Thomas Gaiser

Previous studies have shown that rainfed maize yield can be reduced by increases in heavy rainfall events and the associated loss of nitrogen (N) in low-input cropping systems of Sub-Saharan Africa (SSA). However, the attribution of these yield-reducing factors to human-induced climate change has not yet been performed. Here we assess the impact of climate change on maize productivity at five representative locations in SSA (Mali, Benin, Ghana, Ethiopia, and Rwanda) during 2010–2019 using an ensemble of eight process-based crop models.According to factual (all forcings) and counterfactual (no anthropogenic forcing) climate simulations, climate change altered both temperature and precipitation patterns in SSA compared to 1850-fixed greenhouse gas levels. Local warming reduced potential maize yield by an average of 2% despite elevated CO₂, mostly due to shortened growing periods. In Rwanda, Ghana, and Ethiopia, reduced drought stress reversed this decrease and increased water-limited yield. In Benin and Mali, changes were less pronounced, and both potential and water-limited yields decreased.Crop models depict how climate change locally affects combined water-and-N stresses and resulting limited yields. We show that N stress increased in all locations except Mali due to enhanced N leaching associated with increased rainfall. In Ethiopia and Rwanda, increased N stress outweighed reduced drought stress, decreasing water-and-N-limited yield. Water-and-N-limited yield also decreased in Benin and Mali but increased in Ghana, depending on the balance between increased N stress and reduced drought stress. Overall, water-and-N-limited yield decreased in four of the five locations due to human-induced climate change.Finally, N-use efficiency decreased in Rwanda, Mali, and Benin but increased in Ethiopia and Ghana, indicating contrasting opportunities for intensification through additional N inputs. Although crop model uncertainty remains large, these trends were identified by at least 60% of the models, providing a robust basis for implementing agronomic adaptation practices.

Accurate simulation of crop evapotranspiration (ET) is critical for water management and for understanding the impacts of climate variability and change. Yet evidence on model skill in simulating ET remains limited, and the sources of error remain poorly understood. Daily simulations from 17 wheat crop models were evaluated against field observations from two sites in semi-arid and Mediterranean environments under water-limited and irrigated conditions. Absolute ET errors were larger under irrigation, but this contrast was substantially attenuated after normalization by observed ET. The larger absolute errors under irrigation were mainly associated with a greater lack-of-correlation component rather than stronger systematic bias, indicating greater day-to-day mismatch between simulations and observations. Differences among ET-demand approaches also tended to be more evident under irrigation, with Priestley–Taylor-based models tending to produce larger errors, although the broad method classes also differed in other model components. Mixed-effects attribution showed that the conditional associations between ET error and climatic factors differed between the two water-supply datasets. Under irrigation, ET simulation error was associated with vapour pressure deficit (VPD), wind speed, radiation, and the component of maximum temperature not linearly explained by VPD, whereas under water-limited conditions meteorological attribution was weaker and observed soil–water status provided additional information. Root-zone soil moisture provided greater model support than shallow or deep soil moisture and modified the association between VPD and ET error. These findings identify atmospheric-demand representation and root-zone water availability as priorities for improving the evaluation and development of crop ET simulations.

For spring‐sown crops such as maize ( Zea mays L.), the extended growing season enables a substantial contribution of organically derived nitrogen (N), particularly from soil organic matter (SOM) mineralization. Consequently, the soil N mineralization rate (SMR) is a key determinant of maize N uptake and yield formation. Year‐to‐year and spatial variability in climate, soil properties, and management practices introduce considerable fluctuations in SMR, making it a major source of uncertainty in site‐specific N management. Near‐infrared reflectance spectroscopy (NIRS) offers a rapid, cost‐efficient and scalable tool with strong potential for improving SMR estimation and, consequently, site‐specific N management in maize production. In this study, 268 soil samples were collected across Germany with varying SOM content and incubated for 105 days to simulate the maize growing period, allowing laboratory quantification of SMR. Simultaneously, NIRS spectra (1100–2498 nm) were collected, preprocessed, and SMR was modelled using Partial Least Squares Regression (PLSR) and different machine learning (ML) algorithms. SMR was modelled more accurately by ML approaches, with non‐linear models such as the stacked ensemble model ( R ² = 0.72, RMSE = 0.21 kg N ha ⁻¹ day ⁻¹ ) or random forest ( R ² = 0.70, RMSE = 0.20 kg N ha ⁻¹ day ⁻¹ ) outperforming PLSR ( R ² = 0.55, RMSE = 0.33 kg N ha ⁻¹ day ⁻¹ ). Incorporating ancillary soil and management data did not improve performance. NIRS‐ML modelling improved SMR prediction by up to 36%, with slight underestimation of laboratory‐measured SMR values due to indirect spectral relationship with NIRS. Nevertheless, the approach shows great potential as a rapid and scalable tool for SMR estimation, warranting further model refinement and validation across diverse environments.

Nitrogen fertilization strongly influences crop growth, yet its effect on transpiration remains insufficiently understood. This study investigates how nitrogen availability affects growth, canopy development, and transpiration of a historic (Heines II - released 1940) and a modern (Tommi - released 2002) winter wheat cultivar and evaluates the ability of the crop model LintulCC2 to reproduce these responses. Field experiments were conducted over two growing seasons under three fertilization levels (0, 120, and 240 kg N ha-1). Aboveground biomass, nitrogen uptake, leaf area index (LAI), and canopy transpiration were measured throughout the growing season. Nitrogen fertilization increased biomass, LAI, and cumulative transpiration in both cultivars. While biomass responses were similar between cultivars, Heines II showed generally higher LAI and transpiration, whereas nitrogen concentrations in the above ground biomass were mostly higher in Tommi. Under high nitrogen supply, differences between cultivars were mainly driven by tiller numbers, whereas under nitrogen limitation, cultivar-specific differences in water use at the tiller level indicate differences in the physiological regulation of transpiration. The model managed to capture relative differences between cultivars, treatments and years, but showed reduced performance under nitrogen limitation. The results show that nitrogen fertilization effects on transpiration are influenced by both structural and physiological cultivar differences and highlight the need to consider cultivar specific responses towards nitrogen stress and its interaction with crop transpiration in models. Accurate simulation of these processes is essential for reliably assessing management impacts on crop water use and land-atmosphere interactions.

Breeding has significantly improved grain yields in winter wheat ( Triticum aestivum L.). Grain yield improvements were previously attributed to changes in biomass partitioning and canopy architecture, largely based on measurements at single time points such as flowering or maturity. However, comprehensive physiological data covering the growth of modern and historic cultivars throughout the growing season remain rare. This study investigated how breeding progress influenced canopy structure and biomass partitioning across six German winter wheat cultivars released between 1895 and 2002. Field experiments were conducted over two seasons to measure aboveground biomass, root biomass, and canopy traits such as leaf area index (LAI) and specific leaf area (SLA) at multiple stages. Root biomass was assessed using large pots that were buried within an already sown field. Results showed differences in biomass partitioning patterns between cultivars and a trend toward a lower root to shoot ratio at anthesis in modern cultivars. Compared with modern cultivars, historic cultivars displayed larger flag leaves, a higher maximum LAI around flowering and a larger SLA, but similar light extinction coefficients. Significant season effects were observed, with reduced shoot biomass and increased root allocation at flowering in 2023, a year with a hot and dry spring, particularly in older cultivars. Yield increases were most strongly associated with harvest index, alongside faster phenological development and changes in biomass allocation across developmental stages. These findings provide stage‐specific insights into cultivar physiology that might help in guiding future breeding strategies.

Lagrangian models are widely adopted to study atmospheric transport processes, with applications in various domains, including the investigation of windborne crop diseases and epidemic risks in agriculture. Widely used Atmospheric Transport Modelling frameworks (ATMs) do not exploit the potential for performance gains and advanced computer graphics that GPUs provide, and they impose limitations regarding customization for domain-specific applications in crop epidemiology and agrometeorology. Here we introduce AgPaDS, the Agricultural Pest and Disease Simulator, a GPU-accelerated stochastic Lagrangian model with an option for advanced interactive 3-D in-situ visualization of global-scale atmospheric transport simulations. The tool was developed with two main objectives: (i) accelerate compute times by means of an efficient GPU implementation that enables exploratory visual analyses by means of interactive simulation setup in a graphical user interface with embedded 3-D in situ visualization; (ii) build a new atmospheric transport model dedicated to applications in crop epidemiology with model components not available in widely used ATMs and with flexibility for future domain-specific customizations. It is based on an optimized massively parallelized CUDA C ++ and OpenGL implementation. We report on model formulation, technical implementation and testing, including a systematic comparison with HYSPLIT, one of the most widely used ATMs, and a case-evaluation with in-situ visualization of complex 3-D dynamics of simulated crop pathogen transport during the hurricane that has likely transmitted soybean rust into the USA in 2004. A set of supplementary videos illustrates interactive and in situ 3-D visualization methods. We show that AgPaDS maintains good agreement with HYSPLIT whilst providing substantial speedups for simulations with very large particle numbers (up to three orders of magnitude). The model can simulate the release of millions of Lagrangian particles from heterogeneous crop landscapes on global scales with live 3-D visualization of simulated windborne dispersal of crop pathogens. Examples of future use-cases include (i) exploratory 3-D visual analyses of atmospheric transport simulations, including interactions between meteorological and biological processes; (ii) assessment of global airborne connectivity of agricultural landscapes; (iii) efficient representation of wind dispersal in crop disease forecasting systems.

Climate change is projected to exacerbate food insecurity in sub‐Saharan Africa (SSA) by reducing crop yields and soil fertility. Many climate change impact studies in SSA have overlooked long‐term effects of soil fertility on crop yield. We evaluated maize yields under different scenarios of soil fertility (using soil organic carbon as a proxy) and climate change (considering changes in temperature, rainfall, and CO ₂ ) at four sites in SSA. Using an ensemble of 15 calibrated soil‐crop models, we found a strong consensus that, without fertilization, soil fertility declines over time, impacting maize yields more strongly than changes in temperature, rainfall, or CO ₂ . The model ensemble indicated that when accounting for soil fertility changes, the yield benefits of combined application of organic and mineral inputs increase over time, even under climate change. These findings highlight the importance of considering long‐term change in soil fertility when assessing impacts of climate change and integrated nutrient management on crop production in SSA.