Bernhard Schauberger’s research while affiliated with Potsdam Institute for Climate Impact Research and other places

Adaptation to climate change is often conceptualized as a dichotomy, with responses being either planned (formal and structured) or autonomous (organic and self-organized, often known as “everyday adaptation”). Recent literature on adaptation responses has highlighted the existence and importance of the interplay between autonomous and planned adaptation, but examination of this interaction has been limited to date. We use a global database of 1682 peer-reviewed articles on adaptation responses to systematically examine autonomous and planned adaptations, with an emphasis on how these types of adaptations interact with one another. We propose a third category, mixed adaptation, which demonstrates characteristics of both autonomous and planned types, and which recognizes nuances in how organization, external support, formality, and autonomy manifest in the fuzzy space between the two. We find that more than one-third of articles reporting on adaptation responses fall into this mixed category, with cases across sectors and world regions. We develop a qualitative typology of mixed adaptation that identifies nine ways that autonomous and planned adaptation interact and influence each other both positively and negatively. Based on these findings, we argue for more nuanced examinations of the interplay between autonomous and planned adaptation and for conceptualizing adaptation planning as a continuum between the two rather than a dichotomy. Exploring the patterns of interplay from a large database of adaptation responses offers new insights on the relative roles of both autonomous and planned adaptation for mobilizing adaptation pathways in locally relevant, scalable, effective, and equitable ways.

Whether hydroclimatic extremes cause yield losses or failures not only depends on their intensity but also on local environmental conditions. These conditions shape the capacity to buffer climatic shocks and thus necessitate a regionally specific impact assessment and adaptation planning. However, the degree to which different environmental conditions affect climate impacts on yields and its spatiotemporal variability across Germany is relatively unknown. In this study, we use a regression-based crop-climate modelling approach for 71 regions, classified according to soil and climate characteristics and investigate region-specific vulnerabilities of winter wheat yields to hydroclimatic extremes for the period 1991-2019. We account for the co-occurrence of temperature and moisture impacts (i.e. compound effects) as well as for local soil-climate conditions. On average, our models can explain approx. 67 % of past winter wheat yield variations. Despite the rather homogeneous climate in Germany, the results reveal clear geographic differences across different soil-climate regions. While the northeastern regions show a clear dominance of drought impacts, southern regions show stress due to moisture excess. Heat impacts can clearly be linked to the warm regions along the western part of the country. Overall, compound dry-hot extremes pose the strongest and most widespread risk for winter wheat yields in Germany, being responsible for approx. 38 % and in some regions for up to 50 % of past yield variations. Based on the identified regional differences in hydroclimate susceptibility, we can define four geographic risk clusters, which exhibit vulnerability to climatic extremes such as summer droughts, winter droughts, summer heat waves, and winter moisture excess. The identified risk clusters of heat and moisture stresses could inform regional-specific adaptation planning.

Coffee, an important global commodity, is threatened by climate change. Agroforestry has been considered as one option to maintain or enhance coffee production. In this study, we use a machine learning ensemble consisting of MaxEnt, Random Forest and Boosted Regression Trees to assess climate change impacts on the suitability to grow Arabica coffee, Robusta coffee and bananas in Uganda by 2050. Based on this, the buffering potential of Cordia africana and Ficus natalensis, the two commonly used shading trees in agroforestry systems is assessed. Our robust models (AUC of 0.7–0.9) indicate temperature-related variables as relevant for Arabica coffee suitability, while precipitation-related variables determine Robusta coffee and banana suitability. Under current climatic conditions, only a quarter of the total land area is suitable for growing Arabica coffee, while over three-quarters are suitable for Robusta coffee and bananas. Our results suggest that climate change will reduce the area suitable to grow Arabica coffee, Robusta coffee and bananas by 20%, 9% and 3.5%, respectively, under SSP3-RCP7.0 by 2050. A shift in areas suitable for Arabica coffee to highlands might occur, leading to potential encroachment on protected areas. In our model, implementing agroforestry with up to 50% shading could partially offset suitable area losses for Robusta coffee—but not for Arabica coffee. The potential to produce valuable Arabica coffee thus decreases under climate change and cannot be averted by agroforestry. We conclude that the implementation and design of agroforestry must be based on species, elevation, and regional climate projections to avoid maladaptation.

Northern Kazakhstan is a major wheat exporter, contributing to food security in Central Asia and beyond. However, wheat yields fluctuate and low-producing years occur frequently. It is currently unclear to what extent human-induced climate change contributes to this. The most severe low-producing year in this century was in 2010, which had severe consequences for the food security of wheat-importing countries. Here, we present a climate impact attribution study that quantifies the impact of human-induced climate change on the average wheat production and associated economic revenues in northern Kazakhstan in the 21st century and on the likelihood of a low-production year like 2010. The study uses bias-adjusted counterfactual and factual climate model data from two large ensembles of latest-generation climate models as input to a statistical subnational yield model. We consider the climate data and the yield model as fit for purpose as first, the factual climate simulations represent the observations, second, the out-of-sample validation of the yield model performs reasonably well with a mean R ² of 0.54, and third, the results are robust under the performed sensitivity tests. Human-induced climate change has had a critical impact on wheat production, specifically through increases in daily-minimum temperatures and extreme heat. This has resulted in a decrease in yields during 2000–2019 by approximately 6.2%–8.2% (uncertainty range of two climate models) and an increased likelihood of the 2010 low-production event by 1.5–4.7 times (10th to 90th percentile uncertainty range covering both climate models). During 2000–2019, human-induced climate change caused economic losses estimated at between 96 and 180 million USD per year (10th to 90th percentile uncertainty range covering both climate models). These results highlight the necessity for ambitious global mitigation efforts and measures to adapt wheat production to increasing temperatures, ensuring regional and global food security.

Climate change-induced precipitation anomalies during extremely wet years (EWYs) result in substantial nitrogen losses to aquatic ecosystems (N w). Still, the extent and drivers of these losses, and effective mitigation strategies have remained unclear. By integrating global datasets with well-established crop modeling and machine learning techniques, we reveal notable increases in N w , ranging from 22 to 56%, during historical EWYs. These pulses are projected to amplify under the SSP126 (SSP370) scenario to 29 to 80% (61 to 120%) due to the projected increases in EWYs and higher nitrogen input. We identify the relative precipitation difference between two consecutive years (diffPr) as the primary driver of extreme N w. This finding forms the basis of the CLimate Extreme Adaptive Nitrogen Strategy (CLEANS), which scales down nitrogen input adaptively to diffPr, leading to a substantial reduction in extreme N w with nearly zero yield penalty. Our results have important implications for global environmental sustainability and while safeguarding food security.

That climate variability and change can potentially force multiple simultaneous breadbasket crop yield shocks has been established. But research quantifying the mechanisms behind such simultaneous shocks has been constrained by short records of crop yields. Here we compile a dataset of subnational crop yields in 25 countries dating back to 1900 to study the frequency and trends in multiple breadbasket yield shocks and how large-scale climate anomalies on interannual timescales have affected multiple breadbasket yield shocks over the last century. We find that major simultaneous breadbasket yield shocks have occurred in at least three, four, or five of nine breadbaskets 10.3%, 2.3% and 1.1% of the time for maize and 18.4%, 4.6% and 2.3% of the time for wheat. Furthermore, we find that multiple breadbasket yield shocks decreased in frequency even as those breadbaskets experience increasingly frequent climate-related shocks. For both maize and wheat breadbaskets, there were fewer simultaneous yield shocks during the 1975-2017 time period as compared to 1931-1975. Finally, we find that interannual modes of climate variability-such as the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the North Atlantic Oscillation (NAO)-have all affected the relative probability of simultaneous yield shocks in pairs of breadbaskets by up to 20-40% in both maize and wheat breadbaskets. While past literature has focused on the effects of ENSO, we find that at the global scale the NAO affects the overall number of wheat yield shocks most strongly despite only affecting northern hemisphere breadbaskets.

Adaptation strategies sustaining agricultural production under climate change are urgently required in Sub-Saharan Africa. To quantify the impacts of different adaptation options in Burkina Faso, this study simulated sorghum yields under current and projected climatic conditions with and without adaptation. We used the Decision Support System for Agrotechnology Transfer (DSSAT) at 0.5° spatial resolution (around 55 km) and forced the model with two climate change scenarios. Our calibrated model showed good agreement between reported and simulated yields (Pearson’s r = 0.77; out-of-sample r = 0.68). DSSAT was configured to mimic four distinct adaptation measures: integrated soil fertility management (ISFM), irrigation, an improved variety, and agroforestry. Results show that nationally averaged sorghum yields are projected to decrease by 5.5% under high emissions by 2090 without adaptation. Major yield losses (up to 35%) would occur in the southern and western parts of the country. Our assessments identify ISFM as the most effective adaptation strategy, increasing yield up to 300%, followed by agroforestry (up to 125%), an improved variety (up to 90%), and irrigation (up to 43%) at the regional scale. ISFM is effective across all regions, while irrigation and an improved variety are most effective in the northern and western parts. Agroforestry, meanwhile, is most effective in the south and eastern part of the country. We conclude that climate change in Burkina Faso could negatively affect sorghum yields, but adequate adaptation options exist to enhance agricultural resilience.

Agricultural performance is influenced by environmental conditions, management decisions and economic circumstances. It is important to quantify their respective contribution to allow for detecting major hazards to production, projecting future yields under climate change and deriving adaptation options. For this purpose, time series of agricultural yields with high spatial and long-term temporal resolution are a primary requisite. Here we present a data set of crop performance in France, one of Europe’s major crop producers. The data set comprises ten crops (barley, maize, oats, potatoes, rapeseed, sugarbeet, sunflower, durum wheat, soft wheat and wine) and covers the years 1900 to 2018. It contains harvested area, production and yield data for all 96 French départements (i.e. counties or NUTS3 level) with a total number of 375,264 data points. Entries until 1988 have been digitized manually from statistical yearbooks. The technical validation indicates a high consistency of the data set within itself and with external resources. The data set may contribute to an enhanced understanding of the manifold influences on agricultural performance.