5 resultados para Adaptive Mesh Refinement (AMR)

em eResearch Archive - Queensland Department of Agriculture


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Catches of sharks and bycatch in large-mesh nets and baited drumlines used by the Queensland Shark Control Program were examined to determine the efficacy of both gear types and assess fishing strategies that minimise their impacts. There were few significant differences in the size of both sharks and bycatch in the two gear types, apart from significantly smaller (p < 0.05) tiger sharks Galeocerdo cuvier being taken on drumlines and smaller green turtles Chelonia mydas in nets. Catch per unit effort showed orders of magnitude differences among species, even within the same family. Hammerhead sharks and rays were particularly vulnerable to net capture, whereas higher catch rates of tiger sharks were observed for drumlines. Nets caught more marine mammals, teleost fish and rays, whereas drumlines exhibited higher catch rates of the threatened loggerhead turtle Caretta caretta. Survival of most taxa (particularly obligate ram ventilators) was lower in nets than drumlines. Bycatch species (turtles and marine mammals) were able to swim to the surface to breathe when they were hooked on drumlines, enhancing their survival potential. Fishing strategies that recognise the different selectivity patterns of the gear can be developed to suit local biotic and abiotic conditions, although it is recognised that quantification of both ecological risk and risk to bathers is not a simple task.

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Significant interactions have been demonstrated between production factors and postharvest quality of fresh fruit. Accordingly, there is an attendant need for adaptive postharvest actions to modulate preharvest effects. The most significant preharvest effects appear to be mediated through mineral nutrition influences on the physical characteristics of fruit. Examples of specific influencers include fertilisers, water availability, rootstock, and crop load effects on fruit quality attributes such as skin colour, susceptibility to diseases and physiological disorders, and fruit nutritional composition. Also, rainfall before and during harvest can markedly affect fruit susceptibility to skin blemishes, physical damage, and diseases. Knowledge of preharvest-postharvest interactions can help determine the basis for variability in postharvest performance and thereby allow refinement of postharvest practices to minimise quality loss after harvest. This knowledge can be utilised in predictive management systems. Such systems can benefit from characterisation of fruit nutritional status, particularly minerals, several months before and/or at harvest to allow informed decisions on postharvest handling and marketing options. Other examples of proactive management practices include adjusting harvesting and packing systems to account for rainfall effects before and/or during harvest. Improved understanding of preharvest-postharvest interactions is contributing to the delivery of consistently higher quality of fruit to consumers. This paper focuses on the state of knowledge for sub-tropical and tropical fruits, in particular avocado and mango.

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Significant interactions have been demonstrated between production factors and postharvest quality of fresh fruit. Accordingly, there is an attendant need for adaptive postharvest actions to modulate preharvest effects. The most significant preharvest effects appear to be mediated through mineral nutrition influences on the physical characteristics of fruit. Examples of specific influencers include fertilisers, water availability, rootstock, and crop load effects on fruit quality attributes such as skin colour, susceptibility to diseases and physiological disorders, and fruit nutritional composition. Also, rainfall before and during harvest can markedly affect fruit susceptibility to skin blemishes, physical damage, and diseases. Knowledge of preharvest-postharvest interactions can help determine the basis for variability in postharvest performance and thereby allow refinement of postharvest practices to minimise quality loss after harvest. This knowledge can be utilised in predictive management systems. Such systems can benefit from characterisation of fruit nutritional status, particularly minerals, several months before and/or at harvest to allow informed decisions on postharvest handling and marketing options. Other examples of proactive management practices include adjusting harvesting and packing systems to account for rainfall effects before and/or during harvest. Improved understanding of preharvest-postharvest interactions is contributing to the delivery of consistently higher quality of fruit to consumers. This paper focuses on the state of knowledge for sub-tropical and tropical fruits, in particular avocado and mango.

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Progress in crop improvement is limited by the ability to identify favourable combinations of genotypes (G) and management practices (M) in relevant target environments (E) given the resources available to search among the myriad of possible combinations. To underpin yield advance we require prediction of phenotype based on genotype. In plant breeding, traditional phenotypic selection methods have involved measuring phenotypic performance of large segregating populations in multi-environment trials and applying rigorous statistical procedures based on quantitative genetic theory to identify superior individuals. Recent developments in the ability to inexpensively and densely map/sequence genomes have facilitated a shift from the level of the individual (genotype) to the level of the genomic region. Molecular breeding strategies using genome wide prediction and genomic selection approaches have developed rapidly. However, their applicability to complex traits remains constrained by gene-gene and gene-environment interactions, which restrict the predictive power of associations of genomic regions with phenotypic responses. Here it is argued that crop ecophysiology and functional whole plant modelling can provide an effective link between molecular and organism scales and enhance molecular breeding by adding value to genetic prediction approaches. A physiological framework that facilitates dissection and modelling of complex traits can inform phenotyping methods for marker/gene detection and underpin prediction of likely phenotypic consequences of trait and genetic variation in target environments. This approach holds considerable promise for more effectively linking genotype to phenotype for complex adaptive traits. Specific examples focused on drought adaptation are presented to highlight the concepts.