Herbicide-resistant weeds : from research and knowledge to future needs

Synthetic herbicides have been used globally to control weeds in major field crops. This has imposed a strong selection for any trait that enables plant populations to survive and reproduce in the presence of the herbicide. Herbicide resistance in weeds must be minimized because it is a major limiting factor to food security in global agriculture. This represents a huge challenge that will require great research efforts to develop control strategies as alternatives to the dominant and almost exclusive practice of weed control by herbicides. Weed scientists, plant ecologists and evolutionary biologists should join forces and work towards an improved and more integrated understanding of resistance across all scales. This approach will likely facilitate the design of innovative solutions to the global herbicide resistance challenge

Considering weed management as a social dilemma bridges individual and collective interests

Weeds pose severe threats to agricultural and natural landscapes worldwide. One major reason for the failure to effectively manage weeds at landscape scales is that current Best Management Practice guidelines, and research on how to improve such guidelines, focus too narrowly on property-level management decisions. Insufficiently considered are the aggregate effects of individual actions to determine landscape-scale outcomes, or whether there are collective practices that would improve weed management outcomes. Here, we frame landscape-scale weed management as a social dilemma, where trade-offs occur between individual and collective interests. We apply a transdisciplinary system approach—integrating the perspectives of ecologists, evolutionary biologists and agronomists into a social science theory of social dilemmas—to four landscape-scale weed management challenges: (i) achieving plant biosecurity, (ii) preventing weed seed contamination, (iii) maintaining herbicide susceptibility and (iv) sustainably using biological control. We describe how these four challenges exhibit characteristics of ‘public good problems’, wherein effective weed management requires the active contributions of multiple actors, while benefits are not restricted to these contributors. Adequate solutions to address these public good challenges often involve a subset of the eight design principles developed by Elinor Ostrom for ‘common pool social dilemmas’, together with design principles that reflect the public good nature of the problems. This paper is a call to action for scholars and practitioners to broaden our conceptualization and approaches to weed management problems. Such progress begins by evaluating the public good characteristics of specific weed management challenges and applying context-specific design principles to realize successful and sustainable weed management

Beyond modelling: Considering user-centred and post-development aspects to ensure the success of a decision support system

RIM, or 'Ryegrass Integrated Management', is a model-based decision support system (DSS) for weed management in broadacre cropping systems that was updated to continue aid the delivery of key recommendations to manage herbicide resistance. This article complements earlier publications by documenting the rationales that underpinned the re-development efforts. The objectives are to inform the next development cycle of RIM and its delivery, as well as its adaptation to other situations. Specifically, the article aims at providing developers and project managers with key aspects to be considered before and after (re-)developing this type of model-based agricultural DSS. Reviewers report a lack of similar efforts, with modelling aspects generally better documented than underpinning rationales, including those related to implementation. Yet, this type of initiative is necessary considering that agricultural DSS can become expensive projects, and that uptake by target audiences is typically low in spite of known pitfalls and limitations. The key elements that contributed to the thought process behind upgrade choices are thus provided, as well as practical consequences for modelling. Clearly re-asserting cost-effectiveness objectives and favouring human aspects led to: retaining the 'what-if' learning strategy rather than developing optimisation features; renouncing added modelling intricacies; enhancing the software accessibility; and anticipating future maintenance and distribution requirements. Strategies to maximise the impact of RIM are also discussed, particularly the need for qualified workshop facilitators, as well as transparency and evaluation to build user confidence

RIM: Anatomy of a Weed Management Decision Support System for Adaptation and Wider Application

© 2015 Weed Science Society of America. RIM, or "Ryegrass Integrated Management," is a model-based software allowing users to conveniently test and compare the long-term performance and profitability of numerous ryegrass control options used in Australian cropping systems. As a user-friendly decision support system that can be used by farmers, advisers, and industry professionals, RIM can aid the delivery of key recommendations among the agricultural community for broadacre cropping systems threatened by herbicide resistance. This paper provides advanced users and future developers with the keys to modify the latest version of RIM in order to facilitate future updates, modifications, and adaptations to other situations. The various components of RIM are mapped and explained, and the key principles underlying the construction of the model are explained. The implementation of RIM into a Microsoft Excel® software format is also documented, with details on how user inputs are coded and parameterized. An overview of the biological, agronomic, and economic components of the model is provided, with emphasis on the ryegrass biological characteristics most critical for its effective management. The extreme variability of these parameters and the subsequent limits of RIM are discussed. The necessary compromises were achieved by emphasizing the primary end-use of the program as a decision support system for farmers and advisors

Upgrading the RIM model for improved support of integrated weed management extension efforts in cropping systems

RIM, or "Ryegrass Integrated Management," is a user-friendly weed management software that integrates long-term economics. As a model-based decision support system, RIM enables users to easily build 10-year cropping scenarios and evaluate the impacts of management choices on annual rigid ryegrass populations and long-term profitability. Best used in a workshop format to enable learning through interactions, RIM can provide insights for the sustainable management of ryegrass through "what-if" scenarios in regions facing herbicide resistance issues. The upgrade of RIM is presented, with changes justified from an end-user perspective. The implementation of the model in a new, intuitive software format is presented, as well as the revision, update, and documentation of over 40 management options. Enterprises, establishment systems, and control options were redefined to represent current practices, with the notable inclusion of customizable herbicide options and techniques for weed seed control at harvest. Several examples of how RIM can be used with farmers to demonstrate the benefits of adopting recommended practices for managing or delaying the onset of herbicide resistance are presented. Originally designed for the dryland broadacre systems of the Australian southern grainbelt, RIM's underlying modeling was restructured to facilitate future updates and adaptation to other weed species and cropping regions. Nomenclature: Annual rigid ryegrass, Lolium rigidum Gaud

Methods to study agricultural systems

International audienc

Assessing regional farming system diversity using a mixed methods typology: the value of comparative agriculture tested in broadacre Australia

Most farm and farmer typologies focus on specific aspects and use standard structural and socio-economic indicators. Regional assessments of agricultural diversity based on farming systems are rarely done, as detailed and representative information is difficult to collect. The discipline of comparative agriculture addresses these challenges but remains little known, and seldom applied to broadacre situations. This study demonstrates in Western Australia the value of its mixed methods and multi-disciplinary concepts to determine the level and nature of regional farming system heterogeneity. The typology built comprised six farming systems based on 36 farms that represented half the farming population of a 4000 km 2 area (broadacre rainfed systems dominated by winter cereals and sheep, Mediterranean climate). The farm groups corresponding to these farming systems differed across 36 variables representing biophysical, technical, and social aspects at varied spatial and temporal scales. Results were compared with five sets of farm clusters produced through multivariate clustering procedures commonly employed to build typologies. These farm clusters differed across fewer variables than the farm groups of the comparative agriculture typology. The analytical, methodological and conceptual tools used in comparative agriculture to solve the challenges associated with the holistic study of farming system heterogeneity are discussed. These included basing data collection and analysis on an empirical approach that assessed groups of farms rather than individuals, solving data scarcity through a range of qualitative techniques, and progressively informing the choice of typology criteria. Comparative agriculture thus provides an alternative to standard typology paradigms that deserves wider application

Farming practices at the landscape scale: a novel approach investigating rotations in the WA wheatbelt

International audienc

Varietal Diffusion in Marginal Seed Systems: Participatory Trials Initiate Change in East Timor

Participatory on-farm trials of introduced varieties of the major staple crops were implemented in East Timor to contribute to addressing chronic national food insecurity. This paper confirmed the suitability of the participatory varietal selection approach by measuring high early adoption (over 80% one year after the trials). Three years on, significant areas of the new varieties were managed by adopters, and planting material from a third of the trials had been shared with an average of five non-participating households. However, crop failures (particularly from climatic hazards and animal damage) and the loss of planting material were common, reducing subsequent diffusion with crop characteristics and the availability of planting material playing critical roles. The study showed that on-farm testing was key as a first-stage mechanism in marginal areas but insufficient alone to achieve permanent varietal insertion in these particularly isolated farmers' seed systems. To ensure long-term adoption and broad diffusion, it is essential to combine the approach with comprehensive, flexible and reliable planting material sources. Linking the formal and existing farmers' seed systems at the community level appears to be the most promising option. The study also demonstrated that small, well-defined surveys can be critical, cost-efficient tools to monitor technology diffusion in resource-poor areas. © 2012 Copyright Taylor and Francis Group, LLC