Lantana: current management status and future prospects.

Lantana camara L. is a significant weed of which there are some 650 varieties in over 60 countries or island groups. It has been the focus of biological control attempts for a century, yet still poses major problems in many regions. Lantana has a significant impact on economic and environmental areas and is difficult to control. The key to good management of lantana is constant vigilance. Repeated control of new regrowth is critical to success. Control of new infestations should be a priority because the species is able to expand its range during good seasons, but does not die out during poor conditions. This book is a resource for land managers and researchers on methods of lantana control, particularly biocontrol.

Silverleaf whitefly resistance management

Silverleaf whitefly (SLW) is a major late season pest of cotton due to its potential to contaminate cotton lint with honeydew. To prevent this, management is often reliant on the use of insecticides to control SLW populations. With selection pressure SLW develop resistance to insecticides they are exposed to, resulting in spray failures. Our lab tests resistance levels in SLW populations collected from across the cotton industry. In this presentation I will provide an update of emerging SLW resistance issues the cotton industry is facing.

New developments in UAV remote sensing for pest management and the implications for cotton

Efficient crop monitoring and pest damage assessments are key to protecting the Australian agricultural industry and ensuring its leading position internationally. An important element in pest detection is gathering reliable crop data frequently and integrating analysis tools for decision making. Unmanned aerial systems are emerging as a cost-effective solution to a number of precision agriculture challenges. An important advantage of this technology is it provides a non-invasive aerial sensor platform to accurately monitor broad acre crops. In this presentation, we will give an overview on how unmanned aerial systems and machine learning can be combined to address crop protection challenges. A recent 2015 study on insect damage in sorghum will illustrate the effectiveness of this methodology. A UAV platform equipped with a high-resolution camera was deployed to autonomously perform a flight pattern over the target area. We describe the image processing pipeline implemented to create a georeferenced orthoimage and visualize the spatial distribution of the damage. An image analysis tool has been developed to minimize human input requirements. The computer program is based on a machine learning algorithm that automatically creates a meaningful partition of the image into clusters. Results show the algorithm delivers decision boundaries that accurately classify the field into crop health levels. The methodology presented in this paper represents a venue for further research towards automated crop protection assessments in the cotton industry, with applications in detecting, quantifying and monitoring the presence of mealybugs, mites and aphid pests.

BYGUM – a new tool for BarnYard Grass Understanding and Management

Weed management has become increasingly challenging for cotton growers in Australia in the last decade. Glyphosate, the cornerstone of weed management in the industry, is waning in effectiveness as a result of the evolution of resistance in several species. One of these, awnless barnyard grass, is very common in Australian cotton fields, and is a prime example of the new difficulties facing growers in choosing effective and affordable management strategies. RIM (Ryegrass Integrated Management) is a computer-based decision support tool developed for the south-western Australian grains industry. It is commonly used there as a tool for grower engagement in weed management thinking and strategy development. We used RIM as the basis for a new tool that can fulfil the same types of functions for subtropical Australian cotton-grains farming systems. The new tool, BYGUM, provides growers with a robust means to evaluate five-year rotations including testing the economic value of fallows and fallow weed management, winter and summer cropping, cover crops, tillage, different herbicide options, herbicide resistance management, and more. The new model includes several northernregion- specific enhancements: winter and summer fallows, subtropical crop choices, barnyard grass seed bank, competition, and ecology parameters, and more freedom in weed control applications. We anticipate that BYGUM will become a key tool for teaching and driving the changes that will be needed to maintain sound weed management in cotton in the near future.

A review of weed management in wheat using crop competition

Wheat occupies a principal place in the diet of humans globally, contributing more to our daily calorie and protein intake than any other crop. For this reason, preventing weed induced yield losses in wheat has high significance for world food sustainability. Herbicides and tillage play an important role in weed control, but their use has often unacceptable consequences for humans and the wider environment. Additionally, the range of herbicides effective on key weeds is dwindling due to the evolution of herbicide resistance. Elevating crop competitiveness against weeds, through a combination of wheat breeding and innovative planting design (planting density, row spacing and orientation), has strong potential to reduce weed-induced yield losses in wheat. The last decade of research has provided a solid foundation for the breeding of weed suppressive wheat cultivars, and continued research in this area should be a focus for the future. In the interim, there is cause for optimism that weeds can be effectively suppressed using existing wheat varieties, through careful cultivar selection and choice of planting design. Further research is required to define the nature of relationships between cultivar traits and competitive planting strategies, across diverse weed flora in multiple countries, sites and seasons. Investment in such innovation promises to produce benefits, not only in terms of sustained wheat yields, but also in terms of human and ecosystem health, through ameliorating chemical and sediment contamination, soil degradation, and CO2 pollution.