Towards diverse agricultural land uses: socio-ecological implications of European agricultural pathways for a Swiss orchard region.

Diverse agricultural land uses are a typical feature of multifunctional landscapes. The uncertain change in the drivers of global land use, such as climate, market and policy technology and demography, challenges the long-term management of agricultural diversification. As these global drivers also affect smaller scales, it is important to capture the traits of regionally specific farm activities to facilitate adaptation to change. By downscaling European shared socioeconomic pathways (SSPs) for agricultural and food systems, combined with representative concentration pathways (RCP) to regionally specific, alternative socioeconomic and climate scenarios, the present study explores the major impacts of the drivers of global land use on regional agriculture by simulating farm-level decisions and identifies the socio-ecological implications for promoting diverse agricultural landscapes in 2050. A hilly orchard region in northern Switzerland was chosen as a case study to represent the multifunctional nature of Swiss agriculture. Results show that the different regionalised pathways lead to contrasting impacts on orchard meadows, production levels and biodiversity. Increased financial support for ecological measures, adequate farm labour supplies for more labour-intensive farming and consumer preferences that favour local farm produce can offset the negative impacts of climate change and commodity prices and contribute to agricultural diversification and farmland biodiversity. However, these conditions also caused a significant decline in farm production levels. This study suggests that considering a broader set of land use drivers beyond direct payments, while acknowledging potential trade-offs and diverse impacts across different farm types, is required to effectively manage and sustain diversified agricultural landscapes in the long run. Supplementary information: The online version contains supplementary material available at 10.1007/s10113-023-02092-5.

Cross-sectoral impacts of the 2018-2019 Central European drought and climate resilience in the German part of the Elbe River basin.

The 2018-2019 Central European drought was probably the most extreme in Germany since the early sixteenth century. We assess the multiple consequences of the drought for natural systems, the economy and human health in the German part of the Elbe River basin, an area of 97,175 km2 including the cities of Berlin and Hamburg and contributing about 18% to the German GDP. We employ meteorological, hydrological and socio-economic data to build a comprehensive picture of the drought severity, its multiple effects and cross-sectoral consequences in the basin. Time series of different drought indices illustrate the severity of the 2018-2019 drought and how it progressed from meteorological water deficits via soil water depletion towards low groundwater levels and river runoff, and losses in vegetation productivity. The event resulted in severe production losses in agriculture (minus 20-40% for staple crops) and forestry (especially through forced logging of damaged wood: 25.1 million tons in 2018-2020 compared to only 3.4 million tons in 2015-2017), while other economic sectors remained largely unaffected. However, there is no guarantee that this socio-economic stability will be sustained in future drought events; this is discussed in the light of 2022, another dry year holding the potential for a compound crisis. Given the increased probability for more intense and long-lasting droughts in most parts of Europe, this example of actual cross-sectoral drought impacts will be relevant for drought awareness and preparation planning in other regions. Supplementary Information: The online version contains supplementary material available at 10.1007/s10113-023-02032-3.

Choosing multiple linear regressions for weather-based crop yield prediction with ABSOLUT v1.2 applied to the districts of Germany.

ABSOLUT v1.2 is an adaptive algorithm that uses correlations between time-aggregated weather variables and crop yields for yield prediction. In contrast to conventional regression-based yield prediction methods, a very broad range of possible input features and their combinations are exhaustively tested for maximum explanatory power. Weather variables such as temperature, precipitation, and sunshine duration are aggregated over different seasonal time periods preceding the harvest to 45 potential input features per original variable. In a first step, this large set of features is reduced to those aggregates very probably holding explanatory power for observed yields. The second, computationally demanding step evaluates predictions for all districts with all of their possible combinations. Step three selects those combinations of weather features that showed the highest predictive power across districts. Finally, the district-specific best performing regressions among these are used for actual prediction, and the results are spatially aggregated. To evaluate the new approach, ABSOLUT v1.2 is applied to predict the yields of silage maize, winter wheat, and other major crops in Germany based on two decades of data from about 300 districts. It turned out to be absolutely crucial to not only make out-of-sample predictions (solely based on data excluding the target year to predict) but to also consequently separate training and testing years in the process of feature selection. Otherwise, the prediction accuracy would be over-estimated by far. The question arises whether performances claimed for other statistical modelling examples are often upward-biased through input variable selection disregarding the out-of-sample principle.