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PLoS One. 2015 Aug 12;10(8):e0135328. doi: 10.1371/journal.pone.0135328. eCollection 2015.

iMAR: An Interactive Web-Based Application for Mapping Herbicide Resistant Weeds.

PloS one

Silvia Panozzo, Michele Colauzzi, Laura Scarabel, Alberto Collavo, Valentina Rosan, Maurizio Sattin

Affiliations

  1. National Research Council (CNR)-Institute of Agro-environmental and Forest Biology (IBAF), Legnaro (PD), Italy.
  2. Free-lance webmaster, Padova, Italy.

PMID: 26266545 PMCID: PMC4534039 DOI: 10.1371/journal.pone.0135328

Abstract

Herbicides are the major weed control tool in most cropping systems worldwide. However, the high reliance on herbicides has led to environmental issues as well as to the evolution of herbicide-resistant biotypes. Resistance is a major concern in modern agriculture and early detection of resistant biotypes is therefore crucial for its management and prevention. In this context, a timely update of resistance biotypes distribution is fundamental to devise and implement efficient resistance management strategies. Here we present an innovative web-based application called iMAR (interactive MApping of Resistance) for the mapping of herbicide resistant biotypes. It is based on open source software tools and translates into maps the data reported in the GIRE (Italian herbicide resistance working group) database of herbicide resistance at national level. iMAR allows an automatic, easy and cost-effective updating of the maps a nd provides two different systems, "static" and "dynamic". In the first one, the user choices are guided by a hierarchical tree menu, whereas the latter is more flexible and includes a multiple choice criteria (type of resistance, weed species, region, cropping systems) that permits customized maps to be created. The generated information can be useful to various stakeholders who are involved in weed resistance management: farmers, advisors, national and local decision makers as well as the agrochemical industry. iMAR is freely available, and the system has the potential to handle large datasets and to be used for other purposes with geographical implications, such as the mapping of invasive plants or pests.

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