual evapotranspiration (AET), streamflow, and surface runoff while decreasing groundwater recharge. Surface runoff was more affected by land cover change than by climate change, whereas streamflow and AET were more affected by climate change than land cover change during the last 30 years in the basin. The combined effects of land cover and climate changes played an offsetting effect on groundwater recharge and AET. Overall, the simulated hydrologic responses will have negative effects on water resource availability and agricultural production in the basin and the surroundings. Therefore, implementing integrated watershed management strategies, such as soil and water conservation and afforestation, could minimize the negative impact. [ABSTRACT FROM AUTHOR]

Extreme hydrological events, like floods and droughts, exert considerable effects on both human and natural systems. The frequency, intensity, and duration of these events are expected to change due to climate change, posing challenges for water resource management and adaptation. In this study, the Soil and Water Assessment Tool plus (SWAT +) model was calibrated and validated to simulate flow under future shared socioeconomic pathway (SSP2-4.5 and SSP5-8.5) scenarios in the Baro River Basin with R2 values of 0.88 and 0.83, NSE of 0.83 and 0.74, and PBIAS of 0.39 and 8.87 during calibration and validation. Six bias-corrected CMIP6 Global Climate Models (GCM) were selected and utilized to investigate the effects of climate change on the magnitude and timing of hydrological extremes. All climate model simulation results suggest a general increase in streamflow magnitude for both emission scenarios (SSP2-4.5 and SSP5-8.5). The multi-model ensemble projections show yearly flow increases of 4.8% and 12.4% during the mid-term (MT) (2041–2070) and long-term (LT) (2071–2100) periods under SSP2-4.5, and 15.7% and 35.6% under SSP5-8.5, respectively. Additionally, the analysis revealed significant shifts in the projected annual 1 day, 3 day, 7 day, and 30 day maximum flows, whereas the annual 3 day and 7 day minimum flow fluctuations do not present a distinct trend in the future scenario compared to the baseline (1985–2014). The study also evaluated the timing of hydrological extremes, focusing on low and peak flow events, utilizing the annual 7 day maximum and minimum flow for this analysis. An earlier occurrence was noted for both peak and low flow in the SSP2-4.5 scenario, while a later occurrence was observed in the SSP5-8.5 scenario compared to the baseline. In conclusion, this study showed the significant effect of climate change on river hydrology and extreme flow events, highlighting their importance for informed water management and sustainable planning. [ABSTRACT FROM AUTHOR]

Climate change poses a threat to water security where both current and future generations are concerned, with its accompanying impacts set to be greater in low‐ and middle‐income countries (LMICs). As a result, questions pertaining to climate change adaption in LMICs are receiving increased attention from academics and policymakers alike. It is broadly accepted that top‐down approaches to developing resilience to climate change challenges have been shown to be limited and that concerted efforts need to be made to engage local communities in advancing adaptive strategies. Based on the above, we make two main arguments: (1) while there has been a shift towards acknowledging the importance of community‐driven data in generating a broader and deeper understanding of climate change, far better use could be made of local knowledge and (2) efforts at community‐based solutions to problems of resilience are currently limited by issues of capacity, specifically linked to the need for further education and training, and improved representation with respect to gender, class and caste (as well as financial support). To illustrate these arguments, we present evidence provided by rural communities located in two countries affected heavily by climate change: Ethiopia and Nepal. Local communities in climate change‐affected low‐ and middle‐income countries are on the 'front line' with respect to present and future challenges associated with rising temperatures. These communities, and the governance structures put in place by them to manage climate‐affected resources like water, are critical sources of information for academics and policymakers, offering as they do more complete understanding of how climate change is unfolding, and being mitigated, 'on the ground'. [ABSTRACT FROM AUTHOR]

Climate change has an impact on water resources. Estimations of the variations in water balance under climate change variables are essential for managing and developing the water resource of a catchment. The current investigation evaluated the magnitude of the change in the water balance component of the Akaki catchment, Ethiopia, using the semi-distributed hydrological model, the Soil and Water Assessment Tool (SWAT), with the integration of the Coordinated Regional Downscaling Experiment of Africa under RCP4.5 and 8.5. The SWAT model was developed using spatial and temporal data; it was calibrated (1991–2001) and validated (2002–2004) using SWAT-CUP. The statistical monthly SWAT model performance values of the NSE, PBIAS (%), and R2 showed good agreement between calibration and validation. On an annual basis, projected rainfall is expected to increase by 14.96%, 4.13%, 8.39%, and 10.39% in the 2040s under RCP4.5 and 8.5 and in the 2060s under RCP4.5 and 8.5, respectively, with inconsistent change on a monthly projections basis for each scenario. The projected monthly and yearly temperatures are expected to increase under different climate change scenarios. Annual evapotranspiration and potential evapotranspiration increased under both RCPs, whereas surface runoff, lateral flow, and water yield declined under the climate scenarios of each RCP. Monthly projected water yield showed a non-uniform change in the first 30 years and in the second years under the RCP4.5 and RCP8.5 scenarios. These results show that the catchment is highly vulnerable to hydrological and agricultural drought due to water availability. These research findings provide valuable evidence on the role of climate change in water balance, which will help decision makers to achieve better water resource management. [ABSTRACT FROM AUTHOR]