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The dataset encompassed records from 143,332 animals across 60 farms, integrating animal-level information (i.e., phenotype and pedigree relationships) with farm-level data on environmental conditions (climate, soil classifications, and elevation) and management practices at the rearing sites, hereafter referred to as descriptors. Farm-level descriptors were carefully selected based on three steps: (i) evaluation of each descriptor\u2019s contribution to explaining the variance of YW across farms, (ii) assessment of collinearity among farm management descriptors, and (iii) comparison of models using a stepwise selection procedure. The selected descriptors were subsequently included as fixed effects in the genetic evaluation of YW. The analysis began with a traditional animal model (M1, benchmark model). It was extended to three alternative models that incorporated environmental descriptors (M2), farm management descriptors (M3), or both (M4). Model comparisons were based on the Akaike Information Criterion (AIC) and the proportion of the farm variance in YW explained by the fixed effects. The results indicate that climate and soil classifications, elevation, guidance from animal breeding technicians, period of the breeding season, age and weight of heifers at first breeding, no-till farming, reproductive technique (categorized as natural service, fixed-time artificial insemination\u2014FTAI, synchronization protocols and\/or herd bulls), years enrolled in the breeding program and livestock land area (categorized as small: \u2264100\u2009ha; medium: 101\u2013999\u2009ha; or large: \u22651000\u2009ha) are key factors describing the macro-environmental effects contributing to variation of YW across farms. Among them, guidance from animal breeding technicians, age and weight of heifers at first breeding, and no-till farming were directly or indirectly associated (P\u2009&amp;lt;\u20090.05) with several descriptors of soil, supplemental feeding, and reproductive management. Indeed, when these environmental and farm management descriptors were simultaneously included in the genetic evaluation model (M4), they explained 65.7% of the YW variance across farms, while maintaining the model\u2019s goodness-of-fit. This finding explains substantial sources of environmental variation commonly accounted for by contemporary groups (CG) in genetic evaluations. This suitable characterization of environmental factors might be essential for future genetic evaluation in the context of genotype-by-environment interaction (GxE), as well as for forecasting cattle performance under different environmental conditions.<\/jats:p>","DOI":"10.1093\/tas\/txaf147","type":"journal-article","created":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T13:09:22Z","timestamp":1762002562000},"source":"Crossref","is-referenced-by-count":2,"title":["Comprehensive farm-level analysis of environmental and management descriptors for developing an efficient genetic evaluation of pasture-raised beef cattle"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3835-1100","authenticated-orcid":false,"given":"Talita E Z","family":"Santana","sequence":"first","affiliation":[{"name":"Department of Animal Science, Federal University of Vicosa , Vicosa, MG 36570-900,","place":["Brazil"]},{"name":"Department of Animal and Dairy Sciences, University of Wisconsin-Madison , Madison, WI 53706,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renata","family":"Veroneze","sequence":"additional","affiliation":[{"name":"Department of Animal Science, Federal University of Vicosa , Vicosa, MG 36570-900,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gilberto R O","family":"Menezes","sequence":"additional","affiliation":[{"name":"National Center of Research on Beef Cattle, Brazilian Agricultural Research Corporation , Campo Grande, MS 79106-550,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9172-6461","authenticated-orcid":false,"given":"Guilherme J M","family":"Rosa","sequence":"additional","affiliation":[{"name":"Department of Animal and Dairy Sciences, University of Wisconsin-Madison , Madison, WI 53706,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"2025121118563516900_txaf147-B1","author":"ABIEC","year":"2024"},{"key":"2025121118563516900_txaf147-B2","doi-asserted-by":"publisher","DOI":"10.1093\/jas\/skaa089","article-title":"Forecasting beef production and quality using large-scale integrated data from Brazil","volume":"98","author":"Aiken","year":"2020","journal-title":"J. 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