Department of Primary Industries and Regional Development

A new technology called genomic selection is revolutionizing dairy cattle breeding. Genomic selection refers to selection decisions based on genomic breeding values (GEBV). The GEBV are calculated as the sum of the effects of dense genetic markers, or haplotypes of these markers, across the entire genome, thereby potentially capturing all the quantitative trait loci (QTL) that contribute to variation in a trait. The QTL effects, inferred from either haplotypes or individual single nucleotide polymorphism markers, are first estimated in a large reference population with phenotypic information. In subsequent generations, only marker information is required to calculate GEBV. The reliability of GEBV predicted in this way has already been evaluated in experiments in the United States, New Zealand, Australia, and the Netherlands. These experiments used reference populations of between 650 and 4,500 progeny-tested Holstein-Friesian bulls, genotyped for approximately 50,000 genome-wide markers. Reliabilities of GEBV for young bulls without progeny test results in the reference population were between 20 and 67%. The reliability achieved depended on the heritability of the trait evaluated, the number of bulls in the reference population, the statistical method used to estimate the single nucleotide polymorphism effects in the reference population, and the method used to calculate the reliability. A common finding in 3 countries (United States, New Zealand, and Australia) was that a straightforward BLUP method for estimating the marker effects gave reliabilities of GEBV almost as high as more complex methods. The BLUP method is attractive because the only prior information required is the additive genetic variance of the trait. All countries included a polygenic effect (parent average breeding value) in their GEBV calculation. This inclusion is recommended to capture any genetic variance not associated with the markers, and to put some selection pressure on low-frequency QTL that may not be captured by the markers. The reliabilities of GEBV achieved were significantly greater than the reliability of parental average breeding values, the current criteria for selection of bull calves to enter progeny test teams. The increase in reliability is sufficiently high that at least 2 dairy breeding companies are already marketing bull teams for commercial use based on their GEBV only, at 2 yr of age. This strategy should at least double the rate of genetic gain in the dairy industry. Many challenges with genomic selection and its implementation remain, including increasing the accuracy of GEBV, integrating genomic information into national and international genetic evaluations, and managing long-term genetic gain.

Research on the adoption of rural innovations is reviewed and interpreted through a cross-disciplinary lens to provide practical guidance for research, extension and policy relating to conservation practices. Adoption of innovations by landholders is presented as a dynamic learning process. Adoption depends on a range of personal, social, cultural and economic factors, as well as on characteristics of the innovation itself. Adoption occurs when the landholder perceives that the innovation in question will enhance the achievement of their personal goals. A range of goals is identifiable among landholders, including economic, social and environmental goals. Innovations are more likely to be adopted when they have a high ‘relative advantage’ (perceived superiority to the idea or practice that it supersedes), and when they are readily trialable (easy to test and learn about before adoption). Non-adoption or low adoption of a number of conservation practices is readily explicable in terms of their failure to provide a relative advantage (particularly in economic terms) or a range of difficulties that landholders may have in trialing them.

Abstract Genetic diversity is key to crop improvement. Owing to pervasive genomic structural variation, a single reference genome assembly cannot capture the full complement of sequence diversity of a crop species (known as the ‘pan-genome’ 1 ). Multiple high-quality sequence assemblies are an indispensable component of a pan-genome infrastructure. Barley ( Hordeum vulgare L.) is an important cereal crop with a long history of cultivation that is adapted to a wide range of agro-climatic conditions 2 . Here we report the construction of chromosome-scale sequence assemblies for the genotypes of 20 varieties of barley—comprising landraces, cultivars and a wild barley—that were selected as representatives of global barley diversity. We catalogued genomic presence/absence variants and explored the use of structural variants for quantitative genetic analysis through whole-genome shotgun sequencing of 300 gene bank accessions. We discovered abundant large inversion polymorphisms and analysed in detail two inversions that are frequently found in current elite barley germplasm; one is probably the product of mutation breeding and the other is tightly linked to a locus that is involved in the expansion of geographical range. This first-generation barley pan-genome makes previously hidden genetic variation accessible to genetic studies and breeding.

BACKGROUND: Two key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds. Both findings are a problem for breeds where the number of individuals in the reference population is limited. A multi-breed reference population is a potential solution, and here we investigate the accuracies of GEBV in Holstein dairy cattle and Jersey dairy cattle when the reference population is single breed or multi-breed. The accuracies were obtained both as a function of elements of the inverse coefficient matrix and from the realised accuracies of GEBV. METHODS: Best linear unbiased prediction with a multi-breed genomic relationship matrix (GBLUP) and two Bayesian methods (BAYESA and BAYES_SSVS) which estimate individual SNP effects were used to predict GEBV for 400 and 77 young Holstein and Jersey bulls respectively, from a reference population of 781 and 287 Holstein and Jersey bulls, respectively. Genotypes of 39,048 SNP markers were used. Phenotypes in the reference population were de-regressed breeding values for production traits. For the GBLUP method, expected accuracies calculated from the diagonal of the inverse of coefficient matrix were compared to realised accuracies. RESULTS: When GBLUP was used, expected accuracies from a function of elements of the inverse coefficient matrix agreed reasonably well with realised accuracies calculated from the correlation between GEBV and EBV in single breed populations, but not in multi-breed populations. When the Bayesian methods were used, realised accuracies of GEBV were up to 13% higher when the multi-breed reference population was used than when a pure breed reference was used. However no consistent increase in accuracy across traits was obtained. CONCLUSION: Predicting genomic breeding values using a genomic relationship matrix is an attractive approach to implement genomic selection as expected accuracies of GEBV can be readily derived. However in multi-breed populations, Bayesian approaches give higher accuracies for some traits. Finally, multi-breed reference populations will be a valuable resource to fine map QTL.

Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under a given set of conditions. The analysis of the metabolome is particularly challenging due to the diverse chemical nature of metabolites. Metabolites are the result of the interaction of the system's genome with its environment and are not merely the end product of gene expression but also form part of the regulatory system in an integrated manner. Metabolomics has its roots in early metabolite profiling studies but is now a rapidly expanding area of scientific research in its own right. Metabolomics (or metabonomics) has been labeled one of the new "omics", joining genomics, transcriptomics, and proteomics as a science employed toward the understanding of global systems biology. Metabolomics is fast becoming one of the platform sciences of the "omics", with the majority of the papers in this field having been published only in the last two years. In this review metabolomic methodologies are discussed briefly followed by a more detailed review of the use of metabolomics in integrated applications where metabolomics information has been combined with other "omic" data sets (proteomics, transcriptomics) to enable greater understanding of a biological system. The potential of metabolomics for natural product drug discovery and functional food analysis, primarily as incorporated into broader "omic" data sets, is discussed.

Viral diseases provide a major challenge to twenty-first century agriculture worldwide. Climate change and human population pressures are driving rapid alterations in agricultural practices and cropping systems that favor destructive viral disease outbreaks. Such outbreaks are strikingly apparent in subsistence agriculture in food-insecure regions. Agricultural globalization and international trade are spreading viruses and their vectors to new geographical regions with unexpected consequences for food production and natural ecosystems. Due to the varying epidemiological characteristics of diverent viral pathosystems, there is no one-size-fits-all approach toward mitigating negative viral disease impacts on diverse agroecological production systems. Advances in scientific understanding of virus pathosystems, rapid technological innovation, innovative communication strategies, and global scientific networks provide opportunities to build epidemiologic intelligence of virus threats to crop production and global food security. A paradigm shift toward deploying integrated, smart, and eco-friendly strategies is required to advance virus disease management in diverse agricultural cropping systems.

BACKGROUND: The theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values. METHODS: Simulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated. RESULTS: The gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy. CONCLUSIONS: An animal's relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.

Management areas are used in marine spatial planning to conserve biodiversity of marine ecosystems and to protect fish from fishing pressure. To evaluate the effectiveness of these protected areas, observational techniques are used to determine densities, sizes, biomass, habitat types and distribution of fish species in and around management areas. Two types of observational techniques are used in spatial monitoring: (1) fishery-independent techniques, which include underwater visual census (UVC), underwater video, remote sensing, acoustics, and experimental catch and effort data; and (2) fishery-dependent techniques, which include catch, effort and catch per unit effort data from commercial and recreational fisheries. This review summarises the applications, advantages, disadvantages and biases of each of these observational categories and highlights emerging technologies. The main finding from this review was that a combination of observational techniques, rather than a single method, was the most effective approach to marine spatial monitoring. For example, a combination of hydroacoustics for habitat mapping and UVC or video for fish surveys was one of the most cost-effective and efficient means of obtaining fish-habitat linkages and fish assemblage data. There are also emerging technologies that could increase the precision and efficiency of monitoring surveys. There is a need for continued development of non-intrusive technology for marine monitoring studies.

BACKGROUND: Bread wheat is an allopolyploid species with a large, highly repetitive genome. To investigate the impact of selection on variants distributed among homoeologous wheat genomes and to build a foundation for understanding genotype-phenotype relationships, we performed population-scale re-sequencing of a diverse panel of wheat lines. RESULTS: A sample of 62 diverse lines was re-sequenced using the whole exome capture and genotyping-by-sequencing approaches. We describe the allele frequency, functional significance, and chromosomal distribution of 1.57 million single nucleotide polymorphisms and 161,719 small indels. Our results suggest that duplicated homoeologous genes are under purifying selection. We find contrasting patterns of variation and inter-variant associations among wheat genomes; this, in addition to demographic factors, could be explained by differences in the effect of directional selection on duplicated homoeologs. Only a small fraction of the homoeologous regions harboring selected variants overlapped among the wheat genomes in any given wheat line. These selected regions are enriched for loci associated with agronomic traits detected in genome-wide association studies. CONCLUSIONS: Evidence suggests that directional selection in allopolyploids rarely acted on multiple parallel advantageous mutations across homoeologous regions, likely indicating that a fitness benefit could be obtained by a mutation at any one of the homoeologs. Additional advantageous variants in other homoelogs probably either contributed little benefit, or were unavailable in populations subjected to directional selection. We hypothesize that allopolyploidy may have increased the likelihood of beneficial allele recovery by broadening the set of possible selection targets.

Ascochyta blight (AB), caused by Ascochyta rabiei is a major disease of chickpea (Cicer arietinum L.), especially in areas where cool, cloudy, and humid weather persists during the crop season. Several epidemics of AB causing complete yield loss have been reported. The fungus mainly survives between seasons through infected seed and in infected crop debris. Despite extensive pathological and molecular studies, the nature and extent of pathogenic variability in A. rabiei have not been clearly established. Accumulation of phenols, phytoalexins (medicarpin and maackiain), and hydrolytic enzymes has been associated with host-plant resistance (HPR). Seed treatment and foliar application of fungicides are commonly recommended for AB management, but further information on biology and survival of A. rabiei is needed to devise more effective management strategies. Recent studies on inheritance of AB resistance indicate that several quantitative trait loci (QTLs) control resistance. In this paper we review the biology of A. rabiei, HPR, and management options, with an emphasis on future research priorities.

The relationships between sensory traits (tenderness, juiciness, flavour and overall liking) and objective measures, such as shear force, intramuscular fat, cooking loss, pH and animal age, were derived for M. longissimus thoracis et lumborum (LL) from 471 lamb and sheep carcasses. Tenderness could be predicted with the most accuracy (R2 = 0.24) and flavour with the highest precision (r.s.d. = 7.5 units) when using the objective measures, which may be in part due to the small variation in the range of shear force values of the samples (all carcasses electrically stimulated and meat aged for 5 days) and the use of consumer panels for the assessment of sensory traits. The ultimate pH of the LL, the rate of decline in pH in the LL or the predicted temperature at pH 6.0 were not significant predictors of the sensory traits when tested on a subsample of the carcasses. The model coefficients indicated that all sensory traits (tenderness, flavour, juiciness and overall liking) declined as shear force and age increased, and as intramuscular fat percentage decreased. This translated into a decline of 16 points on average for tenderness and 13 points for overall liking when LL samples from 68.5-month-old sheep were compared with those from unweaned lambs, when adjusted to the same level of intramuscular fat and shear force. Predictions of the sensory traits at varying levels of shear force were made and show that at 49 Newtons (N), the overall liking score would be 51 and the tenderness score 48. Derived relationships between objective meat quality measures and sensory traits suggest that to achieve a failure rate of no more than 10% for loin meat when eaten, it must have a shear force of about 27 N or less.

Oxidation of meat occurs under postmortem conditions and is inevitable. This oxidation includes the biochemical changes in meat leading to changes in color pigments and lipids. As a consequence, color deteriorates, and undesirable flavors and rancidity develop in meat thereby impacting on consumer appeal and satisfaction. Across carcasses, there is variation in the rate at which muscle undergoes chemical reactions under postmortem conditions that reflect inherent variation at the biochemical level. It is expected that this underlying biochemical variation will be reflected in living muscle through oxidative processes. The oxidative process of muscle tissues will vary according to an animal's immunity status, temperament, and ability to cope with stress, with all these affected by nutrition, genetics, management practices, and environmental conditions (hot and cold seasons). Identification of biomarkers that indicate the oxidative status levels of animals or muscle tissues in vivo could provide insight as to how the muscle will respond to the anoxic conditions that produce undesirable results in meat. This review outlines the potential use of 1 group of biomarkers, the isoprostanes, in the context of complex biochemical reactions relating to oxidative processes that take place in the biological systems of live animals (in vivo) and subsequently in meat (in vitro).

Abstract Bactrocera papayae D rew & H ancock, B actrocera philippinensis D rew & H ancock, B actrocera carambolae D rew & H ancock, and B actrocera invadens D rew, T suruta & W hite are four horticultural pest tephritid fruit fly species that are highly similar, morphologically and genetically, to the destructive pest, the O riental fruit fly, B actrocera dorsalis ( H endel) ( D iptera: T ephritidae). This similarity has rendered the discovery of reliable diagnostic characters problematic, which, in view of the economic importance of these taxa and the international trade implications, has resulted in ongoing difficulties for many areas of plant protection and food security. Consequently, a major international collaborative and integrated multidisciplinary research effort was initiated in 2009 to build upon existing literature with the specific aim of resolving biological species limits among B . papayae , B . philippinensis , B . carambolae , B . invadens and B . dorsalis to overcome constraints to pest management and international trade. Bactrocera philippinensis has recently been synonymized with B . papayae as a result of this initiative and this review corroborates that finding; however, the other names remain in use. While consistent characters have been found to reliably distinguish B . carambolae from B . dorsalis , B . invadens and B . papayae , no such characters have been found to differentiate the latter three putative species. We conclude that B . carambolae is a valid species and that the remaining taxa, B . dorsalis , B . invadens and B . papayae , represent the same species. Thus, we consider B . dorsalis ( H endel) as the senior synonym of B . papayae D rew and H ancock syn.n. and B . invadens D rew, T suruta & W hite syn.n. A redescription of B . dorsalis is provided. Given the agricultural importance of B . dorsalis , this taxonomic decision will have significant global plant biosecurity implications, affecting pest management, quarantine, international trade, postharvest treatment and basic research. Throughout the paper, we emphasize the value of independent and multidisciplinary tools in delimiting species, particularly in complicated cases involving morphologically cryptic taxa.

Found throughout the tree of life and in every ecosystem, parasites are some of the most diverse, ecologically important animals on Earth—but in almost all cases, the least protected by wildlife or ecosystem conservation efforts. For decades, ecologists have been calling for research to understand parasites' important ecological role, and increasingly, to protect as many species from extinction as possible. However, most conservationists still work within priority systems for funding and effort that exclude or ignore parasites, or treat parasites as an obstacle to be overcome. Our working group identified 12 goals for the next decade that could advance parasite biodiversity conservation through an ambitious mix of research, advocacy, and management. • Parasite conservation is a rapidly growing field but needs coordinated priorities and metrics of success. • We propose a global plan for parasite conservation over the next decade. • Our proposal includes 12 ambitious goals broadly capturing conservation research, practice, and outreach.

The composition and functional properties of cow’s milk are of considerable importance to the dairy farmer, manufacturer, and consumer. Broadly, there are 3 options for altering the composition and/or functional properties of milk: cow nutrition and management, cow genetics, and dairy manufacturing technologies. This review considers the effects of nutrition and management on the composition and production of milk fat and protein, and the relevance of these effects to the feeding systems used in the Australian dairy industry. Dairy cows on herbage-based diets derive fatty acids for milk fat synthesis from the diet/rumen microorganisms (400–450 g/kg), from adipose tissues (<100 g/kg), and from de novo synthesis in the mammary gland (about 500 g/kg). However, the relative contributions of these sources of fatty acids to milk fat production are highly dependent upon feed intake, diet composition, and stage of lactation. Feed intake, the amount of starch relative to fibre, the amount and composition of long chain fatty acids in the diet, and energy balance are particularly important. Significant differences in these factors exist between pasture-based dairy production systems and those based on total mixed ration, leading to differences in milk fat composition between the two. High intakes of starch are associated with higher levels of de novo synthesis of fat in the mammary gland, resulting in milk fat with a higher concentration of saturated fatty acids. In contrast, higher intakes of polyunsaturated fatty acids from pasture and/or lipid supplements result in higher concentrations of unsaturated fatty acids, particularly oleate, trans-vaccenate, and conjugated linoleic acid (CLA) in milk fat. A decline in milk fat concentration associated with increased feeding with starch-based concentrates can be attributed to changes in the ratios of lipogenic to glucogenic volatile fatty acids produced in the rumen. Milk fat depression, however, is likely the result of increased rates of production of long chain fatty acids containing a trans-10 double bond in the rumen, in particular trans-10 18 : 1 and trans-10-cis-12 18 : 2 in response to diets that contain a high concentration of polyunsaturated fatty acids and/or starch. Low rumen fluid pH can also be a factor. The concentration and composition of protein in milk are largely unresponsive to variation in nutrition and management. Exceptions to this are the effects of very low intakes of metabolisable energy (ME) and/or metabolisable protein (MP) on the concentration of total protein in milk, and the effects of feeding with supplements that contain organic Se on the concentration of Se, as selenoprotein, in milk. In general, the first limitation for the synthesis of milk protein in Australian dairy production systems is availability of ME since pasture usually provides an excess of MP. However, low concentrations of protein in milk produced in Queensland and Western Australia, associated with seasonal variations in the nutritional value of herbage, may be a response to low intakes of both ME and MP. Stage of lactation is important in determining milk protein concentration, but has little influence on protein composition. The exception to this is in very late lactation where stage of lactation and low ME intake can interact to reduce the casein fraction and increase the whey fraction in milk and, consequently, reduce the yield of cheese per unit of milk. Milk and dairy products could also provide significant amounts of Se, as selenoproteins, in human diets. Feeding organic Se supplements to dairy cows grazing pastures that are low in Se may also benefit cow health. Research into targetted feeding strategies that make use of feed supplements including oil seeds, vegetable and fish oils, and organic Se supplements would increase the management options available to dairy farmers for the production of milks that differ in their composition. Given appropriate market signals, milk could be produced with lower concentrations of fat or higher levels of unsaturated fats, including CLA, and/or high concentrations of selenoproteins. This has the potential to allow the farmer to find a higher value market for milk and improve the competitiveness of the dairy manufacturer by enabling better matching of the supply of dairy products to the demands of the market.

Recently a paper authored by ourselves and a number of co-authors about the proportion of phenotypic variation in height that is explained by common SNPs was published in Nature Genetics (Yang et al., 2010). Common SNPs explain a large proportion of the heritability for human height (Yang et al.). During the refereeing process (the paper was rejected by two other journals before publication in Nature Genetics) and following the publication of Yang et al. (2010) it became clear to us that the methodology we applied, the interpretation of the results and the consequences of the findings on the genetic architecture of human height and that for other traits such as complex disease are not well understood or appreciated. Here we explain some of these issues in a style that is different from the primary publication, that is, in the form of a number of comments and questions and answers. We also report a number of additional results that show that the estimates of additive genetic variation are not driven by population structure.

BACKGROUND: The Ralstonia solanacearum species complex includes thousands of strains pathogenic to an unusually wide range of plant species. These globally dispersed and heterogeneous strains cause bacterial wilt diseases, which have major socio-economic impacts. Pathogenicity is an ancestral trait in R. solanacearum and strains with high genetic variation can be subdivided into four phylotypes, correlating to isolates from Asia (phylotype I), the Americas (phylotype IIA and IIB), Africa (phylotype III) and Indonesia (phylotype IV). Comparison of genome sequences strains representative of this phylogenetic diversity can help determine which traits allow this bacterium to be such a pathogen of so many different plant species and how the bacteria survive in many different habitats. RESULTS: The genomes of three tomato bacterial wilt pathogens, CFBP2957 (phy. IIA), CMR15 (phy. III) and PSI07 (phy. IV) were sequenced and manually annotated. These genomes were compared with those of three previously sequenced R. solanacearum strains: GMI1000 (tomato, phy. I), IPO1609 (potato, phy. IIB), and Molk2 (banana, phy. IIB). The major genomic features (size, G+C content, number of genes) were conserved across all of the six sequenced strains. Despite relatively high genetic distances (calculated from average nucleotide identity) and many genomic rearrangements, more than 60% of the genes of the megaplasmid and 70% of those on the chromosome are syntenic. The three new genomic sequences revealed the presence of several previously unknown traits, probably acquired by horizontal transfers, within the genomes of R. solanacearum, including a type IV secretion system, a rhi-type anti-mitotic toxin and two small plasmids. Genes involved in virulence appear to be evolving at a faster rate than the genome as a whole. CONCLUSIONS: Comparative analysis of genome sequences and gene content confirmed the differentiation of R. solanacearum species complex strains into four phylotypes. Genetic distances between strains, in conjunction with CGH analysis of a larger set of strains, revealed differences great enough to consider reclassification of the R. solanacearum species complex into three species. The data are still too fragmentary to link genomic classification and phenotypes, but these new genome sequences identify a pan-genome more representative of the diversity in the R. solanancearum species complex.

One hundred and twenty Bos indicus cross steers were allocated to 3 treatments (good, mixed and poor) on the basis of flight speed, as a measure of cattle temperament. The cattle were lot-fed for 100 days and data collected at intervals on their temperament (flight speeds) and productivity (liveweight changes, body condition, pen feed intakes) during this time. After slaughter, data were collected on carcass traits and meat quality. Eating-quality attributes were measured in meat samples from 22 carcasses from each treatment. Flight speeds were highly correlated across animals and within treatments, showed little change in variability over time and were highly repeatable. Flight speed indicated a slight deterioration in temperament with time in the feedlot until day 70, suggesting an increasing fearfulness in the steers. Differences in flight speeds between treatments were maintained throughout the feedlotting period; poor-temperament animals retained poor temperaments and good retained good. Flight speed was correlated with measures of production, and flight speed measured at feedlot induction was a predictor of performance. Correlations and treatment effects showed that cattle with poor temperaments had poorer average daily gains, feed conversion efficiencies, body conditions and dressing percentages compared with those with good temperaments. Reduced performance in the poor-temperament animals may have resulted from their fearfulness and state of high arousal. Treatment (temperament grouping) did not influence carcass traits, but there was evidence of lower initial pH levels and indicators of 'heat-shortening' in the meat of steers with poor temperament compared with those with good temperament. These findings suggest that the poor temperament steers were more susceptible to pre-slaughter stressors than the good temperament animals. However, the meat quality differences were not detected in eating-quality measurements.

. The data presented herein are based on morphological examination of fresh specimens, coupled with analysis of phylogenetic sequence data to better integrate taxa into appropriate taxonomic ranks and infer their evolutionary relationships.

A computer simulation model to analyse risks of soil erosion to long-term crop production is described. The model, called PERFECT, simulates interactions between soil type, climate, fallow management strategy and crop sequence. It contains six main modules; data input, water balance, crop growth, crop residue, erosion and model output. Modules are arranged in a framework that allows alternative modules to be used as required for the potential range of applications. The model contains dynamic crop growth models for wheat, sorghum and sunflower. Validation of PERFECT against small catchment and contour bay data collected throughout Queensland showed that PERFECT explained up to 84% of the variation in total available soil water, 89% of the variation in daily runoff, and up to 75% of the variation in grain yield. Average annual soil erosion was accurately predicted but daily erosion totals were less accurate due to the exclusion of rainfall intensity in erosion prediction. Variability in climate dominates agricultural production in the subtropical region of Australia. The validated model can be coupled with long-term climate and soils databases to simulate probabilities of production and erosion risks due to climatic variability. It provides a method to determine the impact of soil erosion on long-term productivity.