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
A machine learning approach for evaluating the impact of land use and management practices on streamwater pollution by pesticides
Streamwater pollution by pesticides is a critical environmental issue in farmed catchment areas. Many important factors are involved in this pollution phenomenon, like weather, area topology, land use and crop management practices, which all influence streamwater quality. The purpose of the ongoing study presented in this paper is to evaluate the impact of land use and management practices on streamwater pollution. We use modelling, simulation and machine learning techniques for acquiring knowledge about this complex domain. Our main objective is to learn qualitative rules relating the pollution factors to the temporal distribution of the stream pesticide concentration. The study area is the farmed catchment of Fremeur (similar to 17 km(2)), located in Brittany, France. Our approach relies on a simulation model, called SACADEAU, which is based on two main components: a biophysical transfer model and a decision model. The biophysical transfer model simulates pesticide transfer through the catchment, from application sites on maize parcels, to the river. The decision model simulates farm management practices such as tillage, sowing, and pesticide application. The two other components of the SACADEAU model include a climate model which provides daily rainfall and temperature, and a spatial model which describes land use and catchment topology ( Figure 1). This simulation model is used for generating a large number of scenarios of the catchment system, considering different weather series or spatial distributions of land use and agricultural activities. [GRAPHICS] Machine learning techniques were used to interpret the very complex and large set of results. ICL, an inductive logic programming software, generated a set of simple rules which described the factors influencing streamwater contamination. This demonstrated that soil characteristics, and in particular organic carbon content, are a key factor controlling contamination. Other important factors are: type of pesticide used, timing and quantity of rainfall, and topology of the catchment. The Sacadeau model is not yet fully implemented and The first results have been obtained with a simplified model. We were able to check the coherence and the feasibility of our approach, and to build a first view of the role of some attributes in stream-water quality. When the SACADEAU model is fully operational, it should be possible to develop more specific rules that incorporate a greater level of details about spatial and temporal variations.
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