Survey of weed flora and management relative to cropping practices in the north-eastern grain region of Australia

The main weeds and weed management practices undertaken in broad acre dryland cropping areas of north-eastern Australia have been identified. The information was collected in a comprehensive postal survey of both growers and agronomists from Dubbo in New South Wales (NSW) through to Clermont in central Queensland, where 237 surveys were returned. A very diverse weed flora of 105 weeds from 91 genera was identified for the three cropping zones within the region (central Queensland, southern Queensland and northern NSW). Twenty-three weeds were common to all cropping zones. The major common weeds were Sonchus oleraceus, Rapistrum rugosum, Echinochloa spp. and Urochloa panicoides. The main weeds were identified for both summer and winter fallows, and sorghum, wheat and chickpea crops for each of the zones, with some commonality as well as floral uniqueness recorded. More genera were recorded in the fallows than in crops, and those in summer fallows exceeded the number in winter. Across the region, weed management relied heavily on herbicides. In fallows, glyphosate and mixes with glyphosate were very common, although the importance of the glyphosate mix partner differed among the cropping zones. Use and importance of pre-emergence herbicides in-crop varied considerably among the zones. In wheat, more graminicides were used in northern NSW than in southern Queensland, and virtually none were used in central Queensland, reflecting the differences in winter grass weed flora across the region. Atrazine was the major herbicide used in sorghum, although metolachlor was also used predominantly in northern NSW. Fallow and inter-row cultivation were used more often in the southern areas of the region. Grazing of fallows was more prominent in northern NSW. High crop seeding rates were not commonly recorded indicating that growers are not using crop competition as a tool for weed management. Although many management practices were recorded overall, few growers were using integrated weed management, and herbicide resistance has been and continues to be an issue for the region.

Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI).

Varying the spatial distribution of applied nitrogen (N) fertilizer to match demand in crops has been shown to increase profits in Australia. Better matching the timing of N inputs to plant requirements has been shown to improve nitrogen use efficiency and crop yields and could reduce nitrous oxide emissions from broad acre grains. Farmers in the wheat production area of south eastern Australia are increasingly splitting N application with the second timing applied at stem elongation (Zadoks 30). Spectral indices have shown the ability to detect crop canopy N status but a robust method using a consistent calibration that functions across seasons has been lacking. One spectral index, the canopy chlorophyll content index (CCCI) designed to detect canopy N using three wavebands along the "red edge" of the spectrum was combined with the canopy nitrogen index (CNI), which was developed to normalize for crop biomass and correct for the N dilution effect of crop canopies. The CCCI-CNI index approach was applied to a 3-year study to develop a single calibration derived from a wheat crop sown in research plots near Horsham, Victoria, Australia. The index was able to predict canopy N (g m-2) from Zadoks 14-37 with an r2 of 0.97 and RMSE of 0.65 g N m-2 when dry weight biomass by area was also considered. We suggest that measures of N estimated from remote methods use N per unit area as the metric and that reference directly to canopy %N is not an appropriate method for estimating plant concentration without first accounting for the N dilution effect. This approach provides a link to crop development rather than creating a purely numerical relationship. The sole biophysical input, biomass, is challenging to quantify robustly via spectral methods. Combining remote sensing with crop modelling could provide a robust method for estimating biomass and therefore a method to estimate canopy N remotely. Future research will explore this and the use of active and passive sensor technologies for use in precision farming for targeted N management.

Weed age affects chemical control of Conyza bonariensis in fallows

In the last decade, Conyza bonariensis has become a widespread and difficult-to-control weed in Australian broad-acre cropping, particularly in glyphosate-based zero-tilled fallows of the subtropical grain region. The first Australian populations of C. bonariensis, where it is known as flaxleaf fleabane, were confirmed resistant to glyphosate in 2010. Control with alternative herbicides in fallows has been inconsistent, with earlier research indicating that weed age could be a potential contributing factor. In two field experiments, the impact of weed age (one, two and three months) was measured on the efficacy of six non-selective herbicide mixtures and sequential applications for control in fallows. In another two experiments we evaluated 11 non-selective herbicides, mixtures and sequential applications applied to one and three month old weeds using higher rates on older weeds. When herbicide rates were consistent for different weed ages, efficacy was reduced only by an average of 1% when two month old weeds were treated compared to one month old weeds. However when applied to three month old weeds, efficacy of treatments was significantly (P < 0.001) reduced by 3-30%. When herbicide rates were increased, weed age had no adverse effect on efficacy, which ranged from 90 to 100%, for amitrole, glyphosate mixed with 2,4-D amine plus picloram, and three sequential application treatments of glyphosate mixtures followed with bipyridyl products. Thus, this problem weed can be controlled effectively and consistently at the rosette stage of one to two months old, or three month old weeds with several different treatments at robust rates. These effective glyphosate alternatives and sequential-application tactics will minimise replenishment of the soil seed-bank and further reduce the risk for further evolution of glyphosate resistance. (C) 2012 Elsevier Ltd. All rights reserved.

Green and brown bridges between weeds and crops reveal novel Diaporthe species in Australia

Diaporthe (syn. Phomopsis) species are well-known saprobes, endophytes or pathogens on a range of plants. Several species have wide host ranges and multiple species may sometimes colonise the same host species. This study describes eight novel Diaporthe species isolated from live and/or dead tissue from the broad acre crops lupin, maize, mungbean, soybean and sunflower, and associated weed species in Queensland and New South Wales, as well as the environmental weed bitou bush (Chrysanthemoides monilifera subsp. rotundata) in eastern Australia. The new taxa are differentiated on the basis of morphology and DNA sequence analyses based on the nuclear ribosomal internal transcribed spacer region, and part of the translation elongation factor-1α and ß-tubulin genes. The possible agricultural significance of live weeds and crop residues ('green bridges') as well as dead weeds and crop residues ('brown bridges') in aiding survival of the newly described Diaporthe species is discussed.

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.

The importance of cotton disease surveys in Queensland for monitoring endemic diseases and detecting new pathogens and pests

The cotton industry in Australia funds biannual disease surveys conducted by plant pathologists. The objective of these surveys is to monitor the distribution and importance of key endemic pests and record the presence or absence of new or exotic diseases. Surveys have been conducted in Queensland since 2002/03, with surveillance undertaken by experienced plant pathologists. Monitoring of endemic diseases indicates the impact of farming practices on disease incidence and severity. The information collected gives direction to cotton disease research. Routine diagnostics has provided early detection of new disease problems which include 1) the identification of Nematospora coryli, a pathogenic yeast associated with seed and internal boll rot; and 2) Rotylenchulus reniformis, a plant-parasitic nematode. This finding established the need for an intensive survey of the Theodore district revealing that reniform was prevalent across the district at populations causing up to 30% yield loss. Surveys have identified an exotic defoliating strain (VCG 1A) and non-defoliating strains of Verticillium dahliae, which cause Verticillium wilt. An intensive study of the diversity of V. dahliae and the impact these strains have on cotton are underway. Results demonstrate the necessity of general multi-pest surveillance systems in broad acre agriculture in providing (1) an ongoing evaluation of current integrated disease management practices and (2) early detection for a suite of exotic pests and previously unknown pests.