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Li L, Min X, Guo J, Wu F. The influence mechanism analysis on the farmers' intention to adopt Internet of Things based on UTAUT-TOE model. Sci Rep 2024; 14:15016. [PMID: 38951536 PMCID: PMC11217386 DOI: 10.1038/s41598-024-65415-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 06/20/2024] [Indexed: 07/03/2024] Open
Abstract
Internet of Things (IoT) technologies are widely recognized as one of the most important infrastructures for economic development and technological innovation. By analyzing the influencing factors of vegetable farmers' intention to adopt agricultural IoT, it helps to formulate effective IoT promotion policies and accelerate the realization of agricultural modernization. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology-Organization-Environment (TOE) theory, this study constructed for the first time a mechanism model of the influence of vegetable farmers' intention to adopt IoT, expanding the scope of current research on agricultural IoT and revealing the intrinsic influence mechanism of farmers' adoption of IoT technologies. In this study, 357 quantitative data were obtained by a questionnaire survey, and structural equation modeling was used to test the direct and indirect effects of each factor on vegetable farmers' intention to adopt IoT. The results show that almost all variables in TOE have significant direct impacts on the intention, while no variables in UTAUT have significant direct impacts. Among variables in TOE, government support and complexity are the two most important elements influencing the intention. Although the interactions among variables in TOE and UTAUT are also found, the indirect effects of variables are non-significant. Therefore, it is proposed to reduce the complexity of operation and use of IoT technologies; improve rural information infrastructure and compatibility of IoT platforms and devices; and governments should increase subsidies, and incentives to promote the use of IoT in agriculture and agricultural practices.
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Affiliation(s)
- Lianying Li
- School of Economics and Management & Rural Revitalization Strategy Research Institute, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Xin Min
- School of Economics and Management & Rural Revitalization Strategy Research Institute, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Jinyong Guo
- School of Economics and Management & Rural Revitalization Strategy Research Institute, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Feng Wu
- School of Economics and Management & Rural Revitalization Strategy Research Institute, Jiangxi Agricultural University, Nanchang, 330045, China.
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von Keyserlingk MAG, Hendricks J, Ventura B, Weary DM. Swine industry perspectives on the future of pig farming. Anim Welf 2024; 33:e7. [PMID: 38510419 PMCID: PMC10951666 DOI: 10.1017/awf.2024.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/21/2023] [Accepted: 12/08/2023] [Indexed: 03/22/2024]
Abstract
Understanding the views of those working along the value chain reliant on livestock is an important step in supporting the transition towards more sustainable farming systems. We recruited 31 delegates attending the Pig Welfare Symposium held in the United States to participate in one of six focus group discussions on the future of pig farming. Each of these six group discussions was subjected to a thematic analysis that identified four themes: (1) technical changes on the farm; (2) farm and industry culture; (3) the farm-public interface; and (4) sustainability. The results of this study illustrate the complexity and diversity of views of those working along the associated value chain within the swine industry. Participants spent the majority of their time discussing current challenges, including technical challenges on the farm and public perception of pig farms. Participants were more hesitant to discuss future issues, but did engage on the broader issue of sustainability, focusing upon economic and environmental aspects.
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Affiliation(s)
- Marina AG von Keyserlingk
- Animal Welfare Program, Faculty of Land and Food Systems, The University of British Columbia, 2357 Main Mall, Vancouver, BC, CanadaV6T 1Z4
| | - Jillian Hendricks
- Animal Welfare Program, Faculty of Land and Food Systems, The University of British Columbia, 2357 Main Mall, Vancouver, BC, CanadaV6T 1Z4
| | - Beth Ventura
- Department of Life Sciences, University of Lincoln, Lincoln, LincsLN6 7DL, UK
| | - Daniel M Weary
- Animal Welfare Program, Faculty of Land and Food Systems, The University of British Columbia, 2357 Main Mall, Vancouver, BC, CanadaV6T 1Z4
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Sharifuzzaman M, Mun HS, Ampode KMB, Lagua EB, Park HR, Kim YH, Hasan MK, Yang CJ. Technological Tools and Artificial Intelligence in Estrus Detection of Sows-A Comprehensive Review. Animals (Basel) 2024; 14:471. [PMID: 38338113 PMCID: PMC10854728 DOI: 10.3390/ani14030471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/30/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved.
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Affiliation(s)
- Md Sharifuzzaman
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Department of Animal Science and Veterinary Medicine, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
| | - Hong-Seok Mun
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Department of Multimedia Engineering, Sunchon National University, Suncheon 57922, Republic of Korea
| | - Keiven Mark B. Ampode
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Department of Animal Science, College of Agriculture, Sultan Kudarat State University, Tacurong 9800, Philippines
| | - Eddiemar B. Lagua
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, Suncheon 57922, Republic of Korea
| | - Hae-Rang Park
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, Suncheon 57922, Republic of Korea
| | - Young-Hwa Kim
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Chonnam National University, Gwangju 61186, Republic of Korea;
| | - Md Kamrul Hasan
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Department of Poultry Science, Sylhet Agricultural University, Sylhet 3100, Bangladesh
| | - Chul-Ju Yang
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, Suncheon 57922, Republic of Korea
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Pienwisetkaew T, Wongsaichia S, Pinyosap B, Prasertsil S, Poonsakpaisarn K, Ketkaew C. The Behavioral Intention to Adopt Circular Economy-Based Digital Technology for Agricultural Waste Valorization. Foods 2023; 12:2341. [PMID: 37372552 DOI: 10.3390/foods12122341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/05/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Thailand generates considerable amounts of agricultural food waste. This research focuses on the manufacturing and retail agricultural food system in the northeastern region of Thailand. Our study aimed to investigate the user segments and factors that influence users' behavioral intentions to utilize mobile technology for agricultural waste valorization. This study is based on the Unified Theory of the Adoption and Utilization of Technology (UTAUT2). In order to classify these segments, we performed a cluster analysis using demographic variables: gender, age, and income. In addition, the researchers employed a method known as multigroup structural equation modeling to determine and contrast the users' behavioral intentions. The results showed two types of users: (1) older users with various income ranges, and (2) younger users with a low-income range. Explicitly, age and income were the significant variables for the demographic segmentation, but gender was not. The results also revealed that social influence, price value, and trust highly affected the behavioral intentions of older and various-income users, but did not influence younger and low-income users. However, privacy strongly affected the behavioral intentions in the younger segment, but not those in the older one. Lastly, habit or regularity influenced the behavioral intentions of users in both segments. This study highlights implications for how developers and practitioners might adapt their platform strategies using a circular agricultural platform and user behaviors.
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Affiliation(s)
- Teerapong Pienwisetkaew
- International College, Khon Kaen University, Khon Kaen 40002, Thailand
- Center for Sustainable Innovation and Society, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Sasichakorn Wongsaichia
- International College, Khon Kaen University, Khon Kaen 40002, Thailand
- Center for Sustainable Innovation and Society, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Benyapa Pinyosap
- International College, Khon Kaen University, Khon Kaen 40002, Thailand
| | | | | | - Chavis Ketkaew
- International College, Khon Kaen University, Khon Kaen 40002, Thailand
- Center for Sustainable Innovation and Society, Khon Kaen University, Khon Kaen 40002, Thailand
- Faculty of Business and Economics, University of Antwerp, 2000 Antwerpen, Belgium
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Liu Z, Geng N, Yu Z. Does a Traceability System Help to Regulate Pig Farm Households' Veterinary Drug Use Behavior? Evidence from Pig Farms in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:11879. [PMID: 36231180 PMCID: PMC9564818 DOI: 10.3390/ijerph191911879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/15/2022] [Accepted: 09/18/2022] [Indexed: 06/16/2023]
Abstract
In China, there is a renewed interest in traceability systems as an efficient tool to guarantee pork safety. One of the pathways through which a traceability system can benefit consumers is by easing information asymmetry. However, past literature on the traceability system in China pays more attention to theoretical analysis and less to empirical analysis. Using a large-scale survey of pig farms in China, we investigate the effects influencing farmers' participation in the traceability system. Findings show that a traceability system can influence the safety of pork indirectly through its impacts on farmers' production behaviors. Another important finding is that unsafe pork is a result of non-standard use of veterinary drugs, and the traceability system works well for farmers by pushing them to take stricter safety measurements.
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Affiliation(s)
- Zengjin Liu
- Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
| | - Ning Geng
- School of Public Administration, Shandong Normal University, Jinan 250014, China
| | - Zhuo Yu
- School of Management, Ocean University of China, Qingdao 266100, China
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Wang S, Jiang H, Qiao Y, Jiang S, Lin H, Sun Q. The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22176541. [PMID: 36080994 PMCID: PMC9460267 DOI: 10.3390/s22176541] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/22/2022] [Accepted: 08/27/2022] [Indexed: 05/05/2023]
Abstract
Pork accounts for an important proportion of livestock products. For pig farming, a lot of manpower, material resources and time are required to monitor pig health and welfare. As the number of pigs in farming increases, the continued use of traditional monitoring methods may cause stress and harm to pigs and farmers and affect pig health and welfare as well as farming economic output. In addition, the application of artificial intelligence has become a core part of smart pig farming. The precision pig farming system uses sensors such as cameras and radio frequency identification to monitor biometric information such as pig sound and pig behavior in real-time and convert them into key indicators of pig health and welfare. By analyzing the key indicators, problems in pig health and welfare can be detected early, and timely intervention and treatment can be provided, which helps to improve the production and economic efficiency of pig farming. This paper studies more than 150 papers on precision pig farming and summarizes and evaluates the application of artificial intelligence technologies to pig detection, tracking, behavior recognition and sound recognition. Finally, we summarize and discuss the opportunities and challenges of precision pig farming.
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Affiliation(s)
- Shunli Wang
- College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China
| | - Honghua Jiang
- College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China
| | - Yongliang Qiao
- Australian Centre for Field Robotics (ACFR), Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia
- Correspondence:
| | - Shuzhen Jiang
- College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai’an 271018, China
| | - Huaiqin Lin
- College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China
| | - Qian Sun
- College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China
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7
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Hassoun A, Alhaj Abdullah N, Aït-Kaddour A, Ghellam M, Beşir A, Zannou O, Önal B, Aadil RM, Lorenzo JM, Mousavi Khaneghah A, Regenstein JM. Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies. Crit Rev Food Sci Nutr 2022; 64:873-889. [PMID: 35950635 DOI: 10.1080/10408398.2022.2110033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.
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Affiliation(s)
- Abdo Hassoun
- Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France
- Syrian Academic Expertise (SAE), Gaziantep, Turkey
| | | | | | - Mohamed Ghellam
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Ayşegül Beşir
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Oscar Zannou
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Begüm Önal
- Gourmet International Ltd, Izmir, Turkey
| | - Rana Muhammad Aadil
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad, Pakistan
| | - Jose M Lorenzo
- Centro Tecnológico de la Carne de Galicia, Ourense, Spain
| | - Amin Mousavi Khaneghah
- Department of Fruit and Vegetable Product Technology, Prof. Wacław Dąbrowski Institute of Agricultural and Food Biotechnology - State Research Institute, Warsaw, Poland
| | - Joe M Regenstein
- Department of Food Science, Cornell University, Ithaca, New York, USA
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