This research project analyzes the beef grading system in Australia. Firstly, the Meat Standards Australia (MSA) grading system as a potential value-creating and value chain-coordinating mechanism is investigated. In-depth interviews with value chain stakeholders and industry experts suggest that the implementation of the MSA grading system has had a catalytic effect of moving value chains toward a greater level of coordination. The concept of best value supply chains is also used as a benchmark in determining MSAs effect on value chain performance. Secondly, using a survey of Australian consumers, findings suggest that the MSA certification is perceived as a trustworthy signal for tenderness and quality, reducing information asymmetry at the consumer level. This thesis then addresses the questions of whether or not it is necessary to use a grading system in consumer marketing (e.g. quality label) in order to be successful in terms of adding value to the industry. / Agricultural and Resource Economics

Returned products of many industries are transported backwards through supply chains for recovery, thus forming &ldquo / closed-loop supply chains&rdquo / . Benefits, forthcoming with more effective management of recovery of returns are gaining importance. However, some issues, such as lack of information required to assess the quality of the returned products, may translate into critical uncertainties in the product recovery decisions and prevent closed-loop supply chains from operating efficiently. Hence, it is envisaged that significant economies may be attained by increasing the quantity of information fed into the planning decisions related to returned products. Thus, the objective of this study is to test the hypothesis that ready availability of perfect quality grade information associated with returned products by means of &ldquo / embedded systems&rdquo / , may lead to improved over all performance of recovery operations. To this end, in this thesis, linear programming models of generic multistage recovery processes are built. It is demonstrated by computational studies that significant gains may be obtained especially in environments where the prices of recovered products are decreasing in time.

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Feature extraction is one of the major challenges in object recognition. Features that are extracted from one type of objects cannot always be used directly for a different type of objects, therefore limiting the performance of feature extraction. Having an automatic feature learning algorithm could be a big advantage for an object recognition algorithm. This research first introduces several improvements on a fully automatic feature construction method called Evolution COnstructed Feature (ECO-Feature). These improvements are developed to construct more robust features and make the training process more efficient than the original version. The main weakness of the original ECO-Feature algorithm is that it is designed only for binary classification and cannot be directly applied to multi-class cases. We also observe that the recognition performance depends heavily on the size of the feature pool from which features can be selected and the ability of selecting the best features. For these reasons, we have developed an enhanced evolutionary learning method for multi-class object classification to address these challenges. Our method is called Evolutionary Learning of Boosted Features (ECO-Boost). ECO-Boost method is an efficient evolutionary learning algorithm developed to automatically construct highly discriminative image features from the training image for multi-class image classification. This unique method constructs image features that are often overlooked by humans, and is robust to minor image distortion and geometric transformations. We evaluate this algorithm with a few visual inspection datasets including specialty crops, fruits and road surface conditions. Results from extensive experiments confirm that ECO-Boost performs closely comparable to other methods and achieves a good balance between accuracy and simplicity for real-time multi-class object classification applications. It is a hardware-friendly algorithm that can be optimized for hardware implementation in an FPGA for real-time embedded visual inspection applications.

Improved production and reduced consumption of beef is often highlighted as key aspects for tackling sustainability issues of the food system because the environmental impact of beef is ~100 times higher than plant-based foods. Both scientist and civil society organisations argue that eating “less but better” beef is important for sustainability. Better quality can encompass better eating quality as well as improved sustainability, but despite the two being very important for overall quality, very little research on interactions between them exists. No tools, applicable in Sweden, allowing for joint assessment have been developed. This study investigates the synergies and trade-offs between eating quality and environmental sustainability by using Swedish beef production as a case study. It reviews peer reviewed literature on factors that contribute to eating quality (flavour, tenderness and juiciness), and four factors that contribute to environmental sustainability (climate, biodiversity, feed/food competition and animal welfare). Based on the findings, an indicator-based sustainability assessment framework and a meat quality grading scheme differentiating Premium and Standard eating quality is developed, aimed to be practical tools for Swedish beef assessments. The study provides a systems-based understanding of synergies and trade-offs that may occur when “less but better” is presented as a strategy for tackling the environmental impact of beef. Results show that there are synergies between eating quality and biodiversity, animal welfare and with the right choices of feed, feed/food competition but with consequent trade-offs with climate impact. The discussion addresses the potential of enhanced eating quality to increase the profitability of Swedish beef production without consequent substantial negative impact on sustainability. The suggested methods have the potential to facilitate a shift from quantity- to quality-based consumption, but further empirical studies are required.