Prediction of mortality and conception rates of beef breeding cattle in northern Australia

In current simulation packages for the management of extensive beef-cattle enterprises, the relationships for the key biological rates (namely conception and mortality) are quite rudimentary. To better estimate these relationships, cohort-level data covering 17 100 cow-years from six sites across northern Australia were collated and analysed. Further validation data, from 7200 cow-years, were then used to test these relationships. Analytical problems included incomplete and non-standardised data, considerable levels of correlation among the 'independent' variables, and the close similarity of alternate possible models. In addition to formal statistical analyses of these data, the theoretical equations for predicting mortality and conception rates in the current simulation models were reviewed, and then reparameterised and recalibrated where appropriate. The final models explained up to 80% of the variation in the data. These are now proposed as more accurate and useful models to be used in the prediction of biological rates in simulation studies for northern Australia. © The State of Queensland (through the Department of Agriculture, Fisheries and Forestry) 2012. © CSIRO.

Improving the prediction of odour impacts from meat chicken farms with detailed ventilation and odour data

Odour from meat chicken (broiler) farms is an environmental issue affecting the sustainable development of the chicken meat industry but is a normal part of broiler production. Odour plumes exhausted from broiler sheds interact with the environment, where dispersion and dilution of the odours varies constantly, especially diurnally. The potential for odour impacts is greatest when odour emission rates are high and/or when atmospheric dispersion and dilution of odour plumes is limited (i.e. during stable conditions). We continuously monitored ventilation rate, on-site weather conditions, atmospheric stability, and estimated odour concentration with an artificial olfaction system. Detailed inspection of odour emission rates at critical times, i.e. dawn, dusk and night time, revealed that maximum daily and batch odour emission rates are not necessarily the cause of odour impacts. Periods of lower odour emission rates on each day are more likely to correspond with odour impacts. Odour emission rates need to be measured at the times when odour impacts are most likely to occur, which is likely to be at night. Additionally, high resolution ventilation rate data should be sought after to improve odour emission models, especially at critical times of the day. Consultants, regulators and researchers need to give more thought to odour emission rates from meat chicken farms to improved prediction and management of odour impacts.

Rapid genome wide mapping of phosphine resistance loci by a simple regional averaging analysis in the red flour beetle, Tribolium castaneum

Background Next-generation sequencing technology is an important tool for the rapid, genome-wide identification of genetic variations. However, it is difficult to resolve the ‘signal’ of variations of interest and the ‘noise’ of stochastic sequencing and bioinformatic errors in the large datasets that are generated. We report a simple approach to identify regional linkage to a trait that requires only two pools of DNA to be sequenced from progeny of a defined genetic cross (i.e. bulk segregant analysis) at low coverage (<10×) and without parentage assignment of individual SNPs. The analysis relies on regional averaging of pooled SNP frequencies to rapidly scan polymorphisms across the genome for differential regional homozygosity, which is then displayed graphically. Results Progeny from defined genetic crosses of Tribolium castaneum (F4 and F19) segregating for the phosphine resistance trait were exposed to phosphine to select for the resistance trait while the remainders were left unexposed. Next generation sequencing was then carried out on the genomic DNA from each pool of selected and unselected insects from each generation. The reads were mapped against the annotated T. castaneum genome from NCBI (v3.0) and analysed for SNP variations. Since it is difficult to accurately call individual SNP frequencies when the depth of sequence coverage is low, variant frequencies were averaged across larger regions. Results from regional SNP frequency averaging identified two loci, tc_rph1 on chromosome 8 and tc_rph2 on chromosome 9, which together are responsible for high level resistance. Identification of the two loci was possible with only 5-7× average coverage of the genome per dataset. These loci were subsequently confirmed by direct SNP marker analysis and fine-scale mapping. Individually, homozygosity of tc_rph1 or tc_rph2 results in only weak resistance to phosphine (estimated at up to 1.5-2.5× and 3-5× respectively), whereas in combination they interact synergistically to provide a high-level resistance >200×. The tc_rph2 resistance allele resulted in a significant fitness cost relative to the wild type allele in unselected beetles over eighteen generations. Conclusion We have validated the technique of linkage mapping by low-coverage sequencing of progeny from a simple genetic cross. The approach relied on regional averaging of SNP frequencies and was used to successfully identify candidate gene loci for phosphine resistance in T. castaneum. This is a relatively simple and rapid approach to identifying genomic regions associated with traits in defined genetic crosses that does not require any specialised statistical analysis.

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.

Large-scale characterization of drought pattern: a continent-wide modelling approach applied to the Australian wheatbelt – spatial and temporal trends

* Plant response to drought is complex, so that traits adapted to a specific drought type can confer disadvantage in another drought type. Understanding which type(s) of drought to target is of prime importance for crop improvement. * Modelling was used to quantify seasonal drought patterns for a check variety across the Australian wheatbelt, using 123 yr of weather data for representative locations and managements. Two other genotypes were used to simulate the impact of maturity on drought pattern. * Four major environment types summarized the variability in drought pattern over time and space. Severe stress beginning before flowering was common (44% of occurrences), with (24%) or without (20%) relief during grain filling. High variability occurred from year to year, differing with geographical region. With few exceptions, all four environment types occurred in most seasons, for each location, management system and genotype. * Applications of such environment characterization are proposed to assist breeding and research to focus on germplasm, traits and genes of interest for target environments. The method was applied at a continental scale to highly variable environments and could be extended to other crops, to other drought-prone regions around the world, and to quantify potential changes in drought patterns under future climates.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials.

Assessment of functional forms of crop yield loss models of invasive plant species applied in decision support tools and bioeconomic modelling

AbstractObjectives Decision support tools (DSTs) for invasive species management have had limited success in producing convincing results and meeting users' expectations. The problems could be linked to the functional form of model which represents the dynamic relationship between the invasive species and crop yield loss in the DSTs. The objectives of this study were: a) to compile and review the models tested on field experiments and applied to DSTs; and b) to do an empirical evaluation of some popular models and alternatives. Design and methods This study surveyed the literature and documented strengths and weaknesses of the functional forms of yield loss models. Some widely used models (linear, relative yield and hyperbolic models) and two potentially useful models (the double-scaled and density-scaled models) were evaluated for a wide range of weed densities, maximum potential yield loss and maximum yield loss per weed. Results Popular functional forms include hyperbolic, sigmoid, linear, quadratic and inverse models. Many basic models were modified to account for the effect of important factors (weather, tillage and growth stage of crop at weed emergence) influencing weed–crop interaction and to improve prediction accuracy. This limited their applicability for use in DSTs as they became less generalized in nature and often were applicable to a much narrower range of conditions than would be encountered in the use of DSTs. These factors' effects could be better accounted by using other techniques. Among the model empirically assessed, the linear model is a very simple model which appears to work well at sparse weed densities, but it produces unrealistic behaviour at high densities. The relative-yield model exhibits expected behaviour at high densities and high levels of maximum yield loss per weed but probably underestimates yield loss at low to intermediate densities. The hyperbolic model demonstrated reasonable behaviour at lower weed densities, but produced biologically unreasonable behaviour at low rates of loss per weed and high yield loss at the maximum weed density. The density-scaled model is not sensitive to the yield loss at maximum weed density in terms of the number of weeds that will produce a certain proportion of that maximum yield loss. The double-scaled model appeared to produce more robust estimates of the impact of weeds under a wide range of conditions. Conclusions Previously tested functional forms exhibit problems for use in DSTs for crop yield loss modelling. Of the models evaluated, the double-scaled model exhibits desirable qualitative behaviour under most circumstances.

Genome-wide association analysis and differential expression analysis of resistance to Sclerotinia stem rot in Brassica napus

Brassica napus is one of the most important oil crops in the world, and stem rot caused by the fungus Sclerotinia sclerotiorum results in major losses in yield and quality. To elucidate resistance genes and pathogenesis-related genes, genome-wide association analysis of 347 accessions was performed using the Illumina 60K Brassica SNP (single nucleotide polymorphism) array. In addition, the detached stem inoculation assay was used to select five highly resistant (R) and susceptible (S) B. napus lines, 48 h postinoculation with S. sclerotiorum for transcriptome sequencing. We identified 17 significant associations for stem resistance on chromosomes A8 and C6, five of which were on A8 and 12 on C6. The SNPs identified on A8 were located in a 409-kb haplotype block, and those on C6 were consistent with previous QTL mapping efforts. Transcriptome analysis suggested that S. sclerotiorum infection activates the immune system, sulphur metabolism, especially glutathione (GSH) and glucosinolates in both R and S genotypes. Genes found to be specific to the R genotype related to the jasmonic acid pathway, lignin biosynthesis, defence response, signal transduction and encoding transcription factors. Twenty-four genes were identified in both the SNP-trait association and transcriptome sequencing analyses, including a tau class glutathione S-transferase (GSTU) gene cluster. This study provides useful insight into the molecular mechanisms underlying the plant's response to S. sclerotiorum.

Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia: Species differentiation and prediction for fisheries monitoring and assessment

Four species of large mackerels (Scomberomorus spp.) co-occur in the waters off northern Australia and are important to fisheries in the region. State fisheries agencies monitor these species for fisheries assessment; however, data inaccuracies may exist due to difficulties with identification of these closely related species, particularly when specimens are incomplete from fish processing. This study examined the efficacy of using otolith morphometrics to differentiate and predict among the four mackerel species off northeastern Australia. Seven otolith measurements and five shape indices were recorded from 555 mackerel specimens. Multivariate modelling including linear discriminant analysis (LDA) and support vector machines, successfully differentiated among the four species based on otolith morphometrics. Cross validation determined a predictive accuracy of at least 96% for both models. An optimum predictive model for the four mackerel species was an LDA model that included fork length, feret length, feret width, perimeter, area, roundness, form factor and rectangularity as explanatory variables. This analysis may improve the accuracy of fisheries monitoring, the estimates based on this monitoring (i.e. mortality rate) and the overall management of mackerel species in Australia.

Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range

Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific. © 2015 Macmillan Publishers Limited.

Area-wide releases and evaluation of the parasitoid Eretmocerus hayati(Hymenoptera:Aphelinidae) for silverleaf whitefly control

Silverleaf whitefly (SLW), Bemisia tabaci biotype B, is a major horticultural pest that costs Queensland vegetable growers millions of dollars in lost production and control measures each year. In the Bowen and Burdekin districts of North Queensland, the major cultivated SLW host crops are tomatoes, melons, green beans, pumpkins, eggplants, and cucumbers, which cover a total production area of approximately 6500 ha. Eretmocerus hayati, an effective SLW parasitoid, was imported into Australia by CSIRO in 2002 and released from quarantine in 2004. In 2006, DAFF established a mass-rearing unit for E. hayati at Bowen Research Station to provide E. hayati for release on vegetable farms within its SLW integrated pest management research program. A total of 1.3 million E. hayati were released over three seasons on 34 vegetable farms in the Bowen and Burdekin districts (October 2006 to December 2008). Post-release samplings were conducted across the release area over this time period with parasitism levels recorded in tomatoes, melons, beans, eggplants, pumpkins, and various SLW weed hosts. Sample data show that E. hayati established at most release sites as well as some non-release sites, indicating natural spread. Overall results from these three years of evaluation clearly demonstrated that E hayati releases played a significant role in SLW control. In most crops sampled, E hayati exerted between 30 and 80% parasitism. Even in regularly sprayed crops, such as tomato and eggplant, E. hayati was able to achieve an overall average parasitism of 45%.