Computer simulation modelling is an essential aid in building an integrated understanding of how different factors interact to affect the evolutionary and population dynamics of herbicide resistance, and thus in helping to predict and manage how agricultural systems will be affected. In this review, we first discuss why computer simulation modelling is such an important tool and framework for dealing with herbicide resistance. We then explain what questions related to herbicide resistance have been addressed to date using simulation modelling, and discuss the modelling approaches that have been used, focusing first on the earlier, more general approaches, and then on some newer, more innovative approaches. We then consider how these approaches could be further developed in the future, by drawing on modelling techniques that are already employed in other areas, such as individual-based and spatially explicit modelling approaches, as well as the possibility of better representing genetics, competition and economics, and finally the questions and issues of importance to herbicide resistance research and management that could be addressed using these new approaches are discussed. We conclude that it is necessary to proceed with caution when increasing the complexity of models by adding new details, but, with appropriate care, more detailed models will make it possible to integrate more current knowledge in order better to understand, predict and ultimately manage the evolution of herbicide resistance.
Weed management practices in cotton systems that were based on frequent cultivation, residual herbicides, and some post-emergent herbicides have changed. The ability to use glyphosate as a knockdown before planting, in shielded sprayers, and now over-the-top in glyphosate-tolerant cotton has seen a significant reduction in the use of residual herbicides and cultivation. Glyphosate is now the dominant herbicide in both crop and fallow. This reliance increases the risk of shifts to glyphosate-tolerant species and the evolution of glyphosate-resistant weeds.
Four surveys were undertaken in the 2008–09 and 2010–11 seasons. Surveys were conducted at the start of the summer cropping season (November–December) and at the end of the same season (March–April). Fifty fields previously surveyed in irrigated and non-irrigated cotton systems were re-surveyed.
A major species shift towards Conyza bonariensis was observed. There was also a minor increase in the prevalence of Sonchus oleraceus. Several species were still present at the end of the season, indicating either poor control and/or late-season germinations. These included C. bonariensis, S. oleraceus, Hibiscus verdcourtii and Hibiscus tridactylites, Echinochloa colona, Convolvulus sp., Ipomea lonchophylla, Chamaesyce drummondii, Cullen sp., Amaranthus macrocarpus, and Chloris virgata. These species, with the exception of E. colona, H. verdcourtii, and H. tridactylites, have tolerance to glyphosate and therefore are likely candidates to either remain or increase in dominance in a glyphosate-based system.
This validation work shows that empirical validation of herbicide resistance models is problematic. However, the model simulates the complexities of sub-tropical grains farming in Australia well, and can be used to investigate, generate and improve glyphosate resistance prevention strategies.
Glyphosate resistance will have a major impact on current cropping practices in glyphosate-resistant cotton systems. A framework for a risk assessment for weed species and management practices used in cropping systems with glyphosate-resistant cotton will aid decision making for resistance management. We developed this framework and then assessed the biological characteristics of 65 species and management practices from 50 cotton growers. This enabled us to predict the species most likely to evolve resistance, and the situations in which resistance is most likely to occur. Species with the highest resistance risk were Brachiaria eruciformis, Conyza bonariensis, Urochloa panicoides, Chloris virgata, Sonchus oleraceus and Echinochloa colona. The summer fallow and non-irrigated glyphosate-resistant cotton were the highest risk phases in the cropping system. When weed species and management practices were combined, C. bonariensis in summer fallow and other winter crops were at very high risk. S. oleraceus had very high risk in summer and winter fallow, as did C. virgata and E. colona in summer fallow. This study enables growers to identify potential resistance risks in the species present and management practices used on their farm, which will to facilitate a more targeted weed management approach to prevent development of glyphosate resistance.
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