Dataset of the suitability of major food crops in Africa under climate change.

Understanding the extent and adapting to the impacts of climate change in the agriculture sector in Africa requires robust data on which technical and policy decisions can be based. However, there are no publicly available comprehensive data of which crops are suitable where under current and projected climate conditions for impact assessments and targeted adaptation planning. We developed a dataset on crop suitability of 23 major food crops (eight cereals, six legumes & pulses, six root & tuber crops, and three in banana-related family) for rainfed agriculture in Africa in terms of area and produced quantity. This dataset is based on the EcoCrop model parameterized with temperature, precipitation and soil data and is available for the historical period and until mid-century. The scenarios used for future projections are SSP1:RCP2.6, SSP3:RCP7.0 and SSP5:RCP8.5. The dataset provides a quantitative assessment of the impacts of climate change on crop production potential and can enable applications and linkages of crop impact studies to other socioeconomic aspects, thereby facilitating more comprehensive understanding of climate change impacts and assessment of options for building resilience.

A forecast of staple crop production in Burkina Faso to enable early warnings of shortages in domestic food availability.

Almost half of the Burkinabe population is moderately or severely affected by food insecurity. With climate change, domestic food production may become more under pressure, further jeopardizing food security. In this study, we focus on the production of maize, sorghum and millet as staple cereal crops in Burkina Faso to assess food availability as one component of food security. Based on a statistical weather-driven crop model, we provide a within-season forecast of crop production 1 month before the harvest. Hindcast results from 1984 to 2018 produce an r2 of 0.95 in case of known harvest areas and an r2 of 0.88 when harvest areas are modelled instead. We compare actually supplied calories with those usually consumed from staple crops, allowing us to provide early information on shortages in domestic cereal production on the national level. Despite the-on average-sufficient domestic cereal production from maize, sorghum and millet, a considerable level of food insecurity prevails for large parts of the population. We suggest to consider such forecasts as an early warning signal for shortages in domestic staple crop production and encourage a comprehensive assessment of all dimensions of food security to rapidly develop counteractions for looming food crises.

Climate change and specialty coffee potential in Ethiopia.

Current climate change impact studies on coffee have not considered impact on coffee typicities that depend on local microclimatic, topographic and soil characteristics. Thus, this study aims to provide a quantitative risk assessment of the impact of climate change on suitability of five premium specialty coffees in Ethiopia. We implement an ensemble model of three machine learning algorithms to predict current and future (2030s, 2050s, 2070s, and 2090s) suitability for each specialty coffee under four Shared Socio-economic Pathways (SSPs). Results show that the importance of variables determining coffee suitability in the combined model is different from those for specialty coffees despite the climatic factors remaining more important in determining suitability than topographic and soil variables. Our model predicts that 27% of the country is generally suitable for coffee, and of this area, only up to 30% is suitable for specialty coffees. The impact modelling showed that the combined model projects a net gain in coffee production suitability under climate change in general but losses in five out of the six modelled specialty coffee growing areas. We conclude that depending on drivers of suitability and projected impacts, climate change will significantly affect the Ethiopian speciality coffee sector and area-specific adaptation measures are required to build resilience.

Robustly forecasting maize yields in Tanzania based on climatic predictors.

Seasonal yield forecasts are important to support agricultural development programs and can contribute to improved food security in developing countries. Despite their importance, no operational forecasting system on sub-national level is yet in place in Tanzania. We develop a statistical maize yield forecast based on regional yield statistics in Tanzania and climatic predictors, covering the period 2009-2019. We forecast both yield anomalies and absolute yields at the sub-national scale about 6 weeks before the harvest. The forecasted yield anomalies (absolute yields) have a median Nash-Sutcliffe efficiency coefficient of 0.72 (0.79) in the out-of-sample cross validation, which corresponds to a median root mean squared error of 0.13 t/ha for absolute yields. In addition, we perform an out-of-sample variable selection and produce completely independent yield forecasts for the harvest year 2019. Our study is potentially applicable to other countries with short time series of yield data and inaccessible or low quality weather data due to the usage of only global climate data and a strict and transparent assessment of the forecasting skill.

Impacts of climate change on agro-climatic suitability of major food crops in Ghana.

Climate change is projected to impact food production stability in many tropical countries through impacts on crop potential. However, without quantitative assessments of where, by how much and to what extent crop production is possible now and under future climatic conditions, efforts to design and implement adaptation strategies under Nationally Determined Contributions (NDCs) and National Action Plans (NAP) are unsystematic. In this study, we used extreme gradient boosting, a machine learning approach to model the current climatic suitability for maize, sorghum, cassava and groundnut in Ghana using yield data and agronomically important variables. We then used multi-model future climate projections for the 2050s and two greenhouse gas emissions scenarios (RCP 2.6 and RCP 8.5) to predict changes in the suitability range of these crops. We achieved a good model fit in determining suitability classes for all crops (AUC = 0.81-0.87). Precipitation-based factors are suggested as most important in determining crop suitability, though the importance is crop-specific. Under projected climatic conditions, optimal suitability areas will decrease for all crops except for groundnuts under RCP8.5 (no change: 0%), with greatest losses for maize (12% under RCP2.6 and 14% under RCP8.5). Under current climatic conditions, 18% of Ghana has optimal suitability for two crops, 2% for three crops with no area having optimal suitability for all the four crops. Under projected climatic conditions, areas with optimal suitability for two and three crops will decrease by 12% as areas having moderate and marginal conditions for multiple crops increase. We also found that although the distribution of multiple crop suitability is spatially distinct, cassava and groundnut will be more simultaneously suitable for the south while groundnut and sorghum will be more suitable for the northern parts of Ghana under projected climatic conditions.

The Economic Impact of Exchanging Breeding Material: Assessing Winter Wheat Production in Germany.

Climate change impacts imply that the stabilization and improvement of agricultural production systems using technological innovations has become vital. Improvements in plant breeding are integral to such innovations. In the context of German crop breeding programs, the economic impact of exchanging genetic material has yet to be determined. To this end, we analyze in this impact assessment the economic effects on German winter wheat production that are attributable to exchanging parental material amongst breeders in the breeding process. This exchange is supported by the breeders' exemption, which is an integral part of the German plant variety protection legislation. It ensures that breeders can freely use licensed varieties created by other breeders for their own breeding activities and aims to speed up the development of improved varieties. For our analysis, we created a unique data set that combines variety-specific grain yield, adoption, and pedigree information of 133 winter wheat varieties. We determined the parental pedigree of each variety to see if a variety was created by interbreeding varieties that are internal or external to its specific breeder. Our study is the first that analyzes the economic impact of exchanging genetic material in German breeding programs. We found that more than 90 % of the tested varieties were bred with exchanged parental material, whereby the majority had two external parents. Also, these varieties were planted on an 8.5 times larger area than the varieties that were bred with two internal parents. Due to lower adoption, these only contributed 11 % to the overall winter wheat production in Germany, even though they yielded more. We used an economic surplus model to measure the benefits of exchanging parental breeding material on German winter wheat production. This resulted in an overall estimated economic surplus of 19.2 to 22.0 billion EUR from production year 1972 to 2018. This implies tremendous returns to using the breeder's exemption, which, from an economic perspective, is almost cost-free for the breeder. We conclude that the exchange of breeding material contributes to improving Germany's agricultural production and fosters the development of climate-resilient production systems and global food security.

Mortality impact of low annual crop yields in a subsistence farming population of Burkina Faso under the current and a 1.5°C warmer climate in 2100.

In subsistence farming populations of sub-Saharan Africa reliant on rainfed agriculture, years of low crop yields result in poorer child nutrition and survival. Estimates of such impacts are critical for their reduction and prevention. We developed a model to quantify such health impacts, and the degree to which they are attributable to weather variations, for a subsistence farming population in the Nouna district of Burkina Faso (89,000 people in 2010). The method combines data from a new weather-crop yield model with empirical epidemiological risk functions. We quantify the child mortality impacts for 1984-2012 using observed weather data and estimate potential future burdens in 2050 and 2100 using daily weather data generated by global climate models parameterized to simulate global warming of 1.5°C above pre-industrial levels. For 1984-2012, crop yields below 90% of the period average were estimated to result in the total of 109.8 deaths per 10,000 children <5years, or around 7122.0years of life lost, 72% of which are attributable to unfavourable weather conditions in the crop growing season. If all non-weather factors are assumed to remain unchanged, the mortality burden related to low crop yields would increase about twofold under 1.5°C global warming by 2100. These results emphasize the importance and value of developing strategies to protect against the effects of low crop yields and specifically the adverse impact of unfavourable weather conditions in such settings under the current and future climate.

Farmer typology to understand differentiated climate change adaptation in Himalaya.

Smallholder farmers' responses to the climate-induced agricultural changes are not uniform but rather diverse, as response adaptation strategies are embedded in the heterogonous agronomic, social, economic, and institutional conditions. There is an urgent need to understand the diversity within the farming households, identify the main drivers and understand its relationship with household adaptation strategies. Typology construction provides an efficient method to understand farmer diversity by delineating groups with common characteristics. In the present study, based in the Uttarakhand state of Indian Western Himalayas, five farmer types were identified on the basis of resource endowment and agriculture orientation characteristics. Factor analysis followed by sequential agglomerative hierarchial and K-means clustering was use to delineate farmer types. Examination of adaptation strategies across the identified farmer types revealed that mostly contrasting and type-specific bundle of strategies are adopted by farmers to ensure livelihood security. Our findings show that strategies that incurred high investment, such as infrastructural development, are limited to high resource-endowed farmers. In contrast, the low resourced farmers reported being progressively disengaging with farming as a livelihood option. Our results suggest that the proponents of effective adaptation policies in the Himalayan region need to be cognizant of the nuances within the farming communities to capture the diverse and multiple adaptation needs and constraints of the farming households.

Global evaluation of a semiempirical model for yield anomalies and application to within-season yield forecasting.

Quantifying the influence of weather on yield variability is decisive for agricultural management under current and future climate anomalies. We extended an existing semiempirical modeling scheme that allows for such quantification. Yield anomalies, measured as interannual differences, were modeled for maize, soybeans, and wheat in the United States and 32 other main producer countries. We used two yield data sets, one derived from reported yields and the other from a global yield data set deduced from remote sensing. We assessed the capacity of the model to forecast yields within the growing season. In the United States, our model can explain at least two-thirds (63%-81%) of observed yield anomalies. Its out-of-sample performance (34%-55%) suggests a robust yield projection capacity when applied to unknown weather. Out-of-sample performance is lower when using remote sensing-derived yield data. The share of weather-driven yield fluctuation varies spatially, and estimated coefficients agree with expectations. Globally, the explained variance in yield anomalies based on the remote sensing data set is similar to the United States (71%-84%). But the out-of-sample performance is lower (15%-42%). The performance discrepancy is likely due to shortcomings of the remote sensing yield data as it diminishes when using reported yield anomalies instead. Our model allows for robust forecasting of yields up to 2 months before harvest for several main producer countries. An additional experiment suggests moderate yield losses under mean warming, assuming no major changes in temperature extremes. We conclude that our model can detect weather influences on yield anomalies and project yields with unknown weather. It requires only monthly input data and has a low computational demand. Its within-season yield forecasting capacity provides a basis for practical applications like local adaptation planning. Our study underlines high-quality yield monitoring and statistics as critical prerequisites to guide adaptation under climate change.