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Feed additives for methane mitigation: Assessment of feed additives as a strategy to mitigate enteric methane from ruminants-Accounting; How to quantify the mitigating potential of using antimethanogenic feed additives.
Del Prado A, Vibart RE, Bilotto FM, Faverin C, Garcia F, Henrique FL, Leite FFGD, Mazzetto AM, Ridoutt BG, Yáñez-Ruiz DR, Bannink A. Del Prado A, et al. Among authors: yanez ruiz dr. J Dairy Sci. 2025 Jan;108(1):411-429. doi: 10.3168/jds.2024-25044. J Dairy Sci. 2025. PMID: 39725505
Feed additives for methane mitigation: A guideline to uncover the mode of action of antimethanogenic feed additives for ruminants.
Belanche A, Bannink A, Dijkstra J, Durmic Z, Garcia F, Santos FG, Huws S, Jeyanathan J, Lund P, Mackie RI, McAllister TA, Morgavi DP, Muetzel S, Pitta DW, Yáñez-Ruiz DR, Ungerfeld EM. Belanche A, et al. Among authors: yanez ruiz dr. J Dairy Sci. 2025 Jan;108(1):375-394. doi: 10.3168/jds.2024-25046. J Dairy Sci. 2025. PMID: 39725503
Feed additives for methane mitigation: Recommendations for testing enteric methane-mitigating feed additives in ruminant studies.
Hristov AN, Bannink A, Battelli M, Belanche A, Cajarville Sanz MC, Fernandez-Turren G, Garcia F, Jonker A, Kenny DA, Lind V, Meale SJ, Meo Zilio D, Muñoz C, Pacheco D, Peiren N, Ramin M, Rapetti L, Schwarm A, Stergiadis S, Theodoridou K, Ungerfeld EM, van Gastelen S, Yáñez-Ruiz DR, Waters SM, Lund P. Hristov AN, et al. Among authors: yanez ruiz dr. J Dairy Sci. 2025 Jan;108(1):322-355. doi: 10.3168/jds.2024-25050. J Dairy Sci. 2025. PMID: 39725501
Feed additives for methane mitigation: Recommendations for identification and selection of bioactive compounds to develop antimethanogenic feed additives.
Durmic Z, Duin EC, Bannink A, Belanche A, Carbone V, Carro MD, Crüsemann M, Fievez V, Garcia F, Hristov A, Joch M, Martinez-Fernandez G, Muetzel S, Ungerfeld EM, Wang M, Yáñez-Ruiz DR. Durmic Z, et al. Among authors: yanez ruiz dr. J Dairy Sci. 2025 Jan;108(1):302-321. doi: 10.3168/jds.2024-25045. J Dairy Sci. 2025. PMID: 39725500
Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome.
Carlino N, Blanco-Míguez A, Punčochář M, Mengoni C, Pinto F, Tatti A, Manghi P, Armanini F, Avagliano M, Barcenilla C, Breselge S, Cabrera-Rubio R, Calvete-Torre I, Coakley M, Cobo-Díaz JF, De Filippis F, Dey H, Leech J, Klaassens ES, Knobloch S, O'Neil D, Quijada NM, Sabater C, Skírnisdóttir S, Valentino V, Walsh L; MASTER EU Consortium; Alvarez-Ordóñez A, Asnicar F, Fackelmann G, Heidrich V, Margolles A, Marteinsson VT, Rota Stabelli O, Wagner M, Ercolini D, Cotter PD, Segata N, Pasolli E. Carlino N, et al. Cell. 2024 Oct 3;187(20):5775-5795.e15. doi: 10.1016/j.cell.2024.07.039. Epub 2024 Aug 29. Cell. 2024. PMID: 39214080 Free article.
Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy.
Zhang B, Lin S, Moraes L, Firkins J, Hristov AN, Kebreab E, Janssen PH, Bannink A, Bayat AR, Crompton LA, Dijkstra J, Eugène MA, Kreuzer M, McGee M, Reynolds CK, Schwarm A, Yáñez-Ruiz DR, Yu Z. Zhang B, et al. Among authors: yanez ruiz dr. Sci Rep. 2023 Dec 2;13(1):21305. doi: 10.1038/s41598-023-48449-y. Sci Rep. 2023. PMID: 38042941 Free PMC article.
67 results