Exploring micro-level evidences is critical to fine tune effective adaptation options to cope with the adverse impact of climate change. In this regard, detailed studies on climate change adaptation options are not available in the study areas. Hence, the objective of this study was to assess farmers’ climate change adaptation options and determinant factors that influence their choice. Data were collected from 253 respondents randomly using probability proportional to the sizes (PPS) of the population of each district and peasant association from which sample households to be drawn. Descriptive statistics were employed to assess adaptation options while the multinomial logit model (MNL) was used to identify factors influencing households’ choices. The results revealed that farmers use change in crop type /variety, soil and water conservation practices, crop diversification, change in planting date and irrigation practices as climate change adaptation options. Educational level of the household head, age of the household head, sex of the household head, farm income, access to extension service, access to credit, access to climate information and agro-ecological settings were the most important determinant factors that affect significantly the choice of farmers to climate change adaptations. Therefore, an effort that enhances farmers’ awareness to climate change and creates the capacity to adopt climate resilient options is an important strategy that should be considered by a variety of societal groups, including policy makers, and farmers support organizations. Key words: adaptation options, climate change, determinants, multinomial logit model.

Climate change is affecting agricultural production and other aspects of life but only limited number of studies took interest in characterizing and projecting climate and its impact on crop production at local level. The threat to agricultural sector is more serious in Ethiopia, where climate is influenced by diverse topography and varying landscape features. This study was conducted in Welmera area to model the impacts of climate change on production of two faba bean (Vicia faba L.) varieties (Tums and Gora). Historical climate and crop yield data were obtained from the National Meteorological Agency of Ethiopia and Holeta Agricultural Research Center, respectively. Future climate data were downscaled by an average ensemble of four GCMs (BSS-CSM1-1, HadGEM2-ES, MIROC-ESM, NorESM1-M) in near- and mid-century (2030s and 2050s) under both RCP 4.5 and RCP 8.5. Rainfall by near-century will increase by up to 50% depending on the concentration pathway considered compared to the baseline period (1988–2017). The projected average rainfall total of belg season (FMAM) will increase by 88.17% under RCP 4.5 scenario and 95.38% under RCP 8.5 scenario in near-century. The future projection revealed that the highest mean monthly rainfall and temperature changes will occur in July (147.3 mm/month) and August (0.24 °C/month) under RCP8.5. However, in August and September mean monthly rainfall will decrease dramatically by 50.85 % and 31.05% from 2020 to 2079 under RCP 8.5 and RCP 4.5, respectively. The yield of Tumsa variety will decrease by up to 24.19% under RCP8.5 in mid-century. Gora variety will see an increase of yield by 18.24% under RCP 4.5 in mid-century and 28.03% under RCP 8.5 in near-century. Overall, the area will experience an increase and a decrease in faba bean yield for both varieties in the upcoming decades in the study area. Performance evaluation of the models showed that they were able to predict future yield faba bean varieties in the area with acceptable accuracy. Inconsistency of future climate variables and impact on fababean production underscores the need to develop location-specific adaptation strategies. Further studies that consider wider area could be necessary to better understand the impact of future climate on faba bean production in the study area and similar agroecologies in the country.

Climate change is a major threat to agricultural production and undermines the efforts to achieve sustainable development goals in poor countries such as Ethiopia that have climate-sensitive economies. The objective of this study was to assess characterization of local climate and its impact on productivity faba bean (Vicia faba L.) varieties (Gora and Tumsa) productivity in Welmera watershed area, central Ethiopia. Historical climate (19882017) and eight years of crop yield data were obtained from National Meteorological Agency of Ethiopia and Holeta Agricultural Research Center. Trend, variability, correlation, and regression analyses were carried out to characterize the climate of the area and establish association between faba bean productivity and climate change. The area received mean annual rainfall of 970mm with SD of 145.6 and coefficient of variation (CV ) of 15. The earliest and latest onset of rainfall were April 1 (92 DOY) and July 5 (187 DOY), whereas, the end date of rainy season was on September 2 (246 DOY) and October 31 (305 DOY), respectively. The average length of the growing period was 119 days, with a CV of 35.2. The probability of dry spell less than 7 days was high (>80) until the last decade of May (151 DOY); however, the probability sharply declined and reached 0 on the first decade of July (192 DOY). Kiremt (long rainy season that occurs from June to September) and belg (short rainy season that falls from February to April/May) rainfall had increasing trends at a rate of 4.7mm and 2.32mm/year, respectively. The annual maximum temperature showed increasing trend at a rate of 0.06°C per year and by a factor of 0.34°C, which is not statistically significant. The year 2014 was exceptionally drought year while 1988 was wettest year. Kiremt (JJAS) start of rain and rainy day had strong correlation and negative impact on Gora yield with (r=0.407 and 0.369), respectively. The findings suggests large variation in rainfall and temperature in the study area which constraints faba bean production. Investment on agricultural sector to enhance farmers adaptation capacity is essential to reduce the adverse impacts of climate change and variability on faba bean yield. More research that combines household panel data with long-term climate data is necessary to better understand climate and its impact on faba bean yield.

Sesame, a crucial oilseed crop in Ethiopia, ranks second only to coffee in its importance as an exported agricultural commodity. However, inadequate soil fertility management has hampered its productivity despite its substantial international market demand. Hence, this study was conducted to model the response of sesame to different nitrogen fertilizer levels using the AquaCrop model, and to assess the capability of the model as a decision-support tool for optimizing soil fertility management strategies in the study area. The experiment was laid out in a randomized complete block design, consisting of four nitrogen fertilizer rates (0, 23, 46, and 69 kg/ha nitrogen) and three distinct sesame varieties (Setit-1, Setit-2, and Humera-1). Over the course of the three cropping seasons, data on soil physical and chemical properties, crop growth, yield and yield components were collected for each treatment. Evaluation of model performance relied upon established metrics of coefficient of determination (R²), root mean square error (RMSE), normalized root mean square error (N-RMSE), model efficiency (E), and degree of agreement (D). Analysis of results revealed the AquaCrop model appropriately calibrated for simulation of soil water content, showing R² values ranging from 0.92 to 0.98, RMSE values varying from 6.5 to 13.9 mm, E values from 0.78 to 0.94, and D values from 0.95 to 0.99. Similarly, simulation outputs for aboveground biomass (AB) demonstrated good accuracy of the model, with R² values varying from 0.92 to 0.98, RMSE values ranging from 0.33 to 0.54 tons/ha, and D values from 0.9 to 0.98. Notable accuracy was also observed in the simulation of canopy cover (CC), revealing R² values between 0.95 and 0.99, and RMSE values ranging from 5.3 to 8.6 %. In conclusion, this study substantiates the successful calibration and validation of the AquaCrop model for predicting sesame response to diverse nitrogen fertilizer levels. The performance of the model in predicting soil water content, CC, AB, and yield highlights its potential as a valuable tool for optimizing soil fertility management and enhancing sesame cultivation practices in Ethiopia.