Animal breeding is increasingly recognised as an effective strategy to reduce enteric methane (CH4) emissions from ruminants. However, the lack of individual cow CH4 emission recording has been a limitation to practical application. Consequently, implementation of CH4 mitigation strategies in breeding programs are in their early stages of development. In the Netherlands, two successive projects between 2018 and 2025 aimed to collect a large number of enteric CH4 records of individual cows that were recorded using ‘sniffers’ in the feed bin of milking robots. This resulted in a dataset with 74,569 weekly mean CH4 concentration (ppm) records on 7,139 cows from 68 commercial dairy farms. Sniffers measure concentrations and in order to predict the breeding value for the total grams of CH4 emitted by breath, an additional dataset was collected that included enteric CH4 production (g/day) recorded by GreenFeed units. This dataset included 4,358 weekly mean records from 822 cows and 334 weekly means from 73 cows overlapped with both GF and sniffer records in the same period. Other animals were linked through (genomic) relationships, enabling the estimation of the genetic correlation. Several analyses have been performed on the datasets, with the following objectives: 1) define a CH4 trait from the raw concentration measurements and estimate heritabilities and repeatabilities, 2) investigate the relationship between measurements by sniffers and GreenFeed units, 3) investigate the relationships between CH4 and other breeding goal traits, and 4) investigate the expected impact of breeding for low CH4 emissions. The defined phenotype for weekly mean CH4 concentration measured by sniffers had a moderate heritability of 0.17 ± 0.04 and a repeatability of 0.56 ± 0.03. The genetic correlation between measurements by sniffer and by GreenFeed units was 0.76 ± 0.15, indicating that selection for lower CH4 concentrations will result in a reduction of total CH4 production output in g/day. The genetic relationships among CH4 concentration, DMI, body weight, and milk yield traits were weak: 0.06 ± 0.10 with dry matter intake, -0.04 ± 0.10 with body weight, and -0.04 ± 0.08 with milk yield for first parity cows. Selection index calculations showed that with weak genetic correlations with other breeding goal traits, a large selective weight can be put on mitigating CH4 while continuing to improve other important breeding goal traits including health and fertility. Thereby, reducing enteric CH4 by 1% per year in the population through animal breeding is feasible. This dataset will be the basis for national breeding value estimations for enteric CH4 of Holstein dairy cows, which will be published in early 2025. Here, the breeding goal trait will be CH4 production (g/day), as recorded by GreenFeed units, to which sniffer concentration (ppm) measurements serve as a proxy. Ongoing research will focus on estimating genetic correlations between GreenFeed recorded CH4 production and other breeding goal traits, to ensure effective integration of CH4 concentration and production phenotypes in selection indices that aim to mitigate CH4 emissions. In addition, milk midinfrared spectra will be investigated to be used as an additional proxy for CH4 production.