Narrow Results

This paper presents an empirical analysis of the role of different climate change adaptation strategies in supporting food productivity in Ethiopia. The analysis relies on unique primary survey data on 1000 farms producing cereal crops in the Nile Basin, Ethiopia. Based on monthly collected meteorological station data, the Thin Plate Spline method of spatial interpolation is used to impute the household specific rainfall and temperature values of each household. The rainfall data is disaggregated at season level (Meher and Belg). Econometric results show that the implementation of adaptation strategies supports farm productivity. Changing crops is found to be the most successful strategy, followed by the implementation of soil conservation and tree planting. We complement the analysis with some evidence on the determinants of adaptation. We find that extension services (both formal and farmer-to-farmer) and information on future climate changes affect positively and significantly the probability of adaptation through changing crops and tree planting. This finding highlights the crucial role played by information dissemination in improving farmers' decision-making.

Many studies of climate change adaptation have relied on farmers’ perceptions of climate change to explain why farmers are adopting new farming methods, and to advise adaptation policy framework that justifies Climate Smart Agriculture (CSA) especially in Africa. These studies have rarely verified whether farmers’ perceptions are consistent with observed changes in meteorological conditions to establish sufficient premise. This study compares farmers’ perceptions of changes in precipitation and temperature in a rainfed agriculture region of Ghana against objective measurements made in nearby weather stations in the region. The study finds that farmers correctly perceived the increase in temperature over time but incorrectly perceived a reduction in precipitation, while objective data showed high fluctuations with no clear trend. It is possible that farmers mistakenly assumed reduction in soil moisture meant to support crop growth requirements was caused by less rainfall when in fact it was caused by higher temperature.

The North American Multi-Model Ensemble (NMME) has grown into a fully developed scientific database for seasonal and sub-seasonal climate forecasts, progressing prediction from global to regional scales. The NMME has continuously developed, with new models replacing old ones; it is hypothesized that this development will generate more accurate forecasts over time. However, to date, this hypothesis has not been verified in Central Africa (CA). This study investigates the hypothesis that the skill of NMME models will increase as the forecasting system advances, focusing on rainfall in CA. The study is conducted for the four configuration (phases) of NMME models, from the oldest to the most recent. The analyses are performed with Short Lead (SL) time and Long Lead (LL) time hindcasts very coherent with the perspectives of the CA. The results show from configuration 1 (phase 1) to configurations 4 (phase 4), the NMME models reasonably replicate the spatial structures in the seasonal rainfall climatology of the observations with a remarkable bias at LL. The mean absolute error and root mean square difference reveal small but incremental improvements in the prediction skills of NMME models from phase 1 to phase 4. The Pearson coefficient (r) increased in SL by about 1%, i.e., from 0.94 to 0.95 during June–August (JJA) season and about 4% during the September–November (SON), i.e., from $r=0.90$ in phase 1 to $r=0.94$ in phase 4, about 3% from phase 1 to phase 4 during the March–May (MAM). The categorical scores show that the Probability of Detection (POD) and False Alarm (FAR) increased very slightly from phase 1 to phase 4, but is it noted that the different combinations of the NMME forecasting system present difficulties in predicting rainy and dry events. It should be added that by introducing newer models into a multi-model ensemble as they are developed, and by eliminating older models, small skill gains are observed in the NMME forecasting system in CA.

Climate change poses mounting risks to agricultural development and rural livelihoods in Nigeria. This study investigates the impacts of climate change on agricultural sector employment in Nigeria. Agriculture provides income and sustenance for much of Nigeria’s rural population. However, smallholder rain-fed farming predominates, with minimal resilience to climate shifts. Historical data reveal rising temperatures and declining, erratic rainfall across Nigeria’s agro-ecological zones since the 1970s. Crop modeling predicts further climate changes will reduce yields of key staple crops. This threatens the viability of smallholder agriculture and risks widespread job losses. The study adopts a nonlinear autoregressive distributed lag (NARDL) modeling approach to evaluate climate change effects on agricultural sector employment in Nigeria from 1990 to 2020. Findings reveal reduced rainfall initially raises employment, as farming requires more labor in dry conditions. However, protracted droughts significantly reduce agricultural jobs. Increased temperatures consistently lower farm employment through reduced yields and incomes. Based on these findings, the study recommends that adaptive strategies are urgently needed to build resilience, promote climate-smart agriculture, and safeguard rural livelihoods.

Based on temperature and rainfall recorded at 34 meteorological stations in Bangladesh during 1989–2018, the trends of yearly average maximum and minimum temperatures have been found to be increasing at the rates of 0.025^∘C and 0.018^∘C per year. Analysis of seasonal average maximum temperature showed increasing trend for all seasons except the late autumn season. The increasing trend was particularly significant for summer, rainy and autumn seasons. Seasonal average minimum temperature data also showed increasing trends for all seasons. The trend of yearly average rainfall has been found to be decreasing at a rate of 0.014mm per year in the same period; especially, for most of the meteorological stations the rainfall demonstrates an increasing trend for rainy season and a decreasing trend in the winter season. It means that in Bangladesh dry periods became drier and wet periods became wetter.

As one of the poorest countries in the world, agriculture is Bangladesh’s economic pillar, leading to Bangladesh’s economy becoming vulnerable to global warming. The generation of high-resolution climate predictions in Bangladesh can help to reduce the huge damage and losses inflicted by disasters linked to climate. The statistical downscaling model (SDSM) is the most widely used software on robust climate downscaling and prediction analysis. In this study, by using the SDSM model, we established the statistical relationship between observed climate data in Bangladesh and the large-scale low-resolution NCEP data and used three statistical indicators to evaluate the prediction performance of the SDSM software. Our results show that the SDSM software is more suitable for forecasting humidity/temperature in Bangladesh than rainfall.

Through warming of the oceans, human-induced climate change effects accumulate. The result is higher ocean heat content (OHC), sea levels and sea surface temperatures (SSTs). The resulting environment invigorates tropical cyclones to make them more intense, bigger and longer lasting, and greatly increases their flooding rains. Here, the focus is on Atlantic hurricanes in 2017, featuring Harvey, Irma and Maria, and the huge damage that occurred. Hurricanes keep tropical oceans cooler as a consequence of their strong winds that increase evaporation. Planning for such supercharged hurricanes (adaptation) by increasing resilience (e.g., better building codes, flood protection) and preparing for contingencies (such as evacuation routes, power cuts) is essential but not adequate in many areas, including Texas, Florida and Puerto Rico where Harvey, Irma and Maria took their toll.