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http://hdl.handle.net/123456789/4343
Title: | Predictors of ex-ante adoption of precision agriculture technologies by cocoa farmers in Ghana |
Authors: | Bosompem, Martin |
Keywords: | Predictors Precision agriculture Future adoption Cocoa high technology in Ghana Small scale farmers Sub-saharan A22frica |
Issue Date: | 2019 |
Publisher: | University of Cape Coast |
Abstract: | The purpose of the study was to identify the best predictors of cocoa farmers’ willingness to adopt future precision agriculture technologies (PATs) in Ghana. The target population was all cocoa farmers who benefited from cocoa high technology programme (an initiative of distributing free fertilizer by the government to selected cocoa farmers) in Ghana. A total of 416 cocoa farmers who are beneficiaries of the programme were interviewed. Majority (83%) of the respondents were willing to adopt future PATs development in Ghana. The binary logistic regression model explained between 37.5% to 60.4% of the variances in cocoa farmers’ willingness to adopt any future PATs. The significant predictors of respondents’ willingness to adopt future PATs were: i. educational level of cocoa farmers; ii. cocoa farmers who plant in rows; iii. credit from financial institution; iv. relative advantage of PATs and v. farmers’ perceived ease of use of PATs. The strongest predictor of farmers’ willingness to adopt any future PATs was “row planting” indicating that farmers who had already planted in rows are more likely to adopt future PATs than those who had not yet done so. The study recommended, among others, the need to create awareness among farmers and other major stakeholders in cocoa industry of the potential benefits of PAT development in cocoa industry in Ghana |
Description: | 22p:, ill. |
URI: | http://hdl.handle.net/123456789/4343 |
ISSN: | 23105496 |
Appears in Collections: | Department of Agricultural Economics & Extension |
Files in This Item:
File | Description | Size | Format | |
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PredictorsofEx-AnteAdoptionofPrecisionAgriculture_T.pdf | Article | 474.21 kB | Adobe PDF | View/Open |
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