Baseline responses of Alphitobius diaperinus (Coleoptera : Tenebrionidae) to cyfluthrin and detection of strong resistance in field populations in eastern Australia

Resistance to cyfluthrin in broiler farm populations of lesser mealworm, Alphitobius diaperinus (Panzer) (Coleoptera: Tenebrionidae), in eastern Australia was suspected to have contributed to recent control failures. In 2000-2001, beetles from 11 broiler farms were tested for resistance by comparing them to an insecticide-susceptible reference population by using topical application. Resistance was detected in almost all beetle populations (up to 22 times the susceptible at the LC50), especially in southeastern Queensland where more cyfluthrin applications had been made. Two from outside southeastern Queensland were found to be susceptible. Dose-mortality data generated from the reference population over a range of cyflutbrin concentrations showed that 0.0007% cyfluthrin at a LC99.9 level could be used as a convenient dose to discriminate between susceptible and resistant populations. Using this discriminating concentration, from 2001 to 2005, the susceptibilities of 18 field populations were determined. Of these, 11 did not exhibit complete mortality at the discriminating concentration (mortality range 2.8-97.7%), and in general, cyfluthrin resistance was directly related to the numbers of cyfluthrin applications. As in the full study, populations outside of southeastern Queensland were found to have lower levels of resistance or were susceptible. One population from an intensively farmed broiler area in southeastern Queensland exhibited low mortality despite having no known exposure to cyfluthrin. Comparisons of LC50 values of three broiler populations and a susceptible population, collected in 2000 and 2001 and recollected in 2004 and 2005 indicated that values from the three broiler populations had increased over this time for all populations. The continued use of cyfluthrin for control of A. diaperinus in eastern Australia is currently under consideration.

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 and thermal regimes for field pea in Australia and their implications for breeding.

There is a large gap between the refined approaches to characterise genotypes and the common use of location and season as a coarse surrogate for environmental characterisation of breeding trials. As a framework for breeding, the aim of this paper is quantifying the spatial and temporal patterns of thermal and water stress for field pea in Australia. We compiled a dataset for yield of the cv. Kaspa measured in 185 environments, and investigated the associations between yield and seasonal patterns of actual temperature and modelled water stress. Correlations between yield and temperature indicated two distinct stages. In the first stage, during crop establishment and canopy expansion before flowering, yield was positively associated with minimum temperature. Mean minimum temperature below similar to 7 degrees C suggests that crops were under suboptimal temperature for both canopy expansion and radiation-use efficiency during a significant part of this early growth period. In the second stage, during critical reproductive phases, grain yield was negatively associated with maximum temperature over 25 degrees C. Correlations between yield and modelled water supply/demand ratio showed a consistent pattern with three phases: no correlation at early stages of the growth cycle, a progressive increase in the association that peaked as the crop approached the flowering window, and a progressive decline at later reproductive stages. Using long-term weather records (1957-2010) and modelled water stress for 104 locations, we identified three major patterns of water deficit nation wide. Environment type 1 (ET1) represents the most favourable condition, with no stress during most of the pre-flowering phase and gradual development of mild stress after flowering. Type 2 is characterised by increasing water deficit between 400 degree-days before flowering and 200 degree-days after flowering and rainfall that relieves stress late in the season. Type 3 represents the more stressful condition with increasing water deficit between 400 degree-days before flowering and maturity. Across Australia, the frequency of occurrence was 24% for ET1, 32% for ET2 and 43% for ET3, highlighting the dominance of the most stressful condition. Actual yield averaged 2.2 t/ha for ET1, 1.9 t/ha for ET2 and 1.4 t/ha for ET3, and the frequency of each pattern varied substantially among locations. Shifting from a nominal (i.e. location and season) to a quantitative (i.e. stress type) characterisation of environments could help improving breeding efficiency of field pea in Australia.