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Simulating the impact of climate change on maize production in Ethiopia, East Africa
© The Author(s) 2018
Received: 22 July 2017
Accepted: 1 February 2018
Published: 13 February 2018
Climate change is expected to significantly impact agricultural production across Africa. While a number of studies assessed this impact in semi-arid southern Africa, or tropical West Africa, only a limited number took interest in the mountainous and climatically varying Ethiopia of eastern Africa. This study assesses the impact of climate change on maize production in three representative sites of maize growing areas in Ethiopia. The assessment relies on the DSSAT crop model simulation of maize under current climate and future projections (19 Global Climate Models and 2 Representative Concentration Pathways). The period 1980–2010 was used to represent the baseline climate, while future climate projections cover three periods; near term (2010–2039), mid-century (2040–2069) and end-of-century (2070–2099). Climate, soil and crop management data were collected for the study sites representing the maize growing areas in the country.
Results show that maize yields will decrease by up to 43 and 24% by the end of the century at Bako and Melkassa stations, respectively, while simulated maize yield in Hawassa show an increase of 51%. On the one hand, rainfall variability and rising temperatures are determining factors explaining yield decrease in Bako and Melkassa, while projected rainfall increase in Hawassa explain simulated yield increases.
The terrain and climate high variability of Ethiopia is emphasizing the extremely different responses of current agricultural systems to climate change. Though adaptation approached can address some negative impacts, and in some case can take advantage of changes, this study reveals that dedicated local knowledge is necessary for national and regional decision makers to respond with local relevance to a global exposure, in order to face food security challenges.
Developing countries, including African countries, are vulnerable to climate change due to increase of stresses such as human population, water scarcity, land degradation and food insecurity (IPCC 2012; World Bank 2010). Climate change can affect the sustainability of agriculture systems and will therefore challenge vulnerable people who depend on local food production (Wheeler and von Braun 2013; Müller et al. 2011; Boko et al. 2007). Severity will be accrued in arid and semi-arid areas of Africa where water resources are very sensitive to climate variability, particularly temperature and rainfall. Moreover, the majority of small scale agricultural systems, as commonly found in developing African countries, are rain-fed, hence directly affected by unpredictable temperature and rainfall variations, resulting in yield reduction or crop failure (Kurukulasuriya et al. 2006).
Poor agrarian economies such as Ethiopia are considered among the most at risk from the impact of climate change/variability on agricultural productivity, with direct consequences on national food security. The country’s economy is highly dependent on rain-fed agriculture with only 5% of the agricultural land being under irrigation (AGRA 2014). Agricultural production or crop failure due to shortage of water during the growing season is a threat for the rain-fed cropping systems in semi-arid Ethiopia (Kassie et al. 2013).
Previous studies have shown that climate variability and associated droughts have been major causes of food insecurity and famine in Ethiopia (Tesfaye et al. 2017; Alemayehu and Bewket 2016; Bewket 2009). For instance, the worst disaster in Ethiopia has experienced in 1983/84 failure of the main rainfall season, and resulted in reduction of the agricultural outputs by 21%, and GDP by 9.7% (World Bank 2006). FAO (2016) also reported that because of El Niño caused drought in 2015/2016, the average number of food insecure people in Ethiopia was of more than 10 million.
Agricultural output in Ethiopia is highly dependent on erratic, unevenly distributed and difficult to forecast rainfall intensity and distribution (Tefera 2012; Bewket 2009). Ethiopian economy and especially its agricultural sector are expected to be significantly affected by future climatic conditions. Eshetu et al. (2014) suggested the GDP could decrease by 0.5–2.5% per year in the near future. Rainfall variability alone could account for 2 billion USD loss in the agricultural sector. Ethiopia readiness ranks 151 (GAIN Index 2013), as an indicator of its capacity to cope with the impact of climate change (poor on adaptation). There are high levels of confidence in forecasting ongoing temperature and mean rainfall increase over the country, however, climate change impacts depend on the extent of emission scenarios and climatic models. For instance, there are several studies that showed Ethiopia would experience further warming by the years 2020s and 2050s in all seasons (Hadgu et al. 2015; Jury and Funk 2013; Ayalew et al. 2012; Conway and Schipper 2011). Annual rainfall is also expected to increase, but there is much uncertainty on the spatial and temporal patterns (Conway and Schipper 2011; Bewket and Conway 2007). Consequently climate change is expected to significantly impact crop production (Muluneh et al. 2015; Deressa and Hassan 2009), thus bearing profound effects on the livelihood of local communities (Hadgu et al. 2015; Kassie et al. 2013).
Maize is the most important cereal cultivated in Ethiopia as it ranks second after teff in area coverage, first in total national production and yield per hectare (CSA 2015). The national maize yield average is of 2.95 t/ha (CSA 2012). This is far below the world’s average which is about 5.66 t/ha (USDA 2016). This low productivity is attributed to several factors amongst which frequent droughts, declining soil fertility, poor agronomic practices, limited use of inputs, insufficient access to technology, lack of credit facilities, poor seed quality, incidence of diseases, pests and weeds (Taffesse et al. 2011; Erkossa et al. 2007). According to Muluneh et al. (2015), maize yields will reduce as a result of climate change for the semi-arid areas of Ethiopia by 2080s by up to 46%, but could result an increase by up to 59% result in sub-humid/humid areas. There is a great variety of possible adaptive responses available to deal with climate change/variability. These include technological options such as use of fertilizers, altering planting dates and supplemental irrigation (Bryan et al. 2009). The main challenge, however, continues to be identification of the most effective combination of possible strategies and technologies in a particular context (Burney et al. 2014).
Different studies conducted on climate change impacts over the past decades in Ethiopia have reported mixed relationships between climate change and crop production. Alemayehu and Bewket (2016), Bewket (2009) and Lemi (2005) reported that the existence of significant correlations between climate change and crop production, while Admassu (2004) identified no significant correlations between total annual, main rainy season (Kiremt) and small rainy season (Belg) rainfall, and production of those crops (barley, maize, sorghum, teff and wheat) in most parts of the country. In light of these contradictory views, it is necessary to further quantify the impact of climate change on crop production at the local level.
The previous assessments of climate change impacts on crop production in Ethiopia were either at the national (Deressa and Hassan 2009; Admassu 2004) or larger scale such as the East African regional levels (Bryan et al. 2009; Thornton et al. 2009). There are only a few studies at sub-national levels within Ethiopia exist (e.g., Alemayehu and Bewket 2016; Muluneh et al. 2015). It is necessary understanding the impact of climate change on maize yields at local scales when considering for planning and designing appropriate adaptation strategies. To the best of our knowledge, very few studies have examined the impact of climate change on maize yield at the local scale. Therefore, this study attempted to close this gap by assessing how monthly temperature and rainfall is likely to change in the future, establish how these changes affect maize production and portray how adaptation strategies can enhance future maize production over three distinct maize production areas of Ethiopia viz., Bako, Melkassa and Hawassa.
Description of the study area
Climate characteristics (1980–2010) for the three study areas (Bako, Melkassa and Hawassa stations)
Elevation (m a.s.l)
Mean annual rainfall (mm)
Mean annual rainfall (mm) for main growing season (June–September)
Mean annual maximum temperatures (°C)
Mean annual minimum temperatures (°C)
Baseline data and crop simulation model
Baseline climatic data
The baseline climates are made of daily rainfall, maximum and minimum temperatures, and daily sunshine hours for the period 1980–2010. Daily data was obtained from the Ethiopian National Meteorological Agency (NMA) and Ethiopian Institute of Agricultural Research (EIAR) archives for the three study sites. The gaps in the daily historical records over 1980–2010 were filled with a monthly bias-corrected version of the closest grid point of the AgMERRA data set, following the Agricultural Model Intercomparison and Improvement Project (AgMIP) protocoles (AgMIP 2013a, b). AgMIP is a worldwide cooperative effort linking climate, crop and socio-economic modelling to produce improved modelling capacity and better local to regional to global integrated assessment of climate change impacts on the agricultural sector (Rosenzweig et al. 2013).
Simulating maize yield
The Decision Support System for Agro-technology Transfer (DSSAT) model was used to simulate potential crop yields in response to baseline and future climates. The DSSAT suit is a software application package that encompasses over 28 crop simulation models (Hoogenboom et al. 2012). The DSSAT model is derived from the CERES and CROPSIM, and also many studies conducted by DSSAT models (Jones and Thornton 2003; Soltani and Hoogenboom 2003). It simulates the eco-physiological processes of plant growth, and commonly used to analyse yield response to future climate, relying on the best available description of soil, weather and agronomic managements (Andrew et al. 2007). The DSSAT model requires field level input data including daily weather, soil physical and chemical characteristics, crop variety parameters, and details of the crop management (Hoogenboom et al. 2012; Jones and Thornton 2003). In this study, the CERES-Maize models (Jones and Thornton 2003), which are embedded within the DSSAT version 4.5 (Hoogenboom et al. 2009), were used to simulate the phenology and yield of maize, in response to climate and management factors.
Crop model calibration
Genetic coefficients for BH540 and Melkassa I maize cultivars (Kassie et al. 2014)
Thermal time from emergence to end of the juvenile phase (degree days)
Development delay for each hour increase in photoperiod above a maximum development rate (days)
Thermal time from silking to physiological maturity (degree days)
Maximum possible number of kernels per plant
Kernel optimum filling rate during the linear grain filling stage (mg/day)
Phylochron interval: thermal time between successive leaf tip appearances (degree days)
Crop model validation
The third criterion was determining the correlation coefficient (r) value. It is used to evaluate the linear relationship between the observed and modelled amounts with a value of 1.0. Thus, r tests the ‘‘goodness of fit’’ of the linear model, r = 1 indicates a perfect fit of the model and r = 0 indicates that there is no linear relation.
Future maize simulation
The present analysis is based on multi-downscaled products from CMIP5 dataset, 19 Global Climate Models (GCMs) which able to better reproduce present day climate as compared to observations and causal mechanisms that can provide plausible future projections in various climate changes impact studies over the region (Bhattacharjeea and Zaitchik 2015; Jury 2015; Brands et al. 2013), were selected for future climate change projections under a medium stabilization scenarios (RCP4.5) and a very high baseline emission scenarios (RCP8.5) (Taylor et al. 2012).
Future crop management
The future yields were simulated with a set of early, mid-season and late planting dates. Early plantings were set to 5 May in Bako, 15 June and 17 May in Melkassa and Hawassa, respectively. Mid-season plantings were set to 15 May, 25 June and 27 May in Bako, Melkassa and Hawassa, respectively. Late plantings were set to 25 May, 5 July and 7 June in Bako, Melkassa and Hawassa, respectively. The future fertilizer applications include (1) no urea fertilizer, (2) half- and (3) full-historical urea recommended rates. This translates into (1) 0 kg/ha, (2) 50 kg/ha and (3) 100 kg/ha urea with 100 kg/ha DAP for Bako and Hawassa, and (1) 0 kg/ha, (2) 25 kg/ha and (3) 50 kg/ha urea with 100 kg/ha DAP for Melkassa stations. Urea application follows current recommendation, i.e. half the total amount at sowing, and half at silking at all sites. We simulated all combinations of planting dates and fertilizer rates.
Yield changes under future climate
The DSSAT model was used to simulate maize yields under future climate with the combination of planting and fertilizer rates described above. Crop models come with limitations due to the partial representation of crop systems (e.g., no pests and diseases are modelled). Hence we have not presented or discussed the absolute yields simulated, but rather focus on the percentage of yield change from baseline simulated to future simulated yields. The changes organized to present the various simulations resulting from 2 RCPs, 19 GCMs and three future time periods, compared to a single baseline.
Results and discussion
Future climate and consequent yields changes
Projected change in temperature
We clearly observe that the monthly mean maximum and minimum temperatures increase for all GCMs and both RCPs (see Figs. 2, 3). The maximum temperatures increase gradually in time from under + 1 °C in the near-term period, up to + 2 °C by the end of the century under RCP4.5. Under RCP8.5, the highest maximum temperatures change is projected from under + 1 °C in the near-term period up to 3.5 °C by the end of the century. Minimum temperatures increase is even larger. They rise from under + 1 °C in the near-term up to + 2.5 °C under RCP4.5. Under RCP8.5 increase start above + 1 °C in the near-term period up to + 4.5 °C by the end of the century. Though the presented temperature changes vary spatially (over the three stations) and across the 19 GCMs, projections show a clear and consistent continuous increase of minimum and maximum temperatures at all locations. This agrees with previous reports that indicated future warming of the air in the different parts of Ethiopia (Hadgu et al. 2015; Ayalew et al. 2012; Conway and Schipper 2011; Setegn et al. 2011; Yimer et al. 2009). NMA (2007) was also reported an increase in mean annual temperature by 0.2 °C per decade over the country between 1960 and 2006 period.
Projected changes in rainfall
Rainfall is expected to increase at all stations, for both RCPs, and the percentage of change increases in time from near-term to end-of-century. The largest projected median change of + 20% is expected in Hawassa under RCP8.5 by the end of the century. Both under RCP4.5 and RCP8.5 an increase in rainfall is consistently projected for Hawassa, meanwhile the rainfall increase is limited for Bako under RCP4.5 (which shows the largest number of outliers). The results are in agreement with those previously reported in the other parts of the country (e.g., Hadgu et al. 2015; Muluneh et al. 2015; Tesfaye et al. 2014; Kassie et al. 2013). Kassie et al. (2013) found an increase in annual rainfall and the highest was projected to increase by 28 and 38% for RCP4.5 and RCP8.5, respectively. The study by Muluneh et al. (2015) also reported that the projected main rainy season (Kiremt) rainfall increase up to 32% in the Central Rift Valley (CRV) of Ethiopia.
Future yield simulations
Impact of climate change
Model capacity to represent local systems
Average obs. yield (t/ha)
Average simulated yield (t/ha)
Reddish brown Nitosol
Bako Hybrid 540
100 kg/ha urea and 100 kg/ha DAP
Deep grey Fluvisols
50 kg/ha urea and 100 kg/ha DAP
Bako Hybrid 540
100 kg/ha urea and 100 kg/ha DAP
The order of magnitude of the yield change we found is broadly consistent with previous studies (Kassie et al. 2015; Muluneh et al. 2015; Schlenker and Lobell 2010). Inline to this study, Muluneh et al. (2015) found that an increase and decrease of maize yield at two different stations due to projected climate change. Thornton et al. (2009) reported that maize yield can be expected to increase in the central and southern Ethiopian highlands. The observed range of yield decrease concur with Kassie et al. (2015), they indicated maize yield reductions of about 20% in the Central Rift Valley by 2050s. The decrease in yield from overall climate change impact was due to increased evapotranspiration (ETO) that results from increased temperatures (Kassie et al. 2015; Muluneh et al. 2015).
Bako, a sub humid area
In all climate change scenarios, individual adaptation options, changing planting dates and application of fertilizer, decreased maize yield at Bako (Fig. 5). The largest declines (up to − 40%) are projected under RCP4.5 (end-of-century) and RCP8.5 (mid-century and end-of-century) exclusively under early planting. The smallest declines (up to − 2%) for both RCP4.5 and RCP8.5 are expected under a combination of late planting and half or full recommended fertilizer rates (unlike Melkassa, see next section). Independently of planting, fertilizing or RCPs combinations, maize yields decrease further with time. Without fertilization, planting dates provided little difference in yield reduction, while under half or recommended fertilizer application (i) early planting leads to the largest yields decline and (ii) late planting leads for the smallest yields decline outcomes.
For Bako, the combination of high fertilizer rate with late planting resulted the best response to detrimental effect of future climate on yields. This noticeably differs from Melkassa (presented in the next section) where low fertilization (with late planting as well) appeared to be the best alternative response to future climate.
Melkassa, a semi-arid area
The future maize yields simulated at Melkassa show consistent decrease across RCPs (Fig. 6). The largest yield decrease is unanimously simulated under RCP8.5 during the end of century period independently of fertilization or planting date. The rare and limited yield increases (up to + 4%) are simulated under RCP4.5 (near term and end-of-century) and RCP8.5 (near term) only in combination with late planting and no or half recommended fertilizer application. While under RCP8.5 yield projections decrease further with time (near term, then mid-century, then end-of century), projections under RCP4.5 decrease further until mid-century and recover by the end-of-century to levels comparable to near-term. Independently of RCPs or time period, results suggest that late planting in combination with no or half recommended fertilization amounts are the best possible combinations, either improving yields under RCP4.5 or limiting the yield decrease under RCP8.5. Recommend fertilization application appeared to be detrimental for early or late planting. While yield changes vary largely in response to the planting date (early, mid- and late), fertilizer rates could have very limited effects.
Hawassa, a sub-moist area
The maize production change over Hawassa is shown in Fig. 7. Unlike both previous stations, maize yield in Hawassa are expected to mostly benefit from future climate. The largest yield increases (up to + 50%) are expected in the near-term under both RCP4.5 and RCP8.5, for mid planting period, with no and full fertilizer rates only. The largest yield decreases (up to − 25%) are projected under RCP4.5 either by the end of century in combination with late planting, or by mid-century in combination with early planting. As for Bako, the benefits decline with time under RCP8.5, whereas under RCP4.5 the benefits either (i) severely decline by mid-century before increasing by end-of-century in combination with early planting, or (ii) decline continuously with time with late planting dates.
Starting from comparable yield increases in the near term, the latter leads to RCP8.5 projections unusually offering better outcomes, though reducing with time. It is noticeable that RCP8.5 projections all result in increased yields, while under RCP4.5 projections decline drastically from similar near-term expectations to no increase or decrease in the mid-century and end-of-century. As usual the near term period present the highest (in that case highly beneficial) yield expectations independently of planting, fertilizing or RCPs. However, quite unusually maize yield simulations show higher improvements under RCP8.5 than under RCP4.5. In addition, while RCP8.5 projections remain all beneficial (all increases) despite a decline in time, yield projections under RCP4.5 decline drastically up to − 26% decrease either (i) as soon as mid-century in combination with early planting, or (ii) end-of-century in combination with late planting.
For Hawassa station, planting date play a major role in combination with RCP/time period to improve the yield in the area. While there is no evident “best” planting decision unlike late planting at both Bako and Melkassa, results suggest “worst” planting decisions under RCP4.5 in combination with future period; (1) late planting and end of century, and (2) early planting and mid-century. Near term overall appeared to be the most fruitful period and strongly suggest potential for improvement under increased temperature and increase rainfall whether under RCP4.5 or RCP8.5.
Adjusting planting dates and application of fertilizer are among the most widely studied strategies of adapting to climate change (Kassie et al. 2015; White et al. 2011). Our analysis indicated that late planting in Bako and Melkassa, and mid-window planting in Hawassa, combination with recommend fertilization application provided highest yield in all stations. In line with our results, Muluneh et al. (2017), Kassie et al. (2015) and Biazin and Sterk (2013) in Ethiopia, reported an increase in maize yields with delayed planting dates and nitrogen application for climate change scenarios. The significant impact of late planting would concur with works from Biazin and Sterk (2013) suggesting to wait for sufficient moisture needed for an efficient seed germination. Similar to our result, Kassie et al. (2015) indicated that increasing nitrogen fertilizer rate by 60 kg/ha did yields increase by 78–89% in across the climate change scenarios in Ethiopia.
Climate change/variability and its impact, as projected by the 19 GCMs in combination with 2 Representative Concentration Pathways and crop model, demonstrate variable response of maize crop yield in maize growing areas of Ethiopia. From a climate only perspective, the RCP8.5 translating higher atmospheric greenhouse gas concentrations and higher increase in temperatures, shows as expected larger detrimental impact in Bako and in Melkassa, but it also unusually offers the most stable benefit over time in Hawassa. The yield responses over time varies as well, from a constant decrease of performances over time in Bako independently of the RCP, to a decrease in time from near-term to mid-century before increasing back by the end-of-century in Melkassa and Hawassa under RCP4.5.
From a treatment perspective, planting dates and fertilizer rates always have an effect over simulated yield responses, yet the most promising combinations largely vary from one to another location. In Bako, a combination of late planting and medium-to-high fertilization rates respond best to the detrimental effects of future climate. In Melkassa, a combination of late planting and low-to-medium fertilization rates respond best to the detrimental effects of future climate. And in Hawassa, future climate is mostly shown beneficial to simulated future yields, the largest benefit resulting from low-or-high fertilization rates in combination with mid-window planting dates. Shifting the planting date can reduce the risk of crop failure and offset the predicted yield reduction caused by climate change.
The improved yield responses under future climate in Hawassa could be a direct effect of larger projected increases in monthly rainfall, hence allowing to benefit from higher projected temperatures. Under a comparable increase in monthly temperatures but this time with limited increase in rainfall in Bako and Melkassa, the results support the value of planting later in an attempt to secure adequate moisture in the soil, hence reducing the sensitivity of- and the risk of- prolonged dry spell at early sensitive growth stage.
Considering temperature and rainfall changes, two major determining factors of agricultural production and food security, the results emphasize extremely variable outcomes. On one side, future maize yields are consistently decreasing and, fertilizer and planting treatments variation only offer an opportunity to limit the negative impact of future climate change in Bako and Melkassa. On the other side, future maize yields are consistently increasing and, fertilizer and planting treatments variation become a tool to benefit the most from the impact of future climate change in Hawassa. However, the increase in yield with changing planting date and fertilizer application under climate change scenarios is conditional on the use of other measures, such as adjusted irrigation and crop protection, to enable realization of potential yield increases.
Through three major production areas in Ethiopia, this study improves our understanding of local production variations under global climate change. It provides impact assessment in the light of a range of GCMs, a set of treatments and 2 RCPs, allowing for an exploration of adaptation options best suited at locale scale, to feed into larger provincial and national future production schemes. This study further strengthens our understanding of the impact of global climate changes on local agricultural food production systems, and the need of good local knowledge to better address global climate challenges.
KA, JS and FM have made contributions in the acquisition of the data, data collection, data coding and entry, data analysis, interpretation of the result. OC has been involved in critically advising, revising the manuscript and made possible suggestion. All authors read and approved the final manuscript.
The authors are grateful to the Climate Impacts Research Capacity and Leadership Enhancement (CIRCLE), which gave the opportunity to start a 1 year research project and financial support. The authors would like to thank Melkassa Agricultural Research Center, Bako Research Center, Hawassa Research Center and Debrezeit Research Center for providing important inputs for this research. The authors gratefully acknowledge the support made by Samuel Tesfaye, Mezgebu Getnet and Nkulumo Zinyenger.
The authors declare that they have no competing interests.
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The full cost of this study was covered by Climate Impacts Research Capacity and Leadership Enhancement (CIRCLE) project, co-operated by African Academic Science (AAS) and UKAID.
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- Admassu S (2004) Rainfall variation and its effect on crop production in Ethiopia. MSc. thesis, Department of Civil Engineering, Addis Ababa University, EthiopiaGoogle Scholar
- AgMIP (2013a) Guide for running AgMIP climate scenario generation tools with R in windows. AgMIP, New YorkGoogle Scholar
- AgMIP (2013b) The coordinated climate-crop modeling project C3MP: an initiative of the agricultural model intercomparison and improvement project. C3MP protocols and procedures. AgMIP, New YorkGoogle Scholar
- AgMIP (2013c) Guide for regional integrated assessments: handbook of methods and procedures, version 5. Center for Climate Systems Research, Earth Institute, Columbia University. AgMIP, New YorkGoogle Scholar
- Alemayehu A, Bewket W (2016) Local climate variability and crop production in the central Highlands of Ethiopia. Environ Dev 19:36–48View ArticleGoogle Scholar
- Alemu D, Wilfred M, Nigussie M, David JS (2008) The maize seed system in Ethiopia: challenge and opportunities in drought prone areas. Afr J Agric Res 3(4):305–314Google Scholar
- Alliance for a Green Revolution in Africa (AGRA) (2014) African agriculture status report; climate change and smallholder agriculture in sub-Saharan Africa. Alliance for a Green Revolution in Africa (AGRA), NairobiGoogle Scholar
- Andrew W, Robertson J, Amor V, Ines M, James W (2007) Downscaling of seasonal precipitation for crop simulation. J Appl Meteorol Climatol. https://doi.org/10.1175/JAM.2495.1 Google Scholar
- Araya A, Girma A, Getachew F (2015) Exploring impacts of climate change on maize yield in two contrasting agro-ecologies of Ethiopia. Asian J Appl Sci Eng 4:27–37Google Scholar
- Ayalew D, Tesfaye K, Mamo G, Yitaferu B, Bayu W (2012) Variability of rainfall and its current trend in Amhara region, Ethiopia. Afr J Agric Res 7(10):1475–1486Google Scholar
- Bewket W (2009) Rainfall variability and crop production in Ethiopia: case study in the Amhara region. In: Ege S, Aspen H, Teferra B, Bekele S (eds) Proceedings of the 16th international conference of Ethiopian studies, Trondheim, NorwayGoogle Scholar
- Bewket W, Conway D (2007) A note on the temporal and spatial variability of rain fall in the drought-prone Amhara region of Ethiopia. Int J Climatol 27:1467–1477View ArticleGoogle Scholar
- Bhattacharjeea PS, Zaitchik BF (2015) Perspectives on CMIP5 model performance in the Nile River headwaters regions. Int J Climatol 35:4262–4275. https://doi.org/10.1002/joc.4284 View ArticleGoogle Scholar
- Biazin B, Sterk G (2013) Drought vulnerability drives land-use and land cover changes in the Rift Valley dry lands of Ethiopia. Agric Ecosyst Environ 164:100–113. https://doi.org/10.1016/j.agee.2012.09.012 View ArticleGoogle Scholar
- Boko M, Niang I, Nyong A, Vogel C, Githeko A, Medany M, Osman-Elasha B, Tabo R, Yanda P (2007) Africa, in climate change 2007: impacts, adaptation and vulnerability. Cambridge University Press, CambridgeGoogle Scholar
- Brands S, Herrera S, Fernandez J, Gutiérrez JM (2013) How well do CMIP5 Earth System Models simulate present climate conditions in Europe and Africa? A performance comparison for the downscaling community. Clim Dyn 41:803–817. https://doi.org/10.1007/s00382-013-1742-8 View ArticleGoogle Scholar
- Bryan E, Deressa TT, Gbetibouo GA, Ringler C (2009) Adaptation to climate change in Ethiopia and South Africa: options and constraints. Environ Sci Policy 12:413–426View ArticleGoogle Scholar
- Burney J, Cesano D, Russell J, La Rovere EL, Corral T, Coelho NS, Santos L (2014) Climate change adaptation strategies for smallholder farmers in the Brazilian Sertão. Clim Change 126:45–59View ArticleGoogle Scholar
- Conway D, Schipper ELF (2011) Adaptation to climate change in Africa: challenges and opportunities identified from Ethiopia. Glob Environ Change 21:227–237View ArticleGoogle Scholar
- CSA (Central Statistical Agency) (2012) Agricultural sample survey: report on area and production of major crops (private peasant holdings, Meher season). Statistical Bulletin 1, Addis Ababa, EthiopiaGoogle Scholar
- CSA (central statistical authority) (2015) Agricultural sample survey report on area and production for major crops (private peasant holdings Meher season) for 2007/08. The Federal Democratic Republic of Ethiopia. Statistical Bulletin 278, Addis Ababa, EthiopiaGoogle Scholar
- Deressa TT, Hassan RM (2009) Economic impact of climate change on crop production in Ethiopia: evidence from cross-section measures. J Afr Econ 18:529–554View ArticleGoogle Scholar
- Diaz-Nieto J, Wilby RL (2005) A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom. Clim Change 69:245–268View ArticleGoogle Scholar
- Erkossa T, Itanna F, Stahr K (2007) Indexing soil quality: a new paradigm in soil science research. Aust J Soil Res 45:129–137View ArticleGoogle Scholar
- Eshetu Z, Simane B, Tebeje G, Negatu W, Amsalu A, Berhanu A, Bird N, Welham B, Trujillo NC (2014) Climate finance in Ethiopia. Overseas Development Institute, London and Climate Science Centre, Addis AbabaGoogle Scholar
- Food for Agriculture Organization of the United Nations (FAO) (2016) Challenges facing agriculture and food security: situation report. http://www.fao.org/fileadmin/user_upload/emergencies/docs/FAO%20Ethiopia_El%20Nino%20Situation%20Report_February%202016.pdf. Accessed 8 June 2017
- Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modeling to impacts studies: recent advances in downscaling techniques for hydrological modeling. Rev Int J Climatol 27:1547–1578View ArticleGoogle Scholar
- Notre Dame-Global Adaptation Index (ND-GAIN) (2016) Summarizes a country’s vulnerability to climate change and other global challenges in combination with readiness to improve resilience. https://gain-new.crc.nd.edu/ranking/readiness. Accessed 2 June 2017
- Hadgu G, Tesfaye K, Mamo G (2015) Analysis of climate change in northern Ethiopia: implications for agricultural production. Theor Appl Climatol 121(3):733–747. https://doi.org/10.1007/s00704-014-1261-5 View ArticleGoogle Scholar
- Hoogenboom G, Jones JW, Wilkens PW, Porter CH, Hunt LA, Boote KJ, Singh U, Uryasev O, Lizaso JI, White JW, Ogoshi R, Gijsman AJ, Batchelor WD, Tsuji GY (2009) Decision Support System for Agrotechnology Transfer (DSSAT) version 4.5. University of Hawaii, HonoluluGoogle Scholar
- Hoogenboom G, Jones JW, Traore PC, Boote KJ (2012) In the book. In: Kihara J (ed) Improving soil fertility recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT). Springer Science + Business Media B.V, Dordrecht. https://doi.org/10.1007/978-94-007-2960-52 Google Scholar
- IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner GK, Allen SK, Tignor M, Midgley PM (eds) A special report of working groups I and II of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, p 582Google Scholar
- Jones PG, Thornton PK (2003) The potential impacts of climate change on maize production in Africa and Latin America in 2055. Glob Environ Change Hum Policy Dimens 13(1):51–59View ArticleGoogle Scholar
- Jury MR (2015) Statistical evaluation of CMIP5 climate change model simulations for the Ethiopian highlands. Int J Climatol 35:37–44. https://doi.org/10.1002/joc.3960 View ArticleGoogle Scholar
- Jury MR, Funk C (2013) Climatic trends over Ethiopia: regional signals and drivers. Int J Climatol 33:1924–1935View ArticleGoogle Scholar
- Kassie BT, Rötter RP, Hengsdijk H, Asseng S, Van Ittersum MK, Kahiluto H, Van Keulen H (2013) Climate variability and change in the Central Rift Valley of Ethiopia: challenges for rainfed crop production. J Agric Sci 152:58–74View ArticleGoogle Scholar
- Kassie BT, Van Ittersum MK, Hengsdijk H, Asseng S, Wolf J, Rotter RP (2014) Climate induced yield variability and yield gap of maize (Zea mays L.) in the Central Rift Valley of Ethiopia. Field Crops Res. https://doi.org/10.1016/j.fcr.2014.02.010 Google Scholar
- Kassie BT, Asseng S, Rotter RP, Hengsdijk H, Ruane AC, Van Ittersum MK (2015) Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models. Clim Change 129(1–2):145–158. https://doi.org/10.1007/s10584-014-1322-x View ArticleGoogle Scholar
- Kurukulasuriya P, Mendelsohn R, Hassan R, Benhin J, Deressa T, Diop M, Eid HM, Fosu KY, Gbetibouo G, Jain S, Mahamadou A, Mano R, Kabubo-Mariara J, El Marsafawy S, Molua E, Ouda S, Ouedraogo M, Sene I, Maddison D, Seo SN, Dinar A (2006) Will African agriculture survive climate change? World Bank Econ Rev 20:367–388View ArticleGoogle Scholar
- Lemi A (2005) Rainfall probability and agricultural yield in Ethiopia. East Afr Soc Sci Res Rev 21(1):57–96View ArticleGoogle Scholar
- Ministry of Agriculture and Rural Development (MOARD) (2009) Animal and plant health regulatory, crop variety register issue no. 12. Ministry of Agriculture and Rural Development (MOARD), Addis AbabaGoogle Scholar
- Mourice SK, Rweyemamu CL, Tumbo SD, Amuri N (2014) Maize cultivar specific parameters for Decision Support System for Agrotechnology Transfer (DSSAT) application in Tanzania. Am J Plant Sci 5:821–833. https://doi.org/10.4236/ajps.2014.56096 View ArticleGoogle Scholar
- Müller C, Cramer W, Hare WL, Lotze-Campen H (2011) Climate change risks for African agriculture. Proc Natl Acad Sci 108:4313–4315View ArticleGoogle Scholar
- Muluneh A, Birhanu B, Stroosnijder L, Bewket W, Keesstra S (2015) Impact of predicted changes in rainfall and atmospheric carbon dioxide on maize and wheat yields in the Central Rift Valley of Ethiopia. J Reg Environ Change. https://doi.org/10.1007/s10113-014-0685-x Google Scholar
- Muluneh A, Stroosnijder L, Keesstra S, Biazin B (2017) Adapting to climate change for food security in the Rift Valley dry lands of Ethiopia: supplemental irrigation, plant density and sowing date. J Agric Sci 155:703–724. https://doi.org/10.1017/S0021859616-000897 View ArticleGoogle Scholar
- NMA (National Meteorological Agency) (2007) Climate change national adaptation programme of action (NAPA) of Ethiopia: technical report. Addis Abeba, National Metorological Agency, p 85Google Scholar
- Rosenzweig C, Ruane AC, Winter JM, Boote KJ, Porter C, Jones J, Wasseng S, Hatfield JL, Thorburn P, Antle JM, Nelson GC, Janssen S, Basso B, Ewert F, Wallach D, Baigorria G (2013) The agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studies. J Agric For Meteorol 170:166–182View ArticleGoogle Scholar
- Schlenker W, Lobell DB (2010) Robust negative impacts of climate change on African agriculture. Environ Res Lett 5:014010View ArticleGoogle Scholar
- Seleshi Y, Zanke U (2004) Recent changes in rainfall and rainy days in Ethiopia. Int J Climatol 24:973–983. https://doi.org/10.1002/joc.1052 View ArticleGoogle Scholar
- Setegn SG, Rayner D, Melesse AM, Dargahi B, Srinivasan R, Wörman A (2011) Climate change impact on agricultural water resources variability in the Northern Highlands of Ethiopia. In: Melesse AM (ed) Nile River Basin. Springer, Dordrecht, pp 241–265. https://doi.org/10.1007/978-94-007-0689-7_12 View ArticleGoogle Scholar
- Soltani A, Hoogenboom G (2003) Assessing crop management options with crop simulation models based on generatedwhether data. Field Crops Res 103:198–207View ArticleGoogle Scholar
- Taffesse A, Dorosh P, Asrat S (2011) Crop production in Ethiopia regional patterns and trends. Ethiopia strategy support program IIGoogle Scholar
- Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498View ArticleGoogle Scholar
- Tefera A (2012) Ethiopia: grain and feed annual report. Global Agricultural Information Network. USDA Foreign Agriculture Service, report number ET 1201Google Scholar
- Tesfaye S, Raj AJ, Gebersamuel G (2014) Assessment of climate change impacts on hydrologic response of Geba Catchment, Tekez Basin, and Northern Ethiopia. Am J Environ Eng 4(2):25–32Google Scholar
- Tesfaye S, Birhane E, Leijnse T, van der Zee SEATM (2017) Climatic controls of ecohydrological responses in the highlands of northern Ethiopia. Sci Total Environ 609:77–914. https://doi.org/10.1016/j.scitotenv.2017.07.13 View ArticleGoogle Scholar
- Thornton PK, Jones PG, Alagarswamy G, Andresen J (2009) Spatial variation of crop yield response to climate change in East Africa. Glob Environ Change 19:54–65. https://doi.org/10.1016/j.gloenvcha.2008.08.005 View ArticleGoogle Scholar
- Trzaska S, Schnarr E (2014) A review of downscaling methods for climate change projections. A report made by United States Agency for International Development by Tetra Tech ARD, through a task order under the prosperity, livelihoods, and conserving ecosystems (PLACE) indefinite quantity contract core task order (USAID contract no. AID-EPP-I-00-06-00008, order number AID-OAA-TO-11-00064)Google Scholar
- United States Department of Agriculture (USDA) (2016) World Agricultural Production. Foreign Agricultural Service. Circular Series WAP 6–16 June 2016. Approved by the World Agricultural Outlook BoardGoogle Scholar
- Wheeler T, von Braun J (2013) Climate change impacts on global food security. Science 341:508–513View ArticleGoogle Scholar
- White JW, Hoogenboom G, Kimball BA, Wall GW (2011) Methodologies for simulating impacts of climate change on crop production. Field Crops Res 12:357–368View ArticleGoogle Scholar
- Worku M, Twumasi Afriyie S, Wolde L, Tadesse B, Demisie G, Bogale G, Wegary D, Prasanna, B (2012) Meeting the challenges of global climate change and food security through innovative maize research. In: Proceedings of the third national maize workshop, Ethiopia, Addis AbabaGoogle Scholar
- World Bank (2006) Managing water resources to maximise sustainable growth: a country water resources assistance strategy for Ethiopia. World Bank, Washington, DCGoogle Scholar
- World Bank (2010) Economics of adaptation to climate change: Ethiopia. The World Bank Group, Washington, DC, p 124Google Scholar
- Yang JM, Yang JY, Liu S, Hoogenboom G (2014) An evaluation of the statistical methods for testing the performance of crop models with observed data. J Agric Syst 127:81–89View ArticleGoogle Scholar
- Yimer G, Jonoski A, Griensven AV (2009) Hydrological response of a catchment to climate change in the upper Beles river basin, upper blue Nile, Ethiopia. Nile Basin Water Eng Sci Mag 2:49–59Google Scholar