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Title: Red palm weevil incidence: Spatial pattern and implications in technology adoption
Author(s) : P. Anithakumari K. Muralidharan & K.P. Chandran
Type: Research Articles
Volume: 45
Issue : 2
Month : August
Year : 2017
ISSN: Print -0304-5242, online - 2454-8480
Pages : 101-109
Keywords : technology adoption, red palm weevil, knowledge, GIS, Coconut
Abstract: Red palm weevil (RPW) is one of the major fatal pests of coconut. Conventional extension approaches need to be refined for achieving the desired outcome on adoption of integrated pest management (IPM) strategies against RPW, especially among small and marginal farming communities. The pattern of incidence, severity of infestation and age of palms are the factors motivating farmers for adoption of IPM practices. Taking these into account, a technology delivery approach for RPW management was evolved and implemented in the entire geographical extent of 520 ha area of Edava grama panchayat in Thiruvananthapuram district of Kerala state, India. This approach involves farmer participatory surveillance and monitoring with technical facilitation from research institutions which is a paradigm shift to community based area wide management strategies. Besides holdings’ profile, number of RPW infested/lost palms and GPS (i.e., latlong) coordinates of infested palms were recorded. The spatial distribution pattern was analyzed using Indices of Dispersion (ID), Patchiness (IP) Cluster Frequency (ICF) and Mean Crowding (IMC) at different cluster levels (i.e., holdings, administrative segments and grids). ESRI GIS software was used to depict geospatial patterns of RPW infestation. Among 5410 coconut holdings, 18.7 per cent were having RPW infested palms. The pest incidence in juvenile palms was significantly higher compared to adult palms. The distribution pattern of infested palms was observed to be aggregated. Knowledge level of farmers regarding aspects of pest and management was below 10 per cent. The proposed methodology of participatory data documentation resulted in rapid and reliable collection of data from large area with an additional benefit of experiential learning for farmers in the locality.
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