Reanalysis products are often taken as an alternative solution to observational weather and climate data due to availability and accessibility problems, particularly in data-sparse regions such as Africa. Proper evaluation of their strengths and weaknesses, however, should not be overlooked. The aim of this study was to evaluate the performance of ERA5 reanalysis and to document the progress made compared to ERA-interim for the fields of near-surface temperature and precipitation over Africa. Results show that in ERA5 the climatological biases in temperature and precipitation are clearly reduced and the representation of inter-annual variability is improved over most of Africa. However, both reanalysis products performed less well in terms of capturing the observed long-term trends, despite a slightly better performance of ERA5 over ERA-interim. Further regional analysis over East Africa shows that the representation of the annual cycle of precipitation is substantially improved in ERA5 by reducing the wet bias during the rainy season. The spatial distribution of precipitation during extreme years is also better represented in ERA5. While ERA5 has improved much in comparison to its predecessor, there is still demand for improved products with even higher resolution and accuracy to satisfy impact-based studies, such as in agriculture and water resources.

This study investigated the trends and variability of seasonal and annual rainfall and temperature data over southern Ethiopia using time series analysis for the period 1983–2016. Standard Anomaly Index (SAI), Coefficient of Variation (CV), Precipitations Concentration Index (PCI), and Standard Precipitation Index (SPI) were used to examine rainfall variability and develop drought indices over southern Ethiopia. Temporal changes of rainfall trends over the study period were detected using Mann Kendall (MK) trend test and Sen’s slope estimator. The results showed that the region experienced considerable rainfall variability and change that resulted in extended periods of drought and flood events within the study period. Results from SAI and SPI indicated an inter-annual rainfall variability with the proportions of years with below and above normal rainfall being estimated at 56% and 44% respectively. Results from the Mann Kendall trend test indicated an increasing trend of annual rainfall, Kiremt (summer) and Bega (dry) seasons whereas the Belg (spring) season rainfall showed a significant decreasing trend (p < 0.05). The annual rate of change for mean, maximum and minimum temperatures was found to be 0.042 °C, 0.027 °C, and 0.056 °C respectively. The findings from this study can be used by decision-makers in taking appropriate measures and interventions to avert the risks posed by changes in rainfall and temperature variability including extremes in order to enhance community adaptation and mitigation strategies in southern Ethiopia.

Climate change threatens African countries’ economic development and affects agriculture and food security. Ethiopia is especially vulnerable to the negative effects of climate change because its economy is dependent on climate-sensitive livelihoods that have limited potential for adaptation. Emerging evidence indicates that climate-smart agriculture (CSA) can help smallholder farmers adapt to climate change and increase agricultural productivity, thereby enhancing household income and food security. In the study area, different CSA practices have been adopted to mitigate the negative effects of climate change and improve agricultural productivity, income, and food security. Therefore, this study examines the impact of CSA practices on household income and food security in southern Ethiopia. A total of 385 households were selected using multistage sampling. Primary and secondary data were used, and propensity score matching with different types of matching algorithms, such as nearest neighbor, kernel, and radius matching, was employed to quantify the conditional impacts of CSA intervention on farm income and food security. In comparison with non adopters farmers that have adopted CSA practices had a higher food consumption score between 6.27 and 8.15, which was statistically significant at the 1% level. Overall, 34.55% of interviewed households had acceptable food consumption scores, 44.68% had borderline, and 20.77% had poor food consumption scores. Furthermore, households that adopted CSA practices had a 20.30% higher average annual farm income per hectare than non-adopters. The study suggests that effective extension services, accurate climate information, and sound policy support are required to promote and scale up CSA measures in the study area to improve farmers’ adaptive capacity, farm income, and food security.

This study investigated the impacts of climate change on the hydrology of the Upper Awash Basin, Ethiopia. A soil and water assessment tool (SWAT) model was calibrated and validated against observed streamflow using SWAT CUP. The Mann–Kendall trend test (MK) was used to assess climate trends. Meteorological drought (SPEI) and hydrological drought (SDI) were also investigated. Based on the ensemble mean of five global climate models (GCMs), projected increases in mean annual maximum temperature over the period 2015–2100 (compared with a 1983–2014 baseline) range from 1.16 to 1.73 °C, while increases in minimum temperature range between 0.79 and 2.53 °C. Increases in mean annual precipitation range from 1.8% at Addis Ababa to 45.5% over the Hombole area. High streamflow (Q₅) declines at all stations except Ginchi. Low flows (Q₉₀) also decline with Q₉₀ equaling 0 m³ s⁻¹ (i.e., 100% reduction) at some gauging stations (Akaki and Hombole) for individual GCMs. The SPEI confirmed a significant drought trend in the past, while the frequency and severity of drought will increase in the future. The basin experienced conditions that varied from modest dry periods to a very severe hydrological drought between 1986 and 2005. The projected SDI ranges from modestly dry to modestly wet conditions. Climate change in the basin would enhance seasonal variations in hydrological conditions. Both precipitation and streamflow will decline in the wet seasons and increase in the dry seasons. These changes are likely to have an impact on agricultural activities and other human demands for water resources throughout the basin and will require the implementation of appropriate mitigation measures.

Climate-smart agriculture (CSA) responds in order to sustain agriculture under a changing environment, and is a major priority in the development sphere. However, to achieve impact at scale, CSA innovations must address agricultural systems’ context-specific and multi-dimensional nature and be purveyed through feasible scaling processes. Unfortunately, knowledge on the scaling of CSA innovations under smallholder farming systems and in the context of developing countries remains scant. Understanding scaling processes is essential to the design of a sustainable scaling strategy. This study aimed to draw lessons on scaling from 25 cases of scaling CSA, and related projects in Ethiopia, Kenya, Uganda, and Tanzania implemented by public institutions, local and international research organisations, Non-Govermental Orginsations(NGOs), and community-based organisations. Generally, scaling follows a linear pathway comprising technology testing and scaling. Most cases promoted technologies and models geared towards climate change adaptation in crop-based value chains, and only a few cases incorporated mitigation measures. Efforts to engage the private sector involved building business models as a potential scaling pathway. The cases were very strong on capacity building and institutionalisation from local, national, and even regional levels. However, four critical areas of concern about the sustainability of scaling emerged from the study: (i) There is little understanding and capture of the dynamics of smallholder farming systems in scaling strategies; (ii) climate data, projections, and impact models are rarely applied to support the decision of scaling; (iii) considerations for the biophysical and spatial-temporal impacts and trade-offs analysis in scaling is minimal and just starting to emerge; and (iv) there are still challenges effecting systemic change to enable sustainable scaling. In response to these concerns, we propose investment in understanding and considering the dynamics of the smallholder farming system and how it affects adoption, and subsequently scaling. Programme design should incorporate climate change scenarios. Scaling programmes can maximise synergies and leverage resources by adopting a robust partnerships model. Furthermore, understanding the spatio-temporal impact of scaling CSA on ecological functioning deserves more attention. Lastly, scaling takes time, which needs to be factored into the design of programmes.

Ethiopia has the largest livestock population in Africa with 35 million tropical livestock units. The livestock system relies on natural open grazing which is affected by frequent droughts. However, little research exists that studies the suitability of the biophysical environment for fodder production and the risks due to climate change. The main objectives of the study are to evaluate the potential effects of climate change on land suitability for alfalfa production in Ethiopia and to assess the extent of irrigation requirements for alfalfa growing under the adverse climate change projections. The impact of climate change on land suitability for alfalfa was evaluated using projected changes in rainfall and temperature based on three global circulation models (CCSM4, HadGEM2-AO, and MIROC5). A multi-criteria evaluation in GIS that uses biophysical, climatic and topography factors was applied to identify the suitable land. The highly suitable area under current climate scenarios covered ~472,000 km², while moderately suitable and marginally suitable covered ~397,000 km² and ~16,200 km², respectively. The projected climate alters the suitable land for fodder production across Ethiopia. Expansion of suitable land occurred in the highlands where climate scenarios predict an increase in temperature and precipitation. Dryland regions showed a rainfall deficit for the three model projections. The research provides guidelines for growing alfalfa in Ethiopia considering ecological and climatic variability.