Managing the risk of glyphosate resistance in Australian glyphosate-resistant cotton production systems

BACKGROUND: Glyphosate-resistant cotton varieties are an important tool for weed control in Australian cotton production systems. To increase the sustainability of this technology and to minimise the likelihood of resistance evolving through its use, weed scientists, together with herbicide regulators, industry representatives and the technology owners, have developed a framework that guides the use of the technology. Central to this framework is a crop management plan (CMP) and grower accreditation course. A simulation model that takes into account the characteristics of the weed species, initial gene frequencies and any associated fitness penalties was developed to ensure that the CMP was sufficiently robust to minimise resistance risks. RESULTS: The simulations showed that, when a combination of weed control options was employed in addition to glyphosate, resistance did not evolve over the 30 year period of the simulation. CONCLUSION: These simulations underline the importance of maintaining an integrated system for weed management to prevent the evolution of glyphosate resistance, prolonging the use of glyphosate-resistant cotton.

Integrated Cotton Farming Systems for Central Queensland

This project has delivered outcomes that address major agronomic and crop protection issues closely linked to the profitability and sustainability of cotton production enterprises in CQ. From an agronomic perspective, the CQ environment was always though to support economically viable cotton production in a wide sowing window from the middle of September to early January prior to this research. The ideal positioning of Bollgard II varieties in the CQ planting window was, therefore, critical to the future of the local cotton industry because growers needed baseline information to determine how best to take advantage of the higher yield potential offered by the Bt cotton technology, optimise irrigation water use and fibre characteristics. The project’s outputs include a number of key agronomic findings. Over three growing seasons, Bollgard II crop planted in the traditional sowing window from the middle of September to the end of October consistently produced the highest yields. The project delivers a clear and quantitative assessment of the impacts of planting outside the traditional cropping window - a yield penalty of between 1-4 bales/ha for November and December planted cotton. Whilst yield penalties associated with December-planted crops are clearly linked to declining heat units in the second half of the crop and a cool finish, those associated with November-planted cotton are not consistent with the theoretical yield potential for this sowing date. Further research to understand and minimize the physiological constraints on November-planted cotton would give CQ cotton growers far greater flexibility to develop mixed/double/rotation cropping farming systems that are relevant to the rapidly evolving nature of Agricultural production in Australia. The equivalence of cultivar types with clearly distinguishable, genetically based growth habits, demonstrated in this project, gives growers important information for making varietal choices. The entomological outcomes of this project represent strategic and tactical tools that are highly relevant to the viability and profitability of the cotton industry in Australia. The future of the cotton industry is inextricably linked to the survival and efficacy of GM cotton. Research done in the Callide irrigation area demonstrates the unquestionable potential for development of alternative and highly effective resistance management strategies for Bollgard II using novel technologies and strategies based on products such as Magnet®. Magnet® and similar technologies will be increasingly important in strategies to preserve the shelf life and efficacy of current and future generations of GM technology. However, more research will be required to address logistical and operational issues related to these new technologies before they can be fully exploited in commercial production systems. From an economic perspective, SLW is the sleeping giant in terms of insect nemeses of cotton, particularly from the standpoint of climate change and an increasingly warmer production environment. An effective sampling and management strategy for SLW which has been delivered by this project will go a long way towards minimising production costs in an environment characterised by rapidly rising input costs. SLW has the potential to permanently debilitate the national cotton industry by influencing market sentiment and quality perceptions. Field validation of the SLW population sampling models and management options in the Dawson irrigation area cotton and southern Queensland during 2006-07 documents the robustness of the entomological research outcomes achieved through this project.

APSIM – Evolution towards a new generation of agricultural systems simulation

Agricultural systems models worldwide are increasingly being used to explore options and solutions for the food security, climate change adaptation and mitigation and carbon trading problem domains. APSIM (Agricultural Production Systems sIMulator) is one such model that continues to be applied and adapted to this challenging research agenda. From its inception twenty years ago, APSIM has evolved into a framework containing many of the key models required to explore changes in agricultural landscapes with capability ranging from simulation of gene expression through to multi-field farms and beyond. Keating et al. (2003) described many of the fundamental attributes of APSIM in detail. Much has changed in the last decade, and the APSIM community has been exploring novel scientific domains and utilising software developments in social media, web and mobile applications to provide simulation tools adapted to new demands. This paper updates the earlier work by Keating et al. (2003) and chronicles the changing external challenges and opportunities being placed on APSIM during the last decade. It also explores and discusses how APSIM has been evolving to a “next generation” framework with improved features and capabilities that allow its use in many diverse topics.