Myrtille Lacoste

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.

Upgrading the RIM Model for Improved Support of Integrated Weed Management Extension Efforts in Cropping Systems

Abstract 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, RIMs underlying modeling was restructured to facilitate future updates and adaptation to other weed species and cropping regions. Nomenclature: Annual rigid ryegrass, Lolium rigidum Gaud. Resumen RIM (por sus siglas en inglés) o “Manejo Integrado de Lolium rigidum” es un programa amigable con el usuario para el manejo de malezas que integra factores económicos en el largo plazo. Como un sistema de apoyo para la toma de decisiones basado en un modelo, RIM permite a los usuarios construir escenarios de producción de cultivos de 10 años de duración y evaluar el impacto de las decisiones de manejo en las poblaciones de L. rigidum y en la rentabilidad a largo plazo. Al usarse en un formato de taller que facilite el aprendizaje mediante interacciones, RIM puede brindar una visión para el manejo sostenible de L. rigidum a través de escenarios “y qué pasa si” en regiones con problemas de resistencia a herbicidas. Aquí se presenta una actualización de RIM con cambios justificados desde una perspectiva del usuario final. Se presenta la implementación del modelo en un formato nuevo e intuitivo, además de la revisión, actualización y documentación de 40 opciones de manejo. Proyectos productivos, sistemas de establecimiento, y las opciones de control fueron redefinidas para representar prácticas actuales, con la notable inclusión de opciones de herbicidas personalizables para el control de semillas de malezas durante la cosecha. Adicionalmente, se presentan varios ejemplos de cómo se puede usar RIM con los productores para demostrar los beneficios de la adopción de prácticas recomendadas para el manejo o el atraso en la aparición de resistencia a herbicidas. Aunque originalmente se diseñó para sistemas de producción extensiva sin riego de la zona productora de granos del sur de Australia, el modelaje en el que se basa RIM fue estructurado para facilitar actualizaciones futuras y la adaptación a otras especies de malezas y otras regiones agrícolas.

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

Abstract 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. Nomenclature: Annual rigid ryegrass, Lolium rigidum Gaud.

PAM: Decision Support for Long-Term Palmer Amaranth (Amaranthus palmeri) Control

Palmer amaranth is the most troublesome weed problem in mid-southern US crop production. Herbicides continue to be the most commonly employed method for managing Palmer amaranth, despite the weeds widespread resistance to them. Therefore, farmers need research and extension efforts that promote the adoption of integrated weed management (IWM) techniques. Producers, crop consultants, educators, and researchers would be more likely to deploy diversified chemical and nonchemical weed management options if they are more informed about long-term biological and economic implications via user-friendly decision-support software. Described within is a recently developed software that demonstrates the effects of Palmer amaranth management practices on soil seedbank, risk of resistance evolution, and economics over a 10-year planning horizon. Aiding this objective is a point-and-click interface that provides feedback on resistance risk, yield potential, profitability, soil seedbank dynamics, and error checking of management options. Nomenclature: Palmer amaranth, Amaranthus palmeri S. Wats.

Methods to study agricultural systems

Modern agriculture faces complex and ever-evolving challenges. Productive, environmental and social requirements are to be met while fulfilling the needs of numerous stakeholders across a wide array of conditions. To better meet these challenges, researchers study agricultural systems using a myriad of methods, across varied disciplines and contexts. To help connect and orientate these research efforts, an overview is required to assess and categorize the diversity of approaches and methodologies being used to study agricultural systems. Whilst a plethora of specialized studies are available, broad-scope methodological reviews are lacking. Here we review methods used in Australia and New Zealand to study farms, farmers and their broader environment. Both quantitative and qualitative studies were included across a particularly wide range of publications while retaining a high level of methodological detail. An original overarching framework was produced that coherently summarized, described and categorized the diversity of methods encountered. This included defining classification criteria that can be conveniently applied to compare methods, assess their relative use, and identify linkages between approaches.