Institute of Crop Science

Abandoned oil and gas wells can be significant sources of methane emissions into the atmosphere. However, the extent of these potential emissions is often unclear, and mitigation requires detailed knowledge of the location of emitters. In Germany, the Federal Institute for Geosciences and Natural Resources has investigated a selection of wells of the in total ~25.000 onshore oil and gas wells for the first time. One challenge in investigating these wells is the mandatory decommissioning process, i.e. plugging, cutting and burying. For such soil buried wells, a measurement strategy was developed in which emissions were measured in an area of 30 x 30 m around the well and, for comparison, in a nearby reference area to record the natural background (Jordan et al., 2025). Between 2022 and 2025, nearly 90 wells of varying ages were investigated in northern Germany, where most of Germany's current and historical oil and gas production has taken place. In addition to investigating potential methane and CO2 emissions, soil gas compositions and stable carbon isotopes (and occasionally also hydrogen isotopes) in the soil gas methane were measured at each study site. In analogy to the reference areas and as is typical for forest, arable, and meadow soils, most well sites acted as methane sinks. Abnormalities compared to the reference areas were only determined at a few wells. For instance, a maximum of ~40 mg h-1 methane emissions were detected at one well, where small amounts of crude oil also appear to be escaping. However, covering the wells with soil offer an advantage here, as the gas and oil composition geochemically indicates a strong and depth-increasing influence of hydrocarbon-degrading microorganisms (Blumenberg et al., 2025). Final evaluations have not yet been completed, but for Germany, our results indicate that methane emissions from old oil and gas wells are relatively low. Open questions that are currently being addressed include the temporal variability of methane emissions, but also the importance of e.g., seasonal factors on the effectiveness of the microbial filter.

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Beom Kyu Kang, Jeong Hyun Seo, Jun Hoi Kim, Su Vin Heo, Man Soo Choi, Jee Yeon Ko, Byung Won Lee, In Youl Baek, Jae Seong Park, Choon Song Kim, Ji Ho Choo, Won Young Han, Myeong Cheol Seo, Myung Hee Lee, Nam Geol Kim, Gi Young Kim, Taek Rim Lee, Ji Ae Lee, Sol Ji No, Seung Su Lee, Young Min Jo, Jin Sil Choi, Hyun Tae Jo, and Jin Young Kim. Korean J. Breed. Sci. 2026;58:31-41. https://doi.org/10.9787/KJBS.2026.58.1.31

Legume crops are rich in carbohydrates, proteins, and various secondary metabolites such as isoflavones. Micronutrient treatments regulate plant biological activities, including stomatal function, hormone balance, and antioxidant accumulation, thereby improving resistance to environmental stress. This study investigated the effect of FeCl2 treatment, which changed the seed coat color, on the physical and biochemical characteristics of mungbean sprouts. After 72 h of cultivation with FeCl2 (Fe-72 h), no significant differences in physical traits were observed compared with the control. However, the levels of isoflavone aglycones (daidzein, genistein, and glycitein) were significantly higher. These findings suggest that FeCl2 treatment does not affect the physical quality of mungbean sprouts but enhances their metabolic quality by catalyzing isoflavone biosynthesis, as these compounds function as iron chelators. Tissue-specific analysis revealed that the seed coat and cotyledons accumulated the highest levels of isoflavone aglycones. The seed coat and cotyledons are directly exposed to FeCl2 during soaking, whereas newly developing tissues, such as the hypocotyl, are only indirectly affected. This difference in mineral exposure among tissues likely influences the biosynthesis of secondary metabolites. The observed increase in isoflavone content may improve stress resistance during mungbean cultivation and enhance the nutritional quality of mungbean sprouts as a health-promoting food.

Winter freezing injury is a critical factor limiting wheat(Triticum aestivum L.) productivity in northern China. Since freeze tolerance (FT) correlates with seedling growth traits, this study investigated the relationship between FT and plant architecture (PA) in winter wheat at the tillering stage. We evaluated 550 wheat varieties and advanced lines from the Huang and Huai River Valleys Winter Wheat Zone of China. Seedling PA was classified using the International Union for the Protection of New Varieties of Plants (UPOV) standards, while FT was evaluated through two parameters: severity of leaf necrosis (SLN) and mortality rate of shoots (MRS). The results showed that the PA distribution across germplasms approximated a normal distribution. The relationships between SLN and MRS under freezing stress were highly variable across years with differing winter conditions. SLN and MRS-derived FT levels showed a positive correlation within the same growing season but were inconsistent across different years. PA and MRS showed no correlation whereas correlation between seedling PA and SLN varied substantially across years. Due to inadequate cold acclimation in 2022-2023 and heavy snow cover in 2023-2024, there was no significant correlation between FT levels and seedling PA during these periods. A significant negative correlation was observed between PA and SLN during the 2024-2025 season, indicating that more prostrate growth habits were associated with a reduction in leaf necrosis. These results indicate that architectural traits may contribute to FT only within certain environmental contexts. Thus, enhancing freezing tolerance should focus on direct survival tests in various environments, with secondary traits like SLN and PA considered as context-dependent factors.

Abstract. Lagrangian models are widely adopted to study atmospheric transport processes, with applications in various domains, including the investigation of windborne crop diseases and epidemic risks in agriculture. Widely used Atmospheric Transport Modelling frameworks (ATMs) do not exploit the potential for performance gains and advanced computer graphics that GPUs provide, and they impose limitations regarding customization for domain-specific applications in crop epidemiology and agrometeorology. Here we introduce AgPaDS, the Agricultural Pest and Disease Simulator, a GPU-accelerated stochastic Lagrangian model with an option for advanced interactive 3-D in-situ visualization of global-scale atmospheric transport simulations. The tool was developed with two main objectives: (i) accelerate compute times by means of an efficient GPU implementation that enables exploratory visual analyses by means of interactive simulation setup in a graphical user interface with embedded 3-D in situ visualization; (ii) build a new atmospheric transport model dedicated to applications in crop epidemiology with model components not available in widely used ATMs and with flexibility for future domain-specific customizations. It is based on an optimized massively parallelized CUDA C ++ and OpenGL implementation. We report on model formulation, technical implementation and testing, including a systematic comparison with HYSPLIT, one of the most widely used ATMs, and a case-evaluation with in-situ visualization of complex 3-D dynamics of simulated crop pathogen transport during the hurricane that has likely transmitted soybean rust into the USA in 2004. A set of supplementary videos illustrates interactive and in situ 3-D visualization methods. We show that AgPaDS maintains good agreement with HYSPLIT whilst providing substantial speedups for simulations with very large particle numbers (up to three orders of magnitude). The model can simulate the release of millions of Lagrangian particles from heterogeneous crop landscapes on global scales with live 3-D visualization of simulated windborne dispersal of crop pathogens. Examples of future use-cases include (i) exploratory 3-D visual analyses of atmospheric transport simulations, including interactions between meteorological and biological processes; (ii) assessment of global airborne connectivity of agricultural landscapes; (iii) efficient representation of wind dispersal in crop disease forecasting systems.

Breeding programs prioritize the average performance of a genotype across environments and may overlook promising candidates for specific environments. To address this challenge, we propose a genomic prediction framework to select high-yielding genotypes tailored to individual environments. We compiled winter wheat grain yield data from 13,285 genotypes-6,766 lines and 6,519 hybrids-evaluated in yield plots at 31 central european sites from 2010 to 2022. With integrated genomic data, we show that only as the size of the training dataset increase, convolutional neural networks benchmark competitive to superior compared with traditional genomic best linear unbiased predictions (GBLUP) in predicting average genotype performance of lines. We then extend our prediction models to account for genotype times environment (G × E) interactions by incorporating information about the growth environment. We observe a 23% improvement in predicting environment-specific performance of new hybrids within a network of test environments with GBLUP based models. To better understand the environmental variables driving G × E interactions, we conduct analyses on a core set of 500 genetically diverse lines. Using machine learning, we successfully identify pivotal environment variables driving the clustering of study environments in central europe and highlight the benefit of modelling G × E interactions in selection of enviromically adapted varieties. Our results suggest that big data in combination with machine learning and deep learning methods offers new ways to widen the genetic bottleneck often encountered when advancing candidates from early limited-environment to late stage multi-environment evaluations. This promises faster delivery of breeding progress to farmers' fields.

Root-knot nematodes (RKN; Meloidogyne spp.) are the most prevalent and damaging nematode taxa on solanaceous and cucurbit vegetable crops in the southeastern United States (U.S.). An RKN-resistant pepper hybrid was evaluated for its effects on root galling, RKN population density, and yield in a pepper-cucumber double crop system, relative to a susceptible hybrid and compared with factorial treatments of 1,3-dichloropropene (1,3-D) fumigation in Kinston, NC. Effects of these treatments were also evaluated on the subsequently planted cucumber. These results guided the selection of seven different nematicide and pepper hybrid combinations, as management programs, for a three-year study in Elm City, NC. Treatment effects on yield, root damage, and population densities of free-living and plant-parasitic nematode species were examined. In Kinston, both 1,3-D fumigation and the RKN-resistant hybrid significantly reduced root galling and RKN densities. Use of the RKN-resistant pepper hybrid was the only factor to significantly improve pepper and second crop cucumber yields (P<0.05). In Elm City, higher pepper and cucumber yields were observed in RKN management programs that included the RKN resistant pepper hybrid combined with fluopyram application, regardless of fumigation application. No significant difference in densities of free-living or plant parasitic nematode species were observed across management programs, though RKN densities trended lower when using an RKN resistant hybrid. These results highlight the potential for increasing yields and improving RKN management in pepper-cucumber rotations by using RKN-resistant pepper hybrids, combined with fluopyram application. Reducing dependence on 1,3-D fumigation improves both the economic and environmental sustainability of vegetable production.

Addressing global food security demands more efficient crop breeding. While Genomic Selection (GS) accelerates breeding by predicting phenotypes from genomic data, its accuracy is limited by complex trait architectures and environmental dependencies. To overcome these challenges, we propose DPCformer, a deep learning model integrating convolutional neural networks with self-attention to capture nonlinear genotype-phenotype relationships. Evaluated on 13 traits across five crops (maize, cotton, tomato, rice, chickpea) using chromosomally-ordered SNP data processed with 8-dimensional encoding and PMF feature selection, DPCformer demonstrated superior performance. It achieved accuracy improvements of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.74-2.92 \%$</tex> in maize, up to 8.37% in cotton, and remarkable PCC enhancements of 57.35% in tomato and 16.62% in chickpea compared to baselines. These results establish DPCformer as a robust framework for genomic prediction, excelling in accuracy, small-sample performance, and polyploid data processing.