Please wait a minute...
Journal of Integrative Agriculture  2023, Vol. 22 Issue (1): 292-305    DOI: 10.1016/j.jia.2022.11.002
Agricultural Economics and Management Advanced Online Publication | Current Issue | Archive | Adv Search |
Farmers’ precision pesticide technology adoption and its influencing factors: Evidence from apple production areas in China

YUE Meng1, LI Wen-jing1, 2, 3, JIN Shan2, CHEN Jing4, CHANG Qian4, 5, Glyn JONES2, 3, CAO Yi-ying6, YANG Gui-jun7, LI Zhen-hong8, Lynn J. FREWER2


1 College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, P.R.China

2 School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK

3 Institute for Agri-Food Research and Innovation, Fera Science Ltd., York YO41 1LZ, UK

4 Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China

5 Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, P.R.China

6 RSK ADAS Ltd., Helsby WA6 0AR, UK

7 Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, P.R.China

8 College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, P.R.China

Download:  PDF in ScienceDirect  
Export:  BibTeX | EndNote (RIS)      
摘要  

利用渤海湾和黄土高原地区545名苹果种植户的微观数据,运用Double-hurdle模型,本文实证分析影响农户精准农药技术的采纳意愿和支付意愿的因素。结果表明,78.72%的农户表示,愿意采纳由合作社、专业企业等服务机构提供精准农药技术服务;69.72%的农户表示愿意购买精准农药技术设备。同时,农户感知、农场规模、是否加入合作社、数字信息获取和金融服务可获得性对农户精准农药技术的采纳意愿具有显著的正向影响。而合作成员、技术培训和环境法规则显影响农户精确农药技术的支付意愿,年龄、农业经验与农户精准农药技术服务的采纳意愿和支付意愿之间存在非线性关系。



Abstract  

The research aimed to understand farmers’ willingness to adopt (WTA) and willingness to pay (WTP) for precision pesticide technologies and analyzed the determinants of farmers’ decision-making.  We used a two-stage approach to consider farmers’ WTA and WTP for precision pesticide technologies.  A survey of 545 apple farmers was administered in Bohai Bay and the Loess Plateau in China.  The data were analyzed using the double-hurdle model.  The results indicated that 78.72% of respondents were willing to apply precision pesticide technologies provided by service organizations such as cooperatives and dedicated enterprises, and 69.72% were willing to buy the equipment for using precision pesticide technologies.  The results of the determinant analysis indicated that farmers’ perceived perceptions, farm scale, cooperative membership, access to digital information, and availability of financial services had significant and positive impacts on farmers’ WTA precision pesticide technologies.  Cooperative membership, technical training, and adherence to environmental regulations increased farmers’ WTP for precision pesticide technologies.  Moreover, nonlinear relationships between age, agricultural experience, and farmers’ WTA and WTP for precision pesticide technology services were found.

Keywords:  precision technologies       apple production        precision pesticides        willingness to adopt        willingness to pay  
Received: 04 April 2021   Accepted: 30 August 2022
Fund: 

This research was supported by the National Key Research and Development Program of China (2017YFE0122500), the UK BBSRC-Innovate UK–China Agritech Challenge Funded Project (RED-APPLE; BB/S020985/1), and the project supported by the Fundamental Research Funds for the Central Universities, China (2662022JGQD001). 

About author:  YUE Meng, E-mail: yuemeng_tgwork@126.com; Correspondence LI Wen-jing, E-mail: liwenjing@mail.hzau.edu.cn; CHEN Jing, E-mail: chenjing@caas.cn

Cite this article: 

YUE Meng, LI Wen-jing, JIN Shan, CHEN Jing, CHANG Qian, Glyn JONES, CAO Yi-ying, YANG Gui-jun, LI Zhen-hong, Lynn J. FREWER. 2023. Farmers’ precision pesticide technology adoption and its influencing factors: Evidence from apple production areas in China. Journal of Integrative Agriculture, 22(1): 292-305.

agricultural technology adoption: Empirical evidence from Ethiopia. Food Policy, 38, 82–91.
Aikoh T, Shoji Y, Tsuge T, Shibasaki S, Yamamoto K. 2020. Application of the double-bounded dichotomous choice model to the estimation of crowding acceptability in natural recreation areas. Journal of Outdoor Recreation and Tourism, 32, 100195.
Ali T, Ali J. 2020. Factors affecting the consumers’ willingness to pay for health and wellness food products. Journal of Agriculture and Food Research, 2, 100076.
Barnes A P, Soto I, Eory V, Beck B, Balafoutis A, Sánchez B, Vangeyte J, Fountas S, van der Wal T, Gómez-Barbero M. 2019. Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land Use Policy, 80, 163–174.
Bukchin S, Kerret D. 2020. The role of self-control, hope and information in technology adoption by smallholder farmers - A moderation model. Journal of Rural Studies, 74, 160–168.
Burton M, Tomlinson M, Young T. 1994. Consumers’ decisions whether or not to purchase meat: A double-hurdle analysis of single adult households. Journal of Agricultural Economics, 45, 202–212.
Chen Q, Wachenheim C, Zheng S. 2020. Land scale, cooperative membership and benefits information: Unmanned aerial vehicle adoption in China. Sustainable Futures, 2, 100025.
Chen Z, Zhang J, He K. 2018. Technical perception, environmental awareness and adoption willingness of agricultural cleaner production technology. Chinese Journal of Eco-Agriculture, 26, 936.
Cisternas I, Velásquez I, Caro A, Rodríguez A. 2020. Systematic literature review of implementations of precision agriculture. Computers and Electronics in Agriculture, 176, 105626.
Cragg J G. 1971. Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica, 39, 829–844.
Diiro G M, Fisher M, Kassie M, Muriithi B W, Muricho G. 2021. How does adoption of labor saving agricultural technologies affect intrahousehold resource allocations? The case of push–pull technology in Western Kenya. Food Policy, 102, 102114.
Drogué S, DeMaria F. 2012. Pesticide residues and trade, the apple of discord? Food Policy, 37, 641–649.
Fan Y, Lai K, Rasco B A, Huang Y. 2014. Analyses of phosmet residues in apples with surface-enhanced Raman spectroscopy. Food Control, 37, 153–157.
Fan Y, Lai K, Rasco B A, Huang Y. 2015. Determination of carbaryl pesticide in Fuji apples using surface-enhanced Raman spectroscopy coupled with multivariate analysis. LWT-Food Science and Technology, 60, 352–357.
Feder G, Umali D. 1993. The adoption of agricultural innovations: A review. Technological Forecasting and Social Change, 43, 215–239.
Foong S Y, Ma N L, Lam S S, Peng W, Low F, Lee B H K, Alstrup A K O, Sonne C. 2020. A recent global review of hazardous chlorpyrifos pesticide in fruit and vegetables: Prevalence, remediation and actions needed. Journal of Hazardous Materials, 400, 123006.
Forge T, Neilsen G, Neilsen D. 2016. Organically acceptable practices to improve replant success of temperate tree-fruit crops. Scientia Horticulturae, 200, 205–214.
Foster A D, Rosenzweig M R. 2010. Microeconomics of technology adoption. Annual Review of Economics, 2, 395–424.
Gao Y, Zhao D, Yu L, Yang H. 2020. Influence of a new agricultural technology extension mode on farmers’ technology adoption behavior in China. Journal of Rural Studies, 76, 173–183.
Ghimire R, Huang W C. 2015. Household wealth and adoption of improved maize varieties in Nepal: A double-hurdle approach. Food Security, 7, 1321–1335.
Giné X, Yang D. 2009. Insurance, credit, and technology adoption: Field experimental evidence from Malawi. Journal of Development Economics, 89, 1–11.
Herriges J A, Shogren J F. 1996. Starting point bias in dichotomous choice valuation with follow-up questioning. Journal of Environmental Economics and Management, 30, 112–131.
Higgins V, Bryant M, Howell A, Battersby J. 2017. Ordering adoption: Materiality, knowledge and farmer engagement with precision agriculture technologies. Journal of Rural Studies, 55, 193–202.
Hudson D, Hite D. 2003. Producer willingness to pay for precision application technology: Implications for government and the technology industry. Canadian Journal of Agricultural Economics, 51, 39–53.
Khanna A, Kaur S. 2019. Evolution of Internet of things (IoT) and its significant impact in the field of precision agriculture. Computers and Electronics in Agriculture, 157, 218–231.
Kolade O, Harpham T. 2014. Impact of cooperative membership on farmers’ uptake of technological innovations in Southwest Nigeria. Development Studies Research, 1, 340–353.
Li C, Wang Y, Lu C, Huai H. 2018. Effects of precision seeding and laser land leveling on winter wheat yield and residual soil nitrogen. International Journal of Agriculture and Biology, 20, 2357–2362.
Li J, He R, deVoil P, Wan S. 2021. Enhancing the application of organic fertilisers by members of agricultural cooperatives. Journal of Environmental Management, 293, 112901.
Li W, Ruiz-Menjivar J, Zhang L, Zhang J. 2020. Climate change perceptions and the adoption of low-carbon agricultural technologies: Evidence from rice production systems in the Yangtze River Basin. Science of The Total Environment, 759, 143554. 
Li Z, Taylor J, Frewer L, Zhao C, Sun Z. 2018. A comparative review of the state and advancement of site-specific crop management in the UK and China. Frontiers of Agricultural Science and Engineering, 6, 1–21.
Liu Y, Ruiz-Menjivar J, Zhang L, Zhang J B, Swisher M E. 2019. Technical training and rice farmers’ adoption of low-carbon management practices: The case of soil testing and formulated fertilization technologies in Hubei, China. Journal of Cleaner Production, 226, 454–462.
Lozowicka B. 2015. Health risk for children and adults consuming apples with pesticide residue. Science of The Total Environment, 502, 184–198.
Ma W, Abdulai A, Goetz R. 2018. Agricultural cooperatives and investment in organic soil amendments and chemical fertilizer in China. American Journal of Agricultural Economics, 100, 1–19.
Mi Q, Li X, Gao J. 2020. How to improve the welfare of smallholders through agricultural production outsourcing: Evidence from cotton farmers in Xinjiang, Northwest China. Journal of Cleaner Production, 256, 120636.
Mondal P, Basu M. 2009. Adoption of precision agriculture technologies in India and in some developing countries: Scope, present status and strategies. Progress in Natural Science, 19, 659–666.
Nakano Y, Tsusaka T W, Aida T, Pede V O. 2018. Is farmer-to-farmer extension effective? The impact of training on technology adoption and rice farming productivity in Tanzania. World Development, 105, 336–351.
NBSC (National Bureau of Statistics of China). 2021. National database. [2021-12-1]. https://data.stats.gov.cn/ (in Chinese)
Orea L, Perez J A, Roibas D. 2015. Evaluating the double effect of land fragmentation on technology choice and dairy farm productivity: A latent class model approach. Land Use Policy, 45, 189–198.
Pathak H, Brown P, Best T. 2019. A systematic literature review of the factors affecting the precision agriculture adoption process. Precision Agriculture, 20, 1292–1316.
Paudel K P, Mishra A K, Pandit M, Larkin S, Rejesus R, Velandia M. 2020. Modeling multiple reasons for adopting precision technologies: Evidence from U.S. cotton producers. Computers and Electronics in Agriculture, 175, 105625.
Perino G, Requate T. 2012. Does more stringent environmental regulation induce or reduce technology adoption? When the rate of technology adoption is inverted U-shaped. Journal of Environmental Economics and Management, 64, 456–467.
Pleeging E, van Exel J, Burger M J, Stavropoulos S. 2020. Hope for the future and willingness to pay for sustainable energy. Ecological Economics, 181, 106900.
Shang L, Heckelei T, Gerullis M K, Börner J, Rasch S. 2021. Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction. Agricultural Systems, 190, 103074.
Suvedi M, Ghimire R, Kaplowitz M. 2017. Farmers’ participation in extension programs and technology adoption in rural Nepal: A logistic regression analysis. The Journal of Agricultural Education and Extension, 23, 351–371.
Teixeira G H D A, Meakem V, Morais C, Lima K, Whitehead S R. 2020. Conventional and alternative pre-harvest treatments affect the quality of ‘Golden Delicious’ and ‘York’ apple fruit. Environmental and Experimental Botany, 173, 104005.
Teklewold H, Dadi L, Yami A, Dana N. 2006. Determinants of adoption of poultry technology: A double-hurdle approach. Livestock Research for Rural Development, 18, 75–86.
Tey Y S, Brindal M. 2012. Factors influencing the adoption of precision agricultural technologies: A review for policy implications. Precision Agriculture, 13, 713–730.
Vorobeva D, Scott I J, Oliveira T, Neto M. 2022. Adoption of new household waste management technologies: The role of financial incentives and pro-environmental behavior. Journal of Cleaner Production, 362, 132328.
Voss R C, Jansen T, Mané B, Shennan C. 2021. Encouraging technology adoption using ICTs and farm trials in Senegal: Lessons for gender equity and scaled impact. World Development, 146, 105620.
Wang Y, He K, Zhang J, Chang H. 2020. Environmental knowledge, risk attitude, and households’ willingness to accept compensation for the application of degradable agricultural mulch film: Evidence from rural China. Science of The Total Environment, 744, 140616.
Wilson N. 2021. Why is ageing associated with lower adoption of new technologies? Evidence from voluntary medical male circumcision and a structural model. The Journal of the Economics of Ageing, 19, 100308.
Xie B, Yu J, Zheng X, Qu F, Xu H. 2014. N2O emissions from an apple orchard in the coastal area of Bohai Bay, China. The Scientific World Journal, 3, 164732.
Yang Q, Zhu Y, Wang J. 2020. Adoption of drip fertigation system and technical efficiency of cherry tomato farmers in Southern China. Journal of Cleaner Production, 275, 123980.
Yitayew A, Abdulai A, Yigezu Y A, Deneke T T, Kassie G T. 2021. Impact of agricultural extension services on the adoption of improved wheat variety in Ethiopia: A cluster randomized controlled trial. World Development, 146, 105605.
Zhang Y, Long H, Li Y, Ge D, Tu S. 2020. How does off-farm work affect chemical fertilizer application? Evidence from China’s mountainous and plain areas. Land Use Policy, 99, 104848.
Zhang Y, Wang L, Duan Y. 2016. Agricultural information dissemination using ICTs: A review and analysis of information dissemination models in China. Information Processing in Agriculture, 3, 17–29.
Zhang Y, Zhang Z. 2016. Study on difference between demand willingness and choice behavior of peasant household’s production links. outsourcing - based on empirical study on rice production data from Jiangsu and Jiangxi provinces Journal of Huazhong Agricultural University (Social Science Edition), 122, 9–14, 134. (in Chinese)

[1] WANG Ge, Madison T PLASTER, Bai Yun-li, LIU Cheng-fang. Consumers’ experiences and preferences for plant-based meat food: Evidence from a choice experiment in four cities of China[J]. >Journal of Integrative Agriculture, 2023, 22(1): 306-319.
No Suggested Reading articles found!