Improving knowledge and practices in fresh produce chains

A successful supply chain must delivery the right product, value and satisfaction to the end customer, and profitability for its participants. Critical to getting the product right is the practices used to produce and maintain product quality through the supply chain from production to sale to the end customer. This paper describes the approach used by a R&D team to add value to supply chains through improving knowledge and practices. The desired outcome is better produce quality for consumers and more control and less wastage for chain participants. The team worked with specific supply chains to identify areas for improvement and to develop, test and implement improved practices. The knowledge gained was communicated to the industry to gain wider adoption of results. Three conditions were identified as critical for practice change - motivation, knowledge, and capacity for change. For improvement in practices to occur, a business must be motivated and have the knowledge and capacity to improve. Two case studies of working with Australian supply chains (mango and melons) are presented to illustrate our participatory methodology. A key activity is monitoring produce quality and handling practices and conditions to demonstrate to participants the points where quality deterioration occurs in the supply chain. This participatory approach is successful because working with supply chain participants generates knowledge and solutions to real problems. It enables the participants to observe the effect of handling practices and conditions on produce quality, gain knowledge and assess the benefits of improvements. Where existing knowledge is not present, research is conducted to fill the knowledge gaps. IV International Conference on Managing Quality in Chains - The Integrated View on Fruits and Vegetables Quality

The extent of population genetic subdivision differs among four co-distributed shark species in the Indo-Australian archipelago

Background: The territorial fishing zones of Australia and Indonesia are contiguous to the north of Australia in the Timor and Arafura Seas and in the Indian Ocean to the north of Christmas Island. The area surrounding the shared boundary consists of a variety of bio-diverse marine habitats including shallow continental shelf waters, oceanic trenches and numerous offshore islands. Both countries exploit a variety of fisheries species, including whaler (Carcharhinus spp.) and hammerhead sharks (Sphyrna spp.). Despite their differences in social and financial arrangements, the two countries are motivated to develop complementary co-management practices to achieve resource sustainability. An essential starting point is knowledge of the degree of population subdivision, and hence fisheries stock status, in exploited species. Results: Populations of four commercially harvested shark species (Carcharhinus obscurus, Carcharhinus sorrah, Prionace glauca, Sphyrna lewini) were sampled from northern Australia and central Indonesia. Neutral genetic markers (mitochondrial DNA control region sequence and allelic variation at co-dominant microsatellite loci) revealed genetic subdivision between Australian and Indonesian populations of C. sorrah. Further research is needed to address the possibility of genetic subdivision among C. obscurus populations. There was no evidence of genetic subdivision for P. glauca and S. lewini populations, but the sampling represented a relatively small part of their distributional range. For these species, more detailed analyses of population genetic structure is recommended in the future. Conclusion: Cooperative management between Australia and Indonesia is the best option at present for P. glauca and S. lewini, while C. sorrah and C. obscurus should be managed independently. On-going research on these and other exploited shark and ray species is strongly recommended. Biological and ecological similarity between species may not be a predictor of population genetic structure, so species-specific studies are recommended to provide new data to assist with sustainable fisheries management.

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.