ArticleJournal of animal science2026
Genetic and environmental factors affecting pregnancy loss in Nellore cattle: insights from a large-scale Brazilian field database.
Article in Journal of animal science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pregnancy loss (PL) is a major constraint on reproductive efficiency in beef cattle, yet its genetic basis remains poorly understood. This study aimed to estimate genetic parameters for PL and to quantify sire-related variation in Nellore cattle managed under Fixed-Time Artificial Insemination (FTAI). Records from 305,325 FTAI services involving 667 Nellore sires, collected across 271 farms in Brazil between 2015 and 2022, were analyzed. PL was modeled as a binary trait using a Bayesian threshold sire model, assuming an underlying continuous liability. Fixed effects included contemporary group, dam category, body condition score, and cow age at pregnancy diagnosis, and sire effects were modeled as random. Variance components and sire effects were estimated via Markov Chain Monte Carlo sampling implemented in the BLUPF90+ software suite, and the accuracy of sire evaluations was computed following the Beef Improvement Federation (BIF) guidelines based on prediction error variance. PL exhibited low heritability on the liability scale (h2 = 0.031; 95% HPD: 0.019-0.042), indicating that most of the variation is driven by environmental factors, and meaningful variation among sires was detected. PL rates averaged 6.64% in multiparous cows, 8.28% in primiparous cows, and up to 12.35% in precocious heifers. Approximately 15% of sires achieved BIF accuracy values of 0.24 or greater, and contrasts between the top and bottom deciles of sires were associated with a 31% increase in PL among precocious heifers. PL was also strongly influenced by age and dam category, with higher rates observed in younger females, particularly precocious heifers, and declining with increasing age and parity. Overall, these results demonstrate that, although PL has low heritability, exploitable genetic variation among sires exists when supported by large, well-structured FTAI datasets. Integrating PL indicators into multi-trait selection indices with targeted nutritional and reproductive management offers a practical strategy to improve reproductive efficiency in beef cattle production systems.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.