Climate change will reshape agricultural water consumption in Central Asia, yet the impacts on crop water footprints remain largely unquantified. The Upper Syr Darya Basin, a critical agricultural region where irrigated cotton and other water-intensive crops depend on a shared river system, faces accelerating water stress as climate variables shift dramatically by 2100. To assess future agricultural sustainability and inform transboundary water allocation decisions, this study projects spatiotemporal changes in crop water footprints under alternative climate scenarios. The ACEA crop water productivity model is implemented and forced with downscaled climate projections to quantify how variations in climate variables will alter green (rainfed) and blue (irrigated) water footprints across cropping systems. Expected outcomes include spatial-temporal maps identifying agricultural regions where future water scarcity will intensify, shifts in crop water consumption patterns, and areas of heightened vulnerability to climate-driven water stress. These projections provide critical evidence for climate-adaptive agricultural planning and transboundary water governance, enabling policymakers to anticipate sectoral water competition and design sustainable irrigation management strategies under alternative climate futures.

How to cite: Baviskar, G., Hogeboom, R. J., and Krol, M. S.: Mapping future crop water footprints under alternative climate futures in Upper Syr Darya Basin, Central Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1820, https://doi.org/10.5194/egusphere-egu26-1820, 2026.

Drip fertigation can conserve water in arid and semi-arid regions across the world. Recent field studies have shown that drip fertigation can also mitigate emissions of the powerful greenhouse gas nitrous oxide (N₂O). However, existing process-based models have not been evaluated for simulating N₂O emissions under drip fertigation systems, limiting our capacity to predict the environmental performance of these irrigation technologies under future climatic conditions. Here we assessed the performance of the Canadian version of the DeNitrification-DeComposition model (DNDCv.CAN) in simulating N₂O emissions from drip-fertigated maize systems. The model was calibrated and validated using a comprehensive two-year dataset from a field experiment in Spain that included subsurface and surface drip irrigation with four nitrogen (N) fertigation treatments: ammonium sulfate (AS), AS with nitrification inhibitor DMPP (AS_DMPP), calcium nitrate (CN), and a control without N (N0). The calibrated model accurately simulated crop yield (RMSE < 1,300 kg ha⁻¹), grain N content (RMSE < 12 kg N ha⁻¹), and cumulative N₂O emissions (RMSE < 0.03 kg N ha⁻¹), with R² values of 0.6-0.8 and d-index above 0.8. Under future climate scenarios, both surface and subsurface drip irrigation will likely experience yield reductions and increased N₂O emissions. Subsurface drip showed slightly lower yield losses but higher N₂O emissions compared to surface drip irrigation. CN-based fertilizer integrated with subsurface drip performed best, achieving both higher yields and lower N₂O emissions. Increasing heat stress is likely the primary factor driving the yield losses. Adaptation strategies focused on mitigating heat stress should be explored to support the use of drip fertigation systems in arid and semiarid regions.

How to cite: Liu, H., Guardia, G., Grant, B., Smith, W., Qian, B., Olesen, J., and Abalos, D.: Modelling N2O Emissions and Yield Responses under Drip Fertigation and Future Climate Scenarios with DNDCv.CAN, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5362, https://doi.org/10.5194/egusphere-egu26-5362, 2026.

Crop production exerts substantial pressure on the Earth system and frequently exceeds environmental boundaries, including those related to greenhouse gas emissions, nitrogen use, biomass appropriation, and freshwater use.  International trade redistributes these production related impacts, with high-income countries often externalizing a significant share of the environmental pressures associated with their food consumption.

Using 2020 detailed international food trade data (FAOSTAT) combined with our crop-specific assessment of production sustainability in 2020 (https://doi.org/10.5194/egusphere-egu25-2526), we quantify the environmental unsustainability embedded in international trade flows of crop commodities. We then explore the potential effects of demand-side changes, namely dietary shifts, on global environmental sustainability. This analysis highlights the importance of addressing sustainability from the demand side and provides policy-relevant insights based on the most recent, crop-specific assessment of environmental sustainability 

How to cite: Guilbert, M. and Dalin, C.: How far are global croplands from environmental sustainability: from production to consumption perspectives, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12851, https://doi.org/10.5194/egusphere-egu26-12851, 2026.

Advancing the circular bioeconomy requires climate-friendly valorization of locally available biomass. Organic multispecies grasslands provide multiple outputs, including leaf protein concentrate (LPC) for feed and biogas for energy, while supporting biodiversity, weed suppression, and carbon sequestration.

This study assessed the climate impacts of Protein, Pollinator, and Energy grassland mixtures under different cutting regimes using life cycle assessment (LCA) with the ReCiPe 2016 method, applying both economic allocation and system expansion approaches. The cradle-to-gate system boundary included grassland cultivation, transport, and processing in the biorefinery. Grassland carbon footprints were low, roughly 50–100 kg CO₂ eq per ton of dry matter. LPC from four-cut grass mixtures had a baseline carbon footprint around 1600 kg CO₂ eq per ton DM with no allocation, and reductions possible depending on co-product use. Biogas from energy grass mixtures had a climate impact of roughly 100–400 kg CO₂ eq per 1000 m³.

Climate performance of biorefinery products was strongly influenced by grass yields, protein content, allocation methods, and downstream valorization strategies. These findings highlight the potential of organic multispecies grasslands to provide LPC as a sustainable alternative to soy-based feed and biomethane as a renewable energy source while mitigating climate impacts.

How to cite: Hashemi, F., Mogensen, L., Eriksen, J., Ambye-jensen, M., Andrade, T. A., Møller, H. B., Jørgensen, U., Wahid, R., Dupont, Y. L., Cong, W., Zhen, H., Dorca-Preda, T., and Knudsen, M. T.: Assessing the carbon footprint of leaf protein concentrate and biomethane from organic multispecies grasslands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14447, https://doi.org/10.5194/egusphere-egu26-14447, 2026.

Food consumption drives environmental pressures by shaping global agricultural production systems and international trade patterns. A growing body of literature has quantified consumption-based food footprints by reallocating production-based pressures, such as greenhouse gas (GHG) emissions, land use, and nitrogen application, to final consumers, highlighting the role of global demand in shaping agricultural impacts beyond national borders (Hertwich & Peters, 2009; Weinzettel et al., 2013; Henders et al., 2015; Oita et al., 2016).  However, existing approaches differ in their treatment of GHG emission sources, spatial characterization of production systems, and temporal consistency, often addressing individual pressures in isolation. Developing harmonized frameworks that consistently integrate multiple agricultural GHG emission sources and link them to food consumption through trade is therefore essential for fully assessing the sustainability of the agri-food system. Here we quantify the carbon footprint of food consumption by combining spatially-explicit (5-arc-minute resolution) agricultural GHG emission sources -including land-use change building on prior work by the authors, farm-level production processes (synthetic fertilizer and manure application, peatland drainage, and rice paddy methane), and transport- for 24 crop types with international trade data (FAOSTAT). We also quantify livestock-related emissions derived from feed production (crops and grass) and reallocate these to consumption via trade. Both results are reported at the global scale across four reference years between 2000 and 2020. By reallocating production-based emissions to final consumers through a consumption-based framework, we link global food demand to the geographic origin of agricultural GHG emissions, thereby enabling an analysis of spatial patterns and temporal trends of the carbon footprint of food demand worldwide.

How to cite: Benitez, B., Dalin, C., and Guenet, B.: Consumption-based GHG footprint of global food systems (2000–2020), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14656, https://doi.org/10.5194/egusphere-egu26-14656, 2026.

Assessing the effectiveness of agricultural nitrogen policies requires capturing the interactions between socio-economic decision-making, crop production, and catchment hydrology. Our study looks at the impact of uniform per hectare payments to farmers for reducing nitrogen (N) input to soil in German agriculture on groundwater quality in Rhine and Elbe catchments. To evaluate nitrogen surplus reductions and resultant nitrate leaching at the catchment scale under different subsidy schemes and climatic scenarios, we use an integrated modeling framework linking an agro-economic model (SNAg) – based on simulated yield data from the dynamic global vegetation, hydrology and crop model „Lund-Potsdam-Jena managed Land (LPJmL)“ - with a process-based water quality model (mQM). Our approach captures farmer responses to policy instruments and spatial heterogeneity in productivity, nitrogen use efficiency and hydrological processes.

Specifically, we compare a moderate subsidy scheme characterized by high participation and moderate nitrogen surplus reductions with a tighter scheme featuring lower uptake but stronger reductions. This allows us to examine how different spatial distributions of nitrogen reductions affect nitrate leaching and exceedances of groundwater quality thresholds. In the modeled scenarios, the share of catchments exceeding a nitrate leaching limit of 12 mg L⁻¹ in 2030 is reduced by 48% under the moderate subsidy scheme and by 59% under the tight scheme, relative to the no-policy baseline.

Our results further show that spatial heterogeneity in catchment structure plays a stronger role than interannual climatic variability in shaping nitrate export. Consequently, policy instruments calibrated solely on agricultural N input risk producing outcomes that are misaligned with hydrological processes. For instance, not taking spatial differences in nitrogen retention into account can lead to misleading assessments of policy effectiveness.

This work highlights the importance of integrating agro-economic, biogeophysical and hydrological properties in models to inform policy design capable of accommodating both spatial variability in agricultural systems and the biogeochemical complexity of catchments. By explicitly modeling outcomes across a range of scenarios and including farmers decision-making, we provide insight into how agricultural policy effectiveness can be evaluated and enhanced under global change conditions.

How to cite: Jurak, K., Musolff, A., Kumar, R., and Müller, B.: Tracing agricultural nitrogen policy impacts on groundwater quality through an integrated modeling approach , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20431, https://doi.org/10.5194/egusphere-egu26-20431, 2026.

The socioeconomic development in China has fundamentally reshaped the patterns of grain supply, demand, and interregional flows, with significant implications for both national food security and global environmental sustainability. In this study, we constructed an integrated analytical framework to comprehensively assess and forecast the spatial dynamics of grain flows and their associated environmental impacts under multiple future consumption scenarios. Our findings reveal that by 2040, domestic grain supply will continue to fall short of demand, leading to sustained increases in interprovincial flows, alongside a decelerating trend in overseas grain inflows. Crucially, the environmental footprints along the grain flows, particularly in terms of virtual water and virtual greenhouse gas emissions are increasingly decoupled from flow intensity. This decoupling effect is strongly linked to dietary shifts. The more balanced and health-oriented the diet, the stronger the decoupling effect characterized by increased interprovincial flows but reduced environmental footprints, and reduced overseas flows but increased environmental footprints. These results underscore the environmental trade-offs embedded in dietary transitions, and call for systematic integration of environmental impact assessments into food and nutrition policies. Achieving a sustainable food system requires coordinated efforts in both total quantity control and dietary structural optimization.

How to cite: Fang, G., Sun, X., and Bogaert, J.: Dietary transitions intensify the decoupling between China’s grain flows and their environmental footprints, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20863, https://doi.org/10.5194/egusphere-egu26-20863, 2026.

The global rise in human population has substantially increased reliance on agricultural landscapes to meet food security demands. At the same time, conventional rural agriculture is a major contributor to global anthropogenic greenhouse gas (GHG) emissions. Climate change, coupled with the increasing frequency and intensity of extreme weather events, has intensified pressure to develop more sustainable, resilient, and environmentally friendly agrifood systems. Over recent decades, urban and peri-urban agriculture (UPA) has gained increasing attention as a potential strategy to supply food to growing urban populations while delivering a range of environmental, social, and economic co-benefits. Despite growing scientific and policy interest, multiple meta-analyses indicate that the environmental impacts of UPA systems remain poorly quantified, particularly with respect to their contributions to GHG emissions and their potential role in achieving net-zero climate targets. Evidence regarding the GHG mitigation potential of UPA systems remains mixed. Some studies highlight reductions in emissions due to shorter rural-to-urban supply chains (“food miles”) and enhanced carbon sequestration associated with increased urban green space. In contrast, other studies report substantially higher carbon dioxide (CO₂) emissions per unit of food produced in urban agricultural systems compared to conventional rural agriculture. Here, we synthesize insights from an extensive global literature review of UPA systems with the objectives of: (1) clarifying key definitions and characteristics of UPA systems across spatial and temporal scales; (2) quantifying their reported global environmental impacts, such as effects on GHG emissions; (3) identifying the major vegetation types cultivated and assessed within UPA systems; and (4) evaluating existing research and knowledge gaps in process-based crop simulation models and life cycle assessment (LCA) approaches used to estimate food production and GHG emissions in UPA contexts. This synthesis aims to advance understanding of the carbon footprint reduction potential of UPA systems and their interactions with climatic, social, political, and economic drivers, while informing strategies to strengthen their role as effective nature-based solutions within sustainable urban food systems.

How to cite: Javed, A., Isaac, M., Martin, A., and Arhonditsis, G.: Eating food from my backyard: The role of urban and peri-urban agriculture in greenhouse gas reduction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12688, https://doi.org/10.5194/egusphere-egu26-12688, 2026.

Agricultural decision-making depends on tools that quantify whole-system tradeoffs under climate and market volatility, but significant gaps remain in understanding how these tools perform at the farm scale under real-world constraints. For dairy production systems, transitioning from corn silage monoculture to diversified forage systems offers a pathway toward greater sustainability under global change. However, market and policy constraints, including crop insurance structures, continue to challenge farmers’ willingness to adopt adaptive strategies. To address these barriers, we evaluated six cropping systems: Corn silage as a baseline, corn followed by a cover crop (CCC), corn interseeded with alfalfa (CAL), alfalfa (ALF), intermediate wheatgrass (IWG), and multi-species pasture (PAS) in a two-year field experiment in Wisconsin, USA. Environmental sustainability was quantified through depth-resolved soil C, N, P, K stocks using equivalent soil mass, bulk density, hydrological metrics, and microbial diversity, integrated into a composite soil health index (SHI). Economic outcomes included net returns on a forage basis, potential milk production, risk-adjusted metrics under historical price variability and stress scenarios, and an incremental cost-effectiveness ratio analysis.

Corn-based systems maximized energy-corrected milk per hectare and minimized land use per cow but exhibited the lowest SHI and greatest downside risk under price shocks. CCC and PAS improved SHI and reduced costs relative to Corn, while ALF delivered high per-cow profitability with limited soil health gains. CAL provided intermediate returns with greater variability, and IWG offered strong soil benefits at higher cost. These results reveal a fundamental tradeoff: Corn-centric systems prioritize short-term yield and land efficiency, whereas perennial systems enhance long-term soil resilience and economic stability. This is because pasture-based diets require no concentrate supplementation, reducing feed costs and input dependency, while all corn-based systems rely heavily on concentrates to sustain high milk yields, which increases their vulnerability to market shocks. Greater input requirements for corn further compound this risk. In our study, pasture systems offered profitable alternatives to corn silage diets and improved risk management, which can reduce reliance on crop insurance. By integrating biophysical indicators with risk-aware economics, our framework identifies diversified forage strategies as adaptive pathways that enhance resilience to climate variability and economic shocks. While these findings reflect a single soil type, the approach provides a scalable method for evaluating tradeoffs in agricultural systems under global change.

How to cite: Wiesner, S., Gajbhiye, S., Freedman, Z., and Stoy, P.: Economic and Environmental Tradeoffs of Forage Systems in Climate-Adaptive Dairy Production, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3843, https://doi.org/10.5194/egusphere-egu26-3843, 2026.

Agricultural nitrogen (N) pollution poses major challenges for sustainable food systems, yet policy assessments often neglect feedbacks between biophysical crop responses and economic market dynamics. We couple the process-based global crop model LPJmL with the spatially explicit agricultural trade model SIMPLE-G using crop- and location-specific nitrogen response functions for yields and N leaching derived from extensive LPJmL simulations. This framework is used to assess the effects of a regional N tax targeting highly polluting production systems while accounting for market-mediated spillover effects.

The tax substantially reduces N pollution in targeted regions with comparatively small yield losses, reflecting the non-linear response of leaching to fertilizer inputs. Lower fertilizer demand in taxed regions reduces global fertilizer prices, inducing yield-enhancing input increases elsewhere that raise production with limited additional pollution. At the global scale, total fertilizer use declines and food prices may decrease under inelastic fertilizer supply assumptions, consistent with empirical evidence, while production remains largely stable. Although targeted farmers experience income and production losses, non-targeted regions can benefit from higher output and incomes. A comparison with a uniform, economy‑wide N tax shows that a location-specific targeted tax achieves similar pollution reductions at substantially lower economic cost. The targeted tax is based on a universal, generalizable, and easily applicable formula. Our results demonstrate the importance of integrating crop-model-informed response functions into economic analyses and challenge the notion that environmental taxation necessarily increases food prices.

How to cite: Müller, C., Breier, J., Haqiqi, I., Hertel, T., and Gerten, D.: Crop-model informed economic analysis of nitrogen tax effects on food production, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7806, https://doi.org/10.5194/egusphere-egu26-7806, 2026.

The detrimental impacts of climate change to global agriculture are well documented, but the financial consequences of these climate-driven crop losses remain underexplored. Here, we quantify the economic damages from heat and drought-induced crop losses in maize, wheat, and soybean using a statistical modeling approach and attribute them to individual emitters. Between 2000 and 2019, climate-induced yield impacts resulted in global economic losses totalling roughly $400 billion, corresponding to an average annual loss of about 0.06% of global GDP. Least-developed countries experienced GDP-normalized losses 2.5 times higher than those of rich nations (0.10% versus 0.04% of GDP). Aggregated over 2000–2019, CO2 emissions from the world’s richest 10% contributed to approximately $113 billion in financial losses from associated crop yield declines. This represents about 55% of the total economic damages across all income groups and is over eight times greater than the contribution from the poorest 50%. Attributing damages to the economic activities of Carbon Major companies, we estimate that their CO2 emissions caused about $170 billion in financial losses from associated agricultural yield declines. We also show that global annual losses could quadruple between 2019 and 2070 under a high-emissions scenario (SSP3-7.0), while a sustainable development pathway (SSP1-2.6) could avoid an estimated $40 billion of these damages. By linking climate-induced yield losses to financial outcomes, we provide a more tangible understanding of climate risks from food system impacts and strengthen the basis for loss and damage claims.

How to cite: Lesk, C., Hwong, Y.-L., and Kornhuber, K.: The Financial Toll of Climate-Induced Crop Losses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8148, https://doi.org/10.5194/egusphere-egu26-8148, 2026.

Ensuring global food security while limiting environmental impacts and supporting the economy requires a multidisciplinary perspective in analysis. Crops, which are the foundation for providing humans with food and livestock with feed, significantly impact both biophysical and human systems over time. Thus, we have conceptualised the crop production system into biophysical, human, and integrated systems and assembled relevant datasets for these systems. The dataset choices were gathered through surveys from expert opinions and compiled at a 5-arcminute resolution from 1992 to 2020. Furthermore, we analysed the global gridded dataset characteristics by utilising the Two-step Self-Organising Map method. These characteristics will be grouped into clusters showing differences and similarities in crop production across the globe using hierarchical cluster analysis. The clusters will be further analysed based on the diversity within each country and the changes in clusters over time. The results of this research are expected to contribute to the understanding and communication of global crop production from a socio-ecological perspective from 1992 to 2020. More research is encouraged to validate the conceptualisation and build upon these characteristics to further analyse the system, potentially leading to the creation of more relevant and sustainable policies.

How to cite: Viriyaroj, B. B.: Unveiling Characteristics of the Global Crop Production System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11859, https://doi.org/10.5194/egusphere-egu26-11859, 2026.

Agriculture has major impacts on the environment: it is the first cause of biodiversity loss, freshwater withdrawals and nutrient flows disruption, as well as an important source of GHG emissions. At the same time, it is an essential human activity to sustain the life of current and future generations. Thus, a drastic increase in food systems sustainability is crucial in the coming years. To address this huge challenge, a mix of local- and global-scale studies assessing impacts and exploring possible solutions are needed. At the global scale, studies that are spatially-explicit and account for multiple impacts are particularly precious. Such studies often focus on hotspots of environmental degradation and tend to overlook the analysis of existing best practices.

In this work, we instead look at bright spots, that we define as regions where agricultural production is relatively important but does not cause the exceedance of local environmental sustainability thresholds. Making use of a circa 2020, 5 arcmin resolution dataset on global crops distribution and four associated environmental sustainability indicators (biodiversity loss, freshwater stress, excess nitrogen application and GHG emissions), we derive bright spot maps for 46 crop categories including individual cereals (wheat, maize, rice, barley, …) and other major crops (soybean, rapeseed, …).

We then train a random forest classification model to identify bright spots based on a number of land-use, biophysical and socio-economical variables. Using feature importance metrics such as SHAP values, we identify key characteristics of these regions.

Further, we simulate several prospective scenarios assuming the widespread adoption of the best practices identified, such as allocating more land to natural habitat, reducing irrigation and fertiliser use, or establishing crop rotations. We quantify the consequences of these scenarios in terms of agricultural production loss and sustainability increase, and estimate their ability to feed the human population by combining them with different human diet scenarios.

How to cite: Gamblin, J., Guilbert, M., Makowski, D., and Dalin, C.: Characterisation of global cropland bright spots, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3839, https://doi.org/10.5194/egusphere-egu26-3839, 2026.

Agriculture contributes approximately 80% of ammonia (NH₃) emissions globally and in the United States, with major loss pathways including animal housing, manure storage, and land application of manure and synthetic fertilizers. Although NH₃ is not itself a greenhouse gas, it is a key precursor of nitrous oxide (N₂O), a potent greenhouse gas. Current housing NH₃ emission models, including those implemented in the open-source Ruminant Farm Systems (RuFaS) model, rely on generalized parameters that may not adequately represent region- and management-specific variability, particularly in naturally ventilated barns. The objective of this study was to generate high-temporal resolution housing NH₃ concentration data using IoT-based sensors to inform refinement of housing NH₃ modeling in RuFaS toward more context-specific simulations. Measurements were conducted in a naturally ventilated free-stall dairy barn housing approximately 600 lactating cows with solid flooring and sand bedding in Harford, New York, USA. Manure was mechanically scraped 3 times per day. A total of 7 electrochemical NH₃ sensors (Cynomys, Arenzano, Italy) were deployed evenly throughout the barn at a height of 2 m. Ammonia concentrations were continuously monitored from April 2025 to January 2026 at 10-min intervals. Hourly averages were used to assess diurnal patterns, and monthly averages were calculated to evaluate seasonal trends. Indoor temperature was monitored concurrently. Indoor temperature increased from 12.04±1.52 °C in April to 24.67±0.46 °C in July, before declining to 4.32±0.89 °C in January. Hourly NH₃ concentrations ranged from 0.447±0.497 ppm to 0.714±0.369 ppm, with an overall mean of 0.554±0.088 ppm. Minimum concentrations occurred around 12:00, while maximum concentrations were observed at 23:00. Monthly mean NH₃ concentrations ranged from 0.413±0.090 ppm to 0.752±0.618 ppm, with an overall mean of 0.594±0.133 ppm; the lowest and highest monthly averages occurred in April and August, respectively. These concentration levels are generally consistent with ranges reported in the literature for dairy housing. These measurements provide a high-throughput dataset capturing diurnal and seasonal variability of housing NH₃ concentrations in a naturally ventilated dairy barn. While concentration data alone are insufficient to directly calibrate housing NH₃ emission models, the observed temporal patterns establish essential boundary conditions for subsequent emission estimation when combined with ventilation rates. In this context, the dataset supports future derivation of NH₃ emission fluxes needed to evaluate and calibrate housing NH₃ submodules in whole-farm simulation frameworks such as RuFaS. Ongoing work will integrate ventilation estimates to quantify emission fluxes.

How to cite: Hu, H., Perez, M., Oliver, J., Jacome, A., Scattolini, F., Giordano, J., and Reed, K.: High-temporal-resolution ammonia concentration measurements in a naturally ventilated dairy barn to inform RuFaS housing ammonia prediction refinement, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8438, https://doi.org/10.5194/egusphere-egu26-8438, 2026.

Dietary patterns in India are increasingly shifting toward higher consumption of animal-based foods, with implications for climate change. However, dietary choices in a country with widespread economic disparities are influenced by socioeconomic factors. While previous studies have examined the role of income (or expenditure) on food consumption, their contributions in driving dietary GHG are not well explored. Here, we link literature-derived GHG emission factors for food items with food consumption data obtained from three rounds of the Household Consumer Expenditure Survey (1999-00, 2011-12, 2022-23), further differentiated by deciles based on monthly per-capita expenditure.

The total dietary GHG emissions increased by 25% from 449 Mt CO₂eq in 1999-2000 to 601 Mt CO₂eq in 2022-2023 for India. Dietary GHG emissions are unevenly concentrated among deciles, with the top three expenditure deciles contributing comparably to emissions (30%; 2022) as the lower three deciles (29%; 2022), despite accounting a much smaller section of the population. A clear pattern emerges of higher deciles exhibiting a significantly greater share of animal food emissions. In 2022, animal food emissions accounted for 46% of total dietary emissions for the tenth decile compared to 37% in the lowest decile.

A similar influence of expenditure is also observed in dietary footprints. In 2022, the per-capita GHG emissions ranged from 0.9 to 1.9 kg CO₂eq/day across deciles. Dietary footprints shifted from whole grains (35% to 17%) toward animal-based foods (23% to 33%) from the lowest to highest deciles. Additionally, decile contributions differed spatially, with some states dominated by lower-decile emissions and others by upper-decile emissions. Overall, the dietary GHG intensity increases with rising per-capita expenditure, highlighting the need for climate and nutrition policies that explicitly account for socioeconomic heterogeneity.

How to cite: Yadav, S. and Balasubramanian, S.: Expenditure–Driven Patterns in India’s Dietary GHG Emissions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20094, https://doi.org/10.5194/egusphere-egu26-20094, 2026.

Understanding vineyard scale microclimate variability is essential for adapting viticulture to climate change and increasing water scarcity. This study applies a high-resolution microclimate modelling framework to assess future irrigation requirements in two Mediterranean vineyard living labs, in the Douro Region and Alentejo. The approach integrates the NicheMapR microclimate model, hourly local meteorological observations, ERA5-Land reanalysis, and a high-resolution Digital Elevation Model to generate climate variables at 10m spatial resolution. Local station data are used to bias-correct ERA5-Land through quantile mapping, while topographic effects (elevation, slope, aspect, shading and horizon angles) are explicitly represented via the Digital Elevation Model. The resulting 10 m microclimate outputs are then used to bias-correct EURO-CORDEX regional climate model ensembles, producing vineyard-specific future climate projections. These climate datasets are subsequently used in the STICS crop model to simulate vineyard water balance and irrigation requirements. Irrigation needs are assessed for four climate scenarios (RCP4.5 and RCP8.5, mid- and end-century) under four water stress levels (20%, 40%, 60% and 80%). Results show increasing irrigation demand and variability under higher radiative forcing, with distinct responses between the two vineyards reflecting differences in local microclimate and atmospheric demand. In addition, viticulture climate extreme and bioclimatic indices are derived at the 10m scale, providing insights for vineyard-scale irrigation planning and climate adaptation. Differences between the Douro and Alentejo vineyards emphasise the role of local microclimate in modelling irrigation needs, reinforcing the importance of site-specific adaptation strategies. This work highlights the value of combining microclimate modelling, crop modelling, and bias-corrected climate projections to support sustainable vineyard management under future climate change.

Acknowledgements: Research funded by Vine & Wine Portugal–Driving Sustainable Growth Through Smart Innovation, PRR & NextGeneration EU, Agendas Mobilizadoras para a Reindustrialização, Contract Nb. C644866286-011. The authors acknowledge National Funds by FCT – Portuguese Foundation for Science and Technology, under the projects UID/04033/2025: Centre for the Research and Technology of Agro-Environmental and Biological Sciences (https://doi.org/10.54499/UID/04033/2025) and LA/P/0126/2020 (https://doi.org/10.54499/LA/P/0126/2020).

How to cite: Fonseca, A., Cruz, J., Fraga, H., Andrade, C., Valente, J., Alves, F., Neto, A., Flores, R., and Santos, J.: Assessing future irrigation needs in vineyard living labs through microclimate modelling and climate projections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5656, https://doi.org/10.5194/egusphere-egu26-5656, 2026.

In view of increasing climate pressure and population growth, ensuring food security while progressing towards carbon neutrality has become a key challenge for agricultural development. Although cropping pattern optimization has been widely explored, most existing studies focus on single objectives or static configurations and rarely incorporate long-term cropping dynamics and stakeholder preferences into a unified decision framework, limiting their applicability in agricultural management. This study proposes a hybrid framework that integrates remote sensing data, agricultural systems modeling, life cycle assessment, and multi-objective optimization to identify optimal cropping patterns based on stakeholder preferences. The approach aims to maximize the yield, profitability, and carbon sequestration potential of corn and soybeans while minimizing associated carbon emissions in the typical black soil region of Northeast China. The results show that between 2008 and 2022, both continuous corn cultivation and corn–soybean rotation systems expanded, with continuous corn cultivation accounting for 60–75% of the total cultivated area, whereas continuous soybean cultivation declined steadily. Spatially, most cultivation patterns exhibited a clear northward shift. Overall, the results suggest that continuous corn cultivation can offer the most effective compromise between food production, carbon sequestration, and economic returns, provided that strict measures to reduce emissions are implemented. Among all rotation strategies, the two-year corn and one-year soybean rotation is the most effective in mitigating the adverse effects of continuous cropping while maintaining a balanced food–carbon–profit performance. In contrast, soybean cultivation offers notable environmental benefits but is constrained by relatively low yields and limited economic returns, underscoring the need for targeted optimization measures. This study provides actionable insights for designing sustainable crop patterns that balance agricultural productivity with climate mitigation goals.

How to cite: Zuo, L., Liu, G., Su, X., Volk, M., Witing, F., Wan, L., Zheng, S., and Luo, K.: Optimizing cropping patterns under emission reduction constraints: Balancing food production, carbon sequestration, and profit, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6627, https://doi.org/10.5194/egusphere-egu26-6627, 2026.

Wheat stands as one of the most important staple crops worldwide. However, the vital role of this crop has been increasingly challenged in Lebanon, in recent years by multi-factorial crises from socio-economic, political, security and climate factors, threatening agricultural stability and food supply. Consequently, monitoring wheat production is crucial for managing import and export activities, developing effective policies, achieving resilient agricultural development, and ensuring food security. This study provides the first national, multi-year monitoring of wheat area and production in Lebanon (2017-2025), linking satellite observations with crisis impacts. We conducted a multi-temporal supervised classification from 2017-2018 to 2024-2025 seasons, usinging Sentinel-2 optical images and Random Forest classifier. We estimated wheat area based on a random stratified sample achieving an overall accuracy of 87%. Interannual changes were then related to major crises and input-price dynamics. Wheat area increased during 2019-2021 but dropped sharply in 2021-2022 as subsidies weakened and input costs surged. Indeed, during the transition toward economy dollarization, the computed indicator of production cost expressed in USD peak in 2021-2022 and then ease consistently with the 2022-2023 area rebound reaching the highest level observed during the study period. In 2023-2025, the crop area decreased again dramatically (-34% in 2023-2024 and -38% in 2024-2025) in relation to the conflict with Israel and associated widespread displacement of population that likely constrained field access and reduced sowing, particularly in southern Lebanon. The derived wheat cover maps were then used as input of the agronomic model AgriCarbon-EO to assess biomass and grain yields variability.

How to cite: Ibrahim, M., Faour, G., Le Page, M., Montginoul, M., Al Bitar, A., Ceschia, E., Komati, B., and Jarlan, L.: Wheat in Crisis: Variability of Wheat Area in Lebanon 2017-2025, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6714, https://doi.org/10.5194/egusphere-egu26-6714, 2026.

Rising food demand and intensifying climate stresses are putting growing pressure on African cereal systems. In Morocco, maize is a staple crop for smallholder farmers, yet it remains highly vulnerable to rainfall variability and water scarcity. To address these challenges, model-guided adaptation practices offer a promising pathway to enhance the resilience and sustainability of maize production under future climate conditions. The APSIM Next Generation model was calibrated and validated for irrigated maize in the Souss-Massa region in Morocco using data from three growing seasons (2022–2024), under varying levels of deficit irrigation and nitrogen supply. Adaptation strategies were then evaluated under different planting dates using multi-model climate projections. The model showed good accuracy in both calibration and validation for simulating maize phenology (R² up to 0.91; RMSE = 0.5–1.1 days), leaf area index (R² = 0.96/0.89; RMSE = 0.32/0.49), soil water content (R² = 0.95/0.89; RMSE = 4.70/8.33 mm), and above-ground biomass (R² = 0.97/0.95; RMSE = 1.25/1.01 t ha^-1). Nitrogen dynamics were reasonably reproduced, showing moderate accuracy for soil nitrogen and high precision for nitrogen uptake. Under full irrigation and nitrogen supply, biomass declines by 2–6% by mid-century and 9–15% by late century, reaching 30% losses under severe resource limitation. Seasonal irrigation inputs increase by about 3–8% by mid-century and 9–25% by late century across scenarios, with peaks shifting later into hotter months. Early planting shifts irrigation demand into cooler periods and increases final biomass by 6%, with maximum gains observed under 75% ETc and nitrogen application. Variance decomposition reveals a shift from management-driven variance (sowing date and N-fertilizer 30% at baseline) to rainfall dominance by mid-century (SSP2-4.5) and temperature dominance by late century (SSP5-8.5 > 50%), with increasing higher‑order interactions. Biomass production‑risk analysis shows that full N with ≥75% ETc maintains high final above-ground biomass (75% probability at baseline; 50% under SSP2‑4.5 late‑century; 39% under SSP5‑8.5 late‑century), while early sowing provides a modest, diminishing buffer by late century as heat and drought intensify. (+2–20 percentage points). Adequate nitrogen supply, moderate irrigation, and earlier sowing are recommended to sustain final biomass in the near term, while heat-tolerant varieties are required for long-term silage maize production in the Souss-Massa region.

How to cite: Benaly, M. A., Zhao, G., Kharrou, M. H., Brouziyne, Y., Chen, B., Mamassi, A., EL Janyani, O., Tian, Q., Chehbouni, A., and Bouchaou, L.: Optimizing maize irrigation and fertilization management with APSIM Next Generation for current and future climate scenarios under semi-arid conditions in Morocco, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6718, https://doi.org/10.5194/egusphere-egu26-6718, 2026.

Securing maize crop production is essential in our changing world. However, it remains unclear to what extent climate conditions and farmers’ practices, such as fertility management and irrigation, can impact future maize crop production in Europe. Here we use the AquaCrop model v7.2 in a spatially distributed setup to estimate yields, yield gaps, growing cycles, and water productivity over a 30-year baseline period (1985–2014), and a near-future period (2030–2059) under a range of climate scenarios, forced with meteorological data from the Inter-Sectoral Impact Model Intercomparison Project (simulation round 3). We define a generic maize crop with a temperature-dependent sowing date and growing stages, allowing for acclimatization of the growing cycle, in contrast to some earlier climate impact assessments. The results show that a warmer climate will lead to earlier sowing dates and shorter growing seasons, keeping future yield and yield gaps for rainfed maize relatively unchanged from the baseline. Furthermore, the area of profitable rainfed maize production may shift north and expand. In contrast to the marginal impact of climate change on near-future maize yield, removing fertility stress has the potential to increase average yields by 1.5 ton/ha (mainly in the north). An additional gain of 2 ton/ha can be obtained by optimizing irrigation in the southern regions that are not completely unsuitable for rainfed maize production. For irrigated maize in the south, the stable future yield projections are accompanied with increased water productivity, again due to an earlier and shorter growing season.

How to cite: Deketelaere, V., Busschaert, L., Thiery, W., Raes, D., and De Lannoy, G. J. M.: Future projections of European maize yields using AquaCrop with an adaptive growing season, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7208, https://doi.org/10.5194/egusphere-egu26-7208, 2026.

Agricultural production systems are increasingly constrained by interacting social-ecological pressures. While a growing world population and dietary shifts are increasing the demand for agricultural crop products, seven out of nine planetary boundaries are breached, with climate change at the forefront. On the production side, farmers face the challenge of implementing climate-resilient farming systems that can operate within planetary boundaries. Conservation agriculture, as part of sustainable and regenerative agriculture, is believed to potentially play a significant role in this development. However, this has not been sufficiently assessed at larger scales. Existing global modelling approaches have predominantly focused either on stylized biophysical potential assessments or macroeconomic optimization approaches. Both approaches often neglect the endogenous decision-making of individual land-use actors. Here, we introduce an integrated World–Earth modelling approach that couples farmers' socio-economic decision-making related to the adoption of farming practices with process-based terrestrial biosphere and, in particular, crop modelling. Using the recently bulit InSEEDS model embedded within the copan:LPJmL modelling framework, we investigate social-ecological co-evolutionary feedback mechanisms of land-use systems at the global scale. For this first large-scale application of InSEEDS, it is extended to include socio-economic and supra-regional communication feedbacks, alongside standardized procedures for providing empirical data for model validation. Modelled farmer agents can dynamically choose between tillage systems and decide on crop residue management and cover crop cultivation. We apply the model under a constant-climate scenario as well as SSP1-2.6 and SSP3-7.0 forcing scenarios. Our results indicate a strong influence of climate change on regional and temporal patterns in farmers' decision-making between conventional agriculture and conservation agriculture, taking into account socio-economic as well as socio-cultural factors. This highlights the importance of integrated modelling approaches for understanding co-evolutionary challenges and opportunities under climate change.

How to cite: Breier, J., Prawitz, H., Rimmert, M., Schwarz, L., Sieben, L., Wirth, S. B., Müller, C., Gerten, D., and Jonathan, D.: Modelling planetary transition pathways to conservation agriculture under climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12491, https://doi.org/10.5194/egusphere-egu26-12491, 2026.

Sorghum (Sorghum bicolor L. Moench), a vital crop in semi-arid region Maharashtra, is increasingly threatened by climate change, characterized by erratic monsoons, prolonged droughts, and rising temperatures. As a staple cultivated during kharif and rabi seasons, sorghum supports food security and livelihoods for millions of farmers. However, these environmental shifts are jeopardizing yield and water use efficiency (WUE), particularly in rainfed systems. This study employs the DSSAT-CERES-Sorghum model to assess these impacts, utilizing historical India Meteorological Department (IMD) data (1980–2009) and CMIP6 Global Climate Model (GCM) projections under SSP2-4.5 (moderate emissions) and SSP5-8.5 (high emissions) scenarios. Focused on Maharashtra, the research aims to quantify yield-WUE changes and classify climate stress regimes (hot/dry, cool/wet) using percentile thresholds to understand regional vulnerabilities. The methodology integrates comprehensive datasets, including soil properties, crop management practices, and genotype parameters for kharif and rabi cultivars. The CERES-Sorghum model simulates baseline and future performance, accounting for temperature, rainfall, and irrigation effects. Climate data from GCMs provides projections for mid-century (2040–2069) and end-century (2070–2099), enabling a comparative analysis of climate impacts across Maharashtra’s diverse agro-climatic zones. Simulations are expected to indicate significant yield declines and reduced WUE by 2040–2069, with further deterioration by 2070–2099 in rainfed systems. Elevated temperatures (Tmax > 35°C) and irregular rainfall are anticipated to drive heat and water stress, leading to poor yield-WUE performance in vulnerable regions. This will pose risks to food security and farmer livelihoods, highlighting areas where agricultural output is most at risk. The study will integrate baseline and future data to identify these critical zones, providing a foundation for targeted adaptation strategies. These insights aim to enhance Maharashtra agricultural resilience by informing sustainable practices to mitigate climate variability effects. The research addresses knowledge gaps in integrating yield, WUE, and climate stress indicators for Maharashtra, where localized assessments are limited despite the crop’s importance. By leveraging advanced modelling, the study offers a robust framework to predict future trends and support policy interventions. The expected outcomes will underscore the urgency of adapting to changing climate conditions, ensuring the sustainability of sorghum production. This effort is crucial for safeguarding Maharashtra’s agricultural heritage and supporting its rural economy against the backdrop of escalating environmental challenges.

How to cite: Srivastava, M. and Mall, R. K.: Climate Change and Sorghum Yield-WUE Dynamics in Maharashtra: Unveiling the Impacts of Temperature and Rainfall Variability on Crop Performance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16233, https://doi.org/10.5194/egusphere-egu26-16233, 2026.

Water resources are increasingly shaped by global teleconnections. Through international trade, particularly in agricultural commodities, water is imported and exported as virtual water, linking distant regions via shared dependencies on freshwater resources. While virtual water trade can alleviate local water scarcity and enhance food security, it also amplifies systemic vulnerabilities within global supply chains. Even though the majority of global agricultural production depends on green water resources (soil moisture derived from precipitation), existing research has predominantly focused on blue water resources (surface and groundwater) and the redistribution of blue water stress due to trade. This study addresses this gap by explicitly linking export-side green water stress to import-side water-related risks within the global food system. We integrate a national-scale green water stress assessment, simulated using the LPJmL dynamic global vegetation, crop, and hydrological model, with international trade data from the CWASI database for selected primary crops. This combined framework enables a systematic analysis of the propagation of green water stress through the global food system via trade relationships. For this, we develop and analyze distinct categories of green water scarcity risk at country level: First, risk associated with domestic agricultural production; second, risk arising from the reliance on imports sourced from water-stressed regions; and third, risk that emerges in a country due to its export-oriented production. Using this categorization, we follow and map water-related risks from producers to consumers. Our results demonstrate that dependence on distant green water resources creates a complex and often opaque network of vulnerabilities, whereby local water stress can translate into risks far beyond the region of origin. By revealing how green water stress is embedded in global trade flows, this study underscores the need to move beyond local perspectives on water management. A more holistic, teleconnection-aware approach is required to sustainably govern global water resources and to reduce systemic risks in an increasingly interconnected world.

How to cite: Dahlmann, H., De Petrillo, E., Gerten, D., Busch, F., Fahrländer, S., Tamea, S., and Tuninetti, M.: From scarcity to risk - Propagation of green water stress through the food system due to agricultural trade, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17868, https://doi.org/10.5194/egusphere-egu26-17868, 2026.

Extreme climate events — such as heavy precipitation, heatwaves, and droughts — are increasing in frequency under global climate change and increasingly threaten sustainable agricultural systems. Crop rotation is a key management practice for sustaining soil health and enhancing ecosystem resilience. However, the extent to which extreme climate events drive observable changes in rotation patterns remains insufficiently quantified. This study presents a modeling framework to analyze crop rotation patterns, attribute changes to climatic factors, and identify resilient rotation strategies under future climate conditions.

We examine three core questions: (1) Do extreme climate events significantly alter crop rotation dynamics? (2) Do regions with higher event frequency show systematically different rotation structures? (3) Do rotation sequences that change after extreme events perform differently under climate stress?

Using 1 km resolution meteorological data across Germany, we identify extreme rainfall, heatwave, and drought events. We analyze crop sequences from over 900,000 fields (2012–2024) using Markov chain models to characterize rotation patterns, transition probabilities, and stability indicators. Statistical comparisons are conducted of rotation patterns before and after extreme events, as well as between regions with different event frequencies. To evaluate resilience, a process-based crop model is used to simulate selected crop rotation patterns under various climate conditions, assessing indicators of resistance, recovery, and yield stability.

By integrating climate data, stochastic crop sequence modeling, and process-based crop model simulation, this study establishes a framework for attributing rotation pattern changes to climate factors and evaluating agricultural system resilience. Our findings contribute to understanding climate adaptation in cropping systems and support the development of targeted strategies for sustainable agriculture under global change.

How to cite: Gao, W., Abbasi, N., and Siebert, S.: Attribution and Resilience Assessment in Crop Rotation Patterns under Extreme Climate Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18148, https://doi.org/10.5194/egusphere-egu26-18148, 2026.

How to cite: Haacker, J., Thaler, S., Estévez, E., Eitzinger, J., and Formayer, H.: Severe yield loss every other year through 2039 in the Austrian agriculture based on SSP3-7.0, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19303, https://doi.org/10.5194/egusphere-egu26-19303, 2026.

Wheat is a key staple crop in temperate regions where projected increases in temperature variability, drought frequency and extreme weather events pose a significant threat to yield stability. Understanding how weather variability, climate extremes and, importantly, their timing in relation to crop development affect yield is essential for modelling agricultural systems in the context of climate change. Increasing climate variability and compound stress events challenge static or season-averaged approaches that fail to resolve developmentally sensitive stress periods.

However, it is often difficult to disentangle these effects across phenological stages due to incomplete or inconsistent phenological observations, particularly in long-term, multi-site datasets. This study presents a proxy-based modelling framework that quantifies yield–environment relationships while explicitly accounting for sensitivities specific to developmental phases.

We analyzed a multi-year historical dataset spanning multiple locations, combining wheat yield records with weather and environmental data extracted from the Spartacus dataset by GeoSphere Austria. Our primary objectives were to: (i) quantify the influence of weather and environmental variables on wheat yield; (ii) identify the most relevant stress factors within these environments; and (iii) assess how stress impacts vary across developmental phases. As phenological scoring data were incomplete across years and locations, we used Growing Degree Days (GDD) as a biologically motivated proxy to reconstruct crop developmental timing.

Based on the reconstructed developmental phases, raw weather and soil data were transformed into explanatory envirotyping variables at multiple temporal scales, including annual aggregates, phase-specific windows and daily to weekly resolutions. Explicit stress indicators were derived for thermal, hydrological and precipitation extremes, including compound and duration-based events, described using both frequency and intensity metrics. These were described using both frequency and intensity metrics. Such temporally explicit stress characterization is essential in the context of global change, given the projected increases in heatwaves, drought duration and compound extremes, which are expected to amplify phase-specific yield sensitivities.

Yield responses were modelled using a combination of linear models and machine learning approaches to capture non-linear effects and interactions. This framework enables the identification of critical developmental windows, the quantification of stress sensitivities and the assessment of environmental similarity and transferability across sites and years. Overall, this scalable envirotyping framework enables the identification of critical developmental windows and time-specific environmental drivers of yield variation, supporting more robust crop modelling and breeding decisions under increasing climate variability.

How to cite: Milek, J. J., Koppensteiner, L., Bernardini, L. G., Bodner, G., Zechner, E., and Molin, E. M.: Modelling developmental stage-specific climate effects and extremes on wheat yield under global change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20518, https://doi.org/10.5194/egusphere-egu26-20518, 2026.

Pistachio (Pistacia vera L.) is a high valued perennial crop, and its growth is highly controlled by agro climate especially by the temperature regimes, seasonal variation and water supply. The current climate change is likely to change these conditions in the Mediterranean region, and the long-term sustainability of agricultural systems, crop adaptation, and land-use planning are affected directly. Although the relevance of pistachio as a climate resistant crop is increasing, there is still limited basin scale evaluation of the suitability of this pistachio in Mediterranean. We evaluate the climate-driven land suitability of pistachio in the Mediterranean Basin under the current and future climate scenario by machine learning-driven modelling framework. Data on the occurrence of pistachios in the Mediterranean area were summarized and co-expressed with a collection of agro-climatic and topographic variables based on high-resolution climatic information. The screening of environmental indicators attempted to control multicollinearity and to eliminate a parsimonious, agronomically constructive set to reflect thermal conditions, seasonality of temperature variations, extremes of precipitation and terrain limitations that are pertinent to the cultivation of pistachio. The slope was added as an indicator of land suitability to show the possibility of management and the constraints of the terrain. The Maximum Entropy (MaxEnt) model used to assess the present and future suitability under climate change conditions. Model evaluation metrics suggest a strong predictive performance. Findings demonstrate temperature-related factors and dry-season precipitation as the dominant factors affecting the suitability of pistachio throughout the Mediterranean. The spatial distribution of present suitability indicates the existence of core climatic stable cultivation areas, and the future projections show that there will be significant spatial changes in suitability. Significantly, the findings indicate the development of new potentially appropriate areas where the pistachio can be grown in some parts of the Mediterranean that are marginal or unprofitable today, as well as those areas where suitability could decrease with rising climatic stress.

How to cite: Ijaz, B., Debolini, M., Trabucco, A., and Spano, D.: Climate-Driven Land Suitability of Pistachio (pistachia vera L.) under Current and Future Climate Scenario in Mediterranean Region , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20645, https://doi.org/10.5194/egusphere-egu26-20645, 2026.

The Mediterranean basin is a climate change hotspot, and this will strongly affect key crops such as durum wheat, a staple for millions and a major commodity in southern Europe. Future productivity remains uncertain, as climate change introduces both limiting and beneficial factors. Understanding crop responses is essential to design effective adaptation strategies and ensure food security.

This study assesses the DSSAT-CERES-Wheat model’s ability to simulate durum wheat growth and yield under climate change across three contrasting Mediterranean environments and support adaptive strategies. The model was calibrated for Tirex, a widely cultivated variety, using long-term field data and Monte Carlo optimization, then evaluated with three independent trials in northern (Rovigo), central (Agugliano), and southern (Genzano) Italy, incorporating leaf area index data from Sentinel-2. Two CMIP5 scenarios (RCP 4.5 and RCP 8.5) and three temporal horizons (baseline 1991–2020, near future 2022–2050, far future 2070–2100) were used to simulate yields, evaluate irrigation as adaptative strategy, and assess water use efficiency (WUE).

Calibration showed strong performance for grain yield (R²=0.98, d-stat=0.98, RMSE=0.3 t/ha), canopy biomass (R²=0.98, d-stat=0.62, RMSE=3 t/ha), and anthesis (R²=0.98, d-stat=0.84, RMSE=7.6 days). Evaluation confirmed good agreement for yield and biomass across sites, while LAI was less accurate.

At Rovigo, climate change reduced yields most under RCP 8.5 near future (-1.8 t/ha, -40%). At Agugliano, responses depended on agronomic management: under enhanced conventional (standard nutrition, supplemental irrigation, and integrated pest management), yields declined most under RCP 4.5 near future (-1.8 t/ha, -28%) but increased under RCP 8.5 far future (+1.0 t/ha, +15%). Under zero-stress (fertigation and chemical pest control), yields increased across all scenarios, reaching the highest gain of +1.5 t/ha (+17%) for RCP 8.5 far future. At Genzano, limited effects were observed, with the largest near-future decline (-0.3 t ha⁻¹, -11%) and a far-future increase (+0.7 t/ha, +30%) under RCP 8.5. The achievement of higher yields in the far future compared to the near future across all scenarios may be due to projected increases in atmospheric CO₂, thereby partially offsetting yield losses caused by changes in temperature and precipitation.

Full irrigation mitigated climate impacts. At Rovigo, it led to +3.9 t ha⁻¹ (+84%) under RCP 8.5 far future and improved WUE from 5 to 10–14 kg/mm. At Agugliano, irrigation increased yields under all scenarios, with the largest gain under RCP 8.5 far future (+3.6 t ha⁻¹, +54%) while maintaining a WUE of 10–15 kg/mm. At Genzano, irrigation produced smaller absolute but higher relative gain (+2.0 t/ha, +90%) under RCP 8.5 far future and WUE slightly increased.

Full irrigation effectively stabilizes and increases wheat yields, but only modestly improves WUE, indicating that higher yields do not necessarily translate into greater water efficiency. Full irrigation should therefore be considered a theoretical upper limit rather than a realistic large-scale strategy due to water availability, cost, and infrastructure constraints. Effective climate adaptation in Mediterranean wheat requires combining agronomic management adjustments and genotype choice, supported by crop modeling to assess trade-offs among productivity, water use, and environmental sustainability.

How to cite: Rivosecchi, C., Bianchini, M., Denora, M., Di Tella, B., D'Ottavio, P., Fiorentini, M., Francioni, M., Ledda, L., Mancini, A., Perniola, M., and Deligios, P. A.: Assessing climate change impacts on Mediterranean durum wheat using DSSAT-CERES-Wheat across contrasting environments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22400, https://doi.org/10.5194/egusphere-egu26-22400, 2026.

Soil organic carbon (SOC) persistence is central to climate mitigation yet often framed by the debated concept of mineral-associated organic carbon (MAOC) saturation. At the microscale (MAOC, <50 µm), organic carbon associates with minerals to form primary organo–mineral complexes. The enrichment factor (EFc), the ratio of C concentration in the silt and clay (silt+clay) fraction to SOC content, emerged early as a useful measure of MAOC enrichment or saturation. For example, a higher EFc is interpreted as indicating that coarse-textured soils are more saturated than fine-textured ones. This concept parallels Hassink’s saturation theory, which posits that C sequestration is constrained by mineral sorption capacity currently observable in the silt+clay fraction. Here, I show that both assumptions are not supported by global empirical evidence, and an alternative steady-state framework is proposed. This study assessed whether SOC accumulation is driven by site-specific inputs and decomposition rather than by a fixed saturation capacity. This study draws on updated global data to reconcile the MAOC:silt+clay and MAOC:SOC approaches across a wide range of pedoclimatic conditions. The analysis further highlights future directions for refining sequestration estimates through the development of a pedotransfer function framework. The slope of the MAOC versus SOC regression from global datasets, previously reported, remains linear up to ~13% SOC, then the observed accumulation of MAOC likely reflects a dynamic steady-state rather than a saturation threshold. By contrast, MAOC versus silt+clay content captures variation in C loading, not strictly a universal fixed saturation. Although rarely observed, MAOC may continue to accumulate under varying C flux scenarios, stabilizing beyond the measurable range. This framework improves SOC sequestration predictions and challenges the paradigm that C saturation is determined solely by silt+clay.

How to cite: Matus, F.: Mineral-associated carbon persistence arises from steady-state dynamics, not saturation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-46, https://doi.org/10.5194/egusphere-egu26-46, 2026.

The increasing global demand for food, feed and flexible crops is exerting unprecedented pressure on the global hydrological cycle through landscape conversion and increasing irrigation demand, which altogether contribute to the alteration of land moisture recycling. This alteration influence evapotranspiration and precipitation patters through atmospheric flows. Atmospheric moisture flows connect sources of evapotranspiration to sinks of precipitation, from local to regional and continental scale, up to thousands of kilometres away. Terrestrial sources of evapotranspiration are crucial for global food production, regulating precipitation and climate patterns by redistributing water and latent heat. At the same time, the alteration of evapotranspiration dynamics from these sources is mainly driven by land-use conversion for pasture (cattle meat production), and feed crops (such as soy, and maize) and agricultural practises, such as irrigation.

Current crop water use assessments disregard these atmospheric moisture fluxes in redistributing evapotranspiration from agricultural parcels to precipitation in downwind areas. This understanding is particularly key to better assess the water-related implication of agricultural production. Addressing this research gap, this study aims to advance the understanding of how evapotranspiration from agricultural areas shape precipitation in other agricultural areas.

The agro-hydrological estimates for crop production were performed over the period 2008–2017 by means of the model waterCROP, which solves the daily soil water balance on a global 5 arc-minute grid, with global coverage for both irrigated and rainfed conditions.

These evapotranspiration estimates are then combined with atmospheric moisture connections by means of the RECON dataset (based on the UTrack Lagrangian moisture tracking model), a 4D matrix of annual moisture flow connections between any cell in the world at a spatial resolution of 0.5° including a globally closed water balance at annual scale. Each cultivated cell is linked to its blue and green evapotranspiration shed (i.e. the downwind area receiving precipitation from irrigated or rainfed crop production). Evapotranspiration sheds are finally classified according to their land use category to analyse potential synergies and trade-off between land and water use between the sites at the origin of evapotranspiration and others at the fate of precipitation. By characterizing these connections, this research sheds light on the hidden global links between cultivated land and downwind areas.

How to cite: Cigna, G., De Petrillo, E., Wang-Erlandsson, L., and Tuninetti, M.: Evapotranspiration shed of agriculture: combining agro-hydrological estimates with atmospheric moisture dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7907, https://doi.org/10.5194/egusphere-egu26-7907, 2026.

Rainfed smallholder farming systems in semi-arid sub-Saharan Africa (SSA) are vulnerable to intra-seasonal rainfall variability, prolonged dry spells, and high evaporative demand that constrain crop productivity. AquaCrop—a water driven crop growth model, has been widely applied to assess crop performance and water use in water-limited environments. However, long-term, multivariable evaluations spanning multiple growing seasons remain scarce in SSA dryland.

Thus, this study was conducted to evaluate the capacity of AquaCrop to reproduce maize yield components, crop evapotranspiration (ETc), soil water storage (SWS) and plant growth dynamics. We assessed these variables across multiple growing seasons spanning contrasting hydroclimatic years and quantified how rainfall characteristics relate to yield in a typical semi-arid agrosystem in Africa.

Field experiments in Maktau, Kenya, spanned six growing seasons (2019–2024), covering both long (LR) and short (SR) rains. AquaCrop was calibrated for SR2023 and validated for LR2024, with additional growing seasons adopted for testing. LARS-WG—a stochastic weather generator was utilized to generate a 100–year weather data for Maktau by utilising in-situ meteorological data (2013–2024). This weather data was then employed to quantify how plant–available soil water relate to yield.

Simulated maize final biomass and yields generally tracked observed data, with percent errors for final aboveground biomass and grain yield ranging from −23% to 33% and −19% to 7%, respectively, under total seasonal rainfall of 176–489mm. The model showed satisfactory performance for ETc (R² = 0.41–0.81), mixed performance for SWS across growing seasons (R² = 0.15–0.69) and accurately captured canopy cover (CC) dynamics (R² ≥ 0.91). In the centennial analysis, maize grain yield variability was strongly associated with total seasonal rainfall (R² = 0.44), with grain yield ranging from crop failure to 3.8 t/ha under total seasonal rainfall of 37–592mm.

Crop failure and low yields were associated with lower rainfall in May, which coincided with the tasselling–flowering stage of the dryland maize variety DH02 planted in early March as is typical in Maktau. Soil water deficit in the tasselling–flowering stage disproportionally impacted maize yield.

Beyond reaffirming the established rainfall and yield relationship, the findings provide clear, actionable insights for smallholder dryland systems in Kenya and similar dryland agrosystems: (i) timing of rainfall, particularly during tasselling–silking, is a critical determinant of yield loss or realization of yield, suggesting the value of matching cultivar maturity and sowing windows to the temporal distribution of rainfall; (ii) supplemental irrigation targeted to critical maize growth stages; and (iii) selection of maize varieties with accelerated growth cycles to minimises the exposure to extended periods of no rainfall that leads to soil water deficits as the crops are able to complete critical growth stages (tasselling–flowering) faster averting the risk of crop failure.

How to cite: Odhiambo, M. O., Tuure, J., Heiskanen, J., Wachiye, S., Mganga, K. Z., Mäkelä, P. S. A., Alakukku, L., Pellikka, P., and Räsänen, M.: AquaCrop model performance evaluation and centennial simulations in a rainfed dryland agroecosystem, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10729, https://doi.org/10.5194/egusphere-egu26-10729, 2026.

A key source of uncertainty in climate impact projections is the divergence of climatic variables across global climate models (GCMs) which are used to drive impact models. Here, we demonstrate the extent of this issue for agricultural impact modelling using the latest generation of GCM-driven global gridded crop models, and explore the usefulness of global warming levels (GWL) for aligning crop yield impact estimates across GCMs. To do this, we compare the spread in distributions of spatially aggregated yield change projections across GCMs using the GWL- and the commonly used fixed time window approaches. We find that at the global scale, the GWL approach is particularly effective in reducing GCM uncertainty in projections of interannual yield variability changes, and that this effect is robust across crops. In contrast, for changes in mean yields, the effectiveness of GWLs is strongly crop-dependent. These differences can be explained by different responses to increasing CO₂ concentrations across crops and yield metrics: a strong CO₂ fertilization effect on mean yields of the C3 crop wheat renders the GWL approach less effective, while the relative independence of both maize and wheat variability from CO₂ concentrations makes GWLs particularly effective in these cases. We find that in the agricultural modelling community, the GWL approach offers a means not only to align responses across GCMs but also to better understand impact drivers and components of uncertainty. The relevance of these findings also extends to the broader impact modelling community, particularly in settings where output from multiple climate models is used to drive impact models, such as studies based on the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework.

How to cite: Mithal, V., Jägermeyr, J., Müller, C., Sillmann, J., and Borchert, L.: Exploring the usefulness of global warming levels for aligning agricultural productivity impact trajectories across GCMs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10733, https://doi.org/10.5194/egusphere-egu26-10733, 2026.

Agricultural production relies heavily on the weather, making it especially sensitive to climate change. In the past, high temperatures have had substantial negative effects on crop yields. In a warming climate, these impacts could become even more severe.

Process-based modelling offers a systematic way to examine whether and how future environmental changes may impact crop yields. Many crop models take the 2 m air temperature as an input, allowing the simulated growth and development of the crops to respond directly to that temperature signal. However, depending on the climatic conditions, water status, and the developmental stage of a crop, 2 m air temperatures can be several degrees higher or lower than the actual temperatures at the canopy level. Some crop models therefore compute canopy temperatures based on complex energy balance approaches (EBSC), that have been shown to perform best compared to other approaches. However, these EBSC approaches are computationally expensive and their application in global models can therefore result in considerably higher runtimes. In our work, we developed resource efficient emulators that are based on an EBSC model and can be incorporated in global process-based models without significantly increasing the simulation time.

We applied the emulators in the agricultural modules of the dynamic global vegetation model LPJmL. The validation of daily maximum simulated canopy temperatures shows that LPJmL can reproduce cooling and heating effects of the canopy depending on the water and nitrogen availability of a crop compared to detailed site based observations in different locations throughout the US and Canada. For a global evaluation, we compared our results with skin temperatures from ERA5, which we used as an approximation for canopy temperatures. We show that, on a global scale and for daily maximum values, skin temperatures are significantly better represented by simulated canopy temperatures than by ERA5 2 m air temperatures.

Our results indicate that substituting simulated canopy temperatures for the 2 m air temperatures in processes driven by daily maximum temperatures improves the requirements of modelling heat stress impacts. Particularly, as high temperature processes often follow nonlinear dynamics and are even more affected by small temperature deviations. In a next step, we will use the further developed LPJmL model to analyze such heat stress impacts on crops. For this, we will include high-temperature responses, that will react to the newly implemented canopy temperatures.

The developed emulators can easily be included in other crop models, aiming to improve simulated temperature-dependent process dynamics. With this, we hope to provide a step towards reducing uncertainties in future agricultural yield estimates. 

How to cite: Hemmen, M., Webber, H., and Müller, C.: Canopy temperature emulation in process-based models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11357, https://doi.org/10.5194/egusphere-egu26-11357, 2026.

CLM5, the land model for the Community Earth System Model (CESM), simulates complex terrestrial processes and provides us with a global understanding of the interplay between energy, water and biochemistry fluxes. The crop representation in CLM5 currently lacks the inclusion of specific heat stress that can harm crop yield during crop sensitive stages. As heatwaves are becoming more frequent, we assess the potential of including specific heat-stress functions to target crop development in the working version of CLM5, the Community Terrestrial Systems Model version 5.2 (CTSM5.2). Several model implementations and parameterizations are assessed to target the leaf area index (LAI) or crop grain production directly and are compared to the default model version in terms of impact and to global annual yield data from the FAO in terms of model accuracy. Uncertainties and challenges in crop modelling and model development are also highlighted.

How to cite: de Roos, S., Rabin, S., and Thiery, W.: Introduction of heat stress on global crop production in the Community Land Model (CLM5), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11692, https://doi.org/10.5194/egusphere-egu26-11692, 2026.

A transformation towards sustainable agriculture requires accurate tools to assess how environmental constraints limit food production. However, agricultural productivity faces increasing pressure from the interconnected issues of soil salinization and water stress. As climate conditions become more extreme, rising temperatures intensify evapotranspiration (ET) and crop water stress, while soil salinization further reduces soil moisture availability, particularly in arid and semi-arid regions. Despite these challenges, global agricultural models have historically lacked the capacity to quantify the specific impact of salinity on irrigation requirements at an operational scale.

To bridge this gap, this work advances the waterCROP agrohydrological model by integrating a new modeling layer that accounts for salt build-up in the root zone and its feedback on crop water availability. This represents one of the first operational attempts to simulate irrigation demand under distinct soil salinity conditions at the global scale. The enhanced framework improves the representation of soil-water-plant interactions by (i) simulating more realistic actual crop ET, (ii) estimating irrigation water demand under varying salinity levels, and (iii) incorporating up-to-date agricultural datasets across staple crops (wheat, rice, maize, soybean) and salt-sensitive crops (broadbean, cabbage, potatoes, tomatoes).

Simulations were conducted at a 5 arc-minute resolution (approximately 9×9 km at the Equator) for years centered on 2000 and 2015. Globally, maize and soybean show blue water demand (BWD) increases of 6 - 10 %, but locally BWD can increase up to 50% over this period, highlighting areas of particularly high water demand growth. This operational approach provides a refined, quantitative assessment of BWD, offering essential data to support sustainable land management strategies in the face of increasing climate and salinity pressures.

How to cite: Ghirardo, I., Dalin, C., Perri, S., Sciarra, C., and Tuninetti, M.: Modeling Coupled Impacts of Soil Salinity and Hydroclimatic Stress on Irrigation Demand, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13304, https://doi.org/10.5194/egusphere-egu26-13304, 2026.

Agriculture faces the challenge of securing a food supply for the growing global population while producing it in a sustainable manner, reducing the impacts of crop management on natural resources, air quality, climate change, and biodiversity. Robust representations of crop processes in agroecosystem modelling allow a better understanding of the interactions and feedbacks between agriculture, climate, environment and society, increasing the likelihood of meeting these challenges. In addition, improved agricultural modelling enhances the simulation of carbon cycling and natural vegetation in Dynamic Global Vegetation Models (DGVM). One main limitation for mechanistic agricultural representation in models is the low availability of high-resolution and long-term management datasets at regional or national scales, such as the application rates of nitrogen (N), the most important nutrient for plant growth. Here, we estimated a crop-specific N fertilisation dataset at 3-arc-min resolution for 1961–2015 and also projected future N applications at the same resolution under SSP1-RCP2.6, SSP3-RCP7.0 and SSP5-RCP8.5 for 2016-2100 in Germany. We included the crop groups C3 and C4 cereals, oil crops, starch crops, and fruit and vegetables under high and low-intensity management. Historical estimates were based on HILDA+ land cover data, harmonised by hindcasting the CRAFTY-GERMANY 2020 baseline and using an existing global N fertilisation database. The estimations were bias-corrected to match yearly FAO country-level statistics of fertiliser consumption.

For future projections, a baseline map for Germany of average fertilisation by state for 2005-2015 was generated, as well as a spatial deviation map, filling missing grid cells by ordinary kriging interpolation. In grid cells where a given crop was projected in the future according to the CRAFTY-GERMANY land-cover data obtained for each scenario, the fertilisation was calculated by weighting the average fertilisation baseline according to the future trends, adding the corresponding spatial deviation for that grid cell, plus a random value from the kriging interpolation error, assuming a normal distribution. The resulting historical spatiotemporal N fertilisation dataset was consistent with statistics of the International Fertilizer Association and can be used as an input for crop models and DGVMs. The same approach can be applied at the regional and global scale to improve modelling inputs.

How to cite: Camargo Alvarez, H. and Arneth, A.: A high-resolution crop fertilisation database for Germany under SSP/RCP scenarios, 1960-2100, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15014, https://doi.org/10.5194/egusphere-egu26-15014, 2026.

The UK poultry industry supplies 50% of the nation’s meat demand, serving as a cornerstone of national food security. Industrialisation and the rise of ‘megafarms’ have rendered poultry a cheap, nutritious, and widely available protein source; production nearly doubled from 1.0 million tonnes in 1994 to 1.9 million tonnes in 2024, while per capita consumption rose from 23 kg in 2007 to 31 kg by 2022. However, intensive farming has triggered significant public concern regarding animal welfare and the environmental impact of farm waste on UK watercourses. Furthermore, recent shocks—including Brexit, COVID-19, the war in Ukraine, and increasing extreme weather—underscore the urgent need for systemic resilience against natural, socio-economic, and geopolitical disruptions.

Addressing these challenges requires a comprehensive systems approach. Despite increasing calls for systems thinking, robust modelling methods remain underutilised in the field. This study employs a data-driven System Dynamics approach to explore the complex interdependencies of poultry production and consumption, evaluating the trade-offs between system benefits and harms across human, animal, microbial, and environmental communities.

The model was developed through a participatory framework in collaboration with academics and industry stakeholders. A group model building approach—incorporating workshops, interviews, and collective scenario specification—enabled qualitative and quantitative modelling, optimisation, and the development of a decision-support tool. The simulation captures the dynamic interrelations between medicated feed, waste management, and welfare, tracing the sector’s evolution since the 1950s. By analyzing how the industrialisation of biochemical processes has shifted the dynamics between poultry, people, and the planet, this study identifies key vulnerabilities and pathways for enhancing the resilience and sustainability of the UK poultry sector.

How to cite: Sheffield, J., Parsa, A., Simmonds, P., Stanley, T., Maye, D., Lambton, S., and Roe, E.: Modelling Resilience of the UK Poultry Sector to Socio-Ecological Shocks: A Data-Driven System Dynamics Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16914, https://doi.org/10.5194/egusphere-egu26-16914, 2026.

Nitrogen leaching in agricultural systems is a major environmental risk resulting from irrigation-fertilization practices. Global losses of fertilized agricultural systems are estimated to be about 30% of the applied nitrogen fertilizer. As food and water security are being threatened by the effects of climate change, it is imperative to develop strategies that optimize fertilization application (and irrigation) to mitigate adverse environmental effects of nitrogen losses while maximizing production. Developing and testing such strategies remains challenging, partly because soil functions strongly depend on pedoclimatic conditions, soil degradation, and crop type; and all these parameters may be highly variable, even over relatively small distances and short periods of time. In this work, we present an approach for optimizing agricultural management based on the introduction of a sustainability index (SI). The SI is defined as a function of the net monetary system gain resulting from subtracting the estimated societal cost of soil losses of nitrogen (NO₃ leaching and N₂O emissions) and soil carbon to the brut economic gain of crop yield at current market prices. We considered a management optimization example simulating winter wheat in the United Kingdom using Historical climate simulations (1960-1980) with yearly homogeneous fertilization of 200 kg N/ha/yr applied on 11^th of March. These management variables were integrated into a probabilistic Markov Chain Monte Carlo (MCMC) approach aiming at optimizing the SI. This led to reductions of approximately 34% in annual nitrate leaching (from 44 kg/ha to 29 kg/ha) and 23% in annual nitrous oxide emissions (from 5.2 kg/ha to 4 kg/ha) by only compromising 3% of the annual crop yield (from 7.4 Mg/ha to 7.2 Mg/ha). These results are further discussed in the context of climate change and soil degradation in cropland. For this, we computed the SI for healthy and compacted soils in European cropland using winter wheat simulations under climate projections from the high-emissions climate scenario (SSP585) in the Coupled Model Intercomparison Project (CMIP6). Introducing a SI that weights economic and environmental factors of agroecosystems and utilizing it within a MCMC optimization scheme provides a powerful framework to harness agroecosystem models in order to test and optimize management strategies. Such an approach offers both estimations and uncertainty of management variables, crop yield, nitrogen losses, and the resulting net economic gain, which are crucial for informing and guiding policy-making in agriculture.

How to cite: Romero-Ruiz, A. and Halloran, L.: Bayesian optimization of a sustainability index to reduce nitrogen losses in European cropland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17734, https://doi.org/10.5194/egusphere-egu26-17734, 2026.

Surface ozone air pollution impairs carbon assimilation in terrestrial ecosystems. For crop species, ozone pollution reduces biomass and crop yield and therefore poses challenges on food security in regions with large populations such as India and China. The ozone impacts on crop yield can be examined with a mechanistic crop model, which explicitly simulates plant physiological responses (e.g., gas exchange rate, leaf area index) to changes in environmental conditions. In mechanistic crop models, ozone-induced yield loss is primarily determined by the sensitivity parameter (a_sen) of photosynthetic rate loss to cumulative stomatal ozone uptake. Derivation of a_sen follows different approaches: one based on statistical relationships between relative yield or biomass loss and cumulative ozone uptake, as described in Sitch et al. (2007); another based on relationships between gas exchange rate losses (photosynthesis and stomatal conductance) and cumulative ozone uptake, as described in Lombardozzi et al. (2015).

In this study, gas-exchange measurement data from multiple elevated ozone exposure experiments for maize and soybean are used to calibrate a_sen following Lombardozzi et al. (2015). Validation simulations are conducted using the Terrestrial Ecosystem Model in R (TEMIR) version 2.0, a mechanistic crop model akin to those in land surface models such as JULES and CLM4.5, implemented with two plant-ozone damage schemes following Sitch et al. (2007) and Lombardozzi et al. (2015).

With the newly calibrated a_sen, modeled ozone-induced relative yield loss shows good agreement with observed values for soybean, with a mean error of less than 5 percentage points across different ozone levels. Simulations using the calibrated a_sen following Lombardozzi et al. (2015) exhibit superior performance compared to those using the default a_sen from Lombardozzi et al. (2015) or the calibrated a_sen following Sitch et al. (2007), both of which have mean errors exceeding 25 percentage points in the modeled ozone-induced relative yield loss. The low mean error from the simulations using the calibrated a_sen following Lombardozzi et al. (2015) suggests the sensitivity of relative photosynthetic rate loss to ozone is similar to that for relative yield loss in soybean. In contrast, for maize, with the calibrated a_sen following Lombardozzi et al. (2015), the model overestimates relative ozone-induced yield loss by about 30 percentage points at the highest ozone concentration (~100 ppbv). Sensitivity simulations with varying values of a_sen indicate that the parameter calibrated to photosynthetic rate loss must be reduced to about one-third of its original value to align modeled and observed relative yield and biomass losses for maize. Modelers should account for these differential responses of photosynthetic rates versus yield and biomass losses among crops species, when assessing future ozone impacts on crop productivity.

How to cite: Pang, J. Y. S., Singh, A. A., Agrawal, S. B., Chintala, S., Feng, Z., Ainsworth, E. A., and Tai, A. P. K.: Investigation of the impacts of elevated ozone on maize and soybean using the Terrestrial Ecosystem Model in R (TEMIR) version 2.0: differential responses of biomass and photosynthetic rates to cumulative stomatal ozone uptake in different crops, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17799, https://doi.org/10.5194/egusphere-egu26-17799, 2026.

Taiwan’s agricultural systems face multiple and interconnected challenges under global change, including a high dependence on food imports, stringent land-use constraints imposed by national spatial planning, and increasing pressure to reduce agricultural greenhouse gas (GHG) emissions in line with net-zero targets. This study addresses this gap by applying and validating the Food, Agriculture, Biodiversity, Land-Use, and Energy (FABLE) Calculator as an integrated interdisciplinary modeling framework for Taiwan’s agricultural systems. We evaluate the model’s applicability under Taiwan’s specific agricultural, land-use, and policy contexts, and develop a set of baseline and alternative sustainability-oriented scenarios to explore potential development pathways of food and land-use systems under environmental and climate change. The scenario analysis focuses on key system indicators, including crop production and food self-sufficiency, cropland area dynamics, and agriculture- and land-based GHG emissions. Particular emphasis is placed on cropland availability, which is highly constrained due to limited land resources, and on agricultural GHG emissions, which represent a critical sector for national climate mitigation strategies. The results highlight trade-offs and synergies among food security, land-use allocation, and emission reduction across alternative scenarios. This study provides a transparent and scalable modeling framework that supports integrated assessments of agricultural adaptation and mitigation strategies. Additionally, the proposed approach offers policy-relevant insights for transforming sustainable agricultural systems, thereby contributing to integrated land–food–environment management under conditions of global change.

How to cite: Chen, P.-Y. and Chen, H.-K.: An Integrated Modeling Assessment of Food and Land-Use Systems in Taiwan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17854, https://doi.org/10.5194/egusphere-egu26-17854, 2026.

The impact of climate change on agricultural yields and risk management is fundamentally tied to shifts in crop phenology. As warming climates accelerate plant development, adjusting sowing dates has emerged as a critical adaptation strategy for maintaining productivity. However, the drivers of future sowing decisions remain a subject of debate. Current modeling strategies, ranging from prescribed planting dates and temperature thresholds to sophisticated decision trees, often imply different assumptions regarding farmers' goals. Some approaches assume farmers seek to maximize the growing period to optimize yields, while others suggest they prioritize reducing inter-annual variability to ensure business stability.

This study investigates the determinants of maize sowing dates in Germany. While historical observations and rule-based models highlight temperature as the primary driver in Central Europe, recent findings suggest that integrating soil moisture may offer higher predictive power. To bridge the gap between theoretical modeling and practical management, we surveyed German farmers to identify the primary motivations behind their planting decisions.

Based on these insights, we developed a planting model that incorporates rolling temperature averages, defined "earliest possible" sowing dates, and soil moisture constraints. This decision-making framework was linked to a Bayesian phenological model to simulate the future development of maize in Germany, assuming the use of modern-day cultivars in a shifting climate.

Our results shed light on the factors that determine farmers' motivations for choosing specific planting dates, as well as the effects these decisions have on crop phenology. By refining how we model the "human factor," we provide a more robust assessment of climate change impacts on German maize production.

How to cite: Busch, F.: Integrating Farmer Motivations into Phenological Models: Impacts of Sowing Decisions on German Maize, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19368, https://doi.org/10.5194/egusphere-egu26-19368, 2026.

Global crop models such as LPJmL typically represent environmental stress through reductions in photosynthesis and leaf area development. Such predominantly source-limited approaches allow for strong post-stress compensation and exhibit little persistence of stress effects. This results in a weak dependence of yield losses on stress timing and an underestimation of impacts during sensitive developmental phases.

We present an enhancement of the LPJmL crop module that explicitly represents persistent stress effects through sink limitation and tissue damage. The main novelty is the introduction of sink limitation, which constrains growth during vegetative development and yield formation during grain filling. Specifically, the number and potential size of harvest organs are determined during flowering, a phenological phase that is highly sensitive to stress. Stress during this period leads to a persistent reduction in sink capacity, limits subsequent growth, and reduces harvest index. Sink limitation leads to a dynamic downregulation of photosynthesis, a process currently absent from the LPJmL crop module.

In addition, we introduce tissue damage as a distinct and partially irreversible pathway to represent impacts of conditions such as waterlogging and frost, which are poorly captured by existing stress formulations.

The model is initially implemented and evaluated for wheat, focusing on nitrogen and water limitation as well as newly introduced waterlogging and frost stress. Heat stress is addressed in complementary work. By representing persistence, this development improves LPJmL’s sensitivity to stress timing and severity and strengthens its mechanistic basis for climate impact assessments.

How to cite: Heinke, J. and Müller, C.: Representing persistent stress effects in LPJmL through sink limitation and tissue damage, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21699, https://doi.org/10.5194/egusphere-egu26-21699, 2026.

Global warming has already resulted in higher frequencies and severity of multiple abiotic and biotic stresses occurring concurrently or subsequently in farmers’ fields. This trend will likely amplify in the next decades. Yet, to date, the mechanisms determining interactions between abiotic and biotic stresses and their effects on crop performance under field conditions are unknown for most crops and stress combinations. Field data are particularly scarce and, hence, adequate modelling approaches do not exist so far. While crop‐growth models are the most appropriate tools for quantifying climate change effects, they remain largely radiation use efficiency (RUE)‐based, treating stress effects through empirical reductions in photosynthesis or yield (e.g., drought-related multipliers) rather than using explicit carbon reallocations. Critically, they ignore active defense sinks - the substantial fraction of assimilates moved into mucilage, phenolics and other biochemicals that protect plants under stress.

This paper aims to describe a novel crop science and modelling approach, in which new empirical knowledge from the genetic to the field scale is integrated and formalized in the novel “MultiStress model” - implemented for maize.

There are many examples of crop defence mechanisms towards multiple abiotic and biotic stressors and their interactions that come at carbon costs.  Here, we focus on drought-response and illustrate the implementation of the MultiStress model for mucilage exudation under drought conditions. Many water-stressed plants including maize release root mucilage, a gelatinous polysaccharide that maintains rhizosphere moisture. This “hydraulic sponge” keeps soil around drying roots hydraulically conductive, facilitating higher water uptake in dry soil. Yet, the mucilage benefits come at a cost. It has been estimated that about 10–15% of total carbon assimilation may be diverted into mucilage under drought. This represents a large carbon sink that otherwise could have fueled grain production. Current crop models lack any pool for mucilage, so this carbon diversion is simply “lost” from the crop carbon budget. Empirical stress factors downscale growth but do not track where the saved carbon goes to. Most crop models impose a fractional yield loss under drought but cannot differentiate whether the plant invested extra carbon in mucilage or other survival mechanisms. This leads to misallocation of carbon, and overestimated yield and yield stability, since the metabolic cost of mucilage is never subtracted. The MultiStress model explicitly accounts for such carbon costs.

Current process-based crop models are neither fit for generating the knowledge needed for assessing crop impacts of climate-induced multiple stress interactions; nor for the task of informing breeding of climate-resilient crop cultivars. Overcoming these challenges requires a renewal of crop science and modelling as shown and currently under development by the MultiStress Research Unit.

How to cite: Roetter, R. P., Hoffmann, M. P., Dippold, M. A., Appiah, M., Piepho, H.-P., Siebert, S., Ahmed, M., Ur Rahman, H., Ejaz, I., Martin, K., Köster, M., Neugart, S., Pfordt, A., Rostas, M., Scholten, S., Stetter, M. G., Vosteen, I., Abdulai, I., Bulli, P., and Otieno, D. and the other MultiStress members: A new crop science and modelling approach to improve mechanistic understanding and quantification of abiotic and biotic stress interactions and their impacts , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22021, https://doi.org/10.5194/egusphere-egu26-22021, 2026.

Accurate and timely crop yield prediction is essential for effective agricultural management and global food security. This study assesses the effectiveness of hyperspectral imagery combined with deep learning model for crop yield prediction in agricultural fields of interest. Distinct vegetation indices are derived to reflect key physiological and structural crop traits by using hyperspectral imageries from early crop growth insight for detecting stress and predicting potential yield trends to peak growth information for reliable estimates of final crop yield, along with ground truth yield data. In addition, independent ancillary datasets, such as Digital Elevation Models (DEMs), critical soil parameters, and cropping treatments, are incorporated to capture topographic and edaphic influences on crop growth. The Deep learning algorithms such as Multilayer Perceptron (MLP) are employed, and model performance evaluated using Mean Absolute Error (MAE) and coefficient of determination (R²) values. The critical role of ShortWave InfraRed (SWIR) and Visible and Near-InfraRed (VNIR) based indices are investigated with respect to the yield estimations. The proposed methodology is applied at the field-plot scale as shown in the figure, using long-term experimental data from a temperate agricultural research site in the Midwestern United States. The analysis focuses on agricultural plots within the Main Cropping System Experiment (MCSE), comprising different cropping treatments (T₁-T₄) such as:

• conventional (T₁),
• no-till (T₂),
• reduced-input (T₃), and
• biologically based practices (T₄).

How to cite: Kaparthi, H. V., Vitti, A., Muriithi, D. M., and Mutwiri, F. K.: Crop Yield Prediction Using Multi-Temporal Hyperspectral Data and GeoAI Deep Learning Algorithm, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13806, https://doi.org/10.5194/egusphere-egu26-13806, 2026.