Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1137526
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dc.contributor.authorSILVI, R.
dc.contributor.authorPEREIRA, L. G. R.
dc.contributor.authorPAIVA, C. A. V.
dc.contributor.authorTOMICH, T. R.
dc.contributor.authorTEIXEIRA, V. A.
dc.contributor.authorSACRAMENTO, J. P.
dc.contributor.authorFERREIRA, R. E. P.
dc.contributor.authorCOELHO, S. G.
dc.contributor.authorMACHADO, F. S.
dc.contributor.authorCAMPOS, M. M.
dc.contributor.authorDÓREA, J. R. R.
dc.date.accessioned2021-12-10T14:01:22Z-
dc.date.available2021-12-10T14:01:22Z-
dc.date.created2021-12-10
dc.date.issued2021
dc.identifier.citationAnimals, v. 11, 3488, 2021.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1137526-
dc.descriptionThe use of precision farming technologies, such as milking robots, automated calf feeders, wearable sensors, and others, has significantly increased in dairy operations over the last few years. The growing interest in farming technologies to reduce labor, maximize productivity, and increase profitability is becoming noticeable in several countries, including Brazil. Information regarding technology adoption, perception, and effectiveness in dairy farms could shed light on challenges that need to be addressed by scientific research and extension programs. The objective of this study was to characterize Brazilian dairy farms based on technology usage. Factors such as willingness to invest in precision technologies, adoption of sensor systems, farmer profile, farm characteristics, and production indexes were investigated in 378 dairy farms located in Brazil. A survey with 22 questions was developed and distributed via Google Forms from July 2018 to July 2020. The farms were then classified into seven clusters: (1) top yield farms; (2) medium?high yield, medium‐tech; (3) medium yield and top high‐tech; (4) medium yield and medium‐tech; (5) young medium?low yield and low‐tech; (6) elderly medium?low yield and low‐tech; and (7) low‐tech grazing. The most frequent technologies adopted by producers were milk meters systems (31.7%), milking parlor smart gate (14.5%), sensor systems to detect mastitis (8.4%), cow activity meter (7.1%), and body temperature (7.9%). Based on a scale containing numerical values (1?5), producers indicated ?available technical support? (mean; σ2) (4.55; 0.80) as the most important decision criterion involved in adopting technology, followed by ?return on investment?ROI? (4.48; 0.80), ?user‐ friendliness? (4.39; 0.88), ?upfront investment cost? (4.36; 0.81), and ?compatibility with farm management software? (4.2; 1.02). The most important factors precluding investment in precision dairy technologies were the need for investment in other sectors of the farm (36%), the uncertainty of ROI (24%), and lack of integration with otherfarm systems and software (11%). Farmers indicated that the most useful technologies were automatic milk meters systems (mean; σ2) (4.05; 1.66), sensor systems for mastitis detection (4.00; 1.57), automatic feeding systems (3.50; 2.05), cow activity meter (3.45; 1.95), and in‐line milk analyzers (3.45; 1.95). Overall, the concerns related to data integration, ROI, and user‐friendliness of technologies are similar to those of dairy farms located in other countries. Increasing available technical support for sensing technology can have a positive impact on technology adoption.
dc.language.isoeng
dc.rightsopenAccesseng
dc.subjectFazenda inteligente
dc.titleAdoption of precision technologies by brazilian dairy farms: the farmer's perception.
dc.typeArtigo de periódico
dc.subject.thesagroBovino
dc.subject.thesagroGado
dc.subject.thesagroPecuária
riaa.ainfo.id1137526
riaa.ainfo.lastupdate2021-12-10
dc.identifier.doihttps://doi.org/10.3390/ani11123488
dc.contributor.institutionREBECA SILVI, Universidade Estadual de Santa Cruz; LUIZ GUSTAVO RIBEIRO PEREIRA, CNPGL; CLAUDIO ANTONIO VERSIANI PAIVA, CNPGL; THIERRY RIBEIRO TOMICH, CNPGL; VANESSA A. TEIXEIRA, Universidade Federal de Minas Gerais; JOÃO PAULO SACRAMENTO, Universidade Federal de São João del-Rei; RAFAEL E. P. FERREIRA, University of Wisconsin; SANDRA G. COELHO, Universidade Federal de Minas Gerais; FERNANDA SAMARINI MACHADO, CNPGL; MARIANA MAGALHAES CAMPOS, CNPGL; JOÃO RICARDO. R. DÓREA, University of Wisconsin.
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