Co-cropping, the cultivation of two or more crops simultaneously on the same field, is a nature-based solution that has high potential to improve climate change adaptation and mitigation in arable systems. The short-term benefits of co-cropping, such as higher yields, better productivity, improved soil carbon and enhanced water uptake, are well-established in temperate regions, but evidence is still generally lacking for humid temperate environments. In addition, the interlinkages between water and carbon dynamics in co-cropping and the longer-term functioning, resilience and sustainability of these systems under future scenarios remain unclear. This study focuses on addressing these knowledge gaps by monitoring the short-term (2 years) and modelling the longer-term (~20 years) impact on water and carbon dynamics in different agricultural co-cropping systems for a typical temperate agroecosystem in Scotland.
The experimental study focussed on two barley (Hordeum vulgare) cultivars with contrasting phenotypic traits (high yielding and stress tolerant), co-cropped with pea (Pisum sativum) and their three corresponding monoculture systems. Crops were grown without agrochemical inputs to investigate the potential for co-cropping in low input systems. On 6 occasions during a two-year field experiment, we investigated soil physical, carbon and nitrogen properties at two depths (i.e. upper (<5 cm) and lower (25-30 cm) topsoil). Crop production and grain quality (i.e. grain carbon and nitrogen contents) were also assessed. Analyses of hydrometric monitoring, and soil and plant samples for stable water isotopes further informed the hydro-climatological conditions and plant water uptake interactions. In the short term, we found that co-cropping modified barley water uptake strategies and enhanced soil carbon, crop production and grain quality, although barley cultivar traits determined the specific effects.
The data also informed a modelling study that coupled a soil carbon (RothC) and water balance model (Hydrus-1) to test how crop water uptake patterns and carbon change interact in co-cropping systems throughout the growing season under different conditions of climate change and water availability. The findings of this study provide an evidence-base for sustainable agricultural practices in temperate systems and determine the resilience of co-cropping systems to future climatic conditions.
How to cite: Durodola, O., Hawes, C., Smith, J., Valentine, T. A., and Geris, J.: Short and long-term effects of co-cropping systems in temperate regions: water-carbon interlinkages and the role of cultivar traits, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18549, https://doi.org/10.5194/egusphere-egu25-18549, 2025.
Among other sectors agriculture is under pressure to reduce greenhouse gas (GHG) emission to contribute to national net zero targets. Avoiding all emissions is not possible. Therefore, negative emissions are required to achieve climate neutrality or net zero targets. Croplands are acknowledged to have good capacity to capture and store carbon in form or soil organic matter (SOM). Management changes on croplands are required to increase SOM in cropland. Additionally, monitoring systems must be available to quantify SOM or soil organic carbon (SOC) changes. There are several measuring/monitoring, reporting and verification (MRV) systems in place to provide the required approaches for quantification. However, there are no standards about the structure of an MRV system. Financial constrains driving the applied methods in the available MRV systems for SOC changes, with remote sensing and modelling popular cost-effective solutions. This presentation shows results of an analysis applied in the ClieNFarms project, which assess and advice on solutions to achieve climate neutral farming. Selected MRV systems are analysed for their functionality, applicability and potential accuracy. Further, the available MRV systems are compared for the representation of different compartments that could be implemented for a perfect approach to quantify SOC changes. This is a qualitative analysis highlighting used methods to quantify SOC changes and provides an analysis about the functionality and the applicability of methods being influenced by stakeholder needs and varying levels of data availability. This study also highlights advantages and disadvantages of using the tools and models in MRV systems or for SOC monitoring in general. Models are powerful tools but there is a wide range of different models available, which differ in data demand and accuracy. The results highlight that the available systems are mainly driven by the urgent demand considering an easy applicability, low labour requirements and cost-effectiveness. This is a critical analysis not doubting the quality of available MRV systems, but provide discussion points and views on the available and applied systems.
How to cite: Kuhnert, M., Kashyap, D., and Klumpp, K.: Monitoring of soil carbon storage to achieve climate neutral farming – analysing existing MRV systems and model options, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18001, https://doi.org/10.5194/egusphere-egu25-18001, 2025.
Quantifying carbon outcomes from agroecosystems plays an important role in mitigating global warming and ensuring food security through sustainable production. However, high spatial-temporal-resolution (e.g., ~100m, daily), accurate, well-resolved carbon budgets and crop yield in agroecosystems are extremely challenging to quantify due to the complexity of involved processes and large variations in environmental and management drivers. Traditional process-based-modeling approaches are computationally expensive to achieve field-scale resolution and contain large uncertainty due to underdetermined model structure and parameters. Knowledge-guided machine learning (KGML) is a hybrid modeling approach that leverages recent advances in machine learning combined with known physical principles and relationships to enhance the training and application processes, which helps open the “black box” of conventional ML models, and enable better predictions that capture variability in both time and space. Here we proposed a data-efficient KGML framework that effectively predicts daily variations in agricultural CO2 emissions, crop yields, and soil carbon storage at field scale, as successfully demonstrated for the US Midwest. Multi-source data and pretraining with outputs from a well-validated agroecosystem model were incorporated into a hierarchically structured deep learning neural network that greatly outperformed both process-based and pure machine learning models, especially in data-limited cases. This work demonstrates the advantages of integrating domain knowledge with state-of-the-art artificial intelligence in agroecosystem modeling that will lead toward broader use of KGML in geoscience.
How to cite: Zhou, W., Liu, L., Guan, K., Jin, Z., Peng, B., and Wang, S.: Scalable quantification of agroecosystem carbon budget and crop yield based on knowledge-guided machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7574, https://doi.org/10.5194/egusphere-egu25-7574, 2025.
Conservation agriculture (CA) practices, such as no-tillage and mulching, can contribute to climate change mitigation by enhancing soil organic carbon (SOC) stocks and by influencing nitrous oxide (N2O) emissions. However, their impacts on surface albedo and overall climate benefits, remain underexplored, particularly in Africa. This study tries to better address the net climate impacts of no-tillage and no-tillage with mulching compared to conventional tillage through two long-term experiments conducted in Zimbabwe - one established on an abruptic Lixisol soil (DTC site), the other one on a xanthic Ferralsol soil (UZF site). Over two years, measurements included SOC concentrations to a depth of 1 m, N2O emissions, and surface albedo. The ICBM soil carbon model was employed to predict SOC stocks over 30 years of CA practices. Results indicated that no-tillage with mulching significantly increased SOC in the topsoil (0–30 cm), with stocks projected to reach 0.41 Mg C ha-1y-1 at DTC and 0.56 Mg C ha-1y-1 at UZF after 30 years. Conversely, no-tillage without mulching resulted in slight SOC losses at DTC, with predicted losses of approximately 0.036 Mg C ha-1y-1 over 30 years, while at UZF, SOC stocks increased by 0.11 Mg C ha-1y-1. Both sites exhibited very low N2O emissions, indicating minimal climate impacts from this source. Net climate impacts were evaluated using the Global Warming Potential (GWP) approach at 20- and 100-year time horizons to assess short- and long-term climate effects. Results showed that no-tillage without mulching increased surface albedo on both soil types, inducing net cooling effects of -2.56 Mg CO2 eq ha-1 y-1 and -0.65 Mg CO2 eq ha-1 y-1, with surface albedo contributing 90% and 86%, respectively, on the Lixisol over 20 and 100 years. On the Ferralsol, no-tillage without mulching generated cooling effects of -1.25 Mg CO2 eq ha-1 y-1 and -0.77 Mg CO2 eq ha-1 y-1, with surface albedo contributing 52% and 23%, respectively, over the same periods. In contrast, mulching had contrasting effects at the two sites. On the Ferralsol, mulching enhanced surface albedo, contributing to net cooling effects of -1.82 Mg CO2 eq ha-1 y-1 over 20 years and -1.57 Mg CO2 eq ha-1 y-1 over 100 years, with surface albedo contributing approximately 20% in the short term and 5% in the long term. Conversely, on the Lixisol, mulching reduced surface albedo, offsetting 100% of SOC benefits and resulting in a near-neutral climate effect of +0.09 Mg CO2 eq ha-1 y-1 over 20 years and +0.55 Mg CO2 eq ha-1 y-1 over 100 years. This study underscores the necessity of integrating biogeochemical and biogeophysical effects when assessing the climate mitigation potential of CA practices, particularly in regions with diverse soil types and climatic conditions.
How to cite: Diop, S., Cardinael, R., Lauerwald, R., Sieber, P., Thierfelder, C., Chikowo, R., Corbeels, M., Shumba, A., and Ceschia, E.: Balancing biogeochemical gains and surface albedo shifts: climate impacts of no-tillage and mulching in Southern Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18904, https://doi.org/10.5194/egusphere-egu25-18904, 2025.
Agroforestry (AF) refers to a wide range of agricultural practices that incorporate woody plants into crop- and grasslands. Agroforestry systems (AFS) can be distinguished by their share of trees and their spatial allocation, the selection of tree species, and tree management. While AFS are common in the global south to promote soil fertility, reduce heat stress and improve the water balance, they are less common in the global north. Currently, AFS are discussed as a nature-based solution for terrestrial carbon dioxide removal (CDR). Here, alley-cropping AFS are a promising system because their tree cover is sufficiently large for significant CDR rates and they are still compatible with the use of agricultural machinery that is common in modern agricultural practices. However, estimating the large-scale CDR potential of AFS is challenging because of the variety of potential systems whose performance strongly depends on environmental conditions.
We study the CDR potential of AFS by extending the process based dynamic global vegetation model Lund-Potsdam-Jena managed Land (LPJmL) to represent alley-cropping AFS on cropland. The model explicitly accounts for shading effects of tree rows depending on row and tree distance and row orientation as well as the competition for soil water and nutrients between trees and crops. As an example for potential model applications, we assessed the future CDR potential of timber alley-cropping AFS for Germany assuming a moderate linear annual increase of AF areas by 0.5% of the total cropland area until 2060 and a moderate tree cover.
With the process-based representation of AFS in LPJmL, the model can be applied to study carbon, water, and nitrogen fluxes and pools of different alley-cropping AFS and conventional cropping systems at large spatial scales, including maximum carbon sequestration rates, potential equilibrium states and reversibility.
How to cite: Wirth, S. B., Rolinski, S., and Müller, C.: Estimating the carbon dioxide removal potential of alley-cropping agroforestry systems in Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11022, https://doi.org/10.5194/egusphere-egu25-11022, 2025.
Temporarily flooded depressions within cropland have been identified as substantial hotspots of nitrous oxide (N2O) emission, releasing up to 80 times more N2O than surrounding field areas during the flooded period. Despite their significant contribution, the temporal dynamics of N₂O emissions from these depressions and their impact on regional annual N₂O budgets remain inadequately quantified. The primary drivers of these high emissions are poorly understood, limiting the accuracy of regional estimates and the development of effective mitigation strategies.
To address this knowledge gap, we established two elevation transects in two Danish croplands, each comprising five positions (0, 1, 2, 3, 4; with three replicate plots per position) along a slope gradient from depressions to the uphill areas. Biweekly in-situ N₂O flux measurements were conducted at each plot over a year (March 2020 to March 2021) using static chambers. Concurrently, soil samples were collected for laboratory analysis of physicochemical properties along with each field measurement, and soil water content and temperature were monitored at 30-minute intervals in the depression areas. Additionally, daily photographs of each transect were captured using installed cameras, and daily remote sensing images at 3-m resolution (PlanetScope) were utilized to evaluate relative wetness for each plot. Based on the field data, daily photos, and relative wetness, the study year was divided into three distinct periods: flooded period (with water above the soil surface), flood recover period (characterized by high soil water content typically after flooding), and drained period (with comparable soil moisture between depression and uphill areas).
Our results reveal significant spatial and temporal variability in N₂O fluxes along the transects. Positions within the depressions exhibited significantly higher annual mean N₂O fluxes, ranging from 93.4 to 204.6 µg N₂O m⁻² h⁻¹, compared to 20.6 to 58.2 µg N₂O m⁻² h⁻¹ in the transition areas and 12.1 to 26.4 µg N₂O m⁻² h⁻¹ in the uphill areas. Temporally, flood recover period in depressions showed the highest N₂O fluxes compared to any other periods, whereas the uphill areas maintained consistent emissions throughout the year. Annual cumulative N₂O emissions from positions within the depressions were estimated to be 0.64 to 1.5 g N₂O m⁻², significantly higher than the emissions of 0.16 to 0.39 N₂O m⁻² from transition areas and 0.09 to 0.27 g N₂O m⁻² from uphill areas. Regionally, although depressions cover less than 1% of the total cultivated area, they contribute approximately 10% to the total annual N₂O emissions. Our analysis identified soil moisture and temperature as key drivers for the spatial and temporal variabilities in N₂O emissions along the transects. These findings highlight the importance of incorporating high-emitting depressions into local and regional N₂O inventories to improve the accuracy of agricultural greenhouse gas estimates and inform the development of effective mitigation strategies.
How to cite: Wang, P., Kylborg, S., Tong, X., Elberling, B., and Ambus, P.: Nitrous oxide emission hotspots in temporarily flooded cropland depressions: year-round measurements and regional estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4808, https://doi.org/10.5194/egusphere-egu25-4808, 2025.
Traditional agricultural practices have placed unsustainable pressures on soils, resulting in degraded soil health and losses in biodiversity and fertility. Modern agriculture faces the dual challenge of increasing productivity while building resilience to climate change, particularly in water-scarce regions where crop productivity is at risk. Recognizing the potential of agricultural soils as a nature-based climate solution, climate-smart agriculture (CSA) offers a transformative strategy by integrating conservation practices and efficient water management to enhance soil health and mitigate climate impacts. From an irrigation perspective, this necessitates a comprehensive framework to holistically evaluate practices, moving beyond traditional objectives of maximizing yield and water use efficiency. In this study, we develop a multi-objective optimization framework for climate-smart irrigation (CSI), whereby a dual-index system evaluates irrigation systems (e.g., drip, sprinkler) and strategies (e.g., stress-avoidance, deficit irrigation) across productivity and climate impact dimensions. We first demonstrate the application of this framework by analyzing field studies of different crops (such as wheat and rice), irrigation practices and soil greenhouse gas (GHG) emission compositions, showing how the new indices jointly identify optimal irrigation practices. Additionally, using an ensemble of crop model simulations for corn production using irrigation across major U.S. production regions under varying climate and soil conditions, we explore trade-offs between productivity and climate impact goals. Results reveal a spectrum of Pareto-optimal irrigation practices that balance these dual objectives. These insights underscore the importance of holistic approaches in CSI and are critical for providing actionable insights into nature-based climate solutions in agricultural ecosystems.
How to cite: Anand, S. K., Singh, R., Mohanty, B., Rosa, L., Rajan, N., and Calabrese, S.: Balancing Productivity and Climate Impact: Climate-Smart Potential of Irrigation Practices, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14513, https://doi.org/10.5194/egusphere-egu25-14513, 2025.
Managing jointly the biogeochemical and biogeophysical (e.g. albedo and energy fluxes) impacts of agriculture is essential towards reaching climate-neutral agriculture. Only few observations collected in a small number of sites are available to quantify the impacts of agriculture on both the biogeochemical and biogeophysical effects on climate. The coupling of dedicated crop models with land surface models allows the combined quantification of those effects, but often lacks crop-specific parameterization and accounting of cropland management effects on biogeophysical effects. For these reasons, the biogeophysical and net climatic impact of agriculture on climate remains uncertain.
Here, we refined spatiotemporal bare soil albedo dynamics and the quantification of crop pigmentation and canopy structure effects on cropland albedo in the ORCHIDEE-CROP land surface model. This model develops a detailed crop growing module based on the process-based STICS formalism. We further introduced a new module assessing the effects of crop residues on soil albedo and soil evaporation. The model was parameterized and evaluated at nine European cropland flux sites for which detailed management information, field photos, soil moisture and surface albedo monitoring data were available. In addition, we produced a novel daily bare soil albedo product derived from Sentinel-2 at 300 m spatial resolution for Europe.
Using the refined model we quantified the effect of the presence of crop residues on radiative forcing, soil temperature and soil moisture of winter wheat crops. Simulations with the presence of crop residues left on the soil after harvest in 2-3 months increased surface albedo by approximately 0.08±0.03 in average, with significant spatiotemporal variability influenced by meteorological and soil conditions, as well as tillage practices among sites. We further found that over the same period residue cooled the surface soil by −1.18 ± 1.98 ℃ and enhanced the total soil water content by 35.77 ± 36.23 kg/m2. In a simulation of 10-year dry scenarios, we found that returning crop residues to the field can progressively increase plant available water over multiple years, with the extent of this increase influenced by climatic conditions. This study underscores the significance of the biogeophysical impacts of residue management on surface energy balance and highlights its potential in mitigating climate change, in particular in a warmer drier climate in Europe. The new framework developed in this study allows for a more rigorous assessment of the combined biogeochemical and biophysical impacts of field operations in Earth System Models such as cover crops that could allow climate cooling both through soil organic carbon sequestration and increase in surface albedo.
How to cite: Yu, K., Su, Y., Ciais, P., Lauerwald, R., Makowski, D., Ceschia, E., Tallec, T., and Goll, D.: Quantification of the biogeophysical impact of crop residue management in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17273, https://doi.org/10.5194/egusphere-egu25-17273, 2025.
More Nature-based Solutions (NbS) and related, new farming practices are needed to promote the green transition of agriculture, and to reach the policy targets set in relation to environmental protection, biodiversity preservation, climate change adaptation and mitigation in combination with a sustainable agricultural production. This issue is addressed in a series of research and innovation projects, including the pan-European and China related trans4num.eu Horizon Europe project, and the Sustainscpes.org and Land-CRAFT.dk research centers.
This paper outlines the research-based development of a Decision Support System (DSS), coupled with farm models and data, for farmers and multiple stakeholders to prioritize and implement more NbS in their practices, and thereby meet targets set. Special focus is put on agricultural nutrient management. A new point for innovation is that the DSS should be able to operate at the landscape scale, together with central NatureBased solutions, and thereby used in new types of catchment scale advisory services, relevant to both farmers and other industry related decision makers, as well as for policy development.
NbS measures of particular relevance for the Limfjorden study area are selected (incl. conversion from rotational crops to more permanent crops, in particular more grassland, and related new types of crop rotations). Innovative methods for landscape scale data collection are developed (based on digital farm data sources and remote sensing techniques), and the multiple stakeholder DSS design is developed though workshops in collaboration with local stakeholders, and demonstration of the landscape scale data collected.
Results are presented in the form of solution scenarios for green transitions in the Limfjorden catchment, based on the selected NbS, and the DSS components developed. GIS-based maps are used to illustrate the potentials and implications for farmers as well as local, regional, national and international decision-makers are discussed. Feedbacks to the implications for local farming system development are collected, and potentials and further research needs for upscaling and similar applications in other sites across Europe and beyond are synthesized and discussed.
How to cite: Dalgaard, T.: Decision support for Nature-based Solutions in agricultural nutrient management – Green transition scenarios demonstrated for the landscapes around Limfjorden, Denmark, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15083, https://doi.org/10.5194/egusphere-egu25-15083, 2025.
Understanding the energy, water, and carbon fluxes in dryland ecosystems is essential for maintaining ecosystem functioning and biodiversity. The limited in-situ measurements in drylands pose a significant challenge to the accurate monitoring and modelling of ecosystem dynamics. Satellite remote sensing provides high potential to monitor key surface and carbon variables, such as land surface temperature (LST), evapotranspiration (ET) and gross primary productivity (GPP). Although these data provide valuable insights, their temporal resolution is limited to satellite revisit overpasses, which can limit the continuity of monitoring. To address these gaps, dynamic land surface models serve as effective tools for integrating sparse remote sensing observations with continuous simulations of energy, water, and carbon cycles. The Soil-Vegetation-atmosphere Energy, water, and CO2 traNsfer (SVEN) model exemplifies this approach, offering high temporal resolution simulations that incorporate satellite-based LST and meteorological in-situ inputs. This study focuses on calibrating and validating the model in southeastern Spain, as the only sub-desertic protected area in Europe. Calibration of SVEN was achieved using a combination of MODIS remote sensing data and in-situ LST measurements from an eddy covariance system, ensuring robust parameterization tailored to local field characteristics. Furthermore, the model was validated with in situ measurements, obtained through an eddy covariance tower. The RMSE values for the land surface temperature, latent heat flux, net radiation, sensible heat flux, gross primary productivity, and soil moisture were 1.99 ºC, 25.97 W m-2, 52.71 W m-2, 50.90 W m-2, 1.44 gCm-2s-1 and 1.19 m3m-3, respectively at half-hourly time scale. Normalized root mean square deviations of the simulated values were 7.84%, 10.81%, 5.67%, 7.81%, 13.09% and 6.59%, respectively. Otherwise, it was observed that until 8 days of revisit frequency, the calibration parameters did not affect the model accuracy considerably, increasing the RMSE of variables by 0.42 to 10.53% at the half-hourly time scale. The model’s accuracy across energy, water, and carbon fluxes highlights its potential as a reliable tool for dryland monitoring, offering insights into processes that are critical for ecological management and climate adaptation strategies. By filling the temporal gap between satellite observations, this work demonstrates the value of dynamic models like SVEN in enhancing our understanding of dryland ecosystems and promoting sustainable management practices in water-limited environments. This publication is supported by the EU COST (European Cooperation in Science and Technology) Action CA22136 “Pan-European Network of Green Deal Agriculture and Forestry Earth Observation Science” (PANGEOS).
How to cite: Riba Palou, A., Garcia, M., M. Tarquis, A., Oyonarte, C., Domingo, F., Liu, J., S. Johnson, M., Kim, Y., and Wang, S.: Optimizing the revisiting frequency of remotely sensed thermal observations for continuous estimation of ecosystem evapotranspiration and productivity using Bayesian inference, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21430, https://doi.org/10.5194/egusphere-egu25-21430, 2025.
Posters on site: Wed, 30 Apr, 10:45–12:30 | Hall X1
Riverine wetlands are reservoirs of biodiversity and provide various ecological functions, including carbon absorption. However, they have been subjected to continuous degradation and loss due to river management practices focused on irrigation and flood control. This study aims to quantify the carbon absorption capacity of riverine wetlands and propose strategies for their restoration and management. To achieve this, a laboratory-scale wetland model was developed, and carbon absorption rates were analyzed under varying hydrological conditions. The results revealed that while methane emissions increased under inundation conditions, the absorption of carbon dioxide increased even more significantly. When assessed using the Global Warming Potential (GWP) metric, the overall carbon absorption capacity was found to improve. Wetlands were spatially categorized into waterside wetlands (outside the levee) and landside wetlands (inside the levee) to establish a carbon absorption assessment framework. This framework was used to evaluate restoration needs and propose tailored restoration scenarios for each wetland type. For waterside wetlands, strategies were suggested to regulate carbon absorption based on inundation zones and hydrological characteristics. For landside wetlands, a model was developed to enhance carbon absorption through the creation of carbon forests using Nature-based Solutions (NbS) and biochar application. Additionally, the carbon cycle was established as a closed system, termed the "Carbon-Closing System," to promote sustainability. This study provides standardized models and evaluation frameworks for carbon-neutral riverine wetlands, advancing technologies for wetland creation, restoration, and management while contributing to climate change mitigation and ecological value enhancement.
Keywords: Carbon Absorption, Hydrological Conditions, Restoration Scenarios, Riverine Wetlands
Acknowledgement: This work was supported by Korea Environmental Industry&Technology Institute through Wetland Ecosystem Value Evaluation and Carbon Absorption Value Promotion Technology Development Project, funded by Korea Ministry of Environment(MOE)(2022003630001)
How to cite: Lee, H., Kim, S., Kim, K., and Seo, J.: Quantifying Carbon Absorption of Riverine Wetlands and Proposing Restoration Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2251, https://doi.org/10.5194/egusphere-egu25-2251, 2025.
This article contains a review of whole-farm models for the description of nutrient cycles and greenhouse gas emissions, identifying research needs for the assessment of Nature-based Solutions for reduced emissions, occurring at the interface between the farm and the landscape level. The review thereby aims to give an overview of the state of the art of farm-level models and highlight gaps in the literature with the view of integrating whole-farm models into landscape-level modelling and assessments. The review covers peer-reviewed articles published in the period between 1980 and April 2024, captured in the Web of Science and Scopus databases, as well as using the snowballing method. Google scholar was also used to gather the relevant articles. The articles were described using several characteristics, such as country of origin, year published and complexity of the model. Dynamic process-based models were the most used, particularly the Agricultural Production Systems sIMulator, APSIM and the Integrated Farm System, IFSM, with life cycle assessment (LCA) also being widely used. Dairy and beef farms were the most studied farm types, with most studies published from the USA, followed by Australia and New Zealand; however significant gaps were identified regarding complete whole farm models, including all parts of the farming systems, and links to the landscape level modelling needed to assess key Nature-Based Solutions to reduce emissions from agriculture. The review allowed to highlight these gaps, which will be illustrated by examples from Denmark and studies related to the Land-CRAFT.dk Pioneer Center for Landscape Research in Sustainable Agricultural Futures. The tools most used for the assessment of Nature-based Solutions are also highlighted.
How to cite: Delle Grazie, F., Hutchings, N., Dalgaard, T., and Butterbach-Bahl, K.: A review of whole-farm models - gaps in the literature, links to landscape-level modelling and assessments of Nature-Based Solutions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15993, https://doi.org/10.5194/egusphere-egu25-15993, 2025.
Model predictions are paramount to understanding climate and land management effects on soil organic carbon (SOC) stocks and greenhouse gas (GHG) emissions in forests. However, SOC models remain highly uncertain, and multi-model ensembles can be used to evaluate the level of uncertainty of the predictions due to model choice. One major barrier to the use of multiple models is data availability and the time-scale consistency across models.
In this work, we present me4soc, a Multi-model Ensemble interface For Soil Organic Carbon predictions. This open-source software offers a complete environment to launch six SOC models widely used by the soil community to predict the dynamics of SOC stocks and GHG fluxes (CO2, CH4, and N2O) in forests. It allows users to explore the effect of nature-based climate solutions over multiple decades under climate and land-use changes. The models can be run with either user-provided observational data or data automatically extracted from large-scale open-source datasets for the European region. Available earth system model predictions are used to simulate climate and land-use change scenarios. The tool has been developed in Shiny, a R-based package for simple web application developments.
The obtained results showed the ability of me4soc to simulate the temporal dynamics of SOC stocks and GHG emissions at site-scale under different climate, land-use, and land management change scenarios. Employing multiple models based on different mathematical structures offers a unique opportunity to estimate the uncertainties in the predictions associated with differences in the model structure.
This tool can be applied by the scientific community, forest managers, and policymakers to acquire scientifically-based information about the effects of forest management and disturbances on SOC stocks and GHG emissions. It is an important step towards the use of state-of-the-art models and large-scale datasets to improve model predictions and assess their uncertainties. The software's systematic validation with observational data and parameter optimization to improve model fit are the key priorities of future work. Further software developments to cover other ecosystems (e.g., croplands and grasslands) and data-less sites outside of Europe are also foreseen.
How to cite: Bruni, E., Lehtonen, A., and Guenet, B.: Me4soc: a multi-model ensemble interface for soil organic carbon predictions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3721, https://doi.org/10.5194/egusphere-egu25-3721, 2025.
Accurately estimating top ~5 cm surface soil moisture (SM) is highly valuable for understanding the terrestrial water cycle. Based on the zero-order τ-ω radiative transfer model (RTM), the Soil Moisture Active Passive (SMAP) mission has provided daily global surface SM estimations at 9 km spatial resolution using L-band (1.41 GHz) radiometry since April 2015. As the parameterization of RTM for SMAP's official algorithm highly relies on in-situ measurements, SMAP SM has weaker performance in regions with few calibration sites. To improve the accuracy of global SM estimations, we developed a new radiative transfer Process-Guided Machine Learning (PGML) method, which integrates the mechanistic understanding of RTM and data-driven machine learning approaches to estimate global SM. We generated a synthetic dataset from RTM and developed a pre-trained PGML to quantify SM by using this synthetic dataset. Furthermore, we utilized SM measurements at 1131 in-situ sites collected from International Soil Moisture Network (ISMN) during April 2015 and December 2023 across the globe to fine-tune PGML. The validation result shows that the estimated 9-km daily PGML global SM has a good agreement with in-situ SM measurements from ISMN. Our model has significantly better performance on estimating global SM than the SM retrievals from RTM (R from 0.413 to 0.636, RMSE from 0.132 to 0.100 m3/m3, bias from 0.042 to 0.001 m3/m3, ubRMSE from 0.125 to 0.100 m3/m3). This study highlights the potential of PGML to integrate machine learning and radiative transfer models for accurate remote sensing of SM at the global scale.
How to cite: Feng, S., Li, A., Butterbach-Bahl, K., C. Looms, M., Guan, K., Treat, C., Igel, C., and Wang, S.: Improving satellite microwave sensing of global soil moisture via radiative transfer process-guided machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8696, https://doi.org/10.5194/egusphere-egu25-8696, 2025.
Whilst it is generally understood that grasslands are able to store significant amounts of carbon and that much of our degraded agricultural soil has capacity to build carbon stocks and potentially mitigate on-farm emissions, to date, the greater focus of studies has been on the response of lowland grassland soil carbon to management practices. In contrast, comprehension of current and potential soil carbon stocks in heterogeneric ‘upland’ or marginal farmed environments is currently lacking, and the potential for sustainable livestock production to deliver increased soil carbon sequestration unsubstantiated. With upland farming systems producing 29% and 44% of breeding cows and sheep respectively, understanding the impact of changes in upland livestock management on soil carbon is critical to ensure future land management scenarios are environmentally positive and can sustain food production.
We aim to address this knowledge gap by combining field surveys of soil carbon concentrations and stocks with modelling of potential soil carbon change under nutrient, land use and climate change scenarios using the process-based N14CP model. In this contribution we will present the empirical data and carbon modelling results.
Three 'upland' livestock farms in Cumbria, UK were chosen as representative of diversity of parent material, climate, topography and livestock farming practices. Pedogenic-stratified random sampling of the top 0 – 30cm soil at a rate of 1 sample per 2 hectares; ≥5 metres apart was conducted July-September 2024. Samples were assessed for bulk density (corrected for coarse fragments ≥2mm) and carbon concentration (by dry combustion).
Preliminary analyses suggest high spatial variation in bulk density, soil carbon concentration and stocks within and between farms, reflecting the heterogeneity of ‘upland’ environments. Our sampling approach demonstrates that detecting change in soil carbon empirically, with confidence, is unlikely to be possible in these diverse landscapes, with implications for predicting carbon sequestration potential as climate mitigation.
How to cite: Giles, L., Scott, P., Davies, J., Bebbington, J., and Quinton, J.: Advancing understanding of sustainable production on livestock farms: The importance of accurately assessing upland soil carbon stocks , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9828, https://doi.org/10.5194/egusphere-egu25-9828, 2025.
There is now growing awareness that agricultural land use impacts climate not only through its GHG budget, but also through albedo-mediated changes of the surface energy budget. For instance, grasslands have higher surface albedo (i.e. more incoming solar radiation is reflected instead of being adsorbed and transferred into heat) than forage crops especially during the fallow period.
The project ALBAATRE-Systèmes focuses on reducing the climate impact of forage systems by increasing the share of grassland and by adapting land management practices to increase surface albedo. For this, extensive experimental data is collected from a network of experimental farms from IDELE across France as well as at ICOS flux tower sites. At the same time a modelling framework is being developed to upscale the experimental data at the scale of France. For this task, we use the land surface model ORCHIDEE-GM (Chang et al., 2013), which represents a branch of the global land surface model ORCHIDEE (Krinner et al., 2005) that incorporates main features of the grassland management model PaSim (Riedo et al., 1998). This model is used to study the impact on production and climate of grasslands management such as grazing, fertilization and cutting. At present, however, it has a very simplistic surface albedo description.
Therefore, this study intends to improve albedo formalisms in ORCHIDEE-GM v3.2 in order to better take into account the seasonal and structural changes of different grassland types in France. To evaluate the model, we will use the in-situ data collected over several years at the IDELE farms and at the ICOS grassland flux towers sites.
The meteorological and flux data from ICOS sites were used as input and to calibrate ORCHIDEE. The reflectance of vegetation is now described across the short wave spectrum (400 nm to 2500 nm) as a function of leaf area index, average leaf angle, leaf water content, and pigment concentration. First results show that the new albedo description has a better correlation with the observed data than with the original one but still needs to be investigated further.
This model development will allow us to better account for the albedo changes that happen in response to meteorologic conditions and management practices, thus better quantifying the mitigation potential of French grasslands (forage and natural). Moreover, future simulations will help to adapt management practices and to recommend specific grass species that have a high albedo and/or resilience to heat and drought stress, increasing both the climate change adaptation and mitigation potentials of the French forage systems.
How to cite: Baud Fraile, E., Chang, J., Ceschia, E., Klumpp, K., Mischler, P., Viovy, N., and Lauerwald, R.: Assessment of climate mitigation potential of French grasslands using the land surface model ORCHIDEE-GM, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16922, https://doi.org/10.5194/egusphere-egu25-16922, 2025.
Accurate large-scale crop yield estimation is increasingly critical for agricultural management and understanding the dynamics of food security under climate change. The complex nature of crop growth, influenced by multiple environmental factors across temporal scales, requires advanced approaches for yield prediction. While recent advances in remote sensing provide diverse data sources for enhanced crop monitoring capabilities, effectively integrating heterogeneous data sources at large scales remains challenging for accurate yield prediction. In this study, we developed a temporal multi-modal fusion framework for soft wheat yield prediction at the sub-national level across the European Union from 2001 to 2019. Our framework integrated time-series data from optical remote sensing observations, climate data, and vegetation productivity indicators, along with static soil properties. A Transformer encoder was used to extract temporal patterns of crop growth, and the temporal features were fused with soil features to capture spatial patterns for large-scale wheat yield prediction. The proposed framework achieved much better performance (RMSE = 0.75 t·ha-1) compared with benchmark models including LSTM (RMSE = 0.82 t·ha-1) and Random Forest (RMSE = 1.09 t·ha-1). The study indicates that late fusion strategies are more effective in preserving modality-specific temporal patterns, enhancing the accuracy by 5.9% (RMSE) compared to early fusion. Ablation studies reveal the incremental benefits of multi-modal data integration, with soil properties notably improving prediction performance by 15.0-23.9% (RMSE). Feature importance analysis through explainable machine learning indicates that remote-sensing-related variables contribute more significantly to yield prediction than climatic variables. The novel multi-modal fusion framework developed in this study for large-scale crop yield prediction provides insights into understanding crop-environment relationships in wheat yield formation.
How to cite: Lin, Z., Guan, K., and Wang, S.: Temporal Multi-modal Fusion Framework for Predicting Wheat Yield across the EU from Multi-source Satellite and Environmental Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7753, https://doi.org/10.5194/egusphere-egu25-7753, 2025.
It is widely expected that conservation efforts within protected areas (PAs) can achieve multiple conservation objectives simultaneously. PAs established primarily for biodiversity conservation also contribute to increasing carbon storage in terrestrial ecosystems. However, there is a lack of quantitative studies on the role of China’s existing PAs in carbon storage protection. We proposed an integrated approach to estimate the carbon density of terrestrial ecosystems in China, based on a modified InVEST model. Through a statistical matching method, we evaluated the effectiveness of PAs in conserving carbon storage during 2020-2050. Under the moderate emission scenario (SSP2-RCP4.5), the average carbon density of PAs was projected to increase to 168.3 Mg C ha-1, a 14.2% rise compared to 2020. In contrast, under the low emission scenario (SSP1-RCP2.6) and high emission scenario (SSP5-RCP8.5), the average carbon density of PAs was projected to decrease by 4.8% and 4.6%, respectively. By 2050, approximately 45%-47% of PAs were expected to be effective in conserving carbon storage, with about 80% of PAs experiencing no change in effectiveness during 2020-2050. Additionally, 34.3%-36.2% of the areas of PAs remained effective, while 1.8%-4.0% were projected to transition from ineffective to effective. PAs effective in conserving carbon storage were predominantly located in humid, mid-to-high-altitude regions. Given the spatial mismatch among existing PAs, priority areas for carbon storage protection and effective areas for carbon storage protection, our findings underscored the necessity of expanding China’s PA system to expand the additional benefits of PAs in conserving carbon storage.
How to cite: Wang, S., Peng, J., Lin, Y., and Hu, T.: Revisiting the role of China’s protected areas in carbon storage , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7672, https://doi.org/10.5194/egusphere-egu25-7672, 2025.
Climate-smart agriculture aims to implement a suite of conservation management practices, such as cover crops, reduced tillage, smart irrigation and crop rotations, to maximize agroecosystem productivity and reduce greenhouse gas emissions. Timely and high-resolution agriculture data are crucial for measuring, reporting and verifying the implementation and benefits of climate-smart agriculture practices. However, agricultural data collection through field sampling, laboratory analysis, and/or grower surveys is time-consuming and costly. To address these challenges, we developed an artificial intelligence-empowered cross-scale sensing framework to integrate multi-source ground truth data with multi-modal satellite Earth observations to quantify high spatial and temporal information of essential agroecosystem variables in the EU. Specifically, these essential variables include crop types, harvest time, tillage practices, cover crop adoption and biomass, crop yield, soil moisture, ecosystem gross primary productivity and evapotranspiration. We developed computer vision and machine learning algorithms to obtain ground truth data from in-situ measurements, citizen sciences, census surveys, and ground or aerial vehicle system data. Through process-guided machine learning (PGML), we integrated the domain knowledge of soil-vegetation radiative transfer models and ground truth data to accurately quantify these essential variables from Sentinel-1, 2, 3 and SMAP satellite data. This study highlights the potential of integrating cross-scale sensing and PGML to quantify essential ecosystem variables to support climate-smart agriculture.
How to cite: Wang, S., Guan, K., E. Olesen, J., Zhou, R., Lu, Z., Lin, Z., Feng, S., Gislum, R., Treat, C., and Butterbach-Bahl, K.: Cross-scale Sensing of Field-level Essential Agroecosystem Variables for the EU Climate-smart Agriculture, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8055, https://doi.org/10.5194/egusphere-egu25-8055, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot A
EGU25-4724 | Posters virtual | VPS4
Multiple Redevelopment of BrownfieldsWed, 30 Apr, 14:00–15:45 (CEST) | vPA.28
Pollution places an 'everlasting' burden on brownfields, with a lot of money going towards the management of sites where nothing happens. Action is administratively unattractive, and managers and area developers find it difficult to connect. The development of the surrounding area is also halted. This limitation is becoming increasingly urgent with the growing spatial pressure due to the energy transition, climate adaptation, and housing needs. However, much more is possible than has been achieved so far; redevelopment is often indeed possible.
Public and private parties can work on upgrading brownfields. This can also generate money to better manage risks. In many places, developing parks to make surrounding residential areas more attractive is popular. Parks also play a role in climate adaptation and increasing biodiversity. Solar panels can be installed along the edges of the park in such a way that greenery is also possible underneath.
Altogether, there are twelve known functions that can upgrade brownfields. The value increases if two or more functions enable each other, such as greenery and solar panels. Upgrading brownfields can be done singly, but can also be multiple by stacking functions. What does this yield, and how do you do that, especially how do you finance a multiple project? The paper discusses the multiple redevelopment of former landfills and particularly the financing thereof.
How to cite: van der Heijden, J.: Multiple Redevelopment of Brownfields, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4724, https://doi.org/10.5194/egusphere-egu25-4724, 2025.
