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Datura stramonium L. in soybean in Austria: risk areas, extent of late-season infestation, and management implications Cover

Datura stramonium L. in soybean in Austria: risk areas, extent of late-season infestation, and management implications

Open Access
|May 2026

References

  1. Abia, W.A., Montgomery, H., Nugent, A.P., Elliott, C.T., 2021. Tropane alkaloid contamination of agricultural commodities and food products in relation to consumer health: Learnings from the 2019 Uganda food aid outbreak. Comprehensive Reviews in Food Science and Food Safety 20, 501–525.
  2. Agrarmarkt Austria, 2025. Marktinformation – Getreide und Ölsaaten. Agrarmarkt Austria, https://www.ama.at.
  3. Andrade, C., Villers, A., Balent, G., Bar-Hen, A., Chadoeuf, J., Cylly, D., Cluzeau, D., Fried, G., Guillocheau, S., Pillon, O., Porcher, E., Tressou, J., Yamada, O., Lenne, N., Jullien, J., Monestiez, P., 2021. A real-world implementation of a nationwide, long-term monitoring program to assess the impact of agrochemicals and agricultural practices on biodiversity. Ecology and Evolution 11, 3771–3793.
  4. Bagavathiannan, M.V., Norsworthy, J.K., 2012. Late-season seed production in arable weed communities: Management implications. Weed Science 60, 325–334.
  5. Ballaré, C.L., Scopel, A., Ghersa, C.M., Sánchez, R.A., 1987. The demography of Datura ferox (L.) in soybean crops. Weed Research 27, 91–102.
  6. Bates, B., Maechler M., Bolker B., Walker S., 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Sohware 67, 1–48.
  7. BFW, 2023, eBOD. Digitale Bodenkarte Österreichs. Bundesforschungs- und Ausbildungszentrum für Wald, Naturgefahren und Landschah, https://bodenkarte.at.
  8. Biçici, U.C., Riegler-Nurscher, P.R., 2024. Terrain aware monoplotting for ortho UAV images. In: Agricultural Engineering challenges in existing and new agroecosystems. Proceedings of the AgEng 2024 Converence, 4–7 July 2024, Agricultural University of Athens, Greece, pp. 971–978.
  9. Blackshaw, R.E., Anderson, R.L., Lemerle D., 2007. Cultural weed management. In: Upadhyaya, M.K., Upadhyaya, M.K., Blackshaw, R.E., Blackshaw, R.E. (Eds.). Non-Chemical Weed Management: Principles, Concepts and Technology. CAB International Wallingford, UK, pp. 35–48.
  10. Blank-Landeshammer, B., Ranetbauer, C., Weghuber, J., 2025. Detection of tropane alkaloid contaminations in unprocessed soybeans and their fate in food and feed processing. Food Control 168, 110963.
  11. Commission Regulation (EU) 2023/915 of 25 April 2023 on maximum levels for certain contaminants in food and repealing Regulation (EC) No 1881/2006, Official Journal of the European Union L 119, 5 May 2023, p. 103.
  12. Datta, A., Ullah, H., Tursun, N., Pornprom, T., Knezevic, S.Z., Chauhan, B.S., 2017. Managing weeds using crop competition in soybean (Glycine max (L.) Merr.). Crop Protection 95, 60–68.
  13. de Nijs, M., Crews, C., Dorgelo, F., MacDonald, S., Mulder, P.P.J., 2023. Emerging issues on tropane alkaloid contamination of food in Europe. Toxins 2023, 15, 98.
  14. Doncheva, T., Berkov, S., Philipov, S., 2006. Comparative study of the alkaloids in tribe Datureae and their chemo-systematic significance. Biochemical Systematics and Ecology 34, 478–488.
  15. Essl, F., Follak, S., Glaser, M., 2025. Changes in weed vegetation across transects in maize fields. Basic and Applied Ecology 82, 1–10.
  16. Fernandez-Moral, E., Martins, R., Wolf, D., Rives, P., 2018. A new metric for evaluating semantic segmentation: leveraging global and contour accuracy. IEEE Intelligent Vehicles Symposium (IV), Changshu, China, 2018, pp. 1051–1056.
  17. Follak, S., Schleicher, C., Schwarz, M., Essl, F., 2017. Major emerging alien plants in Austrian crop fields. Weed Research 57, 406–416.
  18. Fried, G., Norton, L.R., Reboud, X., 2008. Environmental and management factors determining weed species composition and diversity in France. Agriculture, Ecosystems & Environment 128, 68–76.
  19. GBIF.org., 2026. GBIF Occurrence Download https://doi.org/10.15468/dl.6nm2gc.
  20. Glaser, M., Berg, C., Buldrini, F., Buholzer, S., Bürger, J., Chiarucci, A. Chytrý, M., Dřevojan, P., Follak, S., Küzmič, F., Lososová, Z., Meyer, S., Moser, D., Pyšek, P., Richner, N., Šilc, U., Wietzke, A., Dullinger, S., Essl, F., 2022. Agri-WeedClim database: A repository of vegetation plot data from Central European arable habitats over 100 years. Applied Vegetation Science 25, 240 e12675.
  21. Henry, W.T., Bauman, T.T., 1991. Interference between Soybean (Glycine max) and Jimsonweed (Datura stramonium) in Indiana. Weed Technology 5, 759–764.
  22. iNaturalist, 2025. Observations of Datura stramonium from Austria, https://www.inaturalist.org.
  23. JACQ consortium, 2004+. Virtual Herbaria, https://www.jacq.org/.
  24. Kirillov, A., Wu, Y., He, K., Girshick, R., 2020. PointRend: image segmentation as rendering. In: Proceedings 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 9796–9805. Seattle, USA.
  25. Koger, C.H., Shaw, D.R., Watson, C.E., Reddy, K.N., 2003. Detecting late-season weed infestations in soybean (Glycine max). Weed Technology 17, 696–704.
  26. Kolářová, M., Tyšer, L., Krähmer, H., 2017. Occurrence of neophytes in agrophytocoenoses - field survey in the Czech Republic. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 65, 661–668.
  27. Lenth, R., 2025. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.11.2-80003, https://rvlenth.github.io/emmeans/.
  28. Lüdecke, D., Ben-Shachar, M.S., Patil, I., Waggoner, P., Makowsk, D., 2021. performance: An R Package for Assessment, Comparison and Testing of Statistical Models. Journal of Open Source Sohware 6, 3139.
  29. Miller, R.T., Soltani, N., Robinson, D.E., Kraus, T.E., Sikkema, P.H., 2012. Biologically effective rate of saflufenacil/dimethenamid-p in soybean (Glycine max). Canadian Journal of Plant Science 92, 517–531.
  30. Pinke, G., Blazsek, K., Magyar, L., Nagy, K., Karácsony, P., Czúcz, B., Botta-Dukát, Z., 2016. Weed species composition of conventional soyabean crops in Hungary is determined by environmental, cultural, weed management and site variables. Weed Research 56, 470–481.
  31. Pinke, G., Karácsony, P., Czúcz, B., Botta-Dukát, Z., 2018. When herbicides don't really matter: weed species composition of oil pumpkin (Cucurbita pepo L.) fields in Hungary. Crop Protection 110, 236–244.
  32. Pinke, G., Vér, A., Réder, K., Koltai, G., Schlögl, G., Be-de-Fazekas, Á., Czúcz, B., Botta-Dukát, Z. 2024. Drivers of species composition in arable-weed communities of the Austrian-Hungarian borderland region: What is the role of the country? Applied Vegetation Science 27, e12764.
  33. R Core Team, 2025. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.r-project.org/.
  34. RAFFS, 2025. RASFF portal — food and feed safety alerts. Directorate-General for Health and Food Safety, https://webgate.ec.europa.eu/rasff-window/.
  35. Richard, D., Leimbrock-Rosch, L., Keßler, S., Stoll, E., Zimmer, S., 2023. Soybean yield response to different mechanical weed control methods in organic agriculture in Luxembourg. European Journal of Agronomy 147, 126842.
  36. Riegler-Nurscher, P., Eder, E., Ufuk, B., Treiblmeier, M., 2023. Erkennung von Stechapfel in hochauflösenden UAV-Bildern von Sojafeldern. 64. Österreichische Pflanzenschutztage, 29–30 November 2023, 32, Wels, Austria.
  37. Söchting, H.-P., Clauß, P., 2022. Untersuchungen zur Lebensdauer der Diasporen von Datura stramonium L. In L. Ulber & D. Rissel (eds.), Tagungsband: 30. Deutsche Arbeitsbesprechung über Fragen der Unkrautbiologie und -bekämpfung (Vol. 468, pp. 173–181), Julius Kühn-Institut, https://doi.org/10.5073/20220117-140535.
  38. Söchting, H.-P., Zwerger, P., 2020. Untersuchungen zur Bedeutung des Auhretens von Datura stramonium in Mais und Rispenhirse. Julius-Kühn-Archiv 464, 57–63.
  39. Stoller, E.W., Woolley, J.T., 1985. Competition for light by broadleaf weeds in soybeans (Glycine max). Weed Science 33, 199–202.
  40. Teasdale, J.R., Mirsky, S.B., Cavigelli, M.A., 2019. Weed species and traits associated with organic grain crop rotations in the mid-Atlantic region. Weed Science 67, 595–604.
  41. Weaver, S.E., Warwick, S.L., 1984. The biology of Canadian weeds. 64. Datura stramonium L. Canadian Journal of Plant Science 64, 979–991.
  42. Wickham H., 2016. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
  43. Wilson, P.J., Aebischer, N.J., 1995. The distribution of dicotyledonous arable weeds in relation to distance from the field edge. Journal of Applied Ecology 32, 295–310.
  44. Xu, S., Chen, M., Feng, T., Zhan, L., Zhou, L., Yu G., 2021. Use ggbreak to effectively utilize plotting space to deal with large datasets and outliers. Frontiers in Genetics 12, 774846.
  45. ZOBODAT, 2025. Biogeografischer Datensatz, https://www.zobodat.at/belege.php.
DOI: https://doi.org/10.2478/boku-2026-0001 | Journal eISSN: 2719-5430 | Journal ISSN: 0006-5471
Language: English, German
Page range: 1 - 11
Submitted on: Nov 7, 2025
Accepted on: Jan 22, 2026
Published on: May 6, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2026 Swen Follak, Ufuk Can Biçici, Antonia Griesbacher, Sabrina Kuchling, Elisabeth Reiter, Michael Schwarz, Michael Treiblmeier, Peter Riegler-Nurscher, published by Universität für Bodenkultur Wien
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.