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.

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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.

  • US
  • Colorado_State_Univ (US)
Data keywords
  • information system
Agriculture keywords
  • agriculture
Data topic
  • modeling
Document type

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

Institutions 10 co-publis
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    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.