Advertisement

Epidemiology and agronomic predictors of herbicide resistance in rice at a large scale

  • Elisa Mascanzoni
  • Alessia Perego
  • Niccolò Marchi
  • Laura Scarabel
  • Silvia Panozzo
  • Aldo Ferrero
  • Marco Acutis
  • Maurizio Sattin
Research Article

Abstract

Herbicide resistance is a major weed control issue that threatens the sustainability of rice cropping systems. Its epidemiology at large scale is largely unknown. Several rice weed species have evolved resistant populations in Italy, including multiple resistant ones. The study objectives were to analyze the impact in Italian rice fields of major agronomic factors on the epidemiology of herbicide resistance and to generate a large-scale resistance risk map. The Italian Herbicide Resistance Working Group database was used to generate herbicide resistance maps. The distribution of resistant weed populations resulted as not homogeneous in the area studied, with two pockets where resistance had not been detected. To verify the situation, random sampling was done in the pockets where resistance had never been reported. Based on data from 230 Italian municipalities, three different statistics, stepwise discriminant analysis, stepwise logistic regression, and neural network, were used to correlate resistance distribution in the main Italian rice growing area with seeding type, rotation rate, and soil texture. Through the integration of complaint monitoring, mapping, and neural network analyses, we prove that a high risk of resistance evolution is associated with traditional rice cropping systems with intense monoculture rates and where water-seeding is widespread. This is the first study that determines the degree of association between herbicide resistance and a few important predictors at large scale. It also demonstrates that resistance is present in areas where it had never been reported through extensive complaint monitoring. However, these resistant populations cause medium-low density infestations, likely not alarming rice farmers. This highlights the importance of integrated agronomic techniques at cropping system level to prevent the diffusion and impact of herbicide resistance or limit it to an acceptable level. The identification of concise, yet informative, agronomic predictors of herbicide resistance diffusion can significantly facilitate effective management and improve sustainability.

Keywords

Echinochloa spp. Soil texture Resistance monitoring Resistance mapping Resistance management Neural network 

Notes

Acknowledgements

The Piedmont and Lombardy Regions kindly provided the land use data and Ente Nazionale Risi the data relative to the seeding technique, i.e., water- and dry-seeded rice. We are grateful to all members of GIRE for contributing to herbicide resistance complaint monitoring and for stimulating discussions. Thanks also to Alison Garside for revising the English text.

Data availability statement

The datasets generated and analyzed during the current study are not publicly available due to the privacy law but are available from the corresponding author on reasonable request.

Compliance with ethical standards

Conflict of interest

Elisa Mascanzoni is an employee of DOW Agrosciences and a PhD candidate at the University of Padova. The whole research program is under the supervision of Maurizio Sattin of the Italian National Research Council (CNR) without any interference by DOW Agrosciences. The other authors declare that they have no conflict of interest.

References

  1. Busi R, Vila-Aiub MM, Beckie HJ, Gaines TA, Goggin DE, Kaundun SS, Lacoste M, Neve P, Nissen SJ, Norsworthy JK, Renton M, Shaner DL, Tranel PJ, Wright T, Yu Q, Powles SB (2013) Herbicide-resistant weeds: from research and knowledge to future needs. Evol Appl 6(8):1218–1221.  https://doi.org/10.1111/eva.12098 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Délye C, Jasieniuk M, Le Corre V (2013) Deciphering the evolution of herbicide resistance in weeds. Trends Genet 29(11):649–658.  https://doi.org/10.1016/j.tig.2013.06.001 CrossRefPubMedGoogle Scholar
  3. Editorial (2018) Resistance is … complex. Nat Ecol Evol 2:405.  https://doi.org/10.1038/s41559-018-0495-5 CrossRefGoogle Scholar
  4. Evans JA, Tranel PJ, Hager AG, Schutte B, Wu C, Chatham LA, Davis AS (2016) Managing the evolution of herbicide resistance. Pest Manag Sci 72(1):74–80.  https://doi.org/10.1002/ps.4009 CrossRefPubMedGoogle Scholar
  5. Ferrero A, Tinarelli A (2008) Rice cultivation in the E.U. Ecological conditions agronomical practices. In: Capri E, Karpouzas DG (eds) Pesticide risk assessment in rice paddies: theory and practice. Elsevier B.V., Amsterdam, pp 1–23.  https://doi.org/10.1016/B978-044453087-5.50002-3 CrossRefGoogle Scholar
  6. Ferrero A, Vidotto F (2006) Weeds and weed management in Italian rice fields. In: Agro-economical traits of rice cultivation in Europe and India. Edizioni Mercurio, Vercelli, pp 55–72Google Scholar
  7. Ferrero A, Vidotto F (2010) History of rice in Europe. In: Sharma SD (ed) Rice, origin antiquity and history. CRC Press, Boca Raton, pp 341–372.  https://doi.org/10.1201/EBK1578086801-c11 CrossRefGoogle Scholar
  8. GIRE - Italian Herbicide Resistance Working Group (2018) Database of herbicide resistance in Italy. www.resistenzaerbicidi.it. Accessed 15 Apr 2018
  9. Gong QH, Zhang JX, Wang J (2018) Application of GIS-based back propagation artificial neural networks and logistic regression for shallow landslide susceptibility mapping in South China-take Meijiang river basin as an example. The Open Civil Engineering Journal 12(1):21–34.  https://doi.org/10.2174/1874149501812010021 CrossRefGoogle Scholar
  10. Haykin SO (2009) Neural networks and learning machines, 3rd edn. Pearson Publisher, LondonGoogle Scholar
  11. Heap I (2014) Herbicide Resistant Weeds. In: Pimentel D, Peshin R (eds) Integrated Pest Management. Springer, Dordrecht.  https://doi.org/10.1007/978-94-007-7796-5_12 CrossRefGoogle Scholar
  12. Heap I (2018) The International Survey of Herbicide Resistant Weeds. www.weedscience.org. Accessed 15 Apr 2018
  13. Hess M, Barralis G, Bleiholder H, Buhr L, Eggers T, Hack H et al (1997) Use of the extended BBCH scale-general for the descriptions of the growth stages of mono and dicotyledonous weed species. Weed Res 37(6):433–441.  https://doi.org/10.1046/j.1365-3180.1997.d01-70.x CrossRefGoogle Scholar
  14. Hicks HL, Common D, Coutts SR, Crook L, Hull R, Norris K, Neve P, Childs DZ, Freckerton RP (2018) The factors driving evolved herbicide resistance at a national scale. Nat Ecol Evol 2:529–536.  https://doi.org/10.1038/s41559-018-0470-1 CrossRefPubMedGoogle Scholar
  15. Holm L, Doll J, Holm E, Pancho J, Herberger J (1997) The world’s worst weeds: natural histories and distribution. Wiley, New YorkGoogle Scholar
  16. Juraimi AS, Uddin MK, Anwar MP, Mohamed MTM, Ismail MR, Man A (2013) Sustainable weed management in direct seeded rice culture: a review. Aust J Crop Sci 7(7):989–1002Google Scholar
  17. Lazcano C, Gómez-Brandón M, Revilla P, Domínguez J (2013) Short-term effects of organic and inorganic fertilizers on soil microbial community structure and function. Biol Fertil Soils 49(6):723–733.  https://doi.org/10.1007/s00374-012-0761-7 CrossRefGoogle Scholar
  18. Loddo D, Kudsk P, Costa B, Dalla Valle N, Sattin M (2018) Sensitivity analysis of Alisma plantago-aquatica L., Cyperus difformis L. and Schoenoplectus mucronatus (L.) Palla to penoxsulam. Agronomy 2018(8):220.  https://doi.org/10.3390/agronomy8100220 CrossRefGoogle Scholar
  19. Maiorano A, Reyneri A, Sacco D, Magni A, Ramponi C (2009) A dynamic risk assessment model (FUMAgrain) of fumonisin synthesis by Fusarium verticillioides in maize grain in Italy. Crop Prot 28(3):243–256.  https://doi.org/10.1016/j.cropro.2008.10.012 CrossRefGoogle Scholar
  20. Mansourian S, Darbandi EI, Mohassel MHR, Rastgoo M, Kanouni H (2017) Comparison of artificial neural networks and logistic regression as potential methods for predicting weed populations on dryland chickpea and winter wheat fields of Kurdistan province, Iran. Crop Prot 93:43–51.  https://doi.org/10.1016/j.cropro.2016.11.015 CrossRefGoogle Scholar
  21. Mortensen D, Egan J, Maxwell B, Ryan M, Smith R (2012) Navigating a critical juncture for sustainable weed management. Bioscience 62(1):65–84.  https://doi.org/10.1525/bio.2012.62.1.12 CrossRefGoogle Scholar
  22. Norris RF (1992) Relationship between inflorescence size and seed production in barnyardgrass (Echinochloa crus-galli). Weed Sci 40(1):74–78.  https://doi.org/10.1017/S0043174500056988 CrossRefGoogle Scholar
  23. Norsworthy JK, Ward SM, Shaw DR, Llewellyn RS, Nichols RL, Webster TM, Bradley KW, Frisvold G, Powles SB, Burgos NR, Witt WW, Barrett M (2012) Reducing the risks of herbicide resistance: best management practices and recommendations. Weed Sci 60(sp1):31–62.  https://doi.org/10.1614/WS-D-11-00155.1 CrossRefGoogle Scholar
  24. Orson J (1999) The cost to the farmer of herbicide resistance. Weed Technol 3(3):607–611.  https://doi.org/10.1017/s0890037x0004628 CrossRefGoogle Scholar
  25. Osuna MD, Vidotto F, Fischer AJ, Bayer DE, De Prado R, Ferrero A (2002) Cross-resistance to bispyribac-sodium and bensulfuron-methyl in Echinochloa phyllopogon and Cyperus difformis. Pestic Biochem Physiol 73(1):9–17.  https://doi.org/10.1016/S0048-3575(02)00010-X CrossRefGoogle Scholar
  26. Panozzo S, Scarabel L, Tranel PJ, Sattin M (2013) Target-site resistance to ALS inhibitors in the polyploid species Echinochloa crus-galli. Pestic Biochem Physiol 105(2):93–101.  https://doi.org/10.1016/j.pestbp.2012.12.003 CrossRefGoogle Scholar
  27. Panozzo S, Scarabel L, Collavo A, Sattin M (2015a) Protocols for robust herbicide resistance testing in different weed species. J Vis Exp (101):e52923.  https://doi.org/10.3791/52923
  28. Panozzo S, Colauzzi M, Scarabel L, Collavo A, Rosan V, Sattin M (2015b) iMAR: an interactive web-based application for mapping herbicide resistant weeds. PLoS One 10(8):e0135328.  https://doi.org/10.1371/journal.pone.0135328 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Powles SB, Yu Q (2010) Evolution in action: plants resistant to herbicides. Annu Rev Plant Biol 61:317–347.  https://doi.org/10.1146/annurev-arplant-042809-112119 CrossRefPubMedGoogle Scholar
  30. Priddy K, Keller PE (2005) Artificial neural networks: an introduction. SPIE Press, BellinghamCrossRefGoogle Scholar
  31. Renton M, Busi R, Neve P, Thornby D, Vila-Aiub M (2014) Herbicide resistance modelling: past, present and future. Pest Manag Sci 70(9):1394–1404.  https://doi.org/10.1002/ps.3773 CrossRefPubMedGoogle Scholar
  32. Sattin M (2005) Herbicide resistance in Europe: an overview. In: Proc. BCPC International Congress – Crop Science & Technology, Glasgow, UK, pp 131–138Google Scholar
  33. Scarabel L, Panozzo S, Varotto S, Sattin M (2011) Allelic variation of the ACCase gene and response to ACCase-inhibiting herbicides in pinoxaden target-site resistant Lolium spp. Pest Manag Sci 67(8):932–941.  https://doi.org/10.1002/ps.2133 CrossRefPubMedGoogle Scholar
  34. Scarabel L, Cenghialta C, Manuello D, Sattin M (2012) Monitoring and management of imidazolinone-resistant red rice (Oryza sativa L., var. sylvatica) in Clearfield® Italian paddy rice. Agron 2(4):371–383.  https://doi.org/10.3390/agronomy2040371 CrossRefGoogle Scholar
  35. Scarabel L, Cenghialta C, Panozzo S, Manuello D, Sattin M (2013) Resistance evolution and sustainability of the rice cropping system: the Italian case study. Proc. of the Conference “Global Herbicide Resistance Challenge”, Fremantle (Australia), 18–22 February 2013, p 105Google Scholar
  36. Tabacchi M, Viggiani P (2017) Piante infestanti di risaie e canali. Botanica e riconoscimento. Edagricole, BolognaGoogle Scholar
  37. Tabacchi M, Mantegazza R, Spada A, Ferrero A (2006) Morphological traits and molecular markers for classification of Echinochloa species from Italian rice fields. Weed Sci 54(6):1086–1093.  https://doi.org/10.1614/WS-06-018R1.1 CrossRefGoogle Scholar
  38. Vogl TP, Mangis JK, Rigler AK, Zink WT, Alkon DL (1988) Accelerating the convergence of the backpropagation method. Biol Cybern 59(4–5):257–263.  https://doi.org/10.1007/BF00332914 CrossRefGoogle Scholar
  39. Zhang Q, Zhang J, Yan D, Bao Y (2013) Dynamic risk prediction based on discriminant analysis for maize drought disaster. Nat Hazards 65(3):1275–1284.  https://doi.org/10.1007/s11069-012-0406-z CrossRefGoogle Scholar

Copyright information

© L’Institut National de la Recherche Agronomique 2018

Authors and Affiliations

  1. 1.DAFNAEUniversity of PadovaLegnaroItaly
  2. 2.DISAA University of MilanoMilanItaly
  3. 3.TESAFUniversity of PadovaLegnaroItaly
  4. 4.Institute of Agro-environmental and Forest Biology (IBAF) – CNRLegnaroItaly
  5. 5.DISAFAUniversity of TorinoGrugliascoItaly

Personalised recommendations