Productive performance and milk quality in cattle herds of the Amazon Savanna

Authors

Abstract

This study aimed to investigate phenotypic trends and the effects of environmental factors on milk yield and composition in herds located in the Amazon Savanna. Milk recording data and samples collected between 2022 and 2024 were analyzed from three cities in the state of Roraima, Brazil (Boa Vista, Cantá, and Iracema), including cows of different breed compositions, predominantly Gir and Girolando crossbreds. Phenotypic trends were estimated using linear regression, and the effects of location and season (dry and rainy) were evaluated by analysis of variance followed by Tukey’s test at a 5 % significance level. In the experimental herd, average milk yield was 2.66 kg/cow/day, and mean contents of fat, protein, lactose, mineral salts, and solids-not-fat were 3.79 %, 3.43 %, 4.80 %, 0.81 %, and 9.04 %, respectively. The phenotypic trend indicated a slight reduction in daily milk yield (−0.0021 kg), whereas milk components remained phenotypically stable: fat (−0.0006 %), protein (0.0003 %), lactose (0.0004 %), and mineral salts (0.00006 %). In commercial herds, no significant interaction was observed between location and season. A significant effect of location was detected for all milk components, with Iracema presenting higher mean contents of protein (4.79 %), lactose (5.21 %), mineral salts (0.88 %), and solids-not-fat (9.89 %), whereas Boa Vista showed the highest fat content (3.81 %). The dry season presented higher values for protein (4.05 %), lactose (5.04 %), mineral salts (0.85 %), and solids-not-fat (9.54 %) compared to the rainy season. It is concluded that average milk yield in the experimental herd remains low under tropical conditions of the Amazon Savanna, although relevant phenotypic variability exists, and milk composition is influenced by geographic location and climatic seasonality, with greater concentration of solids during the dry season.
Keywords: tropical climate; milk components; variability; seasonality.

Downloads

Download data is not yet available.

References

1. Ferrazza RA, Castellani E. Analysis of Brazilian livestock transformations: a focus on dairy farming. Ciência Animal Brasileira. 2021; 22:e-68940. https://doi.org/10.1590/1809-6891v22e-68940

2. Brasil. Instituto Brasileiro de Geografia e Estatística (IBGE). Produção de Leite. Roraima; 2025. Disponível em: https://www.ibge.gov.br/explica/producao-agropecuaria/leite/rr

3. Braga RM, Bendahan AB. Perfil dos produtores e da produção de leite e derivados nos municípios de São Luiz e Rorainópolis em Roraima. Embrapa Roraima – Documentos, n.66, 86 p., 2020.

4. Haug A, Hostmark AT, Harstad OM. Bovine milk in human nutrition – a review. Lipids in Health and Disease. 2007; 6(25):1-16. https://doi.org/10.1186/1476-511X-6-25

5. Gulati A, Galvin N, Hennessy D, McAuliffe S, O’Donovan M, McManus JJ, Fenelon MA, Guinee TP. Grazing of dairy cows on pasture versus indoor feeding on total mixed ration: effects on low-moisture part-skim Mozzarella cheese yield and quality characteristics in mid and late lactation. Journal of Dairy Science. 2018; 101(10):8737-8756. https://doi.org/10.3168/jds.2018-14566

6. Ramírez-Rivera EJ, Rodríguez-Miranda J, Huerta-Mora IR, Cárdenas-Cágal A, Juárez-Barrientos JM. Tropical milk production systems and milk quality: a review. Tropical Animal Health and Production. 2019; 51:1295-1305. https://doi.org/10.1007/s11250-019-01922-1

7. Amalfitano N, Patel N, Haddi M, Benabid H, Pazzola M, Vacca GM, Tagliapietra F, Schiavon S, Bittante G. Detailed mineral profile of milk, whey, and cheese from cows, buffaloes, goats, ewes, and dromedary camels, and efficiency of recovery of minerals in their cheese. Journal of Dairy Science. 2024; 107(11):8887-8907. https://doi.org/10.3168/jds.2023-24624

8. Hayes E, Wallace D, O’Donnell C, Greene D, Hennessy D, O’Shea N, Tobin JT, Fenelon MA. Trend analysis and prediction of seasonal changes in milk composition from a pasture-based dairy research herd. Journal of Dairy Science. 2023; 106(4):2326-2337. https://doi.org/10.3168/jds.2021-21483

9. Bekele R, Taye M, Abebe G, Meseret S. Genetic and non-genetic factors affecting test day milk yield and milk composition traits in crossbred dairy cattle in Ethiopia. Veterinary Integrative Sciences. 2023; 21(3):717-733. https://doi.org/10.12982/VIS.2023.052

10. Kaoian P, Buaban S, Mitsuwan W, Kitpipit W. First-lactation milk fat-to-protein ratio in tropically-raised dairy cows: environmental and genetic influences. Veterinary Integrative Sciences. 2023; 21(2):429-437. https://doi.org/10.12982/VIS.2023.030

11. Reis NS, Ferreira IC, Mazocoo LA, Souza ACB, Pinho GAS, Fonseca Neto AM, Malaquias JV, Macena FA, Muller AG, Martins CF, Balbino LC, McManus CM. Shade modifies behavioral and physiological responses of low to medium production dairy cows at pasture in an integrated crop-livestock-forest system. Animals. 2021; 11:2411. https://doi.org/10.3390/ani11082411

12. O’Connell A, McParland S, Ruegg PL, O’Brien B, Gleeson D. Seasonal trends in milk quality in Ireland between 2007 and 2011. Journal of Dairy Science. 2015; 98(6):3778-3790. https://doi.org/10.3168/jds.2014-9001

13. NASA (National Aeronautics and Space Administration). Power Data Access Viewer. [Internet]. USA: NASA Langley Research Center; 2025. [cited 2025 abr 15]. Available from: https://power.larc.nasa.gov/data-access-viewer/

14. Buffington DE, Collazo-Arocho A, Caton GH, Pitt D, Thatcher WW, Collier RJ. Black globe-humidity index (BGHI) as comfort equation for dairy cows. Transactions of the Asae; 24(3):711-714. 1981. https://doi.org/10.13031/2013.34325

15. Abreu PG, Abreu VMN, Francisco L, Coldebella A, Amaral AG. Estimativa da temperatura de globo negro a partir da temperatura de bulbo seco. Engenharia na Agricultura-REVENG. 2011; 19(6):557-563. https://doi.org/10.13083/reveng.v19i6.273

16. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa da pecuária Roraima 2023: Bovino. Roraima: IBGE, 2023. [citado 2025 abr 15]. Disponível em: https://cidades.ibge.gov.br/brasil/rr/pesquisa/18/164594?ano=2024

17. R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

18. Alves AVNG, Monteschio JO, Fagundes GM, Paiva JT. Avaliação do controle leiteiro e agentes causadores de mastite em vacas Holandesas. Brazilian Journal of Animal and Environmental Research. 2024; 7(1):145-157. https://doi.org/10.34188/bjaerv7n1-012

19. Rauber LP, Coldebella F, Salvadego TA, Comunello L, Giacomin L. Projeto de extensão rural – leite forte: controle reprodutivo de pequenos rebanhos leiteiros. Revista de Extensão do Instituto Federal Catarinense. 2020; 2:47-52. https://publicacoes.ifc.edu.br/index.php/RevExt/article/view/72

20. Gallardo WB, Teixeira IAMA. Associations between dietary fatty acid profile and milk fat production and fatty acid composition in dairy cows: a meta-analysis. Animals. 2023; 13(13):2063. https://doi.org/10.3390/ani13132063

21. Brasil. Ministério da Agricultura, Pecuária e Abastecimento. Instrução Normativa nº 76, de 26 de novembro de 2018. Regulamentos técnicos que fixam a identidade e as características de qualidade que devem apresentar o leite cru refrigerado, o leite pasteurizado e o leite pasteurizado tipo A. Diário Oficial da União. 2018 Nov 26; Seção 1. Português.

22. Kalač P, Samková E. The effects of feeding various forages on fatty acid composition of bovine milk fat: a review. Czech Journal of Animal Science. 2010; 55(12):521-537. https://doi.org/10.17221/2485-CJAS

23. Silva BP, Deboni ACN, Fernandes CB, Carvalho GGM, Christino KC, Saltos LVRA, Santos LC, Pereira PV, Porn TA, Lemos MJ. Qualidade do leite produzido por vacas mestiças. Revista FT. 2023; 1(10). https://doi.org/10.5281/zenodo.10440466

24. Costa A, Lopez-Villalobos N, Sneddon NW, Shalloo L, Franzoi M, DeMarchi M, Penasa M. Invited review: Milk lactose—Current status and future challenges in dairy cattle. Journal of Dairy Science. 2019a; 102(7):5883-5898. https://doi.org/10.3168/jds.2018-15955

25. Costa A, Lopez-Villalobos N, Visentin G, DeMarchi M, Cassandro M, Penasa M. Heritability and repeatability of milk lactose and its relationships with traditional milk traits, somatic cell score and freezing point in Holstein cows. Animal. 2019b; 13(5):909-916. https://doi.org/10.1017/S1751731118002094

26. Dalgleish D, Corredig M. The structure of the casein micelle of milk and its changes during processing. Annual Review of Food Science and Technology. 2012; 3:449-467. https://doi.org/10.1146/annurev-food-022811-101214

27. Tao S, Rivas RMO, Marins TN, Chen Y, Gao J, Bernard JK. Impact of heat stress on lactational performance of dairy cows. Theriogenology. 2020; 150(1):437-444. https://doi.org/10.1016/j.theriogenology.2020.02.048

28. Jingar S, Mehla RK, Singh M, Roy AK. Lactation curve pattern and prediction of milk production performance in crossbred cows. Journal of Veterinary Medicine. 2014; 13:814768. https://doi.org/10.1155/2014/814768

29. Pacheco-Pappenheim S, Yener S, Heck JML, Dijkstra J, VanValenberg HJF. Seasonal variation in fatty acid and triacylglycerol composition of bovine milk fat. Journal of Dairy Science. 2021; 104(8):8479-8492. https://doi.org/10.3168/jds.2020-19856

30. Bokharaeian M, Toghdory A, Ghoorchi T, Ghassemi N J, Esfahani IJ. Quantitative associations between season, month, and temperature-humidity index with milk yield, composition, somatic cell counts, and microbial load: a comprehensive study across ten dairy farms over an annual cycle. Animals. 2023; 13(20). https://doi.org/10.3390/ani13203205

31. Ruiz-Ortega M, González ECG, Guevara-Arroyo AM, Chay-Canul AJ, Valencia-Franco E, Pérez-Sato M, Velázquez-Morales JV, Rodríguez-Castillo JC, Robles-Robles JM, Vázquez-Diosdado JA, Ponce-Covarrubias JL. Physicochemical Characteristics of Dual-Purpose Cow’s Milk During the Dry and Rainy Seasons in a Tropical Environment. Veterinary Sciences. 2025; 12:269. https://doi.org/10.3390/vetsci12030269

32. Beckett L, Gleason CB, Bedford A, Liebe D, Yohe TT, Hall MB, Daniels KM, White RR. Rumen volatile fatty acid molar proportions, rumen epithelial gene expression, and blood metabolite concentration responses to ruminally degradable starch and fiber supplies. Journal of Dairy Science. 2021; 104(8):8857-8869. https://doi.org/10.3168/jds.2020-19622

33. Olawale FO, Kaya I, Secka A. Assessment of composition and physical properties of the Gambian N’Dama cow milk. Journal of Food Composition and Analysis. 2023; 115:104961. https://doi.org/10.1016/j.jfca.2022.104961

34. Kim JE, Lee HG. Amino acids supplementation for the milk and milk protein production of dairy cows. Animals. 2021; 11(7):2118. https://doi.org/10.3390/ani11072118

Downloads

Published

2026-04-07

How to Cite

OLIVEIRA, Yasmin Lima de; SENGER, Jhonathan Carvalho; D’SUZE, Elizangela Zayana Lima; LOPES, Jalison; LIMA, Nilsa Duarte da Silva; PAIVA, José Teodoro de. Productive performance and milk quality in cattle herds of the Amazon Savanna. Brazilian Animal Science/ Ciência Animal Brasileira, Goiânia, v. 27, 2026. Disponível em: https://revistas.ufg.br/vet/article/view/84514. Acesso em: 9 apr. 2026.

Issue

Vol. 27 (2026): Continuous publication

Section

ANIMAL SCIENCE

License

Copyright (c) 2026 Brazilian Animal Science/ Ciência Animal Brasileira

Creative Commons License

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

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

Data statement

  • The research data is available on demand, condition justified in the manuscript