Effectiveness of Information Acquisition via the Internet in Standardizing the Use of Antimicrobials by Hog Farmers: Insights from China
Abstract
:1. Introduction
2. Theoretical Analysis and Research Hypothesis
3. Data and Methods
3.1. Data Source
3.2. Variable Selection
3.2.1. Dependent Variable
3.2.2. Independent Variable
3.2.3. Control Variables
3.3. Model Setting
3.3.1. IV-Heckman Model
3.3.2. Mediating Method
4. Results and Discussion
4.1. Influence of the Internet on the Standardized Use of Antimicrobials by Farmers
4.2. Empirical Analysis of Induction Mechanism
4.2.1. Test Results of the Information Supply Mechanism
4.2.2. Test Results of the Information-Sharing Mechanism
4.2.3. Test Results of the Information Feedback Mechanism
4.3. Heterogeneity Analysis Based on the Age and Breeding Time
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Kuppusamy, S.; Kakarla, D.; Venkateswarlu, K.; Megharaj, M.; Yoon, Y.E.; Lee, Y.B. Veterinary antibiotics (VAs) contamination as a global agro-ecological issue: A critical view. Agric. Ecosyst. Environ. 2018, 257, 47–59. [Google Scholar] [CrossRef]
- Liebana, E.; Carattoli, A.; Coque, T.M.; Hasman, H.; Magiorakos, A.P.; Mevius, D.; Peixe, L.; Poirel, L.; Schuepbach-Regula, G.; Torneke, K.; et al. Public health risks of enterobacterial isolates producing extended-spectrum $β$-lactamases or AmpC $β$-lactamases in food and food-producing animals: An EU perspective of epidemiology, analytical methods, risk factors, and control options. Clin. Infect. Dis. 2013, 56, 1030–1037. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Du, L.; Liu, W. Occurrence, fate, and ecotoxicity of antibiotics in agro-ecosystems. A review. Agron. Sustain. Dev. 2012, 32, 309–327. [Google Scholar] [CrossRef] [Green Version]
- Michael, C.A.; Dominey-Howes, D.; Labbate, M. The antimicrobial resistance crisis: Causes, consequences, and management. Front. Public Health 2014, 2, 145. [Google Scholar] [CrossRef]
- Smith, R.D.; Keogh-Brown, M.R.; Barnett, T. Estimating the economic impact of pandemic influenza: An application of the computable general equilibrium model to the UK. Soc. Sci. Med. 2011, 73, 235–244. [Google Scholar] [CrossRef]
- Sneeringer, S.; Macdonald, J.; Key, N.; McBride, W.; Mathews, K. Economics of Antibiotic Use in U.S. Livestock Production; United States Department of Agriculture, Economic Research Service: Washington, DC, USA, 2015.
- Aarestrup, F.M. The livestock reservoir for antimicrobial resistance: A personal view on changing patterns of risks, effects of interventions and the way forward. Philos. Trans. R. Soc. B Biol. Sci. 2015, 370, 20140085. [Google Scholar] [CrossRef] [Green Version]
- Si, R.; Yu, X.; Liu, M.; Qian, L. Can withdrawal period system reduce veterinary antibacterial drugs overused? Evidence from pig farmers in Hebei, Shandong, Henan, and Hubei Provinces of China. J. Agrotech. Econ. 2023, 6, 115–132. [Google Scholar]
- Callens, B.; Persoons, D.; Maes, D.; Laanen, M.; Postma, M.; Boyen, F.; Haesebrouck, F.; Butaye, P.; Catry, B.; Dewulf, J. Prophylactic and metaphylactic antimicrobial use in Belgian fattening pig herds. Prev. Vet. Med. 2012, 106, 53–62. [Google Scholar] [CrossRef]
- Feola, G.; Lerner, A.M.; Jain, M.; Montefrio, M.J.F.; Nicholas, K.A. Researching farmer behaviour in climate change adaptation and sustainable agriculture: Lessons learned from five case studies. J. Rural Stud. 2015, 39, 74–84. [Google Scholar] [CrossRef]
- Rojo-Gimeno, C.; Dewulf, J.; Maes, D.; Wauters, E. A systemic integrative framework to describe comprehensively a swine health system, Flanders as an example. Prev. Vet. Med. 2018, 154, 30–46. [Google Scholar] [CrossRef]
- Tildesley, M.J.; Smith, G.; Keeling, M.J. Modeling the spread and control of foot-and-mouth disease in Pennsylvania following its discovery and options for control. Prev. Vet. Med. 2012, 104, 224–239. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Si, R.; Zhang, X.; Yao, Y.; Liu, L.; Lu, Q. Influence of contract commitment system in reducing information asymmetry, and prevention and control of livestock epidemics: Evidence from pig farmers in China. One Health 2021, 13, 100302. [Google Scholar] [CrossRef] [PubMed]
- Mevius, D.; Heederik, D. Reduction of antibiotic use in animals “let’s go Dutch”. J. Fur Verbraucherschutz Und Leb. 2014, 9, 177–181. [Google Scholar] [CrossRef] [Green Version]
- Postma, M.; Vanderhaeghen, W.; Sarrazin, S.; Maes, D.; Dewulf, J. Reducing antimicrobial usage in pig production without jeopardizing production parameters. Zoonoses Public Health 2017, 64, 63–74. [Google Scholar] [CrossRef] [PubMed]
- Gigante, A.; Atterbury, R.J. Veterinary use of bacteriophage therapy in intensively-reared livestock. Virol. J. 2019, 16, 10312. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Caudell, M.A.; Dorado-Garcia, A.; Eckford, S.; Creese, C.; Byarugaba, D.K.; Afakye, K.; Chansa-Kabali, T.; Fasina, F.O.; Kabali, E.; Kiambi, S.; et al. Towards a bottom-up understanding of antimicrobial use and resistance on the farm: A knowledge, attitudes, and practices survey across livestock systems in five African countries. PLoS ONE 2020, 15, e0220274. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dyar, O.J.; Yin, J.; Ding, L.; Wikander, K.; Zhang, T.; Sun, C.; Wang, Y.; Greko, C.; Sun, Q.; Lundborg, C.S. Antibiotic use in people and pigs: A One Health survey of rural residents’ knowledge, attitudes and practices in Shandong province, China. J. Antimicrob. Chemother. 2018, 73, 2893–2899. [Google Scholar] [CrossRef]
- Dyar, O.J.; Zhang, T.; Peng, Y.; Sun, M.; Sun, C.; Yin, J.; Ding, L.; Sun, C.; Wang, Y.; Sun, Q.; et al. Knowledge, attitudes and practices relating to antibiotic use and antibiotic resistance among backyard pig farmers in rural Shandong province, China. Prev. Vet. Med. 2020, 175, 104858. [Google Scholar] [CrossRef]
- Coyne, L.; Beningo, C.; Giang, V.N.; Huong, L.Q.; Kalprividh, W.; Padungtod, P.; Patrick, I.; Ngoc, P.T.; Rushton, J. Exploring the socioeconomic importance of antimicrobial use in the small-scale pig sector in vietnam. Antibiotics 2020, 9, 299. [Google Scholar] [CrossRef]
- Kim, D.; Saegerman, C.; Douny, C.; Dinh, T.; Vu, B. First survey on the use of antibiotics in pig and poultry production in the red river delta region of vietnam. Food Public Health 2013, 3, 247–256. [Google Scholar]
- Zhou, X.; Wang, J.; Lu, C.; Liao, Q.; Gudda, F.O.; Ling, W. Antibiotics in animal manure and manure-based fertilizers: Occurrence and ecological risk assessment. Chemosphere 2020, 255, 127006. [Google Scholar] [CrossRef] [PubMed]
- Callens, B.; Faes, C.; Maes, D.; Catry, B.; Boyen, F.; Francoys, D.; De Jong, E.; Haesebrouck, F.; Dewulf, J. Presence of antimicrobial resistance and antimicrobial use in sows are risk factors for antimicrobial resistance in their offspring. Microb. Drug Resist. 2015, 21, 50–58. [Google Scholar] [CrossRef] [PubMed]
- Deng, Z.; Lam, T.J.G.M.; Hogeveen, H.; Spaninks, M.; Heij, N.; Postema, M.; van Werven, T.; Koop, G. Antimicrobial use and farmers’ attitude toward mastitis treatment on dairy farms with automatic or conventional milking systems. J. Dairy Sci. 2020, 103, 7302–7314. [Google Scholar] [CrossRef] [PubMed]
- Mwambi, M.; Depenbusch, L.; Bonnarith, U.; Sotelo-Cardona, P.; Kieu, K.; di Tada, N.; Srinivasan, R.; Schreinemachers, P. Can phone text messages promote the use of integrated pest management? A study of vegetable farmers in Cambodia. Ecol. Econ. 2023, 204, 107650. [Google Scholar] [CrossRef]
- Benavides, J.A.; Streicker, D.G.; Gonzales, M.S.; Rojas-Paniagua, E.; Shiva, C. Knowledge and use of antibiotics among low-income small-scale farmers of Peru. Prev. Vet. Med. 2021, 189, 105287. [Google Scholar] [CrossRef]
- Yan, J.; Su, J. Application of information technology to agricultural knowledge diffusion. Sci. Res. Manag. 2000, 21, 49–55. [Google Scholar]
- Ankrah Twumasi, M.; Jiang, Y.; Asante, D.; Addai, B.; Akuamoah-Boateng, S.; Fosu, P. Internet use and farm households food and nutrition security nexus: The case of rural Ghana. Technol. Soc. 2021, 65, 101592. [Google Scholar] [CrossRef]
- Xie, H.; Zhang, J.; Shao, J. Difference in the influence of internet use on the relative poverty among farmers with different income structures. Econ. Anal. Policy 2023, 78, 561–570. [Google Scholar] [CrossRef]
- Khan, N.; Ray, R.L.; Zhang, S.; Osabuohien, E.; Ihtisham, M. Influence of mobile phone and internet technology on income of rural farmers: Evidence from Khyber Pakhtunkhwa Province, Pakistan. Technol. Soc. 2022, 68, 101866. [Google Scholar] [CrossRef]
- Min, S.; Peng, J.; Qing, P. Does internet use improve food safety behavior among rural residents? Food Control 2022, 139, 109060. [Google Scholar] [CrossRef]
- Yadav, V.S.; Singh, A.R.; Raut, R.D.; Mangla, S.K.; Luthra, S.; Kumar, A. Exploring the application of Industry 4.0 technologies in the agricultural food supply chain: A systematic literature review. Comput. Ind. Eng. 2022, 169, 108304. [Google Scholar] [CrossRef]
- Waldron, S.; Brown, C.; Longworth, J. A critique of high-value supply chains as a means of modernising agriculture in China: The case of the beef industry. Food Policy 2010, 35, 479–487. [Google Scholar] [CrossRef]
- Alshehri, D.M. Blockchain-assisted internet of things framework in smart livestock farming. Internet Things 2023, 22, 100739. [Google Scholar] [CrossRef]
- Albors-Garrigós, J.; Hervas-Oliver, J.L.; Márquez, P. Internet and mature industries. Its role in the creation of value in the supply chain. The case of tile ceramic manufacturers and distributors in Spain. Int. J. Inf. Manag. 2009, 29, 476–482. [Google Scholar] [CrossRef]
- Fuchigami, H.Y.; Tuni, A.; Barbosa, L.Q.; Severino, M.R.; Rentizelas, A. Supporting Brazilian smallholder farmers decision making in supplying institutional markets. Eur. J. Oper. Res. 2021, 295, 321–335. [Google Scholar] [CrossRef]
- Ullah, A.; Arshad, M.; Kächele, H.; Zeb, A.; Mahmood, N.; Müller, K. Socio-economic analysis of farmers facing asymmetric information in inputs markets: Evidence from the rainfed zone of Pakistan. Technol. Soc. 2020, 63, 101405. [Google Scholar] [CrossRef]
- Li, L.; Paudel, K.P.; Guo, J. Understanding Chinese farmers’ participation behavior regarding vegetable traceability systems. Food Control 2021, 130, 108325. [Google Scholar] [CrossRef]
- Liao, P.A.; Chang, H.H.; Chang, C.Y. Why is the food traceability system unsuccessful in Taiwan? Empirical evidence from a national survey of fruit and vegetable farmers. Food Policy 2011, 36, 686–693. [Google Scholar] [CrossRef]
- Sun, R.; Zhou, J. Overuse of veterinary drugs by farmers based on damage control model. J. Agrotech. Econ. 2015, 21, 32–40. [Google Scholar] [CrossRef]
- Kuhn, P.; Skuterud, M. Internet job search and unemployment durations. Am. Econ. Rev. 2004, 94, 218–232. [Google Scholar] [CrossRef] [Green Version]
- Atasoy, H. The effects of broadband Internet expansion on labor market outcomes. Ind. Labor Relat. Rev. 2013, 66, 315–345. [Google Scholar] [CrossRef]
- Liu, Z.; Wang, M.; Jia, L.; Qiao, J. Logic mechanism and countermeasures of pork quality and safety problems under the framework cof traceability and accountability: A survey based on the perspective of whole industry chain. J. China Agric. Uinversity 2018, 23, 206–221. [Google Scholar]
- Si, R.; Yao, Y.; Zhang, X.; Liu, M.; Lu, Q.; Fahad, S. Assessing the role of internet in reducing overuse of livestock antibiotics by utilizing combination of novel damage control and 2-SLS approaches: Risk, responsibility, and action. Prev. Vet. Med. 2022, 208, 105754. [Google Scholar] [CrossRef]
- Xu, X.; Xu, C.; Li, C. The effect of relationship network on farmers’ forestland inflow behavior: An analysis based on survey data from Zhejiang province. Chin. Rural Econ. 2018, 9, 62–78. [Google Scholar]
- Wen, Z.; Ye, B. Analyses of mediating effects: The development of methods and models. Adv. Psychol. Sci. 2014, 22, 731–745. [Google Scholar] [CrossRef]
- Sun, G.; Wang, Y.; Li, Q. The influence of green credits on credit risks of commercial banks. Financ. Forum 2017, 11, 31–40. [Google Scholar] [CrossRef]
- Talanow, K.; Topp, E.N.; Loos, J.; Martín-l, B. Farmers’ perceptions of climate change and adaptation strategies in South Africa’s Western Cape. J. Rural Stud. 2021, 81, 203–219. [Google Scholar] [CrossRef]
- Zhao, Q.; Pan, Y.; Xia, X. Internet can do help in the reduction of pesticide use by farmers: Evidence from rural China. Environ. Sci. Pollut. Res. 2021, 28, 2063–2073. [Google Scholar] [CrossRef]
- Dankar, I.; Hassan, H.; Serhan, M. Knowledge, attitudes, and perceptions of dairy farmers regarding antibiotic use: Lessons from a developing country. J. Dairy Sci. 2022, 105, 1519–1532. [Google Scholar] [CrossRef]
- Nhung, N.T.; Cuong, N.V.; Thwaites, G.; Carrique-Mas, J. Antimicrobial usage and antimicrobial resistance in animal production in Southeast Asia: A review. Antibiotics 2016, 5, 26. [Google Scholar] [CrossRef] [Green Version]
- Garcia, J.F.; Diez, M.J.; Sahagun, A.M.; Diez, R.; Sierra, M.; Garcia, J.J.; Fernandez, M.N. The online sale of antibiotics for veterinary use. Animals 2020, 10, 503. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lekagul, A.; Tangcharoensathien, V.; Yeung, S. Patterns of antibiotic use in global pig production: A systematic review. Vet. Anim. Sci. 2019, 7, 100058. [Google Scholar] [CrossRef] [PubMed]
- Zhong, Y.; Huang, Z.; Wu, L. Safe production behavior of pig farmers and analysis of its influencing factors. Chin. J. Anim. Sci. 2016, 52, 1–11. [Google Scholar]
- Shao, Y.; Wang, Y.; Yuan, Y.; Xie, Y. A systematic review on antibiotics misuse in livestock and aquaculture and regulation implications in China. Sci. Total Environ. 2021, 798, 149205. [Google Scholar] [CrossRef] [PubMed]
- Zheng, Y.; Fan, Q.; Jia, W. How much did Internet use promote grain production?—Evidence from a survey of 1242 farmers in 13 provinces in China. Foods 2022, 11, 1389. [Google Scholar] [CrossRef]
- Nie, P.; Ma, W.; Sousa-Poza, A. The relationship between smartphone use and subjective well-being in rural China. Electron. Commer. Res. 2021, 21, 983–1009. [Google Scholar] [CrossRef]
- Ji, C.; Jin, S.; Wang, H.; Ye, C. Estimating effects of cooperative membership on farmers’safe production behaviors: Evidence from pig sector in China. Food Policy 2019, 83, 231–245. [Google Scholar] [CrossRef]
- Li, H.; Liu, Y.; Zhao, X.; Zhang, L.; Yuan, K. Estimating effects of cooperative membership on farmers’ safe production behaviors: Evidence from the rice sector in China. Environ. Sci. Pollut. Res. 2021, 28, 25400–25418. [Google Scholar] [CrossRef]
- Si, R.; Yao, Y.; Liu, X.; Lu, Q.; Liu, M. Role of risk perception and government regulation in reducing over-utilization of veterinary antibiotics: Evidence from hog farmers of China. One Health 2022, 15, 100448. [Google Scholar] [CrossRef]
- Abdullahi, K.A.; Oladele, O.I.; Akinyemi, M. Attitude, knowledge and constraints associated with the use of mobile phone applications by farmers in North West Nigeria. J. Agric. Food Res. 2021, 6, 100212. [Google Scholar] [CrossRef]
- Ma, Q.; Zheng, S.; Deng, P. Impact of Internet use on farmers’ organic fertilizer application behavior under the climate change context: The role of social network. Land 2022, 11, 1601. [Google Scholar] [CrossRef]
- Qian, J.; Fan, B.; Wu, X.; Han, S.; Liu, S.; Yang, X. Comprehensive and quantifiable granularity: A novel model to measure agro-food traceability. Food Control 2017, 74, 98–106. [Google Scholar] [CrossRef]
- Wang, J.; Yue, H.; Zhou, Z. An improved traceability system for food quality assurance and evaluation based on fuzzy classification and neural network. Food Control 2017, 79, 262–370. [Google Scholar] [CrossRef]
- Hu, X.; Zhong, F. The impact of rural population aging on food production. Chin. Rural Econ. 2012, 7, 29–39. [Google Scholar]
- Li, F.; Zang, D.; Chandio, A.A.; Yang, D.; Jiang, Y. Farmers’ adoption of digital technology and agricultural entrepreneurial willingness: Evidence from China. Technol. Soc. 2023, 73, 102253. [Google Scholar] [CrossRef]
Variables | Classification | Ratio % |
---|---|---|
Gender | Male | 73.4% |
Female | 26.5% | |
Age | <36 years | 3.85% |
36–60 | 62.52% | |
>60 | 33.63% | |
Educational time | 0–6 years | 41.93% |
7–9 | 33.93% | |
10–12 | 10.22% | |
>12 | 0.59% | |
Breeding time | <5 years | 25.63% |
5–15 | 62.37% | |
>15 | 12.00% | |
Breeding scale | <150 heads | 60.59% |
150–300 | 24.30% | |
>300 | 15.11% | |
Male laborers | <0.3 | 1.33% |
0.3–0.6 | 22.67% | |
>0.6 | 76.00% |
Variables | Variable Assignment | Sample Mean | Mean Difference (A − B) | |
---|---|---|---|---|
IAI Group (A) | Non-IAI Group (B) | |||
The decision of standardized use | Standardized use = 1, non-standardized use = 0 | 0.326 | 0.213 | −0.113 *** |
Degree of standardized use | The ratio of the payment amount for standardized use of antimicrobials to the number of hogs | 12.536 | 12.125 | −0.411 *** |
Internet | Have you obtained knowledge of veterinary antimicrobial use through a mobile phone or computer? (Yes = 1, no = 0) | —— | —— | —— |
Gender | Male = 1, female = 0 | 0.716 | 0.748 | 0.032 |
Age | Actual age (year) | 54.819 | 56.649 | 1.830 ** |
Educational time | Actual educational time (year) | 5.965 | 5.941 | −0.023 |
Organizational participation | Joining = 1, non-joining = 0 | 0.564 | 0.601 | 0.037 |
Peer effect | Is your standardized use of antimicrobials influenced by the behavior of other farmers? (No effect at all = 1—very significant effect = 5) | 3.486 | 3.422 | −0.063 |
Breeding time | Time spent breeding hogs (year) | 8.535 | 8.608 | 0.073 |
Family laborers | Actual family laborers (people) | 2.972 | 3.254 | 0.283 * |
Transaction mode | Vertical order transaction = 1, loose market transaction = 0 | 0.227 | 0.410 | 0.183 *** |
Breeding mode | Cooperative, family farm or company breeding = 1, small family breeding = 0 | 0.439 | 0.401 | 0.038 |
Were you in Henan? | Yes = 1, no = 0 | 0.280 | 0.255 | 0.025 |
Were you in Shandong? | Yes = 1, no = 0 | 0.234 | 0.199 | 0.036 |
Were you in Hebei? | Yes = 1, no = 0 | 0.214 | 0.298 | −0.084 ** |
Variables | Regression 1 | Regression 2 | Regression 3 | Regression 4 | Regression 5 | Regression 6 |
---|---|---|---|---|---|---|
Decision | Degree | Decision | Degree | Decision | Degree | |
IV-Probit | 2SLS | Heckman | IV-Heckman | |||
IAI | 0.272 *** (0.092) | −0.104 * (0.060) | 0.332 *** (0.113) | −0.301 * (0.171) | 0.335 *** (0.114) | −0.372 ** (0.169) |
Gender | −0.054 (0.094) | −0.081 (0.186) | 0.024 (0.122) | −0.036 (0.165) | 0.027 (0.139) | −0.012 (0.163) |
Age | −0.009 ** (0.003) | −0.013 * (0.007) | −0.003 (0.004) | 0.000 (0.007) | −0.003 (0.005) | −0.001 (0.007) |
Educational time | 0.009 (0.010) | 0.017 (0.020) | 0.013 (0.014) | −0.005 (0.026) | 0.013 (0.055) | −0.001 (0.022) |
Organizational participation | −0.013 (0.09) | 0.378 ** (0.019) | −0.022 ** (0.011) | 0.002 (0.036) | −0.023 ** (0.011) | −0.012 (0.020) |
Peer effect | −0.026 (0.024) | −0.066 (0.051) | −0.022 (0.028) | −0.050 (0.052) | 0.021 (0.031) | −0.046 (0.042) |
Breeding time | 0.089 (0.085) | −0. 110 (0.165) | 0.078 (0.108) | −0.063 (0.187) | 0.076 (0.115) | −0.002 *** (0.159) |
Family laborers | 0.011 (0.033) | 0.002 (0.067) | 0.004 (0.041) | −0.013 (0.057) | 0.005 (0.045) | −0.029 (0.056) |
Transaction mode | 0.018 * (0.108) | −0.139 (0.234) | 0.435 *** (0.112) | −0.229 (0.629) | 0.433 *** (0.125) | −0.130 (0.337) |
Breeding mode | 0.119 *** (0.031) | −0.265 *** (0.055) | 0.227 *** (0.037) | −0.068 (0.326) | 0.228 *** (0.037) | −0.074 (0.178) |
Were you in Henan? | −0.057 (0.121) | −0.327 (0.259) | −0.154 (0.154) | −0.131 (0.317) | −0.155 (0.166) | −0.117 (0.078) |
Were you in Shandong? | −0.032 (0.128) | −0.527 ** (0.260) | 0.045 (0.158) | −0.537 ** (0.239) | 0.051 (0.169) | −0.587 *** (0.175) |
Were you in Hebei? | 0.027 ** (0.121) | 0.104 (0.252) | 0.136 (0.156) | −0.352 (0.029) | 0.135 (0.161) | 0.000 (0.000) |
Distance between the enclosure and the veterinary service station | —— | —— | 0.120 *** (0.043) | —— | 0.121 *** (0.043) | —— |
IMR value | —— | —— | 8.25 *** | 8.12 *** | ||
DWH test value | 23.14 ** | 20.69 ** | —— | 18.25 ** | ||
The T value of the tool variable | 5.12 *** | 5.29 *** | —— | 5.25 *** | ||
F value in stage one | 121.56 | 102.56 | —— | 229.5 |
Variables | Regression 7 | Regression 8 | Regression 9 | Regression 10 | Regression 11 | Regression 12 | Regression 13 | Regression 14 | Regression 15 |
---|---|---|---|---|---|---|---|---|---|
Information Supply Mechanism | Decision | Degree | Information-Sharing Mechanism | Decision | Degree | Information Feedback Mechanism | Decision | Degree | |
IAI | 1.287 ** (0.570) | 0.334 *** (0.114) | −0.201 *** (0.021) | 1.190 * (0.661) | 0.331 *** (0.114) | −0.201 *** (0.032) | 1.170 ** (0.303) | 0.322 *** (0.115) | −0.160 *** (0.023) |
Information supply mechanism | —— | 0.017 (0.020) | −0.014 (0.030) | —— | —— | —— | —— | —— | —— |
Information-sharing mechanism | —— | —— | —— | —— | 0.123 ** (0.058) | −0.089 *** (0.019) | —— | —— | —— |
Information feedback mechanism | —— | —— | —— | —— | —— | —— | —— | 0.062 *** (0.021) | −0.069 *** (0.019) |
Control variables | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
Sample size | 675 | 675 | 188 | 675 | 675 | 188 | 675 | 675 | 188 |
Variables | Under 36 Years Old | 36–60 Years Old | Over 60 Years Old | |||
---|---|---|---|---|---|---|
Regression 16 | Regression 17 | Regression 18 | Regression 19 | Regression 20 | Regression 21 | |
Decision | Degree | Decision | Degree | Decision | Degree | |
IAI | 0.378 *** (0.145) | −0.579 ** (0.239) | 0.374 * (0.192) | −0.276 (0.386) | 0.129 (0.211) | −0.240 (0.367) |
Control variables | controlled | controlled | controlled | controlled | Controlled | controlled |
Sample size | 27 | 5 | 421 | 125 | 227 | 58 |
Variables | Less than 5 Years | 5–15 Years | More than 15 Years | |||
---|---|---|---|---|---|---|
Regression 22 | Regression 23 | Regression 24 | Regression 25 | Regression 26 | Regression 27 | |
Decision | Degree | Decision | Degree | Decision | Degree | |
IAI | 0.036 *** (0.004) | −0.099 (0.078) | 0.049 (0.101) | −0.005 (0.016) | −0.038 (0.276) | −0.028 (0.093) |
Control variables | controlled | controlled | controlled | controlled | Controlled | controlled |
Sample size | 173 | 39 | 421 | 119 | 81 | 30 |
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Si, R.; Yao, Y.; Liu, M. Effectiveness of Information Acquisition via the Internet in Standardizing the Use of Antimicrobials by Hog Farmers: Insights from China. Agriculture 2023, 13, 1586. https://doi.org/10.3390/agriculture13081586
Si R, Yao Y, Liu M. Effectiveness of Information Acquisition via the Internet in Standardizing the Use of Antimicrobials by Hog Farmers: Insights from China. Agriculture. 2023; 13(8):1586. https://doi.org/10.3390/agriculture13081586
Chicago/Turabian StyleSi, Ruishi, Yumeng Yao, and Mingyue Liu. 2023. "Effectiveness of Information Acquisition via the Internet in Standardizing the Use of Antimicrobials by Hog Farmers: Insights from China" Agriculture 13, no. 8: 1586. https://doi.org/10.3390/agriculture13081586
APA StyleSi, R., Yao, Y., & Liu, M. (2023). Effectiveness of Information Acquisition via the Internet in Standardizing the Use of Antimicrobials by Hog Farmers: Insights from China. Agriculture, 13(8), 1586. https://doi.org/10.3390/agriculture13081586