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
Using district-level occurrences in Max Ent for predicting the invasion potential of an exotic insect pest in India
Insect pests are a major threat to agricultural biosecurity across the world, causing substantial economic losses. Majority of the species distribution modeling studies use precise coordinates (latitude/longitude) of species occurrences in Max Ent (or maximum entropy model). However, lack of precise coordinates of insect pest occurrences at national/regional level is a common problem for many countries including India. This is because of the limited resources, lack of nationally coordinated surveys, and growers/farmers' privacy issues; district-level occurrences are commonly available (e.g., National Agricultural Pest Information System or NAPIS in the United States; http://pest.ceris.purdue.edu/). We demonstrated the use of Max Ent to generate a preliminary, district-level map of the potential risk of invasion by an exotic cotton mealybug Phenacoccus solenopsis (Tinsley) (Hemiptera: Pseudococcidae) in India. District-level occurrence data were integrated with bioclimatic variables (values averaged within districts) using MaxEnt. The Max Ent model performed better than random with an average test AUC value of 0.86 (0.05). Our model predictions matched closely with the documented occurrence of P. solenopsis in all nine cotton growing states, and also predicted suitable habitats in other districts across India. The greatest threat of P. solenopsis infestations were predicted in most districts of Gujarat, Maharashtra, Andhra Pradesh, southwestern Punjab, northwestern Rajasthan, and western Haryana. Precipitation of coldest quarter, temperature annual range, and precipitation seasonality were the strongest predictors associated with P. solenopsis distribution. Precipitation of coldest quarter was negatively correlated with P. solenopsis occurrence. Mapping the potential distribution of invasive species is an iterative process, and our study is the first attempt to model national-level risk assessment of P. solenopsis in India. Our results can be used for selecting monitoring and surveillance sites and designing local, regional and national-level integrated pest management policies for cotton and other cultivated crops in India. The maps of potential pest distributions are urgently needed by agriculture managers and policymakers. Our approach can be used in other countries that lack precise coordinates of insect pest occurrences and generate a preliminary map of potential risk because it may be too late to wait for the precise coordinates of pest occurrences to generate a perfect map. (C) 2014 Elsevier B.V. All rights reserved.
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