Given the sensitivity of the agricultural sector to climate variability and change, a comprehensive understanding of environmental factors to this sector and the extent to which climate change may alter such factors is critical for planning and adaptation strategies. This study aims to assess the extent of extreme climate conditions and livestock-relevant maps of agro-climatic zones across East Africa in current and future climates. Ensembles of seven global climate models selected from the sixth Coupled Model Intercomparison Project (CMIP6) are considered under SSP245 and SSP585 socio-economic pathways. The water extreme/stress indicators considered include indices to indicate drought and flood situations. The heat stress indicators are composed of the frequency of hot spells and the duration and intensity of heat waves. As expected, all heat stress indicators are projected to increase in future climates. The frequency and intensity of heavy precipitation events are also projected to increase with increased GHG emissions. The drought stress indicators follow the precipitation pattern and are projected to decline over most of the domain. A heterogeneous response of the agro-climate regime to climate changes is projected for eastern Africa, with some areas (e.g., southern Tanzania) experiencing alterations towards drier zones while others (e.g., northern Somalia, South Sudan) are experiencing a shift towards wetter zones. The increase in short-duration heavy precipitation events together with the enhanced pace of heat stress over the region, will have critical implications for agriculture in general and local livestock production in particular.

Given the sensitivity of the agricultural sector to climate variability and change, a comprehensive understanding of environmental factors to this sector and the extent to which climate change may alter such factors is critical for planning and adaptation strategies. This study aims to assess the extent of extreme climate conditions and livestock-relevant maps of agro-climatic zones across East Africa in current and future climates. Ensembles of seven global climate models selected from the sixth Coupled Model Intercomparison Project (CMIP6) are considered under SSP245 and SSP585 socio-economic pathways. The water extreme/stress indicators considered include indices to indicate drought and flood situations. The heat stress indicators are composed of the frequency of hot spells and the duration and intensity of heat waves. As expected, all heat stress indicators are projected to increase in future climates. The frequency and intensity of heavy precipitation events are also projected to increase with increased GHG emissions. The drought stress indicators follow the precipitation pattern and are projected to decline over most of the domain. A heterogeneous response of the agro-climate regime to climate changes is projected for eastern Africa, with some areas (e.g., southern Tanzania) experiencing alterations towards drier zones while others (e.g., northern Somalia, South Sudan) are experiencing a shift towards wetter zones. The increase in short-duration heavy precipitation events together with the enhanced pace of heat stress over the region, will have critical implications for agriculture in general and local livestock production in particular.

The South Atlantic Convergence Zone (SACZ) is an intrinsic characteristic of the South American Summer Monsoon. In a recent study, we verified that the main mode of coupled variability over the South Atlantic (South Atlantic Dipole (SAD)) plays a role in modulating the position of extratropical cyclones that affect the SACZ precipitation. In this study, we perform numerical experiments to further investigate the mechanisms between SAD and the SACZ. Numerical experiments forced with prescribed SST anomalies showed that, even though the Atlantic SST affects the position of the cyclone associated with the SACZ, the atmospheric response and precipitation patterns over land are opposed to the observations. On the other hand, experiments forced with prescribed anomalous driving fields showed that the atmospheric component of SAD plays a significant role for the right position and intensity of precipitation associated with the SACZ. SAD negative anomalies provide the low-level and upper-level atmospheric support for the intensification of the cyclone at surface and for the increase in precipitation over the land portion of the SACZ. Therefore, the numerical experiments suggest that, during El Niño Southern Oscillation neutral conditions, the SACZ precipitation variability associated with SAD is largely dependent on the atmospheric variability rather than the underlying SST.

The South Atlantic Convergence Zone (SACZ) is an intrinsic characteristic of the South American Summer Monsoon. In a recent study, we verified that the main mode of coupled variability over the South Atlantic (South Atlantic Dipole (SAD)) plays a role in modulating the position of extratropical cyclones that affect the SACZ precipitation. In this study, we perform numerical experiments to further investigate the mechanisms between SAD and the SACZ. Numerical experiments forced with prescribed SST anomalies showed that, even though the Atlantic SST affects the position of the cyclone associated with the SACZ, the atmospheric response and precipitation patterns over land are opposed to the observations. On the other hand, experiments forced with prescribed anomalous driving fields showed that the atmospheric component of SAD plays a significant role for the right position and intensity of precipitation associated with the SACZ. SAD negative anomalies provide the low-level and upper-level atmospheric support for the intensification of the cyclone at surface and for the increase in precipitation over the land portion of the SACZ. Therefore, the numerical experiments suggest that, during El Niño Southern Oscillation neutral conditions, the SACZ precipitation variability associated with SAD is largely dependent on the atmospheric variability rather than the underlying SST.

Maize is grown by millions of smallholder farmers in South Asia (SA) under diverse environments. The crop is grown in different seasons in a year with varying exposure to weather extremes, including high temperatures at critical growth stages which are expected to increase with climate change. This study assesses the impact of current and future heat stress on maize and the benefit of heat-tolerant varieties in SA. Annual mean maximum temperatures may increase by 1.4–1.8 °C in 2030 and 2.1–2.6 °C in 2050, with large monthly, seasonal, and spatial variations across SA. The extent of heat stressed areas in SA could increase by up to 12 % in 2030 and 21 % in 2050 relative to the baseline. The impact of heat stress and the benefit from heat-tolerant varieties vary with the level of temperature increase and planting season. At a regional scale, climate change would reduce rainfed maize yield by an average of 3.3–6.4 % in 2030 and 5.2–12.2 % in 2050 and irrigated yield by 3–8 % in 2030 and 5–14 % in 2050 if current varieties were grown under the future climate. Under projected climate, heat-tolerant varieties could minimize yield loss (relative to current maize varieties) by up to 36 and 93 % in 2030 and 33 and 86 % in 2050 under rainfed and irrigated conditions, respectively. Heat-tolerant maize varieties, therefore, have the potential to shield maize farmers from severe yield loss due to heat stress and help them adapt to climate change impacts.

Maize is grown by millions of smallholder farmers in South Asia (SA) under diverse environments. The crop is grown in different seasons in a year with varying exposure to weather extremes, including high temperatures at critical growth stages which are expected to increase with climate change. This study assesses the impact of current and future heat stress on maize and the benefit of heat-tolerant varieties in SA. Annual mean maximum temperatures may increase by 1.4–1.8 °C in 2030 and 2.1–2.6 °C in 2050, with large monthly, seasonal, and spatial variations across SA. The extent of heat stressed areas in SA could increase by up to 12 % in 2030 and 21 % in 2050 relative to the baseline. The impact of heat stress and the benefit from heat-tolerant varieties vary with the level of temperature increase and planting season. At a regional scale, climate change would reduce rainfed maize yield by an average of 3.3–6.4 % in 2030 and 5.2–12.2 % in 2050 and irrigated yield by 3–8 % in 2030 and 5–14 % in 2050 if current varieties were grown under the future climate. Under projected climate, heat-tolerant varieties could minimize yield loss (relative to current maize varieties) by up to 36 and 93 % in 2030 and 33 and 86 % in 2050 under rainfed and irrigated conditions, respectively. Heat-tolerant maize varieties, therefore, have the potential to shield maize farmers from severe yield loss due to heat stress and help them adapt to climate change impacts.

Monitoring of genetic gain in crop genetic improvement programs is necessary to measure the efficiency of the program. Periodic measurement of genetic gain also allows the efficiency of new technologies incorporated into a program to be quantified. Genetic gain within the International Maize and Wheat Improvement Centre (CIMMYT) breeding program for eastern and southern Africa were estimated using time series of maize (Zea mays L.) hybrids. A total of 67 of the best-performing hybrids from regional trials from 2000 to 2010 were selected to form an era panel and evaluated in 32 trials in eight locations across six countries in eastern and southern Africa. Treatments included optimal management, managed and random drought stress, low-nitrogen (N) stress and maize streak virus (MSV) infestation. Genetic gain was estimated as the slope of the regression of grain yield on the year of hybrid release. Genetic gain under optimal conditions, managed drought, random drought, low N, and MSV were estimated to have increased by 109.4, 32.5, 22.7, 20.9 and 141.3 kg ha−1 yr−1, respectively. These results are comparable with genetic gain in maize yields in other regions of the world. New technologies to further increase the rate of genetic gain in maize breeding for eastern and southern Africa are also discussed. | 959-970

Soil moisture is one of the essential climate variables with a potential impact on local climate variability. Despite the importance of soil moisture, studies on soil moisture characteristics in Ethiopia are less documented. In this study, the spatiotemporal variability of Ethiopian soil moisture (SM) has been characterized, and its local and remote influential driving factors are investigated. An empirical orthogonal function (EOF) and KMeans clustering algorithm have been employed to classify the large domain into homogeneous zones. Complex maximum covariance analysis (CMCA) is applied to evaluate the covariability between SM and selected local and remote variables such as rainfall (RF), evapotranspiration (ET), and sea surface temperature (SST). Inter-comparison among SM datasets highlight that the FLDAS dataset better depicts the country’s SM spatial and temporal distribution (i.e., a correlation coefficient, with observations). Results also indicate that regions located in northeastern Ethiopia are drier irrespective of the season (JJAS, MAM, and OND) considered. In contrast, the western part of the country consistently depicted a wetter condition in all seasons. During summer (JJAS), the soil moisture variability is characterized by a strong east–west spatial contrast. The highest and lowest soil moisture values were observed across the country’s central western and eastern parts, respectively. Furthermore, analyses indicate that interannual variability of SM is dictated substantially by RF, though the impact on some regions is weaker. It is also found that ET likely drives the SM in the eastern part of Ethiopia due to a higher atmospheric moisture demand that ultimately invokes changes in surface humidity and rainfall. A composite analysis based on the extreme five wettest and driest SM years revealed a similar spatial distribution of wet SM with positive anomalies of RF across the country and ET over the southern regions. Remote SSTs are also found to have a significant influence on SM distribution. In particular, equatorial central Pacific and western Indian oceans SST anomalies are predominant factors for spatiotemporal SM variations over the country. Major global oceanic indices: Oceanic Nino Index (ONI), Indian Ocean Dipole (IOD), Pacific warm pool (PACWARMPOOL), and Pacific Decadal Oscillations (PDO) are found to be closely associated with the SM anomalies in various parts of the country. The associationship between these remote SST anomalies and local soil moisture is via large-scale atmospheric circulations that are linked to regional factors such as precipitation and temperature anomalies.

Soil moisture is one of the essential climate variables with a potential impact on local climate variability. Despite the importance of soil moisture, studies on soil moisture characteristics in Ethiopia are less documented. In this study, the spatiotemporal variability of Ethiopian soil moisture (SM) has been characterized, and its local and remote influential driving factors are investigated. An empirical orthogonal function (EOF) and KMeans clustering algorithm have been employed to classify the large domain into homogeneous zones. Complex maximum covariance analysis (CMCA) is applied to evaluate the covariability between SM and selected local and remote variables such as rainfall (RF), evapotranspiration (ET), and sea surface temperature (SST). Inter-comparison among SM datasets highlight that the FLDAS dataset better depicts the country’s SM spatial and temporal distribution (i.e., a correlation coefficient r=0.95 , rmsd=0.04m3m−3 with observations). Results also indicate that regions located in northeastern Ethiopia are drier irrespective of the season (JJAS, MAM, and OND) considered. In contrast, the western part of the country consistently depicted a wetter condition in all seasons. During summer (JJAS), the soil moisture variability is characterized by a strong east–west spatial contrast. The highest and lowest soil moisture values were observed across the country’s central western and eastern parts, respectively. Furthermore, analyses indicate that interannual variability of SM is dictated substantially by RF, though the impact on some regions is weaker. It is also found that ET likely drives the SM in the eastern part of Ethiopia due to a higher atmospheric moisture demand that ultimately invokes changes in surface humidity and rainfall. A composite analysis based on the extreme five wettest and driest SM years revealed a similar spatial distribution of wet SM with positive anomalies of RF across the country and ET over the southern regions. Remote SSTs are also found to have a significant influence on SM distribution. In particular, equatorial central Pacific and western Indian oceans SST anomalies are predominant factors for spatiotemporal SM variations over the country. Major global oceanic indices: Oceanic Nino Index (ONI), Indian Ocean Dipole (IOD), Pacific warm pool (PACWARMPOOL), and Pacific Decadal Oscillations (PDO) are found to be closely associated with the SM anomalies in various parts of the country. The associationship between these remote SST anomalies and local soil moisture is via large-scale atmospheric circulations that are linked to regional factors such as precipitation and temperature anomalies. | publishedVersion

This study investigates the spatial-temporal trends and variability of rainfall within East and South Africa (ESA) region. The newly available Climate Hazards group Infrared Precipitation with Stations (CHIRPS-v2) gridded data spanning 37 years (1981 to 2017) was validated against gauge observations (N = 4243) and utilised to map zones experiencing significant monotonic rainfall trends. Standardised annual rainfall anomalies revealed the spatial-temporal distribution of below and above normal rains that are associated with droughts and floods respectively. Results showed that CHIRPS-v2 data had a satisfactory skill to estimate monthly rainfall with Kling-Gupta efficiency (KGE = 0.68 and a high temporal agreement (r = 0.73) while also preserving total amount (β = 0.99) and variability (γ = 0.8). Two contiguous zones with significant increase in annual rainfall (3–15 mm year−1) occurred in Southwest Zambia and in Northern Lake Victoria Basin between Kenya and Uganda. The most significant decrease in annual rainfall (− 20 mm year−1) was recorded at Mount Kilimanjaro in Tanzania. Other significant decreases in annual rainfall ranging between − 4 and − 10 mm year−1 were observed in Southwest Tanzania, Central-South Kenya, Central Uganda and Western Rwanda. CHIRPS-v2 rainfall product provides reliable high spatial resolution information on amount of rainfall that can complement sparse rain gauge network in rain-fed agricultural systems in ESA region. The observed spatial-temporal trends and variability in rainfall are important basis for guiding targeting of appropriate adaptive measures across multiple sectors.

Journal Article | This study investigates the spatial-temporal trends and variability of rainfall within East and South Africa (ESA) region. The newly available Climate Hazards group Infrared Precipitation with Stations (CHIRPS-v2) gridded data spanning 37 years (1981 to 2017) was validated against gauge observations (N = 4243) and utilised to map zones experiencing significant monotonic rainfall trends. Standardised annual rainfall anomalies revealed the spatial-temporal distribution of below and above normal rains that are associated with droughts and floods respectively. Results showed that CHIRPS-v2 data had a satisfactory skill to estimate monthly rainfall with Kling-Gupta efficiency (KGE = 0.68 and a high temporal agreement (r = 0.73) while also preserving total amount (β = 0.99) and variability (γ = 0.8). Two contiguous zones with significant increase in annual rainfall (3–15 mm year−1 ) occurred in Southwest Zambia and in Northern Lake Victoria Basin between Kenya and Uganda. The most significant decrease in annual rainfall (− 20 mm year−1 ) was recorded at Mount Kilimanjaro in Tanzania. Other significant decreases in annual rainfall ranging between − 4 and − 10 mm year−1 were observed in Southwest Tanzania, Central-South Kenya, Central Uganda and Western Rwanda. CHIRPS-v2 rainfall product provides reliable high spatial resolution information on amount of rainfall that can complement sparse rain gauge network in rain-fed agricultural systems in ESA region. The observed spatial-temporal trends and variability in rainfall are important basis for guiding targeting of appropriate adaptive measures across multiple sectors.

In the Sahel region, seasonal predictions are crucial to alleviate the impacts of climate variability on populations' livelihoods. Agricultural planning (e.g., decisions about sowing date, fertilizer application date, and choice of crop or cultivar) is based on empirical predictive indices whose accuracy to date has not been scientifically proven. This paper attempts to statistically test whether the pattern of rainfall distribution over the May–July period contributes to predicting the real onset date and the nature (wet or dry) of the rainy season, as farmers believe. To that end, we considered historical records of daily rainfall from 51 stations spanning the period 1920–2008 and the different agro-climatic zones in Burkina Faso. We performed (1) principal component analysis to identify climatic zones, based on the patterns of intra-seasonal rainfall, (2) and linear discriminant analysis to find the best rainfall-based variables to distinguish between real and false onset dates of the rainy season, and between wet and dry seasons in each climatic zone. A total of nine climatic zones were identified in each of which, based on rainfall records from May to July, we derived linear discriminant functions to correctly predict the nature of a potential onset date of the rainy season (real or false) and that of the rainy season (dry or wet) in at least three cases out of five. These functions should contribute to alleviating the negative impacts of climate variability in the different climatic zones of Burkina Faso.

In the Sahel region, seasonal predictions are crucial to alleviate the impacts of climate variability on populations' livelihoods. Agricultural planning (e.g., decisions about sowing date, fertilizer application date, and choice of crop or cultivar) is based on empirical predictive indices whose accuracy to date has not been scientifically proven. This paper attempts to statistically test whether the pattern of rainfall distribution over the May–July period contributes to predicting the real onset date and the nature (wet or dry) of the rainy season, as farmers believe. To that end, we considered historical records of daily rainfall from 51 stations spanning the period 1920–2008 and the different agro-climatic zones in Burkina Faso. We performed (1) principal component analysis to identify climatic zones, based on the patterns of intra-seasonal rainfall, (2) and linear discriminant analysis to find the best rainfall-based variables to distinguish between real and false onset dates of the rainy season, and between wet and dry seasons in each climatic zone. A total of nine climatic zones were identified in each of which, based on rainfall records from May to July, we derived linear discriminant functions to correctly predict the nature of a potential onset date of the rainy season (real or false) and that of the rainy season (dry or wet) in at least three cases out of five. These functions should contribute to alleviating the negative impacts of climate variability in the different climatic zones of Burkina Faso.