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Barnyardgrass (Echinochloa crus-galli) is one of the most problematic weeds infesting rice fields and causes huge crop yield losses. To clarify the resistance pattern and possible mechanisms of resistance in barnyardgrass population JS12 in Jiangsu Province, whole-plant dose-response experiments were conducted to determine the sensitivity to metamifop, and its target- and non-target based mechanisms were clarified by gene sequencing and expression analysis of ACCase gene, as well as synergistic effects on resistance of two metabolic enzyme inhibitors. Finally, the sensitivities of JS12 population to ACCase inhibitors and other herbicides with different modes of action were analyzed to determine the cross- and multi-resistance patterns. Results showed that the JS12 population exhibited 13.71 times high-level resistance to metamifop. No amino acid substitutions conferring metamifop resistance were detected in six copies of ACCase genes in JS12 population, and the gene expression levels of ACCase were significantly lower than that of the susceptible population after herbicide treatment. Both cytochrome P450 inhibitor malathion and glutathione-S-transferase (GST) inhibitor NBD-Cl could significantly increase the susceptibility of JS12 population to metamifop, causing the GR50 (herbicide rate causing growth reduction by 50%) value to decrease from 227.90 g/hm² to 77.51 g/hm2 and 137.93 g/hm², respectively. In addition, the resistant population JS12 had evolved cross-resistance to ACCase inhibitors cyhalofop-butyl, fenoxaprop-p-ethyl, clethodim, and pindoxaden, and multi-resistance to acetolactate synthase (ALS)-inhibiting herbicide penoxsulam and auxin herbicide quinclorac. Nevertheless, it was still susceptible to the new herbicides florpyrauxifen-benzyl and triadimefon. This study suggested that the enhanced herbicide metabolism mediated by cytochrome P450 and GST might be an important reason for herbicide resistance in JS12 population. Florpyrauxifen-benzyl and triadimefon can be used to control the resistant population. [ABSTRACT FROM AUTHOR]

Barnyardgrass (Echinochloa crus-galli (L.) P. Beauv.), one of the worst weeds in paddy fields in China, has been frequently reported evolving resistance to acetyl-CoA carboxylase (ACCase) inhibiting herbicides. However, in the previous research, more attention was paid to target-site resistance (TSR) mechanisms, the non-target-site resistance (NTSR) mechanisms have not been well-established. In this study, the potential mechanism of resistance in a metamifop-resistant E. crus-galli collected from Kunshan city, Jiangsu Province, China was investigated. Dose-response assays showed that the phenotypic resistant population (JS-R) has evolved 4.3-fold resistance to metamifop compared with the phenotypic susceptible population (YN-S). The ACCase CT gene sequencing and relative ACCase gene expression levels studies showed that no mutations were detected in the ACCase CT gene in both YN-S and JS-R, and there was no significant difference in the relative ACCase gene expression between YN-S and JS-R. After the pre-processing of glutathione-S-transferase (GSTs) inhibitor NBD-Cl, the resistance level of JS-R to metamifop was reversed 18.73%. Furthermore, the GSTs activity of JS-R plants was significantly enhanced compared to that of YN-S plants. UPLC-MS/MS revealed that JS-R plants had faster metabolic rates to metamifop than YN-S plants. Meanwhile, the JS-R popultion exhibited resistant to cyhalofop-butyl and penoxsulam. In summary, this study presented a novel discovery regarding the global emergence of metabolic resistance to metamifop in E. crus-galli. The low-level resistance observed in the JS-R population was not found to be related to TSR but rather appeared to be primarily associated with the overexpression of genes in the GSTs metabolic enzyme superfamily. [Display omitted] • Echinochloa crus-galli evolved resisitant to metamifop in China. • The resistance of Echinochloa crus-galli to metamifop was not related to target-site resistance. • We provide evidence of the metabolic resistance to metamifop mediated by GSTs in Echinochloa crus-galli in China. • Echinochloa crus-galli metamifop-resistant population also evolved resisitant to cyhalofop-butyl and penoxsulam. [ABSTRACT FROM AUTHOR]

• We developed a new approach (WeedyCoSMo) for modeling crop-weed interaction. • The model was evaluated for rice-barnyard grass and rice-red rice. • Model accuracy was good, considering the assumptions to limit model complexity. • The model is suitable for operational contexts and for integration in DSS. Despite their potential to support the optimization of weed management, available ecophysiological models for the simulation of crop-weed interaction are still not adopted in operational contexts. For some of them the reasons deal with the insufficient validation in farming conditions, whereas others are either too complex for being used in operational contexts or too empiric for being free from site- or context-specific effects. Here we present a new approach (WeedyCoSMo) to support strategic decisions on weed management, derived from the CoSMo process-based model for the simulation of phytocoenosis dynamics. The model dynamically reproduces on a yearly basis the interaction between crop and weeds at canopy level through the daily quantification of the suitability of each species to weather conditions and management practices, as well as to the simulated system state variables. Dynamically predicted outputs are the relative abundance of crop and weeds and state variables for each species like, e.g., aboveground biomass, biomass of different plant organs, grain yield, leaf area index, plant height. WeedyCoSMo was calibrated and validated using data from different sites (in the Jiangsu province, China, and in Arkansas, USA) and years (from 1982 to 2014), where different rice varieties and two major rice weeds–i.e., red rice (Oryza sativa L., var. sylvatica) and barnyardgrass (Echinocloa crus-galli L.) – were grown in monoculture or mixture. Model performances were satisfying: for rice crops grown in interaction with weeds, relative root mean square error never exceeded 25.2%, regardless of the variable considered, and Nash-Sutcliffe modeling efficiency was always higher than 0.63. Despite the low number of inputs and parameters needed to run the simulations, the degree of accuracy was similar to the ones achieved with other models for crop-weed interaction. This allows considering WeedyCoSMo as a promising approach in light of the possible integration in decision support systems targeting operational farming conditions. [ABSTRACT FROM AUTHOR]