POTENTIAL IMPACTS OF CLIMATE CHANGE ON MAIZE (Zea mays L.) PRODUCTION AND ADAPTATION OPTIONS IN TIGRAY REGION, NORTHERN ETHIOPIA

Abstract

The livelihoods of smallholder farmers in the Tigray region have been impacted by severe climate-related risks, including highly unpredictable rainfall and severe droughts. This study focuses on the densely populated semi-arid areas of Tigray (northern Ethiopia), where increasing land productivity is the only way to produce food sustainably. It is predicted that climate change will make some current issues worse and bring up new risks that go beyond what is now known. The Tigray region of northern Ethiopia's crop production is anticipated to be seriously threatened by this problem. The study's main goal was to evaluate the present and potential effects of climate change on maize production in Tigray region, northern Ethiopia and to pinpoint potential adaptation mechanisms. In order to meet these objectives, the study combined observed, perceived, and projected climate analysis, field experimentation, and crop modeling (DSSAT model) techniques. The DSSAT model simulates yield under various scenarios while estimating maize yield by taking climate, soil, crop, and management techniques into account. The Mann Kendall test was used to assess trends in long-term historical (1989–2018) and prospective (2050–2080) climate data from 10 meteorological sites. The variability of rainfall was evaluated using rainfall anomalies and concentration indices. A total of 250 sample households from five districts—Tselemti, Medebay Zana, Na'eder Adet, Kolla Tembien, and Kilte Awla’elo—were utilized to assess the farmers' perceptions of climate change. The data were analyzed using descriptive statistics and the Multinomial Logit Model (MNL). For the objectives of model calibration and evaluation field experiments on three maize varieties—BH-546, Melkassa-2, and Qeyih Elbo (local)—were carried out in an RCBD design with four replications, at Selekhlekha research station. Future climate data (mid and enc centuries) for Shire area under two emission pathways (RCP4.5 and RCP8.5) were downscaled for the climate impact assessment using the MarkSim weather generator model. Yield responses of the three maize varieties to the impact of future climate were simulated using the DSSAT model under three planting dates combined with four levels of nitrogen. Results of the Mann-Kendall test revealed that rainfall recorded in the region is generally low and highly variable, with a mean annual rainfall of 666.26mm. Nevertheless, both annual and Kiremt rainfall showed a significant (p<0.05) increasing trends in May Tsebri, Axum and Adwa stations, while rainfall during Belg season showed a decreasing trend in all the stations. The coefficient of variation (CV) for annual (25%), Kiremt (30%) and Belg (71.5%) rainfall showed high inter-annual variability. Most of the stations showed high PCI values (>20), indicating a strong seasonality of rainfall in the region. The rainfall anomality index (RAI) revealed the occurrence of severe drought in most of the stations. Likewise, xx temperature during the last three decades has increased in all the studied stations with an average of 0.04 0C year -1 . Similarly, temperature will be increased in the studied stations in the coming decades, the highest warming being expected at the end of the century. The survey’s results indicated that 91.2% of the respondents perceived and believed that climate change is occurring, and its main signs include unpredictable rainfall (88.4%), warming temperatures (83.2%), and more frequent droughts (79.2%). The findings show that farmers' perceptions of rising temperatures and weather data matched; however, there was a discrepancy between perceived and observed rainfall records. Reduced maize yields (78%) and declining soil fertility (83%) were the two biggest impacts of climate change perceived by the farmers. Accordingly, 92.8% of farmers have developed their best adaptation, primarily through the combination of crops and livestock (24%) and the adoption of improved maize varieties (20.8%). The econometric model's findings indicated that the variables significantly (p < 0.05) influencing farmers' choice and application of adaptation options were age, gender, education, farm size, animal ownership, and poverty. The DSSAT-CERES-maize model simulated the days to flowering, days to maturity, and grain yield with very good accuracy with the normalized root mean square error (nRMSE) values of less than 10 indicating a good performance of the model. Results for the evaluation of the LAI also indicated excellent simulation of the model for the LAI for the three maize varieties with RMSE of 0.21-0.35, nRMSE of 6.9-13.49, R2 >0.94 and d-values of >0.97. As compared to the baseline period (1989–2018), analysis of the impacts of climate change revealed a decline in maize grain yield by the 2050s and 2080s. Maize yield was projected to decrease by 13% and 17% in the 2050s and by 19% and 24% in the 2080s under the RCP4.5 and RCP8.5 emission pathways, respectively. Hence, increasing the nitrogenous fertilizer rate and early planting could be considered as potential adaptation options to reduce the adverse effects of the future climate on maize production. Therefore, it can be concluded that proper selection of adaptation options, raising farmers’ awareness, and supporting the adaptation techniques of maize farmers from a variety of institutional, policy, and technological angles at the local level are important to lessen the negative impacts of climate change at present and in the future