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Predicting Contract Participation in the Mekong Delta, Vietnam: A Comparison Between the Artificial Neural Network and the Multinomial Logit Model

Huy Duc Dang ORCID logo and Thuyen Thi Pham ORCID logo


The research aims of this study are bi-fold: to study factors influencing the uptake of contract farming (CF) and to compare the predicting power of the artificial neural network model (ANN) and the Multinomial Logit Model (MNL) on predicting CF participation in the Mekong Delta, Vietnam. ANN and MNL were employed to analyze on the basis of the transaction cost theory. To validate the ANN, a 10-fold cross-validation procedure was applied to avoid model overfitting. The sensitivity analysis of ANN was used to elicit the magnitude of the correlation between predictors. Multicollinearity was examined with all VIFs lower than two. Among predictors, the most influential roles of the cooperatives and the extension agents/services in supporting CF participation are reported. Also, farmers who conduct frequent access to the market incline to participate in CF. Risk perceptions and preferences are dissimilar across domains, which are also mainly interpreted that risk-averse farmers tend to opt for CF as an effective solution to risks perceived. Thus, heterogeneous approaches should be tailored to promote CF. The findings suggest that MNL outperforms ANN in terms of accuracy percentage and mean absolute error (MAE). However, this result should not be generalized base on the constraint of the data threshold as articulated in the study. The sensitivity analysis of ANN and the estimation results of the MNL relatively agreed on the importance of model predictors. This study is the first to investigate the impacts of the domain-specific risk perceptions and attitudes on CF and also contribute to the debate over the performance between the conventional econometric models versus machine learning techniques.

Corresponding author: Huy Duc Dang, Department of Business Administration, Economics Faculty, Nong Lam University, Ho Chi Minh, Viet Nam, E-mail:

Funding source: Nong Lam University

Award Identifier / Grant number: CS-CB18-KT-02


The help of the local extension agents of the An Giang Province in coordinating focus group meetings and facilitating the recruitment of key farmers and the data collection process is fully acknowledged. We also extend our thanks to experts in Nong Lam university for their guidance in identifying risk sources and insightful comments to facilitate the completion of the paper. Thanks again go to an anonymous reviewer.

  1. Research funding: This research was partially funded by Nong Lam University under grant no. (CS-CB18-KT-02).


Table 7:

Definition of and statistics of risk items.

Risk measurement statements Mean (Std. dev.)
Please rate the likelihood of impacts of the following risky items that you experience (1 “no impact” to 7 “very high impact”)
Production risk
 P1: Seed ungermination 3.393 (2.280)
 P2: Low yield varieties 3.459 (2.184)
 P3: Low pest-resistant varieties 3.203 (1.892)
 P4: Seed damage 3.127 (1.997)
 P5: Seed mixturea 3.118 (2.160)
 P6: Failure in crop scheduling to be in line with neighbors 3.677 (1.997)
 P7: Unqualified fertilizer 3.601 (2.010)
 P8: Unqualified pesticide 3.696 (2.096)
 P9: Unable to hire labor 3.568 (2.177)
 P10: High rate of rice loss during harvesting 3.595 (1.760)
 P11: Bad effects of unpredictable climate conditions 5.464 (1.762)
 P12: Delay inputs delivery from the contractor 2.867 (1.907)
Market risk
 M1: High and fluctuating input price 3.753 (1.965)
 M2: Low and fluctuating output price 4.037 (2.151)
 M3: Market inaccessibility 3.118 (1.927)
Financial risk
 F1: Difficult to access funding for rice production 1.900 (1.674)
 F2: High-interest rate for agricultural credits 2.417 (1.785)
 F3: Delay in payment from the contractor 2.748 (1.871)
Human risk
 H1: Lack of knowledge and experience in applying fertilizer/pesticide 3.417 (2.101)
 H2: Lack of knowledge and experience in market accessibility 3.677 (1.872)
 H3: Lack of awareness in environmental protection in pesticide usage 3.672 (1.995)
 H4: Hired laborers are lack of skills and experience in rice production 3.616 (1.951)
Legal risk
 L1: Changes in government policies on product development strategies 2.895 (1.576)
 L2: Changes in regulation on food safety and safe production practices 2.943 (1.646)
 L3: Changes in tax policies 2.592 (1.790)

  1. aHigh percentage of mixed unqualified seed.


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Received: 2020-07-09
Accepted: 2021-01-10
Published Online: 2021-01-20

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