Growers’ fertilizer application behavior and their willingness to pay for the fertilizer: a study in coconut triangle of Sri Lanka
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Published:
Apr 30, 2021
Keywords:
attitude contingency valuation method intention perceived behavioral control subjective norm theory of planned behavior
Main Article Content
Abstract
Sri Lankan small−scale coconut growers do not have the proper habit of applying fertilizer for their coconut palms due to their norms, beliefs, and attitudes. Therefore, the objectives of the study were to identify the factors that determine growers’ fertilizer application, ranking them, and assess the growers’ willingness to pay for a 50 kg bag of adult palm mixture. The data were collected from 366 coconut growers in the main three coconut growing districts in Sri Lanka, and the stratified random sampling method was used. Statistics such as binary logistic regression, linear regression, Pearson correlation, and contingency valuation method were utilized to analyze the data. The results showed that three types of beliefs were contributed to the fertilizer application behavior: behavioral beliefs [yield increase (β = 0.335; p = 0.006), coconut palm grow vigorously (β = 0.182; p = 0.040), income increase (β = 0.029; p = 0.010), and gives sustainable yield over the years (β = 0.027; p = 0.000)], normative beliefs [Coconut Development Officer (β = 0.074; p = 0.003) and fertilizer shopkeeper (β = 0.003; p = 0.020)], and control beliefs [price of the fertilizer (β = 0.643; p = 0.006), technical knowledge on fertilizer (β = 0.204; p = 0.001), labor scarcity (β = 0.179; p = 0.020), and having no interest (β = 0.071; p = 0.007)]. Furthermore, attitudes, subjective norms, and perceived behavioral control were also significantly affected the growers’ fertilizer application. The growers’ willingness to pay for a 50 kg bag of adult palm mixture was 1,672.08 LKR (1 USD = 195 LKR). Therefore, apart from the price of the fertilizer, policymakers should pay attention to the growers’ technical knowledge, the issue of labor scarcity, and different agricultural extension approaches.
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Research Article
References
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Ajzen, I. 1991. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2): 179–211.
Ajzen, I. 2002. Perceived behavioral control, self−efficacy, locus of control, and the theory of planned behavior. J. Appl. Soc. Psychol. 32(4): 665−683.
Ajzen, I. and M. Fishbein. 1980. Understanding Attitude and Predicting Social Behavior. Pearson Prentice Hall Publisher, New Jersey, USA.
Alarcon, P., B. Wieland, A.L.P. Mateus and C. Dewberry. 2014. Pig farmers’ perceptions, attitudes, influences and management of information in the decision−making process for disease control. Prev. Vet. Med. 116(3): 223−242.
Arunrat, N., C. Wang, N. Pumijumnong, S. Sereenonchai and W. Cai. 2017. Farmers’ intention and decision to adapt to climate change: a case study in the Yom and Nan basins, Phichit province of Thailand. J. Clean. Prod. 143: 672−685.
Aubert, B.A., A. Schroeder and J. Grimaudo. 2012. IT as enabler of sustainable farming: an empirical analysis of farmers’ adoption decision of precision agriculture technology. Decis. Support Syst. 54(1): 510−520.
Bergevoet, R.H.M., C.J.M. Ondersteijn, H.W. Saatkamp, C.M.J. van Woerkum and R.B.M. Huirne. 2004. Entrepreneurial behavior of Dutch dairy growers under a milk quota system: goals, objectives and attitudes. Agric. Syst. 80(1): 1−21.
Blaine, T.W., F.R. Lichtkoppler and R. Stanbro. 2003. An assessment of residents’ willingness to pay for green space and farmland preservation conservation easements using the contingent valuation method (CVM). J. Ext. 41(4): 1−4.
Campbell, J.T., T.M. Koontz and J.E. Bonnell. 2011. Does collaboration promote grass−roots behavior change? Farmer adoption of best management practices in two watersheds. Soc. Nat. Resour. 24(11): 1127−1141.
Chen, M.F. 2016. Extending the theory of planned behavior model to explain people’s energy savings and carbon reduction behavioral intentions to mitigate climate change in Taiwan−moral obligation matters. J. Clean. Prod. 112(2): 1746−1753.
Clayton, D.A. 2004. Understanding and Predicting Food Handlers’ Implementation of Specific Food Safety Practices using Social Cognition Models. PhD Thesis, University of Wales, Cardiff, UK.
Dang, H.L., E. Li, I. Nuberg and J. Bruwer. 2014. Understanding farmers’ adaptation intention to climate change: a structural equation modelling study in the Mekong Delta, Vietnam. Environ. Sci. Policy 41: 11−22.
Daxini, A., C. O’Donoghue, M. Ryan, C. Buckley, A.P. Barnes and K. Daly. 2018. Which factors influence farmers’ intentions to adopt nutrient management planning? J. Environ. Manage. 224: 350−360.
Diiro, G.M. 2013. Impact of off−farm income on agricultural technology adoption intensity and productivity: evidence from rural maize farmers in Uganda. Uganda Strategy Support Program, International Food Policy Research Institute, Washington, D.C., USA.
Dillman, D.A. 2007. Mail and Internet Surveys: The Tailored Design Method. 2nd Edition. John Wiley & Sons, Inc., New Jersey, USA.
Ding, S., L. Meriluoto, W.R. Reed, D. Tao and H. Wu. 2011. The impact of agricultural technology adoption on income inequality in rural China: evidence from southern Yunnan province. China Econ. Rev. 22(3): 344−356.
Elliott, J., J. Sneddon, J.A. Lee and D. Blache. 2011. Producers have a positive attitude toward improving lamb survival rates but may be influenced by enterprise factors and perceptions of control. Livest. Sci. 140(1−3): 103−110.
Fielding, K.S., D.J. Terry, B.M. Masser, P. Bordia and M.A. Hogg. 2005. Explaining landholders’ decisions about riparian zone management: the role of behavioral, normative, and control beliefs. J. Environ. Manage. 77(1): 12−21.
Francis, J.J., M.P. Eccles, M. Johnston, A. Walker, J. Grimshaw, R. Foy, E.F.S. Kaner, L. Smith and D. Bonetti. 2004. Constructing Questionnaires Based on the Theory of Planned Behavior: A Manual for Health Services Researchers. Centre for Health Services Research, University of Newcastle upon Tyne, Newcastle upon Tyne, UK.
Gao, L., S. Wang, J. Li and H. Li. 2017. Application of the extended theory of planned behavior to understand individual’s energy saving behavior in workplaces. Resour. Conserv. Recycl. 127: 107−113.
Gebrehiwot, T. and A. van der Veen. 2015. Farmers prone to drought risk: why some farmers undertake farm−level risk−reduction measures while others not? Environ. Manage. 55(3): 588−602.
Gholamrezai, S. and F. Sepahvand. 2017. Farmers’ participation in water user association in western Iran. J. Water Land Dev. 35(X−XII): 49−56.
Gockowski, J. and M. Ndoumbe. 2004. The adoption of intensive monocrop horticulture in southern Cameroon. Agric. Econ. 30(3): 195−202.
Hair, J.F., R.E.J. Anderson, R.L. Tatham and W.C. Black. 1998. Multivariate Data Analysis. 5th Edition. Prentice−Hall, New Jersey, USA.
Helitzer, D.L., G. Hathorn, J. Benally and C. Ortega. 2014. Culturally relevant model program to prevent and reduce agricultural injuries. J. Agric. Saf. Health. 20(3): 175−198.
Herath, C.S. 2012. Do belief differences lead to change in behavior? A study of Sri Lankan coconut farmers. CORD. 28(1): 54−66.
Herath, C.S. 2016. Identification of training needs of the coconut growers in Sri Lanka. CORD. 32(2): 9−20.
Herath, C.S. and R. Wijekoon. 2013. Study on attitudes and perceptions of organic and non−organic coconut growers towards organic coconut farming. Idesia (Arica). 31(2): 5−14.
Issa, I. and U. Hamm. 2017. Adoption of organic farming as an opportunity for Syrian farmers of fresh fruit and vegetables: an application of the theory of planned behavior and structural equation modelling. Sustainability 9(11): 2024.
Kiptot, E., S. Franzel, P. Hebinck and P. Richards. 2006. Sharing seed and knowledge: grower to grower dissemination of agro forestry technologies in western Kenya. Agrofor. Syst. 68(3): 167−179.
Kleebbua, C. and T. Siriparp. 2016. Effects of education and attitude on essential learning outcomes. Procedia. Soc. Behav. Sci. 217: 941−949.
Kuhfuss, L., R. Préget, S. Thoyer and N. Hanley. 2016. Nudging farmers to enroll land into agri−environmental schemes: the role of a collective bonus. Eur. Rev. Agric. Econ. 43(4): 609−636.
Lee, C.K. and S.Y. Han. 2002. Estimating the use and preservation values of national parks’ tourism resources using a contingent valuation method. Tour. Manag. 23(5): 531−540.
Li, H., D. Huang, Q. Ma, W. Qi and H. Li. 2020. Factors influencing the technology adoption behaviours of litchi farmers in China. Sustainability 12: 271.
Li, J., J. Zuo, H. Cai and G. Zillante. 2018. Construction waste reduction behavior of contractor employees: an extended theory of planned behavior model approach. J. Clean. Prod. 172: 1399−1408.
Long, B. 2013. Understanding farmer decision making and adoption behavior. Available Source: https://grdc.com.au/resources−and−publications/grdc−update−papers/tab−content/grdc−update−papers/2013/02/grdc−updatepaper−long2013−decisionmakingandadoption. September 19, 2020.
Martínez−García, C.G., P. Dorward and T. Rehman. 2013. Factors influencing adoption of improved grassland management by small−scale dairy farmers in central Mexico and the implications for future research on smallholder adoption in developing countries. Livest. Sci. 152(2−3): 228−238.
Mekonnen, S.A., G. Koop, T.J.G.M. Lam and H. Hogeveen. 2017. The intention of North−Western Ethiopian dairy farmers to control mastitis. PLoS ONE 12(8): e0182727.
Morris, M.G. and V. Venkatesh. 2000. Age differences in technology adoption decisions: implications for a changing work force. Pers. Psychol. 53(2): 375−403.
Mukhopadhyay, P., M. Sinha and P.P. Sengupta. 2020. Decision−making process of farmers: a conceptual framework. In: A. Das, J. Nayak, B. Naik, S.K. Pati and D. Pelusi, (Eds.), Computational Intelligence in Pattern Recognition: Advances in Intelligent Systems and Computing, 999. Springer, Singapore.
Mullan, B., C. Wong and E.J. Kothe. 2013. Predicting adolescents’ safe food handling using an extended theory of planned behavior. Food Control 31(2): 454−460.
Neil, S.P. and D.R. Lee. 2001. Explaining the adoption and disadoption of sustainable agriculture: the case of cover crops in Northern Honduras. Econ. Dev. Cult. Change. 49(4): 793−820.
Nomura, N. and M. Akai. 2004. Willingness to pay for green electricity in Japan as estimated through contingent valuation method. Appl. Energy. 78(4): 453−463.
Nöremark, M., S.S. Lewerin, L. Ernholm and J. Frössling. 2016. Swedish farmers’ opinions about biosecurity and their intention to make professionals use clean protective clothing when entering the stable. Front. Vet. Sci. 3: 46.
Rose, D.C., C. Keating and C. Morris. 2018. Understanding How to Influence Farmers’ Decision−Making Behavior: A Social Science Literature Review. Agriculture and Horticulture Development Board, UEA Consulting Ltd., UK.
Savari, M. and H. Gharechaee. 2020. Utilizing the theory of planned behavior to predict Iranian farmers’ intention for safe use of chemical fertilizers. J. Clean. Prod. 263: 121512.
Seaman, P. and A. Eves. 2010. Efficacy of the theory of planned behavior model in predicting safe food handling practices. Food Control 21(7): 983−987.
Shapiro, M.A., N. Porticella, L.C. Jiang and R.B. Gravani. 2011. Predicting intentions to adopt safe home food handling practices: applying the theory of planned behavior. Appetite 56(1): 96−103.
Shin, Y.H. and M. Hancer. 2016. The role of attitude, subjective norm, perceived behavioral control, and moral norm in the intention to purchase local food products. J. Foodserv. Bus. Res. 19(4): 338−351.
Solano, C., H. Leon, E. Perez and M. Herrero. 2003. The role of personal information sources on the decision−making process of Costa Rican dairy farmers. Agric. Syst. 76(1): 3−18.
Udimal, T.B., Z. Jincai, O.S. Mensah and A.E. Caesar. 2017. Factors influencing the agricultural technology adoption: the case of improved rice varieties (Nerica) in the Northern region, Ghana. J. Econ. Sustain. Dev. 8(8): 137−148.
Ugur, M. and A. Mitra. 2017. Technology adoption and employment in less developed countries: a mixed−method systematic review. World Dev. 96: 1−18.
Viklund, M. 2002. An Expectancy−Value Approach to Determinants of Trust. SSE/EFI Working Paper Series in Business Administration No 2002: 13. Center for Risk Research, Stockholm School of Economics, Stockholm, Sweden.
Wimalagunasekara, T.U., J.C. Edirisinghe and W. Wijesuriya. 2012. Neighbors’ influence on farmer adoption of fertilizer recommendations in rubber cultivation. J. Rubber Res. 15(3): 179−186.
Zhou, J., Z. Yan and K. Li. 2016. Understanding farmer cooperatives’ self−inspection behavior to guarantee agri−product safety in China. Food Control 59: 320−327.
Zhou, Y., H. Yang, H.J. Mosler and K.C. Abbaspour. 2010. Factors affecting farmers’ decisions on fertilizer use: a case study for the Chaobai watershed in Northern China. Consilience 4(1): 80−102.
Ajayi, A.O. 2006. An assessment of growers’ willingness to pay for extension services using the contingent valuation method (CVM): the case of Oyo State, Nigeria. J. Agr. Educ. Ext. 12(2): 97−108.
Ajzen, I. 1985. From intentions to actions: a theory of planned behavior, pp.11−39. In: J. Kuhl and J. Beckman, (Eds.), Action Control: From Cognition to Behavior. Springer, Heidelberg.
Ajzen, I. 1991. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2): 179–211.
Ajzen, I. 2002. Perceived behavioral control, self−efficacy, locus of control, and the theory of planned behavior. J. Appl. Soc. Psychol. 32(4): 665−683.
Ajzen, I. and M. Fishbein. 1980. Understanding Attitude and Predicting Social Behavior. Pearson Prentice Hall Publisher, New Jersey, USA.
Alarcon, P., B. Wieland, A.L.P. Mateus and C. Dewberry. 2014. Pig farmers’ perceptions, attitudes, influences and management of information in the decision−making process for disease control. Prev. Vet. Med. 116(3): 223−242.
Arunrat, N., C. Wang, N. Pumijumnong, S. Sereenonchai and W. Cai. 2017. Farmers’ intention and decision to adapt to climate change: a case study in the Yom and Nan basins, Phichit province of Thailand. J. Clean. Prod. 143: 672−685.
Aubert, B.A., A. Schroeder and J. Grimaudo. 2012. IT as enabler of sustainable farming: an empirical analysis of farmers’ adoption decision of precision agriculture technology. Decis. Support Syst. 54(1): 510−520.
Bergevoet, R.H.M., C.J.M. Ondersteijn, H.W. Saatkamp, C.M.J. van Woerkum and R.B.M. Huirne. 2004. Entrepreneurial behavior of Dutch dairy growers under a milk quota system: goals, objectives and attitudes. Agric. Syst. 80(1): 1−21.
Blaine, T.W., F.R. Lichtkoppler and R. Stanbro. 2003. An assessment of residents’ willingness to pay for green space and farmland preservation conservation easements using the contingent valuation method (CVM). J. Ext. 41(4): 1−4.
Campbell, J.T., T.M. Koontz and J.E. Bonnell. 2011. Does collaboration promote grass−roots behavior change? Farmer adoption of best management practices in two watersheds. Soc. Nat. Resour. 24(11): 1127−1141.
Chen, M.F. 2016. Extending the theory of planned behavior model to explain people’s energy savings and carbon reduction behavioral intentions to mitigate climate change in Taiwan−moral obligation matters. J. Clean. Prod. 112(2): 1746−1753.
Clayton, D.A. 2004. Understanding and Predicting Food Handlers’ Implementation of Specific Food Safety Practices using Social Cognition Models. PhD Thesis, University of Wales, Cardiff, UK.
Dang, H.L., E. Li, I. Nuberg and J. Bruwer. 2014. Understanding farmers’ adaptation intention to climate change: a structural equation modelling study in the Mekong Delta, Vietnam. Environ. Sci. Policy 41: 11−22.
Daxini, A., C. O’Donoghue, M. Ryan, C. Buckley, A.P. Barnes and K. Daly. 2018. Which factors influence farmers’ intentions to adopt nutrient management planning? J. Environ. Manage. 224: 350−360.
Diiro, G.M. 2013. Impact of off−farm income on agricultural technology adoption intensity and productivity: evidence from rural maize farmers in Uganda. Uganda Strategy Support Program, International Food Policy Research Institute, Washington, D.C., USA.
Dillman, D.A. 2007. Mail and Internet Surveys: The Tailored Design Method. 2nd Edition. John Wiley & Sons, Inc., New Jersey, USA.
Ding, S., L. Meriluoto, W.R. Reed, D. Tao and H. Wu. 2011. The impact of agricultural technology adoption on income inequality in rural China: evidence from southern Yunnan province. China Econ. Rev. 22(3): 344−356.
Elliott, J., J. Sneddon, J.A. Lee and D. Blache. 2011. Producers have a positive attitude toward improving lamb survival rates but may be influenced by enterprise factors and perceptions of control. Livest. Sci. 140(1−3): 103−110.
Fielding, K.S., D.J. Terry, B.M. Masser, P. Bordia and M.A. Hogg. 2005. Explaining landholders’ decisions about riparian zone management: the role of behavioral, normative, and control beliefs. J. Environ. Manage. 77(1): 12−21.
Francis, J.J., M.P. Eccles, M. Johnston, A. Walker, J. Grimshaw, R. Foy, E.F.S. Kaner, L. Smith and D. Bonetti. 2004. Constructing Questionnaires Based on the Theory of Planned Behavior: A Manual for Health Services Researchers. Centre for Health Services Research, University of Newcastle upon Tyne, Newcastle upon Tyne, UK.
Gao, L., S. Wang, J. Li and H. Li. 2017. Application of the extended theory of planned behavior to understand individual’s energy saving behavior in workplaces. Resour. Conserv. Recycl. 127: 107−113.
Gebrehiwot, T. and A. van der Veen. 2015. Farmers prone to drought risk: why some farmers undertake farm−level risk−reduction measures while others not? Environ. Manage. 55(3): 588−602.
Gholamrezai, S. and F. Sepahvand. 2017. Farmers’ participation in water user association in western Iran. J. Water Land Dev. 35(X−XII): 49−56.
Gockowski, J. and M. Ndoumbe. 2004. The adoption of intensive monocrop horticulture in southern Cameroon. Agric. Econ. 30(3): 195−202.
Hair, J.F., R.E.J. Anderson, R.L. Tatham and W.C. Black. 1998. Multivariate Data Analysis. 5th Edition. Prentice−Hall, New Jersey, USA.
Helitzer, D.L., G. Hathorn, J. Benally and C. Ortega. 2014. Culturally relevant model program to prevent and reduce agricultural injuries. J. Agric. Saf. Health. 20(3): 175−198.
Herath, C.S. 2012. Do belief differences lead to change in behavior? A study of Sri Lankan coconut farmers. CORD. 28(1): 54−66.
Herath, C.S. 2016. Identification of training needs of the coconut growers in Sri Lanka. CORD. 32(2): 9−20.
Herath, C.S. and R. Wijekoon. 2013. Study on attitudes and perceptions of organic and non−organic coconut growers towards organic coconut farming. Idesia (Arica). 31(2): 5−14.
Issa, I. and U. Hamm. 2017. Adoption of organic farming as an opportunity for Syrian farmers of fresh fruit and vegetables: an application of the theory of planned behavior and structural equation modelling. Sustainability 9(11): 2024.
Kiptot, E., S. Franzel, P. Hebinck and P. Richards. 2006. Sharing seed and knowledge: grower to grower dissemination of agro forestry technologies in western Kenya. Agrofor. Syst. 68(3): 167−179.
Kleebbua, C. and T. Siriparp. 2016. Effects of education and attitude on essential learning outcomes. Procedia. Soc. Behav. Sci. 217: 941−949.
Kuhfuss, L., R. Préget, S. Thoyer and N. Hanley. 2016. Nudging farmers to enroll land into agri−environmental schemes: the role of a collective bonus. Eur. Rev. Agric. Econ. 43(4): 609−636.
Lee, C.K. and S.Y. Han. 2002. Estimating the use and preservation values of national parks’ tourism resources using a contingent valuation method. Tour. Manag. 23(5): 531−540.
Li, H., D. Huang, Q. Ma, W. Qi and H. Li. 2020. Factors influencing the technology adoption behaviours of litchi farmers in China. Sustainability 12: 271.
Li, J., J. Zuo, H. Cai and G. Zillante. 2018. Construction waste reduction behavior of contractor employees: an extended theory of planned behavior model approach. J. Clean. Prod. 172: 1399−1408.
Long, B. 2013. Understanding farmer decision making and adoption behavior. Available Source: https://grdc.com.au/resources−and−publications/grdc−update−papers/tab−content/grdc−update−papers/2013/02/grdc−updatepaper−long2013−decisionmakingandadoption. September 19, 2020.
Martínez−García, C.G., P. Dorward and T. Rehman. 2013. Factors influencing adoption of improved grassland management by small−scale dairy farmers in central Mexico and the implications for future research on smallholder adoption in developing countries. Livest. Sci. 152(2−3): 228−238.
Mekonnen, S.A., G. Koop, T.J.G.M. Lam and H. Hogeveen. 2017. The intention of North−Western Ethiopian dairy farmers to control mastitis. PLoS ONE 12(8): e0182727.
Morris, M.G. and V. Venkatesh. 2000. Age differences in technology adoption decisions: implications for a changing work force. Pers. Psychol. 53(2): 375−403.
Mukhopadhyay, P., M. Sinha and P.P. Sengupta. 2020. Decision−making process of farmers: a conceptual framework. In: A. Das, J. Nayak, B. Naik, S.K. Pati and D. Pelusi, (Eds.), Computational Intelligence in Pattern Recognition: Advances in Intelligent Systems and Computing, 999. Springer, Singapore.
Mullan, B., C. Wong and E.J. Kothe. 2013. Predicting adolescents’ safe food handling using an extended theory of planned behavior. Food Control 31(2): 454−460.
Neil, S.P. and D.R. Lee. 2001. Explaining the adoption and disadoption of sustainable agriculture: the case of cover crops in Northern Honduras. Econ. Dev. Cult. Change. 49(4): 793−820.
Nomura, N. and M. Akai. 2004. Willingness to pay for green electricity in Japan as estimated through contingent valuation method. Appl. Energy. 78(4): 453−463.
Nöremark, M., S.S. Lewerin, L. Ernholm and J. Frössling. 2016. Swedish farmers’ opinions about biosecurity and their intention to make professionals use clean protective clothing when entering the stable. Front. Vet. Sci. 3: 46.
Rose, D.C., C. Keating and C. Morris. 2018. Understanding How to Influence Farmers’ Decision−Making Behavior: A Social Science Literature Review. Agriculture and Horticulture Development Board, UEA Consulting Ltd., UK.
Savari, M. and H. Gharechaee. 2020. Utilizing the theory of planned behavior to predict Iranian farmers’ intention for safe use of chemical fertilizers. J. Clean. Prod. 263: 121512.
Seaman, P. and A. Eves. 2010. Efficacy of the theory of planned behavior model in predicting safe food handling practices. Food Control 21(7): 983−987.
Shapiro, M.A., N. Porticella, L.C. Jiang and R.B. Gravani. 2011. Predicting intentions to adopt safe home food handling practices: applying the theory of planned behavior. Appetite 56(1): 96−103.
Shin, Y.H. and M. Hancer. 2016. The role of attitude, subjective norm, perceived behavioral control, and moral norm in the intention to purchase local food products. J. Foodserv. Bus. Res. 19(4): 338−351.
Solano, C., H. Leon, E. Perez and M. Herrero. 2003. The role of personal information sources on the decision−making process of Costa Rican dairy farmers. Agric. Syst. 76(1): 3−18.
Udimal, T.B., Z. Jincai, O.S. Mensah and A.E. Caesar. 2017. Factors influencing the agricultural technology adoption: the case of improved rice varieties (Nerica) in the Northern region, Ghana. J. Econ. Sustain. Dev. 8(8): 137−148.
Ugur, M. and A. Mitra. 2017. Technology adoption and employment in less developed countries: a mixed−method systematic review. World Dev. 96: 1−18.
Viklund, M. 2002. An Expectancy−Value Approach to Determinants of Trust. SSE/EFI Working Paper Series in Business Administration No 2002: 13. Center for Risk Research, Stockholm School of Economics, Stockholm, Sweden.
Wimalagunasekara, T.U., J.C. Edirisinghe and W. Wijesuriya. 2012. Neighbors’ influence on farmer adoption of fertilizer recommendations in rubber cultivation. J. Rubber Res. 15(3): 179−186.
Zhou, J., Z. Yan and K. Li. 2016. Understanding farmer cooperatives’ self−inspection behavior to guarantee agri−product safety in China. Food Control 59: 320−327.
Zhou, Y., H. Yang, H.J. Mosler and K.C. Abbaspour. 2010. Factors affecting farmers’ decisions on fertilizer use: a case study for the Chaobai watershed in Northern China. Consilience 4(1): 80−102.