Vol. 84 (2024)
Original Articles - Appraisal and rural economics

Simultaneous evaluation of dairy farmers’ behaviour and intention to adopt technological devices

PDF
  • Roberta Selvaggi,
  • Raffaele Zanchini,
  • Carla Zarbà,
  • Biagio Pecorino,
  • Gioacchino Pappalardo
Roberta Selvaggi
Department of Agriculture, Food and Environment, University of Catania
Raffaele Zanchini
Department of Agricultural, Forest and Food Sciences, University of Turin
Carla Zarbà
Department of Agriculture, Food and Environment, University of Catania
Biagio Pecorino
Department of Agriculture, Food and Environment, University of Catania
Gioacchino Pappalardo
Department of Agriculture, Food and Environment, University of Catania
DOI: https://doi.org/10.36253/aestim-15362

Published 2024-08-04

Keywords

  • Animal welfare,
  • Precision Livestock Farming,
  • Dairy cattle,
  • PLS-SEM,
  • Theory of Planned Behaviour

Copyright (c) 2024 Roberta Selvaggi, Raffaele Zanchini, Carla Zarbà, Biagio Pecorino, Gioacchino Pappalardo

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Funding data

Crossref
Scopus
Google Scholar
Europe PMC

Abstract

Society's awareness of livestock production conditions has increased interest in animal welfare (AW), prompting farmers to consider it in their strategies. However, the adoption of digital devices and sensors to ensure AW is still relatively low. The aim of this study was to assess simultaneously the stated behaviour and intention of dairy farmers towards adopting technological tools for AW. The extended Theory of Planned Behaviour (e-TPB) was selected as theoretical base. It is “extended” since new predictors are integrated in the standard framework of the TPB. The research questions were addressed using a partial least squares structural equation modelling. The findings suggest the existence of a gap between farmers' intentions and behaviour. Perceived Behavioural Control plays a significant role in behaviour, indicating the predominant influence of self-confidence in farmers' choices. Operating margin and technological specialization of the farms are significant predictors of farmers' behavior.

PDF

Metrics

Metrics Loading ...

References

  1. Abeni, F., Petrera F., & Galli, A. (2019). A survey of Italian dairy farmers’ propensity for precision livestock farming tools. Animals (Basel), 9(5), 202. DOI: https://doi.org/10.3390/ani9050202
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. DOI: https://doi.org/10.1016/0749-5978(91)90020-T
  3. Akbar, M. O., Shahaz khan, M. S., Ali, M. J., Hussain, A., Qaiser, G., Pasha, M., Missen, M. S., & Akhtar, N. (2020). IoT for development of smart dairy farming. Journal of Foody Quality, 2020, 4242805, 1–8. DOI: https://doi.org/10.1155/2020/4242805
  4. Akhigbe, B. I., Munir, K., Akinade, O., Akanbi, L., & Oyedele, L. O. (2021). IoT technologies for livestock management: a review of present status, opportunities, and future trends. Big data and cognitive computing, 5(1), 10. DOI: https://doi.org/10.3390/bdcc5010010
  5. Alonso, M. E., González-Montaña, J. R., & Lomillos, J. M. (2020). Consumers’ concerns and perceptions of farm animal welfare. Animals, 10(3), 385. DOI: https://doi.org/10.3390/ani10030385
  6. Aquilani, C., Confessore, A., Bozzi, R., Sirtori, F., & Pugliese, C. (2022). Precision livestock farming technologies in pasture-based livestock systems. Animal, 16(1), 100429. DOI: https://doi.org/10.1016/j.animal.2021.100429
  7. Bagheri, A., & Teymouri, A. (2022). Farmers’ intended and actual adoption of soil and water conservation practices. Agricultural Water Management, 259, 107244. DOI: https://doi.org/10.1016/j.agwat.2021.107244
  8. Banhazi, T. M., Lehr, H., Black, J. L., Crabtree, H., Schofield, P., & Tscharke, M. (2012). Precision livestock farming: an international review of scientific and commercial aspects. International Journal of Agricultural and Biological Engineering, 5(3), 1–9.
  9. Bartlett, J. E., Kotrlik, J. W., & Higgins, C. C. (2001). Organizational research: determining appropriate sample size in survey research. Information Technology, Learning, and Performance Journal, 19(1), 43–50. DOI: https://doi.org/10.5032/jae.2002.03001
  10. Berckmans, D. (2017). General introduction to precision livestock farming. Animal Frontiers, 7(1), 6–11. DOI: https://doi.org/10.2527/af.2017.0102
  11. Bianchi, M. C., Bava L., Sandrucci, A., Tangorra, F. M., Tamburini, A., Gislon, G., & Zucali, M. (2022). Diffusion of precision livestock farming technologies in dairy cattle farms. Animal, 16(11), 100650. DOI: https://doi.org/10.1016/j.animal.2022.100650
  12. Blanc, S., Massaglia, S., Borra, D., Mosso, A., & Merlino, V. M. (2020). Animal welfare and gender: a nexus in awareness and preference when choosing fresh beef meat?. Italian Journal of Animal Science, 19(1), 410–420. DOI: https://doi.org/10.1080/1828051X.2020.1747952
  13. Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach’s alpha. BMJ, 314(7080), 572. DOI: https://doi.org/10.1136/bmj.314.7080.572
  14. Borges, J. A. R, de Faria Domingues, C. H., Caldara, F. R., da Rosa, N. P., Senger, I., & Freire Guidolin, D. G. (2019). Identifying the factors impacting on farmers’ intention to adopt animal friendly practices. Preventive Veterinary Medicine, 170, 104718. DOI: https://doi.org/10.1016/j.prevetmed.2019.104718
  15. Borges, J. A. R., & Lansink, A. G. O. (2016). Identifying psychological factors that determine cattle farmers’ intention to use improved natural grassland. Journal of Environmental Psychology, 45, 89–96. DOI: https://doi.org/10.1016/j.jenvp.2015.12.001
  16. Chapa, J. M., Maschat, K., Iwersen, M., Baumgartner, J., & Drillich, M. (2020). Accelerometer systems as tools for health and welfare assessment in cattle and pigs–a review. Behavioural Processes, 181, 104262. DOI: https://doi.org/10.1016/j.beproc.2020.104262
  17. Chavas, J. P., & Nauges, C. (2020). Uncertainty, learning, and technology adoption in agriculture. Applied Economic Perspectives and Policy, 42(1), 42–53. DOI: https://doi.org/10.1002/aepp.13003
  18. CLAL (2022). Italy: Monthly deliveries of cow milk. Available at: https://www.clal.it/en/?section=consegne_reg_it (accessed 28 April 2023).
  19. da Borso, F., Kic, P., & Kante, J. (2022). Analysis of management, labor and economics of milking systems in intensive goat farms. Agriculture, 12(4), 513. DOI: https://doi.org/10.3390/agriculture12040513
  20. Despotović, J., Rodić, V., & Caracciolo, F. (2019). Factors affecting farmers’ adoption of integrated pest management in Serbia: an application of the theory of planned behavior. Journal of Cleaner Production, 228, 1196–1205. DOI: https://doi.org/10.1016/j.jclepro.2019.04.149
  21. do Valle, P. O., & Assaker, G. (2016). Using partial least squares structural equation modeling in tourism research: a review of past research and recommendations for future applications. Journal of Travel Research, 55(6), 695–708. DOI: https://doi.org/10.1177/0047287515569779
  22. Dong, H., Zhang, Y., Chen, T., & Li, J. (2023). Acceptance intention and behavioral response to soil-testing formula fertilization technology: an empirical study of agricultural land in Shaanxi Province. International Journal of Environmental Research and Public Health, 20(2), 951. DOI: https://doi.org/10.3390/ijerph20020951
  23. Dorce, L. C., da Silva, M. C., Mauad, J. R. C., de Faria Domingues, C. H., & Borges, J. A. R. (2021). Extending the theory of planned behavior to understand consumer purchase behavior for organic vegetables in Brazil: the role of perceived health benefits, perceived sustainability benefits and perceived price. Food Quality and Preference, 91, 104191. DOI: https://doi.org/10.1016/j.foodqual.2021.104191
  24. Ferguson, J. D., Azzaro, G., Gambina, M., & Licitra, G. (2007). Prevalence of Mastitis Pathogens in Ragusa, Sicily, from 2000 to 2006. Journal of Dairy Science, 90(12), 5798–5813. DOI: https://doi.org/10.3168/jds.2006-903
  25. Fraser, D. (2008). Toward a global perspective on farm animal welfare. Applied Animal Behaviour Science, 113(4), 330–339. DOI: https://doi.org/10.1016/j.applanim.2008.01.011
  26. Gargiulo, J. I., Eastwood, C. R., Garcia, S. C., & Lyons, N. A. (2018). Dairy farmers with larger herd sizes adopt more precision dairy technologies. Journal of Dairy Science, 101, 5466–5473. DOI: https://doi.org/10.3168/jds.2017-13324
  27. Gómez, Y., Stygar, A. H., Boumans, I. J. M. M., Bokkers, E. A. M., Pedersen, L. J., Niemi, J. K., Pastell, M., Manteca, X., & Llonch, P. (2021). A systematic review on validated precision livestock farming technologies for pig production and its potential to assess animal welfare. Frontiers in Veterinary Science, 8, 660565. DOI: https://doi.org/10.3389/fvets.2021.660565
  28. Groher, T., Heitkämper, K., & Umstätter, C. (2020). Digital technology adoption in livestock production with a special focus on ruminant farming. Animal, 14(11), 2404–2413. DOI: https://doi.org/10.1017/S1751731120001391
  29. Guarino, M., Norton, T., Berckmans, D., Vranken, E., & Berckmans, D. (2017). A blueprint for developing and applying precision livestock farming tools: a key output of the EU-PLF project. Animal Frontiers, 7, 12–17. DOI: https://doi.org/10.2527/af.2017.0103
  30. Guyomard, H., Bouamra-Mechemache, Z., Chatellier, V., Delaby, L., Détang-Dessendre, C., Peyraud, J. L., & Réquillart, V. (2021). Why and how to regulate animal production and consumption: the case of the European Union. Animal, 15(1), 100283. DOI: https://doi.org/10.1016/j.animal.2021.100283
  31. Hair, J. F., Hult, T. M., Ringle, C., Sarstedt, M., Magno, F., Cassia, F., & Scafarto, F. (2020). Le equazioni strutturali Partial Least Squares: Introduzione alla PLS-SEM. Milano, Franco Angeli.
  32. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139–152. DOI: https://doi.org/10.2753/MTP1069-6679190202
  33. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. DOI: https://doi.org/10.1108/EBR-11-2018-0203
  34. Hartung, J., Banhazi, T., Vranken, E., & Guarino M. (2017). European farmers’ experiences with precision livestock farming systems. Animal Frontiers, 7(1), 38–44. DOI: https://doi.org/10.2527/af.2017.0107
  35. Henchion, M. M., Regan, Á., Beecher, M., & MackenWalsh, Á. (2022). Developing ‘smart’ dairy farming responsive to farmers and consumer-citizens: a review. Animals, 12, 360. DOI: https://doi.org/10.3390/ani12030360
  36. Herlin, A., Brunberg, E., Hultgren, J., Högberg, N., Rydberg, A., & Skarin, A. (2021). Animal welfare implications of digital tools for monitoring and management of cattle and sheep on pasture. Animals, 11, 829. DOI: https://doi.org/10.3390/ani11030829
  37. Jukan, A., Masip-Bruin, X., & Amla, N. (2017). Smart computing and sensing technologies for animal welfare: a systematic review. ACM Computing Surveys, 50(1), 1–27. DOI: https://doi.org/10.1145/3041960
  38. Kebebe, E. G., Oosting, S. J., Baltenweck, I., & Duncan, A. J. (2017). Characterisation of adopters and non-adopters of dairy technologies in Ethiopia and Kenya. Tropical Animal Health and Production, 49(4), 681–690. DOI: https://doi.org/10.1007/s11250-017-1241-8
  39. Kellert, S. (1980). American attitudes toward and knowledge of animals: An update. International Journal for the Study of Animal Problems, 1(2), 87–119.
  40. Krampe, C., Serratosa, J., Niemi, J. K., & Ingenbleek, P. T. M. (2021). Consumer Perceptions of Precision Livestock Farming–A Qualitative Study in Three European Countries. Animals, 11, 1221. DOI: https://doi.org/10.3390/ani11051221
  41. Kureshi, S., & Sujo, T. (2019). Online grocery retailing – exploring local grocers beliefs. International Journal of Retail & Distribution Management, 47(2), 157–185. DOI: https://doi.org/10.1108/IJRDM-05-2018-0087
  42. Licata, M., Ruggeri, R., Iacuzzi, N., Virga, G., Farruggia, D., Rossini, F., & Tuttolomondo, T. (2021). Treatment of combined dairy and domestic wastewater with constructed wetland system in Sicily (Italy). Pollutant removal efficiency and effect of vegetation. Water, 13, 1086. DOI: https://doi.org/10.3390/w13081086
  43. Lima, E., Hopkins, T., Gurney, E., Shortall, O., Lovatt, F., Davies, P., & Kaler, J. (2018). Drivers for precision livestock technology adoption: a study of factors associated with adoption of electronic identification technology by commercial sheep farmers in england and wales. PLoS ONE, 13(1), e0190489. DOI: https://doi.org/10.1371/journal.pone.0190489
  44. Loera, B., Murphy, B., Fedi, A., Martini, M., Tecco, N., & Dean, M. (2022). Understanding the purchase intentions for organic vegetables across EU: a proposal to extend the TPB model. British Food Journal, 124(12), 4736–4754. DOI: https://doi.org/10.1108/BFJ-08-2021-0875
  45. Lomax, S., Colusso, P., & Clark, C. E. F. (2019). Does virtual fencing work for grazing dairy cattle? Animals, 9, 429. DOI: https://doi.org/10.3390/ani9070429
  46. Lopez-Mosquera, N. (2016). Gender differences, theory of planned behavior and willingness to pay. Journal of Environmental Psychology, 45, 165–175. DOI: https://doi.org/10.1016/j.jenvp.2016.01.006
  47. Lovarelli, D., Bacenetti, J., & Guarino, M. (2020). A review on dairy cattle farming: Is precision livestock farming the compromise for an environmental, economic and social sustainable production?. Journal of Cleaner Production, 262, 121409. DOI: https://doi.org/10.1016/j.jclepro.2020.121409
  48. Lovarelli, D., Brandolese, C., Leliveld, L., Finzi, A., Riva, E., Grotto, M., & Provolo, G. (2022). Development of a new wearable 3D sensor node and innovative open classification system for dairy cows’ behavior. Animals, 12, 1447. DOI: https://doi.org/10.3390/ani12111447
  49. Makinde, A., Islam, M. M., Wood, K.M., Conlin, E., Williams, M., & Scott, S. D. (2022). Investigating perceptions, adoption, and use of digital technologies in the Canadian beef industry. Computers and Electronics in Agriculture, 198, 107095. DOI: https://doi.org/10.1016/j.compag.2022.107095
  50. Mattachini, G., Antler, A., Riva, E., Arbel, A., & Provolo, G. (2013). Automated measurement of lying behavior for monitoring the comfort and welfare of lactating dairy cows. Livestock Science, 158(1-3), 145–150. DOI: https://doi.org/10.1016/j.livsci.2013.10.014
  51. Mazrier, H., Tal, S., Aizinbud, E., & Bargai, U. (2006). A field investigation of the use of the pedometer for the early detection of lameness in cattle. The Canadian Veterinary Journal, 47(9), 883–886.
  52. Menozzi, D., Giraud, G., Saïdi, M., & Yeh, C. H. (2021). Choice drivers for quality-labelled food: a cross-cultural comparison on PDO cheese. Foods, 10(6), 1176. DOI: https://doi.org/10.3390/foods10061176
  53. Menozzi, D., Halawany-Darson, R., Mora, C., & Giraud, G. (2015). Motives towards traceable food choice: a comparison between French and Italian consumers. Food Control, 49, 40–48. DOI: https://doi.org/10.1016/j.foodcont.2013.09.006
  54. Mensching, A., Zschiesche, M., Hummel, J., Grelet, C., Gengler, N., Dänicke, S., & Sharifi, A. R. (2021). Development of a subacute ruminal acidosis risk score and its prediction using milk mid-infrared spectra in early-lactation cows. Journal of Dairy Science, 104(4), 4615–4634. DOI: https://doi.org/10.3168/jds.2020-19516
  55. Meuwissen, M. P., Feindt, P. H., Spiegel, A., Termeer, C. J. A. M., Mathijs, E., De Mey, Y., Finger, R., Balmann, A., Wauters, E., Urquhart, J., Vigani, M., Zawalińska, K., Herrera, H., Nicholas-Davies, P., Hansson, H., Paas, W., Slijper, T., Coopmans, I., Vroege, W., Ciechomska, A., Accatino, F., Kopainsky, B., Poortvliet, P. M., Candel, J. J. L., Maye, D., Severini, S., Senni, S., Soriano, B., Lagerkvist, C. J., Peneva, M., Gavrilescu, C., & Reidsma, P. (2019). A framework to assess the resilience of farming systems. Agricultural Systems, 176, 102656. DOI: https://doi.org/10.1016/j.agsy.2019.102656
  56. Morrone, S., Dimauro, C., Gambella, F., & Cappai, M. G. (2022). Industry 4.0 and Precision Livestock Farming (PLF): an up to date overview across animal productions. Sensors, 22, 4319. DOI: https://doi.org/10.3390/s22124319
  57. Nadal-Roig, E., Plà-Aragonès, L. M., & Alonso-Ayuso, A. (2019). Production planning of supply chains in the pig industry. Computers and Electronics in Agriculture, 161, 72–78. DOI: https://doi.org/10.1016/j.compag.2018.08.042
  58. Napolitano, F., Bragaglio, A., Sabia, E., Serrapica, F., Braghieri, A., & De Rosa, G. (2020). The human−animal relationship in dairy animals. Journal of Dairy Research, 87(S1), 47–52. DOI: https://doi.org/10.1017/S0022029920000606
  59. Neethirajan, S., & Kemp, B. (2021). Digital livestock farming. Sensing and Bio-Sensing Research, 32, 100408. DOI: https://doi.org/10.1016/j.sbsr.2021.100408
  60. Pfeiffer, J., Gabriel, A., & Gandorfer, M. (2021). Understanding the public attitudinal acceptance of digital farming technologies: a nationwide survey in Germany. Agriculture and Human Values, 38(1), 107–128. DOI: https://doi.org/10.1007/s10460-020-10145-2
  61. Pieniak, Z., Aertsens, J., & Verbeke, W. (2010). Subjective and objective knowledge as determinants of organic vegetables consumption. Food Quality and Preference, 21, 581–588. DOI: https://doi.org/10.1016/j.foodqual.2010.03.004
  62. Pierpaoli, E., Carli, G., Pignatti, E., & Canavari, M. (2013). Drivers of precision agriculture technologies adoption: a literature review. Procedia Technology, 8, 61–69. DOI: https://doi.org/10.1016/j.protcy.2013.11.010
  63. Poulopoulou, I., Lambertz, C., & Gauly, M. (2019). Are automated sensors a reliable tool to estimate behavioural activities in grazing beef cattle?. Applied Animal Behaviour Science, 216, 1–5. DOI: https://doi.org/10.1016/j.applanim.2019.04.009
  64. Pugliese, M., Biondi, V., Passantino, A., Licitra, A., Alibrandi, A., Zanghi, A., Conte, F., & Marino, G. (2021). Welfare assessment in intensive and semi-intensive dairy cattle management system in Sicily. Animal Science Journal, 92, e13546. DOI: https://doi.org/10.1111/asj.13546
  65. Raimondo, M., Hamam, M., D’Amico, M., Caracciolo, F. (2022). Plastic-free behavior of millennials: an application of the theory of planned behavior on drinking choices. Waste Management, 138, 253–261. DOI: https://doi.org/10.1016/j.wasman.2021.12.004
  66. Ramón-Moragues, A., Carulla, P., Mínguez, C., Villagrá, A., & Estellés, F. (2021). Dairy cows activity under heat stress: a case study in Spain. Animals, 11, 2305. DOI: https://doi.org/10.3390/ani11082305
  67. Rauw, W. M., Rydhmer, L., Kyriazakis, I., Øverland, M., Gilbert, H., Dekkers, J. C., Hermesch, S., Bouquet, A., Gómez Izquierdo, E., Louveau, I., & Gomez-Raya, L. (2020). Prospects for sustainability of pig production in relation to climate change and novel feed resources. Journal of the Science of Food and Agriculture, 100(9), 3575–3586. DOI: https://doi.org/10.1002/jsfa.10338
  68. Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of research in Marketing, 26(4), 332–344. DOI: https://doi.org/10.1016/j.ijresmar.2009.08.001
  69. Rezaei, R., Safa, L., Damalas, C. A., & Ganjkhanloo, M. M. (2019). Drivers of farmers’ intention to use integrated pest management: integrating theory of planned behavior and norm activation model. Journal of Environmental Management, 236, 328–339. DOI: https://doi.org/10.1016/j.jenvman.2019.01.097
  70. Richards, E., Signal, T., & Taylor, N. (2013). A different cut? Comparing attitudes toward animals and propensity for aggression within two primary industry cohorts—farmers and meatworkers. Society & Animals, 21, 395–413. DOI: https://doi.org/10.1163/15685306-12341284
  71. Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares structural equation modeling in HRM research. The International Journal of Human Resource Management, 31(12), 1617–1643. DOI: https://doi.org/10.1080/09585192.2017.1416655
  72. Roelofs, J. B., Van Eerdenburg, F. J. C. M., Soede, N. M., & Kemp, B. (2005). Pedometer readings for estrous detection and as predictor for time of ovulation in dairy cattle. Theriogenology, 64, 1690–1703. DOI: https://doi.org/10.1016/j.theriogenology.2005.04.004
  73. Rowe, E., Dawkins, M. S., & Gebhardt-Henrich, S. G. (2019). A systematic review of precision livestock farming in the poultry sector: is technology focussed on improving bird welfare?. Animals, 9, 614. DOI: https://doi.org/10.3390/ani9090614
  74. Ruff, C. C., Alexander, I. M., & McKie, C. (2005). The use of focus group methodology in health disparities research. Nursing outlook, 53(3), 134–140. DOI: https://doi.org/10.1016/j.outlook.2005.03.010
  75. Rutten, C. J., Steeneveld, W., Oude Lansink, A. G. J. M., & Hogeveen, H. (2018). Delaying investments in sensor technology: the rationality of dairy farmers’ investment decisions illustrated within the framework of real options theory. Journal of Dairy Science, 101(8), 7650–7660. DOI: https://doi.org/10.3168/jds.2017-13358
  76. Rutten, C. J., Velthuis, A. G. J., Steeneveld, W., & Hogeveen, H. (2013). Invited review: sensors to support health management on dairy farms. Journal of Dairy Science, 96(4), 1928–1952. DOI: https://doi.org/10.3168/jds.2012-6107
  77. Santo, N., König von Borstel, U., & Sirovnik, J. (2020). The influence of maternal contact on activity, emotionality and social competence in young dairy calves. Journal of Dairy Research, 87(S1), 138–143. DOI: https://doi.org/10.1017/S0022029920000527
  78. Sarstedt, M., Radomir, L., Moisescu, O. I., & Ringle, C. M. (2022). Latent class analysis in PLS-SEM: a review and recommendations for future applications. Journal of Business Research, 138, 398–407. DOI: https://doi.org/10.1016/j.jbusres.2021.08.051
  79. Sarstedt, M., Ringle, C. M., Cheah, J. H., Ting, H., Moisescu, O. I., & Radomir, L. (2020). Structural model robustness checks in PLS-SEM. Tourism Economics, 26(4), 531–554. DOI: https://doi.org/10.1177/1354816618823921
  80. Savari, M., & Gharechaee, H. (2020). Application of the extended theory of planned behavior to predict Iranian farmers’ intention for safe use of chemical fertilizers. Journal of Cleaner Production, 263, 121512. DOI: https://doi.org/10.1016/j.jclepro.2020.121512
  81. Schillings, J., Bennett, R., & Rose, D. C. (2021). Exploring the potential of precision livestock farming technologies to help address farm animal welfare. Frontiers in Animal Science, 2, 639678. DOI: https://doi.org/10.3389/fanim.2021.639678
  82. Shepley, E., Berthelot, M., & Vasseur, E. (2017). Validation of the ability of a 3D pedometer to accurately determine the number of steps taken by dairy cows when housed in tie-stalls. Agriculture, 7(7), 53. DOI: https://doi.org/10.3390/agriculture7070053
  83. Shepley, E., Lensink, J., Leruste, H., & Vasseur, E. (2020). The effect of free-stall versus strawyard housing and access to pasture on dairy cow locomotor activity and time budget. Applied Animal Behaviour Science, 224, 104928. DOI: https://doi.org/10.1016/j.applanim.2019.104928
  84. Silva, S. R., Araujo, J. P., Guedes, C., Silva, F., Almeida, M., & Cerqueira, J. L. (2021). Precision technologies to address dairy cattle welfare: focus on lameness, mastitis and body condition. Animals, 11, 2253. DOI: https://doi.org/10.3390/ani11082253
  85. Silvi, R., Pereira, L. G. R., Paiva, C. A. V., Tomich, T. R., Teixeira, V. A., Sacramento, J. P., & Dórea, J. R. R. (2021). Adoption of precision technologies by brazilian dairy farms: the farmer’s perception. Animals, 11(12), 3488. DOI: https://doi.org/10.3390/ani11123488
  86. Simitzis, P., Tzanidakis, C., Tzamaloukas, O., & Sossidou, E. (2022). Contribution of precision livestock farming systems to the improvement of welfare status and productivity of dairy animals. Dairy, 3, 12–28. DOI: https://doi.org/10.3390/dairy3010002
  87. Spina, D., Caracciolo, F., Chinnici, G., Di Vita, G., Selvaggi, R., Pappalardo, G., Pecorino, B., & D’Amico, M. (2023). How do farmers plan to safeguard the environment? Empirical evidence on farmers’ intentions to adopt organic pest management practices. Journal of Environmental Planning and Management, 1–21. DOI: https://doi.org/10.1080/09640568.2023.2218021
  88. Stachowicz, J., Nasser, R., Adrion, F., & Umstätter, C. (2022). Can we detect patterns in behavioral time series of cows using cluster analysis?. Journal of Dairy Science, 105(12), 9971–9981. DOI: https://doi.org/10.3168/jds.2022-22140
  89. Stachowicz, J., & Umstätter, C. (2021). Do we automatically detect health- or general welfare-related issues? A framework. Proceedings of the Royal Society B, 288(1950), 20210190. DOI: https://doi.org/10.1098/rspb.2021.0190
  90. Steeneveld, W., & Hogeveen, H. (2014). Characterization of Dutch dairy farms using sensor systems for cow management. Journal of Dairy Science, 98(1), 709–717. DOI: https://doi.org/10.3168/jds.2014-8595
  91. Stevenson, P. (2023). Precision livestock farming: could it drive the livestock sector in the wrong direction? Compassion in world farming. Available at: https://www.ciwf.org.uk/research/animal-welfare/precision-livestock-farming-could-it-drive-the-livestock-sector-in-the-wrong-direction/ (accessed 24 July 2023 Month Year).
  92. Stone, A. E. (2020). Symposium review: The most important factors affecting adoption of precision dairy monitoring technologies. Journal of Dairy Science, 103(6), 5740–5745. DOI: https://doi.org/10.3168/jds.2019-17148
  93. Stygar, A. H., Gómez, Y., Berteselli, G. V., Dalla Costa, E., Canali, E., Niemi, J. K., Llonch, P., & Pastell, M. (2021). A systematic review on commercially available and validated sensor technologies for welfare assessment of dairy cattle. Frontiers in Veterinary Science, 8, 634338. DOI: https://doi.org/10.3389/fvets.2021.634338
  94. Tekin, A., Yurdakök-Dikmen, B., Kanca, H., & Guatteo, R. (2021). Precision livestock farming technologies: novel direction of information flow. Ankara Universitesi Veteriner Fakultesi Dergisi, 68(2), 193–212. DOI: https://doi.org/10.33988/auvfd.837485
  95. Temple, D., & Manteca, X. (2020). Animal welfare in extensive production systems is still an area of concern. Frontiers in Sustainable Food Systems, 4, 545902. DOI: https://doi.org/10.3389/fsufs.2020.545902
  96. Timpanaro, G., Chinnici, G., Selvaggi, R., Cascone, G., Foti, V. T., & Scuderi, A. (2023). Farmer’s adoption of agricultural insurance for Mediterranean crops as an innovative behavior. Food Economy, 25(2), art. 7, 155–188. DOI: https://doi.org/10.3280/ecag2023oa14966
  97. Tobin, C. T., Bailey, D. W., Stephenson, M. B., Trotter, M. G., Knight, C. W., & Faist, A. M. (2022). Opportunities to monitor animal welfare using the five freedoms with precision livestock management on rangelands. Frontiers in Animal Science, 3, 928514. DOI: https://doi.org/10.3389/fanim.2022.928514
  98. Tullo, E., Finzi, A., & Guarino, M. (2019). Environmental impact of livestock farming and Precision Livestock Farming as a mitigation strategy. Science of the Total Environment, 650, 2751–2760. DOI: https://doi.org/10.1016/j.scitotenv.2018.10.018
  99. Umega, R., & Raja, M. A. (2017). Design and implementation of livestock barn monitoring system. 2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT), Coimbatore, India, 2017, 1-6. DOI: https://doi.org/10.1109/IGEHT.2017.8094063
  100. Vasseur, E. (2017). Animal behavior and well-being symposium: optimizing outcome measures of welfare in dairy cattle assessment. Journal of Animal Science, 95(3), 1365–1371. DOI: https://doi.org/10.2527/jas.2016.0880
  101. Vázquez, J. J., Cebolla, M. P. C., & Ramos, F. S. (2019). Digital transformation in the spanish agri-food cooperative sector: situation and prospects. CIRIEC-Espana Revista De Economia Publica, Social y Cooperativa, 95, 39-70. DOI: https://doi.org/10.7203/CIRIEC-E.95.13002
  102. Veissier, I., Kling-Eveillard, F., Mialon, M. M., Silberberg, M., de B. des Roches, A., Terlouw, C., Ledoux, D., Meunier, B., & Hostiou, N. (2019). Precision livestock farming and animal welfare: is the numerical revolution in agriculture able to take into account animals’ and farmers’ needs?. INRA Productions Animales, 32(2), 281–290. DOI: https://doi.org/10.20870/productions-animales.2019.32.2.2478
  103. Venturini, S., & Mehmetoglu, M. (2019). Plssem: a stata package for structural equation modeling with partial least squares. Journal of Statistical Software, 88, 1–35. DOI: https://doi.org/10.18637/jss.v088.i08
  104. Wauters, E., Bielders, C., Poesen, J., Govers, G., & Mathijs, E. (2010). Adoption of soil conservation practices in Belgium: an examination of the theory of planned behaviour in the agri-environmental domain. Land Use Policy, 27, 86e94. DOI: https://doi.org/10.1016/j.landusepol.2009.02.009
  105. Yang, X., Zhou, X., & Deng, X. (2022). Modeling farmers’ adoption of low-carbon agricultural technology in jianghan plain, china: An examination of the theory of planned behavior. Technological Forecasting and Social Change, 180, 121726. DOI: https://doi.org/10.1016/j.techfore.2022.121726
  106. Yatribi, T. (2020). Factors affecting precision agriculture adoption: a systematic literature review. Economics, 8(2), 103–121. DOI: https://doi.org/10.2478/eoik-2020-0013
  107. Zarbà, C., Bracco, S., Pecorino, B., Pappalardo, G., Chinnici, G., & D’Amico, M. (2022). Supporting Agri-Food SMEs in Italy in the Post-COVID-19 context: from Horizon 2020 to Horizon Europe. Sustainability, 14, 7615. DOI: https://doi.org/10.3390/su14137615
  108. Zarbà, C., Chinnici, G., La Via, G., Bracco, S., Pecorino, B., & D’Amico, M. (2021). Regulatory elements on the circular economy: driving into the agri-food system. Sustainability, 13, 8350. DOI: https://doi.org/10.3390/su13158350
  109. Zhang, M., Wang, X., Feng, H., Huang, Q., Xiao, X., & Zhang, X. (2021). Wearable Internet of Things enabled precision livestock farming in smart farms: A review of technical solutions for precise perception, biocompatibility, and sustainability monitoring. Journal of Cleaner Production, 312, 127712. DOI: https://doi.org/10.1016/j.jclepro.2021.127712
  110. Zhong, Z., Luo, J., Zhang, M. (2015). Understanding antecedents of continuance intention in mobile travel booking service. International Journal of Business and Management, 10(9), 156–162. DOI: https://doi.org/10.5539/ijbm.v10n9p156