Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-23T03:44:26.948Z Has data issue: false hasContentIssue false

Motivations and attitudes of Brazilian dairy farmers regarding the use of automated behaviour recording and analysis systems

Published online by Cambridge University Press:  16 August 2021

Aline C. Vieira*
Affiliation:
Animal Science Post-Graduation Research Program, Brazil
Vivian Fischer
Affiliation:
Animal Science Department, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
Maria Eugênia A. Canozzi
Affiliation:
Programa Producción de Carne y Lana, Uruguay
Lisiane S. Garcia
Affiliation:
Animal Science Post-Graduation Research Program, Brazil
Jessica Tatiana Morales-Piñeyrúa
Affiliation:
Programa Nacional de Producción de Leche, Instituto Nacional de Investigación Agropecuaria (INIA), Estación Experimental INIA La Estanzuela, Colonia del Sacramento, Uruguay
*
Author for correspondence: Aline C. Vieira, Email: [email protected]

Abstract

In this Research Communication we investigate the motivations of Brazilian dairy farmers to adopt automated behaviour recording and analysis systems (ABRS) and their attitudes towards the alerts that are issued. Thirty-eight farmers participated in the study distributed into two groups, ABRS users (USERS, n = 16) and non-users (NON-USERS, n = 22). In the USERS group 16 farmers accepted being interviewed, answering a semi-structured interview conducted by telephone, and the answers were transcribed and codified. In the NON-USERS group, 22 farmers answered an online questionnaire. Descriptive analysis was applied to coded answers. Most farmers were young individuals under 40 years of age, with undergraduate or graduate degrees and having recently started their productive activities, after a family succession process. Herd size varied with an overall average of approximately 100 cows. Oestrus detection and cow's health monitoring were the main reasons given to invest in this technology, and cost was the most important factor that prevented farmers from purchasing ABRS. All farmers in USERS affirmed that they observed the target cows after receiving a health or an oestrus alert. Farmers believed that they were able to intervene in the evolution of the animals' health status, as the alerts gave a window of three to four days before the onset of clinical signs of diseases, anticipating the start of the treatment.The alerts issued by the monitoring systems helped farmers to reduce the number of cows to be observed and to identify pre-clinically sick and oestrous animals more easily. Difficulties in illness detection and lack of definite protocols impaired the decision making process and early treatment, albeit farmers believed ABRS improved the farm's routine and reproductive rates.

Type
Research Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of Hannah Dairy Research Foundation

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

Benaissa, S, Tuyttens, FAM, Plets, D, Trogh, J, Martens, L, Vandaele, L, Joseph, W and Sonck, B (2020) Calving and estrus detection in dairy cattle using a combination of indoor localization and accelerometer sensors. Computers and Electronics in Agriculture 168, 105153.CrossRefGoogle Scholar
Borchers, MR and Bewley, JM (2015) An assessment of producer precision dairy farming technology use, prepurchase considerations, and usefulness. Journal of Dairy Science 98, 41984205.CrossRefGoogle ScholarPubMed
Drewry, JL, Shutske, JM, Trechter, D, Luck, BD and Pitman, L (2019) Assessment of digital technology adoption and access barriers among crop, dairy and livestock producers in Wisconsin. Computers and Electronics in Agriculture 165, 104960.CrossRefGoogle Scholar
Eastwood, C, Klerkx, L and Nettle, R (2017) Dynamics and distribution of public and private research and extension roles for technological innovation and diffusion: case studies of the implementation and adaptation of precision farming technologies. Journal of Rural Studies 49, 112.CrossRefGoogle Scholar
Eckelkamp, EA and Bewley, JM (2020) On-farm use of disease alerts generated by precision dairy technology. Journal of Dairy Science 103, 15661582.CrossRefGoogle ScholarPubMed
Gargiulo, JI, Eastwood, CR, Garcia, SC and Lyons, NA (2018) Dairy farmers with larger herd sizes adopt more precision dairy technologies. Journal of Dairy Science 101, 54665473.CrossRefGoogle ScholarPubMed
Grinter, LN, Campler, MR and Costa, JHC (2019) Technical note: validation of a behavior-monitoring collar's precision and accuracy to measure rumination, feeding, and resting time of lactating dairy cows. Journal of Dairy Science 102, 34873494.CrossRefGoogle ScholarPubMed
Isgin, T, Bilgic, A, Forster, DL and Batte, MT (2008) Using count data models to determine the factors affecting farmers’ quantity decisions of precision farming technology adoption. Computers and Electronics in Agriculture 62, 231242.CrossRefGoogle Scholar
Maltz, E (2020) Individual dairy cow management: achievements, obstacles and prospects. Journal of Dairy Research 87, 145157.CrossRefGoogle ScholarPubMed
Mayo, LM, Silvia, WJ, Ray, DL, Jones, BW, Stone, AE, Tsai, IC, Clark, JD, Bewley, JM & Heersche, G Jr (2019) Automated estrous detection using multiple commercial precision dairy monitoring technologies in synchronized dairy cows. Journal of Dairy Science 102, 26452656.CrossRefGoogle ScholarPubMed
Michels, M, von Hobe, C and Musshoff, O (2020) A trans-theoretical model for the adoption of drones by large-scale German farmers. Journal of Rural Studies 75, 8088.CrossRefGoogle Scholar
Michie, C, Andonovic, I, Davison, C, Hamilton, A, Tachtatzis, C, Jonsson, N, Duthie, C-A, Bowen, J and Gilroy, M (2020) The internet of things enhancing animal welfare and farm operational efficiency. Journal of Dairy Research 87, 2027.CrossRefGoogle ScholarPubMed
Norton, T and Berckmans, D (2017) Developing precision livestock farming tools for precision dairy farming. Animal Frontiers 7, 1823.CrossRefGoogle Scholar
Schimidt, AP (2020) [Use of the rumination profile by collar sensors for diagnosing mastitis in dairy cows]. Dissertation (Masters), Postgraduate Program in Animal Science, Faculty of Agronomy Eliseu Maciel, University of Pelotas.Google Scholar
Stangaferro, ML, Wijma, R, Caixeta, LS, Al-Abri, MA and Giordano, JO (2016a) Use of rumination and activity monitoring for the identification of dairy cows with health disorders: part I. Metabolic and digestive disorders. Journal of Dairy Science 99, 73957410.CrossRefGoogle Scholar
Stangaferro, ML, Wijma, R, Caixeta, LSS, Al-Abri, MA and Giordano, JOO (2016b) Use of rumination and activity monitoring for the identification of dairy cows with health disorders: part II. Mastitis. Journal of Dairy Science 99, 74117421.CrossRefGoogle Scholar
Sumner, CL, von Keyserlingk, MAG and Weary, DM (2018) How benchmarking motivates farmers to improve dairy calf management. Journal of Dairy Science 101, 33233333.CrossRefGoogle ScholarPubMed
Supplementary material: PDF

Vieira et al. supplementary material

Vieira et al. supplementary material

Download Vieira et al. supplementary material(PDF)
PDF 206.6 KB