A model of macadamia with application to pruning in orchards

A self-organising model of macadamia, expressed using L-Systems, was used to explore aspects of canopy management. A small set of parameters control the basic architecture of the model, with a high degree of self-organisation occurring to determine the fate and growth of buds. Light was sensed at the leaf level and used to represent vigour and accumulated basipetally. Buds also sensed light so as to provide demand in the subsequent redistribution of the vigour. Empirical relationships were derived from a set of 24 completely digitised trees after conversion to multiscale tree graphs (MTG) and analysis with the OpenAlea software library. The ability to write MTG files was embedded within the model so that various tree statistics could be exported for each run of the model. To explore the parameter space a series of runs was completed using a high-throughput computing platform. When combined with MTG generation and analysis with OpenAlea it provided a convenient way in which thousands of simulations could be explored. We allowed the model trees to develop using self-organisation and simulated cultural practices such as hedging, topping, removal of the leader and limb removal within a small representation of an orchard. The model provides insight into the impact of these practices on potential for growth and the light distribution within the canopy and to the orchard floor by coupling the model with a path-tracing program to simulate the light environment. The lessons learnt from this will be applied to other evergreen, tropical fruit and nut trees.

The effects of holding space on growth and survival of individually reared three-spot crab (Portunus sanguinolentus).

The problem of cannibalism in communally reared crabs can be eliminated by separating the growing crabs into holding compartments. There is currently no information on optimal compartment size for growing crabs individually. 136 second instar crablets (Portunus sanguinolentus) (C2 ca. 7-10 mm carapace width (CW)) were grown for 90 days in 10 different-sized opaque and transparent walled acrylic compartments. The base area for each compartment ranged from small (32 mm × 32 mm) to large (176 mm × 176 mm). Effects of holding space and wall transparency on survival, CW, moult increment, intermoult period and average weekly gain (AWG) were examined. Most crabs reached instars C9-C10 (50-70 mm CW) by the end of experiment. The final survival rate in the smallest compartment was 25% mainly due to moult-related mortality predominantly occurring at the C9 instar. However, crabs in these smaller compartments had earlier produced significantly larger moult increments from instar to instar than those in the larger compartments (P < 0.05). Crabs in the smaller compartments (<65 mm × 65 mm) also showed significantly longer moult periods (P < 0.05). The net result was that AWG in CW was 5.22 mm week-1 for the largest compartment and 5.15 mm week-1 in smallest and did not differ significantly between compartment size groups (P = 0.916). Wall transparency had no impact on survival (P = 0.530) but a slight impact on AWG (P = 0.014). Survival rate was the best indicator of minimum acceptable compartment size (?43 mm × 43 mm) for C10 crablets because below this size death occurred before growth rate was significantly affected. For further growth, it would be necessary to transfer the crablets to larger compartments.

Space allowances for confined livestock and their determination from allometric principles

The amount of space provided to animals governs important elements of their behaviour and, hence, is critical for their health and welfare. We review the use of allometric principles and equations to estimate the static space requirements of animals when standing and lying, and the space required for animals to feed, drink, stand-up and lie-down. We use the research literature relating to transportation and intensive housing of sheep and cattle to assess the validity of allometric equations for estimating space allowances. We investigated these areas because transportation and intensive housing provide points along a continuum in terms of the duration of confinement, (from hours to months) and spatial requirements are likely to increase with increasing duration of confinement, as animals will need to perform a greater behavioural repertoire for long-term survival, health and welfare. We find that, although there are theoretical reasons why allometric relationships to space allowances may vary slightly for different classes of stock, space allowances that have been demonstrated to have adverse effects on animal welfare during transportation correlated well with an inability to accommodate standing animals, as estimated from allometry. For intensive housing, we were able to detect a space allowance below which there were adverse effects on welfare. For short duration transportation during which animals remain standing, a space allowance per animal described by the allometric equation: area (m^2) = 0.020W^0.66, where W = liveweight (kg), would appear to be appropriate. Where it is desirable for all animals to lie simultaneously, then a minimum space allowance per animal described by the allometric equation: area (m^2) = 0.027W^0.66 appears to permit this, given that animals in a group time-share space. However, there are insufficient data to determine whether this allowance onboard a vehicle/vessel would enable animals to move and access food and water with ease. In intensive housing systems, a minimum space allowance per animal described by the allometric equation: area (m^2) = 0.033W^0.66 appears to be the threshold below which there are adverse effects on welfare. These suggested space allowances require verification with a range of species under different thermal conditions and, for transportation, under different conditions of vehicular/vessel stability. The minimum length of trough per animal (L in m) required for feeding and drinking can be determined from L = 0.064W^0.33, with the number of animals required to feed/drink simultaneously taken into account, together with any requirement to minimise competition. This also requires verification with a range of species. We conclude that allometric relationships are an appropriate basis for the formulation of space allowances for livestock.

The sensitivity of updated NIR PLS models for fruit sorting to initial parameter settings

Partial least squares regression models on NIR spectra are often optimised (for wavelength range, mathematical pretreatment and outlier elimination) in terms of calibration terms of validation performance with reference to totally independent populations.

Parameter estimation and sensitivity analysis of fat deposition models in beef steers using acslXtreme.

The Davis Growth Model (a dynamic steer growth model encompassing 4 fat deposition models) is currently being used by the phenotypic prediction program of the Cooperative Research Centre (CRC) for Beef Genetic Technologies to predict P8 fat (mm) in beef cattle to assist beef producers meet market specifications. The concepts of cellular hyperplasia and hypertrophy are integral components of the Davis Growth Model. The net synthesis of total body fat (kg) is calculated from the net energy available after accounting tor energy needs for maintenance and protein synthesis. Total body fat (kg) is then partitioned into 4 fat depots (intermuscular, intramuscular, subcutaneous, and visceral). This paper reports on the parameter estimation and sensitivity analysis of the DNA (deoxyribonucleic acid) logistic growth equations and the fat deposition first-order differential equations in the Davis Growth Model using acslXtreme (Hunstville, AL, USA, Xcellon). The DNA and fat deposition parameter coefficients were found to be important determinants of model function; the DNA parameter coefficients with days on feed >100 days and the fat deposition parameter coefficients for all days on feed. The generalized NL2SOL optimization algorithm had the fastest processing time and the minimum number of objective function evaluations when estimating the 4 fat deposition parameter coefficients with 2 observed values (initial and final fat). The subcutaneous fat parameter coefficient did indicate a metabolic difference for frame sizes. The results look promising and the prototype Davis Growth Model has the potential to assist the beef industry meet market specifications.

Water use studies and implications for management of subtropical C4 turfgrasses in dryland and irrigated urban open space

A 2000-03 study to improve irrigation efficiency of grassed urban public areas in northern Australia found it would be difficult to grow most species in dry areas without supplementary watering. Sporoboulus virginicus and sand couch, Zoysia macrantha, were relatively drought-tolerant. Managers of sporting fields, parks and gardens could more than halve their current water use by irrigating over a long cycle, irrigating according to seasonal conditions and using grasses with low water use and sound soil management practices that encourage deep rooting. The use of effluent water provides irrigation and fertiliser cost savings and reduced nitrogen and phosphorus discharge to local waterways. Projected savings are $8000/ha/year in water costs for a typical sporting field.

Searching for common threads in threadfins: phylogeography of Australian polynemids in space and time

Proper management of marine fisheries requires an understanding of the spatial and temporal dynamics of marine populations, which can be obtained from genetic data. While numerous fisheries species have been surveyed for spatial genetic patterns, temporally sampled genetic data is not available for many species. We present a phylogeographic survey of the king threadfin Polydactylus macrochir across its species range in northern Australia and at a temporal scale of 1 and 10 yr. Spatially, the overall AMOVA fixation index was Omega(st) = 0.306 (F-st' = 0.838), p < 0.0001 and isolation by distance was strong and significant (r(2) = 0.45, p < 0.001). Temporally, genetic patterns were stable at a time scale of 10 yr. However, this did not hold true for samples from the eastern Gulf of Carpentaria, where populations showed a greater degree of temporal instability and lacked spatial genetic structure. Temporal but not spatial genetic structure in the Gulf indicates demographic interdependence but also indicates that fishing pressure may be high in this area. Generally, genetic patterns were similar to another co-distributed threadfin species Eleutheronema tetradactylum, which is ecologically similar. However, the historical demography of both species, evaluated herein, differed, with populations of P. macrochir being much younger. The data are consistent with an acute population bottleneck at the last glacio-eustatic low in sea level and indicate that the king threadfin may be sensitive to habitat disturbances.

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.