Climate variability and change in Ethiopia : exploring impacts and adaptation options for cereal production

Key words: Climate change, Adaptation, Crop modelling, Uncertainty, Maize (Zea mays), Central Rift Valley. Smallholder farmers in Ethiopia have been facing severe climate related hazards, in particular highly variable rainfall and severe droughts that negativelyaffect their livelihoods.Anticipated climate change is expected to aggravate some of the existing challenges and impose new risks beyond the range of current experiences. This study aimed at understanding current climate variability and future climate change and associated impacts, and providing insights on current climate risk management strategies and future adaptation options for adapting agriculture, in particular maize production.The study was conducted in the Central Rift Valley, which represents major cereal-based farming systems of the semi-arid environments of Ethiopia. A second case study area, Kobo Valley was also used for additional analysis in part of the study. Empirical statistical analyses, field survey methods, and a systems analytical approach, using field experimental data in combination with crop-climate simulation modelling were used to achieve the objectives of the study.Crop growth simulation modelling was carried out using two well-accepted crop models, which is an innovative feature of the methodology used in this thesis. The analysis revealed that rainfall exhibited high inter-annual variability (coefficient of variation 15-40%) during the period 1977-2007 in the CRV. The mean annual temperature significantly increased with 0.12 to 0.54 oC per decade during 1977-2007. Projections for future climate suggested that annual rainfall will change by -40 to +10% and the annual temperature is expected to increase in the range of 1.4 to 4.1 oC by 2080s. Simulated water-limited yields are characterized by high inter-annual variability (coefficient of variation 36%) and about 60% of this variability is explained by the variation in growing season rainfall. Actual yields of maize in the CRV are only 28-30% of the simulated water-limited yield. Analysis of climate change scenarios showed that maize yield will decrease on average by 20% in the 2050s relative to a baseline climate due to an increase in temperature and a decrease in growing season rainfall. The negative impact of climate change is very likely, however, the extent of the negative impact has some uncertainties ranging from -2 to -29% depending on crop model and climate change scenario. From the selection of models used, it was concluded that General Circulation Models to assess future climate are the most important source of uncertainty in this study. In response to perceived impacts, farm households are implementing various coping and adaptation strategies. The most important current adaptive strategies include crop selection, adjusting planting time, in situ moisture conservation and income diversification. Lack of affordable technologies, high costs for agricultural inputs, lack of reliable information on weather forecasts, and insecure land tenure systems were identified as limiting factors of farmers’ adaptive capacity. The crop model-based evaluation of future adaptation options indicates that increasing nitrogen fertilization, use of irrigation and changes in planting dates can compensate for some of the negative impacts of climate change on maize production. Developing more heat tolerant and high yielding new cultivars is critical to sustain crop production under future climate change. It was clear from the study that enabling strategies targeted at agricultural inputs, credit supply, market access and strengthening of local knowledge and information services need to become an integral part of government policies to assist farmers in adapting to the impacts of current climate variability and future climate change

Climate-induced yield variability and yield gaps of maize (Zea mays L.) in the Central Rift Valley of Ethiopia

There is a high demand for quantitative information on impacts of climate on crop yields, yield gaps and their variability in Ethiopia, yet, quantitative studies that include an indication of uncertainties in the estimates are rare. A multi-model crop growth simulation approach using the two crop models, i.e. Decision Support System for Agro-Technology (DSSAT) and WOrld FOod STudies (WOFOST) was applied to characterize climate-induced variability and yield gaps of maize. The models were calibrated and evaluated with experimental data from the Central Rift Valley (CRV) in Ethiopia. Subsequently, a simulation experiment was carried out with an early maturing (Melkassa1) and a late maturing (BH540) cultivar using historical weather data (1984-2009) of three locations in the CRV. Yield gaps were computed as differences among simulated water-limited yield, on-farm trial yields and average actual farmers' yields. The simulation experiment revealed that the potential yield (average across three sites and 1984-2009) is 8.2-9.2 and 6.8-7.1 Mg/ha for the late maturing and early maturing cultivars, respectively; ranges indicate mean differences between the two models. The simulated water-limited yield (averaged across three sites and 1984-2009) is 7.2-7.9 Mg/ha for the late maturing and 6.1-6.7 Mg/ha for the early maturing cultivar. The water-limited yield shows high inter-annual variability (CV 36%) and about 60% of this variability in yield is explained by the variation in growing season rainfall. The gap between average farmers yield and simulated water-limited yield ranges from 4.7 to 6.0 Mg/ha. The average farmers' yields were 2.0-2.3 Mg/ha, which is about 1.1-3.1 Mg/ha lower than on-farm trial yields. In relative terms, average farmers' yields are 28-30% of the water-limited yield and 44-65% of on-farm trial yields. Analysis of yield gaps for different number of years to drive average yields indicates that yield gap estimation on the basis of few years may result in misleading conclusions. Approximately ten years of data are required to be able to estimate yield gaps for the Central Rift Valley in a robust manner. Existing yield gaps indicate that there is scope for significantly increasing maize yield in the CRV and other, similar agro-ecological zones in Africa, through improved crop and climate risk management strategies. As crop models differ in detail of describing the complex, dynamic processes of crop growth, water use and soil water balances, the multi-model approach provides information on the uncertainty in simulating crop-climate interactions. (C) 2014 Elsevier B.V. All rights reserved

Climate-induced yield variability and yield gaps of maize (Zea mays L.) in the Central Rift Valley of Ethiopia

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Climate variability and change in the Central Rift Valley of Ethiopia: challenges for rainfed crop production

Ethiopia is one of the countries most vulnerable to the impacts of climate variability and change on agriculture. The present study aims to understand and characterize agro-climatic variability and changes and associated risks with respect to implications for rainfed crop production in the Central Rift Valley (CRV). Temporal variability and extreme values of selected rainfall and temperature indices were analysed and trends were evaluated using Sen's slope estimator and Mann–Kendall trend test methods. Projected future changes in rainfall and temperature for the 2080s relative to the 1971–90 baseline period were determined based on four General Circulation Models (GCMs) and two emission scenarios (SRES, A2 and B1). The analysis for current climate showed that in the short rainy season (March–May), total mean rainfall varies spatially from 178 to 358 mm with a coefficient of variation (CV) of 32–50%. In the main (long) rainy season (June–September), total mean rainfall ranges between 420 and 680 mm with a CV of 15–40%. During the period 1977–2007, total rainfall decreased but not significantly. Also, there was a decrease in the number of rainy days associated with an increase (statistically not significant) in the intensity per rainfall event for the main rainy season, which can have implications for soil and nutrient losses through erosion and run-off. The reduced number of rainy days increased the length of intermediate dry spells by 0·8 days per decade, leading to crop moisture stress during the growing season. There was also a large inter-annual variability in the length of growing season, ranging from 76 to 239 days. The mean annual temperature exhibited a significant warming trend of 0·12–0·54 °C per decade. Projections from GCMs suggest that future annual rainfall will change by +10 to -40% by 2080. Rainfall will increase during November–December (outside the growing season), but will decline during the growing seasons. Also, the length of the growing season is expected to be reduced by 12–35%. The annual mean temperature is expected to increase in the range of 1·4–4·1 °C by 2080. The past and future climate trends, especially in terms of rainfall and its variability, pose major risks to rainfed agriculture. Specific adaptation strategies are needed for the CRV to cope with the risks, sustain farming and improve food security

Climate variability and change in the Central Rift Valley of Ethiopia: challenges for rainfed crop production

Ethiopia is one of the countries most vulnerable to the impacts of climate variability and change on agriculture. The present study aims to understand and characterize agro-climatic variability and changes and associated risks with respect to implications for rainfed crop production in the Central Rift Valley (CRV). Temporal variability and extreme values of selected rainfall and temperature indices were analysed and trends were evaluated using Sen's slope estimator and Mann–Kendall trend test methods. Projected future changes in rainfall and temperature for the 2080s relative to the 1971–90 baseline period were determined based on four General Circulation Models (GCMs) and two emission scenarios (SRES, A2 and B1). The analysis for current climate showed that in the short rainy season (March–May), total mean rainfall varies spatially from 178 to 358 mm with a coefficient of variation (CV) of 32–50%. In the main (long) rainy season (June–September), total mean rainfall ranges between 420 and 680 mm with a CV of 15–40%. During the period 1977–2007, total rainfall decreased but not significantly. Also, there was a decrease in the number of rainy days associated with an increase (statistically not significant) in the intensity per rainfall event for the main rainy season, which can have implications for soil and nutrient losses through erosion and run-off. The reduced number of rainy days increased the length of intermediate dry spells by 0·8 days per decade, leading to crop moisture stress during the growing season. There was also a large inter-annual variability in the length of growing season, ranging from 76 to 239 days. The mean annual temperature exhibited a significant warming trend of 0·12–0·54 °C per decade. Projections from GCMs suggest that future annual rainfall will change by +10 to -40% by 2080. Rainfall will increase during November–December (outside the growing season), but will decline during the growing seasons. Also, the length of the growing season is expected to be reduced by 12–35%. The annual mean temperature is expected to increase in the range of 1·4–4·1 °C by 2080. The past and future climate trends, especially in terms of rainfall and its variability, pose major risks to rainfed agriculture. Specific adaptation strategies are needed for the CRV to cope with the risks, sustain farming and improve food security

Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions

Despite widespread application in studying climate change impacts, most crop models ignore complex interactions among air temperature, crop and soil water status, CO2 concentration and atmospheric conditions that influence crop canopy temperature. The current study extended previous studies by evaluating Tc simulations from nine crop models at six locations across environmental and production conditions. Each crop model implemented one of an empirical (EMP), an energy balance assuming neutral stability (EBN) or an energy balance correcting for atmospheric stability conditions (EBSC) approach to simulate Tc. Model performance in predicting Tc was evaluated for two experiments in continental North America with various water, nitrogen and CO2 treatments. An empirical model fit to one dataset had the best performance, followed by the EBSC models. Stability conditions explained much of the differences between modeling approaches. More accurate simulation of heat stress will likely require use of energy balance approaches that consider atmospheric stability conditions

The Hot Serial Cereal Experiment for modeling wheat response to temperature: Field experiments and AgMIP-Wheat multi-model simulations.

The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models

Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019

Background Given the projected trends in population ageing and population growth, the number of people with dementia is expected to increase. In addition, strong evidence has emerged supporting the importance of potentially modifiable risk factors for dementia. Characterising the distribution and magnitude of anticipated growth is crucial for public health planning and resource prioritisation. This study aimed to improve on previous forecasts of dementia prevalence by producing country-level estimates and incorporating information on selected risk factors. Methods We forecasted the prevalence of dementia attributable to the three dementia risk factors included in the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 (high body-mass index, high fasting plasma glucose, and smoking) from 2019 to 2050, using relative risks and forecasted risk factor prevalence to predict GBD risk-attributable prevalence in 2050 globally and by world region and country. Using linear regression models with education included as an additional predictor, we then forecasted the prevalence of dementia not attributable to GBD risks. To assess the relative contribution of future trends in GBD risk factors, education, population growth, and population ageing, we did a decomposition analysis. Findings We estimated that the number of people with dementia would increase from 57·4 (95% uncertainty interval 50·4–65·1) million cases globally in 2019 to 152·8 (130·8–175·9) million cases in 2050. Despite large increases in the projected number of people living with dementia, age-standardised both-sex prevalence remained stable between 2019 and 2050 (global percentage change of 0·1% [–7·5 to 10·8]). We estimated that there were more women with dementia than men with dementia globally in 2019 (female-to-male ratio of 1·69 [1·64–1·73]), and we expect this pattern to continue to 2050 (female-to-male ratio of 1·67 [1·52–1·85]). There was geographical heterogeneity in the projected increases across countries and regions, with the smallest percentage changes in the number of projected dementia cases in high-income Asia Pacific (53% [41–67]) and western Europe (74% [58–90]), and the largest in north Africa and the Middle East (367% [329–403]) and eastern sub-Saharan Africa (357% [323–395]). Projected increases in cases could largely be attributed to population growth and population ageing, although their relative importance varied by world region, with population growth contributing most to the increases in sub-Saharan Africa and population ageing contributing most to the increases in east Asia. Interpretation Growth in the number of individuals living with dementia underscores the need for public health planning efforts and policy to address the needs of this group. Country-level estimates can be used to inform national planning efforts and decisions. Multifaceted approaches, including scaling up interventions to address modifiable risk factors and investing in research on biological mechanisms, will be key in addressing the expected increases in the number of individuals affected by dementia