Responses in carcass lean pH at 24 hours post-mortem in lines of Large White pigs selected for growth rate on a fixed ration over a set time

A restricted maximum likelihood analysis applied to an animal model showed no significant differences (P > 0.05) in pH value of the longissimus dorsi measured at 24 h post-mortem (pH24) between high and low lines of Large White pigs selected over 4 years for post-weaning growth rate on restricted feeding. Genetic and phenotypic correlations between pH24 and production and carcass traits were estimated using all performance testing records combined with the pH24 measurements (5.05-7.02) on slaughtered animals. The estimate of heritability for pH24 was moderate (0.29 ± 0.18). Genetic correlations between pH24 and production or carcass composition traits, except for ultrasonic backfat (UBF), were not significantly different from zero. UBF had a moderate, positive genetic correlation with pH24 (0.24 ± 0.33). These estimates of genetic correlations affirmed that selection for increased growth rate on restricted feeding is likely to result in limited changes in pH24 and pork quality since the selection does not put a high emphasis on reduced fatness.

Time-dependent instantaneous mortality rates from multiple tagging experiments with exact times of release and recovery

Instantaneous natural mortality rates and a nonparametric hunting mortality function are estimated from a multiple-year tagging experiment with arbitrary, time-dependent fishing or hunting mortality. Our theory allows animals to be tagged over a range of times in each year, and to take time to mix into the population. Animals are recovered by hunting or fishing, and death events from natural causes occur but are not observed. We combine a long-standing approach based on yearly totals, described by Brownie et al. (1985, Statistical Inference from Band Recovery Data: A Handbook, Second edition, United States Fish and Wildlife Service, Washington, Resource Publication, 156), with an exact-time-of-recovery approach originated by Hearn, Sandland and Hampton (1987, Journal du Conseil International pour l'Exploration de la Mer, 43, 107-117), who modeled times at liberty without regard to time of tagging. Our model allows for exact times of release and recovery, incomplete reporting of recoveries, and potential tag shedding. We apply our methods to data on the heavily exploited southern bluefin tuna (Thunnus maccoyii).

Testing the simulation capability of APSIM-ORYZA under different levels of nitrogen fertiliser and transplanting time regimes in Korea

APSIM-ORYZA is a new functionality developed in the APSIM framework to simulate rice production while addressing management issues such as fertilisation and transplanting, which are particularly important in Korean agriculture. To validate the model for Korean rice varieties and field conditions, the measured yields and flowering times from three field experiments conducted by the Gyeonggi Agricultural Research and Extension Services (GARES) in Korea were compared against the simulated outputs for different management practices and rice varieties. Simulated yields of early-, mid- and mid-to-late-maturing varieties of rice grown in a continuous rice cropping system from 1997 to 2004 showed close agreement with the measured data. Similar results were also found for yields simulated under seven levels of nitrogen application. When different transplanting times were modelled, simulated flowering times ranged from within 3 days of the measured values for the early-maturing varieties, to up to 9 days after the measured dates for the mid- and especially mid-to-late-maturing varieties. This was associated with highly variable simulated yields which correlated poorly with the measured data. This suggests the need to accurately calibrate the photoperiod sensitivity parameters of the model for the photoperiod-sensitive rice varieties in Korea.

Applying a simulation model to the management of an infestation of Miconia calvescens in the wet tropics of Australia

A simulation model that combines biological, search and economic components is applied to the eradication of a Miconia calvescens infestation at El Arish in tropical Queensland, Australia. Information on the year M. calvescens was introduced to the site, the number of plants controlled and the timing of control, is used to show that currently there could be M. calvescens plants remaining undetected at the site, including some mature plants. Modelling results indicate that the eradication programme has had a significant impact on the population of M. calvescens, as shown by simulated results for uncontrolled and controlled populations. The model was also used to investigate the effect of changing search effort on the cost of and time to eradication. Control costs were found to be negligible over all levels of search effort tested. Importantly, results suggest eradication may be achieved within several decades, if resources are increased slightly from their current levels and if there is a long-term commitment to funding the eradication programme.

Application of a model to assess aflatoxin risk in peanuts.

When exposed to hot (22-35 degrees C) and dry climatic conditions in the field during the final 4-6 weeks of pod filling, peanuts (Arachis hypogaea L.) can accumulate highly carcinogenic and immuno-suppressing aflatoxins. Forecasting of the risk posed by these conditions can assist in minimizing pre-harvest contamination. A model was therefore developed as part of the Agricultural Production Systems Simulator (APSIM) peanut module, which calculated an aflatoxin risk index (ARI) using four temperature response functions when fractional available soil water was <0.20 and the crop was in the last 0.40 of the pod-filling phase. ARI explained 0.95 (P <= 0.05) of the variation in aflatoxin contamination, which varied from 0 to c. 800 mu g/kg in 17 large-scale sowings in tropical and four sowings in sub-tropical environments carried out in Australia between 13 November and 16 December 2007. ARI also explained 0.96 (P <= 0.01) of the variation in the proportion of aflatoxin-contaminated loads (>15 mu g/kg) of peanuts in the Kingaroy region of Australia during the period between the 1998/99 and 2007/08 seasons. Simulation of ARI using historical climatic data from 1890 to 2007 indicated a three-fold increase in its value since 1980 compared to the entire previous period. The increase was associated with increases in ambient temperature and decreases in rainfall. To facilitate routine monitoring of aflatoxin risk by growers in near real time, a web interface of the model was also developed. The ARI predicted using this interface for eight growers correlated significantly with the level of contamination in crops (r=095, P <= 0.01). These results suggest that ARI simulated by the model is a reliable indicator of aflatoxin contamination that can be used in aflatoxin research as well as a decision-support tool to monitor pre-harvest aflatoxin risk in peanuts.

A hidden Markov model approach for determining vessel activity from vessel monitoring system data.

Many fisheries worldwide have adopted vessel monitoring systems (VMS) for compliance purposes. An added benefit of these systems is that they collect a large amount of data on vessel locations at very fine spatial and temporal scales. This data can provide a wealth of information for stock assessment, research, and management. However, since most VMS implementations record vessel location at set time intervals with no regard to vessel activity, some methodology is required to determine which data records correspond to fishing activity. This paper describes a probabilistic approach, based on hidden Markov models (HMMs), to determine vessel activity. A HMM provides a natural framework for the problem and, by definition, models the intrinsic temporal correlation of the data. The paper describes the general approach that was developed and presents an example of this approach applied to the Queensland trawl fishery off the coast of eastern Australia. Finally, a simulation experiment is presented that compares the misallocation rates of the HMM approach with other approaches.

Evaluation of super-heated steam vacuum drying viability and development of a predictive drying model for four Australian hardwood species.

The results of drying trials show that vacuum drying produces material of the same or better quality than is currently being produced by conventional methods within 41 to 66 % of the drying time, depending on the species. Economic analysis indicates positive or negative results depending on the species and the size of drying operation. Definite economic benefits exist by vacuum drying over conventional drying for all operation sizes, in terms of drying quality, time and economic viability, for E. marginata and E. pilularis. The same applies for vacuum drying C. citriodora and E. obliqua in larger drying operations (kiln capacity 50 m3 or above), but not for smaller operations at this stage. Further schedule refinement has the ability to reduce drying times further and may improve the vacuum drying viability of the latter species in smaller operations.

Lack of release of bound anthocyanins and phenolic acids from carrot plant cell walls and model composites during simulated gastric and small intestinal digestion

Separately, polyphenols and plant cell walls (PCW) are important contributors to the health benefits associated with fruits and vegetables. However, interactions with PCW which occur either during food preparation or mastication may affect bioaccessibility and hence bioavailability of polyphenols. Binding interactions between anthocyanins, phenolic acids (PAs) and PCW components, were evaluated using both a bacterial cellulose-pectin model system and a black carrot puree system. The majority of available polyphenols bound to PCW material with 60-70% of available anthocyanins and PAs respectively binding to black carrot puree PCW matter. Once bound, release of polyphenols using acidified methanol is low with only similar to 20% of total anthocyanins to similar to 30% of PAs being released. Less than 2% of bound polyphenol was released after in vitro gastric and small intestinal (S.I.) digestion for both the model system and the black carrot puree PCW matter. Confocal laser scanning microscopy shows localised binding of anthocyanins to PCW. Very similar patterns of binding for anthocyanins and PAs suggest that PAs form complexes with anthocyanins and polysaccharides. Time dependent changes in extractability with acidified methanol but not the total bound fraction suggests that initial nonspecific deposition on cellulose surfaces is followed by rearrangement of the bound molecules. Minimal release of anthocyanins and PAs after simulated gastric and S.I. digestion indicates that polyphenols in fruits and vegetables which bind to the PCW will be transported to the colon where they would be expected to be released by the action of cell wall degrading bacteria.

Modelling temperature, photoperiod and vernalization responses of Brunonia australis (Goodeniaceae) and Calandrinia sp (Portulacaceae) to predict flowering time

Crop models for herbaceous ornamental species typically include functions for temperature and photoperiod responses, but very few incorporate vernalization, which is a requirement of many traditional crops. This study investigated the development of floriculture crop models, which describe temperature responses, plus photoperiod or vernalization requirements, using Australian native ephemerals Brunonia australis and Calandrinia sp. A novel approach involved the use of a field crop modelling tool, DEVEL2. This optimization program estimates the parameters of selected functions within the development rate models using an iterative process that minimizes sum of squares residual between estimated and observed days for the phenological event. Parameter profiling and jack-knifing are included in DEVEL2 to remove bias from parameter estimates and introduce rigour into the parameter selection process. Development rate of B. australis from planting to first visible floral bud (VFB) was predicted using a multiplicative approach with a curvilinear function to describe temperature responses and a broken linear function to explain photoperiod responses. A similar model was used to describe the development rate of Calandrinia sp., except the photoperiod function was replaced with an exponential vernalization function, which explained a facultative cold requirement and included a coefficient for determining the vernalization ceiling temperature. Temperature was the main environmental factor influencing development rate for VFB to anthesis of both species and was predicted using a linear model. The phenology models for B. australis and Calandrinia sp. described development rate from planting to VFB and from VFB to anthesis in response to temperature and photoperiod or vernalization and may assist modelling efforts of other herbaceous ornamental plants. In addition to crop management, the vernalization function could be used to identify plant communities most at risk from predicted increases in temperature due to global warming.

Stochastic model for simulating maize yield

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment. © 2012 American Society of Agricultural and Biological Engineers.

A Bayesian hurdle model for analysis of an insect resistance monitoring database

Motivated by the analysis of the Australian Grain Insect Resistance Database (AGIRD), we develop a Bayesian hurdle modelling approach to assess trends in strong resistance of stored grain insects to phosphine over time. The binary response variable from AGIRD indicating presence or absence of strong resistance is characterized by a majority of absence observations and the hurdle model is a two step approach that is useful when analyzing such a binary response dataset. The proposed hurdle model utilizes Bayesian classification trees to firstly identify covariates and covariate levels pertaining to possible presence or absence of strong resistance. Secondly, generalized additive models (GAMs) with spike and slab priors for variable selection are fitted to the subset of the dataset identified from the Bayesian classification tree indicating possibility of presence of strong resistance. From the GAM we assess trends, biosecurity issues and site specific variables influencing the presence of strong resistance using a variable selection approach. The proposed Bayesian hurdle model is compared to its frequentist counterpart, and also to a naive Bayesian approach which fits a GAM to the entire dataset. The Bayesian hurdle model has the benefit of providing a set of good trees for use in the first step and appears to provide enough flexibility to represent the influence of variables on strong resistance compared to the frequentist model, but also captures the subtle changes in the trend that are missed by the frequentist and naive Bayesian models. © 2014 Springer Science+Business Media New York.

High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

BACKGROUND: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). RESULTS: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. CONCLUSION: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.

Evaluation and histological examination of a Campylobacter fetus subsp. venerealis small animal infection model

Bovine genital campylobacteriosis (BGC), caused by Campylobacter fetus subsp. venerealis, is associated with production losses in cattle worldwide. This study aimed to develop a reliable BGC guinea pig model to facilitate future studies of pathogenicity, abortion mechanisms and vaccine efficacy. Seven groups of five pregnant guinea pigs (1 control per group) were inoculated with one of three strains via intra-peritoneal (IP) or intra-vaginal routes. Samples were examined using culture, PCR and histology. Abortions ranged from 0 to 100 and re-isolation of causative bacteria from sampled sites varied with strain, dose of bacteria and time to abortion. Histology indicated metritis and placentitis, suggesting that the bacteria induce inflammation, placental detachment and subsequent abortion. Variation of virulence between strains was observed and determined by culture and abortion rates. IP administration of C. fetus subsp. venerealis to pregnant guinea pigs is a promising small animal model for the investigation of BGC abortion.

Hazard Function Models to Estimate Mortality Rates Affecting Fish Populations with Application to the Sea Mullet (Mugil cephalus) Fishery on the Queensland Coast (Australia)

Fisheries management agencies around the world collect age data for the purpose of assessing the status of natural resources in their jurisdiction. Estimates of mortality rates represent a key information to assess the sustainability of fish stocks exploitation. Contrary to medical research or manufacturing where survival analysis is routinely applied to estimate failure rates, survival analysis has seldom been applied in fisheries stock assessment despite similar purposes between these fields of applied statistics. In this paper, we developed hazard functions to model the dynamic of an exploited fish population. These functions were used to estimate all parameters necessary for stock assessment (including natural and fishing mortality rates as well as gear selectivity) by maximum likelihood using age data from a sample of catch. This novel application of survival analysis to fisheries stock assessment was tested by Monte Carlo simulations to assert that it provided unbiased estimations of relevant quantities. The method was applied to the data from the Queensland (Australia) sea mullet (Mugil cephalus) commercial fishery collected between 2007 and 2014. It provided, for the first time, an estimate of natural mortality affecting this stock: 0.22±0.08 year −1 .