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All Journal Emerging Science Journal
Nupueng, Somjai
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Environmental Science

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The Impact of Land Transfer on Farmers' Happiness: The Mediating Effect of Social Aspects Zhang, Weiwei; Zhou, Qiuxiang; Li, Wei; Nupueng, Somjai
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-010

Abstract

This study investigated the mechanism through which land transfer impacts farmers' happiness in China, focusing on the mediating roles of household income and social equity, and the moderating effect of social capital. Utilizing convenience sampling through WJX platform, 431 farmers in Guangxi (2024) were selected as samples, and conducted structural equation modeling with Smart-PLS 4.0. Key findings reveal: (1) Land transfer exerts a significant positive effect on farmers' happiness; (2) Household income and social equity mediate 69.63% of this effect, with social equity demonstrating stronger mediation; (3) Social capital amplifies the equity pathway while showing nonsignificant moderation on income effects. Methodologically, this study applied multi-mediation moderated SEM in farmers' happiness studies, integrating both economic and psychosocial dimensions. Theoretically, these results challenge conventional income-centric paradigms by establishing social equity as the dominant mechanism, revealing that policy effectiveness in land reforms depends more on equity perceptions than absolute income gains. They provide empirical support for the application of social capital theory and social equity theory in rural land issue studies, highlighting critical factors that should be considered in policy formulation, and provide valuable empirical evidence for the government and policymakers, aiding in the optimization of land transfer policies to enhance farmers' happiness.
Hybrid Neural Networks vs. Econometric Models for Fresh Durian Export Value Forecasting: A Comparative Analysis Damrongsakmethee, Thitimanan; Chanthawong, Anuman; Nupueng, Somjai; Ade Kesuma, Sambas
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-018

Abstract

This study compares machine learning and econometric approaches for forecasting agricultural export values in volatile global markets, examining predictive accuracy and economic interpretability trade-offs. Monthly data from January 2014 to December 2023 were analyzed using five models: Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Hybrid ANN-LSTM, Ordinary Least Squares (OLS), and Autoregressive Distributed Lag (ARDL). Key predictors included durian, mangosteen, and longan export values/volumes, plus China's GDP. Performance evaluation used MAE, RMSE, MAPE, and R² metrics with systematic hyperparameter optimization through grid search and 5-fold cross-validation. ANN achieved the highest absolute accuracy (MAE: 1,684,667,401.55; RMSE: 2,602,671,952.28), while Hybrid ANN-LSTM delivered superior relative accuracy (MAPE: 1.58%). ARDL demonstrated exceptional explanatory power (R²=0.83) for structural economic relationships. China's GDP emerged as the strongest determinant across all models. Longan export value showed contrasting effects between approaches, positive in machine learning models versus negative in econometric models, reflecting different paradigmatic interpretations of market substitution dynamics. This research introduces the first comprehensive comparative framework integrating advanced hybrid neural networks with traditional econometric methods for multi-commodity agricultural forecasting, addressing cross-commodity substitution effects previously unexplored while offering complementary perspectives for both predictive accuracy and economic policy interpretation.
Co-Authors Ade Kesuma, Sambas Chanthawong, Anuman Damrongsakmethee, Thitimanan Wei Li Zhang, Weiwei Zhou, Qiuxiang