Climate change impact on wheat and maize growth in Ethiopia: A multi-model uncertainty analysis

Future climate change impacts on wheat grain yield and protein in the North China Region

The threat of global climate change on wheat production may be underestimated by the limited capacity of many crop models to predict grain quality and protein composition. This study aimed to integrate a wheat quality module of protein components into the CROPSIM-CERES-Wheat model to investigate the impact of climate change on wheat grain yield and protein quality in the North China Region (NCR) using five Global Climate Models (GCMs) from CMIP6 under three shared socioeconomic pathways. The CERES-Wheat model with a quality module was developed and calibrated and validated using data from several sites in the NCR. The results of the calibration and validation showed that the modified CERES-Wheat model can accurately predict grain yield, protein content and its components in field experiments. Compared with the baseline period (1981-2010), the annual mean temperature and annual cumulative precipitation increased in the NCR in the 2030's, 2050's and 2080's. The radiation was higher under the SSP126 and SSP585 scenarios, and lower under the SSP370 scenario compared to the baseline period. The anthesis and maturity date occurred earlier under the three future scenarios. The average grain yield increased by 13.3-30.9 % under three future scenarios. However, the regional average grain protein content of winter wheat in the future decreased by 2.0 %- 3.5 %. The reduction in wheat grain protein at the regional was less pronounced under SSP370 than that under SSP126 and SSP585. The structural protein content of winter wheat decreased under future climate conditions compared with the baseline period, but the storage protein content showed the opposite tendency. The model provided a useful tool to study the effects of future climate on grain quality and protein composition. These findings are important for developing agricultural practices and strategies to mitigate the potential impacts of climate change on wheat production and wheat quality in the future.

How Does Climate Change Affect Tomato and Okra Production? Evidence from Nigeria

This study examined the impacts of climate change on okra and tomato yields. Fertilizer consumption and credit to the crop sector were considered as covariates in the analysis. Time-series data, spanning a period of 40 years, were obtained from various sources. An autoregressive distributed lag model was applied to analyze short- and long-term impacts of climate change and agricultural inputs on okra and tomato yields. Not all variables were stationary at levels (order zero), but they were all significant at first difference, indicating the presence of cointegration. The Bound's test F-ratio was statistically significant and implied the presence of long- and short-term relationships among the variables studied. The mean temperatures had negative impacts on okra and tomato yields in both the short and long terms. Credit guaranteed to the crop sector had positive short- and long-term impacts on tomato yield; fertilizer consumption had a negative long-term impact on okra yield. Our study concludes that climate change, particularly rising temperature, impacts herbaceous fruit crop production in Nigeria. Therefore, we recommend that breeding and disseminating climate-smart tomato and okra varieties will help fruit crop farmers respond to rising temperatures.

Impact and adaptation of climate variability and change on small-holders and agriculture in Ethiopia: A review

Ethiopia is highly vulnerable to climate variability and change due to depend on the rain-fed agricultural system. The paper provides comprehensive review of impact and adaptation of climate variability and change on small-holder farmers and agriculture in Ethiopia. The study used secondary data from journal articles, books and technical reports, and discussed and organized with desk review. The study explored that climate variability and change has significantly affected crop and livestock production, fisheries and aquaculture, and food security. The study found that over 38 million people seriously interrupted from their living condition associated to climate related crises since 2000s up to 2010s in Ethiopia. Locust upsurge with the pest spreads reduces 1,228,352, 1,026,132 and 843,241 quintals of cereal crop productions in Oromia, Somali and Tigray regions of Ethiopia, respectively in 2020 production year. Drought declined 26% of number of cattle herd sizes in Dire and Yabelo District, Borana zone, southern Ethiopia in 2010/2011year. Although Effect of climate variability and change has become a serious problem on crop cultivation, pastoralism, and agro-pastoralism in Ethiopia, pastoralism, and agro-pastoralism are more vulnerable comparatively. Small-holder farmers have practiced soil and water conservation, improved crop and livestock variety, tree planting, livestock mobility, crop diversification, planting date adjustment, irrigation, agronomic practices, livelihood diversification, integrating livestock with crop production to reduce adverse impact of climate variability and change in the country. Livestock ownership, farm size, extension service, credit service, distance to market, and access to climate information were major factor of adaptation strategies. It concluded that farmers have practiced different adaptation strategies to reduce impact of climate variability and change in different part of the country. The study suggest that scholars should conduct their studies in disaggregate way for impact and adaptation to climate related problem and the corresponding factors across agro-ecologies in Ethiopia.

High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method

High-resolution climate model projections for a range of emission scenarios are needed for designing regional and local adaptation strategies and planning in the context of climate change. To this end, the future climate simulations of global circulation models (GCMs) are the main sources of critical information. However, these simulations are not only coarse in resolution but also associated with biases and high uncertainty. To make the simulations useful for impact modeling at regional and local level, we utilized the bias correction constructed analogues with quantile mapping reordering (BCCAQ) statistical downscaling technique to produce a 10 km spatial resolution climate change projections database based on 16 CMIP6 GCMs under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). The downscaling strategy was evaluated using a perfect sibling approach and detailed results are presented by taking two contrasting (the worst and best performing models) GCMs as a showcase. The evaluation results demonstrate that the downscaling approach substantially reduced model biases and generated higher resolution daily data compared to the original GCM outputs.