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Farmers’ adoptability of integrated pest management of cotton revealed by a new methodology
Author(s) -
Rajinder Peshin
Publication year - 2013
Publication title -
agronomy for sustainable development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 102
eISSN - 1774-0746
pISSN - 1773-0155
DOI - 10.1007/s13593-012-0127-4
Subject(s) - integrated pest management , agriculture , business , agricultural science , marketing , agronomy , geography , biology , archaeology
International audienceThe huge research efforts to develop integrated pest management (IPM) have failed to reduce pesticide use and to foster IPM adoption by farmers. Indeed, despite five decades since the concept of integrated control and threshold theory was developed, and four decades since IPM programs have been implemented in USA, Asia, Latin America, Australia, and India, the widespread use of complex IPM practices has not been adopted. This failure can be explained by IPM complexity, policy restrictions, and counteracting forces of the pesticide industry. This article is a study of drivers that rule the adoption or rejection of IPM by 150 farmers from the Indian state of Punjab. Cotton was cultivated under an insecticide resistance management-based IPM program. This program was implemented in Punjab from 2002 to 2007. A rating scale was developed to measure farmers’ perceived attitudes. An adoptability index was developed. Results show that farmers exhibited very different adoption attitudes. Specifically, farmers adopted widely practices that have no complexity, higher economic advantage, and observability. IPM practices with adoptability indices higher than 0.60 have been widely adopted. The predicted adoptability and effective actual adoption of IPM practices were well correlated with a correlation coefficient of 0.88. Technological attributes complexity and relative economic advantage induced a variation of 99 % in the adoptability. Overall the findings show that relative economic advantage, benefit visibility, compatibility with past experiences, and complexity are the most effective drivers in predicting adoption or rejection. Whereas, unexpectedly, socio-personal and economic factors used by most scientists are relatively insignificant. The new methodological frame can be applied to predict the adoption of agricultural innovations

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