Literature DB >> 11378139

Linkages between FAO agroclimatic data resources and the development of GIS models for control of vector-borne diseases.

M Bernardi1.   

Abstract

The Food and Agriculture Organization (FAO) of the United Nations is the largest specialized UN Agency dealing with agriculture, forestry and fishery, particularly in the developing countries. One of its technical services, placed under the Sustainable Development Department, has the responsibility to provide information on environment and natural resources as related to food and agriculture. It includes, among others, expertise in remote sensing, geographic information systems and agrometeorology, production of global environmental digital datasets, meteorological and remote sensing data collection and analysis at near real-time, development of methodologies, models and tools for data standardization, collection, spatialization, analysis and dissemination, networking and information sharing, development of integrated information management systems. Some experience has also been gained in the use of climatic digital datasets for spatial modeling of crop pests and diseases. The description of mapping the distribution of the Western Corn rootworm (Diabrotica virgifera) in Europe as a function of environmental conditions is presented as well as the global assessment of environmental potential constraints based on processing of digital datasets. A simple spatial interpolation routine is briefly explained.

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Year:  2001        PMID: 11378139     DOI: 10.1016/s0001-706x(01)00100-0

Source DB:  PubMed          Journal:  Acta Trop        ISSN: 0001-706X            Impact factor:   3.112


  3 in total

1.  Models for predicting bulinids species habitats in southwestern Nigeria.

Authors:  Opeyemi G Oso; Joseph O Sunday; Alex B Odaibo
Journal:  Parasite Epidemiol Control       Date:  2022-05-30

2.  Modelling the emergence dynamics of the western corn rootworm beetle (Diabrotica virgifera virgifera).

Authors:  Rodelyn Jaksons; Katharina Falkner; Elena Moltchanova
Journal:  Sci Rep       Date:  2022-02-11       Impact factor: 4.379

3.  Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria.

Authors:  Uwem F Ekpo; Chiedu F Mafiana; Clement O Adeofun; Adewale Rt Solarin; Adewumi B Idowu
Journal:  BMC Infect Dis       Date:  2008-05-31       Impact factor: 3.090

  3 in total

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