What motivates farmers’ decision to organic farming conversion: The case of conventional mango farming in Vietnam

This research is aimed at analyzing perception and identifying determinants of the decision to convert to organic mango farming in Mekong Delta (MD) Vietnam. The research was conducted by using a direct survey data set from 109 household heads in this region collected by stratified random sampling method. The research method used was descriptive statistics and the binary Logit model. The research results revealed some interesting points. In the total observations gathered, only about half of households decided to convert, mainly due to local implementation and awareness of safety for consumers and environmental protection. Still, the most important reason for farmers to convert was to get a higher selling price. The binary Logit model analyzing the determinants found that the older the farmer, the more difficult it is to decide to convert. At the same time, training and enhancing awareness about organic farming will increase the probability of conversion decisions. Based on the research results, several relevant solutions on investment, production linkage, and propaganda to raise people's awareness were recommended, thereby increasing the probability of deciding to convert.

Authors

Publication Details

Journal
Decision Science Letters
Published
2024-12-11
DOI
https://doi.org/10.5267/j.dsl.2024.12.003
Citations
1
Primary Topic
Agricultural Systems and Practices
Type
article
Field-Weighted Citation Impact
1.03

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

What motivates farmers’ decision to organic farming conversion: The case of conventional mango farming in Vietnam

Tiến Dũng Khổng, Bui Le Thai Hanh, Huynh Thi Dan Xua
1 citations
Decision Science Letters
Agricultural Systems and Practices
1.03
article

What motivates farmers’ decision to organic farming conversion: The case of conventional mango farming in Vietnam

Tiến Dũng Khổng, Bui Le Thai Hanh, Huynh Thi Dan Xua
article en
1 citations

Abstract

This research is aimed at analyzing perception and identifying determinants of the decision to convert to organic mango farming in Mekong Delta (MD) Vietnam. The research was conducted by using a direct survey data set from 109 household heads in this region collected by stratified random sampling method. The research method used was descriptive statistics and the binary Logit model. The research results revealed some interesting points. In the total observations gathered, only about half of households decided to convert, mainly due to local implementation and awareness of safety for consumers and environmental protection. Still, the most important reason for farmers to convert was to get a higher selling price. The binary Logit model analyzing the determinants found that the older the farmer, the more difficult it is to decide to convert. At the same time, training and enhancing awareness about organic farming will increase the probability of conversion decisions. Based on the research results, several relevant solutions on investment, production linkage, and propaganda to raise people's awareness were recommended, thereby increasing the probability of deciding to convert.

Decision Science LettersVol. 14(1)
Bộ Giáo dục và Ðào tạo
Life in Land
Openalex Percentile: Top 16%
Agricultural Systems and Practices
1.03
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

What motivates farmers’ decision to organic farming conversion: The case of conventional mango farming in Vietnam — Tiến Dũng Khổng, Bui Le Thai Hanh, et al. · Decision Science Letters (2024) | TGRS Research Map | TGRS