Agricultural soils are a major source of nitrous oxide (N2O), yet estimates of these emissions vary depending on the accounting method applied. The Intergovernmental Panel on Climate Change (IPCC) defines a tiered framework: Tier 1 uses activity data combined with global default emission factors, Tier 2 refines these estimates using emission factors calibrated for national conditions, and Tier 3 employs detailed process-based models or measurement systems that explicitly represent soil, climate, and management effects. Austria currently applies Tiers 1 and 2 in its national greenhouse gas inventory. This study compares these approaches with the Tier 3 model LandscapeDNDC to refine regional estimates of N2O emissions from agricultural soils. Calculations were conducted for six selected production regions across Austria over a ten-year period. Using the National Inventory approach, estimated regional median N2O emissions ranged between 3.3 and 3.8 kg N2O ha-1 yr-1, which was 36-342% higher than the Tier 3 model results. LandscapeDNDC indicated that grassland soils are more prone to N2O emissions than cropland soils, whereas the National Inventory estimates showed little difference between intensive land use types. This comparison highlights the trade-offs between the two approaches: National Inventory methods (Tier 1, 2) are simple and scalable, they produced higher and less spatially differentiated N2O estimates than those from the LandscapeDNDC model (Tier 3), which is data- and computation-intensive but explicitly represents site-specific drivers such as soils, climate and management. Our results support prioritising investment in developing and implementing Tier 3 reporting tools, alongside with existing National Inventory methods, to better characterise uncertainty, increase transparency and enhance the policy relevance of mitigation strategies.

Forest carbon exchange is strongly influenced by climatic conditions, yet its response to drought remains difficult to quantify because of complex interactions among climate, tree growth, and ecosystem processes. In this study, we use the process-based model LandscapeDNDC to investigate the spatial and temporal variability of carbon exchange processes across German forests between 2011 and 2023, encompassing the extreme drought year 2018. To isolate the direct climate signal, simulations exclude forest management, and disturbances, thereby representing the response of an undisturbed forest system to climatic variability. Using E-OBS climate forcing the model reproduces the overall magnitude and variability of carbon fluxes when compared to FLUXCOM (X-VIIRS), a machine-learning-based upscaling of eddy covariance observations. The 2018 drought emerges as the dominant disturbance, with a marked reduction in productivity and a strong weakening of the carbon sink strength (-46.3%) compared to the baseline period 2011–2017. Although carbon uptake partially recovered in subsequent years, reduced sink strength persisted in drought-prone regions, particularly in beech and pine-dominated forests in northern and northeastern Germany, whereas southern regions remained comparatively resilient. A sensitivity analysis comparing E-OBS with ERA5 climate forcing datasets reveals that carbon flux estimates are strongly dependent on climate inputs. Despite higher precipitation in ERA5, increased interception losses and evaporation associated with more frequent low-intensity rainfall events reduce transpiration and gross primary productivity (GPP), while higher minimum temperatures enhance ecosystem respiration, resulting in a systematically weaker net carbon sink. Overall, our results highlight both the spatial heterogeneity of drought impacts and the critical role of climate forcing uncertainty in shaping simulated forest carbon dynamics.

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.

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.

Abstract. In this study, we have simulated inventories of arable production and soil carbon and nitrogen cycling on a national scale (0.25 × 0.25-degree) for Greece with the bio-geochemical ecosystem model LandscapeDNDC. Based on observation data, we have aggregated for each grid cell 4 most likely crop rotations, including nitrogen and manure fertilization, tilling and irrigation. The arable management was continuously projected into the future until 2100, while plant phenology was adapted to local conditions, general properties of the arable management were kept constant into the future, such as the selection of crops or the share of irrigated arable land. To understand the impacts of climate change, we used the EURO-CORDEX-11 regional climate ensemble to drive the LandscapeDNDC impact model under scenarios RCP4.5 (16 datasets) and RCP8.5 (32 datasets). The simulation timespan was from 1990 until 2100, using the first 10 years as spin-up to obtain equilibrium in the model's internal carbon and nitrogen pools from the model initialization. Arable production declines from 2045 onwards by 9.5% or 144 kg C ha-1 yr-1 under RCP4.5 and by 29% or 484 kg C ha-1 yr-1 towards 2100. At present, the ensemble results show an average soil carbon loss of 122.1 kg C ha-1 yr-1 versus 139.7 kg C ha-1 yr-1 in the future. The gaseous outfluxes of the ensemble simulations show N2O emissions of 0.494 to 0.453 kg N2O–N ha−1 yr−1, NO emissions of 0.031 kg NO–N ha−1 yr−1, N2 emissions of 4.806 to 3.377 kg N2–N ha−1 yr−1, NH3 emissions of 24.662 to 35.040 or 34.205 kg NH3–N ha−1 yr−1 and nitrate leaching losses from 54.304 to 58.213 kg NO3-N ha−1 yr−1 comparing present versus future conditions. The overall nitrogen balance of the ensemble simulations reveals a mean nitrogen loss of 4.7 versus 5.7 kg-N ha-1 yr-1 comparing present to future conditions.

Plants are the main connection between soil and atmosphere. Below ground, nitrogen, carbon, and water fluxes are mediated by roots, which therefore strongly influence nitrogen, carbon, and water distributions throughout the soil profile and impact, for instance, if conditions favorable for denitrification occur or not. However, the representation of roots in biogeochemical models is often strongly simplified, allowing only for a static prescribed root development. Further, the root system is normally not taken into account during model calibration, due to a lack of measurements. This disregard of roots prevents model veracity. In this study, we evaluate three model settings of the biogeochemical model framework LandscapeDNDC and compare them to site measurements of winter wheat and maize on a stony and a silty soil to illuminate and quantify these shortcomings. As a baseline, the model is calibrated regarding above ground parameters and measurements only. These results are compared to calibrations on above and below ground parameters and measurements with two different root models. One static root model and one dynamic root model proposed by Jones et al. in 1991. The calibrated settings yield overall comparable qualities of fit for the above ground properties. As expected, the root depth and the root length density are better represent after calibration. The best qualities of fit in the validation are relative root mean square errors (coefficients of determination) of 0.76 (0.36) and 0.39 (0.86) for the root length density and root depth, respectively. At last, for the best-fit model run of each setting, the nitrogen balance is analysed. On the stony soil, the simulated nitrate leaching from the baseline is 80 % smaller than in a setting where the roots were properly calibrated. In line, the plant nitrogen uptake was on average 40 kgNha-1 bigger in the baseline compared to the other settings. These large impacts on the nitrogen cycle illustrate the need for joined measurements of roots and nitrogen fluxes.

Context: Nitrogen is an essential macronutrient in agriculture, affecting both crop yields and soil health. In Denmark, one of the most densely farmed regions in the world, excess reactive nitrogen (Nr) compounds are lost to the environment along gaseous and hydrological pathways in forms such as nitrate, ammonia, nitrogen oxides and dinitrogen. Objectives: Here, we aim to assess the effect of different field management practices (fertilisation, crop residue management or cultivation of catch crops) on environmental Nr losses and the field scale soil net GHG balance (i. e., sum of soil C stock changes and direct and indirect N2O emissions). Methods: For this purpose, highly detailed data from the Danish Agricultural Watershed Monitoring Program (LOOP-program; 2013-2019) were used in combination with the process-based model LandscapeDNDC. Results and conclusions: The results indicate that a mixture of organic and synthetic fertilisers turns soils to a stronger net sink of GHGs (similar to 70 - similar to 514 kgCO(2-)eq ha(-1) yr(-1)) compared to exclusive use of only one type of fertiliser. In addition, incorporating crop residue and cultivation of catch crops increases the nitrogen use efficiency (NUE) by 3-11 % on average and decreases environmental Nr losses. Significance: These findings emphasize the potential of targeted fertiliser, residue and catch crop management to increase the sustainability of crop production systems in Denmark.

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.

The Mediterranean Basin is one of the regions most affected by climate change, which poses significant challenges to agricultural efficiency and food security. While rising temperatures and decreasing precipitation levels already impose great risks, the effects of compound extreme events (CEEs) can be significantly more severe and amplify the risk. It is therefore of high importance to assess these risks under climate change on a regional level to implement efficient adaption strategies. This study focuses on false-spring events (FSEs), which impose a high risk of crop losses during the beginning of the vegetation growing period, as well as heat–drought compound events (HDCEs) in summer, for a high-impact future scenario (Representative Concentration Pathway (RCP) 8.5). The results for 2070–2099 are compared to 1970–1999. In addition, deviations of the near-surface atmospheric state under FSEs and HDCEs are investigated to improve the predictability of these events. We apply a multivariate, trend-conserving bias correction method (MBCn) accounting for temporal coherency between the inspected variables derived from the European branch of the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX). This method proves to be a suitable choice for the assessment of percentile-threshold-based CEEs. The results show a potential increase in frequency of FSEs for large portions of the study domain, especially impacting later stages of the warming period, caused by disproportionate changes in the behavior of warm phases and frost events. Frost events causing FSEs predominantly occur under high-pressure conditions and northerly to easterly wind flow. HDCEs are projected to significantly increase in frequency, intensity, and duration, mostly driven by dry, continental air masses. This intensification is several times higher than that of the univariate components. This study improves our understanding of the unfolding of climate change in the Mediterranean and shows the need for further, locally refined investigations and adaptation strategies.

Climate change poses a significant threat to agriculture, highlighting the need for adaptation strategies to reduce its impacts. Agronomic adaptation strategies, such as changes in planting dates, fertilization, and irrigation, might sustain crop yield. However, their impact on soil greenhouse gas (GHG) emission is unknown under future climate scenarios. Using the LandscapeDNDC model, we assessed the effect of agronomic adaptation strategies (early sowing, increased fertilization dose, and increased irrigation amount) on soil GHG emission, yield, and yield-scaled GHG emission. A diversified crop rotation (potato – winter wheat – spring barley – faba bean) of a long-term experiment in Denmark was used for model validation. The adaptation practices to climate change were implemented for two representative concentration pathways (RCPs; 4.5 and 8.5) and five coupled global circulation and regional climate models. The adaptation scenarios were contrasted against a baseline scenario under current management practices. Soil-related variables showed better model fit (refined index of agreement ≥ 0.38) and lower errors (mean absolute error ≤ 8.18) than crop-based outputs for model validation. A total yield of ∼29 (± 3) t DW ha−1, and soil GHG emission of ∼3.02 (± 1.39) t CO2e ha−1 (RCP8.5) were obtained for the crop rotation system under the baseline for 2071–2100. Early sowing and its combination with increased fertilization decreased the yield compared to the baseline by 6.1 and 4.8 %, respectively (RCP8.5). Conversely, early sowing with increased irrigation, and early sowing with increased fertilization and irrigation, produced higher yields by 2.3 and 4.0 %, respectively (RCP8.5). All the agronomic adaptation strategies increased soil GHG emissions (ranging from 4.1 to 17.8 %) as well as yield-scaled GHG emissions (varying from 3.0 to 12.9 %) (RCP8.5). The highest soil GHG emission was simulated for early sowing in combination with increased fertilization and irrigation. Our study indicates that soil GHG emission will increase in the coming decades and that the agronomic adaptation strategies needed to sustain food production may further exacerbate this emission.

Nitrous oxide (N2O) emissions are closely linked to agricultural fertilisation. European and national policy incentives have been set to reduce greenhouse gas (GHG) emissions; however, only a few evaluations have been conducted. Avoiding such emissions is an important climate change mitigation measure, but it is still uncertain which management measures over a long-term, best out-balance crop yield and GHG balances in agricultural systems. We here used the process-based LandscapeDNDC model to simulate N2O emissions and trade-offs in yield and soil nitrogen budget for four alternative arable cropping systems in three Austrian agricultural production zones belonging to different climatic regions. We evaluated statistical data on crop rotations and management practices, predominant soil types, and 10-year daily weather conditions for four cropping systems: (1) conventional farming receiving the maximum allowed nitrogen fertilisation rate (Nmax), (2) conventional farming receiving 15% less fertiliser, (3) conventional farming receiving 25% less fertiliser, and (4) organic farming. Our results showed that soil N2O emissions could be best reduced in wet, high-yield regions. Reducing nitrogen fertilisation by 15% and 25% mitigated N2O emissions by, on average, 22% and 39%, respectively, while the yield was reduced by 5% and 9%, respectively. In comparison, the same crops grown in the organic cropping system released 60% less N2O, but yield declined on average by 23%. Corn, winter barley, and vegetables showed the highest N2O reduction potential under reduced fertiliser input in conventional farming. In addition to N2O emissions, reduced fertilisation substantially decreased other nitrogen losses into the water and atmosphere. Generally, the soils under all cropping systems maintained a positive mean nitrogen budget. Our results suggest a significant emission reduction potential in certain production zones which, however, were accompanied by yield reductions. Knowledge of the emission patterns from cropping systems under different environmental conditions is essential to set the appropriate measures. In addition, region-specific measures to reduce soil N2O emissions have to be in line with farmers' interests in order to facilitate the successful implementation of targeted nitrogen management.

Denitrification is a key process in the global nitrogen (N) cycle, causing nitrous oxide (N2O) and dinitrogen (N-2) emissions. Biogeochemical models allow field-scale estimates of N2O and N-2, extrapolating important yet often limited experimental results. However, such predictions rely mostly on N2O data, and the lack of N-2 data hinders validating total denitrification, which remain a major uncertainty for N budgets. This study investigated denitrification losses and N budgets in two tropical sugarcane systems using the Agricultural Production Systems sIMulator (APSIM) and the LandscapeDNDC (LDNDC) simulation framework using a unique dataset of both N2O and N-2 emissions measured in the field over a complete growing season. Key soil N parameters influencing N2O and N-2 emissions in APSIM and LDNDC were identified via global sensitivity analysis, followed by generalised likelihood uncertainty estimation to determine their posterior distributions using (i) N2O data only and (ii) both N2O and N-2 data. The simulation of N2O emissions in APSIM and LDNDC were improved in both calibration approaches, resulting in 0.7-1.3 kg N ha(-1) of RMSE. However, simulated N-2 emissions increased and agreed better with the observed values only when calibrated with both N2O and N-2 (RMSE 30.1-45.0 kg N ha(-1) before calibration and 19.3-19.9 kg N ha(-1) after). The simulated N loss pathway shifted from leaching to N-2 emissions after calibration including N-2. The simulated N balance was larger when sugarcane residues were retained as compared to burning consistently across the different soil N parameter configurations. These findings indicate that biogeochemical models, when used with default soil N parameters or calibration limited to N2O data, are likely to underestimate denitrification losses (>50 %), leading to a bias in N budgets simulation. Accurate N loss estimates are essential for understanding the long-term management impacts on soil organic matter dynamics, as demonstrated by the improved N budgets from both simulation models denote N mining when sugarcane is burnt, and the potential to sequester N when cane residues are retained. These outcomes emphasise the importance of integrating in-situ measurements of N2O and N-2 in simulation exercises, ensuring more accurate N budget estimates across scales.

A complete understanding of the nexus between productivity and sustainability of agricultural production systems calls for a comprehensive assessment of the nitrogen budget (NB). In our study, data from the well-monitored Danish Agricultural Watershed Monitoring Program (LOOP-program; 2013–2019) is used for a quantitative inter-comparison of three different approaches to drive the process-based model LandscapeDNDC on the regional scale. The aim is to assess how assumptions and simplifications about farm management activities at a regional scale induce previously unquantified uncertainties in the simulation of yields and the NB of cropping systems. Our findings reveal that the approach based on detailed field-level management data (A) performs the best in simulation of yield (r2 = 0.93). In contrast, the other two different data aggregation approaches (B: Sequential mono-cropping of six major crops with simulation results averaged according to proportional area, and C: simulation of 20 most frequent crop rotations) have lower correlations to the observed yields (r2 = 0.92 and 0.77, respectively) but are still statistically significant at p < 0.05 level. Notable differences arise between detailed and more aggregated crop system simulations concerning the NB, particularly concerning N losses to the environment. Compared to the detailed approach (A) (gaseous N fluxes: 24.3 kg-N ha−1 year−1; nitrate leaching: 14.7 kg-N ha−1 year−1), the aggregation approach B leads to a 31.4% over-estimation in total gaseous N fluxes (+7.6 kg-N ha−1 year−1), while nitrate leaching shows a similar average with a distinct pattern. Conversely, employing aggregation approach C leads to a 17.6% over-estimation in total gaseous fluxes (+4.3 kg-N ha−1 year−1) and a 204.9% over-estimation in nitrate leaching (+30.2 kg-N ha−1 year−1). These findings suggest that management representation should be chosen carefully because it can induce large uncertainties, especially when simulating large-scale NBs or assessing the environmental impact of cropping management. This may compromise the accuracy of national and international nutrient budgets, and preclude comparisons among different sources when the approaches for management representation differ.

The assessment of cropland carbon and nitrogen (C and N) balances plays a key role in identifying cost-effective mitigation measures to combat climate change and reduce environmental pollution. In this paper, a biogeochemical modelling approach is adopted to assess all C and N fluxes in a regional cropland ecosystem of Thessaly, Greece. Additionally, the estimation and quantification of the modelling uncertainty in the regional inventory are realized through the propagation of parameter distributions through the model, leading to result distributions for modelling estimations. The model was applied to a regional dataset of approximately 1000 polygons, deploying model initializations and crop rotations for the five major crop cultivations and for a time span of 8 years. The full statistical analysis on modelling results (including the uncertainty ranges given as +/- values) yields for the C balance carbon input fluxes into the soil of 12.4 +/- 1.4 t C ha - 1 yr - 1 and output fluxes of 11.9 +/- 1.3 t C ha - 1 yr - 1 , with a resulting average carbon sequestration of 0.5 +/- 0.3 t C ha - 1 yr - 1 . The averaged N influx was 212.3 +/- 9.1 kg N ha - 1 yr - 1 , while outfluxes of 198.3 +/- 11.2 kg N ha - 1 yr - 1 were estimated on average. The net N accumulation into the soil nitrogen pools was estimated to be 14.0 +/- 2.1 kg N ha - 1 yr - 1 . The N outflux consists of gaseous N fluxes composed of N 2 O emissions of 2.6 +/- 0.8 kg N 2 O-N ha - 1 yr - 1 , NO emissions of 3.2 +/- 1.5 kg NO-N ha - 1 yr - 1 , N 2 emissions of 15.5 +/- 7.0 kg N 2 -N ha - 1 yr - 1 and NH 3 emissions of 34.0 +/- 6.7 kg NH 3 -N ha - 1 yr - 1 , as well as aquatic N fluxes (only nitrate leaching into surface waters) of 14.1 +/- 4.5 kg NO 3 -N ha - 1 yr - 1 and N fluxes of N removed from the fields in yields, straw and feed of 128.8 +/- 8.5 kg N ha - 1 yr - 1 .

Food, feed, and fiber production needs to increase to support demands of the growing population in Sub-Saharan Africa (SSA), while soil fertility continues to decline. Intercropping, the cultivation of two or more crop species on the same field, can provide yield benefits and is suggested to positively affect soil organic carbon (C) and nitrogen (N) stocks. This study uses the biogeochemical model system LandscapeDNDC with the objective to (a) represent maize-legume intercropping systems in different bioregions in SSA by simultaneously simulating both crops and their interactions and (b) assess long-term (20 years) impacts of intercropping under varying mineral fertilizer inputs (0-150 kg N ha-1 yr-1) on productivity as well as soil organic C and N stocks. We test LandscapeDNDC on 82 field data sets (site-year-treatment combinations) from 18 sites to represent yields and soil C/N dynamics of maize-legume intercropping systems. Using the model for long-term scenario simulations showed that intercropping allows to sustain productivity and to improve or maintain SOC stock in low or zero fertilizer systems if all residues are returned to the soil. In contrast, for sole-cropped maize systems, a decline in SOC stocks was simulated unless a minimum of 35 kg N ha-1 yr-1 of fertilizer was applied at full residue return. We conclude that intercropping using legumes alongside sufficient residue return allows for stabilizing long-term yields while avoiding SOC losses even with low fertilizer N inputs. Overall, our study confirms the potential of intercropping as a sustainable agricultural practice that could significantly contribute to food security in SSA.

Crop residues are important inputs of carbon (C) and nitrogen (N) to soils and thus directly and indirectly affect nitrous oxide (N2 O) emissions. As the current inventory methodology considers N inputs by crop residues as the sole determining factor for N2 O emissions, it fails to consider other underlying factors and processes. There is compelling evidence that emissions vary greatly between residues with different biochemical and physical characteristics, with the concentrations of mineralizable N and decomposable C in the residue biomass both enhancing the soil N2 O production potential. High concentrations of these components are associated with immature residues (e.g., cover crops, grass, legumes, and vegetables) as opposed to mature residues (e.g., straw). A more accurate estimation of the short-term (months) effects of the crop residues on N2 O could involve distinguishing mature and immature crop residues with distinctly different emission factors. The medium-term (years) and long-term (decades) effects relate to the effects of residue management on soil N fertility and soil physical and chemical properties, considering that these are affected by local climatic and soil conditions as well as land use and management. More targeted mitigation efforts for N2 O emissions, after addition of crop residues to the soil, are urgently needed and require an improved methodology for emission accounting. This work needs to be underpinned by research to (1) develop and validate N2 O emission factors for mature and immature crop residues, (2) assess emissions from belowground residues of terminated crops, (3) improve activity data on management of different residue types, in particular immature residues, and (4) evaluate long-term effects of residue addition on N2 O emissions.