Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-23T06:16:37.407Z Has data issue: false hasContentIssue false

Human behavioral influences and milk quality control programs

Published online by Cambridge University Press:  20 July 2017

L. N. Freitas*
Affiliation:
Animal Science Department, ‘Luiz de Queiroz’ School of Agriculture, University of São Paulo, Av Padua Dias, 13418-900 Piracicaba, São Paulo, Brazil
P. H. R. Cerqueira
Affiliation:
Exact Science Department, ‘Luiz de Queiroz’ School of Agriculture, University of São Paulo, Av Padua Dias, 13418-900 Piracicaba, São Paulo, Brazil
H. Z. Marques
Affiliation:
Animal Science Department, ‘Luiz de Queiroz’ School of Agriculture, University of São Paulo, Av Padua Dias, 13418-900 Piracicaba, São Paulo, Brazil
R. A. Leandro
Affiliation:
Exact Science Department, ‘Luiz de Queiroz’ School of Agriculture, University of São Paulo, Av Padua Dias, 13418-900 Piracicaba, São Paulo, Brazil
P. F. Machado
Affiliation:
Animal Science Department, ‘Luiz de Queiroz’ School of Agriculture, University of São Paulo, Av Padua Dias, 13418-900 Piracicaba, São Paulo, Brazil
*
Get access

Abstract

Mastitis is a major disease affecting the herds of dairy farmers worldwide. One of the indicators directly related to the widespread infection of this disease in herds is the bulk tank somatic cell count (BTSCC). Recent studies have shown that one of the risk factors associated with mastitis is the human factor. Therefore, understanding the influence of humans is essential to control and prevent the disease. The main goal of this study was to determine whether the motivations and barriers perceived by farmers could explain the variation in the BTSCC. This study was conducted at 75 dairy farms in southern Brazil. In the interviews with farmers, a survey based on Likert scale items was used to collect data. Structural equation models were used to explain the subjectivity in the ratio of observed variables and latent variables elucidating the possible causal relationships between the variables. The model indicated that some of the variation in the BTSCC can be explained by the farmer’s behavior, which is elucidated by his/her motivations and barriers. The correlations between motivations and the BTSCC and between barriers and the BTSCC were positive. These findings suggest that variations in the BTSCC can be explained by the motivations and barriers perceived by farmers and that the Fogg Behavior Model used in this study can be used to explain how human behaviors influence mastitis control. This study also indicates that consulting companies focused on improving milk quality should pay attention to the human factor to reduce these barriers.

Type
Research Article
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Azar, KMJ, Lesser, LI, Laing, BY, Stephens, J, Aurora, MS, Burke, LE and Palaniappan, LP 2013. Mobile applications for weight management: theory-based content analysis. American Journal of Preventive Medicine 45, 583589.CrossRefGoogle ScholarPubMed
Barkema, HW, Van der Ploeg, JD, Schukken, YH, TJGM, Lam, Benedictus, G and Brand, A 1999. Management style and its association with bulk milk somatic cell count and incidence rate of clinical mastitis. Journal of Dairy Science 82, 16551663.CrossRefGoogle ScholarPubMed
Bollen, KA 1989. Structural equations with latent variables. John Wiley and Sons, New York City, NY, USA.CrossRefGoogle Scholar
Bollen, KA 2002. Latent variables in psychology and the social sciences. Annual Review of Psychology 53, 605634.CrossRefGoogle ScholarPubMed
Cheek, C, Piercy, KW and Grainer, S 2015. Leaving home: how older adults prepare for intensive volunteering. Journal of Applied Gerontology 34, 181198.CrossRefGoogle ScholarPubMed
Curado, MASC, Teles, J and Marôco, J 2014. Analysis of variables that are not directly observable: influence on decision-making during the research process. Revista da Escola de Enfermagem da USP 48, 149156.CrossRefGoogle Scholar
Demidenko, E 2016. The p-value you can’t buy. The American Statistician 70, 3338.CrossRefGoogle ScholarPubMed
Diamantopoulos, A and Siguaw, JA 2006. Formative versus reflective indicators in organizational measure development: a comparison and empirical illustration. British Journal of Management 17, 263282.CrossRefGoogle Scholar
Filho, DBF, Paranhos, R, Rocha, EC, Batista, M, Silva Junior, JA, Santos, MLWD and Marino, JG 2013. When is statistical significance not significant? Brazilian Political Science Review 7, 3135.CrossRefGoogle Scholar
Fogg, BJ 2009. A behavior model for persuasive design. Retrieved on 16 November 2016 from http://www.bjfogg.com/fbm_files/page4_1.pdf.CrossRefGoogle Scholar
Hair, JF, Black, WC, Babin, BJ, Anderson, RE and Tatham, RL 2005. Multivariate data analysis, 5th edition. Bookman, Porto Alegre, Rio Grande do Sul, Brazil.Google Scholar
Halasa, T, Huijps, K, Østerås, O and Hogeveen, H 2007. Economic effects of bovine mastitis and mastitis management: a review. Veterinary Quarterly 29, 1831.CrossRefGoogle ScholarPubMed
Hogeveen, H, Huijps, K and Lam, TJGM 2011. Economic aspects of mastitis: new developments. New Zealand Veterinary Journal 59, 1623.CrossRefGoogle ScholarPubMed
Huijps, K, Hogeveen, H, Lam, TJGM and Huirne, RBM 2009. Preferences of cost factors for mastitis management among Dutch dairy farmers using adaptive conjoint analysis. Preventive Veterinary Medicine 92, 351359.CrossRefGoogle ScholarPubMed
Jansen, J, Van den Borne, BHP, Renes, RJ, Van Schaik, G, Lam, TJGM and Leeuwis, C 2009. Explaining mastitis incidence in Dutch dairy farming: the influence of farmers’ attitudes and behavior. Preventive Veterinary Medicine 92, 210223.CrossRefGoogle Scholar
Jansen, J, van Schaik, G, Renes, RJ and Lam, TJGM 2010. The effect of a national mastitis control program on the attitudes, knowledge, and behavior of farmers in the Netherlands. Journal of Dairy Science 93, 57375747.CrossRefGoogle ScholarPubMed
Jia, G, Yang, P, Zhou, J, Zhang, H, Lin, C, Chen, J, Cai, G, Yan, J and Ning, G 2015. A framework design for the mHealth system for self-management promotion. Bio-Medical Materials and Engineering 26, 17311740.CrossRefGoogle ScholarPubMed
Kenny, DA 2011. Terminology and basics of SEM. Retrieved on 16 November 2016 from http://davidakenny.net/cm/basics.htm.Google Scholar
Kline, RB 2011. Principles and practice of structural equation modeling. The Guilford Press, New York City, NY, USA.Google Scholar
Leach, KA, Whay, HR, Maggs, CM, Barker, ZE, Paul, ES, Bell, AK and Main, DCJ 2010. Working towards a reduction in cattle lameness: 1. Understanding barriers to lameness control on dairy farms. Research in Veterinary Science 89, 311317.CrossRefGoogle ScholarPubMed
Miller, GY and Bartlett, PC 1991. Economic effects of mastitis prevention strategies for dairy producers. Journal of the American Veterinary Medicine Association 198, 227231.CrossRefGoogle ScholarPubMed
R Core Team 2015. R: A language and environment for statistical computing. Retrieved on 16 November 2016 from https://www.R-project.org.Google Scholar
Vaarst, M, Paarup-Laursen, B, Houe, H, Fossing, C and Andersen, HJ 2002. Farmers’ choice of medical treatment of mastitis in danish dairy herds based on qualitative research interviews. Journal of Dairy Science 85, 9921001.CrossRefGoogle ScholarPubMed
Valeeva, NI, Lam, TJGM and Hogeveen, H 2007. Motivation of dairy farmers to improve mastitis management. Journal of Dairy Science 90, 44664477.CrossRefGoogle ScholarPubMed
Van der Borne, BHP, Jansen, J, Lam, TJGM and Van Schaik, G 2014. Associations between the decrease in bovine clinical mastitis and changes in dairy farmers’ attitude, knowledge, and behavior in the Netherlands. Research in Veterinary Science 97, 226229.CrossRefGoogle Scholar
Yalcin, C and Stott, AW 2000. Dynamic programming to investigate financial impacts of mastitis control decisions in milk production systems. Journal of Dairy Research 67, 515528.CrossRefGoogle ScholarPubMed
Yalcin, C, Stott, AW, Longue, DN and Gunn, J 1999. The economic impact of mastitis-control procedures used in Scottish dairy herds with high bulk-tank somatic-cell counts. Preventive Veterinary Medicine 41, 135149.CrossRefGoogle ScholarPubMed
Supplementary material: File

Freitas supplementary material

Freitas supplementary material 1

Download Freitas supplementary material(File)
File 18.8 KB