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FACTORS AFFECTING THE WILLING TO JOIN IN COFFEE CROP INSURANCE IN DAK LAK PROVINCE, VIETNAM: A NOVEL APPLICATION OF BAYESIAN MODEL AVERAGING APPROACH
Corresponding Author(s) : Le Dinh Thang
Humanities & Social Sciences Reviews,
Vol. 8 No. 5 (2020): September
Purpose of the study: this paper aims to determine factors affecting the willingness to join crop insurance. Besides, this paper is the purpose of developing a coffee tree insurance program.
Methodology: The authors used a systematic random sampling technique. The authors used the Bayesian Model Average (BMA) that calculated the probability of all independent variables affecting the dependent variable with significance level 0.05. Besides, the data based on 480 coffee farmers in Dak Lak province, Vietnam.
Main Findings: Authors calculated the probability of all independent variables affecting the dependent variable with significance level 0.05. Independent variables, including loans, drought risks, educational level, experiences, and productivity.
Applications of this study: This result is a vital science document for insurance companies and managers to apply and suggest recommendations for developing coffee tree insurance in the future.
Novelty/Originality of this study: Vietnam is an agricultural country, 60-70% of the population lives in rural areas, and agricultural insurance should have a considerable market. Farmers’ agrarian insurance cultivated the coffee trees that are currently underdeveloped and challenging.
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Abraham Falola, Opeyemi Eyitayo Ayin, and Babatola Olasunkanmi Agboola (2018). Willingness to take agricultural insurance by cocoa farmers in Nigeria. International Journal of Food and Agricultural Economics, 1(1), 97-107. https://www.foodandagriculturejournal.com/97.pdf
Adam Was and Paweł Kobus (2018). Factors determining the crop insurance level in Pola,nd taking into account the level of farm subsidizing. The Common Agricultural Policy of the European Union, 3(1), 125-146. https://doi.org/10.30858/pw/9788376587431.11 DOI: https://doi.org/10.30858/pw/9788376587431.11
Aidoo R, Mensah Osei J, Wie P, Awunyo-Vitor D. (2014). Prospects of crop insurance as a risk management tool among arable crop farmers in Ghana. Asian Economic Financial Review, 4(3), 341-354.
A. Lawrence Gould (2018). BMA‐Mod: A Bayesian model averaging strategy for determining dose‐response relationships in the presence of model uncertainty. Biometrical Journal, 61(5), 1-13. https://doi.org/10.1002/bimj.201700211 DOI: https://doi.org/10.1002/bimj.201700211
Axel Theorell, Katharina Nöh (2018). Model Uncertainty Analysis for Metabolic Network Inference: A Case Study in Bayesian Model Averaging. IFAC-PapersOnLine, 51(19), 124-135, ISSN 2405-8963. https://doi.org/10.1016/j.ifacol.2018.09.010 DOI: https://doi.org/10.1016/j.ifacol.2018.09.010
BalmaIssaka, Yakubu, Buadu Latif Wumbei, Joy Buckner, and Richard Yeboah Nartey (2016). willingness to participate in the market for crop drought index insurance among farmers in Ghana. African Journal of Agricultural Research, 11(4), 1257-1265. https://doi.org/10.5897/AJAR2015.10326 DOI: https://doi.org/10.5897/AJAR2015.10326
Barnett, B. J., C. B. Barrett, and J. R. Skees (2006). Poverty Traps and Index-Based Risk Transfer Products. Department of Agricultural and Applied Economics: University of Georgia. https://doi.org/10.2139/ssrn.999399 DOI: https://doi.org/10.2139/ssrn.999399
Barrett, C. B., and J. G. McPeak (2005). Poverty Traps and Safety Nets.” Poverty, Inequality, and Development: Essays in Honor of Erik Thorbecke. A. de Janvry, and R. Kanbur, eds. Norwell, MA: Kluwer Academic Publishers. https://doi.org/10.1007/0-387-29748-0_8 DOI: https://doi.org/10.1007/0-387-29748-0_8
Bruce J. Sherrick, Peter J. Barry, Paul N. Ellinger, Gary D. Schnitkey (2004). Factors Influencing Farmers’ Crop Insurance Decisions. American Journal of Agricultural Economics, 86(1), 103-114 https://doi.org/10.1111/j.0092-5853.2004.00565.x
Carter, M. R., and C. B. Barrett (2006). The Economics of Poverty Traps and Persistent Poverty: An Asset-Based Approach. Journal of Development Studies, 42(1), 178-199. https://doi.org/10.1080/00220380500405261 DOI: https://doi.org/10.1080/00220380500405261
Carter, M. R., P.D. Little, T. Mogues, and W. Negatu (2007). Poverty Traps and Natural Disasters in Ethiopia and Honduras. World Development, 35(1), 835-856. https://doi.org/10.1016/j.worlddev.2006.09.010 DOI: https://doi.org/10.1016/j.worlddev.2006.09.010
Chao Feng, Lu-Xuan Sun, Yin-Shuang Xia (2020). Clarifying the “gains” and “losses” of transport climate mitigation in China from technology and efficiency perspectives. Journal of Cleaner Production, 263(1), 14-23. https://doi.org/10.1016/j.jclepro.2020.121545 DOI: https://doi.org/10.1016/j.jclepro.2020.121545
Danso-Abbeam G, Setsoafia ED, Gershon I, Ansah K. (2014). Modeling farmer’s investment in agrochemicals: the experience of smallholder cocoa farmers in Ghana. Res Appl Econ, 6(4), 1-15. https://doi.org/10.5296/rae.v6i4.5977 DOI: https://doi.org/10.5296/rae.v6i4.5977
Elvis Dartey Okoffo, Elisha Kwaku Denkyirah, Derick Taylor Adu & Benedicta Yayra Fosu-Mensah (2016). Double-e‑hurdle model estimation of cocoa farmers’ willingness to pay for crop insurance in Ghana. Okoffo et al. SpringerPlus, 4(2), 1-19. https://springerplus.springeropen.com/track/pdf/10.1186/s40064-016-2561-2
Filippa Pyk and Assem Abu Hatab (2018). Fairtrade and Sustainability: Motivations for Fairtrade Certification among Smallholder Coffee Growers in Tanzania. Sustainability, 10(5), 1-18. https://doi.org/10.3390/su10051551 DOI: https://doi.org/10.3390/su10051551
Fonta, W.M., Sanfo, S., Kedir, A.M. et al. (2018). Estimating farmers’ willingness to pay for weather index-based crop insurance uptake in West Africa: Insight from a pilot initiative in Southwestern Burkina Faso. Agric Econ, 6(1), 1-10. https://doi.org/10.1186/s40100-018-0104-6 DOI: https://doi.org/10.1186/s40100-018-0104-6
Girma Gezimu Gebre, Hiroshi Isoda, Dil Bahadur Rahut Yuichiro Amekawa, Hisako Nomura (2019). Gender differences in agricultural productivity: evidence from maize farm households in southern Ethiopia. GeoJournal, 3(1), 21-34. https://link.springer.com/article/10.1007%2Fs10708-019-10098-y
Guoqiang Tang, Yingzhao Ma, DiLong, LingzhiZhong, Yang Hong (2016). Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales. Journal of Hydrology, 533(1), 152-167. https://doi.org/10.1016/j.jhydrol.2015.12.008 DOI: https://doi.org/10.1016/j.jhydrol.2015.12.008
Hasen, M., & Mekonnen, H. (2017). The impact of agricultural cooperatives membership on the wellbeing of smallholder farmers: Empirical evidence from Eastern Ethiopia. Agricultural and Food Economics, 5(6), 1-20. https://doi.org/10.1186/s40100-017-0075-z DOI: https://doi.org/10.1186/s40100-017-0075-z
Jennifer A. Hoeting, David Madigan, Adrian E. Raftery, and Chris T. Volinsky (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382-401. https://doi.org/10.1214/ss/1009212519 DOI: https://doi.org/10.1214/ss/1009212519
John Mano Raj (2014). Marketing of rain fall insurance in coffee: a concept failure or promotion failure? A Journal of Radix International Educational and Research Consortium, 3(3),1-12.
Khanal Arjun Prasad, Khanal Suman, Dutta Jay Prakash, Dhakal Shiva Chand,ra and Kattel Rishi Ram (2019). An assessment of factors determining the productivity of coffee the western hills of Nepal. International Journal of Agricultural Sciences and Veterinary Medicine, 7(2),11-17. https://www.researchgate.net/publication/333479102
Khalil Ur Rahman; Songhao Shang; Muhammad Shahid; Yeqiang Wen; Zeeshan Khan (2020). Application of a Dynamic Clustered Bayesian Model Averaging (DCBA) Algorithm for Merging Mul tisatellite Precipitation Products over Pakistan. J. Hydrometeor, 21(1), 17-37. https://doi.org/10.1175/JHM-D-19-0087.1 DOI: https://doi.org/10.1175/JHM-D-19-0087.1
Koloma, Y. (2015). Crop Microinsurance for Maize Farmers in Burkina Faso: Access and Agriculture Performance in the Dandé Village. Strategic Change, 24(1), 115-129. https://doi.org/10.1002/jsc.2001 DOI: https://doi.org/10.1002/jsc.2001
Kenneth W. Sibiko, Prakashan C. Veettil, and Matin Qaim (2018). Small farmers’ preferences for weather index insurance: insights from Kenya. Sibiko et al. Agric & Food Secityur, 1(1), 1-14. https://doi.org/10.1186/s40066-018-0200-6 DOI: https://doi.org/10.1186/s40066-018-0200-6
Krzysztof Drachal (2018). Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices. Sustainability, 10(8), 1-27. https://doi.org/10.3390/su10082801 DOI: https://doi.org/10.3390/su10082801
Lele Lu, Hanchen Wang, Sophan Chhin, Aiguo Duan, Jianguo Zhang, Xiongqing Zhang (2019). A Bayesian Model AveraginA approach for moelling tree mortality in relation tthe o site, competiti,on and climatic factors for Chinese fir plantations. Forest Ecology and Management, 40(1), 169-177. https://doi.org/10.1016/j.foreco.2019.03.003 DOI: https://doi.org/10.1016/j.foreco.2019.03.003
Madigan, D., Raftery, A.., (1994). Model selection and accounting for model uncertainty in graphical models using Occam’s window. J. Am. Stat. Assoc, 89(428), 1535-1546. https://doi.org/10.1080/01621459.1994.10476894 DOI: https://doi.org/10.1080/01621459.1994.10476894
Man, Georg (2015). Competition and the growth of nations: International evidence from Bayesian model averaging. Economic Modelling, 51(1), 491-501. https://doi.org/10.1016/j.econmod.2015.08.012 DOI: https://doi.org/10.1016/j.econmod.2015.08.012
Mark F.J. Steel (2019). Model Averaging and its Use in Economics. Department of Statistics, University of Warwick, 3(1), 1-106. https://arxiv.org/pdf/1709.08221.pdf
Notaro, Vincenza & Liuzzo, Lorena & Freni, Gabriele (2016). A BMA Analysis to Assess the Urbanization and Climate Change Impact on Urban Watershed Runoff. Procedia Engineering. 154(1), 868-876. https://doi.org/10.1016/j.proeng.2016.07.461 DOI: https://doi.org/10.1016/j.proeng.2016.07.461
Okoffo, E.D., Denkyirah, E.K., Adu, D.T. et al. (2016). Double-hurdle model estimation of cocoa farmers’ willingness to pay for crop insurance in Ghana. SpringerPlus, 5(1), 873-879. https://doi.org/10.1186/s40064-016-2561-2 DOI: https://doi.org/10.1186/s40064-016-2561-2
Rafia Afroz, Rulia Akhtar, Puteri Farhana (2017). Willingness to pay for crop insurance to adopt flood risk by Malaysian farmers: an empirical investigation of Kedah. International Journal of Economics and Financial Issues, 7(1), 1-9.
Raftery, A. (1995). Bayesian model selection in social research. In: Marsden, R.V.(Ed.), Sociological Methodology. Blackwell: Cambridge, Mass. https://doi.org/10.2307/271063 DOI: https://doi.org/10.2307/271063
Raftery, A.., (1996). Approximate Bayes factors and accounting for model uncertainty in generalized linear models. Biometrika, 83(2), 251-266. https://doi.org/10.1093/biomet/83.2.251 DOI: https://doi.org/10.1093/biomet/83.2.251
Ray P. K. (2001). Agricultural Insurance: Theory and practice and application to developing countries. 2nd edition. Oxford: Pergamon Press.
Robert Ochago (2017). Barriers to women’s participation in coffee pest management learning groups ithe n Mt Elgor Region. Uganda Ochago, Cogent Food & Agriculture, 3(1), 1-19. https://doi.org/10.1080/23311932.2017.1358338 DOI: https://doi.org/10.1080/23311932.2017.1358338
Sarah Lyon, Tad Mutersbau,h and Holly Worthen (2018). Constructing the female coffee farmer: Do corporate smart‐economic initiatives promote gender equity within agricultural value chains?. The American Anthropological Association, 2(1), 1-9. https://anthrosource.onlinelibrary.wiley.com/doi/epdf/10.1002/sea2.12129
Sein Mar, Hisako Nomura, Yoshifumi Takahashi, Kazuo Oga,ta and Mitsuyasu Yabe (2018). Impact of Erratic Rainfalonom Climate Change on Pulse Production Efficiency in Lower Myanmar. Sustainability, 10(1), 1-16. https://doi.org/10.3390/su10020402 DOI: https://doi.org/10.3390/su10020402
Roland Azibo Balgah (2019). Factors Influencing Coffee Farmers’ Decisions to Join Cooperatives. Sustainable Agriculture Research, 8(1), 42-58. https://doi.org/10.5539/sar.v8n1p42 DOI: https://doi.org/10.5539/sar.v8n1p42
Sherrick, B. J., Barry, P. J., Ellinger, P. N., and Schnitkey, G. D. (2004). Factors influencing farmers’ crop insurance decisions. American Journal of Agricultural Economics, 86(1), 103-114. https://doi.org/10.1111/j.0092-5853.2004.00565.x DOI: https://doi.org/10.1111/j.0092-5853.2004.00565.x
Tiago M. Fragoso, Wesley Berto,li and Francisco Louzada (2017). Bayesian Model Averaging: A Systematic Review and Conceptual Classification. International Statistical Review, 86(1), 12-24. https://doi.org/10.1111/insr.12243
Taplin, R. H. (1993). “Robust likelihood calculation for time series”. J Roy. Statist. Soc. Ser. B 55 829-836 DOI: https://doi.org/10.1111/j.2517-6161.1993.tb01943.x
Tapiador, F. J., and Coauthors (2017). Global precipitation measurements for validating climate models. Atmospheric Research, 197(1), 1-20. https://doi.org/10.1016/j.atmosres.2017.06.021 DOI: https://doi.org/10.1016/j.atmosres.2017.06.021
Wang, D., Zhang, W., Bakhai, A. (2004). Comparison of Bayesian model averaging and stepwise methods for model selection in logistic regression. Stat. Med, 23(22), 3451-3467. https://doi.org/10.1002/sim.1930 DOI: https://doi.org/10.1002/sim.1930
Xiao Huang, Guorui Huang, Chaoqing Yu, ShaoQiang Ni, Le Yu (2017). A multiple crop model ensemble for improving broad-scale yield prediction using Bayesian model averaging. An International Journal, 211(1), 114-124. https://doi.org/10.1111/insr.12243 DOI: https://doi.org/10.1111/insr.12243
Yanlai Zhou, Fi-John Chang, Hua Chen, Hong Li (2020). Exploring Copula-based Bayesian Model Averaging with multiple ANNs for PM2.5 ensemble forecasts. Journal of Cleaner Production, 263(1), 121-132. https://doi.org/10.1016/j.jclepro.2020.121528 DOI: https://doi.org/10.1016/j.jclepro.2020.121528
Zhang Yan-yuan, JU Guang-w,ei and Zhan Jin-tao (2019). Farmers using insurance and cooperatives to manage agricultural risks: A case study of the swine industry in China. Journal of Integrative Agriculture, 18(12). https://doi.org/10.1016/S2095-3119(19)62823-6 DOI: https://doi.org/10.1016/S2095-3119(19)62823-6
Zhang, Wei & Yang, Jun. (2015). Forecasting natural gas consumption in China by Bayesian Model Averaging. Energy Reports, 1(1), 216-220. https://doi.org/10.1016/j.egyr.2015.11.001 DOI: https://doi.org/10.1016/j.egyr.2015.11.001