ORIGINAL RESEARCH article
Front. Sustain. Food Syst.
Sec. Agricultural and Food Economics
Tobacco Cultivation in Yunnan: An Integrated Analysis of Economic Trends and Diversification
Provisionally accepted- 1China Tobacco Yunnan lmport and Export Co., Ltd.,, Kunming, China
- 2China Tobacco Yunnan lmport and Export Co., Ltd., Kunming, China, Kunming, China
- 3Yunnan Tobacco Company Qujing City Company Luliang Branch, Qujing, China
- 4Yunnan Agricultural University, Kunming, China
- 5Tsinghua Shenzhen International Graduate School,, Shenzhen, China
- 6Demetech Limited, Hongkong, Hong Kong, SAR China
- 7LKS Faculty of Medicine, The University of Hong Kong, Hongkong, Hong Kong, SAR China
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This study conducts an integrated analysis of the impacts of tobacco cultivation on farmers in Yunnan Province using machine learning and econometric methods, drawing on detailed micro-level survey data collected from 253 households. Our findings highlight the significant role of tobacco income within household income structures, demonstrating its competitiveness compared to other crops. Through interpretable machine learning techniques and advanced econometric methods, the analysis reveals that expanding tobacco cultivation is associated with improvements in certain indicators of farmers’ quality of life, although the benefits vary with the scale of production. Additionally, the promotion of agricultural diversification by the Tobacco Bureau has shown positive effects, despite challenges such as localized disadvantages, such as poor soil quality, limited access to irrigation, and remoteness from markets. The results emphasize the need for optimizing cropping structures by adjusting crop combinations, planting schedules, and resource allocation to improve yield and sustainability and improving management practices to maximize the benefits of tobacco cultivation while supporting sustainable agricultural development.
Keywords: tobacco cultivation, household income, Scale efficiency, Agricultural diversity, Qualityof life, Interpretable machine learning
Received: 06 May 2025; Accepted: 30 Nov 2025.
Copyright: © 2025 Song, Wu, Li, Ning, Liu, Liu, Yuan, Huang, Guo, Zhuang, Bai, Ge and Zi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yipeng Zhuang
Yuxiang Bai
Wang Ge
Dingchun Zi
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