Farming Systems: Beyond Production Technologies for Dairy RD&E

The challenge of Research, Development and Extension (R,D&E) is to apply agricultural science to serve the real needs of production systems. The ideal is to have community partnerships involving a variety of stakeholders with equal representation, and a sharing in the design of R, D&E actions. R,D&E policy in Australia is stressing the participation of industry in new projects. The Dairy Research and Development Corporation (DRDC) in Australia, and the Brazilian Agricultural Research Corporation for Dairy (Embrapa Dairy), have developed initiatives to identify priorities for R,D&E design with participation of the industry. However, weaknesses in the methods have been identified. The present study describes the results of a strategy to involve a broader range of stakeholders in the identification of regional dairy industry needs. The findings show that overall communication, finance and marketing as the three major priorities of three study regions, meaning that primary needs for the industry are not in production technologies. This is an apparent contradiction with what some stakeholders considered valuable for dairy farms, which are pasture, genetics and nutrition technologies. The results reflect the large amount of research activity into production technology, and the relative success of R,D&E. However, it is necessary to consider issues beyond production technologies before developing R,D&E projects or presenting technologies.

Bayesian neural network approach for determining the risk of re-intervention after endovascular aortic aneurysm repair

This article proposes a Bayesian neural network approach to determine the risk of re-intervention after endovascular aortic aneurysm repair surgery. The target of proposed technique is to determine which patients have high chance to re-intervention (high-risk patients) and which are not (low-risk patients) after 5 years of the surgery. Two censored datasets relating to the clinical conditions of aortic aneurysms have been collected from two different vascular centers in the United Kingdom. A Bayesian network was first employed to solve the censoring issue in the datasets. Then, a back propagation neural network model was built using the uncensored data of the first center to predict re-intervention on the second center and classify the patients into high-risk and low-risk groups. Kaplan-Meier curves were plotted for each group of patients separately to show whether there is a significant difference between the two risk groups. Finally, the logrank test was applied to determine whether the neural network model was capable of predicting and distinguishing between the two risk groups. The results show that the Bayesian network used for uncensoring the data has improved the performance of the neural networks that were built for the two centers separately. More importantly, the neural network that was trained with uncensored data of the first center was able to predict and discriminate between groups of low risk and high risk of re-intervention after 5 years of endovascular aortic aneurysm surgery at center 2 (p = 0.0037 in the logrank test).