Understanding the spatial and temporal variability of environmental factors is crucial for managing and preserving agricultural landscapes and adapting to climate change's current and future impacts.
This requires a deep understanding of plants’ mechanisms for acclimation, keeping in mind that functional traits (e.g., phenology,etc.) can be indicators and proxies of plant status, plasticity and resilience. Moreover, it involves applied research and technological innovation in agriculture, including the use of sensors to monitor environmental variables, remote sensing and drones for crop monitoring, predictive models for yield and disease, and advanced methods to study nutrient cycles and soil health.
Furthermore, growing public awareness of the importance of ecosystem health and sustainability has led to adopting quantitative approaches to understand the link between agricultural practices and ecosystem services, which are crucial for achieving long-term environmental goals. Agroecological approaches, such as cover cropping, organic amendments, and integrated pest management, are being increasingly adopted to enhance biodiversity, soil health, water and nutrient retention, and resilience to climate change.
On these bases, the session will delve into:
- Quantifying and Spatially Modeling Environmental Factors: Examining the complex interplay of climate, soil, and water and their influence on plant growth, yield, and quality.
- Agricultural Resilience to Climate Change: Exploring the adaptability of agricultural systems in the face of a changing climate and identifying strategies for adaptation and mitigation.
- Sustainable Agricultural Practices and Ecosystem Services: Analyzing the impact of diverse agricultural practices on soil and water quality, biodiversity, and related ecosystem services.
- Precision Agriculture and Technological Innovation: Utilizing advanced technologies to optimize resource use, improve crop management, and enhance sustainability.
Orals: Mon, 4 May, 14:00–18:00 | Room 0.16
Agrivoltaic systems integrate photovoltaic energy systems with agricultural practices on shared land. Installation of overhead photovoltaic devices can benefit crops in certain situations, for example by creating partial shade in high light environments, improving water-use efficiency due to microclimate effects, and protecting crops from extreme weather events. Nevertheless, integrating solar cells in agricultural systems imposes a fundamental constraint on biological productivity due to the partitioning of incident sunlight between either electricity generation or plants. Semi‑transparent photovoltaics present a promising technology for integrating solar energy production into agriculture and other multi‑use solar landscapes. However, current approaches to assessing the biological productivity of semi-transparent agrivoltaic systems are relatively slow as they typically rely on crop yields and therefore require full cropping cycles before results can be obtained.
Here, we present a lab‑scale experimental framework for combined measurements of photosynthesis and electric power generation on plant leaves positioned beneath a semi‑transparent photovoltaic panel. We present the results of simultaneous measurements of electricity generation and photosynthesis using four different semi‑transparent photovoltaic devices and two plant species exposed to a range of light intensities. To facilitate the interpretation of our combined measurements across photovoltaic devices and plant species, we developed a new metric termed Photon‑use Equivalent Ratio (PER). The PER conceptually resembles the Land Equivalent Ratio (LER) and expresses the combined photovoltaic-photosynthesis output of leaves underneath semi-transparent solar panels, relative to stand-alone non-transparent solar cells and stand-alone leaves. Notably, our results show that certain device-species combinations achieve synergistic photon use, with PER > 1.
Our experimental approach may guide innovation of semi-transparent photovoltaic devices towards synergy in electricity generation and photosynthesis. In a broader context, our finding that certain device-species combinations achieve synergistic photon use challenges the assumption that solar panels and crops fundamentally compete for light.
How to cite: de Boer, H., Terricabres-Polo, R., Driever, S., van Sark, W., De Mello Donega, C., and Jans, T.: Synergistic photon-use in semi-transparent photovoltaic-photosynthesis systems for agrivoltaics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11988, https://doi.org/10.5194/egusphere-egu26-11988, 2026.
Climate change represents a major challenge for agricultural production today. Sclerotinia sclerotiorum (Lib.) de Bary is a necrotrophic pathogen capable of affecting over 500 species of dicotyledonous plants, leading to severe global economic losses. S. sclerotiorum also has developed highly resilient structures—sclerotia—that make this pathogen extremely difficult to control, as they can remain viable in the soil for 4-10 years. This study investigated the efficacy of the multicomponent bioproduct Sclerocid® as a science-based strategy for improving agroecosystem resilience.
Research on the reproductive potential of S. sclerotiorum revealed that under favorable conditions, it can form up to 6 generations of sclerotia, with each mother structure capable of producing approximately 20 daughter sclerotia. Experiments using successive transfers on potato dextrose agar medium recorded specific counts of 31, 30, 18, 19, 20, and 18 new sclerotia per generation, respectively. New sclerotia appear on days 3–4 and reach full maturity by day 7. Furthermore, sclerotia in the soil act as ecological reservoirs for other pathogens, including the genera Aspergillus, Fusarium, and Penicillium, which increases the overall disease pressure on host plants.
Since the superior ability of S. sclerotiorum to persist in the soil makes it difficult to control with conventional pesticides, biocontrol agents represent a promising strategy. Consequently, the biofungicide Sclerocid®, based on a microbial consortium with antagonistic properties, was developed by the BTU Biotech company. The decision to test a microbial consortium was made due to the robustness and synergistic interactions of multi-strain communities. While highly specialized mycoparasites like Paraphaeosphaeria minitans effectively destroy sclerotia, they have a narrow host range and do not control the associated soil-borne pathogens. By combining compatible strains of Trichoderma and Bacillus species, the inhibitory action of the consortium significantly exceeds that of individual monocultures.
The efficacy of Sclerocid® is based on the activities of its specific microbial constituents. Paraphaeosphaeria minitans IMV F-100120 acts as a specialized hyperparasite, colonizing sclerotia and forming pycnospores that lead to their degradation. Trichoderma harzianum IMV F-100097 is a broad-spectrum hyphal mycoparasite that effectively inhibits the growth of associated pathogens, including Alternaria alternata, Verticillium lateritium, Drechslera sorokiniana, and Cladosporium herbarum. Additionally, Bacillus subtilis IMV B-7678 and B. licheniformis IMV B-7778 produce a range of antifungal metabolites, with B. subtilis demonstrating 100% inhibition of S. sclerotiorum. The Sclerocid® consortium demonstrated high biocontrol efficacy, showing inhibition rates of 91.2–100% against S. sclerotiorum and effectively suppressing other pathogens such as Botrytis cinerea (80.4–100%) and Fusarium solani (74.2–100%). Furthermore, joint cultivation trials showed that the microbial component P. minitans could suppress the development of white rot mycelium for up to 35 days.
The synergistic action of the consortium—combining sclerotia hyperparasitism, hyphal degradation, and induction of systemic plant resistance—provides a sustainable and efficient biological solution to control phytopathogens and reduce pesticide load.
How to cite: Bukhonska, Y., Bolokhovska, V., Nagorna, O., Voloshchuk, N., Bolokhovskyi, V., Sterlikova, O., Khomenko, T., and Boroday, V.: Efficacy of the Complex Bioproduct Sclerocid® and its Microbial Constituents against White Rot and Associated Soil-Borne Pathogens, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10901, https://doi.org/10.5194/egusphere-egu26-10901, 2026.
Abstract
Global food insecurity is on the rise, having worsened since 2015 primarily due to conflicts and climate-related extremes, and jeopardizing the UN Sustainable Development Goal of zero hunger by 2030. Especially in sub-Saharan Africa, where the population is estimated to double by 2050, food demand is expected to increase by 3.9 percent annually. It is essential to increase agricultural production sustainably, without undermining the ecosystems’ capacity to sustain human well-being. In this context, neglected/orphan crops are gaining attention as an avenue to alleviate food insecurity. These crops are often produced under subsistence farming and have gained less attention from the scientific communities. Enset (Ensete ventricosum), a multipurpose, perennial, herbaceous-like plant domesticated only in Ethiopia, is a prime example of an underexploited crop. Although a model enset-based homegarden agroforestry exists, it is mainly produced as a monocrop in most Ethiopian regions, which exposes the crop to stressors such as climate extremes, soil fertility imbalance, and diseases. In certain regions, scattered agroforestry tree species exist inside enset farms, while information on the tree species' potential benefits and ensets’ response to tree canopy shade is not well documented. Our study examined how scattered trees in enset farms affect the microclimate and soil properties and evaluated the phenotypic responses of ensets. The trees significantly reduced the daily air, soil surface, and soil temperatures, ranging from -0.5 to -1.9 °C, -0.4 to -2.1 °C, and +0.4 to -1.0 °C, respectively, and maintained the minimum soil moisture by +0.8% to +5.7% compared to open areas. On the other hand, although the tree species had high-quality leaf litter, their effect on the soil fertility indicators was minimal. However, our on-field observation suggests that the old big trees improved total carbon, C/N ratio, cations (Ca2+), and CEC, suggesting the importance of conserving the already existing trees besides planting the new ones. Moreover, our observations on enset phenotypic response to the changing microenvironment by tree canopy covers revealed that enset can adapt to a new environment through plastic responses. Overall, our findings suggest that introducing woody trees into the enset farming system can enhance enset productivity by preventing extreme heat and frost and enhancing soil quality. Besides, they can help to reduce landslide risks commonly affecting farmers in the region.
Key words: Enset, Trees, Microclimate, Soil fertility, Phenotypic plasticity
How to cite: Bireda, A. Y., Gale, G. G., Swennen, R., Everaert, M., Shara, S., Muys, B., Honnay, O., and Vancampenhout, K.: Paradise lost? A case for (re)-introducing trees in the enset farming systems of the Ethiopian rift, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-217, https://doi.org/10.5194/egusphere-egu26-217, 2026.
Soils constitute a central component of the Earth’s Critical Zone, and agricultural land is a strategic resource essential for ensuring national food security, maintaining ecological stability, and supporting rural revitalization. In the context of rising global food security challenges and increasing land-use constraints, scientifically assessing soil agricultural suitability is vital for shaping agricultural production patterns that align with natural geographical structures and agricultural production laws. Responding to the national food security strategy, this study develops a systematic, standardized, and scalable framework for soil agricultural suitability assessment based on the ongoing Third National Soil Survey of China. The framework is applied at the national scale and validated through full-process practice in representative pilot counties.
The study establishes a technical specification system encompassing an evaluation index system, classification standards, and mapping procedures, and clarifies the hierarchical evaluation logic and deliverables from county to municipal and provincial levels. National-scale results show that climate- and landform-driven macro-geographical patterns largely determine the spatial boundaries and suitability grades of different agricultural land types. Significant regional differences are observed in the optimization potential and structural adjustment of cropland, forestland, and grassland, while the availability of effective reserve arable land remains limited, underscoring the need for refined spatial planning and strict protection. Validation in typical pilot counties demonstrates that the proposed framework is scientifically robust, operationally feasible, and capable of transforming soil survey data into practical decision-support products for cropland optimization and agricultural spatial restructuring. This provides a clear and implementable technical pathway for local agricultural land-use planning.
Overall, the research presents a comprehensive and scalable full-process solution for soil agricultural suitability assessment. It highlights the foundational and strategic value of nationwide soil survey data in supporting macro-level policy decisions, safeguarding agricultural productivity, and advancing sustainable agricultural development. The framework also offers a referenceable model for countries or regions with large-scale agricultural production needs, providing scientifically grounded and practical pathways to alleviate human–land conflicts and enhance the efficiency of land resource allocation.
How to cite: Wu, K., Feng, Z., and Li, J.: Technical Approaches and Practices for Assessing Agricultural Suitability of Soils in China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2912, https://doi.org/10.5194/egusphere-egu26-2912, 2026.
How to cite: Feng, Z., Wu, K., and Li, J.: Cultivated Land Resource Resilience Evaluation and Zoning in the Northeast Black Soil Region Based on, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2927, https://doi.org/10.5194/egusphere-egu26-2927, 2026.
The Mediterranean cropping systems continue to face drought risk, with the changing climate intensifying its effects. Adaptation strategies are essential and must be assessed for their efficacy to guarantee their sustainability and relevance in specific regions, thereby enhancing adoption and optimizing resource utilization. This study investigated two climate adaptation strategies, namely shifting planting date (mid-November, late-November, or early-December) and adjusted irrigation (full, 75%, 50%, 25% deficit irrigation (DI), and rainfed), in reducing drought-induced yield loss risk (i.e., the magnitude of the yield loss and the likelihood of its occurrence) for durum wheat in the two provinces of Basilicata (Matera and Potenza) in southern Italy.
The study employed crop and probabilistic modeling to simultaneously assess the effectiveness of shifting planting dates and irrigation adjustments while quantifying the yield loss risk caused by drought. The crop model, CSM-CERES Wheat, was utilized in simulating the yield at different management combinations initialized using the weather (1991–2023), soil, and management data. Copula functions were used to model the dependency structure between drought intensity and yield anomaly calculated from the simulated yield, allowing the assessment of drought-induced yield loss risk.
Drought-induced yield loss risk in Matera was characterized by a relatively high likelihood of experiencing yield reductions with probabilities ranging from 36.6% to 78.4%, whereas Potenza exhibited negligible to low probabilities with the exception for early-December planting under rainfed condition (i.e., 55.9%). Shifting the planting date to mid-November and increasing irrigation levels reduced the likelihood of drought-induced yield loss, with significant results observed in Potenza. In Potenza, planting durum wheat in mid- to late-November under rainfed conditions showed a low risk of yield loss due to drought. This suggests that durum wheat can be grown with little or no irrigation. However, in Matera, even with full irrigation, yield loss risk was marginally high during intense dry conditions.
In Matera under rainfed conditions, substantial yield loss estimates (i.e., 70% likelihood) for late-November and early-December planting were 0.1 and 0.7 t ha-1, respectively, for moderate drought severity (-0.84 > spei ≥ -1.28), while for exceptional droughts (spei < -2.33), yield loss estimates were 4.0 and 1.2 t ha⁻¹, respectively. In Potenza, substantial yield loss was only experienced for early-December planting under rainfed conditions during extreme (-1.65 > spei ≥ -2.33) to exceptional drought, with estimated yield losses of 0.1 and 0.5 t ha⁻¹, respectively.
The results demonstrated that shifting the planting date to mid-November and implementing deficit and full irrigation largely mitigated drought‑induced yield loss risk in Basilicata, most notably in Potenza. Earlier planting and minimal irrigation in Potenza can help reduce yield loss risk due to drought. In Matera, on the other hand, early planting combined with full and deficit irrigation also mitigated the impact of drought except during extremely dry conditions, indicating additional or alternative adaptation strategies to cope with its impact.
How to cite: Chummac, A. and Bonaccorso, B.: Mitigating Drought-Induced Yield Loss Risk for Durum Wheat through Optimized Planting Schedule and Irrigation in Southern Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12892, https://doi.org/10.5194/egusphere-egu26-12892, 2026.
Cover cropping and diversified crop rotations are widely promoted as strategies to enhance soil carbon inputs and contribute to climate change mitigation. However, including additional crop cover could have detrimental impact of water use efficiency (WUE) and has not been sufficiently quantified under continuous field conditions. We evaluated two contrasting crop management systems over two full rotation cycles (2017–2022): a diversified winter wheat–corn–soybean rotation with cover crops and a conventional soybean–corn–soybean rotation without cover crops. Evapotranspiration (ET) was measured at half-hourly resolution using large weighing lysimeters at the Elora Research Station in Ontario, Canada. Analyses focused on the growing season (1 May–1 November), and cumulative seasonal ET was calculated for each lysimeter. Crop yield was measured annually, and WUE was calculated as the ratio of yield to cumulative ET. The experiment included 12 lysimeters across two soil types (silt loam and loamy sand). Across six growing seasons, cumulative ET ranged from 350 to 800 mm, yield from 903 to 13,203 kg ha⁻¹, and WUE from 0.17 to 2.1 kg m⁻³. Despite clear differences in management practices, ET, yield, and WUE did not differ significantly between the diversified and conventional systems in five of six years. In 2017, significant differences were observed between managements for ET in loamy sand soil and for yield and WUE in silt loam soil, largely reflecting differences in crop type between systems. In contrast, soil type consistently exerted a strong influence on ET, yield, and WUE across all years. These results suggest that, in the studied agroecosystem, there are minimal trade-offs between climate change mitigation benefits associated with cover crop use and the water-use efficiency of diversified cropping systems, with soil properties exerting dominant control on crop water use and productivity.
How to cite: Wagner-Riddle, C., Alamdar, S., Berg, A., Basu, N., and Dara, R.: Effects of diversified and conventional crop management on evapotranspiration, yield, and water-use efficiency measured with weighing lysimeters over six years, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14272, https://doi.org/10.5194/egusphere-egu26-14272, 2026.
Agriculture faces significant challenges under a changing climate, with increasingly unpredictable weather patterns disrupting farming operations, degrading soil health, impacting crop growth and thus threatening food security. Extreme rainfall and prolonged droughts during critical planting and growth periods can severely reduce yields and profitability, underscoring the urgent need for adaptive strategies.
Regenerative agriculture has emerged as a farmer-led movement aimed at improving soil health and enhancing system resilience. It is guided by six core principles: understanding farm context, minimising soil disturbance, maintaining year-round soil cover, increasing diversity, integrating livestock, and sustaining a living root throughout the year. However, regenerative agriculture encompasses a wide range of practices, and implementation varies across farms with different soils and climates. Transitioning to a regenerative agriculture system is complex, often requiring new skills, equipment, and management approaches. Furthermore, soil health improvements occur gradually, thus creating a transition period that can increase farm business risk, particularly under climate extremes.
At the University of Leeds farm in Yorkshire, England, we have established a replicated, large-plot field trial to evaluate the impacts of different transition strategies to regenerative agriculture. Our research measures changes in soil health, water regulation, biodiversity, greenhouse gas emissions, and economic viability, providing evidence to determine if regenerative agriculture practices can enhance farm resilience and deliver ecosystem services. Findings from recent extreme weather events - flooding in 2024 and drought in 2025 - highlight that adaptation is a long-term process requiring technical support and financial incentives to ensure a just transition for farmers.
How to cite: Wade, R., Smith, K., Grayson, R., Collins, L., and Chapman, P.: Regenerative Agriculture: A Pathway to Climate Resilience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21482, https://doi.org/10.5194/egusphere-egu26-21482, 2026.
Feeding a growing global population while limiting biodiversity loss remains a central challenge for sustainable development. Although biodiversity decline and food availability have been widely studied, their combined trajectories and trade-offs at the national scale are not well quantified. This study integrates measures of biodiversity intactness and food security to examine long-term national trajectories from 1970 to 2014, together with future emission scenarios. Substantial variation exists across countries, alongside a persistent trade-off between food security gains and biodiversity conservation. Countries such as Norway and Canada remain closest to sustainability, combining relatively high biodiversity intactness with adequate food availability. In contrast, several countries in the Global South, including Bangladesh, Angola, and Mongolia, remain far from the sustainability frontier due to low food availability, biodiversity loss, or both. Large agricultural and economic producers, such as China, India, and Brazil, exhibit predominantly horizontal or downward-sloping trajectories, indicating that increases in food availability have occurred alongside declines in biodiversity intactness. Over the period 1970-2014, countries with sustained improvements in food security show a consistent temporal imbalance between nutritional gains and biodiversity outcomes. Across China, India, Brazil, biodiversity losses offset approximately 30-60% of food-security gains. In these countries, positive contributions from food security accumulate over time while biodiversity contributions remain persistently negative. Assessing food availability alongside biodiversity therefore provides a more complete assessment of sustainability outcomes. Placing national food-system trajectories within a planetary-boundaries framework helps identify pathways that meet nutritional requirements without exceeding limits of ecological change.
How to cite: Datta, A., Goyal, S. S., Kumar, R., and Bhatia, U.: Tracing the Synergies and Trade-off Between the Global Food Security and Biodiversity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16987, https://doi.org/10.5194/egusphere-egu26-16987, 2026.
Cover crops (CCs) are widely promoted as a cornerstone of climate-smart and diversified agriculture, yet their effectiveness remains highly context-dependent. Here, we present a coherent, multi-faceted framework for understanding when, where, and how CCs can deliver net climate and productivity benefits. First, drawing on a global meta-analysis of over 2,300 observations, we demonstrate that CCs can substantially enhance agroecosystem multifunctionality—including crop yield, soil carbon storage, and erosion control—when practices are optimized. Long-term implementation, climate-smart management (e.g. no-tillage), and diversified CC mixtures emerge as key determinants of synergistic outcomes, particularly in environmentally constrained regions. Second, we show that these benefits are frequently constrained by a climate–productivity trade-off driven by elevated nitrous oxide (N2O) emissions. By integrating multiple global meta-analyses with machine-learning approaches, we identify aridity and soil acidity as dominant controls of this trade-off through their influence on microbial nitrogen cycling. Region-specific optimization of nitrogen inputs can substantially reduce trade-off intensity, mitigating up to ~2.65% of global crop-specific N2O emissions while sustaining yield gains, especially in semi-arid alkaline systems. Finally, moving beyond CCs as a standalone solution, we explore the potential of coupling CCs with enhanced rock weathering (ERW) as a complementary nature-based strategy. Evidence from systematic reviews and field observations suggests that CC–ERW synergies can improve biogeochemical synchrony, strengthen soil food webs, and further reconcile climate mitigation with agricultural productivity. Together, these findings highlight a pathway from practice optimization to system-level integration for building resilient, multifunctional agroecosystems under climate change.
How to cite: Qiu, T., Liu, J., Xu, Z., Lamichhane, J. R., Peñuelas, J., and Fang, L.: Towards 2030: advancing climate-resilient and sustainable cover cropping systems under global change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20169, https://doi.org/10.5194/egusphere-egu26-20169, 2026.
The agricultural landscape of South-Eastern Romania, specifically in the counties of Constanța and Tulcea, represents a complex socio-ecological system that is increasingly vulnerable to climate-driven stressors. This study quantifies and models the spatial interplay between climate variables, soil characteristics, and water availability to determine their collective influence on plant growth, final yield, and crop quality. The research employs a multi-source data fusion approach, integrating high-resolution satellite imagery (Sentinel-2 and Landsat 8/9) with CORINE Land Cover, EUCropmap, and AGRI4CAST datasets to map spatial heterogeneity in crop coverage and land-use transitions. To bridge the gap between spectral indices (such as NDVI, EVI, and NDWI) and actual agricultural output, the model incorporates multi-annual statistics on crop harvesting. Using Geographically Weighted Regression (GWR) and Machine Learning algorithms (Random Forest), we analyze the non-stationary relationships between: (i) climate drivers of crop production, such as precipitation patterns, thermal accumulation (GDD), and evapotranspiration rates; (ii) edaphic factors, such as soil moisture retention and organic matter content specific to the Chernozem and Fluvisol profiles of the region; (iii) water-availability constrains, such as proximity to the Danube, the influence of the Black Sea’s maritime climate, and availability of irrigation. Preliminary findings indicate that while soil fertility remains high in parts of Constanța, water scarcity due to limited irrigation and reduced air humidity acts as the primary limiting factor, creating high-yield variability. In the Tulcea region, the influence of the Danube Delta creates a distinct micro-environmental signature that benefits certain crop types while increasing the risk of salinity-induced quality degradation. As part of the transboundary research project “The impact of European agricultural policies on land use: Romania's experience and lessons for the Republic of Moldova in a European perspective – MapLURoMd”, this study provides a good framework for regional land-use planning and useful insights for the Republic of Moldova’s climate change adaptation policy in agriculture based on Romanian experience.
How to cite: Sirodoev, I., Cracu, G.-M., Nicoara, R.-G., Paraschiv, M., Schvab, A., Secareanu, G., Vaidianu, N., Cantir, A., Chiriac, I., Crivova, O., Curcubat, S., and Sirodoev, G.: Multi-dimensional Spatial Modeling of Climate-Soil-Water Dynamics: Assessing Crop Productivity and Quality Factors in South-Eastern Romania, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20059, https://doi.org/10.5194/egusphere-egu26-20059, 2026.
Under pressure from climate change and the need to ensure production resilience, modern agriculture is shifting towards management strategies based on advanced monitoring systems. This approach, known as Agriculture 4.0 or 5.0, aims to optimize resource use and adapt to evolving conditions. However, the deployment of such tools cannot be incidental; to optimize resources and resilience, it is necessary to strategically determine the optimal positioning and the specific typology of sensors to be employed.
Optimizing resources requires correct knowledge of the soil-plant-atmosphere (SPA) system. This objective demands a holistic understanding of the soil, plant, and atmosphere. Based on this knowledge, management decisions regarding nutrients and irrigation are made by subdividing the field into Management Zones or Homogeneous Zones (HZs). A detailed analysis of soil properties and crop responses allows for site-specific distribution of resources and the identification of Functional Homogeneous Zones (fHZs), which are directly related to SPA dynamic processes and ecosystem functions.
The identification of HZs and fHZs passes through a critical characterization phase where geosciences play a transformative role. This contribution demonstrates how UAVs equipped with different cameras enable the classification of these zones through varying levels of complexity. These methods promote Agriculture 4.0 goals by optimizing field sensor deployment—significantly reducing costs—and improving resource allocation, which enhances farm incomes and systemic resilience.
Geophysical investigations provide the spatial intelligence to plan sensor networks correctly, ensuring they capture actual landscape variability. We propose an integrated framework combining UAVs, geophysical surveys, and mechanistic modeling. UAVs with multispectral sensors and topographic tools monitor crop status and phenological plasticity with high precision. These data, integrated with proximal geophysical investigations like Electromagnetic Induction (EMI) and magnetic mapping, allow for non-invasive characterization of sub-surface soil heterogeneity. This approach reduces costs associated with blind sensor installation and trial-and-error management, providing a roadmap for Decision Support Systems (DSS). Through mechanistic modeling, the framework simulates water and nutrient dynamics, providing indicators of soil health and plant stress. Ultimately, UAV-based geophysics and hydropedological modeling represent transformative tools to safeguard ecosystem services and promote sustainable, economically viable agricultural landscapes.
Keywords: Agriculture 4.0 & 5.0, UAV Support, Sensor Network Optimization, Geoscience, SPA Continuum, Climate Resilience.
How to cite: Accomando, F., Tizzani, P., Iannuzzi, A., Buonanno, M., and Bonfante, A.: The importance of UAV applications and uses in Agriculture 4.0, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20379, https://doi.org/10.5194/egusphere-egu26-20379, 2026.
The recent technological advancements in the field of Unmanned Aerial Vehicles (UAVs) and Artificial Ιintelligence (ΑΙ) have led to their widespread adoption across different sectors of agriculture. In particular, there has been a growing interest in the application of these technologies in viticulture, but their operational implementation and validation under diverse environmental and management conditions remain limited. To this end, the evaluation of different AI methodological frameworks for vine detection across different settings constitutes an important step for the sufficient and cost-effective deployment in real-world vineyards.
Our study aims at contributing towards this direction by evaluating different segmentation approaches for vines detection tested in a real-world vineyard of Ktima Lazaridi vinery located in the prefecture of Drama, Macedonia, Northern Greece. The vineyard acting as the study’s experimental site spanned across approximately 4.94 hectares and consisted of Sauvignon Blanc vines. In this site, multispectral imagery was acquired at 40 meters Above Ground Level (AGL) on 30 July 2025 from a UAV equipped with a high-definition RGB camera, a red-edge and Near infrared bands. Experiments were performed using state-of-the-art segmentation methods such as Segment Anything Model (SAM) and object-based image analysis frameworks using multimodal UAV imagery (RGB, NIR, Red Edge bands, Vegetation Indices). Standard statistical metrics were employed to quantitatively assess the modelling results using as reference ground truth masks generated with direct photointerpretation. ArcGIS Pro was used for the implementation of the AI algorithms as well as for the evaluation of the experimental analysis.
Our study findings suggest that UAV-based multimodal imagery combined with advanced AI algorithms, can serve as a cost-effective and scalable solution for vineyard monitoring, management, and decision-making. Future work will focus on evaluating these methods under different grape varieties, phenological stages, and environmental conditions to further generalize their applicability and optimize vineyard management strategies.
Keywords: UAVs, Artificial Intelligence, SAM, Vineyards, ACCELERATE
Acknowledgement
This study is financially supported by the ACCELERATE MSCA SE program of the European Union’s Horizon research and innovation program under grant agreement No. 101182930
How to cite: Asimakopoulos, C., Petropoulos, G. P., Saitis, G., Detsikas, S. E., Evelpidou, N., Grigoriadis, K., Polychronos, V., Mamagiannou, E.-M., and Litke, A.: Detecting vineyards using multispectral UAV imagery and artificial intelligence: A case study from Northern Greece, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3117, https://doi.org/10.5194/egusphere-egu26-3117, 2026.
To investigate the climate-driven mechanism of nicotine formation, this study was based on flue-cured tobacco leaf samples (n=5321) and corresponding climate data from three major typical tobacco-growing regions in China—Southwest, Huanghuai, and Northeast—from 2010 to 2024. Methods including analysis of variance, Mantel test, variance partitioning, correlation analysis, and ARIMA time series models were employed to systematically analyze the contribution rates and interactions of production region, cultivar, leaf position, and climatic factors on nicotine content. The results showed an overall increasing trend in nicotine content across leaf positions in the Southwest region, with upper and middle leaves increasing by 20.3% and 16.7%, respectively, over the 10-year period. The Huanghuai and Northeast regions exhibited decreasing trends in nicotine content. The climatic drivers of nicotine content differed significantly among leaf positions. Nicotine in upper leaves was primarily regulated by sunshine duration (contribution rate 42.5%), while nicotine in middle and lower leaves was driven by precipitation. Time series analysis indicated a significant decrease in sunshine duration in the Southwest region at a rate of 2.1 hours per year (p<0.05), and increasing precipitation trends of 21 mm and 17 mm per year in the Huanghuai and Northeast regions, respectively. Based on ARIMA model, the nicotine content of upper leaves in the Southwest region is projected to rise to 3.6–3.8% over the next five years, posing a risk of exceeding the suitable range for industrial availability. The response of nicotine to climatic factors showed significant cultivar specificity. Yunyan 87 was sensitive to sunshine and precipitation, while Yunyan 99, Zhongyan 100, and Longjiang 911 were insensitive to sunshine duration. This study utilized ARIMA models to predict and issue an early warning that nicotine content in upper leaves of the Southwest region risks exceeding standards in the next five years, providing a critical window for proactive industry response. The identified climate-resilient cultivars such as 'Yunyan 97' and 'Longjiang 911' establish a germplasm foundation for ensuring the future quality stability and industrial usability of tobacco leaf raw materials.
How to cite: Wu, B., Zheng, C., Feng, W., Wang, Y., and Wang, J.: Climate drivers of nicotine content variation and ARIMA model prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17119, https://doi.org/10.5194/egusphere-egu26-17119, 2026.
Sustainable management practices are promoted to achieve dual outcomes of mitigating climate change and sustaining crop productivity. In particular, no-till with cover crops introduces greater plant-derived biomass C and enhances nutrient cycling for subsequent seasons. However, whether this practice improves soil health and further facilitates crop productivity and net biome production (NBP) in the short term remains unclear. Therefore, this study aimed to evaluate the effects of management practices on NBP and soil health, and the relationship between soil health and yield. A soybean field trial was conducted at the Field Science Center for Northern Biosphere, Hokkaido University, Japan, to examine the first two-year effects of rye [Secale cereale, cv. R-007] cover cropping under no-till without fertilizer (NT-WC), compared with CT-CF (tillage + chemical fertilizer), CT-NF (tillage + no chemical fertilizer), and NT-NF (no-till + no chemical fertilizer). Haney’s Soil Health Test (HSHT), which quantifies soil health score (SHS), was employed to evaluate seeding and harvest stages in 2024 and 2025 by measuring the parameters: CO₂ burst, water extractable organic carbon and nitrogen (WEOC, WEON) (calculated as WEN minus NO3--N and NH4+-N). NBP was calculated from plant-derived biomass carbon and heterotrophic respiration (Rh), measured using the static closed-chamber technique. Three-way ANOVA, Pearson’s correlation analysis, and regression analysis were used to examine the differences among treatments over two years, the driver parameter of SHS, and the relationship between SHS and grain yield, respectively. The results showed that the only positive NBP value in the first year under NTWC, with higher rye net primary production (NPP), declined significantly in the second year from 1.56 to −4.70 Mg ha-1 due to reduced soybean aboveground NPP and increased Rh. In addition, grain yields in the second year under NT were significantly lower than those under CT and also significantly decreased relative to the first year. Second-year yield was strongly associated with soil bulk density, particularly in the 0–5 cm layer (r=－0.82; P<0.0001). Tillage reduced bulk density in the CT treatment and thereby alleviated yield and biomass limitations. SHS ranges from 0 to 50, with higher values indicating higher microbial activity and nutrient cycling potential. SHS had no significant differences among treatments but varied over time (mean 16.4 → 7.0 → 8.4 → 29.4), driven by elevated CO₂ burst, WEON, and WEOC. These patterns coincided with significantly higher Rh in the second year (except for CT-NF), likely reflecting enhanced microbial activity and increased residue decomposition. SHS at seeding was significantly correlated with grain yield under NT treatments. Overall, the no-till with cover crops system decreased yield and NBP and did not result in a clear improvement in soil health. Reduced tillage and the inclusion of physical soil properties in managing soil health may guide to optimize management, prevent crop yield loss, and enhance biomass production in further trials in this pedoclimatic context.
How to cite: Tseng, W.-Y., Hirata, T., Kuramochi, K., and Toma, Y.: Short-term responses of soil health and net biome production to no-till cover cropping: a case study of silty clay soil in a humid continental climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19551, https://doi.org/10.5194/egusphere-egu26-19551, 2026.
Drought stress is a major constraint on crop productivity, yet its impact on plant physiological status remains difficult to monitor non-destructively across contrasting soil conditions. In this study, we investigated drought-induced changes in the optical properties of spring barley (Hordeum vulgare L.) leaves using visible–near infrared (VIS–NIR, 350–2500 nm) spectroscopy in a controlled pot experiment.
Barley was grown on four mineral soils differing in texture and water retention capacity, which imposed contrasting soil water availability conditions. Plants were subjected to early drought stress, late drought stress, combined stress, and a well-watered control. Leaf reflectance spectra were collected repeatedly throughout the experiment using a PSR-3500 spectroradiometer with a leaf clip, allowing the analysis of temporal stress development under different soil settings.
Drought stress induced consistent spectral responses across all soils, including increased reflectance in the visible range associated with chlorophyll degradation, reduced near-infrared reflectance linked to changes in leaf internal structure, and pronounced changes in short-wave infrared water absorption features. Vegetation indices related to greenness and water content (e.g. NDVI, NDWI) declined progressively with increasing stress intensity.
Principal component analysis (PCA) revealed a clear and reproducible trajectory of spectral change corresponding to drought progression (control → early stress → combined stress → late stress). This temporal stress signal dominated the spectral variability, while soil type exerted a secondary influence compared to both drought intensity and measurement timing. Loadings indicated that red-edge dynamics and leaf water absorption bands (970, 1450 and 1940 nm) were the primary contributors to stress discrimination.
The results demonstrate that VIS–NIR spectroscopy provides a sensitive, non-destructive tool for tracking drought-induced physiological responses in barley leaves and for disentangling primary plant stress signals from secondary soil-mediated effects related to soil water availability. This approach offers strong potential for proximal and remote sensing applications aimed at improving drought monitoring and crop resilience assessment under changing climatic conditions.
How to cite: Bartosiewicz, B. and Debaene, G.: Tracking drought-induced physiological trajectories in spring barley leaves using VIS–NIR spectroscopy across contrasting soil types, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19781, https://doi.org/10.5194/egusphere-egu26-19781, 2026.
Drought stress is an increasingly dominant constraint on maize (Zea mays L.) production under changing climatic conditions. While modern hybrid varieties are optimized for high yield potential, their performance often declines under variable and limited water availability. This limitation is particularly pronounced during early developmental stages. In contrast, maize landraces may express adaptive root and rhizosphere traits; however, the consistency of these responses across contrasting environmental conditions remains poorly understood. In this study, we investigated variety-specific drought responses in maize by integrating controlled greenhouse experiments with complementary field trials. In a high-throughput phenotyping facility, six maize varieties representing contrasting breeding histories were grown under five distinct water regimes and two soil types. We quantified growth dynamics of above- and belowground biomass, functional root and shoot traits including plant height, biomass allocation, and root morphological properties, as well as physiological responses to characterize drought-response strategies under controlled conditions. To date, the data suggests pronounced soil-type effects on root and shoot traits. In addition, variety-specific patterns emerge for selected above- and belowground traits across the two soil types. Additionally, field experiments were established at four sites in Bavaria to assess genotype performance under realistic agronomic conditions, representing contrasting precipitation and soil textures. To enhance information gain, the field trials included both single-variety and variety-mixture systems, in which maize genotypes with contrasting drought-response strategies were grown either individually or in two-genotype mixtures within the same plot allowing potential genotype interactions under field conditions to be evaluated. By focusing on greenhouse-based trait expression while embedding the study in a field context, this work aims to identify root and rhizosphere traits associated with drought responses and to evaluate their relevance across contrasting environmental conditions. The results contribute to a mechanistic understanding of maize drought adaptation and explore the potential of landraces as genetic resources for the development of maize varieties suited to increasingly dry and variable climates.
How to cite: Thiel, J., Wild, A. J., Gupta, S., Heid, A., Geyer, M., Groth, J., Lüders, T., Zare, M., Pausch, J., and Wiesmeier, M.: Maize drought responses across experimental scales: linking above- and belowground traits , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8929, https://doi.org/10.5194/egusphere-egu26-8929, 2026.
Agroforestry represents one of the oldest and most sustainable forms of land use, integrating woody perennials with crops and grasslands to enhance resource efficiency while meeting human requirements for food, timber, and fiber. While its potential to conserve natural resources is well-recognized, a full understanding of the interactions between system components remains limited, particularly regarding the hydrological processes that regulate plant growth, nutrient dynamics, and energy exchange. This study presents ongoing research at a silvoarable experimental site in Gladbacherhof, Hesse, Germany, designed to quantify soil moisture dynamics across multiple spatial scales.
To characterize these dynamics at a high resolution, volumetric soil moisture is monitored using sensors installed along three transects oriented perpendicular to apple tree rows at specific distances of 1, 2.5, 6, and 10.5 meters from the trees and at soil depths of 10, 40, and 60 centimeters. These point-scale observations are complemented by field-scale variability captured through cosmic-ray neutron sensing (CRNS). To bridge these disparate scales, the study employs machine learning approaches—including random forest models, multilayer perceptron neural networks, and deep learning techniques—to derive spatially continuous representations of soil moisture at an intermediate scale.
Furthermore, the acquisition of high-temporal-resolution data allows for the investigation of hydraulic lift, which is inferred from observed nocturnal increases in soil moisture during prolonged dry periods. Building on these findings, a subsequent phase of the experiment will apply water stable isotope techniques to track the specific spatio-temporal patterns of water uptake by trees, grassland, and arable plants. As an ongoing study, this research aims to clarify the key factors and spatial controls governing soil moisture dynamics, ultimately supporting more informed design and management of resilient, multifunctional agroforestry systems.
How to cite: Asadi, F., Felipe, A. J., Dubbert, M., Jacobs, S. R., and Breuer, L.: Investigating Multi-Scale Soil Moisture Dynamics and Hydrological Processes in Silvoarable Agroforestry in Hessen, Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21251, https://doi.org/10.5194/egusphere-egu26-21251, 2026.
Posters on site: Mon, 4 May, 10:45–12:30 | Hall X3
Arable lands support food production alongside a range of agro-ecosystem services (AES), yet widespread degradation has resulted from their continued exploitation. Within this context, conservation agriculture (CA) has emerged as a potential resource-conservation technology (RCT) to improve system performance and mitigate environmental costs. However, evidence on how conventional and CA-based practices shape the full spectrum of agro-ecosystem services remains limited for rice–wheat systems of tropical and subtropical regions, particularly within lateritic belts where soils are inherently vulnerable. To address this gap, a multi-year experiment (2022–2025) was conducted to evaluate the effect of varying tillage regimes and residue management practices on AES offered by rice-wheat systems on lateritic soils. Rice establishment comprised zero-tilled direct-seeded rice (ZTDSR) and transplanted rice (TPR), including variants with alternate wetting and drying (TPRAWD), while wheat was established under zero tillage (ZTW) or conventional tillage (CTW) with rice residue retention (RR), incorporation (RI), or no residue. Results from the study showed that provisioning AES attained the maximum value in case of ZTDSR-ZTW(RR) for both rice (US$ 1322 ha-1 season-1) and wheat (US$ 977 ha-1 season-1). Regulatory AES values ranged from US$ 397 ha-1 y-1 (TPR-CTW) to US$ 489 ha-1 y-1 (ZTDSR-ZTW (RR). The monetary values of soil parameters influencing regulatory AES under CA followed the trend ZTDSR-ZTW(RR) > ZTDSR-CTW(RI) > ZTDSR-CTW, where CA-based practices exhibited 1.54-8.5% and 2.71-19.9% higher AES values compared to conventional (TPR-CTW) in rice and wheat seasons, respectively. The results of supporting AES which comprised of soil fertility, N mineralization, air purification etc. were observed to be US$ 509 and US$ 328 ha-1 season-1 for rice and wheat, respectively. ZTDSR-ZTW(RR) exhibited highest economic value of soil fertility (US$ 8.73 ha-1 season-1) followed by ZTDSR-CTW(RI) (US$ 3.77 ha-1 season-1), TPRAWD-ZTW(RR) (US$ 5.79 ha-1 season-1) and ZTDSR-CTW (US$ 1.39 ha-1 season-1) averaged for both rice and wheat seasons. With regard to ecosystem disservices, plots managed under ZTDSR-ZTW(RR) resulted in 41 and 28% higher direct emissions for rice and wheat season, respectively, than that of ZTDSR-CTW. The CH4 emissions were largely prevalent in rice season and exerted the most negative impact for TPR-CTW(RI) in economic terms (US$ -31.29 ha-1 y-1) while the least was observed for ZTDSR-ZTW (RR) (US$ -0.07 ha-1 y-1). Similarly, CA-based treatments showed 61 and 72% lower soil-erosion-based environmental costs than conventionally cultivated treatments in rice and wheat seasons, respectively. Considering all ES together, the net AES value extended from US$ 3657 to US$ 4544 ha-1 y-1, with ZTDSR-ZTW being highest (US$ 4544 ha-1 y-1) followed by TPRAWD-ZTW(RR) (US$ 4343 ha-1 y-1) and ZTDSR-CTW(RI) (US$ 4155 ha-1 y-1) and TPR-AWD being the lowest (US$ 3657 ha-1 y-1). These results indicate that ZTDSR-ZTW (RR) can potentially be better RCT for improved net AES offered by agricultural systems in lateritic soils and can be included in policy intervention to achieve sustainability of these ecologically fragile tracts.
How to cite: Dey, P., Rana, S., Swain, D. K., and Bhattacharya, P.: Assessment of agro-ecosystem services under contrasting tillage and residue management in rice–wheat system of Lateritic soils, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2173, https://doi.org/10.5194/egusphere-egu26-2173, 2026.
Molecular biomarkers are increasingly used in agriculture to provide objective indicators of plant health and responses to environmental stress. In grapevine (Vitis vinifera L.), genomic biomarkers are particularly valuable as they integrate physiological and environmental information. Among these, telomere length has emerged as a rapid and cost-effective biomarker, already applied in several plant and animal systems.
Telomeres are repetitive DNA sequences located at chromosome ends that preserve genomic stability and are known to respond dynamically to aging and stress conditions. In this study, telomere length was quantified by quantitative real-time PCR (qPCR), using a grapevine-specific single-copy reference gene to ensure reliable measurements.
The protocol was applied to Aglianico grapevines subjected to different water regimes (irrigated and non-irrigated) under identical soil conditions in a high-quality wine production area of southern Italy (Taurasi DOCG area, Tenuta Donna Elvira, Montemiletto, AV). Results obtained over two consecutive growing seasons allowed evaluation of telomere length dynamics across years. Consistent differences associated with water regime were observed between seasons, supporting telomere length as a sensitive indicator of vine well-being and environmental pressure.
These findings strengthen the potential of telomere length as a genomic biomarker for long-term monitoring of grapevine health under climate-related stress conditions.
How to cite: Iannuzzi, A., Pistucci, R., Erbaggio, A., Albrizio, R., Accomando, F., Liccardo, M., and Bonfante, A.: Telomere length as a genomic biomarker of well-being in grapevines: a two-year study in Aglianico grapevine., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3140, https://doi.org/10.5194/egusphere-egu26-3140, 2026.
In building an environmentally harmonized food system, it is essential to examine measures to stabilize food production under climate change, grounded in quantitative evidence. This requires a framework that quantifies the impacts of climate change on crop production and utilizes those results to design crop production strategies. Approaches that estimate changes in yield with crop production models are widely used and are effective for identifying national-scale trends. However, uncertainty in regional-scale future projections persists because region-specific characteristics, such as soil conditions and farming practices, are not sufficiently represented. In addition, in process-based models, it is often difficult to define the numerous parameters governing crop growth in ways that are consistent with local soil and management conditions, and this parameter uncertainty strongly influences projections.
In this study, we performed Bayesian calibration of crop-file parameters in AquaCrop [1] using the Markov Chain Monte Carlo (MCMC) method to address the practical difficulty of specifying model parameters consistent with local conditions. To support this calibration, we developed a method to generate a region-specific characteristic dataset by integrating climate drivers with soil information as regional characteristics. AquaCrop, a process-based crop model developed by the Food and Agriculture Organization of the United Nations (FAO), was driven by daily maximum and minimum temperature, precipitation, and ETo. Climate forcing was obtained from historical data, and soil characteristics were derived from the Japanese Soil Inventory [2] provided by the National Agriculture and Food Research Organization (NARO), including soil maps and gridded property layers (e.g., saturated hydraulic conductivity and water-retention metrics such as pF-based water contents and available water capacity). We harmonized the coordinate systems of the climate and soil datasets and implemented a data-generation procedure to produce climate-grid-consistent regional inputs. This enables multi-year, multi-site calibration of crop parameters and subsequent yield simulations under local conditions.
Furthermore, to estimate key crop-file parameters from observations, we developed robust procedures for preparing daily weather time series. This includes standardizing the required variables and performing Bayesian estimation via MCMC. Using multi-year observed yields, we estimate posterior distributions of major crop parameters and quantify associated uncertainty. Using the developed system, we validate it with historical data and conduct yield projections under temperature-increase conditions, enabling evaluation of the contributions of climate warming and soil-mediated regional differences to yield changes. By combining an integrated regional input data foundation with explicit treatment of crop-parameter uncertainty, this framework provides a basis for improving the reliability of regional-scale future projections.
[1] FAO (2024) AquaCrop, Version 7.2. Food and Agriculture Organization of the United Nations
[2] NARO, https://soil-inventory.dc.affrc.go.jp/
How to cite: Kono, K., Tokunaga, D., and Kawata, H.: Crop-Parameter Identification through Bayesian analysis for Regional-Scale Yield Estimation with AquaCrop, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6430, https://doi.org/10.5194/egusphere-egu26-6430, 2026.
Multiple cropping systems are widely adopted as a key climate adaptation strategy to ensure food security in China, however, they also impose significant pressure on freshwater ecosystems. While trade-offs between yield and water use are well-documented, the spatiotemporal impacts of specific rotation systems on aquatic biodiversity remain poorly quantified at fine spatial scales. To address this, we present a high-resolution framework integrating 30m-resolution crop rotation maps with monthly gridded characterization factors to quantify the potential fraction of species loss (PDF) at the grid level. This approach specifically distinguishes the impacts of seasonal rotation patterns from annual aggregates. We anticipate three key findings: (1) the identification of seasonal biodiversity hotspots driven by groundwater reliance in winter; (2) a quantification of the biodiversity leakage or savings resulting from China’s Crop Rotation and Fallow Policy; and (3) the revelation of spatial mismatches between agricultural intensification and ecosystem vulnerability. By shifting from a static, national-level perspective to a dynamic, spatially explicit one, this study underscores the urgency of incorporating seasonal biodiversity footprints into climate-smart agricultural policymaking to achieve food targets with ecosystem integrity.
How to cite: Niu, Q. and Zhao, D.: Unveiling the Invisible Ecological Cost: Seasonal Biodiversity Footprint of Crop Rotation in China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7358, https://doi.org/10.5194/egusphere-egu26-7358, 2026.
The importance of automated stress monitoring is growing, as early detection of infestation events can significantly reduce yield losses and the use of chemical synthetic plant protection products. This approach aligns with the agricultural policies of the European Union and Germany.
Hyperspectral Imaging (HSI) is a key non-destructive technique for detecting plant stress and identifying symptoms of pest infestations and abiotic stress. While demand for HSI applications is increasing, its practical implementation remains challenging. Most existing studies have been conducted under controlled laboratory conditions, limiting direct transferability to real-world environments such as greenhouses.
In greenhouse settings, uncontrolled factors—for instance variable ambient light and plant self-shadowing—pose significant challenges to accurate spectral measurements. To address these issues, this study utilized a VNIR hyperspectral camera (500–1000 nm) with an integrated VNIR broadband LED illumination system to ensure consistent lighting conditions. We collected spectral data from tomato plants affected by russet mite infestation and drought stress under real greenhouse conditions.
The data were processed using key vegetation indices which were calculated and analysed to identify spectral signatures associated with stress symptoms. Additionally, machine learning algorithms were applied to develop predictive models for early stress detection.
In our project, we are performing this approach to include a broader range of plant stresses—including different pests, pathogens, and abiotic stressors—to enhance the robustness and generalizability of the detection system. The ultimate goal is to improve precision crop management in greenhouses through early, automated, and non-destructive stress monitoring.
In the poster, the current results from the russet mite and drought stress trials will be presented. The poster shows the effectiveness of vegetation indices, spectral responses, and model performance, in distinguishing stress types based on the two aforementioned stressors.
How to cite: Nakamura, Y., Kreklow, T., Hachen, M., Wuttke, D., and Böckmann, E.: Hyperspectral imaging for detection of russet mite infestation and drought stress in the greenhouse tomato cultivation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1763, https://doi.org/10.5194/egusphere-egu26-1763, 2026.
Detecting spatial variability and delineating management zones are key steps in supporting good agricultural practices and promoting precision agriculture. In this context, proximal soil sensing techniques, such as gamma-ray spectrometry, have shown promising potential over the last decades. However, their application is not yet fully standardized, and their performance has not always been consistent across the studies conducted to date.
In this study, a novel gamma spectrometer prototype equipped with a 1-L NaI(Tl) scintillator was used to characterize soil properties spatial variability at two experimental sites in the Emilia-Romagna region (Italy). The first site is a 13-ha bi-varietal vineyard with permanent grass cover, whereas the second site is a 60-ha walnut orchard, also grass-covered. During each survey counts associated with the emissions of the radionuclides 40K, 232Th, and 238U, as well as total gamma counts, were acquired using a window-analysis approach. The collected data were subsequently processed within a GIS environment and statistical analyses were performed using R software.
At the first site, different surveys were carried out, both in mobile on-the-go mode and in mobile stop-and-go mode. The on-the-go surveys exhibited satisfactory repeatability for total gamma counts, whereas the spatial patterns of individual radionuclide emissions showed limited repeatability. The delineation of homogeneous zones based on total gamma counts was consistent with soil texture analyses; however, an inverse relationship was observed compared to that commonly reported in literature, with higher gamma counts corresponding to areas with higher sand content. The stop-and-go survey yielded results consistent with both the zoning derived from the on-the-go surveys and the textural analysis but only for total gamma counts. The low variability of measurements collected at the same location appears to indicate greater precision and, consequently, higher repeatability for this acquisition mode; however, its main drawback is the loss of spatial resolution compared to the on-the-go approach.
At the second site, an on-the-go survey conducted along a transect showed strong agreement with the 1:50,000 soil map of the Emilia-Romagna region and with laboratory-based texture analyses, both in terms of spatial discontinuities and relationship between soil texture and gamma counts.
Future developments of this research will include the extension to additional study sites, the development of a soil-moisture-based correction model for spatialized gamma counts and the refinement of count-extraction methods for individual radionuclides.
How to cite: Benfenati, M., Vinzio, F., and Baroni, G.: How Repeatable Is Gamma-Ray Spectrometry for Agricultural Soil Mapping? Preliminary results and future perspective., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7828, https://doi.org/10.5194/egusphere-egu26-7828, 2026.
Soil-derived gases, including carbon dioxide, methane, nitrous oxide, ammonia, and carbon monoxide, are major contributors to greenhouse gas emissions. These gases, primarily biogenic in origin, are released from soils into the atmosphere, with concentrations influenced by factors such as temperature, humidity, photosynthetic activity, soil type, and location.
A key challenge in soil gas research is developing accurate, simple, and rapid methodologies for measuring soil fluxes directly in the field. Fourier-transform infrared (FTIR) spectroscopy is a recommended technology for this purpose, as it allows for precise quantification and speciation of gases at low concentrations, generating reliable datasets over short timescales.
In this study, soil gas fluxes were measured using a static chamber approach integrated with a closed-loop system. The static chamber method involves placing an open-ended chamber on the soil surface to accumulate emitted gases for analysis. Despite widespread use, the scientific community has not reached a consensus on the optimal chamber type for accurate flux measurements.
This research compares soil flux data obtained from transparent versus opaque chambers under identical environmental conditions. The results aim to inform best practices in chamber design and provide guidance for more accurate field measurements of soil-derived greenhouse gas emissions.
How to cite: Ketonen, S., Szymon, O., and Seiberlich, S.: Impact of Chamber Transparency on Soil-Derived Greenhouse Gas Flux Measurements Using FTIR Spectroscopy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11667, https://doi.org/10.5194/egusphere-egu26-11667, 2026.
Progressive climate change is affecting agriculture in many ways. Increasing yield fluctuations and crop failures in particular are placing an economic burden on farmers. In addition, the general supply of sufficient high-quality agricultural products is also at risk. A key aspect is the increasing drought stress, which is noticeable across large areas and in various crop types. In Austria, Switzerland and Germany, temperatures have risen above average in global comparison. Since pre-industrial times, average temperatures in Austria have risen by 2.9°C, in Switzerland by 2.8°C and in Germany by 2.3°C. The summer months of June, July and August are particularly affected in Austria. Data collected by Statistics Austria on maize yields in the federal states already show a correlation between extreme temperatures – especially in August – and reduced yields. Global forecasts assume that today's maximum yield losses due to drought stress could become the new average within the next 30 years.
Against this background, the question arises as to how Austrian variety testing can adequately reflect this increasingly frequent abiotic stress in variety descriptions. A current attempt to classify the drought stress tolerance of varieties is the DROST research project (‘Methods for Evaluation of Drought Stress Tolerance in VCU Testing’). Using maize and winter wheat as examples, variety trials from the value assessment that are regularly affected by drought are to be intensively sampled. Soil moisture will be monitored using data loggers and relevant weather data will be collected. An extended soil analysis will provide information not only on nutrient supply and soil quality, but also on water retention capacity. In addition to the usual phenotypic surveys in the value assessment, multispectral indices on the leaves will also be measured using a handheld device. Drone flights will be used to record various parameters such as chlorophyll content, leaf discolouration and biomass. In order to also take physiological processes within the plants into account, the protein profile of the grains at different stages of maturity will be examined using high-resolution LC-MS/MS to identify relevant marker proteins for drought stress, mostly enzymes. The plan is to sample three locations per crop type in Austria's Pannonian Plain over a period of two years. In addition, five varieties per crop type will be specifically exposed to drought stress in a rainout shelter and compared with an adjacent control area. Additional phenotypic and proteomic surveys are also planned here. Finally, a SWOT analysis will be carried out to evaluate the various survey methods. The aim is to develop a sound perspective for the implementation of efficient methods for assessing drought stress tolerance in Austrian variety testing. This should particularly benefit farmers, who will be able to rely on reliable, officially tested results.
How to cite: Wawra, A., Fuchs, M., D'Amico, S., and Spiegel, H.: Innovative Methods for Assessing Drought Tolerance in Crop Varieties: Towards Sustainable VCU Testing in Austria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11778, https://doi.org/10.5194/egusphere-egu26-11778, 2026.
Crop yield gaps in rainfed agricultural systems pose a persistent challenge to food security under increasing climate variability, particularly in semi-arid and Mediterranean regions. Although climate drivers, macronutrient inputs, and management practices are widely used to explain yield variability, the contribution of comprehensive soil chemistry to crop resilience remains insufficiently understood. This study assesses whether integrating heavy metals with conventional soil, climate, and management variables improves wheat yield gap modeling and supports agricultural adaptation strategies. Field surveys were conducted across 54 rainfed wheat sites in three Moroccan provinces (Meknes, Khemisset, and Settat) during the 2021 growing season. A total of 216 soil samples were analyzed for 25 physicochemical properties, including soil texture, macronutrients, micronutrients, and eight trace elements/heavy metals (Fe, Cu, Ni, Cd, As, Pb, Cr, Se). Climate data were derived from ERA5-Land, and farm management information was collected through farmer interviews. Yield gaps ranging from 2,000 to 4,000 kg ha⁻¹ were estimated using a 90th-percentile benchmark approach. To reduce dimensionality and identify the most relevant predictors, Boruta feature selection was applied prior to model training. The selected variables were then used to develop machine learning models (XGBoost, Random Forest, and Support Vector Regression) for yield gap prediction. Model performance was evaluated using cross-validation, and feature importance analysis was subsequently applied to interpret the contribution of individual predictors. Among the tested models, XGBoost achieved the highest accuracy (R² =0.64, RMSE =1,030 kg ha⁻¹). Growing season precipitation showed a strong negative relationship with yield gaps (r =−0.65), indicating that higher rainfall consistently reduced yield gaps and enhanced resilience in rainfed systems. Nitrogen inputs, represented by total N rate (r =−0.58) and NPK applied (r =−0.59), also had clear gapreducing effects, while phosphorus application exhibited a weaker but still negative relationship with yield gaps (r =−0.26). Among micronutrients, manganese showed a weak negative relationship with yield gaps (r =−0.30), with low to moderate concentrations associated with reduced yield gaps, consistent with its positive role in plant nutrition. In contrast, cadmium exhibited a positive relationship with yield gaps (r =+0.28), indicating a negative influence on crop performance and yield gap widening. These results suggest that trace elements capture soil chemical variability relevant to crop performance and resilience, likely through interactions with nutrient availability and soil buffering processes. Incorporating comprehensive soil chemistry into yield gap modeling enhances predictive performance and provides a more integrated basis for climate adaptation in rainfed agriculture.
Keywords: Yield gap analysis, predictive modeling, soil chemistry, heavy metals, machine learning, XGBoost, wheat, Morocco
How to cite: Bendou, N., Jemo, M., Bijker, W., and Belgiu, M.: Predictive modeling of wheat yield gaps using soil heavy Metals, climate, and Nutrient management in rainfed Morocco, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15533, https://doi.org/10.5194/egusphere-egu26-15533, 2026.
Agroecosystems in southern Saxony-Anhalt are under increasing ecological stress due to climate change. The widespread absence of field margins exacerbates this challenge; large-scale land consolidation over recent decades, combined with inadequate management, has led to a substantial reduction in these critical landscape features. However, wooden field margins - hedgerows and tree rows with shrub components - are increasingly recognized as important elements for enhancing soil functions and supporting climate-resilient agroecosystems.
This study quantified the effects of structurally distinct wooded field margins on soil properties and crop productivity. Soil indicators included inorganic parameters (pH and bulk density) and organic indicators (soil organic matter content and Tea Bag Index), complemented by agronomic traits (crop yield and plant height).
Field investigations were conducted at two sites in southern Saxony-Anhalt representing four distinct field margin structures, adjacent to conventionally and organically managed fields. At site one, there were two variants of closed hedgerows with individual trees of differing structural characteristics, and at site two, there were two types of tree rows with differentiated shrub components. Each plot comprised a length of 100 m. Soil and crop parameters were assessed along transects extending from the field margin into the adjacent cropland to capture distance-dependent effects (25 m, 50 m and 100 m). Soil indicators were sampled at five points per transect at 20-m intervals. Yield and plant height measurements were recorded at the proximity of the field margin (approximately 1 m) and at transect positions (25 m, 50 m and 100 m) with replicated sampling.
Preliminary results show significant differences in pH and microbial activity, as measured by the Tea Bag Index, between field margins and open field areas. Measurable margin effects extend up to 25 m into the field. Differences between conventional and organic management were comparatively small. Conversely, crop yield and plant height increased within the 25 m zone adjacent to field margin and declined with increasing distance from the margin. Further analysis will incorporate site management practices and field margin vegetation structure in order to elucidate their interactions with agronomic performance parameters.
These findings demonstrate measurable positive spillover effects of wooded field margins for soil ecosystem functions and crop performance. They provide a basis for evidence-based design and spatial planning of climate-resilient agricultural landscapes under variable conditions.
How to cite: Pawelke, M., Teucher, M., Büschel, L., and Pöhlitz, J.: Effects of Wooden Field Margins on Soil Ecosystem Functions and Crop Productivity in Southern Saxony-Anhalt (Germany), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19184, https://doi.org/10.5194/egusphere-egu26-19184, 2026.
