Explaining Growth in Dutch Agriculture: Prices, Public R&D, and Technological Change

This paper analyzes the sources of growth of Dutch agriculture (arable, meat, and dairy sectors). Because the time series data (1950-1997) are non-stationary and not cointegrated, it is argued that a model estimated in first differences should be used. Estimated price elasticities turn out to be very inelastic, both in the short-run and the long-run. The direct distortionary effect of price support has therefore been rather limited. However, price support has an important indirect effect by improving the sectors investment possibilities and therewith the capital stock. Public R&D expenditure mainly affected agriculture by contributing to yield improvement therewith favoring intensification of production.growth, technology, cointegration, non-stationarity, agricultural policy, Agribusiness, Q18, O13,

Using Corpus to Facilitate Vocabulary Teaching in the Data-driven Learning Classroom

The synthesized paper covers the topics of “corpus linguistics” and “language instruction and pedagogies”. I would like to do a presentation to highlight the key points in my paper

Partitioning Clustering Based on Support Vector Ranking

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e-Distance Weighted Support Vector Regression

We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR).e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data. The proposed e-DWSVR optimizes the minimum margin and the mean of functional margin simultaneously to tackle these two issues. In addition, we use both dual coordinate descent (CD) and averaged stochastic gradient descent (ASGD) strategies to make e-DWSVR scalable to large scale problems. We report promising results obtained by e-DWSVR in comparison with existing methods on several benchmark datasets

A Bayesian Belief Network to Infer Incentive Mechanisms to Reduce Antibiotic Use in Livestock Production

Efficient policy intervention to reduce antibiotic use in livestock production requires knowledge about the rationale underlying antibiotic usage. Animal health status and management quality are considered the two most important factors that influence farmers’ decision-making concerning antibiotic use. Information on these two factors is therefore crucial in designing incentive mechanisms. In this paper, a Bayesian belief network (BBN) is built to represent the knowledge on how these factors can directly and indirectly determine antibiotic use and the possible impact on economic incentives. Since both factors are not directly observable (i.e. latent), they are inferred from measurable variables (i.e. manifest variables) which are influenced by these factors. Using farm accounting data and registration data on antibiotic use and veterinary services in specialized finisher pig production farms, a confirmatory factor analysis was carried out to construct these factors. The BBN is then parameterized through regression analysis on the constructed factors and manifest variables. Using the BBN, possible incentive mechanisms through prices and management training are discussed.Livestock Production/Industries,

The state of innovation in European agriculture: Innovators are few and far between

Innovation and adoption of innovation are considered key indicators of competitiveness and sustainability. Analysing data from 821 farms from eight Member States of the European Union in the frame of the EU Framework 7 project FLINT, this study provides an insight into the different adoption rates of fi ve types of innovation in agriculture across Europe and suggests the potential effects of different factors, including farm type and farm size, subsidies and age, on farmers’ decision to innovate

Adoption of risk management strategies in European agriculture

Given the increased attention to risk management in the European Union’s (EU) Common Agricultural Policy (CAP), it is important to monitor and evaluate the rates of adoption by farmers and their determinants over time. Current European Agricultural statistics (Farm Accountancy Data Network) capture few indicators that assess such strategies, but complementing data collected during the EU Framework 7 project FLINT have allowed the adoption of risk management strategies and the determinants of farmers’ preference for complementary or substitute instruments to be assessed. Adoption rates of risk managementinstruments such as insurance contracts, price contracts, off-farm income, other types risk of reduction measures and other gainful activities vary signifi cantly across EU Member States and farming types. Econometric analysis indicates that larger farms more often adopt crop insurance, occupational accident insurance, price contracts and diversifi cation but are less likely to adopt credit avoidance and off-farm employment (at a signifi cance level of 1 per cent). For policy analyses these indicators are a step forward for the determination of the net impacts and establishment of counterfactuals in the long term (i.e. time seriesencompassing also adverse years) for measuring the impact of the CAP at farm level