Abstract
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.
Funding source: Nong Lam University
Award Identifier / Grant number: CS-CB18-KT-02
Acknowledgment
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.
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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) |
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aHigh percentage of mixed unqualified seed.
References
Abebe, G. K., J. Bijman, R. Kemp, O. Omta, and A. Tsegaye. 2013. “Contract Farming Configuration: Smallholders’ Preferences for Contract Design Attributes.” Food Policy 40: 14–24, https://doi.org/10.1016/j.foodpol.2013.01.002.Search in Google Scholar
Arouna, A., P. Adegbola, R. Zossou, O. Babatunde, and A. Diagne. 2017. “Contract Farming Preferences by Smallholder Rice Producers in Africa: A Stated Choice Model Using Mixed Logit.” Tropicultura 35: 179–91.Search in Google Scholar
Ba, H. A., Y. de Mey, S. Thoron, and M. Demont. 2019. “Inclusiveness of Contract Farming Along the Vertical Coordination Continuum: Evidence from the Vietnamese Rice Sector.” Land Use Policy 87: 104050, https://doi.org/10.1016/j.landusepol.2019.104050.Search in Google Scholar
Bakar, N. M. A., and I. M. Tahir. 2009. “Applying Multiple Linear Regression and Neural Network to Predict Bank Performance.” International Business Research 2 (4): 176–83, https://doi.org/10.5539/ibr.v2n4p176.Search in Google Scholar
Barboza, F., H. Kimura, and E. Altman. 2017. “Machine Learning Models and Bankruptcy Prediction.” Expert Systems with Applications 83: 405–17, https://doi.org/10.1016/j.eswa.2017.04.006.Search in Google Scholar
Barrett, C., M. E. Bachke, M. Bellemare, H. Michelson, S. Narayanan, and T. F. Walker. 2012. “Smallholder Participation in Contract Farming: Comparative Evidence from Five Countries.” World Development 40 (4): 715–30, https://doi.org/10.1016/j.worlddev.2011.09.006.Search in Google Scholar
Bellemare, M. F. 2012. “As You Sow, So Shall You Reap: The Welfare Impacts of Contract Farming.” World Development 40 (7): 1418–34, https://doi.org/10.1016/j.worlddev.2011.12.008.Search in Google Scholar
Cai, J., L. Ung, S. Setboonsarng, and P. Leung. 2008. “Rice Contract Farming in Cambodia: Empowering Farmers to Move Beyond the Contract Toward Independence.” In Making Globalization Work Better for the Poor Through Contract Farming, edited by S. Setboonsarng, and P. S. Leung. Mandaluyong, Manila: Asian Development Bank.Search in Google Scholar
Chiang, W. K., D. Zhang, and L. Zhou. 2006. “Predicting and Explaining Patronage Behavior Toward Web and Traditional Stores Using Neural Networks: A Comparative Analysis with Logistic Regression.” Decision Support Systems 41 (2): 514–31, https://doi.org/10.1016/j.dss.2004.08.016.Search in Google Scholar
Chong, A. Y.-L. 2013. “Predicting M-Commerce Adoption Determinants: A Neural Network Approach.” Expert Systems with Applications 40 (2): 523–30, https://doi.org/10.1016/j.eswa.2012.07.068.Search in Google Scholar
Crane, L., G. Gantz, S. Isaacs, D. Jose, and R. Sharp. 2013. “Introduction to Risk Management.” In Understanding Agricultural Risks: Production, Marketing, Financial, Legal, Human. 2nd ed. Extension Risk Management Education and USDA-Risk Management Agency.Search in Google Scholar
Dang, H. D., A. H. Thi Dam, T. T. Pham, and T. M. Thi Nguyen. 2019. “Determinants of Credit Demand of Farmers in Lam Dong, Vietnam: A Comparison of Machine Learning and Multinomial Logit.” Agricultural Finance Review, https://doi.org/10.1108/AFR-06-2019-0061 (Epub ahead of print).Search in Google Scholar
Duéñez-Guzmán, A. E., and M. D. Vose. 2012. “No Free Lunch and Benchmarks.” Evolutionary Computation 21 (2): 293–312, https://doi.org/10.1162/EVCO_a_00077.Search in Google Scholar
Duinen, R. V., T. Filatova, P. Geurts, and A. van der Veen. 2015. “Empirical Analysis of Farmers’ Drought Risk Perception: Objective Factors, Personal Circumstances, and Social Influence.” Risk Analysis 35 (4): 741–55, https://doi.org/10.1111/risa.12299.Search in Google Scholar
Eaton, C., and A. Shepherd. 2001. Contract Farming: Partnerships for Growth. Rome, Italy: FAO.Search in Google Scholar
Fischer, S., and M. Wollni. 2018. “The Role of Farmers’ Trust, Risk and Time Preferences for Contract Choices: Experimental Evidence from the Ghanaian Pineapple Sector.” Food Policy 81: 67–81, https://doi.org/10.1016/j.foodpol.2018.10.005.Search in Google Scholar
Fishbein, M., and I. Ajzen. 1975. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.Search in Google Scholar
Grassi, F., T. R. Paris, and T. N. C. Truong. 2017. Rice-Rice and Rice-Shrimp Production. A Gender Perspective on Labour, Time Use and Access to Technologies and Services in Southern Viet Nam. Rome: FAO.Search in Google Scholar
Greene, W. H. 2011. Econometric Analysis, 7 ed. New Jersey: Pearson Prentice Hall.Search in Google Scholar
Hair, F. J., W. Black, B. Babin, and R. Anderson. 2010. Multivariate Data Analysis: A Global Perspective. New Jersey: Pearson Education Inc.Search in Google Scholar
Hansson, H., and C. J. Lagerkvist. 2012. “Measuring Farmers’ Preferences for Risk: A Domain-Specific Risk Preference Scale.” Journal of Risk Research 15 (7): 737–53.Search in Google Scholar
Hudson, D., and J. Lusk. 2004. “Risk and Transactions Cost in Contracting: Results from a Choice-Based Experiment.” Journal of Agricultural & Food Industrial Organization 2 (1). Article 2, https://doi.org/10.2202/1542-0485.1046.Search in Google Scholar
Hyndman, R. J., and G. Athanasopoulos. 2018. Forecasting: Principles and Practice. Melbourne, Australia: OTexts.Search in Google Scholar
Ito, J., Z. Bao, and Q. Su. 2012. “Distributional Effects of Agricultural Cooperatives in China: Exclusion of Smallholders and Potential Gains on Participation.” Food Policy 37 (6): 700–9, https://doi.org/10.1016/j.foodpol.2012.07.009.Search in Google Scholar
Jia, X., and J. Huang. 2011. “Contractual Arrangements between Farmer Cooperatives and Buyers in China.” Food Policy 36 (7): 656–66, https://doi.org/10.1016/j.foodpol.2011.06.007.Search in Google Scholar
Kiwanuka, R., and C. Machethe. 2016. “Determinants of Smallholder Farmers’ Participation in Zambian Dairy Sector’s Interlocked Contractual Arrangements.” Journal of Sustainable Development 9 (2): 230–45, https://doi.org/10.5539/jsd.v9n2p230.Search in Google Scholar
Mao, H., L. Zhou, J. Ifft, and R. Y. Ying. 2019. “Risk Preferences, Production Contracts and Technology Adoption by Broiler Farmers in China.” China Economic Review 54: 147–59, https://doi.org/10.1016/j.chieco.2018.10.014.Search in Google Scholar
McFadden, D. 1977. Quantitative Methods for Analyzing Travel Behaviour of Individuals: Some Recent Developments, Cowles Foundation Discussion Papers 474. Cowles Foundation for Research in Economics, Yale Station New Haven. Connecticut: Yale University.Search in Google Scholar
Meuwissen, M. P. M., R. B. M. Huirne, and J. B. Hardaker. 2001. “Risk and Risk Management: An Empirical Analysis of Dutch Livestock Farmers.” Livestock Production Science 69 (1): 43–53, https://doi.org/10.1016/s0301-6226(00)00247-5.Search in Google Scholar
Minot, N. 1986. “Contract Farming and its Effect on Small Farmers in Less Developed Countries.” Food Security International Development Working Papers 54740, https://doi.org/10.22004/ag.econ.54740.Search in Google Scholar
Nguyen, H. K., C. Raymond, M. Chica, R. Middleton, and D. T. Kim Pham. 2019. “Contract Farming in the Mekong Delta’s Rice Supply Chain: Insights from an Agent-Based Modeling Study.” The Journal of Artificial Societies and Social Simulation 22 (3): 1. https://doi.org/10.18564/jasss.4008.Search in Google Scholar
Nhan, T. Q., I. Takeuchi, and D. van Hoang. 2013. “Rice Contract Farming – The Potential Key to Improve Rice Growers’ Income: A Farm Level Study in An Giang Province.” Journal of Science and Development 11 (7): 1062–72.Search in Google Scholar
Ochieng, D. O., P. C. Veettil, and M. Qaim. 2017. “Farmers’ Preferences for Supermarket Contracts in Kenya.” Food Policy 68: 100–11, https://doi.org/10.1016/j.foodpol.2017.01.008.Search in Google Scholar
Otsuka, K., Y. Nakano, and K. Takahashi. 2016. “Contract Farming in Developed and Developing Countries.” Annual Review of Resource Economics 8 (1): 353–76, https://doi.org/10.1146/annurev-resource-100815-095459.Search in Google Scholar
Prowse, M. 2012. Contract Farming in Developing Countries: A Review. France: AFD’s Research Department.Search in Google Scholar
Rehber, E., 1998. “Vertical Integration in Agriculture and Contract Farming.” Regional Research Project No. NE-165: Private Strategies, Public Policies, and Food System Performance. Amherst, MA: Department of Resource Economics, University of Massachusetts.Search in Google Scholar
Rodríguez‐Entrena, M., M. Salazar‐Ordóñez, and D. Becerra‐Alonso. 2016. “An Assessment of the Barriers to the Consumers’ Uptake of Genetically Modified Foods: A Neural Network Analysis.” Journal of the Science of Food and Agriculture 96 (5): 1548–55, https://doi.org/10.1002/jsfa.7247.Search in Google Scholar
Shepherd, A. 2013. An Introduction to Contract Farming. Vol. 29, 10–3. Technical Centre for Agricultural and Rural Cooperation (ACP-EU).Search in Google Scholar
Simmons, P., W. Paul, and I. Patrick. 2005. “An Analysis of Contract Farming in East Java, Bali, and Lombok, Indonesia.” Agricultural Economics 33: 513–25, https://doi.org/10.1111/j.1574-0864.2005.00096.x.Search in Google Scholar
Sivramkrishna, S., and A. Jyotishi. 2008. “Monopsonistic Exploitation in Contract Farming: Articulating a Strategy for Grower Cooperation.” Journal of International Development 20: 280–96, https://doi.org/10.1002/jid.1411.Search in Google Scholar
Sokchea, A., and R. J. Culas. 2015. “Impact of Contract Farming with Farmer Organizations on Farmers’ Income: A Case Study of Reasmey Stung Sen Agricultural Development Cooperative in Cambodia.” Australasian Agribusiness Review 23 (1): 1442–6951, https://www.doi.org/10.22004/ag.econ.262469.Search in Google Scholar
Storm, H., K. Baylis, and T. Heckelei. 2020. “Machine Learning in Agricultural and Applied Economics.” European Review of Agricultural Economics 47 (3): 849–92, https://doi.org/10.1093/erae/jbz033.Search in Google Scholar
Tkáč, M., and R. Verner. 2016. “Artificial Neural Networks in Business: Two Decades of Research.” Applied Soft Computing 38: 788–804, https://doi.org/10.1016/j.asoc.2015.09.040.Search in Google Scholar
Ton, G., D. Sam, W. Vellema, S. Weituschat, and M. D’Haese. 2017. “The Effectiveness of Contract Farming for Raising Income of Smallholder Farmers in Low- and Middle-Income Countries: A Systematic Review.” Campbell Systematic Reviews 13 (1): 1–131, https://doi.org/10.4073/csr.2017.13.Search in Google Scholar
Tripathi, R. S., R. Singh, and S. Singh. 2005. “Contract Farming in Potato Production: An Alternative for Managing Risk and Uncertainty.” Agricultural Economics Research Review 18: 47–60.Search in Google Scholar
Tsai, C.-F., Y.-F. Hsu, and D. C. Yen. 2014. “A Comparative Study of Classifier Ensembles for Bankruptcy Prediction.” Applied Soft Computing 24: 977–84, https://doi.org/10.1016/j.asoc.2014.08.047.Search in Google Scholar
Venkatesh, V., and S. Goyal. 2010. “Expectation Disconfirmation and Technology Adoption: Polynomial Modeling and Response Surface Analysis.” MIS Quarterly 34 (2): 281–303, https://doi.org/10.2307/20721428.Search in Google Scholar
Wanglin, M., and A. Awudu. 2016. “Linking Apple Farmers to Markets: Determinants and Impacts of Marketing Contracts in China.” China Agricultural Economic Review 8 (1): 2–21, https://doi.org/10.1108/CAER-04-2015-0035.Search in Google Scholar
Williamson, O. E. 1979. “Transaction-Cost Economics: The Governance of Contractual Relations.” The Journal of Law and Economics 22 (2): 233–61, https://www.doi.org/10.1086/466942.Search in Google Scholar
Williamson, O. E. 1989. “Chapter 3 Transaction Cost Economics.” In Handbook of Industrial Organization, Vol. 1, 135–82, https://doi.org/10.1016/S1573-448X(89)01006-X.Search in Google Scholar
Zhao, Z., S. Xu, and B. H. Kang. 2015. “Mir Md Jahangir Kabir, Yunling Liu, and Rainer Wasinger Investigation and Improvement of Multi-Layer Perceptron Neural Networks for Credit Scoring.” Expert Systems with Applications 42 (7): 3508–16, https://doi.org/10.1016/j.eswa.2014.12.006.Search in Google Scholar
Zheng, X., T. Vukina, and C. Shin. 2008. “The Role of Farmers’ Risk aversion for Contract Choice in the US Hog Industry.” Journal of Agricultural & Food Industrial Organization 6: 1220, https://doi.org/10.2202/1542-0485.1220.Search in Google Scholar
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