Using Alkanes to Predict Voluntary Intake, Faecal Output and Digestibility of Buffel Grass Hay Fed to Steers

The cuticular waxes of forage plants contain long chain n-alkanes with odd carbon chain lengths in the range C25-C37 which are quantitatively recovered in faeces. When these concentrations are used with the concentrations of administered synthetic even chain length alkanes, the voluntary intake (VI), faecal output (FO) and digestibility (DMD) of forages can be estimated (Dove and Mayes 1991, 1996).

Selection and characterisation of two attenuated vaccine lines of Eimeria tenella in Australia

Objective To attenuate two strains of Eimeria tenella by selecting for precocious development and evaluate the strains in characterisation trials and by field evaluation, to choose one precocious line for incorporation into an Australian live coccidiosis vaccine for poultry. Design Two strains from non-commercial flocks were passaged through chickens while selecting for precocious development. Each strain was characterised for drug sensitivity, pathogenicity, protection against homologous and heterologous challenge, and oocyst output in replicated experiments in which the experimental unit was a cage of three birds. Oocyst output and/or body weight gain data collected over a 10 to 12 day period following final inoculation were measured. Feed conversion ratios were also calculated where possible. Results Fifteen passages resulted in prepatent periods reduced by 24 h for the Redlands strain (from 144 h to 120 h)and 23 h for the Darryl strain (from 139 h to 116 h). Characterisation trials demonstrated that each precocious line was significantly less pathogenic than its parent strain and each effectively induced immunity that protected chickens against challenge with both the parent strain and other virulent field strains. Both lines had oocyst outputs that, although significantly reduced relative to the parent strains, remained sufficiently high for commercial vaccine production, and both showed susceptibility to coccidiostats. Conclusion Two attenuated lines have been produced that exhibit the appropriate characteristics for use in an Australian live coccidiosis vaccine.

Remote sensing of nitrogen and water stress in wheat

Nitrogen (N) is the largest agricultural input in many Australian cropping systems and applying the right amount of N in the right place at the right physiological stage is a significant challenge for wheat growers. Optimizing N uptake could reduce input costs and minimize potential off-site movement. Since N uptake is dependent on soil and plant water status, ideally, N should be applied only to areas within paddocks with sufficient plant available water. To quantify N and water stress, spectral and thermal crop stress detection methods were explored using hyperspectral, multispectral and thermal remote sensing data collected at a research field site in Victoria, Australia. Wheat was grown over two seasons with two levels of water inputs (rainfall/irrigation) and either four levels (in 2004; 0, 17, 39 and 163 kg/ha) or two levels (in 2005; 0 and 39 kg/ha N) of nitrogen. The Canopy Chlorophyll Content Index (CCCI) and modified Spectral Ratio planar index (mSRpi), two indices designed to measure canopy-level N, were calculated from canopy-level hyperspectral data in 2005. They accounted for 76% and 74% of the variability of crop N status, respectively, just prior to stem elongation (Zadoks 24). The Normalised Difference Red Edge (NDRE) index and CCCI, calculated from airborne multispectral imagery, accounted for 41% and 37% of variability in crop N status, respectively. Greater scatter in the airborne data was attributable to the difference in scale of the ground and aerial measurements (i.e., small area plant samples against whole-plot means from imagery). Nevertheless, the analysis demonstrated that canopy-level theory can be transferred to airborne data, which could ultimately be of more use to growers. Thermal imagery showed that mean plot temperatures of rainfed treatments were 2.7 °C warmer than irrigated treatments (P < 0.001) at full cover. For partially vegetated fields, the two-Dimensional Crop Water Stress Index (2D CWSI) was calculated using the Vegetation Index-Temperature (VIT) trapezoid method to reduce the contribution of soil background to image temperature. Results showed rainfed plots were consistently more stressed than irrigated plots. Future work is needed to improve the ability of the CCCI and VIT methods to detect N and water stress and apply both indices simultaneously at the paddock scale to test whether N can be targeted based on water status. Use of these technologies has significant potential for maximising the spatial and temporal efficiency of N applications for wheat growers. ‘Ground–breaking Stuff’- Proceedings of the 13th Australian Society of Agronomy Conference, 10-14 September 2006, Perth, Western Australia.

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.