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.
Climate change is a real natural phenomenon. It is affecting agricultural productivity, especially in rain-fed agriculture. This paper provides comprehensive review studies on the impacts of climate change on crop and water productivity, soil water balance and food security. Global total annual anthropogenic GHG emission was grown by 70% between 1970 and 2004. The IPCC developed four emission scenarios or storylines, A1, A2, B1 and B2 and three groups of family storylines of A1FI, A1T and A1B. Climate predictions indicate a warmer world within the next 50 years, maximum and minimum temperatures increasing causing substantial yield decrease in low latitude areas; whereas, projected rainfall has no distinct variability pattern. By 2080, arid and semi-arid lands in Africa will increase by 5% to 8%. Global Climate Change Models (GCMs) have been used for different climate change impact assessment; however, due to lack of accuracy at local or smaller spatial simulation capacity; regional climate modeling, are being used to downscale climate scenarios at local and smaller scale around the world. Therefore, identifying and assessing suitable adaptation and mitigation practices have paramount importance and contributions to improve crop productivity, reduce the negative impacts of climate change on water availability and productivity. Global and regional climate models have been used as decision support tools for climate change impact assessment, and hence, application of such models to generate present and future climate data outputs for crop modeling and climate change impact assessment on crop production, water balance and food security is very essential.
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. The model has been calibrated and validated to simulate growth and productivity of crops, soil moisture balance, water use efficiency, evapo-transpiration and climate change impact assessment in different climate, management (water, fertilizer, sowing date, spacing etc.) practices around the world, especially in areas where soil moisture stress prevails. Maize, wheat, barley, tee, sorghum, pulse crops such as groundnut, soybean, vegetables (tomato, cabbage) have been tested using this model. The model comprehensively uses stress coefficients (water stress, fertilizer and temperature coefficients) to compute the effect of the factors on crop canopy, dry matter, stomatal closure, flowering, pollination and harvest index build up.Aqua crop model is calibrated for canopy cover expansion (development), dry matter accumulation and Soil Moisture Content (SMC) in the root zone and initial Harvest Index (HIo) under optimal growth conditions, to simulate the crop canopy development, dry matter and soil moisture content under actual growth conditions. The model is validated for its performance to simulate the crop canopy, dry matter, soil moisture balance grain yield (using normalized water productivity function and dry matter and harvest index). There are different statistical indices used to measure the performance or model goodness of fit such as the Root Mean Square Error (RMSE), normalized-root mean square error, index of agreement (d), model efficiency (E) and coefficient of determination (R2). Hence, application of Aqua crop model as a decision support tool to assess impact of climate change, water management strategies (rain-fed, soil water conservation practices like mulching and bunds), sowing date, plant spacing and fertilizer management strategies under different climatic conditions.
Background: Though nitrogen and water are key factors for tomato production, their optimum integration is not well identified in the study area. Therefore, optimizing irrigation level and nitrogen fertilizer rates are crucial to boost tomato yield as well as for better nutrient and Water Use Efficiency (WUE). Objective: The objective of the present study was to determine the optimum irrigation water and nitrogen fertilizer levels for higher tomato yield, improved water and nutrient use efficiencies. Methods: Split plot design was implemented with three irrigation levels expressed as a percentage of potential Evapotranspiration (ETc) allotted to the main plots and four nitrogen levels as sub-plots. Climate data were imported to AquaCrop model climate dataset for determining irrigation water amount and irrigation scheduling. Irrigation scheduling was determined using the FAO AquaCrop model and the crop evapotranspiration (ETc) in AquaCrop model was determined using Penman-Monteith method. Results: Irrigation water and nitrogen fertilizer levels markedly influenced the growth and yield performance of tomato, nutrient residue, Agronomic Efficiency (AE), Partial Factor Productivity (PFP) as well as Water Use Efficiency (WUE). With this, the most influential factor for tomato production was the nitrogen level rather than irrigation. Conclusion: In this study, higher growth and yield performance as well as, better water and nutrient use efficiencies of tomato were obtained while the irrigation level of 75% ETc is interacted with a nitrogen fertilizer rate of 92 kg N/ha.
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