What influences fungal communities in cotton soils

Soilborne diseases such as Fusarium wilt, Black root rot and Verticillium wilt have significant impact on cotton production. Fungi are an important component of soil biota with capacity to affect pathogen inoculum levels and their disease causing potential. Very little is known about the soil fungal community structure and management effects in Australian cotton soils. We analysed surface soils from ongoing field experiments monitoring cotton performance and disease incidence in three cotton growing regions, collected prior to 2013 planting, for the genetic diversity and abundance as influenced by soil type, environment and management practices and link it with disease incidence and suppression. Results from the 28S LSU rRNA sequencing based analysis indicated a total of 370 fungal genera in all the cotton soils and the top 25 genera in abundance accounted for the major portion of total fungal community. There were significant differences in the composition and genetic diversity of soil fungi between the different field sites from the three cotton growing regions. Results for diversity indices showed significantly greater diversity in the long-term crop rotation experiment at Narrabri (F6E) and experiments at Cowan and Goondiwindi compared to the Biofumigation and D1 field experiments at ACRI, Narrabri. Diversity was lowest in the soils under brassica crop rotation in Biofumigation experiment. Overall, the diversity and abundance of soil fungal community varied significantly in the three cotton growing regions indicating soil type and environmental effects. These results suggest that changes in soil fungal community may play a notable role in soilborne disease incidence in cotton.

Growth and development of three key summer grasses in Australian cotton systems

Awnless barnyard grass, feathertop Rhodes grass, and windmill grass are important weeds in Australian cotton systems. In October 2014, an experiment was established to investigate the phenological plasticity of these species. Seed of these species were planted in a glasshouse every four weeks and each cohort grown for 6 months. A developmental response to day length was observed in barnyard grass but not in the other species. Days to maturity increased with each planting for feathertop Rhodes and windmill grass for the first six cohorts. Barnyard grass showed a similar pattern in growth for seeds planted from October to December with an increase in the onset of maturity from 51 to 58 days. However, the onset of maturity for cohorts planted between January and March decreased to between 50 and 52 days. All species had a decrease in the total number of panicles produced from the first four plantings. Feathertop Rhodes grass planted in October produced 41 panicles compared to those planted at the end of December producing 30 panicles, barnyard grass had a decrease from 99 to 47 panicles and windmill grass 37 to 15 panicles on average. By comparing the development of these key weed species over 12 months, detailed information on the phenological plasticity of these species will be obtained. This information will contribute to more informed management decisions by improving our understanding of appropriate weed control timings or herbicide rates depending on weed emergence and development.

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.

Response of irrigated cotton to applied nitrogen

Like all high yielding farming systems nitrogen (N) is a key component to their productivity and profitability and Australian irrigated cotton growers are tending to apply more N than is required for the level of lint yield that is being achieved. This suggests either over application of N or inefficient systems limiting the response of cotton to N inputs. To investigate this four replicated trials were established in commercial fields during the 2014/15 season. The trials were aiming to measure the difference in response of irrigated cotton to the application of N under flood and overhead irrigation systems. The application treatments utilized eight upfront rates of applied N, ranging from 0 N kg/ha to a maximum of 410 kg N/ha, with three of the fours trials receiving a growerdetermined in-crop application of N in the irrigation water. The two flood irrigation systems had lower lint yields from similar levels of N input compared to one of the overhead irrigated sites; the result from the second overhead site was impacted by disease. This paper discusses the response of plant N uptake, lint yield and fertilizer N recovery to N application..

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.

Composts addition may improve biology in cotton soils

Composts can provide a source of organic carbon and nutrients for soil biota and increase soil fertility as well as provide other biological and structural benefits hence compost addition to cotton soils is seen as a way to improve cotton soil biological health and fertility. In a six month incubation experiment we analysed the changes in microbial populations and activities related to C and N cycling following the application of feedlot, poultry manure and gin trash compost materials. A significant variation in the chemical composition, e.g. major nutrients and trace elements, was found between the three compost products. The feedlot compost generally contained higher levels of dissolved organic carbon, total nitrogen and bicarbonate extractable phosphorus whereas the Gin trash compost had lower carbon and nutrient concentrations. The effect of compost addition @ 5 and 10t/ha generally increased microbial activity but the effect was only evident during the first two weeks of incubation. Composts effects on the abundance of total bacteria (16S), nitrifying (amoA), nitrogen fixing (nifH) and denitrifying bacteria (nosZ) and total fungi (ITS gene) varied between different composts. The addition of feedlot and poultry compost material significantly increased the levels of dissolved organic carbon (DOC) and nitrogen (DON) in soil compared to that in control soils while ‘Gin trash’ compost had no effect. These differences reflected in the microbial catabolic diversity changes in the compost amended soils. Therefore, chemical analysis of the compost material before application is recommended to more fully consider its’ potential benefits.

Effects of silicon treatment and inoculation with Fusarium oxysporum f. sp. vasinfectum on cellular defences in root tissues of two cotton cultivars

BACKGROUND AND AIMS: Silicon has been shown to enhance the resistance of plants to fungal and bacterial pathogens. Here, the effect of potassium silicate was assessed on two cotton (Gossypium hirsutum) cultivars subsequently inoculated with Fusarium oxysporum f. sp. vasinfectum (Fov). Sicot 189 is moderately resistant whilst Sicot F-1 is the second most resistant commercial cultivar presently available in Australia. METHODS: Transmission and light microscopy were used to compare cellular modifications in root cells after these different treatments. The accumulation of phenolic compounds and lignin was measured. KEY RESULTS: Cellular alterations including the deposition of electron-dense material, degradation of fungal hyphae and occlusion of endodermal cells were more rapidly induced and more intense in endodermal and vascular regions of Sicot F-1 plants supplied with potassium silicate followed by inoculation with Fov than in similarly treated Sicot 189 plants or in silicate-treated plants of either cultivar not inoculated with Fov. Significantly more phenolic compounds were present at 7 d post-infection (dpi) in root extracts of Sicot F-1 plants treated with potassium silicate followed by inoculation with Fov compared with plants from all other treatments. The lignin concentration at 3 dpi in root material from Sicot F-1 treated with potassium silicate and inoculated with Fov was significantly higher than that from water-treated and inoculated plants. CONCLUSIONS: This study demonstrates that silicon treatment can affect cellular defence responses in cotton roots subsequently inoculated with Fov, particularly in Sicot F-1, a cultivar with greater inherent resistance to this pathogen. This suggests that silicon may interact with or initiate defence pathways faster in this cultivar than in the less resistant cultivar.

Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves.