Background: Africa is the most vulnerable continent in the world; which recurrent droughts, extreme temperature and rainfall affects agriculture and food security. The aim of this study was to analyze the trends in extreme temperature and rainfall in major sesame producing areas in western Tigray using RClimDex software. We selected eight temperature and nine rainfall indices from 27 extreme temperature and rainfall indices, which are recommended by joint CCL/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). A non-parametric Mann–Kendall test and Sen’s slope estimates were used to test the statistical significance and trend of each of the extreme temperature and rainfall indices, respectively. Results: Number of heavy rainy days, number of very heavy rainy days, very wet days, extremely wet days, and maximum 5 days precipitation showed a negative trend, with significant (p < 0.05) decrease throughout the study area. Monthly maximum value of maximum and minimum value of maximum temperature, monthly maximum and minimum value of minimum temperature, hot days and hot nights revealed positive trend throughout the study areas. Total rainfall was decreasing significantly (p < 0.05) by 13.34 mm, 13.8 mm, 14.65 mm, 10.9 mm and 8.4 mm/year at Humera and Dansha, Adiremets, Maygaba, Maytsebri and Sheraro, and Adigoshu, respectively. Spatial analysis on extreme temperature also indicated there was relatively lower variability on minimum temperature in Humera, Dansha, Adiremets, and Adigoshu. On average, the western part of Tigray experienced a reduction in total rainfall ranging 8.45 to 14.7 mm/year; and increase in average maximum temperature of 0.04 to 0.051 °C/year since 1983 to 2016. The results also revealed an increase in warm nights and warm days ranging from 0.31 to 0.62 days, and 0.38 to 0.71 days/year, respectively. Conclusions: Increase in temperature and decrease in amount of rainfall may have a negative impact on crop transpiration, photosynthetic rate and soil water balance; exacerbating distribution and infestation of malaria and leishmaniasis. The results in this study could have an important role in identifying possible present and future production strategies on sesame, cotton, and sorghum crops, which are essential cash crops produced by farmers and investors.

Oilseeds are the second most export crops next to coffee in Ethiopia. Among the different oil crops, sesame is the second most important agricultural commodity next to coffee in foreign exchange earning to more than $449 million. It is the most ancient oil crop adapted to tropics and sub-tropics around the world. Sesame grows in Tigray, Amhara, Gambella, Benshangul Gumuz, Somalia, SNNP and Oromia regions in Ethiopia. However, productivity of sesame is very low due to several reasons. Traditional production technology, weeds, insect pests and diseases, climate change impacts, domestic and global market fluctuations and low research and expert knowledge and skill, are among the major challenges for sesame production in Ethiopia. On the contrary, vast area for sesame cultivation, irrigable area, low labor, government initiation in support of the commodity and varietal diversity are the production opportunities for production of sesame in Ethiopia. Hence; the national productivity of sesame is 730kg/ha. These necessitates improving the production techniques, improve productivity, and developing varieties with higher productivity potential, wider adaptability, application of improved crop protection techniques, and capacity building for experts on agronomic practices; will have vital role in providing modern sesame production systems in Ethiopia.

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.

Introduction: Climate is the most important factor in determining crop production systems in Ethiopia. However, land suitability evaluation using climate variables for specific crops has been barely conducted to demonstrate the suitability of a location for the growth and yield of the crop. This study was, therefore, conducted to analyze land suitability evaluation for sesame production using agro-climatic variables by employing a multi-criteria approach. Materials and Methods: Temperature (maximum, minimum, and average temperature), annual rainfall, elevation, slope, growing degree days (GDD), and length of growing period were classified, and re-classified. Finally, a suitability map overlay was produced using ArcMap version 10.3.1 Results and Discussion: The results indicated that areas with higher elevations, like Adi Remets being 1860 meters above sea level, were classified as moderately suitable, whereas the majority of the study area was suitable for sesame production. After re-classifying the climate variables, elevation, and GDD, a suitability map overlay was produced for sesame production. The overall suitability map showed that areas around Humera, Dansha, Sheraro, Badime, Adigoshu, and some parts of Dedebit, Adiremets, and Maygaba were found from highly suitable to suitable, with 1,024,543.2 and 664,545.5 hectares of land, respectively. There has been no evidence with regards to land suitability classification for sesame production in the study area; hence, the results obtained in this study demonstrated the majority of the cultivated areas in the western and northwestern zones of Tigray to be high to moderately suitable for sesame production. Conclusion: Based on the results, a land suitability map representing a homogenous environment for improved sesame production was produced using a multi-criteria approach in the northwestern and western zones of Tigray. While the results obtained in this study might be helpful for agricultural experts, researchers, decision-makers, and farmers for crop intensification and expansion strategies, they can explicitly help in decisions in the marketing processes with respect to the crop’s value chain and provide a homogenous environment in the study area.

To quantify, integrate and assess the impacts from weather and climate change/variability on crop growth and productivity, crop models have been used for several years as decision support tools in the world. This paper is reviewed to assess applications of Aqua crop model as a decision support tool for simulating and validating crop management practices and climate change adaptation strategies. This model is devised by the FAO irrigation and drainage team. This model is very important especially, to guide as a decision support tool for dry land areas where soil moisture is very critical to affect crop productivity. It maintains the balance between simplicity, accuracy and robustness.