Flying-Fox Species Density-A Spatial Risk Factor for Hendra Virus Infection in Horses in Eastern Australia

Hendra virus causes sporadic but typically fatal infection in horses and humans in eastern Australia. Fruit-bats of the genus Pteropus (commonly known as flying-foxes) are the natural host of the virus, and the putative source of infection in horses; infected horses are the source of human infection. Effective treatment is lacking in both horses and humans, and notwithstanding the recent availability of a vaccine for horses, exposure risk mitigation remains an important infection control strategy. This study sought to inform risk mitigation by identifying spatial and environmental risk factors for equine infection using multiple analytical approaches to investigate the relationship between plausible variables and reported Hendra virus infection in horses. Spatial autocorrelation (Global Moran’s I) showed significant clustering of equine cases at a distance of 40 km, a distance consistent with the foraging ‘footprint’ of a flying-fox roost, suggesting the latter as a biologically plausible basis for the clustering. Getis-Ord Gi* analysis identified multiple equine infection hot spots along the eastern Australia coast from far north Queensland to central New South Wales, with the largest extending for nearly 300 km from southern Queensland to northern New South Wales. Geographically weighted regression (GWR) showed the density of P. alecto and P. conspicillatus to have the strongest positive correlation with equine case locations, suggesting these species are more likely a source of infection of Hendra virus for horses than P. poliocephalus or P. scapulatus. The density of horses, climate variables and vegetation variables were not found to be a significant risk factors, but the residuals from the GWR suggest that additional unidentified risk factors exist at the property level. Further investigations and comparisons between case and control properties are needed to identify these local risk factors.

Variation in water extraction with maize plant density and its impact on model application

The use of maize simulation models to determine the optimum plant population for rainfed environments allows the evaluation of plant populations over multiple years and locations at a lower cost than traditional field experimentation. However the APSIM maize model that has been used to conduct some of these 'virtual' experiments assumes that the maximum rate of soil water extraction by the crop root system is constant across plant populations. This untested assumption may cause grain yield to be overestimated in lower plant populations. A field experiment was conducted to determine whether maximum rates of water extraction vary with plant population, and the maximum rate of soil water extraction was estimated for three plant populations (2.4, 3.5 and 5.5 plants m(-2)) under water limited conditions. Maximum soil water extraction rates in the field experiment decreased linearly with plant population, and no difference was detected between plant populations for the crop lower limit of soil water extraction. Re-analysis of previous maize simulation experiments demonstrated that the use of inappropriately high extraction-rate parameters at low plant populations inflated predictions of grain yield, and could cause erroneous recommendations to be made for plant population. The results demonstrate the importance of validating crop simulation models across the range of intended treatments. (C) 2013 Elsevier E.V. All rights reserved.

Harvest weed seed control: is there a role in northern region farming systems?

Harvest weed seed control (HWSC) is a new approach which targets weed seed removal and/or destruction during the crop harvest operation. The success of HWSC is dependant upon weed seed retention at harvest. To identify and define the potential value of HWSC in northern farming systems, we conducted a field survey. In total 1400 transects across 70 paddocks assessed weed distribution, density and seed production at harvest time in wheat, chickpea and sorghum crops. Seventy weed species were identified, of which many had large seed numbers retained at crop harvest. The most prevalent included common sowthistle, flaxleaf fleabane, awnless barnyard grass, wild oat, and African turnip weed. Our field survey has shown there is a role for HWSC in the northern farming system. Therefore the efficacy of specific HWSC systems on problematic weeds should be evaluated in the northern region.

Optimizing Hill Seeding Density for High-Yielding Hybrid Rice in a Single Rice Cropping System in South China

Mechanical hill direct seeding of hybrid rice could be the way to solve the problems of high seeding rates and uneven plant establishment now faced in direct seeded rice; however, it is not clear what the optimum hill seeding density should be for high-yielding hybrid rice in the single-season rice production system. Experiments were conducted in 2010 and 2011 to determine the effects of hill seeding density (25 cm 615 cm, 25 cm 617 cm, 25 cm 619 cm, 25 cm 621 cm, and 25 cm 623 cm; three to five seeds per hill) on plant growth and grain yield of a hybrid variety, Nei2you6, in two fields with different fertility (soil fertility 1 and 2). In addition, in 2012 and 2013, comparisons among mechanical hill seeding, broadcasting, and transplanting were conducted with three hybrid varieties to evaluate the optimum seeding density. With increases in seeding spacing from 25 cm615 cm to 25 cm623 cm, productive tillers per hill increased by 34.2% and 50.0% in soil fertility 1 and 2. Panicles per m2 declined with increases in seeding spacing in soil fertility 1. In soil fertility 2, no difference in panicles per m2 was found at spacing ranging from 25 cm617 cm to 25 cm623 cm, while decreases in the area of the top three leaves and aboveground dry weight per shoot at flowering were observed. Grain yield was the maximum at 25 cm 617 cm spacing in both soil fertility fields. Our results suggest that a seeding density of 25 cm617 cm was suitable for high-yielding hybrid rice. These results were verified through on-farm demonstration experiments, in which mechanical hill-seeded rice at this density had equal or higher grain yield than transplanted rice

Effects of light conditions and plant density on growth and reproductive biology of Cascabela thevetia (L.) Lippold

Cascabela thevetia (L.) Lippold (Apocynaceae) is an invasive woody weed that has formed large infestations at several locations in northern Australia. Understanding the reproductive biology of C. thevetia is vital to its management. This paper reports results of a shade house experiment that determined the effects of light conditions (100% or 30% of natural light) and plant densities (one, two, four or eight plants per plot) on the growth, time to flowering and seed formation, and monthly pod production of two C. thevetia biotypes (peach and yellow). Shaded plants were significantly larger when they reached reproductive maturity than plants grown under natural light. However, plants grown under natural light flowered earlier (268 days compared with 369 days) and produced 488 more pods per pot (a 5-fold increase) over 3 years. The yellow biotype was slightly taller at reproductive maturity but significantly taller and with significantly greater aboveground biomass at the end of the study. Both biotypes flowered at a similar time under natural light and low plant densities but the yellow biotype was quicker to seed (478 versus 498 days), produced significantly more pods (364 versus 203 pods) and more shoot growth (577 g versus 550 g) than the peach biotype over 3 years. Higher densities of C. thevetia tended to significantly reduce the shoot and root growth by 981 g and 714 g per plant across all light conditions and biotypes over 3 years and increase the time taken to flower by 140 days and produce seeds by 184 days. For land managers trying to prevent establishment of C. thevetia or to control seedling regrowth once initial infestations have been treated, this study indicates that young plants have the potential to flower and produce seeds within 268 and 353 days, respectively. However, with plant growth and reproduction most likely to be slower under field conditions, annual surveillance and control activities should be sufficient to find and treat plants before they produce seeds and replenish soil seed banks. The most at-risk part of the landscape may be open areas that receive maximum sunlight, particularly within riparian habitats where plants would consistently have more favourable soil moisture conditions.

Crop design for specific adaptation in variable dryland production environments

Climatic variability in dryland production environments (E) generates variable yield and crop production risks. Optimal combinations of genotype (G) and management (M) depend strongly on E and thus vary among sites and seasons. Traditional crop improvement seeks broadly adapted genotypes to give best average performance under a standard management regime across the entire production region, with some subsequent manipulation of management regionally in response to average local environmental conditions. This process does not search the full spectrum of potential G × M × E combinations forming the adaptation landscape. Here we examine the potential value (relative to the conventional, broad adaptation approach) of exploiting specific adaptation arising from G × M × E. We present an in-silico analysis for sorghum production in Australia using the APSIM sorghum model. Crop design (G × M) is optimised for subsets of locations within the production region (specific adaptation) and is compared with the optimum G across all environments with locally modified M (broad adaptation). We find that geographic subregions that have frequencies of major environment types substantially different from that for the entire production region show greatest advantage for specific adaptation. Although the specific adaptation approach confers yield and production risk advantages at industry scale, even greater benefits should be achievable with better predictors of environment-type likelihood than that conferred by location alone.

Increasing the productivity of avocado orchards using high-density plantings: A review

There is strong interest in the use of high-density plantings to increase the productivity of avocado (Persea americana) orchards. Close plantings have the potential for higher yields and returns than standard or traditional plantings, especially in the early years of production. The success of this technology is dependent on the use of methods to control shoot growth and maximise light interception as the trees begin to bear fruit. We reviewed the performance of high-density orchards in different environments, and the success of efforts to control the growth of the trees through the use of dwarfing material, canopy management and growth regulators. Close plantings generally produce higher yields in the first few years of bearing compared with the yields of standard plantings. However, in most growing areas, the trees in the close plantings soon begin to crowd each other and yields decline. This usually occurs despite efforts to control shoot growth by pruning the trees or by applying growth regulators. Efforts to breed dwarfing rootstocks that can control the growth of mature trees have been largely unsuccessful. In the absence of dwarfing material, effective canopy management appears to be the largest barrier to success of high-density orchards. Further research on the use of different pruning strategies and growth regulators to control the growth of the trees and maximise light interception is required. There are potential problems with some of the growth regulators persisting in the harvested fruit and soil under certain circumstances.

Growth analysis and biomass partitioning of Cyperus iria in response to rice planting density and nitrogen rate

Cyperus iria is a weed of rice with widespread occurrence throughout the world. Because of concerns about excessive and injudicious use of herbicides, cultural weed management approaches that are safe and economical are needed. Developing such approaches will require a better understanding of weed biology and ecology, as well as of weed response to increases in crop density and nutrition. Knowledge of the effects of nitrogen (N) fertilizer on crop-weed competitive interactions could also help in the development of integrated weed management strategies. The present study was conducted in a screenhouse to determine the effects of rice planting density (0, 5, 10, and 20 plants pot−1) and N rate (0, 50, 100, and 150 kg ha−1) on the growth of C. iria. Tiller number per plant decreased by 73–88%, leaf number by 85–94%, leaf area by 85–98%, leaf biomass by 92–99%, and inflorescence biomass by 96–99% when weed plants were grown at 20 rice plants pot−1 (i.e., 400 plants m−2) compared with weed plants grown alone. All of these parameters increased when N rates were increased. On average, weed biomass increased by 118–389% and rice biomass by 121–275% with application of 50–150 kg N ha−1, compared to control. Addition of N favored weed biomass production relative to rice biomass. Increased N rates reduced the root-to-shoot weight ratio of C. iria. Rice interference reduced weed growth and biomass and completely suppressed C. iria when no N was applied at high planting densities (i.e., 20 plants pot−1). The weed showed phenotypic plasticity in response to N application, and the addition of N increased the competitive ability of the weed over rice at densities of 5 and 10 rice plants pot−1 compared with 20 plants pot−1. The results of the present study suggest that high rice density (i.e., 400 plants m−2) can help suppress C. iria growth even at high N rates (150 kg ha−1).

Optimising initial population density, growth time and nitrogen nutrition for assessing resistance of wheat cultivars to root-lesion nematode (Pratylenchus thornei)

Pratylenchus thornei is a major pathogen of wheat in Australia. Two glasshouse experiments with four wheat cultivars that had different final populations (Pf) of P. thornei in the field were used to optimise conditions for assessing resistance. With different initial populations (Pi) ranging up to 5250 P. thornei/kg soil, Pf of P. thornei increased to 16 weeks after sowing, and then decreased at 20 weeks in some cultivar x Pi combinations. The population dynamics of P. thornei up to 16 weeks were best described by a modified exponential equation P f (t) = aP i e kt where P f (t) is the final population density at time t, P i is the initial population density, a is the proportion of P i that initiates population development, and k is the intrinsic rate of increase of the population. The cultivar GS50a had very low k values at Pi of 5250 and 1050 indicating its resistance, Suneca and Potam had high k values indicating susceptibility, whereas intolerant Gatcher had a low value at the higher Pi and a high value at the lower Pi. Nitrate fertiliser increased plant growth and Pf values of susceptible cultivars, but in unplanted soil it decreased Pf. Nematicide (aldicarb 5 mg/kg soil) killed P. thornei more effectively in planted than in unplanted soil and increased plant growth particularly in the presence of N fertiliser. In both experiments, the wheat cultivars Suneca and Potam were more susceptible than the cultivar GS50a reflecting field results. The method chosen to discriminate wheat cultivars was to assess Pf after growth for 16 weeks in soil with Pi ~1050–5250 P. thornei/kg soil and fertilised with 200 mg NO3–N/kg soil.

Plant composition modulates arthropod pest and predator abundance: Evidence for culling exotics and planting natives

We investigate the role of plant species in crops, pasture and native vegetation remnants in supporting agronomic pests and their predators. The study was conducted in three Australian States and across 290 sites sampled monthly for two years. Pastures played a key role in harbouring pest species consistent across States, while native vegetation hosted relatively more predators than other habitat types within each State. Furthermore, native plant species supported the lowest pest density and more predators than pests; in contrast, 75 of the exotic weed species surveyed hosted more pests than predators. Despite the role of pasture in harbouring pests, we found in NSW that pasture also supported the highest proportion of juvenile predators, while native vegetation remnants had the lowest. Our results indicate that non-crop habitat (native remnants or pasture) with few exotic weeds supports high predator and low pest arthropod densities, and that weeds are associated with high pest densities. By linking broad response variables such as ‘all pests’ with specific predictors such as ‘plant species’, our study will inform on-farm management actions of which weeds to control and which natives to plant or regenerate. This study shows the importance of knowing the function of habitats and plants species in supporting pests and predators in agricultural landscapes across multiple regions.

First report of Cotton leafroll dwarf virus in Thailand using a species-specific PCR validated with isolates from Brazil

Partial virus genome sequence with high nucleotide identity to Cotton leafroll dwarf virus (CLRDV) was identified from two cotton (Gossypium hirsutum) samples from Thailand displaying typical cotton leaf roll disease symptoms. We developed and validated a PCR assay for the detection of CLRDV isolates from Thailand and Brazil.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.