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
We examine here daily minimum and maximum temperatures recorded at 7 climatic stations, all located in Lazio, Italy. These 14 time series were provided by the Italian "Agro-meteorological National Data Base" (BDAN) of the National Agricultural Information System (SIAN) and cover the second half of the XX century. The purposes of the signal processing were, first, to extract the linear trend and the two main seasonal cycles present in the series, second, after their subtraction from the signal, to assess the relative importance of the residual stochastic component and, finally, to identify a stochastic model for the latter, in order to arrive at an artificial simulation of the original series. After retrieving and filling the data gaps, we obtained uninterrupted series of daily data. Then, after detrending and filtering away the seasonal components (those with 6-month and 12-month periods), it was possible to determine correlograms and power spectra of the residual zero-mean stochastic component. Also, a successful attempt was carried out to model this stochastic residual by means of an AR(1) process, thus yielding an efficient representation of the time variability of each of the 14 temperature series. In all cases, the residual white noise obtained is definitely non-Gaussian. This model including the trend, the seasonal oscillation and the AR(1) process permitted to build a fairly good artificial reconstruction of the given temperature series via computer simulations specific for each given climatic station. This reconstruction, on capturing the essential features of each given series, represents a useful tool to describe and understand the recurrence of weather patterns and the possible occurrences of weather-linked phenomena interesting the local vegetation and the related biological processes. As a by-product, the analysis has permitted to evaluate the relative incidence of the two main seasonal components and their importance with respect to the residual variability associated to purely stochastic fluctuations. From a comparison with the results of other similar studies carried out in other countries of Europe and Oceania, it appears that the trends found by us for both minimum and maximum temperature daily series, when statistically significant, are generally lower than the corresponding values reported by the last IPCC (2007) for those areas that, at least from a geographical viewpoint, appear similar to ours.
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