Ralf Kiese

Agricultural soils are the dominant source of anthropogenic N₂O emissions, yet their high spatial and temporal heterogeneity provides a major challenge for accurately quantifying emissions and evaluating mitigation options. Most national greenhouse gas inventories rely on empirical Tier-1 or Tier-2 emission-factor approaches and therefore do not fully capture the effects of climate variability, soil properties, or management practices. Here, we present a transferable, process-based modelling framework based on the biogeochemical model LandscapeDNDC for determining direct and indirect N₂O emissions from major crops cultivated on mineral soils at the national scale. We apply the method to Germany making use of high-resolution input data provided by the national reporting agencies, estimating N₂O emissions of 35 (29–44) kt N yr^-1(2017–2022 average). This is 28 % higher than the national inventory report (submission 2025), but well within the uncertainty range. In contrast to conventional inventory methods, the framework explicitly accounts for interannual climate variability and can be spatially disaggregated at high resolution, taking into account local variations in soil type, weather and agricultural management practices. Because the model simulates coupled carbon and nitrogen cycling, it also quantifies multiple nitrogen loss pathways and potential changes in carbon stocks simultaneously, providing a consistent basis for evaluating mitigation strategies and their potential trade-offs. Our results demonstrate that process-based modelling can substantially improve the spatial and temporal resolution of agricultural N₂O emissions and provide a platform for developing next-generation national greenhouse gas inventories. While further work is required before the framework fully satisfies all IPCC Tier-3 requirements, it offers a pathway towards a more mechanistic and policy-relevant assessment of agricultural greenhouse gas emissions.

Agricultural soils are the dominant source of anthropogenic N₂O emissions, yet their high spatial and temporal heterogeneity provides a major challenge for accurately quantifying emissions and evaluating mitigation options. Most national greenhouse gas inventories rely on empirical Tier-1 or Tier-2 emission-factor approaches and therefore do not fully capture the effects of climate variability, soil properties, or management practices. Here, we present a transferable, process-based modelling framework based on the biogeochemical model LandscapeDNDC for determining direct and indirect N₂O emissions from major crops cultivated on mineral soils at the national scale. We apply the method to Germany making use of high-resolution input data provided by the national reporting agencies, estimating N₂O emissions of 35 (29–44) kt N yr^-1(2017–2022 average). This is 28 % higher than the national inventory report (submission 2025), but well within the uncertainty range. In contrast to conventional inventory methods, the framework explicitly accounts for interannual climate variability and can be spatially disaggregated at high resolution, taking into account local variations in soil type, weather and agricultural management practices. Because the model simulates coupled carbon and nitrogen cycling, it also quantifies multiple nitrogen loss pathways and potential changes in carbon stocks simultaneously, providing a consistent basis for evaluating mitigation strategies and their potential trade-offs. Our results demonstrate that process-based modelling can substantially improve the spatial and temporal resolution of agricultural N₂O emissions and provide a platform for developing next-generation national greenhouse gas inventories. While further work is required before the framework fully satisfies all IPCC Tier-3 requirements, it offers a pathway towards a more mechanistic and policy-relevant assessment of agricultural greenhouse gas emissions.

One part for tackling agricultural challenges such as the need for increased food production, reduced environmental impacts, and adaptation to climate change is the optimization of agricultural management. This requires improved monitoring of agricultural areas with respect to vegetation status over dense time series. The combined use of different types of satellite missions has the potential for a more detailed monitoring of agricultural fields. The increasing availability of spaceborne hyperspectral data (from EnMAP, PRISMA and upcoming CHIME) calls for fast processing chains and efficient retrieval algorithms. Therefore, this contribution aims to (i) accelerate and improve existing retrieval workflows, (ii) evaluate currently available hyperspectral EnMAP/PRISMA retrievals to monitor crops, and (iii) test the combined use of hyperspectral and multispectral Sentinel-2 data for crop monitoring.

Agricultural soils are the dominant source of anthropogenic N₂O emissions, yet their high spatial and temporal heterogeneity provides a major challenge for accurately quantifying emissions and evaluating mitigation options. Most national greenhouse gas inventories rely on empirical Tier-1 or Tier-2 emission-factor approaches and therefore do not fully capture the effects of climate variability, soil properties, or management practices. Here, we present a transferable, process-based modelling framework based on the biogeochemical model LandscapeDNDC for determining direct and indirect N₂O emissions from major crops cultivated on mineral soils at the national scale. We apply the method to Germany making use of high-resolution input data provided by the national reporting agencies, estimating N₂O emissions of 35 (29–44) kt N yr^-1(2017–2022 average). This is 28 % higher than the national inventory report (submission 2025), but well within the uncertainty range. In contrast to conventional inventory methods, the framework explicitly accounts for interannual climate variability and can be spatially disaggregated at high resolution, taking into account local variations in soil type, weather and agricultural management practices. Because the model simulates coupled carbon and nitrogen cycling, it also quantifies multiple nitrogen loss pathways and potential changes in carbon stocks simultaneously, providing a consistent basis for evaluating mitigation strategies and their potential trade-offs. Our results demonstrate that process-based modelling can substantially improve the spatial and temporal resolution of agricultural N₂O emissions and provide a platform for developing next-generation national greenhouse gas inventories. While further work is required before the framework fully satisfies all IPCC Tier-3 requirements, it offers a pathway towards a more mechanistic and policy-relevant assessment of agricultural greenhouse gas emissions.

Agricultural soils are the dominant source of anthropogenic N₂O emissions, yet their high spatial and temporal heterogeneity provides a major challenge for accurately quantifying emissions and evaluating mitigation options. Most national greenhouse gas inventories rely on empirical Tier-1 or Tier-2 emission-factor approaches and therefore do not fully capture the effects of climate variability, soil properties, or management practices. Here, we present a transferable, process-based modelling framework based on the biogeochemical model LandscapeDNDC for determining direct and indirect N₂O emissions from major crops cultivated on mineral soils at the national scale. We apply the method to Germany making use of high-resolution input data provided by the national reporting agencies, estimating N₂O emissions of 35 (29–44) kt N yr^-1(2017–2022 average). This is 28 % higher than the national inventory report (submission 2025), but well within the uncertainty range. In contrast to conventional inventory methods, the framework explicitly accounts for interannual climate variability and can be spatially disaggregated at high resolution, taking into account local variations in soil type, weather and agricultural management practices. Because the model simulates coupled carbon and nitrogen cycling, it also quantifies multiple nitrogen loss pathways and potential changes in carbon stocks simultaneously, providing a consistent basis for evaluating mitigation strategies and their potential trade-offs. Our results demonstrate that process-based modelling can substantially improve the spatial and temporal resolution of agricultural N₂O emissions and provide a platform for developing next-generation national greenhouse gas inventories. While further work is required before the framework fully satisfies all IPCC Tier-3 requirements, it offers a pathway towards a more mechanistic and policy-relevant assessment of agricultural greenhouse gas emissions.

Food security and avoiding land use change in Sub‐Saharan Africa (SSA) requires increasing agricultural productivity, necessitating greater fertilizer use. This may increase soil nitrous oxide (O) emissions, a potent greenhouse gas. This study used Machine learning (ML) models to predict O emissions under future climatic and fertilizer scenarios across SSA. Three models were trained (Random Forest (RF), XGBoost (XGB), and feedforward neural networks (FNN)) on existing forest, grassland, and cropland O measurements. The analysis identified the main drivers influencing O emissions: temperature, soil moisture, rainfall, and fertilizer (cropland). SSA O emissions totaled 253–538 Gg N (1 Gg = g), with forests contributing 119–342 Gg N , grasslands 70–132 Gg N , and croplands 63–64 Gg N . Ranges reflect the full uncertainty across all models. Unexpectedly, severe climate change (SSP5‐8.5 scenario) may decrease total O emissions by 4%–37% across SSA, possibly due to drier soils. However, when climate change was combined with tripled fertilizer use (0–63 to 0–189 kg N ), the models predicted a wide range of cropland emission increases of 6%–23% (XGB–RF), with FNN predicting 139% from baseline projections. These findings highlight that while climate change may reduce overall O emissions from forests and grasslands, agricultural intensification will likely become an increasingly significant emission source. To improve prediction accuracy, more comprehensive O monitoring across SSA is needed. This work underscores the need for international investment in monitoring infrastructure and data repositories to guide sustainable agricultural development and SSA climate policy.

Accurately quantifying crop yield reductions from heat and drought stress is essential for predicting how a changing climate will impact future crop production. Here, we assess the relative contributions of heat and drought stress on crop yield losses across Germany using the process-based ecosystem model LandscapeDNDC. This is achieved via a two-step calibration process, in which key model parameters governing the growth of wheat and maize (the two major crops in Germany) are optimized to simulate crop yields in both stressed and unstressed conditions. We show that recent yield losses in Germany were predominantly caused by drought stress, with heat stress playing only a negligible role. Consequently, yield losses could have been largely eliminated through irrigation, with an average of 156 ± 60 mm of water in 2018 across Germany necessary. We show that irrigation demand increases exponentially with decreasing plant-available water, indicating that the importance of this mitigation measure will grow under future climate conditions. The models’ ability to accurately capture the consequences of recent extreme climatic events on crop yields makes it a suitable candidate for evaluating the implications of climate change on both crop production and, more generally, on the carbon, nitrogen and water cycles of cropping systems in Central Europe.

Agricultural soils are the dominant source of anthropogenic N2O emissions, yet their high spatial and temporal heterogeneity provides a major challenge for accurately quantifying emissions and evaluating mitigation options. Most national greenhouse gas inventories rely on empirical Tier-1 or Tier-2 emission-factor approaches and therefore do not fully capture the effects of climate variability, soil properties, or management practices. Here, we present a transferable, process-based modelling framework based on the biogeochemical model LandscapeDNDC for determining direct and indirect N2O emissions from major crops cultivated on mineral soils at the national scale. We apply the method to Germany making use of high-resolution input data provided by the national reporting agencies, estimating N2O emissions of 35 (29–44) kt N yr-1(2017–2022 average). This is 28 % higher than the national inventory report (submission 2025), but well within the uncertainty range. In contrast to conventional inventory methods, the framework explicitly accounts for interannual climate variability and can be spatially disaggregated at high resolution, taking into account local variations in soil type, weather and agricultural management practices. Because the model simulates coupled carbon and nitrogen cycling, it also quantifies multiple nitrogen loss pathways and potential changes in carbon stocks simultaneously, providing a consistent basis for evaluating mitigation strategies and their potential trade-offs. Our results demonstrate that process-based modelling can substantially improve the spatial and temporal resolution of agricultural N₂O emissions and provide a platform for developing next-generation national greenhouse gas inventories. While further work is required before the framework fully satisfies all IPCC Tier-3 requirements, it offers a pathway towards a more mechanistic and policy-relevant assessment of agricultural greenhouse gas emissions.

Understanding responses of temperate grasslands to combined climate and land use changes is needed to secure yields as well as to mitigate future climate change (CC). This study aims to analyse the long-term effects of CC and grassland management on the water balance, yield, nitrogen uptake and species composition of montane grasslands in S-Germany. We used a space-for-time approach with large intact grassland monoliths (1 m ² , 1.5 m deep) translocated along an elevation and thus climatic gradient to simulate CC. The monoliths were operated with four treatments formed by full-factorial combination of climate (current and approx. +2 °C warming) and management (high and low frequency of cutting and cattle slurry fertilization events). Measurements of various climate, water balance, and vegetation variables were analysed over a nine-year period (2012–2020). The effect of management on yield was notably higher than the effect of CC. In general, high management intensity led to higher annual yields (mean dry matter of control site: 1020 +/− 160 g m ⁻² , CC site: 1075 +/− 200 g m ⁻² ), than low management intensity (control: 784 +/−66 g m ⁻² , CC: 786 +/− 98 g m ⁻² ). CC conditions increased grassland yields only under high management intensity and during the first 5 years of the experiment (2012–2016), mainly due to higher yields at the first cut. Reduced nitrogen availability, in conjunction with water limitations, emerged as a key constraint on grassland productivity under CC conditions, explaining the decline in CC–induced yield responses toward the end of the experiment. The pronounced inter-annual differences and trends illustrated the importance of long-term field experiments. Understanding the drivers of biomass production and forage quality is crucial not only for a better understanding of ecosystem functioning but also for improving grassland management, for instance by adjusting cutting and fertilization practices to future climate conditions.