The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
From the perspective of agricultural business, the market price of certain crop reflects the demand of that crop in current stage. Therefore, to track and to forecast the market prices are both important tasks in agri-management, by which the production schedule can be adjusted to increase the profit. For tracking the crop prices, the Council of Agriculture (COA) establishes an official website that provides open data of daily market prices from over 15 local markets with more than 100 different crops in Taiwan. Recently, the smart agri-management platform (S.A.M.P.) is developed by the Institute for Information Industry (III) as an integrated cloud service for agri-business. Inspired by the open data of crop prices, in this paper we develop a crop price forecasting service on S.A.M.P., which automatically retrieves the historical prices on the official website as training dataset, and provides the price forecasting service with some well-known algorithms for time series analysis. The algorithms implemented in this paper are the autoregressive integrated moving average (ARIMA), the partial least square (PLS), and the artificial neural network (ANN). In addition, for PLS we further integrate the response surface methodology (RSM), deriving a new algorithm RSMPLS, by which the non-linear relationship between historical prices can be investigated. We compare the performance of these four algorithms with the price data obtained from the First Fruit and Vegetable Wholesale Market in Taipei. The experimented crops are cabbage, bok choy, watermelon, and cauliflower. According to the experimental results, PLS and ANN are of lower error in percentages. In addition, PLS and ANN are recommended for short term and long term forecasting, respectively.
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