Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/140832
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Type: | Journal article |
Title: | Understanding heterogeneity in technology adoption among Indonesian smallholder dairy farmers |
Author: | Azkar, R. Umberger, W. Peralta, A. |
Citation: | Agribusiness: an international journal, 2023; 39(2):347-370 |
Publisher: | Wiley |
Issue Date: | 2023 |
ISSN: | 0742-4477 1520-6297 |
Statement of Responsibility: | Rida Akzar, Wendy Umberger, Alexandra Peralta |
Abstract: | This study aims to understand and profile smallholder farmers' technology adoption status. We collected cross‐ sectional data from 600 smallholder dairy farming households in West Java, Indonesia. A Latent class cluster analysis identified two unique clusters of smallholder dairy farmers based on patterns in their adoption status of multiple dairy farming technologies. Cluster 1 (Low awareness/low adoption) had significantly lower awareness of all technologies, and among the “aware” farmers, technology adoption rates were also significantly lower compared to Cluster 2 (High awareness/high adoption). The Low awareness/low adoption cluster was older, had less formal education, managed fewer dairy cows, had less productive and less profitable dairy enterprises, lived further away from their cooperative and farmer group leader, and had fewer contacts with dairy extension staff. Farmers' responses to questions regarding reasons underpinning nonadoption decisions suggest that farmers face multilayered and heterogenous constraints to adopting dairy technologies. This insight can assist government, policymakers, and development professionals in designing technology dissemination programs that meet the unique characteristics of subgroups of farmers, ultimately improving the adoption of technologies. |
Keywords: | adoption; Indonesia; latent class cluster analysis; multiple technologies; smallholder dairy farmers; West Java |
Rights: | © 2022 The Authors. Agribusiness published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
DOI: | 10.1002/agr.21782 |
Published version: | http://dx.doi.org/10.1002/agr.21782 |
Appears in Collections: | Research Outputs |
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hdl_140832.pdf | Published version | 1.7 MB | Adobe PDF | View/Open |
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