e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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:

Discover all records
Home page


Assessment of monthly solar radiation estimates using support vector machines and air temperatures


Solar radiation (Rs), a critical variable in agricultural and eco-environmental processes, is not measured at many meteorological stations. Prediction of Rs has drawn increasing attention in the recent years. However, due to the dynamic nature of atmosphere, accurate estimation of Rs from routinely measured meteorological variables is a challenging task. Studies have demonstrated that machine-learning approaches outperformed traditional statistical methods. This paper presents an application of Support Vector Machines (SVMs) for monthly mean daily Rs estimates using measured maximum, minimum, and mean air temperatures (Tmax, Tmin, and Tmean, respectively). Twenty-four stations covering different climatic regionscold (C), severe cold (SC), mild (M), hot summer and cold winter within the Yangtze River Plain (HSCW-A), and hot summer and cold winter within the Sichuan Basin (HSCW-B)across China were gathered and analysed. Five SVM models with different input attributes were created. These models were also compared with two empirical temperature-based methods. Root mean squared error (RMSE), Nash-Sutcliffe coefficient (NSE), and coefficient of residual mass (CRM) were employed to compare the performances of different methods. The newly developed model, SVM using Tmax-Tmin, and Tmean, outperformed other models with an averaged RMSE of 1.637 MJ m-2 and NSE of 0.813. On a regional scale, when Rs was estimated using the parameters developed at other sites, estimation of Rs within HSCW-B and SC regions were more reliable than in other zones. Especially in HSCW-B region, estimates of Rs using the parameters developed at Zunyi gave better performance (RMSE = 1.117 MJ m-2, NSE = 0.922) than that using the parameters obtained from their own data (RMSE = 1.309 MJ m-2, NSE = 0.894). The results showed that the SVM methodology may be a promising alternative to the traditional approach for predicting solar radiation at any locations where the records of air temperatures are available. Copyright (C) 2010 Royal Meteorological Society

  • CN
  • SW_Univ (CN)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • big data
  • modeling
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
  • SW_Univ (CN)
Powered by Lodex 8.20.3
logo commission europeenne
e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.