PICO
The European Green Deal provides for two targets by 2030: 1) the strict protection of at least 10% of the European Union’s land area and 2) the expansion of organic farming to 25% of agricultural land. To address these independent objectives competing for land use, we construct a spatially explicit partial equilibrium model that fulfills both targets either consecutively or simultaneously and at an EU or national level. Results indicate that the 25% organic farming target is the restricting constraint with high marginal costs, leading to less cropland use, higher land prices and higher farming revenues. Less than 1% of cropland area in the EU is needed to fulfill the strict protection target. Therefore, both targets can be fulfilled without major conflicts over cropland use. While targets at the EU level lead to better resource utilization and significantly lower price effects, the uneven distribution of additional strictly protected area and organically managed cropland between countries could be perceived as unfair and should be compensated. Half of the newly strictly protected areas are re-designations of already protected area. Thus, a comprehensive approach that combines expansion with proper management of protected areas is crucial to achieving conservation goals.
How to cite: Gensch, L., Jantke, K., Rasche, L., and Schneider, U. A.: Land of opportunities: Aligning organic farming and conservation targets in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1225, https://doi.org/10.5194/egusphere-egu25-1225, 2025.
Crop migration is a vital strategy for alleviating the adverse effects of climate change on agricultural production and minimizing yield losses. Although previous studies have emphasized the importance of crop migration, there remains a significant gap in quantitative assessments of its effectiveness in mitigating climate-induced production losses. To address this gap, we constructed a MaxEnt–SPAM–EPIC framework that integrates a crop distribution model with a crop model. Using this framework, we quantified the effectiveness of crop migration in alleviating climate-induced production losses. Taking the North China Plain (NCP) as a case study, we projected the migration patterns of winter wheat in near-term, mid-term, and long-term periods under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, along with its efficacy in reducing climate-induced production losses. The results indicate that under climate change, the center of gravity of winter wheat cultivation (GCW) on the NCP will shift northwest by 2.4-6.3 km, while the mean center of winter wheat cultivation (MCW) will move westward by 11.22-17.90 km in the long term. Additionally, the planting boundary of winter wheat on the NCP will expand northwest and contract southeast, leading to an average increase of 0.42% in the winter wheat planting area under future SSP scenarios. Among the three scenarios, the SSP2-4.5 scenario exhibits the largest scale and most complex trajectory of crop migration. In contrast, under the SSP1-2.6 scenario, there is minimal change in cultivation patterns. In the short term, crop migration can temporarily alleviate climate-induced production losses, but it cannot reverse the long-term trend of production decline on the NCP. Compared to the baseline, winter wheat migration on the NCP can mitigate climate-induced production losses and enhance production by more than 4.15% in the near- and mid-term. However, in the long-term, except for the SSP1-2.6 scenario where winter wheat production remains roughly consistent with the baseline, crop migration has limited effectiveness in reducing production losses, with winter wheat production facing substantial reductions of 9.54% and 24.02% under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. Our study reveals that while crop migration may provide temporary relief from the adverse effects of climate change on agricultural production, its long-term sustainability is questionable. Therefore, prioritizing on-site adaptation strategies to enhance crop resilience remains crucial for ensuring food security. Our research contributes to a deeper understanding of the practical effectiveness of crop migration as a climate mitigation strategy and provides evidence-based insights for policymakers to develop region-specific adaptation measures.
How to cite: Guo, X., Wang, L., and Yue, Y.: Crop migration could temporarily alleviate the impact of climate change on production, but it is not sustainable, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1908, https://doi.org/10.5194/egusphere-egu25-1908, 2025.
Agricultural land systems globally face escalating pressures from rising food demands, climate change, environmental degradation, and biodiversity loss. China, as a critical case, exemplifies the urgent need for strategies that reconcile food security with ecological sustainability. Here, we demonstrate that adopting a systematic approach to spatially allocate existing land policy tools—such as cropland reforestation, agricultural intensification, non-grain cropland restoration, and agricultural expansion—has the potential to simultaneously achieve multiple sustainability goals. Using a predictive model based on a socio-ecological-technical framework and machine learning, we evaluated the outcomes of six counterfactual scenarios for China’s agricultural land-use transitions at the county level. Results indicate that under a maximum land-sparing scenario (maximizing intensification of exist cropland, restoring unstable cropland, and maintaining non-grain cropland), compared to the 2020 baseline, China could increase grain output by 8%, reduce crop carbon emission intensity by 1%, enhance carbon sequestration by 63%, while substantially mitigating biodiversity loss across key taxa. However, the spatial distribution of land policy tools remains uneven, leading to varying types and degrees of trade-offs across specific counties under any given scenario. This highlights the critical need for coordinated national leadership to achieve sustainable objectives at a broader scale, offering valuable insights for global land-use transitions.
How to cite: Han, B.: Exploring sustainable pathways through AI-based simulation of China’s agricultural land-use transitions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2739, https://doi.org/10.5194/egusphere-egu25-2739, 2025.
Abstracts: The utilization of cultivated land and its evaluation has gradually transitioned from a singular focus on food production to encompassing socio-economic development, maintenance of ecological functions, and enhancement of landscape experience. Cultivated land multifunctionality has thus become a key area of agricultural land systems research. Especially the over-intensified utilization of cultivated land, lacking comprehensive consideration of utilization, supply, and demand, impairs the adaptive capacity of cultivated land multifunctionality. The resulting soil nutrient imbalance, decline in biodiversity, and homogenization of landscapes undermine its sustainable contribution to human well-being.
This study integrates land use, socio-economic data, remote sensing monitoring, and point-of-interest data to develop an adaptive optimization model for cultivated land multifunctionality. Based on a quantitative assessment of the utilization, supply, and demand of cultivated land multifunctionality, three supply-demand matching scenarios serve as the foundation for modeling. In the scenario with supply exceeding demand, the supply and demand indices define the lower and upper thresholds. In the supply-demand balance scenario, the range of balanced values is used as the threshold. In the scenario where demand exceeds supply, the supply index establishes the lower limit of the threshold. Through this modeling process, five utilization characteristics of cultivated land multifunctionality are identified: potential type, transition type, stabilization type, critical type, and surpass type. Among these five types, the potential type indicates that resources are underutilized, the surpass type signifies that the utilization of cultivated land multifunctionality has surpassed resource and environmental constraints, while the other three types are in a relatively safe state. Their spatial attribution informs the development of a composite zoning scheme for cultivated land multifunctionality, designed to support its adaptive optimization. An empirical study in the Yangtze River Delta, China, explores the spatial differentiation patterns, utilization characteristics, and optimization strategies of cultivated land multifunctionality.
The findings indicate that cultivated land multifunctionality in the Yangtze River Delta is characterized by uneven utilization levels, robust supply capacity, and relatively lagging demand conditions. Influenced by the spatial heterogeneity of utilization, supply, and demand, the utilization characteristics—analyzed using the supply-demand matching relationship as the threshold—indicate persistent challenges of cultivated land multifunctionality. Specifically, the agricultural production function reveals dual challenges of surpass and potential types coexisting, the social security function is predominantly of the potential type, the surpass type of ecological maintenance function accounts for 32.1% of the region, and the cultural landscape function generally remains within a safe range. Building on this analysis, the study proposes a composite zoning scheme that integrates dominant and refined zoning approaches. In this zoning, the agricultural production function necessitates reduced inputs of production factors in major grain production areas, while agricultural productivity can be appropriately enhanced in ecological protection areas. The ecological protection function must be constrained within the limits of the resource and environmental carrying capacity. The social security function requires further exploration to strengthen its contribution to rural socio-economic development. Lastly, the cultural landscape function is expected to operate effectively.
How to cite: Wang, S., Jin, X., and Christian, F.: Modeling Adaptive Optimization of Cultivated Land Multifunctionality in the Yangtze River Delta, China , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5989, https://doi.org/10.5194/egusphere-egu25-5989, 2025.
Changing crop patterns are the primary driver of global land use change and impact Earth’s hydrological and ecosystem processes. While existing studies have mapped the distribution of some individual food crops in China, harvest area maps for a complete set of crops over the past few decades are currently lacking. This study pioneered the development of a spatiotemporally continuous dataset of harvest area maps for 16 crop types in China from 1990 to 2020 at a 1-km resolution. Prefecture-level crop statistics were allocated to grids based on the crop suitability score, which is evaluated by multi-source natural and economic factors influencing crop cultivation. County-level validations demonstrated that the built dataset is highly consistent with statistical data, especially for primary grains and oilseed crops. Moreover, crop harvest area attribution at the sub-pixel level can better represent gradient changes within urban-rural transition zones. The built crop maps revealed that the harvest zones of maize, rice, and soybeans in Northern China have steadily expanded over the past three decades, with their cultivation centers shifting northeast by more than 200 kilometres. In comparison, wheat cultivation has become increasingly concentrated in Northern China. This dataset fully supports the identification of spatiotemporal changes in China’s crop patterns and can serve as a critical input to biogeochemical models and dynamic agricultural models such as LPJmL. The datasets can be obtained at https://www.scidb.cn/en/s/yeAfme.
How to cite: Dai, K., Cheng, C., Li, B., Wang, Z., Mu, N., Li, Z., Yang, S., and Wu, X.: Mapping harvest area of comprehensive crop types in China from 1990 to 2020 at a 1-km resolution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7663, https://doi.org/10.5194/egusphere-egu25-7663, 2025.
Population growth, evolving consumption preferences, technological advancements, globalized trade, and environmental factors have all significantly influenced global agriculture. The rising demand for agricultural commodities has driven increased production through both land area expansion and intensification (reflected as higher yields). However, the connections between global agricultural expansion and intensification remain unclear.
Using a data-driven approach to map past cropland use and productivity changes on a global scale, we aim to (1) quantify the spatiotemporal patterns of global changes in cropland systems, particularly focusing on area expansion and contraction, as well as yield increases and decreases over the last six decades (1960-2020), and (2) explore the relationship between cropland intensification and expansion across different countries and regions.
Our findings reveal that high-income countries have followed a trajectory of yield increases and land contraction on croplands, aligning with the concept of land sparing and influenced by policy. In contrast, low-income countries have seen less yield increase but substantial cropland area expansion over time. Notably, emerging countries in tropical regions (e.g., Brazil, Indonesia, Thailand, Colombia, and Malaysia) have experienced both the highest crop yield increases and cropland expansion rates. This suggests potential knock-on effects of yield increases in high-profit crops such as soybean, oil palm, and sugar cane, primarily used for exports. These yield increases are linked to and likely triggered significant agricultural expansion into natural ecosystems. We find that the increase in tree crops is the underlying cause of more than half of the global deforestation for cropland expansion.
Overall, we demonstrate how the relationship between yield increases and cropland expansion varies by region and crop type. This relationship is also likely influenced to varying degrees by political intervention, global trade, technology transfer, and climate change.
How to cite: Winkler, K., Fuchs, R., Rounsevell, M., and Herold, M.: Six decades of global crop yield increase and cropland expansion from 1960 to 2020, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8826, https://doi.org/10.5194/egusphere-egu25-8826, 2025.
The global food system is responsible for up to a third of greenhouse gas emissions and is a major cause of biodiversity loss. A low-emission food system transition towards more plant-rich diets is urgently needed in the EU to mitigate environmental crises including climate change. Linking global physical input-output models with public agro-economic data reveals that animal-sourced food (ASF) dominates EU agriculture, consuming the majority of agricultural land, Common Agricultural Policy (CAP) subsidies, fixed assets, and farm employment, while contributing disproportionally to greenhouse gas emissions, net farm profits, and caloric intake. ASFs account for most EU food-related greenhouse gas emissions (84%) yet provide only a fraction of the dietary calories (35%) and proteins (65%), highlighting their inefficiency.
Dietary shifts away from ASFs would free up significant agricultural land and CAP subsidies, unlocking opportunities for alternative land uses, such as rewilding, and CAP budget redirection to support plant-based alternatives. However, transitioning to more plant-rich diets could also lead to the stranding of ASF-related assets, currently evaluated at €258 billion (78% of all agricultural fixed assets). Our findings suggest that as agricultural assets depreciate over time, a systemic phase-out of ASF-related assets without further investments, would leave minimal residual value and limit the risk of stranded assets.
How to cite: Kortleve, A., Mogollón, J., and Behrens, P.: EU food system transformations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9686, https://doi.org/10.5194/egusphere-egu25-9686, 2025.
Geopolitical tensions are increasingly affecting global trade in food and essential agronomic inputs such as fertilizers. This imperils food production and security in import-dependent countries. Major disruptions, such as armed conflicts or the formation of isolated political blocs, are expected to further disrupt bilateral trade as countries tend to save resources for their own populations or because of the destruction of trade infrastructure. Countries not directly involved in such conflicts may also choose to stop exporting and start stockpiling products as a precautionary measure. This will create a situation where the global trade network will be fragmented.
This study estimates the consequences of such trade disruptions on fertilizer supply and food security through network analysis and statistical modeling using global data on food and fertilizer trade, fertilizer inputs, and crop yields. We consider several hypothetical scenarios, including a military conflict between major military alliances, political separation into major (emerging) blocs, economic scenario, where the world is divided into Global North and Global South, and stochastic scenarios that model probable division into groups based on the structure and intensity of historical trade between partners through community detection in graphs. A first prototype considers major staple crops: rice, wheat, maize, potato, and cassava.
The results demonstrate that in the event of a political, military, or economic separation that disrupts trade, Non-Aligned and Global South countries will experience dramatic reductions in the availability of certain critical crops and fertilizers, with losses of more than 25 percent compared to uninterrupted supplies in 2022. In the military scenario, Non-Aligned nations will be most sensitive to the decline in maize, wheat, and fertilizer, while in the political scenario, access to maize, potatoes, rice, and wheat will be problematic. The economic scenario shows drops in availability of maize, rice, cassava, wheat, and fertilizer for the Global South block. Military alliances, political blocs, and Global North countries have limited supplies of at least two critical crops in every scenario, but their losses are less disruptive (excluding cassava, which is expected to decline by 95 percent in the Global North). For all groups of countries, the drops in food supply are compounded by a further reduction in expected agricultural output due to the loss of fertilizer supplies. Stochastic network simulations generally provide more balanced scenarios as they are based on interwoven historical trade data. Further research will refine the results using process-based crop modeling and explore scenarios for improving the resilience of the global food system.
How to cite: Kiparisov, P. and Folberth, C.: Modeling impacts of food and fertilizer trade disruptions on global food security, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13837, https://doi.org/10.5194/egusphere-egu25-13837, 2025.
Agriculture is one of the largest sectors in terms of human land and water use, occupying over 38% of the Earth’s ice-free land surface and 80% of the global human water footprint. In addition, various scenarios project a continuous expansion of agricultural land use at least until the 2050s, therefore emphasizing the growing importance of agriculture as a major channel of human influence on the Earth system. At the same time, urbanization and declining rural populations, accompanied by economic growth, suggest a potential decrease in the share of agricultural employment within the labor market. In this study, we introduce agricultural workforce availability, in addition to environmental suitability and policy, into the projection of future potential cropland supply and compare it with the projections of future cropland demand. A simple model framework was developed to project workforce-available cropland area, which includes the estimation of potential agricultural workforce and technological advancements. Under the SSP1-RCP2.6 scenario, environmentally cultivable land is projected to remain underutilized due to limitations imposed by workforce availability, while under the SSP5-RCP8.5 scenario, the global cultivation capability is expected to exceed environmentally cultivable land area by the 2080s. Under both scenarios, total potential cropland supply is projected to surpass the cropland demand globally. On the other hand, regional insufficiencies in the potential cropland supply are anticipated. For instance, under the SSP1-RCP2.6 scenario, a group of high-latitude nations is expected to face a 10% shortfall in potential cropland supply by the 2050s, which is projected to decrease to 5% by the end of the 21st century. Even under the SSP5-RCP8.5 scenario with the fast technological advancements, Brazil is expected to have 20% deficiency in potential cropland supply throughout the century. The results of this study suggest that there is room for improvement in the cultivable land area as input dataset for Earth system simulation models. Additionally, it highlights regions where technological investments are necessary to meet current projections of cropland demand.
How to cite: Lee, H. and Kim, H.: Evaluation on Future Potential Cropland Supply with Considering Agricultural Workforce Availability , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15799, https://doi.org/10.5194/egusphere-egu25-15799, 2025.
Food loss and waste (FLW) represent a critical challenge to global sustainability, with significant implications for food security and environmental conservation. As about one third of the food that is produced is lost or wasted along the value chain, water, land and energy resources employed in its many stages (e.g., production, transformation, storage) are wasted together with it. Despite extensive research on this topic, a significant gap remains in understanding how FLW will evolve in the future, particularly under the influence of key drivers such as economic development, urbanization, and access to electricity. Changes in FLW patterns have far-reaching consequences for the Water-Energy-Food Nexus, particularly in regions where local resources are already under stress. Current projections frequently employ static assumptions or simplified scenarios, overlooking the dynamic socio-economic trends that have the potential to reshape FLW profiles of countries. This limitation is especially relevant in rapidly developing regions like Sub-Saharan Africa, where present per capita FLW levels are relatively low compared to high-income regions. However, rapid socio-economic transformations in these regions have the potential to drastically alter this scenario in the near future, thereby deviating from current estimates.
In order to address these challenges, a random forest algorithm was employed, leveraging data from the FAO Food Loss and Waste Database. The integration of these data with socio-economic predictors such as GDP, urbanisation rates, and technological adoption has enabled the development of a predictive framework capable of estimating future FLW shares at the country level. The analysis reveals diverse trajectories in FLW evolution across regions. While technological advancements and increased mechanisation in agriculture and food processing may reduce supply-side losses in rapidly developing economies, there is likely to be a reciprocal increase in consumption-side waste, which could potentially offset gains achieved through technological improvements and amplify pressures on critical resources such as water, land, and energy. These findings emphasise the urgent need for the design and implementation of sustainable transformation pathways to reduce FLW generation in agri-food systems in present and future conditions, while also addressing the existing trade-offs between FLW reduction and energy security.
How to cite: Semeria, F., Falchetta, G., Vinca, A., Laio, F., Ridolfi, L., and Tuninetti, M.: Predicting future food loss and waste patterns under changing socio-economic conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16002, https://doi.org/10.5194/egusphere-egu25-16002, 2025.
Dynamic global vegetation models have been established as a useful tool in environmental and agricultural sciences for many purposes, e.g. modeling crop growth, fire disturbances, or biosphere-climate interactions. Nevertheless, DGVMs are often very limited in terms of interactions with the anthroposphere, particularly human-Earth interactions. DGVMs such as LPJmL have been successfully connected to integrated assessment models such as Remind-MAgPIE or IMAGE. Still, the model coupling of those approaches often remains loose and static over the simulation period. copan-LPJmL addresses this issue by providing a standardized Python interface, consisting of the LPJmL coupler extension and the pycoupler, that can be used to exchange LPJmL inputs and outputs annually during the simulation period. In addition to LPJmL and the coupling interface, copan:LPJmL also integrates the world-earth modeling framework copan:CORE, which provides useful standardized abstractions of key entities of such models as the world (the simulation space as a whole), the cell, or the individual (an agent in agent-based modeling (ABM)). With copan:LPJmL, any LPJmL output can be retrieved at the world and cell level, and any input to LPJmL can be returned at the same level. This allows for any modeling to be carried out easily using this structure to interact with LPJmL. We here show three examples making use of this: (1) The model of Integrated social-ecological resilient land systems (InSEEDS), which uses a classical ABM approach to model management decisions by farmers, (2) an adaption of an established crop calendar model and (3) a novel LLM (Large Language model) ABM approach. These three examples show the diversity of models that can be implemented using the copan:LPJmL modeling framework to gain new insights into future potential land use and agricultural pathways in the context of global change.
How to cite: Breier, J., Müller, C., Schwarz, L., Prawitz, H., von Bloh, W., Bechthold, M., Gerten, D., and Donges, J.: copan:LPJmL: A new hybrid DGVM-based modeling framework for dynamic land use and agricultural management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19244, https://doi.org/10.5194/egusphere-egu25-19244, 2025.
The Mediterranean region faces critical water scarcity issues exacerbated by climate change, posing significant threats to agricultural sustainability and food security. Adaptive strategies for agriculture are vital to cope with these challenges and ensure long-term resilience. This research focuses on the prioritization and socio-economic evaluation of adaptation measures in the Arroyo de la Balisa sub-basin (SCAB), a representative case study in the Duero River Basin, in Segovia (Spain).
For the prioritization process, a participatory multicriteria approach was used, in which 41 stakeholders representing public administration, agronomic engineering companies, farmers, ranchers, environmentalists and experts, ranked 14 adaptive measures under current and future climatic scenarios, based on four criteria: effectiveness, economic benefit, environmental benefit and ease of implementation. Among the measures considered, the modernization and optimization of irrigation systems was valued as the top strategy for climate change adaptation, followed by the introduction of more resilient crops and several Common Agricultural Policy 2023-2027 eco-schemes, such as crop rotation or no-till farming. These adaptive measures were particularly well-valued for the future scenario, marked by increased evapotranspiration and reduced rainfall. In contrast, other measures requiring significant infrastructure investment, such as transitioning rainfed areas to irrigation, were ranked lower under future scenarios due to anticipated water scarcity.
In parallel, a structured survey targeting 150 local farmers and ranchers is being conducted to assess the socio-economic impacts of implementing these prioritized measures in the SCAB, focusing on potential variations in production costs, income and environmental externalities. The ongoing analysis aims to complement the prioritization results, offering a more comprehensive understanding of the viability of these measures, taking into account economic, social, environmental and institutional dimensions.
The preliminary findings of this research highlight the importance of integrating advanced technologies with sustainable agricultural practices to enhance water use efficiency and mitigate climate risks. Furthermore, the participatory approach employed in this study ensures the relevance and local acceptance of the proposed adaptation measures, fostering their practical implementation. By aligning these technical solutions with stakeholder priorities, this work drives the adoption of effective and sustainable agricultural adaptive strategies. Through this approach, it aims to inform agricultural policies that enhance resilience to climate change, contributing to the implementation of the National Climate Change Adaptation Plan (PNACC) 2021-2030.
How to cite: Ballesteros-Olza, M., Esteve-Bengoechea, P., Bardají, I., Soriano, B., Blanco-Gutiérrez, I., Jiménez-Aguirre, M., Garde-Cabellos, S., Galea, C., Lizaso, J., Díaz-Ambrona, C. H., Pérez, D., Ruiz-Ramos, M., and Tarquis, A. M.: Building resilience in Mediterranean agriculture through participatory approaches: an evaluation of climate adaptation strategies in the Arroyo de la Balisa Sub-basin (Segovia, Spain), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8894, https://doi.org/10.5194/egusphere-egu25-8894, 2025.
Irrigation increases cropland productivity, improves resilience to intensifying climatic stressors, and is accordingly recognized as an effective strategy for climate change adaptation. Irrigation also produces greenhouse gas emissions through energy use for pumping, increased N2O emissions, and degassing of CO2 from supersaturated groundwater, and therefore involves a potential tradeoff between climate change adaptation and mitigation goals. However, irrigation may also decrease global demand for agricultural land by increasing yields, preventing land use change emissions. Here, we quantify the net greenhouse gas impact of US irrigation via both direct emissions and avoided land use change. First, we find that irrigation produces 18.9 Mt CO2e yr-1, 72% of which is due to energy use and thus can be mitigated through adoption of electric pumps coupled with decarbonization of the electric grid. Next, we use empirical models of irrigated to rainfed yield ratios to estimate the production benefits of irrigation in the US for 16 crop groups. Based on these production estimates, we use a global economic model for evaluating land use (GTAP-AEZ) to project hypothetical land use change in response to the loss of irrigated crop production in the US. Land use change projections are downscaled to 300 m resolution using the Spatial Economic Allocation Landscape Simulator (SEALS) model, calibrated on historical land use change. Finally, we leverage existing estimates of biomass and soil carbon stocks to quantify the carbon impacts of the projected land use change. Preliminarily, we find the carbon benefits attributable to avoided land use to be ~4.6 Gt CO2e in total, equivalent to roughly 240 years of annual direct emissions from irrigation. These findings improve clarity regarding the environmental and economic tradeoffs of irrigation, particularly with respect to irrigation expansion for the sake of climate change adaptation.
How to cite: Driscoll, A. W., Johnson, J. A., Blumberg, J., King, A. E., Spawn-Lee, S. A., and Mueller, N. D.: Net Greenhouse Gas Impacts of US Irrigation: Integrating Local Emissions and Global Land Sparing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10248, https://doi.org/10.5194/egusphere-egu25-10248, 2025.
The Mongolian Steppe Ecosystem is a highly vulnerable system, prone to degradation, driven in part by increasing livestock densities. While reducing livestock numbers is often suggested to alleviate pressure on grasslands, there remains a lack of tools for projecting forage availability, potential livestock densities, and the utilization of net primary productivity (NPP) into the future. Such tools could provide valuable guidance for developing effective policies and sustainable management strategies.
To address this gap, we employed a Dynamic Global Vegetation Model (DGVM), LPJ-GUESS, adapted with a daily allocation scheme for grasses, to which we added a simplified livestock submodule to simulate the effects of grazing on aboveground biomass. Forage availability was modeled using historical climate data (ERA5-Land, 0.1° resolution), while NPP utilization was assessed by comparing model runs with and without observed livestock numbers included. Using an iterative approach, potential livestock densities were determined as the maximum densities at which forage sufficiency was maintained over the period from 1970 to 2023.
Our results show a reasonable alignment between LPJ-GUESS modeled GPP and NPP, and GOSIF GPP as well as MODIS NPP. We show spatially explicit utilisation rates and compared actual livestock densities with potential densities, revealing areas of overutilisation that to some extent agree with degradation patterns. When combined with future climate projections, this approach offers a valuable tool for stakeholders and policymakers aiming to sustain the ecological balance and productivity of the Mongolian Steppe under changing climate and grazing scenarios.
How to cite: Van Laere, J., Traylor, W., and Hickler, T.: Sustaining livestock in Mongolia through integrated livestock–vegetation modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15806, https://doi.org/10.5194/egusphere-egu25-15806, 2025.
Central Asia, characterised by arid climate and water scarcity, is climate change hotspot, warming at rates above the global average. This research models how changing climatic conditions, irrigation practices, and co-produced adaptation strategies impact soybean productivity in southeastern Kazakhstan, biggest Central Asian economy. Agricultural modelling incorporated different methodologies including AquaCrop crop modeling, field data collection, in-depth interviews, surveys, and group discussions with stakeholders. AquaCrop crop model was applied to simulate the effects of stakeholder-proposed adaptation strategies and assess soybean yield sensitivity to temperature and precipitation changes under various irrigation scenarios.
For the first time in this region, the AquaCrop model was calibrated and validated for soybean using data from the 2016–2022 growing seasons, showing its suitability for local conditions. Results highlighted the importance of irrigation timing, with maximum yields achieved when irrigation was applied during flowering, pod formation, and pod filling, especially at the first two stages. Smaller irrigation applications increased water productivity by 0.93 kg/m³ and yield by 14.7 % compared to current practices. However, inadequate irrigation infrastructure emerged as a critical challenge for stakeholders. Sensitivity analysis revealed that a 2°C temperature increase reduced the growing season by 10 days due to faster accumulation of growing degree days, highlighting the need for adaptive management under a warming climate.
These findings have important implications for improving soybean production in Kazakhstan and Central Asian region. The results demonstrate the importance of holistic agricultural modeling including modeling of the effect of adaptation strategies co-produced with the stakeholders. Research carries significant implications for regional sustainable food future and food security, emphasising the need for informed adaptation strategies to the changing environmental and economic conditions.
How to cite: Suleimenova, S. and Lukac, M.: Is food secure in Central Asia: agricultural modelling of soybean productivity and adaptation to climate change., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20344, https://doi.org/10.5194/egusphere-egu25-20344, 2025.
Due to its geographical location and its poor economic conditions, Africa is the continent most exposed to the adverse consequences of climate change, particularly on agriculture. The very low percentage of land equipped for irrigation, 3.5% in Sub-Saharan Africa, is another element of concern, sensibly reducing the ability to mitigate the likely productivity losses caused by increasing climate variability and extreme events. Fostering irrigation in Africa is therefore a priority, but due to a limited amount of resources, both in economic and physical (e.g. harvestable water) terms, irrigation projects have to be planned carefully and appropriate locations should be prioritized. The present paper tries to assess the potentials of irrigation in Sub-Saharan Africa and to individuate the locations to be prioritized. The analysis focuses on four cereals, maize, millet, sorghum and wheat, among the most common staples in the region, and relies on a mix of crop modelling (DSSAT) and machine learning (XGBoost) to draw its conclusions. Specifically, for all four crops, crop simulations under rain-fed conditions and optimal irrigation, with DSSAT adding water every time a need for it is observed, are performed on a sample of all Sub-Saharan agricultural plots. Yields differentials and water requirements for optimal irrigation are then computed. Subsequently, yields and water requirements are predicted for all remaining agricultural locations through machine learning, using as explanatory variables the same inputs, soil characteristics, management practices and weather variables, required by DSSAT. Water productivity, defined as the ratio of yields differentials over water requirements for irrigation, is finally computed to individuate the locations where irrigation projects would be most beneficial. By further relying on a continental map of run-off values, we individuate two types of priority locations: areas where simple water capture and storage devices are viable and areas where more complex systems are necessary. The paper points out the importance of irrigation in Sub-Saharan Africa, showing significant gains in yields, up to 100% compared to rain-fed conditions. It also finds high potentials for water capture and storage devices in the south-eastern part of the continent and in South Africa, while the western part and the stripe bordering the Sahara desert would have to rely on more complex irrigation systems.
How to cite: Rogna, M., Klinnert, A., Barbosa, A. L., Tillie, P., and Baldoni, E.: The potential of irrigation for cereals production in Sub--Saharan Africa: A machine learning application for emulating crop growth at large scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1471, https://doi.org/10.5194/egusphere-egu25-1471, 2025.
Wind plays a crucial role in the growth of plants. Recent decades have witnessed a global phenomenon of wind stilling and reversal; however, the implications of these long-term wind changes on crop yields have been insufficiently explored. In this study, we evaluated wind's impact on rice, wheat, maize, and soybean yields in China from 1980 to 2017. Utilizing statistical models across various agro-climatic zones and spatial scales, our findings consistently indicate that crop yields increase with a reduction in growing-season wind speed, even after controlling for temperature and precipitation variables. Over three decades of wind stilling, a total production gain of 212 million tons was realized, effectively compensating for the production losses attributed to rising temperatures. Nevertheless, as the trend of wind has reversed and wind speeds have returned to levels observed in the 1990s, the production gains attributable to wind effects have diminished from 109% to 76% relative to the losses incurred from warming. Additionally, we observed an increase in the annual fluctuations of both wind speed and temperature, which has introduced further instability to crop yields. Consequently, wind-related climatic changes may pose an unrecognized threat to food security, warranting further investigation into their underlying mechanisms and broader implications.
How to cite: Xu, J. and Zhang, Z.: Assessing the Impact of Long-Term Wind Speed Changes on Crop Yields in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1509, https://doi.org/10.5194/egusphere-egu25-1509, 2025.
Ozone (O3) threatens food security by reducing rice yields, a staple food for half of the world’s population. While numerical research has shown the negative impact of O3 on rice through mathematical methods and crop models, existing global assessments have not incorporated data from rice-specific Free Air Concentration Enrichment (FACE) experiments into the mechanical models that simulate the interactions among crop phenology, physiology, and O3. FACE experiments are novel field experiments with O3 distributed directly to the crops in the field. This provides a realistic environment for studying how rice responds to O3 and is well-suited for evaluating its impact.
To perform this study, we use the calibrated JULES-crop model based on data from O3-FACE experiments, to simulate the effects of O3 on rice. We investigate the response of rice under various shared socio-economic pathways (SSPs) as part of CMIP6. These SSPs represent a range of potential future anthropogenic emissions and different climate projections, from scenarios of regional conflict to those of global cooperation. By assessing the effects of O3 on rice under these future scenarios, we gain valuable insights into pathways that could mitigate damage to food security. This research provides a critical foundation for policymakers facing the dual challenges of air pollution and climate change.
How to cite: Xu, B., Dobbie, S., Yang, H., Yang, L., Jiang, Y., Challinor, A., Williams, K., Wang, Y., and Wang, T.: Ozone (O3) risks to rice yields under warming climate using O3-FACE observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4826, https://doi.org/10.5194/egusphere-egu25-4826, 2025.
The efficient management of water resources is critical for sustainable agricultural practices, particularly in arid and semi-arid regions. This study aims to assess crop yield and water use efficiency (WUE) for maize and wheat in Northern China, with a focus on irrigation management, using the Agricultural Production Systems sIMulator (APSIM). APSIM, a widely used crop modeling tool, provides a robust framework for simulating crop growth, yield, and water consumption under different climatic and management scenarios.
Our research integrates historical climate data and crop management practices to evaluate how irrigation strategies influence crop water consumption and yield in the region. By simulating different irrigation regimes, including deficit and full irrigation, we explore their impacts on crop water use efficiency (WUE) and overall yield. The results indicate that optimal irrigation scheduling can significantly enhance water use efficiency, reducing water consumption while maintaining crop productivity. Moreover, the model highlights the sensitivity of crop yield to varying water availability, demonstrating the importance of timely and appropriate irrigation interventions.
The study underscores how crop water consumption can be better managed to enhance WUE and achieve sustainable agricultural production. Future research will focus on refining the model to account for the effects of soil salinity and other environmental factors, further enhancing its applicability for water resource management in arid regions.
How to cite: Yang, X., Zhang, Y., Tian, J., Zhang, X., and Ma, N.: Optimizing irrigation schedule improves water use efficiency of maize and wheat, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4958, https://doi.org/10.5194/egusphere-egu25-4958, 2025.
Climate change significantly threatens global food security, while advancements in negative emission technologies, such as Bioenergy with Carbon Capture and Storage (BECCS) from crop residues, offer potential for climate mitigation. Crop yields are influenced by climatic factors, including temperature, precipitation, and atmospheric CO2, as well as human management practices such as irrigation and fertilization. Crop residues, as unavoidable byproducts of food production, provide a sustainable resource for bioenergy generation without requiring additional cropland. To synergistically achieve the Sustainable Development Goals (SDGs) of Zero Hunger and Climate Action, a comprehensive analysis of future food crop yields through numerical modelling and exploration of diverse climatic and socio-economic scenarios incorporating region-specific adaptation strategies is crucial.
A new crop emulator, blending information from state-of-the-art global gridded crop models (GGCMs) and observational data from field experiments, has been developed to facilitate probabilistic projections of crop yields under diverse climatic and socio-economic scenarios. It can be integrated into simple climate models, such as the compact Earth system model OSCAR, or used standalone. For policy relevance, it is constructed at a sub-national scale with the flexibility to be aggregated to broader regional levels while remaining computationally efficient for large scenario ensembles. Aligned with the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework, it simulates yields for four major food crops: maize, rice (two growing seasons), soybean, and wheat (spring and winter varieties) driven by atmospheric CO2(C), growing season temperature (T), water availability (W), including precipitation and irrigation, and nitrogen fertilization (N). While crop yield responses to C, T, and W are calibrated using ISIMIP3b simulations conducted under fixed human forcing, responses to N are calibrated against long-term field experiments, addressing inter-model uncertainty and integrating diverse data sources. Applying observational constraints via Bayesian inference further improves the model’s accuracy.
This paper describes the calibration, integration, and validation of the crop emulator and illustrates its performance and potential through two example studies. The first examines historical crop yields under static human inputs, and the consistency of these results with ISIMIP3a outputs validates the emulator’s ability to emulate GGCMs. The second study uses dynamic human inputs and constraints derived from field experiments (e.g., open-top chamber and free-air CO2 enrichment experiments), showing good agreement with FAO statistics and demonstrating the emulator’s capability to represent human management impacts. Beyond these examples, the crop emulator's potential extends to various future applications, such as coupling with integrated assessment models (IAMs), reanalysis of the Sixth Assessment Report (AR6) scenarios, and contributions to the upcoming AR7.
How to cite: Liu, X., Gasser, T., Ma, J., and Liu, J.: A New Probabilistic Crop Yield Emulator: Development and Applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6625, https://doi.org/10.5194/egusphere-egu25-6625, 2025.
Climate change poses significant challenges to global food security, particularly in regions relying on double cropping systems. Developing sustainable adaptation strategies for these systems is essential to mitigate climate-induced yield losses and ensure sustainable crop production under changing climate. However, the effectiveness of these strategies remains underexplored in many regions, especially in areas where double cropping systems are a cornerstone of agricultural productivity and food security. This study aims to establish a global framework for climate change adaptation in single and double cropping systems, focusing on optimizing management practices such as sowing dates and cultivar selection. As a first prototype, we assessed the impacts of climate change on rainfed soybean and maize in single and double cropping systems in Brazil.
Using an advanced crop model emulator, the CROp model Machine learning Emulator Suite (CROMES), we projected crop yields under two shared socioeconomic pathways (SSP126 and SSP585) for 2016–2100. Our results reveal that optimizing sowing dates and cultivar selection is crucial for adapting cropping systems to climate change. Double cropping soybean faces yield declines up to 40% under SSP585 but gains up to 10% under SSP126, with early-sown and early-maturing varieties suffering sharper losses (up to 75%). Double cropping maize grown in the second season shows greater resilience, with declines ranging down to only -20%, while single cropping maize again faces sharper losses, reaching down to -60%. Single cropping soybean can increase yields by up to 30% under SSP126 with later planting and longer maturity groups but declines up to -30% under SSP585.
These findings provide valuable insights for understanding the vulnerabilities and potential adaptation strategies for single and double cropping systems in Brazil, setting the stage for broader global studies. Future work will extend this analysis to other key cereal-based double cropping systems in China, the United States, and Indonesia, contributing to a comprehensive global framework for transitioning to sustainable double cropping systems and securing food production under the pressures of climate change.
How to cite: Niu, Q., Folberth, C., Khabarov, N., and Balkovič, J.: Evaluating Climate Change Impacts and Adaptation Potential in Single and Double Cropping Systems using Crop Model Emulators, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7954, https://doi.org/10.5194/egusphere-egu25-7954, 2025.
Extreme climate events like drought and heatwave are increasingly co-occurring and considerably threaten global food security. Global gridded crop models (GGCMs) are widely used to assess the impacts of climate extremes on crop yields; however, in which way and to what extent the uncertainty of assessment can be reduced remains largely unknown. Here, we jointly improve the CERES-Wheat model from model inputs, structure, and parameterization at 10-km resolution to reduce the uncertainties globally. The improved model parameterization remarkably increase the model explanatory power of observed global wheat yield losses from drought, heatwave, and their compounds during 1981-2015 by 25% to 60% compared to the multi-model ensemble (MME) approach. Improved temperature response functions for key physiological processes particularly contribute to a better representation of wheat response to heatwave by 20%. Taking 2003 European drought and heatwave events as examples, the improved model is capable of closely replicating the observed yield declines (> 90%), whereas most of the existing GGCMs fail to show any impact and MME merely explains < 25% of the reported influences. Our findings provide the first evidence for comprehensively constraining crop model uncertainty in extreme climate impact assessment, benefiting the accurate understanding of climate risk and the design of effective adaptation strategies.
How to cite: Luo, Y., Zhang, Z., Han, J., Cao, J., Tang, Q., and Tao, F.: Comprehensive global gridded crop model improvements reduce the uncertainty of extreme climate impact assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7666, https://doi.org/10.5194/egusphere-egu25-7666, 2025.
The dynamic response and adaptation mechanisms of crop phenology to climate change have not been extensively studied. At present, the mainstream process mechanism models have not yet considered the adaptability of crop phenology to climate. The simulation of crop development process is generally driven by temperature, especially the development rate during the reproductive growth season is assumed to be only affected by temperature. The development rate of wheat is also affected by photoperiod, temperature cycle, vernalization, and growth continuity effects. This article uses long-term and multi variety wheat phenological observation data, combined with historical climate data and field management data, to identify the dynamic changes in wheat phenology and accumulated temperature demand in China from 1981 to 2018. It reveals the mechanism of wheat dynamic response and adaptation to climate change, couples indicators reflecting phenological plasticity, and improves the models of nutritional growth period and reproductive growth period respectively. The main results and conclusions of the research are as follows: (1) The dynamic changes in wheat accumulated temperature demand, even for the same variety and stage, there are differences in accumulated temperature demand in different environments. The dynamic nature of accumulated temperature indicates that previous models based on the assumption of constant accumulated temperature are difficult to apply to changing environments. (2) The increase in temperature shortened the reproductive growth period of winter wheat, and this effect tended to intensify during the study period. The rhythmicity of day night temperature can slow down the accelerated development of nutrient growth due to warming. The wheat variety is shifting towards a weaker winter orientation, and the weakened vernalization effect leads to an increase in accumulated temperature required for phenological occurrence. For the reproductive growth period, the flowering period will affect the development rate of wheat during the reproductive growth period, and the response of temperature to maturity period is delayed. The effect of flowering temperature on phenology will continue for 8-15 days after flowering. When the flowering temperature exceeds 26 ° C, the impact on maturity period will last for more than 20 days. (3) For the flowering period of spring wheat, the model considering the effects of photoperiod and temperature cycle on accumulated temperature has the best performance, while for the flowering period of winter wheat, the model considering the effects of photoperiod and vernalization on accumulated temperature has the best performance. The nonlinear plasticity model is the optimal model for simulating the maturity period of spring and winter wheat. The use of optimized models to simulate the flowering and ripening stages of wheat reduced the average simulation error by 22.71% and 22.19% compared to traditional models.
How to cite: Liu, Y., Chen, J., Huang, S., and Cheng, W.: Improving wheat developmental model based on dynamic changes in accumulated temperature demand, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7675, https://doi.org/10.5194/egusphere-egu25-7675, 2025.
Optimising nitrogen management has the potential to enhance crop productivity while mitigating greenhouse gas emissions. Nevertheless, it has low adoption rates, due to the complex interactions of crop types, environments (climate and soil) and management combinations, posing significant challenges to advancing climate-smart agriculture. In this study, a hybrid modelling approach was developed to target a minimum of 90% of the potential yield, while simultaneously increasing nitrogen use efficiency and optimising N inputs, reducing net GHG emissions and GHG intensity. A 30-year field trial was conducted on a wheat-maize rotation system in the North China Plain. The observations (annual yields, SOC and N2O emissions) were then used to validate the process-based DNDC model, and the NSGA-Ⅲ machine learning algorithm was applied for multi-objective optimisation. This hybrid modelling approach simulated and optimised three levels of nitrogen management under future climate scenarios (level 1: fertilizer rates; level 2: fertiliser rates, timing, frequency, and crop schedules; level 3: level 2 plus irrigation and residue retention). From 1990 to 2100, the optimised practice combinations were identified: delaying and reducing basal fertilization (+5 d, -52.8 kg N ha-1) while advancing top-dressing in wheat (-5 d) and both events in maize (-9 d, -3 d); postponing wheat sowing (+5 d) and advancing maize sowing (-9 d); aligning irrigation event with fertilization, and adding one irrigation event during the maize bell stage; and lowering residue retention (-0.2). Integrating additional practices with fertiliser rates (levels 2 and 3) proves effective in meeting these climate-smart objectives. Under SSP245 and SSP585, the optimal level 3 practices, compared to maintaining current practices unchanged (conventional practices), increase annual crop yields by 5.6% and 1.7%, respectively, while concurrently reducing net GHG emissions by 9.4% and 8.4%, respectively. Optimal level 3 practices, in comparison to level 2, increased yields by only 0.7%, but significantly reduced net GHG emissions by 8.7%. Furthermore, the implementation of optimal level 3 practices, compared to conventional practices, led to a reduction in N inputs, irrigation water use and residue inputs by 17.2%, 6.7% and 20.0%, respectively. The findings of this study demonstrate that the optimal practices continually adapted in order to respond to the changing climate conditions. It is imperative for decision-makers to consider the trade-off between achieving greater GHG reductions and the potentially higher implementation costs associated with adjusting practices, given the minimal yield differences but significant GHG emission disparities across levels.
How to cite: Liu, D., Pan, B., Lam, S. K., Wang, E., and Chen, D.: Optimising nitrogen management for climate-smart agriculture: A hybrid modelling approach in wheat-maize rotations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7722, https://doi.org/10.5194/egusphere-egu25-7722, 2025.
Soybean, the most produced leguminous crop on Earth, serves as a vital source of vegetable oil and a major provider of protein for animal feed and human consumption. By fixing atmospheric nitrogen via rhizobia, soybean reduces reliance on synthetic fertilizers, promoting soil sustainability, reducing surface water eutrophication and N2O emissions. Most soybean is produced in the US and South America. In contrast, Europe and China are major importers, and produce only a small fraction of their soybean consumption. However, there is growing interest of increasing the soybean self-sufficiency in these regions, to decrease dependence on US exports, reduce environmental impacts of soybean expansion in South America, and for the sake of crop diversification in Europe and the agronomical and environmental benefits of leguminous crops. In order to explore the potential to cultivate soybean around the world, including probable yields, yield stability, and the agronomical and environmental effects mentioned above, comprehensive, process-based models are needed. Moreover, such models could permit for future predictions accounting for climate change, which has the potential to shift regions where soybean production is promising to higher latitudes. Here we present our recent developments of the land surface model ORCHIDEE-CROP (Organizing Carbon and Hydrology in Dynamic Ecosystems-Crop), for which we developed a representation of soybean as a major crop besides wheat, maize, and rice. For this crop, we developed a new parametrization of crop phenology, biomass production and allocation, and yield production. A new scheme was also introduced to represent the effects of fertilization on biomass development and yield production. Experimental data from ten flux tower sites were used to calibrate and validate the model. We find that the simulated gross primary productivity, evapotranspiration, leaf area index, and biomass agree well with the observations. Our model development provides an essential tool for assessing the agronomical and environmental benefits of legume crops in agroecosystems at regional to global scales.
How to cite: Zhang, X., Goll, D., Ciais, P., Su, Y., and Lauerwald, R.: Modelling soybean growth processes in the land surface model ORCHIDEE-CROP, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8129, https://doi.org/10.5194/egusphere-egu25-8129, 2025.
Irrigation plays a vital role in sustaining agricultural productivity, particularly in the Mediterranean region, which is characterized by limited water resources and heightened vulnerability to water scarcity. Meeting the irrigation demands of both water and energy in such environments requires efficient management strategies to ensure long-term agricultural sustainability. This work aims to provide a comprehensive understanding of the current use of water and energy in Mediterranean agriculture, with implications for food production and environmental sustainability. Using high-resolution data on irrigated and rainfed areas, crop-specific water consumption, and regional irrigation infrastructure, the blue water (BW) consumption and energy demand for irrigation were calculated across the region. The results reveal that cereals account for the largest irrigated area, representing 54% of the total area and consuming 30% of the energy. Conversely, fruits and nuts, which cover just 14% and 7% of the irrigated area, respectively, contribute significantly to energy demand, requiring 30% and 17% of the total energy consumption. In total, irrigation across the Mediterranean region utilizes 88.34 km³/y of blue water and 85.19 × 10⁶ GJ/y of energy, covering an irrigated area of 17.88 Mha. These results offer important insights into the interlinkages within the WEFE Nexus, highlighting the resource intensity of irrigation. By quantifying energy demands, the study helps assess the broader environmental impacts of irrigation within the Nexus.
How to cite: Chiarelli, D. D., Nanesha, H., and Rulli, M. C.: Sustaining Irrigated Agriculture in the Mediterranean: A Comprehensive Assessment of Water and Energy Resources, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9810, https://doi.org/10.5194/egusphere-egu25-9810, 2025.
Accurate crop yield estimation is important for global food security. Data-driven deep learning approaches have shown great potential for agricultural system monitoring, but are limited by their out-of-sample prediction failure and low interpretability. How to embed knowledge into deep learning models to address the above challenges remains an open question. In this study, we developed a deep learning model named PSNet following the concept of hierarchical yield levels to estimate county-level crop yield. The PSNet model mainly consists of PotentialNet and StressNet to capture the interactions among crop, environment, and technological trend. The PotentialNet is developed to capture the spatiotemporal pattern of the rice yield potential based on environmental and local technological conditions. The StressNet is designed to capture the negative impact of climate stresses, which caused the yield gap between yield potential and actual yield. We applied the model to analyze the county-level rainfed corn yield in the US Corn Belt from 2006 to 2020. The Random Forest (RF) and Long Short-term Memory (LSTM) models were chosen as baselines. The results showed that the PSNet model achieved better yield estimation accuracy than baselines under the normal (R2 = 0.82) and stressful climate conditions (R2 = 0.77). The ablation results indicated that PotentialNet contributed to the yield estimation under normal climate conditions, while the StressNet was better at capturing the yield losses under climate stresses. This study provided a promising approach to extract the pattern of yield potential and stress impact to achieve good estimation performance across various growth conditions.
How to cite: Zhong, R., Xiong, X., Tian, Q., Huang, J., and Lin, T.: PSNet: a knowledge guided deep learning approach for county-level corn yield estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5306, https://doi.org/10.5194/egusphere-egu25-5306, 2025.
The United States (US) is a major producer and exporter of cotton (Gossypium hirsutum L.). The US produces about 20% of the world’s cotton and cotton production in the country is concentrated in the southern states, also known as the “Cotton Belt”. Air temperature and carbon dioxide (CO2) concentration are important abiotic factors that control the growth and development of cotton. Global climate models (GCMs) project an increase in air temperature and CO2 concentration, and changes in precipitation amounts and patterns in the future. Thus, cotton production across the Cotton Belt could face severe challenges due to projected warmer and drier future climatic conditions and changes in availability of irrigation water. The objective of this study was to investigate the effects of climate change on cotton production across the US Cotton Belt and develop appropriate adaptation strategies for sustaining cotton production in the future using the DSSAT CROPGRO-Cotton model.
Five sites across the Cotton Belt including Maricopa in Arizona, Lubbock and Chillicothe in Texas, Camilla in Georgia, and Lewiston-Woodville in North Carolina were selected for this study. The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) climate projections of nine GCMs from 1950 to 2100 were obtained for the study sites for four Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP4-8.5. Data was categorized into four time periods: Historic (1950-2014), Near-future (2015-2040), Mid-century (2041-2070), and Late-century (2071-2100) to assess the effects of projected climate change on seed cotton yield and irrigation water requirement at the study sites. Modifications to planting date and row spacing were evaluated as potential climate adaptation strategies.
Results indicated that the simulated irrigated seed cotton yield is expected to increase within a range of 10-24% at all sites, except at arid Maricopa site, where irrigated seed cotton yield is simulated to decrease within a range of 24-60%. While the negative effects of projected increases in already higher temperatures dominated the positive effects of CO2 fertilization at Maricopa site, the opposite effects were found at the remaining four sites. The future irrigation requirement is expected to increase at all sites within a range of 4-30% to meet higher evapotranspiration requirements due to projected warmer and drier climates. Identified potential climate adaptation strategies differed across the study sites. For example, mid-season cotton planting in a narrow row spacing (75 cm) was found to be a promising climate adaptation strategy to improve irrigated seed cotton yield at Halfway while an early planted cotton with wide row spacing (100 cm) was found to be the most promising strategy for Maricopa. Findings from this study will be useful to US cotton producers in modifying agronomic practices conducive to cotton growth and development under projected future changes in climate.
How to cite: Ale, S., Singh, B., Samanta, S., and Barnes, E.: Potential Impacts of Climate Change on Cotton Production across the United States Cotton Belt and Evaluation of Adaptation Strategies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12059, https://doi.org/10.5194/egusphere-egu25-12059, 2025.
Research on the impacts of climate change on crop yield is necessary for improving agricultural management practices and increasing crop adaptability to climate change. Future climate change not only alters long-term climate trends but also changes the amplitude of their fluctuations. Currently, there is a lack of studies that comprehensively consider the effects of climate trend and fluctuation on crop yield. The North China Plain is the largest wheat producing area in China, this study utilizes the DSSAT crop model to analyzes the impacts of future climate trends and climate fluctuations on winter wheat yields in the North China Plain, explores the dominant climatic factors affecting irrigated and rainfed winter wheat in the North China Plain under different climate scenarios in the future and proposes feasible recommendations for management options to cope with climate change with a view to guaranteeing food security. It was found that winter wheat yield in the North China Plain increased by 1.5% in the 2030s and decreased by 13.4% in the 2080s. The main reason for the decrease was the increase in the future temperature trend, which could lead to an average potential decrease of 8.4 %, and the increase in precipitation in the future could play an alleviating role. Irrigated and rainfed agriculture respond differently to climate change, with future temperatures dominating yield reduction changes in irrigated winter wheat and precipitation dominating yield increase changes in rainfed winter wheat. Delaying the sowing date of winter wheat and increasing field fertility can effectively mitigate the negative effects of temperature increases, whereas the mitigation effect of increasing irrigation is limited. In the future, we should pay attention to the potential threat of high temperatures and heat damage to winter wheat planting, and rationally use regional climate resources to guide agricultural production.
How to cite: Hu, J., Li, Y., and Shi, P.: Assessment of the impact of future climate trend and fluctuation on winter wheat yield in the North China Plain and exploration of adaptation strategies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12356, https://doi.org/10.5194/egusphere-egu25-12356, 2025.
Nitrogen plays a fundamental role in agri-food systems, as the primary component of proteins, a key limiting soil nutrient, and a significant driver of environmental pollution. The Generalized Representation of Agri-Food Systems (GRAFS; Billen et al., 2014) model provides a comprehensive and robust framework for quantifying nitrogen flows across regional, national, or continental scales. By employing a metabolic approach to analyze nitrogen dynamics, GRAFS enables detailed diagnostic assessments of historical and current trends in crop production linked to socio-technical, pedological, and climatic variables. The predictive capacity of this approach yet remains limited by the lack of explicit incorporation of climatic drivers on N flows, particularly those related to crop harvest.
Utilizing a newly compiled annual dataset from 1990 onwards, the model offers high-resolution diagnostics across Europe, capturing spatio-temporal variability across 127 subnational regions. This study focuses on quantifying the influence of climatic variables on the historical evolution of arable crop yields. The methodology is based on an empirical relationship between total nitrogen yields at the crop-rotation scale and total nitrogen inputs from synthetic fertilizers, manure, biological fixation, and atmospheric deposition. This yield response to nitrogen fertilization follows a hyperbolic curve characterized by a single parameter (Ymax; Lassaletta et al., 2014), representing the theoretical maximum yield for a given territory.
We analyze the temporal evolution of this parameter for the 127 European regions in relation with shifts in climate-related factors, including precipitation, water balance, temperature, and extreme weather events. We establish a relationship between climatic variables and shifts in the Ymax value, which characterizes the yield-fertilization relationship. Our results provide foundation for developing prospective scenarios addressing the combined effects of climate change and transformations in agricultural systems on agronomic and environmental performances of food systems.
Reference
Billen, G., Lassaletta, L., Garnier, J., 2014. A biogeochemical view of the global agro-food system: Nitrogen flows associated with protein production, consumption and trade. Glob. Food Sec. 3, 209–219. https://doi.org/10.1016/j.gfs.2014.08.003.
Lassaletta, L., Billen, G., Grizzetti, B., Anglade, J., Garnier, J., 2014. 50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland. Environ. Res. Lett. 9. https://doi.org/10.1088/1748-9326/9/10/105011.
How to cite: Harter, L., Einarsson, R., and Le Noë, J.: How has climate variability affected regional crop production across Europe in the last 30 years?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15965, https://doi.org/10.5194/egusphere-egu25-15965, 2025.
The Mediterranean Basin is recognized as a "climate change hotspot" due to its high exposure and vulnerability to interconnected climatic risks. Agriculture in this region must not only adapt to these challenges but also contribute to mitigation goals, as it is a significant source of greenhouse gas emissions. This study evaluates the impacts of climate change on durum wheat productivity in two sites in southern Sardinian (Italy), representative of Mediterranean cereal farming, and compares conservation tillage practices (reduced tillage and no-tillage) with conventional management. Crop modelling was performed using the CSM-CERES-Wheat model, implemented in the DSSAT software, parameterized with data from two long-term experiments on conservation agriculture. Climate projections from the Euro-Cordex platform under three Representative Concentration Pathways (RCP2.6, RCP4.5, RCP8.5) were considered for future projections. Results indicate significant increases in temperatures across all scenarios, with shortened crop growing cycles and earlier maturation by up to three weeks under the most extreme scenarios. Yield variations ranged from -9% to +20% by the end of the century, influenced by the direct effect of increased atmospheric CO2 concentration on photosynthesis rate and water use efficiency. Grain yields obtained with conservation tillage practices are comparable with the values obtained with conventional practices, but with several related advantages, including reduced operational costs, time savings, and lower greenhouse gas emissions. These findings highlight the dual role of conservation agriculture as a strategy for climate adaptation and mitigation in Mediterranean cereal systems. However, further research is needed to better address uncertainties related to extreme weather, pests and diseases, and greenhouse gas emissions, to ensure sustainable agricultural productivity in the face of climate change.
How to cite: Mereu, V., Trabucco, A., Aslam, M. F., and Carboni, G.: Durum Wheat in a Changing Climate: Comparing Conservation and Conventional Practices in a Mediterranean environment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19936, https://doi.org/10.5194/egusphere-egu25-19936, 2025.
One third of the annual world's crop production is directly or indirectly damaged by insects, with an even increasing burden in a warming climate. Early detection of invasive insect pests is key for optimal treatment before infestation. Existing detection devices are based on pheromone traps: attracting pheromones are released to lure insects into the traps, with the number of captures indicating the population levels. Promising new sensors are on development to directly detect pheromones produced by the pests themselves and dispersed in the environment. Inferring the pheromone emission would allow locating the pest's habitat, before infestation. This early detection enables to perform pesticide-free elimination treatments and reduce the negative impact of agricultural practices on biodiversity, environment and human health, in a precision agriculture framework.
In order to identify the sources of pheromone emission from signals produced by sensors spatially positioned in the landscape, the inference of the pheromone emission (inverse problem) is performed. In the present case, classical inference framework consists in combining the data from the pheromone sensors and the fluid mechanic-based pheromone concentration dispersion model that is a 2D reaction-diffusion-convection model. The proposed inference framework further incorporates into this combination additional a priori biological knowledge on pest behaviour (favourite habitat, insect clustering for reproduction, population dynamic behaviour...) [1]. This information is introduced to constrain the inference problem towards biologically relevant solutions. Different biology-informed constraints are tested, and the accuracy of the solutions of the inverse problems is assessed on simulated noisy data using a dedicated package [2].
In addition, optimal experimental design will be presented to deduce optimal sensor position in order to reduce the uncertainty of the inference and to improve the prediction of pest’s habitat localization.
Reference:
[1] Malou T., Parisey N., Adamczyk-Chauvat K., Vergu E., Laroche B., Calatayud P.-A., Lucas P. and Labarthe S. (2024). Biology-Informed inverse problems for insect pests detection using pheromone sensors. Submitted for publication. https://doi.org/10.5281/ZENODO.11506617
[2] Malou T. and Labarthe S. (2024). Pherosensor-toolbox: a Python package for Biology-Informed Data Assimilation. Journal of Open Source Software, 29 (101), 6863. https://doi.org/10.21105/joss.06863.
Acknowledgements:
This work was carried out with the financial support of the French Research Agency through the Pherosensor project with grant agreement ANR-20-PCPA-0007.
How to cite: Malou, T., Labarthe, S., Laroche, B., Vergu, E., Adamczyk, K., Parisey, N., Lucas, P., and Calatayud, P.-A.: Pest detection from a biology-informed inverse problem and pheromone sensors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5850, https://doi.org/10.5194/egusphere-egu25-5850, 2025.
The increased likelihood and severity of extreme weather events in the future affect the key processes of terrestrial ecosystems such as plant growth, water fluxes and biogeochemical cycles of all elements. It is urgent need to adopt climate change adaptive measures in agricultural production to overcome the negative effects of climate change on crop production and nutrient losses. This study aims to develop a comprehensive framework for investigating optimized crop rotations that balance environmental sustainability and economic benefits while enhancing resilience to future climate conditions . The research involves three key steps. First, crop rotations at the catchment scale were generated using the Crop Generator tool. Second, the water quality model (mHM-Nitrate) and the crop growth model (WOFOST) were coupled using a process-based modeling approach. Third, environmental and economic indicators—such as crop yields, farmers' income, and nitrate leaching—were employed to evaluate crop production activities under different climate scenarios. The study will be conducted in the Bode catchment, Germany, where 45 feasible crop rotations have been planned. The study is going to explore how these crop rotations may evolve over the next two decades under four distinct climate scenarios (SSP 1-2.6, 2-4.5, 3-7.0, and 5-8.5). Additionally, the study aims to identify recommended crop rotations by quantifying their impacts on nitrogen dynamics and crop yields. This research provides an useful and comprehensive framework for devising crop adaptation strategies at the watershed scale in the face of future climate change.
How to cite: Ding, W. and Rode, M.: Crop adaptation and its impact on non-point source pollution under future climate challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5971, https://doi.org/10.5194/egusphere-egu25-5971, 2025.
Tailoring straw return strategies to specific regional conditions can optimize soil health, enhance crop yields, and contribute to climate change mitigation. By using the Rothamsted carbon model (RothC) and the bioenergy-emission-economic model (BEE), we assessed the spatially explicit, optimal straw harvest strategies to maintain soil organic carbon (SOC), and evaluated the climate benefits aquired from straw-based bioenergy. We found that the national average straw return rate needs to reach 43% to meet the 4 per mille SOC target. Most crop straws in Northeast China must be returned to cropland to maintain SOC level, while straws in East China and Central China could provide substantial quantities of biomass feedstock for energy production without SOC loss. Under future climate scenarios and designed straw harvest strategies, 0.3 to 0.7 Pg C of straw could become available annually for energy production, providing a greenhouse gas mitigation potential of 1.4 to 2.5 Pg CO2e using the combined heat and power (CHP) and integrated gasification combined cycle (IGCC) technologies (2020-2100). These region-specific straw management strategies offer insights into sustainable agricultural practices, soil carbon enhancement, and agricultural sector’s climate policies.
How to cite: Lu, X., Xu, Y., Lin, Z., Wang, G., and Qin, Z.: Optimizing crop straw management in China: valuing bioenergy potential and greenhouse gas reduction opportunities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10203, https://doi.org/10.5194/egusphere-egu25-10203, 2025.
The notable increase in insect populations over the recent years has been closely linked to rising global temperatures and more frequent drought events, both consequences of climate change. This surge in insect activity has had a significant impact on agricultural production [1]. Among the crops most affected is sugar beet in the eastern part of Austria [2], where outbreaks of the sugar beet weevil (Asproparthenis punctiventris) have been become increasingly common. Identifying regions more prone to such infestations could aid crop planning and management practices, mitigate agricultural losses, improving energy efficiency, and increase crop yield. Previous publications have already shown the influence of weather conditions on the reproduction and survival rates of insects and linked these factors to their distinct life cycle stages [3,4]. These investigations employed simple regression models and statistical frameworks to study the correlation of the infestation level with weather parameters as well as degree-day models that aimed at predicting the time of insects’ outbreak.
In our study we extend this approach by incorporating soil composition data, historical crop records alongside the most relevant meteorological parameters. We use these data to train machine learning algorithms, specifically species distribution models together with random forests, aiming at forecasting infestation levels. By integrating data from diverse and heterogeneous sources, we construct a comprehensive database used as the foundation for developing our machine learning trained prediction algorithm. We propose a multi-layered model in which each layer processes data from a different source, spatially represented on a map. Furthermore, we integrate geospatial information of the previous sugar beet crops and derive a population spread function, which is subsequently used to refine the prediction results. Initial findings validate the feasibility of the proposed approach and its potential for geographically predicting infestation levels of the sugar beet weevil.
References
[1] Skendžić S, Zovko M, Živković IP, Lešić V, Lemić D. The Impact of Climate Change on Agricultural Insect Pests. Insects 2021; 12(5).
[2] Strotmann K., Pflanzenschutzverbot: 4.000 ha Rüben in Österreich vernichtet. Agrarmarkt Österreich; Jun.2023.
[3] Drmić Z, Čačija M, Virić Gašparić H, Lemić D, Bažok R. Phenology of the sugar beet weevil, Bothynoderes punctiventris Germar (Coleoptera: Curculionidae), in Croatia. Bull Entomol Res. 2019 Aug;109(4):518-527. doi: 10.1017/S000748531800086X. Epub 2018 Nov 27. PMID: 30477591.
[4] Lydia Jarmer. Masterarbeit: Auftreten des Rübenderbrüsslers (Asproparthenis punctiventris) in Ostösterreich unter besonderer Berücksichtigung von Witterungsverhältnissen. Universität für Bodenkultur; 2022.
How to cite: Bernadó, L., Cerqueira, F., Thiele, P. L., Dokal, M., Seiter, M., Lampert, J., and Molin, E.: Data-informed Machine Learning Modeling for Infestation Level Prediction of the Sugar Beet Weevil, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11538, https://doi.org/10.5194/egusphere-egu25-11538, 2025.
Agriculture significantly contributes to greenhouse gas (GHG) emissions, mainly via enteric and manure methane (CH4) from livestock and fertilizer-induced nitrous oxide (N2O) from soils. Mitigation strategies include dietary changes, feed additives, and fertilisation with circularity approaches. Agroforestry further offsets GHGs through carbon sequestration (soil and biomass) while enhancing soil health and ecosystem services. Achieving carbon-neutral farms by 2050 requires sustainable agricultural transformation. System-based modelling is crucial for understanding agriculture, supporting informed decision-making, and balancing data needs. HOLOS-IE, evolving into HOLOS-EU, simplifies complex modelling for farmers and stakeholders, empowering them to reduce their environmental footprint and achieve sustainable production.
The HOLOS-IE v3.0 (www.ucd.ie/holos-ie) utilises large datasets, evidence-based algorithms, GIS, Machine Learning, and C#.NET coding. The ongoing development focuses on refining model components (crops, grasses, livestock, agroforestry and farm infrastructure), and their sub-components. These components are driven by key soil, climate and relevant variables, which are automated or user-defined inputs. As a case study, HOLOS-IE was applied to a 30-hectare Irish dairy farm to explore agroforestry scenarios (silvopastoral systems with Oak and Sycamore hedgerows) by sparing 5% of land without reducing livestock density. The model predicted sectoral GHG emissions, carbon removal, and total/net carbon balance, quantifying soil and biomass carbon sequestration. This analysis highlighted the offsetting potential and provided insights into total and net carbon balances, guiding future land-use planning for climate change mitigation.
The model successfully simulated GHGs, soil organic carbon (SOC), biomass carbon, and farm energy. On the dairy farm, the main GHG contributors were enteric CH4 (76%, 5148 tCO2eq ha-1), direct N2O (13%), and manure CH4 (9%), with indirect N2O contributing 2%, respectively. SOC density in grassland increased by 0.16 t C ha-1 y-1 over 23 years. After introducing silvopasture, grassland GHG contributions remained similar, but SOC density in the tree zone increased, especially in hedgerows. Silvopasture and hedgerows, covering 5% of the land, offset 19% of the farm’s carbon footprint without reducing livestock density, supporting future steps toward carbon neutrality.
This paper introduces HOLOS-IE as a foundational step towards the development of HOLOSEU. As the model is still under development, a relatively comprehensive scenario demonstrating how to achieve carbon neutrality including soil health indices, production metrics, cost-benefit analyses and maintaining profitability on a dairy farm will be presented at the conference. Feedback from stakeholders will be gathered to guide further improvements, followed by validation and calibration.
The HOLOS-IE project is funded by the Science Foundation Ireland (Currently Research Ireland) through the Gov.ie and the ECRRF (Grant No. 22/NCF/FD/10947) in collaboration with ReLive and HOLOSEU funded by Transnational ERA-NET and ICT-AGRI-FOOD, respectively through the Department of Agriculture, Food and the Marine, Ireland.
How to cite: Khalil, M. I., Sen, B., Siddque, M., Chatzichristou, T., McPherson, A., Rakin, S., Herron, J., Kröbel, R., Osborne, B., and Collier, R.: HOLOS-IE: A System Model for Assessing Carbon Emissions and Balance in Agricultural Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12648, https://doi.org/10.5194/egusphere-egu25-12648, 2025.
Biochar application to soil shows promise for enhancing soil properties, increasing crop yields, improving water retention, and promoting carbon sequestration. While the direct effects of biochar on soil properties have been studied to some extent, the overall impact on ecosystem carbon balance remains uncertain, as field and lab studies typically do not account for interactions with vegetation. The LiDELS (LiBry-DETECT Layer Scheme) model offers a process-based approach to assess these soil-vegetation interactions and the potential for carbon sequestration in response to biochar application under diverse environmental conditions. This study presents an overview of the LiDELS model and its application to a sandy soil profile under the climate conditions of Northern Germany. LiDELS simulates the impacts of biochar on key soil functions, including water retention, thermal properties, evapotranspiration rates, and net primary production (NPP). Model validation shows strong agreement with observed data for soil moisture, temperature, and CO2 flux, confirming LiDELS’s applicability across varying soil textures, vegetation types, and biochar treatments. Results indicate that biochar application to sandy soil in Northern Germany enhances soil water availability by 35%, increases NPP by 5%, raises soil CO2 by 19%, and has nosignificant impact on soil respiration or soil temperature. LiDELS thus represents a valuable predictive tool for evaluating environmental feedback of biochar in agriculture and carbon management, supporting sustainable land use practices.
How to cite: Maslouski, M., Eschenbach, A., Beer, C., Thomsen, S., and Porada, P.: Soil and vegetation responses to biochar application in terms of its feedback on carbon sequestration under different environmental conditions – LiDELS model overview, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16450, https://doi.org/10.5194/egusphere-egu25-16450, 2025.
Mediterranean hilly landscapes, surface runoff is one of the main hydrological processes that redistributes water from upslope to downslope. In agricultural catchments, surface runoff causes rainfall water to transfer from upstream plots to downstream plots due to hydrological connectivity. The water thus redistributed can then infiltrate into the soil of the downstream plot, depending on the soil's infiltration capacity, thereby increasing water availability in the root zone. While the impact of hydrological connectivity on hydrological processes such as streamflow generation is well recognized, few studies evaluate its effect on crop functioning. In general, crop functioning is studied using multilocal methods that assume hydrological independence between plots, overlooking the influence of hydrological connectivity. In the development of catchment agro-hydrological models, the coupling between the crop model and the hydrological model is partly conditioned by the effect of hydrological connectivity.
The objective is to study the effect of water redistribution through runoff on crop functioning in the context of Mediterranean rainfed annual crops, using a modelling approach. A numerical experiment using the AquaCrop model was performed, considering two hydrologically connected plots. The experiment explored a range of agro-pedo-climatic conditions upstream and downstream: crop type, soil texture and depth, climate forcing, and the size of the upstream plot. Data collected over the past 25 years from the OMERE Environmental Research Observatory in northeastern Tunisia (Molénat et al., 2018) were used, along with data from the literature. The Aquacrop model was previously parametrised and validated for the soil, crop and climate conditions of this in northeastern Tunisia site (Dhouib et al., 2022).
Results show that annual crop production under semi-arid and subhumid climatic conditions can be increased due to hydrological processe in a moderate number of cases (16% for wheat and 33% for faba bean on average for above-ground biomass and yield) (Dhouib et al., 2024). Positive impacts are mainly observed for higher soil water retention capacity and under semi-arid and dry subhumid climate conditions, with a significant effect of the intra-annual distribution of rainfall in relation to crop phenology.
Dhouib M., Zitouna-Chebbi R., Prevot L., Molénat J., Mekki I., Jacob F. (2022). Multicriteria evaluation of the AquaCrop crop model in a hilly rainfed Mediterranean agrosystem. Agricultural Water Management, 273, 107912, https://dx.doi.org/10.1016/j.agwat.2022.107912
Dhouib M, Molénat J., Prevot L., Mekki M, Zitouna-Chebbi, C et Jacob F.. Numerical exploration of the impact of hydrological connectivity on rainfed annual crops in Mediterranean hilly landscapes. Agronomy for Sustainable Development, 2024, 44 (6), 51 p. ⟨10.1007/s13593-024-00981-5⟩. ⟨hal-04752688⟩
Molénat J., Raclot D., Zitouna R., et al., 2018. OMERE, a long-term observatory of soil and water resources in interaction with agricultural and land management in Mediterranean hilly areas. Vadose Zone journal, 17(1), doi:10.2136/vzj2018.04.0086
How to cite: Molénat, J., Zitouna-Chebbi, R., Dhouib, M., Prévot, L., Mekki, I., and Jacob, F.: Effect of upslope runoff on crop functioning under Mediterranean conditions: an analysis based on a modelling approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17144, https://doi.org/10.5194/egusphere-egu25-17144, 2025.
Abstract _
Crop protection covers are increasingly a necessary tool to face the increasing abiotic stresses under climate change, threatening both fruit quality and yield. Cover systems have been widely studied as they can modify the microclimate and consequently the ecophysiological responses of the crops growing underneath. The effects of covers on the propagation of radiation below the covers is crucial in determining the plant microclimate. Also the training system affects the canopy radiative regime via modifying the plant structural properties. Finally, the meteorological conditions and latitude affect the available radiation, resulting in very context specific modification of the microclimate.
In this study a 3D radiative transfer model (Discrete Anisotropic Radiative Transfer, DART) was used to represent the light propagation across a cherry orchard covered by a rain exclusion net.
The cherry orchard was represented by the repetition in all directions of a single tree, covered by a rain exclusion screen and in absence of covers. The DART scene was characterized by geometrical properties of the tree and the rain exclusion net, measured in the orchard, which were used for model calibration. The trunk and canopy volumes were described as a trapezoids based on trunk diameter and height, and crown dimensions, while the leaves as triangles with a certain leaf angle distribution.
The cherry tree canopy and cover were optically characterized based on spectrophotometric measurements, while the soil based on DART optical libraries. Field measured values of top of the canopy global short wave radiation recorded around noon were used as input for simulation of light propagation. The angles of incident sun rays were determined by DART starting from time (date, local time zone) and scene location. The simulated radiation values obtained at three canopy heights were then were compared with ceptometer measurements performed at the same time. Following, sequence of simulations were run to obtain the spatial and temporal variations in light propagation during the growing season.
To the authors knowledge, this is the first application of a 3D radiative model on covered orchard systems. The proposed approach can give important insights into the effects of canopy covers on the radiative regime across climatic and context specific conditions. Considering the increasing use of covers to protect crops from climate change, the proposed approach may possibly contribute to drive agricultural advisers and farmers to more aware selections of the type of cover, according to their features.
The study was funded by the PRIN CHOICE project (Optimizing CHerry physiOlogIcal performanCE
through the correct choice of multifunctional covers
How to cite: Destefanis, C. and Reyes, F.: Unravelling the effect of tree protection covers on the propagation of radiation within a cherry tree canopy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18534, https://doi.org/10.5194/egusphere-egu25-18534, 2025.
The rhizosphere priming effect (RPE) is a key process where the mineralization of soil organic carbon (SOC) by microorganisms is modified by the presence and activity of plant roots compared to SOC mineralization on bare soil, increasing carbon fluxes from soils to the atmosphere. However, its magnitude in agricultural systems remains uncertain. Moreover, since the RPE is not specifically accounted for in earth system models it is a source of uncertainty in global carbon loss estimates relevant to achieve climate change targets. The PrimeSCale model offers a simple framework to quantify RPE-induced SOC respiration at large spatial scales. Here we aim to estimate the RPE in specific European croplands.
The PrimeSCale model estimates the RPE using root carbon input to the soil derived from the combination of MODIS gross primary production (GPP) and net primary production (NPP) data, root depth distribution, heterotrophic respiration, soil bulk density and soil organic carbon content. A central component of PrimeSCale is the RPE ratio, the relative increase in heterotrophic respiration induced by priming based on literature using living plants. Our analysis of the time period 2010-2020 covers six types of croplands (maize, wheat, oat, barley, legumes, and soy) across Europe at a 5 km resolution, down to a depth of 200 cm. The model outputs include the magnitude of the RPE ratio and RPE-induced SOC loss in these croplands and how they vary within Europe according to climate and crops. Our findings will enhance understanding of the processes behind carbon cycling in managed environments and provide insights for carbon-farming policies to better suit mitigation strategies.
How to cite: Nicolas, B. L., Bernhard, A., Birgit, W., Gustaf, H., Hugues, C., Gwenaëlle, L., Sylvain, M., Matti, K., and Frida, K.: Estimating Rhizosphere Priming in European Agricultural Soils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19302, https://doi.org/10.5194/egusphere-egu25-19302, 2025.
The Paris Agreement commits 197 countries to stabilizing global average surface temperatures at less than 2 °C above pre-industrial levels. Many industrialized nations, including Italy, aim for climate neutrality by 2050 through “net zero” greenhouse gas (GHG) emissions policies, aimed at decarbonizing all the energy intensive sector. In this context, the role of agriculture, forestry, and other land use (AFOLU) sector play an ambiguous role. Challenges include balancing GHG mitigation with food security, addressing synergies with the energy sector (e.g., bio commodities), and leveraging AFOLU as a net sink to offset emissions from other sectors.
Energy system optimization models (ESOMs), as widely used to design cost-optimal decarbonization policies, can be used to determine effective AFOLU management strategies at a national level. Nevertheless, their focus on energy-intensive processes had previously limited detailed AFOLU representation, despite its prominent role in emission mitigation. ESOMs often lack the integration of natural capital constraints, such as land and water availability, as well as the ability to model specific AFOLU commodities like crops, livestock, and forest products. To address this gap, we introduce a novel AFOLU module designed to couple with ESOMs, enabling the formulation of national decarbonization scenarios incorporating a technology-explicit AFOLU representation, biophysical constraints and the possibility to evaluate climate change impacts on the sector.
The AFOLU module tracks GHG emissions from livestock, crops, and bioenergy production while optimizing sectoral contributions to national decarbonization goals. Additionally, it projects the evolution of AFOLU commodities, including shifts in crop types, livestock production, and forest management strategies in response to climate and policy drivers. Finally, it can account for biophysical constraints such as land use limitations, crop yield sensitivity to fertilizer and climate change, and forest absorption potential. The module is designed to be directly fed by the Global Agro-Ecological Zones (GAEZ) database from FAO, allowing for the automatized creation of national instances based on up-to-date geospatial datasets.
To demonstrate the utility of the module, we integrate it with the open-source energy system optimization model TEMOA, which has been validated in Italian case studies and shown coherence with established models like TIMES, and similar in structure to other ESOMs like MESSAGE, and OSeMOSYS. The integrated model evaluates Italy’s national climate mitigation plans, focusing on the interplay between energy and AFOLU sectors, including land competition for bio crop production.
Key outputs of the model include detailed accounting and optimization of AFOLU emissions, land and water use, and cost-effective decarbonization pathways for all the energy intensive sectors. For instance, scenarios explore the potential of organic farming to reduce crop-related emissions, the role of manure management in mitigating livestock emissions, and the benefits of afforestation for carbon sequestration. Preliminary results from the Italian case study reveal critical trade-offs and synergies, such as the tension between bioenergy production and food security, while identifying least-cost pathways to achieve climate neutrality.
This research bridges a critical gap in decarbonization modeling by integrating a flexible AFOLU module with energy systems, offering a reproducible framework for other national applications.
How to cite: Mosso, D., Savoldi, L., and Nicoli, M.: From Crops to Carbon Sequestration: A Technology-Explicit AFOLU Module for Energy Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20520, https://doi.org/10.5194/egusphere-egu25-20520, 2025.
Achieving the food-related UN Sustainable Development Goals, particularly global food security and sustainable agriculture, requires sustainable intensification. This approach strives to close yield gaps by efficiently utilizing land, water, and agricultural inputs, while protecting natural ecosystems. Agriculture stands as the largest global consumer of freshwater, and its demand is expected to rise as a result of Global Change, making the enhancement of water use efficiency crucial for sustainable agriculture.
The Danube River Basin encompasses some of Europe’s most fertile regions, with its wide agricultural plains forming an important part of the continent’s breadbasket. However, agriculture in this region remains largely extensive due to insufficient resource inputs and water limitations. By adopting resource-efficient management (esp. fertilization, irrigation), yield gaps could be closed, thereby contributing to global food security. Nevertheless, in the Danube River Basin—the world’s most international river basin—20 countries and their water-using sectors are in competition for the basin’s freshwater resources.
In this presentation, we share research highlights, primarily from Probst et al. (2024), employing the mechanistic hydro-agroecological model PROMET in the Danube River Basin. PROMET integrates biophysically-based vegetation modelling and dynamic hydrological modelling at a high spatial and temporal resolution (1 km², 1 h). The model concept allows for systematic analyses of agricultural management effects (e.g. fertilization, irrigation) on crop yields, water use efficiency, and water balance through irrigation water withdrawal. This enables the identification of underutilized yield potential and hotspots of inefficient water use, facilitates understanding of inter-sectoral economic trade-offs (e.g. with hydroenergy production), pinpoints ecological impacts, and identifies opportunities for more efficient land management. Thus, this modelling approach offers valuable decision-support for both the agricultural and other sectors in the Danube River Basin.
References:
Probst, E., Fader, M. & Mauser, W. (2024): The water-energy-food-ecosystem nexus in the Danube River Basin: Exploring scenarios and implications of maize irrigation. Science of The Total Environment 914: 169405. https://doi.org/10.1016/j.scitotenv.2023.169405
How to cite: Probst, E., Fader, M., and Mauser, W.: Towards Water-Efficient Agriculture in the Danube River Basin: Insights from Hydro-Agroecological Modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21495, https://doi.org/10.5194/egusphere-egu25-21495, 2025.
How to cite: Saldivia Gonzatti, I., Biemans, H., and Paparrizos, S.: Integrating Seasonal Forecasts with Process-Based Crop Modeling for Responsive Adaptation to Food Risks in Sub-Saharan Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16514, https://doi.org/10.5194/egusphere-egu25-16514, 2025.
Paddy rice cultivation is a major source of methane emissions, a greenhouse gas with a relatively short atmospheric lifetime. Reducing methane emissions from rice fields offers significant potential for near-term climate change mitigation. Large rice-producing countries in tropical and subtropical regions, such as China, India, and Vietnam, commonly adopt multiple cropping systems to maximize rice yields. However, according to the IPCC Guidelines for National Greenhouse Gas Inventories (2019), continuous rice cultivation on the same land over consecutive seasons can more than double methane emissions compared to intermittent cropping. The anticipated rapid growth in the rice-consuming population, particularly in Asia, will likely drive further increases in rice demand and production. To mitigate methane emissions from rice cultivation, additional strategies are required beyond existing and emerging agronomic practices such as crop improvement and alternate wetting and drying. Here we propose a season-spatial redistribution of rice cultivation as an immediately effective strategy for reducing rice methane emissions. Using emission factors for various cropping patterns from the IPCC 2019 Guidelines and a temporal-spatial cropland cover database developed by the Taiwan Agricultural Research Institute (TARI), we computed the methane-minimizing season-spatial reallocation for a two-year period (four cropping seasons) in all townships of Yunlin, Taiwan's key rice-growing county, while keeping aggregate harvested area constant. Results indicate that the maximum methane mitigation potential for a single township could reach reductions of 12.63% in the first year, 44.31% in the second year, and 32.80% over the entire two-year period compared to scenarios without such reallocation. This reallocation strategy aligns with existing policies aimed at reducing irrigation water use and promoting self-sufficiency in non-rice staple crops. It can be implemented without incurring additional costs for subsidies or the establishment of new policies. The TARI cropland cover database, which incorporates Sentinel-2 satellite imagery, aerial photographs, and ground truth data analyzed using Geographic Information System (GIS) technologies, provides a detailed season-spatial map of crop cultivation in Taiwan, where two rice cropping seasons are feasible annually. Similar to the Crop Data Layer (CDL) database maintained by the USDA, the TARI database was originally designed for crop production forecasting. However, our study demonstrates its additional utility in informing policies to advance agricultural sustainability. With the increasing accessibility and affordability of digital imaging technologies, the proposed season-spatial reallocation approach could be adopted by other countries with multiple rice-cropping systems, complementing agronomic efforts to cut methane emissions from rice cultivation.
How to cite: Lee, H.-L. and Zhang, Y.-T.: Season-spatial redistribution of rice cultivation as an immediately effective strategy for cutting methane emissions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19626, https://doi.org/10.5194/egusphere-egu25-19626, 2025.
How to cite: Safitri, L., Valadares Galdos, M., Pradiko, I., Comber, A., and Challinor, A. J.: Assessing potential climate smartness of agronomic practices in oil palm plantations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20785, https://doi.org/10.5194/egusphere-egu25-20785, 2025.
