Context: The global shift toward sustainable agriculture has increased interest in using process-based models to design and optimize intercropping systems. However, these models differ fundamentally in how they represent trade-offs (competition for light, water, and nutrients) and synergies (facilitation, complementarity) in resource sharing between species. This conceptual variation creates uncertainty in model selection and application, particularly since most models were originally developed for monoculture and subsequently adapted for intercropping. Objective: In this study, we describe how current crop models represent interspecies resource-sharing mechanisms, analyse their structural differences, and synthesize findings from existing validation studies to understand how their structural differences affect model performance. The models examined include APSIM (APSIM-Canopy, APSIM-Micromet, APSIM-Strip, APSIM-Alternating, APSIM-APSwim, APSIM-SoilArbitrator), DayCent, DSSAT-Mixed, DSSAT-MPI, LandscapeDNDC, LUCIA, MONICA, SIMPLACE Lintul5-Intercrop, STICS-Big-Leaf, STICS-Multilayer, and WaNuLCAS. Methods: Through the Agricultural Model Intercomparison and Improvement Project (AgMIP) platform, we engaged with model developers and expert users to collect detailed information on how crop models represent intercropping systems using structured interviews and questionnaires. We then developed a framework that groups models by their core conceptual approaches to simulating resource sharing. Finally, we synthesize findings from existing quantitative validation studies to connect conceptual intercomparison to predictive performance across different intercrop characteristics and environments. Results and Conclusions: Our analysis identifies six distinct conceptual approaches for simulating light sharing and four for belowground resource (water and nutrient) competition. Intercrop models show greater structural divergence in canopy than in belowground representation. Furthermore, competitive trade-offs (light, water, and nutrients) are widely represented while facilitative and other complex processes like Na-fixation, plasticity (shoot and root), microclimate effects or hydraulic lift are often simplified or omitted. Our analysis of model validation studies reveals a critical trade-off: structurally complex models often perform well in simulating intercropping when calibration is done on sole crops, whereas simpler models require extensive intercrop-specific calibration to achieve better prediction performance. This distinction is vital for model application in data-scarce environments, as more complex architectures can leverage existing sole-crop data to effectively simulate intercrop systems. The classification of the structural resource capture differences in combination with the evaluated model performance analysis allowed us to devise an evidence-based model selection criteria framework useful also for for non-specialist, while the unique detailed description provided are highly valuable for the model research community. Significance: This study establishes a conceptual framework that provides the necessary foundation for a meaningful quantitative intercomparison of intercrop models, as structural understanding enables the interpretation of numerical differences in model outputs. It also guides hypothesis testing, model choice, priorities in model development and improvements.

Context Lower nitrogen (N) use efficiency and direct N losses are adversely affecting global cereal production. Yet, the exact fate of applied N in the soil and the interactions that occur are largely unknown. Aims We aimed to quantify the fate, recovery and magnitude of losses from applied N and compare the effectiveness of compost application on N recovery and yield. We hypothesised that high N losses and low N use efficiency from tropical soils can be combatted by integrated application of compost and synthetic N. Methods A randomised complete block design with two N rates and two compost rates (0 and 15 Mg ha−1) with four replicates was used. Isotopically (15N) labelled urea was applied to tropical maize and the 15N recovery, grain yield and dry matter were analyzed. Key results The combination of compost and urea increased N recovery (16% reduction in N loss), and significantly increased maize yield by 50% and stover yield by 33% in applied urea 150N (urea at 150 kg N ha−1) + compost treatment relative to the 150N only treatment, demonstrating that compost-derived N considerably increased crop N acquisition compared to urea alone. Compost addition significantly increased both apparent N recovery and mineralised N by 31% and 62%, respectively. Up to 71% N harvested was obtained from non-fertiliser sources, with 80–90% recovered in the surface 40 cm of soil. Application of urea and compost demonstrated the considerable potential to increase N recovery and enhance crop productivity of cereals. Conclusion Proving the hypothesis, beyond the effect of mineral N alone, additional organic N provided by compost significantly contributed to crop productivity and N retention in the soil. Compost is thus characterised as an ecologically balanced alternative to synthetic fertiliser, with potential decreases in losses of N, while enhancing N recovery in the plant–soil system. Implications As compost is unlikely to be commercially feasible at the rates applied and difficult to handle as a bulk, further studies should explore the most cost-effective and practical rates of compost application combined with optimised urea rates.

Nitrous oxide (N2O) is a major GHG and ozone-depleting substance which is produced by microbial processes in soils, with mineral nitrogen availability, carbon availability, soil moisture, soil temperature, oxygen availability and pH being important controlling factors. Emissions of N2O are notorious for being short-lived with the magnitude of emissions being difficult to predict due to the interplay of the aforementioned controlling factors. In Europe, the major share of anthropogenic N2O emissions result from fertilizer application to agricultural land. National reporting typically relies on so-called Tier 1 or 2 approaches which relate activity data (N inputs) to an emission factor to estimate a national total. However, this method does not consider the full set of spatially and temporally varying controlling factors, so that the latter approaches may be biased. For this reason, reconciliation with an independent, top-down method has large potential to improve national GHG budgets and to review mitigation strategies.Here we present results from the Horizon Europe project Process Attribution of Regional emISsions (PARIS), where we calculate bottom-up and top-down N2O emission inventories for Germany, the UK and Switzerland at monthly time resolution for the timeframe 2018 – 2024. Bottom-up estimates are obtained using the biogeochemical model LandscapeDNDC and state-of-the-art European datasets. Top-down estimates are averaged results from three different inverse modeling systems: InTEM (UK MetOffice), RHIME (University of Bristol), ELRIS (EMPA) and two different atmospheric transport models: NAME-UM and FLEXPART-ECMWF.We find the emission estimates from both top-down and bottom-up methods to be consistently higher than the corresponding national inventories, but bottom-up approaches are within the uncertainty of the top-down estimate. In terms of seasonality, bottom-up and top-down methods indicate a seasonal cycle, although its magnitude is country dependent. Across all countries, the discrepancy between bottom-up and top-down estimates is greatest in autumn, where LandscapeDNDC predicts an emission peak following planting of winter crops. Discrepancies regarding magnitude and seasonality of top-down and bottom-up approaches will be discussed considering controlling factors for N2O emissions simulated using LandscapeDNDC.

Abstract. 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.

Current life cycle assessments (LCAs) of milk production often underestimate environmental impacts by overlooking significant greenhouse gas (GHG) emissions from drained peatlands. This study applied a novel approach to quantify the contribution of GHG emissions from drained peat soils to the carbon footprint (CF) of milk production in German pre-alpine dairy farms, addressing this critical knowledge gap. Carbon footprints (CFs) of milk production were calculated for three distinct dairy farms in Southern Germany, both with and without the inclusion of peatland emissions. Three methodological approaches were applied for emission quantification: (i) IPCC Tier 1, (ii) implied emission factors (EFs) from German national inventory reporting, and (iii) water table depth (WTD)-dependent response functions. A near-natural peatland reference scenario was also developed for contextualization. Results reveal that peatland emissions are a highly significant contributor, more than doubling average milk CFs at farm-level. A positive correlation was found between the extent of drained peatland area and carbon emissions, with the CF from drained peat soils being 3 to 6.5 times higher than those from mineral soils if the entire farm area was located on drained peat soil (i.e., 'under full peatland drainage’). The chosen methodology significantly influenced CFs, where WTD-dependent approaches consistently yielded higher GHG estimates. These findings underscore the crucial importance of incorporating peatland emissions into dairy LCA studies for accurate environmental assessments. They highlight the urgent need for targeted mitigation strategies, especially water table (WT) management, to effectively reduce agriculture’s climate impact. Future research and policy should prioritize developing and implementing effective WT management techniques. Encouraging the integration of peatland emission data into standard agricultural LCA methodologies is also vital to generate realistic and complete and to drive sustainable practices.

Peatland rewetting is crucial for Germany to achieve net greenhouse gas (GHG) neutrality by 2045 as it can help to drastically reduce emissions in the LULUCF sector. This study combines farm-level Life Cycle Assessment with the biogeochemical model LandscapeDNDC to evaluate three different Bavarian dairy farm systems with varying peatland shares, quantifying GHG mitigation potential of rewetting and associated trade-offs in forage production. Rewetting reduces peatland emissions by about 84%, lowering total product carbon footprint by 14-53%. However, a biophysical compensation failure threshold (Xcrit) is identified at a peatland share of 21.8 %. Farms below this threshold can maintain forage production by intensifying mineral soils, while those exceeding it, such as high peat share farms (40% peatland), face structural shortfalls and can realistically rewet only half of their peatland area without production losses. At regional scale, a circular solution appears biophysically feasible. Aggregated results for the Ammer region indicate that the potential forage surplus from intensification on mineral soils exceeds deficits associated with peatland rewetting by nearly two-fold, while additional emissions from intensified production and increased transport remain small relative to mitigation gains. Practical implementation, however, needs to manage increased nitrogen losses on intensified mineral soils and account for site-specific socio-economic constraints. Realizing this transformation requires EU Common Agricultural Policy payments to incentivize regional cooperation and address associated socio-economic implications of transitioning away from drained peatland use, providing the necessary framework for integrating alternative land-use and restoration goals.

The ITMS (Integriertes Treibhausgas Monitoring System) Sources and Sinks module, funded by the German Federal Ministry of Research, Technology and Space, develops modelling approaches to simulate greenhouse gas (GHG) fluxes in Germany at high spatial and temporal resolution. By integrating existing measurement data from national and Bavarian research initiatives with new field observations from natural, drained, and rewetted peatlands collected in the MODELPEAT project, we aim to refine statistical modeling approaches of peatland GHG exchange. While the current German national GHG inventory approach for landuse specific peatlands relies on functional relationships in dependency on water table depth and the type of organic soil (Tiemeyer et al. 2020), this project introduces a machine learning framework that leverages an extensive monthly dataset (approximately 190 site years) to capture peatland GHG dynamics in more detail. The poster presents the methodological implementation of a eXtreme Gradient Boosting (XGB) decision tree model, which incorporates predictors representing seasonal dynamics, vegetation activity, meteorological conditions, and management practices, along with initial findings. As the project progresses, the approach is aimed to be applied across Bavaria on a 30×30 m grid to generate spatially explicit simulations of peatland GHG fluxes (CO2, CH4, N2O). This work is essential for identifying emission hotspots and supporting the development of effective mitigation strategies.

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.

Understanding how ecosystems sustain plant nitrogen (N) supply under climate change is critical, yet whether increasing plant N demand is met by external inputs or mobilization of soil N reserves remains unresolved. Here we show that climate change increases plant reliance on soil N reserves despite intensive fertilization. Using a two-year 15N-tracing experiment combining elevated CO2, warming, and drought in a montane grassland, we found that plants obtained 82-88% of their N from soil and acquired 4.6-7.3 times more N from soil than from fertilizer despite high N inputs. Elevated CO2 and warming increased plant uptake of soil-derived N but not fertilizer N. Consequently, plant N export exceeded fertilizer inputs, causing ecosystem N deficits and depletion of soil N stocks, with the strongest soil N mining under combined elevated CO2 and warming. Our findings reveal that climate change accelerates biological mining of soil N reserves, potentially constraining the long-term sustainability of intensively managed agroecosystems.

There is need to calibrate raw data of N2 and N2O isotopocules due to effects of non-linearity, instability, matrix effects and interference with trace gases. Our objective was thus to supply a variety of suitable standard gases for members of the DASIM research unit (www.DASIM.de) and their partners in sufficient amount for routine use to enable calibration for extended time. In total 23 different mixtures were produced to cover all isotopic approaches to study N2 and N2O production and cycling in soils with stable isotopes and suitable for IRMS and laser spectroscopy.Standards for the 15N gas flux method should mimic mixtures of N2 and N2O emitted from highly 15N enriched nitrate in soil and atmospheric background. These must thus contain unlabelled, single-labelled as well as double-labelled N2 and N2O.N2O standards for natural abundance must cover a range of N2O concentrations and isotopocule values typically found in field flux and laboratory incubation studies to correct for non-linearity and bias.Premixtures were prepared by mixing isotopically enriched or depleted gases which were either commercially available or produced in the lab. Moreover, pure N2O of natural abundance was supplied from a previous project (Mohn et al., 2022, https://doi.org/10.1002/rcm.9296). Premixtures were diluted in artificial atmospheres and compressed in commercial tanks.We will explain the production of mixtures, give an overview of the manufactured mixtures and show first results of analysis in comparison with ideal values.

In the last century, the global N (nitrogen) cycle has been profoundly disturbed by human influences. One of the most detrimental consequences is the release of large quantities of N2O (nitrous oxide) into the atmosphere, which significantly contributes to global warming (Gulev et al., 2021). Most of the anthropogenic contribution to atmospheric N2O originates from the transformation of excessive reactive N inputs in agricultural food production systems (Tian et al., 2019). Mitigation strategies propose the use of EEFs (Enhanced Efficiency Fertilizers), which have shown large potential in decreasing N2O emissions from various types of agricultural systems (Akiyama et al., 2009; Fan et al., 2022). Two types of EEFs are generally considered: NIs (Nitrification Inhibitors) and CRNFs (Controlled Release Nitrogen Fertilizers). However, the effectiveness of EEFs is yet to be estimated at large spatial and temporal scales. The use of process-based biogeochemical models allows for the estimation of N2O emissions at various spatial and temporal scales and with greater accuracy than widely applied emission factors from the IPCC methodology. Within this thesis, a new routine to model EEFs is implemented in the LandscapeDNDC model framework (Haas et al., 2013). The routine largely follows the recent implementation in the DAYCENT model described by Gurung et al. (2021). For accurate results, biochemical models require their parameters to be calibrated on field data. Therefore, the new LandscapeDNDC routine was calibrated on measurement data from three corn cropping systems in the US. Contrary to DAYCENT model calibration in Gurung et al. (2021), it is the pretense of LandscapeDNDC to not only quantify cumulative emissions but to predict N2O emissions dynamics in higher temporal, e.g., daily time resolution. Thus, the calibration was performed over the entirety of available measurements instead of only on cumulative emissions. Moreover, it was investigated whether calibrating the model over every site simultaneously instead of separately for every site significantly contributes to overall uncertainty in the final results. Our results demonstrate how LandscapeDNDC is able to recreate site and year-specific differences in EEF mitigation potentials. The RRMSE for NIs during the growing season ranges between 1.42 and 2.42. For CRNFs, the range is between 1.05 and 3.52. When reduction factors based on cumulative emissions are concerned, for NIs, the posterior reduction factor proposed by LandscapeDNDC is -12% (- 36% to 12%) (mean and 95% confidence interval), which is lower than the reduction factor suggested by the DAYCENT model -12% (-61.8% to 3.1%) and large observational datasets -38% (-44% to -31%) (Akiyama et al., 2009). For CRNFs, LandscapeDNDC returns a reduction factor of -2% (-28% to +25%), which is again lower than the DAYCENT reduction factor of -12% (52% to +1%) and the reduction factor suggested by large global datasets -35% (-58% to -14%) but compares with a larger observational dataset of multiple US corn cropping systems of -5% (-18% to +7%) (Eagle et al., 2017). However, considering the simulated magnitude and relative EEF reduction potential, large uncertainties remain, which are attributed to site-specific edaphic characteristics and growing season variability.

The special issue summarises and highlights key findings of the research unit DASIM funded by the German Research Foundation (DFG) on the process of denitrification. Progress was made in several areas including the development of new and advanced methods to quantify N2 fluxes such as a new 15N gas flux method, enhanced Raman spectroscopy and a new incubation system to study plant-soil interactions in He-O2 atmosphere. Understanding of denitrification in disturbed and structured soil was gained by combining X-ray CT scanning and microbial ecology methods. High resolution models developed as part of DASIM were able to successfully simulate experimental data and provide valuable insights for the improvement of existing ecosystem models. Improved 15N tracing tools for the analysis of 15N tracing data in soil-plant systems have been developed that are extensively used by associated partners. DASIM brought together an interdisciplinary network of researchers interested in analytical but also modelling aspects. This includes close collaboration with the FAO/IAEA centre of Nuclear Techniques in Food and Agriculture of the United Nations which resulted in an open access book that describes the methods used in DASIM. The impact of the DASIM research unit on the scientific community is manifold and will most likely have a lasting impact on the understanding of nitrogen cycling in terrestrial ecosystems.

Soil freeze-thaw (FT) cycles induce high nitrous oxide (N2O) emissions across all ecosystems, whereby flux rates are highest for agricultural systems, where more than half of the annual N2O emissions may result from FT related fluxes. Globally, neglecting FT related N2O emissions may lead to an underestimation of the annual N2O budget by almost a quarter. However, FT related N2O emissions are hardly implemented in and simulated by state-of-the-art ecosystem models yet, because of a lack of knowledge about the actual mechanisms explaining timing and magnitude of the observed N2O emission peaks.Here we review recent advances in process understanding, which can be summarized into three approaches: (i) a frozen (top)soil (or snow) layer that acts as physical barrier for gas diffusion, (ii) the production of additional decomposable substrate during freezing-thawing, and (iii) temperature-depending changes in the biochemical balances within the denitrification process. We implemented the different mechanisms in the LandscapeDNDC ecosystem model, which provides an advanced representation of soil nitrogen processes, and validate their effects on site scale, before we evaluate their importance on regional scale and as part of the annual N2O budget. This will enable us to improve national to global estimates of annual N2O emissions and lower the current uncertainty due to the neglect of FT related N2O fluxes.

Climate change poses a significant threat to agriculture, primarily through yield losses due to droughts and heat waves. The flowering phase of most crops is a critical period during which they are highly susceptible to heat, resulting in long-term damage and substantial yield reduction. Significant heat-induced yield cuts have already been observed in Europe, especially during the frequent and widespread heat waves occurring in the years 2018 to 2022. By imposing the large-scale atmospheric circulation of the 2018 to 2022 heatwaves onto CMIP6 projections, the impact of such a multi-year event within future climate is made tangible as a storyline (Sánchez-Benítez et al., 2022). The +4K storyline, which gives a flavour of possible atmospheric conditions in the 2090s in the ssp370 scenario, indicates a potential increase of up to 7°C during the flowering phase of major crops in Europe. Using these storylines, we evaluated the impact of such a heatwave on cereal production in Europe under a warmer climate. To achieve this, we developed a heat stress index, which gauges the amount of stress experienced by crops due to heat exposure during flowering relative to unstressed conditions. This index was then applied to the dynamically downscaled nudged storylines over the European domain and evaluated for major cereal crops (maize and wheat). As part of this evaluation, we modelled how a changing climate would affect planting dates and the area suitable for growing winter cropsand investigated the potential impact of heat on different crop cultivars. In 2021, we estimate that approximately 4% of cropland in Europe experienced severe heat stress (i.e., yield losses of up to 50%) due to heat waves during flowering. Extrapolating to a scenario with global warming of +4 K, we show that almost 80% of the total European crop area for maize could be affected by heat stress, with 30% of the area experiencing a severe heat stress. This could lead to a 20% yield reduction across Europe. In south-eastern Europe, where the 2021 heatwave was particularly intense, 40% of the harvested area would be severely affected, leading to a yield loss of 32% relative to current conditions. Our investigation of different stress vulnerabilities shows that some crop varieties may exhibit minimal stress while others face severe damage, leading to considerable intra-crop variability in yield reduction. Planting date plays a major role in the impact of heat stress, since an earlier planting shifts the sensitive window during which the plant is flowering to earlier in the year. For winter crops, such as winter wheat, the increased temperatures in winter could lead to a reduction of the winter wheat growing area of 50% by 2093. Addressing these challenges will require proactive management changes, including strategic decisions on planting dates, crop, and variety selection. Sánchez-Benítez, A., Goessling, H., Pithan, F., Semmler, T., Jung, T., 2022. The July 2019 European Heat Wave in a Warmer Climate: Storyline Scenarios with a Coupled Model Using Spectral Nudging. Journal of Climate.

Spatio-temporal patterns of extreme climate events have been extensively studied, yet two questions remain underexplored: Do such events occur regularly, and how do regularity patterns change under global warming? We address these questions by investigating dominant periods in crop failure, heatwave, and wildfire data. Here, we show that under pre-industrial conditions dominant periods emerge in 28% of cropland exposed to crop failure and 10% of wildfire-affected areas, likely related to climatic oscillations such as the El Niño-Southern Oscillation, while heatwaves occur irregularly. The number of dominant periods increases by 2–13% during the transition from the pre-industrial era to the anthropocene. In the anthropocene, the occurrence of extreme events shifts towards monotonic growth, replacing previous natural regularity patterns. Linearly de-trended projections reveal an additional shift towards smaller dominant periods due to climate change. These shifts in regularity are crucial for adaptation planning, and our method offers an additional approach for studying extreme events.

The Integrated Greenhouse Gas Monitoring System for Germany (ITMS) is a national initiative to establish an operational service for the provision of independent estimates of GHG fluxes for Germany. The main aim is to enhance transparency in reporting of emissions and natural fluxes on the path to net zero emissions. ITMS is a highly interdisciplinary project, bringing together diverse scientific communities involved in atmospheric observations, satellite observations, biosphere and agriculture research, inventory experts, and atmospheric transport and inverse modelling. ITMS utilizes observational datastreams from research infrastructures such as ICOS and IAGOS, and tailored remote sensing products, to constrain Germany’s GHG fluxes into the atmosphere using inverse atmospheric transport modelling. Detailed a priori emissions are generated consistent with UNFCCC reported emissions, while priors for natural fluxes are based on various process based as well as diagnostic models. Inverse modelling is deployed at mesoscale resolution, using the CarboScope-Regional (CSR) inversion system operated at the MPI-BGC as a back-bone and reference system, while developing ICON-ART based data assimilation for future operational services. The presentation will give an overview of recent progress and show some research highlights achieved so far.

An evaluation of the susceptibility of different N management systems to nitrogen (N) losses into the environment requires either the in-situ determination of the individual components of the nitrogen balance or the determination of the recovery of fertilizer N in plants and soil. For both aspects, 15N methods are essential as the 15N gas flux method (15NGF) is the only widespread in-situ method for the determination of dinitrogen (N2) emissions, and 15N labelled fertilizers can be used to assess the allocation of fertilizer N to plants and soil.To evaluate the influence of management history on N losses, we quantified N loss pathways (NH3, N2O, N2, NO3- leaching), total N balance and 15N recovery in soil and plants of two adjacent sites over a two-year cropping sequence. One site was under integrated farming (IF) and the other under organic farming (OF) with frequent legume cultivation and occasional fertilizer input.Though integrated farming had resulted in significantly higher pH, soil organic C and N content, the emissions of ammonia, dinitrogen and nitrous oxide after cattle slurry application as well as nitrate leaching were low and not significantly different. High 15N recovery rates in plants and soil agreed well with the low directly measured N losses. Integrating the directly measured losses into the 15N balance resulted in high overall recoveries of 84 to 100%. Conversely, unrecovered 15N was on a low level, but higher for OF (12%) than for IF (6%).Our results confirm that 15N labelled fertilizers and their recovery can be used as an indicator for N losses, but the spatial variability is high, complicating statistically significant findings. Consideration of N2 fluxes using the 15NGF method could not close the 15N balance, indicating that unaccounted N losses have occurred. Since the directly measured N losses were not significantly different, unaccounted losses could be due to N2 emissions as their quantification was limited to two weeks after fertilizer application.Overall, integrated farming history reduced the vulnerability towards N loss, but continuous methods for determination of N2 emissions, such as isotopomer measurements, need to be tested concomitantly, and uncertainty of 15N recovery in plants and soil needs to be reduced by more sophisticated sample mixing approaches.

Climate change poses a significant threat to agriculture, primarily through yield losses due to droughts and heatwaves. The flowering phase is a particularly critical period during which many crops are highly susceptible to heat, resulting in long-term damage and substantial yield reduction. By imposing the large-scale atmospheric circulation of the 2018 to 2022 heatwaves in a CMIP6 model, we explore the potential impact of such a multi-year event within future climate scenarios as a storyline. We developed a heat stress index to quantify the amount of stress experienced by crops due to heat exposure during flowering relative to unstressed conditions. This index was then applied to the storylines over the European domain and evaluated for major cereal crops (maize and wheat). Extrapolating 2022 conditions to a scenario with global warming of +4 K, we show that over 30% of the harvested area would experience severe heat stress, resulting in a 10% yield reduction across Europe. Our investigations highlight that the timing and severity of a heatwave can have a much higher impact than the mean warming level, emphasizing the need for accurate seasonal forecasts. Addressing these challenges will require proactive management adaptations, including dynamic forecast-based decisions on planting dates, crop, and variety selection.

Denitrification represents a major nitrogen (N) loss pathway in agriculture, reducing plant N uptake, lowering crop N use efficiency, while emitting the potent greenhouse gas nitrous oxide (N 2 O). However, due to methodological challenges, field measurements of N 2 emissions remain rare, leaving denitrification losses poorly characterised across most cropping systems and comprehensive N budgets lacking. This constrains parameterization and validation of biogeochemical models, impeding efforts to forecast denitrification losses under changing conditions and design effective mitigation strategies. This study (i) reviewed the current literature on field-based N 2 measurements, (ii) tested the ability of five different models to accurately simulate denitrification losses, and (iii) simulated denitrification losses from agricultural soils in Germany using gridded and point-based modelling frameworks. Results show that field studies suitable for model calibration remain scarce and mostly limited to temperate systems. Simulated cumulative N 2 emissions varied widely between models, ranging from 0.89 to 6.07 kg N ha −1 , far below the observed N 2 emissions of 21 ± 3 kg N ha −1 . The ratio of N 2 O to (N 2 O + N 2 ) emitted ( R N 2 O ), a key determinant of the climate impact of denitrification, was not only strongly overestimated by all models but also exhibited considerable variations, ranging from 0.056 to 0.707, compared to the measured ratio of 0.006. Models also failed to capture expected responses of to environmental drivers. For Germany, simulated average N 2 emissions from agricultural soils ranged from 1.7 to 14.5 kg N ha −1 yr −1 , with corresponding R N 2 O ratios ranging from 0.18 to 0.59. Spatial patterns of emissions also differed significantly across modelling frameworks, reflecting large structural uncertainties. These findings highlight that both N 2 emissions and R N 2 O ratios remain poorly represented in current models, severely limiting our ability to establish reliable N budgets and develop targeted N management strategies. Coordinated advancements in field-based measurements and modelling are urgently needed to improve the representation of denitrification in biogeochemical models.