Eddy covariance (EC) measurements are a backbone of ecological research and have provided valuable insights into the variability of carbon and water fluxes in different ecosystems and under varying environmental conditions. Since these measurements are integrative and weighted over changing areas (footprint), species-specific information cannot be easily derived except for homogenous monocultures. However, EC sites are increasingly established in mixed forest stands which are considered to be more resilient under changing environmental conditions. This leads to the question of how species-specific responses can be determined, and whether the magnitude of fluxes derived from temporally varying flux footprint predictions (FFPs) can provide insight into these responses. At a site in southwestern Germany's Black Forest, primarily composed of mature beech and Douglas fir trees, we investigate the dependence of EC flux measurements on different FFP areas and explore how species-specific contributions to gas exchange can be disentangled using a combined measurement and modeling framework. We applied an ecosystem model that has been calibrated from EC measurements at various sites with beech- and Douglas fir monocultures, and evaluated it with data of soil water content and soil respiration taken at homogeneous parts of the investigated mixed forest site. Then we compared hourly aggregated measurements of net carbon exchange (NEE) and evapotranspiration (ET) with model simulations under four configurations: (i) pure beech, (ii) pure Douglas fir, (iii) a static weighted average of both species, and (iv) a dynamic weighted average based on FFP variations. The results show that weighted combinations of the two species generally provide a better match with hourly EC measurements than single-species simulations, while differences between static and dynamic weighting approaches remain relatively small. However, species-specific flux responses can be significantly different during transitional periods such as autumn and spring when physiological differences between Douglas fir and beeches are most pronounced. We demonstrate that accounting for seasonal differences is particularly important for gap-filling EC measurements in mixed forests and, consequently, for determining annual carbon and water budgets. Furthermore, EC measurements over mixed forests provide valuable information for detailed model evaluation, while species-specific modeling helps disentangle and attribute underlying ecosystem dynamics to individual species.

The systematic acquisition of field data is a major bottleneck for identifying scalable solutions that effectively reduce emissions while maintaining productivity in agricultural systems such as rice. This 2nd volume of a multilayered presentation of Greenhouse Gas (GHG) emission measurements in rice fields links up with a review of scientific findings achieved with well-established measurement approaches. Special emphasis is given to advanced systems with laser-based trace gas analyzers (TGA) integrated into an upgraded closed chamber system. A synchronized field experiment was conducted under Alternate Wetting and Drying (AWD) and Continuous Flooding (CF), comparing a) manual sampling with gas chromatography representing a time-tested reference method, b) a TGA in a stand-alone (portable) configuration, and c) a TGA assembled with a semi-automated multi-valve system. Following a preparatory test resulting in an optimum sampling interval of 4 min, the reliability of the TGA measurements was assessed by calculating R² from linear regression of gas concentration versus sampling time. Based on a paired t-test, the three approaches did not present any significant difference except for rare outliers with p ≤ 0.01 reaching a maximum difference of 12.62 mg m-² d-¹. In total, these disparities were small compared to overall emission levels and occurred randomly across treatments, indicating that there was no systematic bias between approaches. In the second part of this volume, we broadened the perspective to a comparative assessment of methods supplemented by projecting future developments in GHG measurements in rice. Both portable and multi-valve TGA systems provide greater efficiency and real-time data acquisition while their mutual comparison is a function of research objectives and project settings. Regarding technical features of future measurement systems in rice, we highlighted the multi-valve TGA system as a feasible core component of a high-throughput screening platform intended to identify low-emission rice varieties for immediate dissemination across scales and integration into breeding programs. Finally, we assessed the possible synergies of these high-frequency TGA data sets with other emerging technologies, namely Remote Sensing and Machine Learning, under a diversified regulatory framework for GHG accounting that will likely dissolve the distinction of Tier 2 and 3 approaches for rice production.

Nitrogen-rich agricultural headwater streams are known hotspots for fluvial greenhouse gas (GHG) emissions and denitrification, yet the underlying processes driving these elevated rates are not fully understood. In this study, we examined these mechanisms by combining measurements of gross nitrogen turnover processes, open-channel GHG and N2 saturation (%) and fluxes, and δ15N isotopic analysis of stream sediment and water at nine headwater stream sites with varying levels of agricultural land use. To assess seasonal patterns, data were collected across two transitional periods: spring–summer and winter–spring. Catchment land use emerged as an important environmental driver of variability, as open channel GHG emissions and denitrification rates were up to 11 times higher in fertilized grasslands and croplands compared to those in forested areas. In-vitro nitrogen turnover rates followed a similar trend and were mainly positively related to both GHG and N2 oversaturation. This finding suggests that the excess nitrogen inputs in agricultural streams promote enhanced nitrogen turnover and gaseous carbon and nitrogen losses. We also observed a proportional increase in CO2, CH4, N2O, and N2 saturation in the water column with sediment δ15N enrichment, a known indicator of long-term nitrogen turnover processes. Because the highest GHG emissions and denitrification N2 losses occurred within streams in fertilized areas, our findings highlight the potential of using sediment δ15N as an indicator of long-term anthropogenic hotspots of fluvial GHG emissions and denitrification rates.

The overuse of nitrogen (N) fertilizers in southern Chinese citrus orchards is associated with significant environmental losses of reactive N, including ammonia (NH3) volatilization, nitrous oxide (N2O) and nitric oxide (NO) emissions, and nitrate leaching. This study evaluated the effectiveness of three advanced urea-based nitrogen fertilizers-polyurethane-coated urea (PCU), humic acid urea (HAU), and urease inhibitor-based urea (UIU)-in reducing N losses from citrus orchards compared with conventional urea (U). A two-year field experiment was conducted in the Sichuan Basin of southern China to measure fruit yield and quality, gaseous emissions (NH3, N2O, and NO), and hydrological N losses via surface water runoff (total nitrogen [TN], NO3--N, and NH4+-N). In parallel, a pot experiment using N-15 tracer technique was conducted to investigate soil N transformations. The results showed that, in the second year, PCU significantly increased fruit yield and quality (e.g., vitamin C content and sugar-to-acid ratio) compared to U. Over the two-year experimental period, PCU most effectively mitigated gaseous N losses, reducing cumulative NH3 volatilization by 51.1%, NO emissions by 36.3%, and surface runoff losses of TN and NO3--N by 14.3% and 25.5%, respectively. Although PCU suppressed N2O flux peaks, it did not significantly reduce cumulative N2O emissions. In contrast, HAU increased N2O emissions by 16.1% (p < 0.05) without providing additional benefits for NH3 or runoff N losses. UIU failed to reduce cumulative NH3 and N2O losses, despite reducing NO emissions and NO3--N loss. The superiority of PCU is attributed to its ability to physically control N release and stimulate soil NO3- immobilization (+56.5%). Overall, PCU represents the most balanced strategy for promoting citrus productivity while minimizing reactive N losses in subtropical citrus orchards. These findings provide critical insights into N cycling processes and the environmental impacts of enhanced-efficiency fertilizers, offering scientific support for the development of next-generation urea products for sustainable citrus production.

The ratios of dissolved carbon, nitrogen, and phosphorus in rivers constrain downstream carbon and nutrient cycling, yet their long-term trends remain unclear. Using a 20-year ML-modeled dataset spanning 5084 catchments, we show that 27% exhibited coupled C-N-P trends with increasing or decreasing patterns, while 73% exhibited divergent trends. These divergent trends resulted in global declines in DOC-to-nutrient ratios, possibly linked to river warming, anthropogenic land-use expansion, and forest cover declines.

The bulk soil δ15N signature in grassland soils serves as a fingerprint of nitrogen inputs, turnover and losses, reflecting the influence of land management, climate, plant biodiversity and abiotic soil properties. Although natural abundance δ15N has been proposed as an indicator for these factors, previous studies yielded mixed results regarding the importance of its drivers. In this study, we quantified changes in soil δ15N across 72 German grassland sites after 12 years (2011 vs. 2023) and analysed relationships with detailed records of land use intensity, plant biodiversity, climate, and soil properties. Land use intensity was quantified on a continuous scale based on mowing, grazing and fertilisation. In the top 10 cm of soil, δ15N changes ranged from −0.86 to + 2.49‰, equating to an average increase of + 0.35‰ per decade. Over two-thirds of our study sites showed a long-term increase in δ15N associated with high land use intensity. Our findings indicate that fertiliser application and grazing are the dominant drivers of δ15N increases, while a biodiversity-mediated decrease via reduced N losses was not observed. The slow decadal-scale shifts suggest that single soil δ15N measurements without known initial values are only informative for long-term management processes and not for assessing recent management impacts.

Actual evapotranspiration (ETa) is a vital terrestrial ecosystem process that links water, energy, and carbon cycles. ETa can be limited by either energy or water availability. The transition between water-and energy-limited regimes is related to soil moisture and is often characterized as a threshold, denoted as critical soil moisture threshold (Ocrit). However, the determination of Ocrit is subject to uncertainties due to the different methods used to evaluate the relationship between ETa and soil moisture (SM), such as SM depths, definitions of ETa and curve fitting functions. Typically, surface SM is used to identify Ocrit as it is easily accessible and assumed to represent root zone SM status. Weighable lysimeter technology provides a unique opportunity to assess the role of root zone SM on the transition between water and energy limited ETa. It is widely regarded as the gold standard for measuring in-situ ET, and at the same time allows for in-situ SM measurements at different depths. In this study, we estimated Ocrit using in situ SM measurements at 10 cm depth and root zone SM by vertically integrating in situ SM (0-60 cm) observations. In addition, we applied three different definitions of relative evapotranspiration (evaporative fraction, the ratio of ETa to grass reference evapotranspiration and the ratio of actual ETa to calculated potential evapotranspiration) as well as two different fitting curves to investigate the sensitivities of Ocrit. We found robust Ocrit estimates across different definitions and fitting curve methods, but the estimates were significantly higher for root zone than for surface Ocrit. Our results also highlight the high correlation (0.83) between root zone and surface Ocrit. However, the relation between both values is not unique since it depends on the actual moisture profile and plant root system and, herewith, on the soil type and previous weather conditions. We further observed that both surface and root zone Ocrit decreased with increasing sand fraction. Under changing climatic conditions but with identical soil and ecosystem types, both surface and root zone Ocrit decreased with increasing aridity. Additionally, we found that using the midpoint between field capacity and wilting point provides a reliable range of root zone Ocrit for a given soil texture.

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.

Conversion of tropical peatland forests to oil palm plantations has been associated with elevated nitrous oxide (N2O) emissions and nitrate (NO3-) leaching, yet the biogeochemical mechanisms driving these nitrogen (N) losses remain poorly understood. To address this gap, soil N2O fluxes, gross N transformation rates, microbial gene abundances, annual NO3- leaching rates, and bulk soil δ15N signatures were quantified along transects in Malaysia that included intact tropical peat swamp forest and drained oil palm plantation. Oil palm plantation sites had higher topsoil oxygen (O2) concentrations than forest sites, indicating that drainage shifts soils from predominantly anaerobic to aerobic conditions. These redox shifts, together with fertilizer inputs, increased nitrifier gene abundances and enhanced gross nitrification rates, which exceeded ammonification rates by up to 186%, promoting soil NO3- accumulation. The elevated soil NO3- concentrations stimulated N2O production, probably through coupled nitrification-denitrification, and increased NO3- leaching. Overall, these results showed that oil palm plantation sites had higher gaseous and hydrological N losses than peat forest sites. Additionally, nitrification rates, hydrological N export, and gaseous N losses were positively correlated with more enriched bulk soil δ15N values along the land use gradient. These findings demonstrated that bulk soil δ15N patterns may have the potential to serve as indicators of long-term N cycling and losses in tropical peatlands associated with human-induced land use changes.

Reducing the high nitrogen (N) losses during fertilization with cattle slurry is key to reduce environmental impacts of grassland farming. We tested the hitherto unknown potential of separated versus regular unseparated slurry (control) to mitigate total N losses in a three-year experiment using 15N-labelled slurry. Slurry separation was enhanced using starch, clay minerals, and centrifugation, yielding an organic-rich solid fraction and a liquid fraction with low dry-matter content and ca 70 % ammonium-N. The use of separated slurry significantly increased plant productivity (+12 %), plant N uptake (+21 %), and total biomass harvest N export (+20 %) compared to the control. Additionally, fertilizer N retention in topsoil organic N (SON) increased by 8 %. Due to higher plant uptake, and higher soil storage of fertilizer N, total gaseous N losses from separated slurry were lower (33.5 % of added N) than from regular slurry (57.6 %). Leaching of fertilizer N remained negligible in both treatments. However, this did not apply for N2O emissions, which were of low relevance for N balance considerations, but tripled after the addition of the liquid phase of separated slurry in summer. This undesired effect however might be prevented if the solid phase is applied in summer and the liquid phase in spring when soil microbial activity is still low. In summary, separated slurry reduced N losses, increased productivity, fodder quality, and fertilizer N retention, thereby mitigating N deficits and soil N mining. Thus, with appropriate application timing, use of separated slurry can enhance both ecological and economic soil functions and ecosystem services.

Global fluvial ecosystems are increasingly impacted by human activities, such as climate warming and land use changes; however, the combined effects of these pressures on river greenhouse gas (GHG) supersaturation and deoxygenation remain poorly understood. This study modeled past global trends (2002-2022) in river GHG saturation, dissolved oxygen (DO) levels, water temperature, and eight other water quality parameters using machine learning models powered by satellite observations. Our findings show significant global increases in river GHG supersaturation and deoxygenation, mainly driven by rising water temperatures (0.27°C ± 0.03°C per decade), increased precipitation, higher labile carbon and nitrogen inputs, and urban and cropland expansion. We estimate that anthropogenic GHG emissions from rivers due to these pressures totaled 1.5 Pg-CO2-eq over the 20-year period. The increase in GHGs was accompanied by a global river deoxygenation rate of 0.058 ± 0.01 mg L-1 per decade, suggesting rivers may be losing oxygen up to 2.5 times faster than lakes and oceans globally.

Changes in land use and land management can have significant effects on the global emissions budget, influencing the climate through biogeochemical processes. However, their impacts on major soil greenhouse gas (GHG) emissions in West Africa remain poorly documented and understood. This study provides the assessment of soil GHG emissions in the Sudanian savanna region of West Africa using a chamber-based experimental setup. The measurements are taken at four sites with contrasting land use and land management practices: pristine savanna forest, cropland, degraded grassland, and a rainfed rice field. Over two consecutive years (2023-2024) of weekly chamber measurements during the rainy season (corresponding to the rice-growing period), our results reveal significant variation in methane (CH4) fluxes across the sites. However, nitrous oxide (N2O) fluxes did not vary significantly, likely due to uniformly low nitrogen input across all systems. The highest seasonal CH4 emissions were recorded in the rainfed rice field (0.69 ± 0.17 and 0.82 ± 0.22 kg C ha- 1 season- 1, on average), while the forest reserve acted as a net CH4 sink (- 0.019 ± 0.20 and - 0.42 ± 0.13 kg C ha- 1 season- 1). In contrast, soils across all sites, both managed and natural, were sources of N2O, with fluxes ranging from 0.01 kg N ha- 1 season- 1 in the forest reserve to 0.16 kg N ha- 1 season- 1 in the rice field. This study also analyzed the environmental drivers of GHG fluxes and found that CH4 variability was significantly influenced by soil water content and soil temperature (partial R² between 0.21 and 0.42). No significant relationship was observed between these variables and N2O emissions. These results highlight that land cover degradation in the Sudanian savanna can substantially increase CH4 emissions, while its impact on N2O fluxes is marginal but leads to higher CO2-equivalent.

Water use efficiency (WUE) is a key indicator of ecosystem balance, reflecting how productivity responds to hydrological constraints under climate change. However, variability in WUE and its environmental drivers across West African agroecosystems remains poorly understood. Here, we integrate multi-year (2019–2024), half-hourly eddy-covariance observations of carbon and water-vapor fluxes from four contrasting land-use types in northern Ghana: a reserve savanna forest, rain-fed paddy rice, grassland, and rain-fed cropland. WUE exhibited pronounced diurnal and seasonal variability, shaped by hydrological, atmospheric, and land-management drivers. Diurnal patterns were bimodal, with morning and afternoon peaks shifting between wet and dry seasons. During the wet season, mean WUE was highest in the savanna forest (3.1 ± 0.26 g C kg⁻¹ H₂O), followed by paddy rice (2.08 ± 0.21), cropland (1.93 ± 0.20), and grassland (1.66 ± 0.18). Seasonal analyses highlighted ecosystem-specific controls, reflecting differences in radiation, soil moisture, and cultivation practices.

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.

Mount Kilimanjaro, with its steep elevational gradient (770-5886 m a.s.l.) and pronounced land-use heterogeneity, supports high biodiversity and diverse nature's contributions to people (NCP), but it is underrepresented in global spatial assessments. We address this gap by mapping NCP supply across 12 ecosystem types on the southern slopes identifying hotspots and coldspots and quantifying synergies and trade-offs among NCP categories. We use 25 context-specific NCP categories that integrate local and scientific knowledge with field measurements and remote-sensing-derived proxies. Combining long-term field data with remote sensing and machine learning, we upscaled plot-scale indicators into standardized supply maps. Total NCP supply is strongly concentrated in mid-elevation ecosystems: the 1100-2200 m band alone accounted for similar to 59% of total supply compared with similar to 18% in the lowlands (700-1100 m), and the 1100-2800 m belts together provide similar to 73%, whereas high-elevation zones (2800-4600 m) contribute <9%. Hotspots clustered in lower montane forest, Ocotea forest and homegardens at mid-elevations, while coldspots occur in Erica forest and Helichrysum vegetation at high elevations and in maize fields and savanna at low elevations. We detected moderate (r = 0.55) to strong synergies (r = 0.83) among the three NCP groups (material, regulating, non-material). After accounting for climatic co-variation, the correlations among NCP groups weakened (r = 0.23-0.44), underscoring the critical role of climate for NCP supply. Our study maps NCP hotspots and coldspots across Mt. Kilimanjaro and provides a decision-support layer for conservation, restoration and agroforestry management, as well as a blueprint for spatially-explicit NCP mapping and analyses.

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.

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.