Epidemiology and agronomic predictors of herbicide resistance in rice at a large scale
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.
KeywordsEchinochloa spp. Soil texture Resistance monitoring Resistance mapping Resistance management Neural network
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.
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