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
Mapping and monitoring of the agricultural production systems on a regular interval provide important spatial matrix on the status, trend, and options for effective intervention at multiple scales. The recent advances in agrogeoinformatics big-data enriched with increasing open-access protocols become an integral part of solving the food security equation. This paper demonstrates use of an integrated earth observation system (EOS) for mapping and monitoring major agricultural production systems. The approach uses multitemporal and multi-scale remote sensing data coupled with insitu observation to map the legume and cereal production systems. The support vector machine (SVM) classification was found to be the best with overall classification accuracy of 82%. The in-situ data on crop grain and straw yields were measured using nested sampling approach. The best fit equation of yield values were regressed with remote sensing indices (NDVI and EVI). The significant correlation (R-2) value of cereal and lentil crop were 0.74 and 6.9 at p < 0.01 respectively. The R-2 value between observed yield and predicted yield was 0.80 and 0.97 in cereal and lentil crops respectively. The predicted yield based on remote sensing data varies from 3,303 to 5,710 kg ha(-1) and mean yield is 3,840 kg ha(-1). The productivity of the cereal crop was varies from 4228 kg ha(-1) to 4598 kg ha(-1) while lentil crop was between 304 to 1,500 kg ha(-1). The huge inter and intra field variably was observed through the study areas. Such information yielded vital information about yield gaps exists within and across the fields. Study is in progress to develop systematic and semi-automated algorithms to map and monitor the agricultural production on regular interval to quantify the changes in the cropping pattern, rotation, production and impacts of the technological interventions and ex-ante analysis.
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