Revisiting Environmental Sustainability in Ruminants: A Comprehensive Review

Agriculture · 2026-01-07 · 1 citations

Ruminant livestock production faces increasing pressure to reduce environmental impacts while maintaining productivity and food security. This comprehensive review examines current strategies and emerging technologies for enhancing environmental sustainability in ruminant systems. The review synthesizes recent advances across four interconnected domains: genetic and genomic approaches for breeding environmentally efficient animals, rumen microbiome manipulation, nutritional strategies for emission reduction, and precision management practices. Specifically, genetic and genomic strategies demonstrate significant potential for long-term sustainability improvements through selective breeding for feed efficiency, methane reduction, and enhanced longevity. Understanding host–microbe interactions and developing targeted interventions have also shown promising effects on optimizing fermentation efficiency and reducing methane production. Key nutritional interventions include dietary optimization strategies that improve feed efficiency, feed additives, and precision feeding systems that minimize nutrient waste. Furthermore, management approaches encompass precision livestock farming technologies including sensor-based monitoring systems, automated feeding platforms, and real-time emission measurement tools that enable data-driven decision making. Integration of these approaches through system-based frameworks offers the greatest potential for achieving substantial environmental improvements while maintaining economic viability. In addition, this review identifies key research gaps including the need for standardized measurement protocols, long-term sustainability assessments, and economic evaluation frameworks. Future directions emphasize the importance of interdisciplinary collaboration, policy support, and technology transfer to accelerate adoption of sustainable practices across diverse production systems.

PS6-7. Modeling Feed Intake in Lactation Sows Using Temperature and Humidity Covariates

Journal of Animal Science · 2026-04-01

Abstract Intensive genetic selection has increased reproductive output in modern sows; however, voluntary feed intake (FI) during lactation often limits milk production, particularly under heat stress. This study evaluated lactation FI trajectories and environmental covariates using 17,171 daily FI records from 898 sows (P1=503, P2=138, P3 + =257) farrowing between May and July 2024 on a commercial farm in Washington, Iowa. Records with negative intake, incomplete lactations, or missing farrowing dates were excluded, and data were truncated at 22 days in lactation. Nine alternative models, including generalized Michaelis–Menten (GMM) and cubic polynomial mixed-effects functions with varying random-effect structures, were compared using conditional R², residual standard deviation (RSD), and cross-validated RMSE. The cubic polynomial with three sow-specific random effects provided the best overall performance (R² = 0.8473; RSD = 0.9099 kg/d, RMSE=1.01), outperforming the best GMM specification (R²=0.8218, RSD=0.9832 kg/d, RMSE=2.33). The polynomial model accommodated a modest post-peak decline in FI during late lactation, which was not fully captured by the asymptotic GMM form. Across parities, FI increased to a concave peak near day 18 (P3 + > P2 > P1) and then slightly declined. Environmental covariates included daily maximum air temperature (MT), degree-hours above 24 °C (DGH24), and daily maximum dew point (MD). Models allowing environmental covariates to interact with lactation day consistently outperformed constant additive effects and proportional modifiers, indicating lactation-day–dependent environmental sensitivity. Among single covariates, MD provided the greatest improvement in model fit. When both MT and MD were included with day interactions, model fit improved further (ΔAIC=48; likelihood-ratio P < 0.001), indicating that temperature contributed additional information beyond absolute humidity. Marginal effects showed that a 1 SD increase in MD reduced FI by 0.129 kg/d, whereas a 1 SD increase in MT increased FI by 0.075 kg/d when MD was held constant. When both increased simultaneously, predicted FI declined by 0.054 kg/d. These findings suggest that moisture-related heat load accounts for most intake suppression, while temperature exerts a smaller conditional modifying influence. Overall, parity and environmental heat load meaningfully shaped both the level and form of lactation FI trajectories. Incorporating parity together with dew point and temperature into precision feeding and thermal-management strategies may improve sow performance under warm conditions. Results are based on a single commercial herd during the warm season, and environmental variables were derived from external ambient data rather than in-barn measurements; broader validation under diverse environmental and management conditions is warranted.

PSIV-22 Genome-wide association studies for binary traits: A comparison of methods.

Journal of Animal Science · 2025-10-01

Abstract Genome-wide association studies (GWAS) are widely used in animal breeding and genetics research to identify candidate genes and genomic regions associated with traits of interest. Many methodologies and software tools have been developed to perform GWAS. However, binary traits present unique challenges in GWAS due to their categorical nature, requiring specialized approaches for accurate analysis. The POSTGSF90 program from the BLUPF90 family can estimate SNP effects using the single-step GBLUP framework. For binary traits analyzed under threshold models, the window-based variance approach is an option, but it lacks the statistical power to statistically detect significance, and threshold selection remains subjective. An alternative is to use BLUPF90+ to obtain p-values before running POSTGSF90. Another method suited for binary traits is fastGWA-GLMM, a computationally efficient tool for generalized linear mixed model (GLMM)-based GWAS, implemented in the GCTA software. This study aimed to compare GWAS results between POSTGSF90 and GCTA using the same dataset. Phenotypic records included 182,964 observations for a binary morphological defect (pigmentation; affected/unaffected) in Nellore cattle, with an incidence of 6.8%. Quality control was performed using preGSF90, starting with 24,729 genotyped animals and 588,846 SNPs. Markers with a minor allele frequency (MAF ≤ 0.05), call rate ≤ 0.90, extreme deviations from Hardy-Weinberg equilibrium (p ≤ 10⁻⁵), unknown or duplicated positions, and Mendelian conflicts were removed, resulting in a final dataset of 24,562 genotyped animals and 583,769 SNP markers. Contemporary groups were included as fixed effects in both analyses. For POSTGSF90, the analysis incorporated the full dataset, including 340,991 animals with pedigree information, requiring 4 days, 8 hours, 46 minutes, and 53 seconds to complete. In contrast, GCTA used only animals with both genotypic and phenotypic data (8,920 records), completing the analysis in 26 minutes and 52 seconds. Both POSTGSF90 and GCTA identified the same five significant chromosomes for the binary trait. In the GWAS performed with POSTGSF90, a total of 1,365 markers were identified as significant (p-value < 0.05 after Bonferroni correction), and 466 genes were found, of which 318 were protein-coding genes. In GCTA, 1,165 markers were identified as significant (p-value < 0.05 after Bonferroni correction), and 363 genes were found, of which 243 were protein-coding genes. A total of 342 genes (~74%) overlapped between both analyses. These results demonstrate that GCTA achieved comparable detection power while using fewer records and significantly less computational time. Additional analyses will be performed to better understand the importance of the genomic regions identified solely by one software.

326 Genetic evaluation of lamb survival in Canadian sheep using a random regression model.

Journal of Animal Science · 2025-10-01

Abstract Improving lamb survival is essential for sustainable sheep production, particularly because high prolificacy can lead to increased mortality. The objective of this study was to implement a random regression model to evaluate lamb survival (LS) and estimate genetic parameters across different survival ages, from 1 to 50 days (weaning age). The dataset analyzed included 1,951,084 animals, progeny of 409,304 ewes and 35,118 rams, raised in 2,208 flocks across Canada between 1986 to 2024. A single-trait Bayesian random regression model (RRM), which included the systematic effects of year-month of lambing and age of the dam-class of sex of lambs born, and the random effects of flock-year-management group, litter of birth dam, direct animal additive genetic, animal permanent environment, maternal genetic, and maternal permanent environment. Third-degree Legendre orthogonal polynomials were used to fit the longitudinal direct animal additive genetic, animal permanent environment, maternal genetic random effects and year-month of lambing systematic effect. A homogeneous residual variance across ages was assumed. The average heritability estimates for LS across ages ranged from 0.15 to 0.20 and were consistently higher than when analyzing survival as a categorical trait with five categories (heritability= 0.05), i.e. death at birth: mummified (1) or stillborn (2), between birth and 10 days (3), between 10 and 50 days (4), and survival until weaning at 50 days (5), which is currently the model used to evaluate LS in the Canadian sheep evaluation. Maternal heritability estimates were high across all ages (ranged from 0.52 to 0.53), showing a significant and consistent genetic maternal influence on survival across all ages. This highlights the importance of considering maternal traits in breeding and management strategies to improve lamb survival. The estimated direct genetic correlations across ages ranged from moderate to high (0.75 to 0.90), while the maternal genetic correlations were slightly higher (0.88 to 0.95). These findings suggest that LS between 1 and 50 days of age has a common genetic background indicated by similar heritabilities and moderate to high genetic correlation across ages. Genetic selection based on RRM breeding values is expected to yield greater selection responses for improving lamb survival in Canadian sheep compared to the current evaluation based on a categorical LS trait.

Genome-Wide Association Study of Morphological Defects in Nellore Cattle Using a Binary Trait Framework

Genes · 2025-10-14

BACKGROUND/OBJECTIVES: such as Nellore, the genetic basis of these traits remains poorly characterized. This study aimed to investigate the genetic architecture of six morphological defects in Nellore cattle, namely feet and legs malformation, chamfer asymmetry, fallen hump, loin deviation, jaw misalignment, and navel irregularities, via a genome-wide association study (GWAS) approach tailored for binary traits. METHODS: Depending on the trait, the number of genotyped animals analyzed ranged from 3369 to 23,206, using 385,079 SNPs (after quality control). Analyses were conducted using a linear mixed model framework adapted for binary outcomes. RESULTS: Significant associations were identified for four traits: feet and legs, chamfer, hump, and loin. No significant markers were detected for jaw or navel defects, likely due to lower sample sizes and trait incidence. Gene annotation revealed 49 candidate genes related to feet and legs, 4 for chamfer, 4 for hump, and 6 for loin. CONCLUSIONS: Candidate genes were enriched for biological functions, including bone remodeling, muscle development, lipid metabolism, and epithelial organization. Overlaps with QTL related to conformation, feed intake, reproductive traits, and carcass quality were also observed. These findings provide novel insights into the genetic control of morphological defects in Nellore cattle and may inform breeding strategies aimed at improving structural soundness.

Genomic-based genetic parameters for daily milk yield and various lactation persistency traits in American Holstein cattle

Journal of Dairy Science · 2025-05-12 · 4 citations

Genomic-based genetic parameters for daily milk yield and lactation persistency were estimated for the first 3 lactations in American Holstein cattle. Data included 5,235,411 daily milk yield records on automatic milking systems and milking parlors from 11,788 genotyped cows that calved from 2012 to 2019. A total of 62,029 SNPs remained after quality control. Single-trait random regression models were used to estimate variance and covariance components based on Bayesian inference. The systematic effects in the model included calving year, calving season, milking system, and calving age as linear and quadratic covariates. Random effects included additive genetic, permanent environment, and residual effects modeled with parametric (i.e., Wood, Wilmink, and Ali and Schaeffer) and non-parametric functions (i.e., Legendre orthogonal polynomials and B-splines). The same number of coefficients was considered for all fixed and random regressions in each analysis. Twenty-six models were compared for each lactation and models were selected based on goodness of fit and parsimony, estimates of variance components, heritabilities, correlations, credibility and interpretation of the results, and computational demand and time. Consequently, the model using fourth-order Legendre orthogonal polynomials and 10 classes of heterogeneous residual variance was considered the optimal model. The daily heritability estimates across DIM ranged from 0.12 to 0.22 for milk yield in parity 1 (MY1), 0.11 to 0.19 for milk yield in parity 2 (MY2), and from 0.03 to 0.15 for milk yield in parity 3 (MY3). For lactation persistency, the heritability estimates ranged from 0.05 to 0.18 for MY1, from 0.06 to 0.20 for MY2, and from 0.05 to 0.16 for MY3. Overall, the heritability estimate values were of low to moderate magnitude. In general, null to moderate genetic correlations were observed between lactation persistency and 305-d milk yield, indicating weak association between lactation persistency and milk yield, which is desirable (but should not be neglected). Ten measures of lactation persistency were evaluated using genomic breeding values. The third lactation persistency measurement (derived by subtracting the area under the curve of lactation in the final third from the area under the curve in the initial third of lactation) is recommended as the selection criterion, as these measures showed a moderate heritability (0.18 for MY1, 0.20 for MY2, and 0.15 for MY3) and a low to moderate genetic correlation (0.38 for MY1, 0.23 for MY2, and 0.20 for MY3) with 305-d milk yield. In summary, this study provides genetic parameter estimates for longitudinal daily milk yield and various lactation persistency traits in American Holstein cattle. These estimates will be useful to optimize lactation persistency in high-producing dairy cows through selection as they suggest null or weak association between LP and milk yield, and therefore, enhance the profitability of the dairy industry through extended lactations.

238 Applying the algorithm for proven and young in genome-wide association studies for reproductive traits in Nellore cattle.

Journal of Animal Science · 2025-10-01

Abstract Due to their significant economic impact, reproductive traits are crucial in beef cattle breeding programs. However, identifying the genomic regions associated with these traits requires large datasets to capture the complex genetic architecture underlying their variability. Traits such as age at first calving (AFC), stayability (STAY), and scrotal circumference measured at 365 and 450 days (SC365 and SC450, respectively) are highly polygenic, making it essential to leverage robust genomic methodologies to uncover meaningful associations. In this study we aimed to identify candidate genes and genomic regions associated with reproductive traits in Nellore cattle. A dataset containing genomic estimated breeding values (GEBVs) for 304,782 Nellore animals genotyped with 437,650 SNPs (after quality control) was made available by the Brazilian Association of Zebu Breeders (ABCZ). The Algorithm for Proven and Young (APY), implemented in the PREGSF90 software, was used to compute the matrix using 36,000 core animals. Subsequently, the SNP solutions were estimated by back-solving the GEBVs predicted by ABCZ using the single-step GBLUP method. We identified genomic regions associated with these traits using sliding windows of 175 consecutive SNPs, and the top 1% of genomic windows were used to annotate positional candidate genes. The top 1% genomic windows for these traits explained between 2.8% (STAY) to 3.0% (AFC) of the additive genetic variance, highlighting their polygenic nature. Functional analysis of the candidate genes within these genomic regions provided valuable insights into the genetic architecture underlying reproductive traits in Nellore cattle. For instance, our results revealed genes with important functions for each trait, such as SERPINA14 (plays a key role for the endometrial epithelium) and CCNB1 (essential for spindle checkpoint regulation, meiosis, and mitosis) identified for AFC. ARHGAP18 (involved in maintaining endothelial cell alignments) and KCNC1 (regulates potassium ion flow crucial for animal longevity) were identified for STAY. Additionally, genes such as EVI5 (important for centrosome stability), BRDT (essential for male germ cell differentiation), KIT (associated with the male germ cells), and (involved in spermatogenesis and intraflagellar transport) were identified for both SC365 and SC450. We identified genomic regions and candidate genes, some of which have been previously reported in the literature, while others are novel discoveries that warrant further investigation. These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.

PSVI-10 Genome-wide association study of mature cow weight in American Angus cattle.

Journal of Animal Science · 2025-10-01

Abstract Mature cow weight (MWT) significantly affects cowherd profitability by directly impacting maintenance feed requirements, which constitute a significant portion of production costs. The objectives of this study were to perform genome-wide association and functional genomic analyses to better characterize the genetic background of MWT in Angus cattle. A total of 434,746 phenotypic records from 222,907 Angus cows were used for the analyses. Of these, 33,764 cows were genotyped using various single nucleotide polymorphism (SNP) panels and imputed to a common marker density of 51,395 SNPs. Quality control procedures included filtering out markers with low call rates (< 0.90), extreme deviations from Hardy–Weinberg equilibrium (>0.15), parent-progeny Mendelian conflicts >0.01, and minor allele frequency < 0.01. A final dataset of 52,575 SNPs from 33,746 animals remained after quality control for further analyses. The single-step genome-wide association study (ssGWAS) method was used in the analyses. The Bonferroni correction method was applied to adjust for multiple tests based on the number of independent chromosomal segments at the chromosome-wide level. A total of 64 significant SNPs located on 10 chromosomes were significantly associated with MWT. These SNPs were located near 116 annotated genes, including 57 protein-coding genes, 50 long non-coding RNAs, 7 microRNAs, 1 ribosomal RNA, and 1 small nuclear RNA. Additionally, 31 quantitative trait loci were associated with key traits related to production (e.g., Body weight, metabolic body weight, dry matter intake, average daily gain, and length of productive life), meat and carcass (e.g., Bone weight, carcass weight, connective tissue amount, lean meat yield, longissimus muscle area, muscle creatine content), health (Tick resistance), milk production (Milk iron content, milk fat percentage, milk C14, C16, C18 indices, milk acid content), and reproduction (Calving index). These results contribute to a better understanding of the genomic regions associated with MWT in Angus cattle.

Beyond black and white: dissecting the genetic basis of skin depigmentation in Nellore cattle

Mammalian Genome · 2025-08-04 · 1 citations

Depigmentation defects in cattle are characterized by the absence of pigment in specific skin regions, increasing susceptibility to health issues and often leading to early culling. In Nellore cattle, depigmentation is primarily observed at the tail tip, mucous membranes, and as small patches across the body. This study aimed to estimate genetic parameters and perform a genome-wide association study (GWAS) for depigmentation in Nellore cattle. Data were sourced from the DeltaGen® breeding program, provided by Gensys®. Phenotypic records included 182,964 Nellore cattle, with a 6.8% incidence of depigmentation. Of these, 28,655 genotyped animals and 385,079 SNPs were available for the analysis. The ultra-fast generalized linear mixed model for binary traits (fastGWA-GLMM) was used for the GWAS, while variance components were estimated using a Bayesian threshold model and single-step methodology. The heritability of depigmentation was estimated at 0.12 on the observed scale and 0.54 on the liability scale. The GWAS identified 1,011 significant SNPs (p < 0.05 after Bonferroni correction) associated with depigmentation defects, located across chromosomes BTA6, BTA12, and BTA22. However, after performing a conditional GWAS to account for the top signal on BTA22, the original signal in the MITF region was no longer detected. In total, 234 genes were identified near the associated SNPs, including 129 protein-coding genes. Functional enrichment highlighted MITF, KIT and EDNRB as biologically relevant candidate genes. The gene ontology analysis highlighted biological processes related to melanogenesis, pigmentation, and hypopigmentation phenotypes, while the QTL enrichment analysis identified significant associations on BTA6 and BTA22. These findings improve our understanding of the genetic basis of depigmentation in Nellore cattle and may contribute to future selection strategies.

Genome-wide association and functional genomic analyses for udder conformation traits derived from data recorded by robotic milking systems in American Holstein cattle

Journal of Dairy Science · 2025-10-10 · 2 citations

The implementation of automatic milking systems (AMS) in modern dairy farming has significantly facilitated genetic evaluation of udder conformation traits in dairy cattle. As AMS-based udder conformation traits are highly heritable in Holstein cattle, this study aimed to identify genomic regions, QTL, and genes significantly associated with 3 udder conformation traits derived from Cartesian coordinates recorded by AMS in American Holstein cows. The traits evaluated included udder depth (UD), udder balance (UB), and distance front-rear (DFR). Phenotypic data consisted of 4,232,026 visit records to 36 Lely Astronaut A5 AMS robots for 4,280 American Holstein cows. A total of 4,118 cows also had genomic information for 57,598 SNPs. The SNP effects, together with their approximate P-values and proportion of the total additive genetic variance explained by them, were back solved from the GEBV using the POSTGSF90 software (version 2024-12-26, BLUPF90 Family of Programs, University of Georgia, Athens, GA). Multiple testing correction was applied using a modified Bonferroni method based on the number of independent chromosomal segments, and functional enrichment analyses were performed for the candidate positional genes and QTL. We identified 5 (BTA4, BTA22, and BTA29), 15 (BTA2, BTA8, BTA9, BTA15, BTA18, BTA23, and BTA26), and 19 (BTA1, BTA4, BTA6, BTA9, BTA10, BTA14, BTA17, BTA18, BTA23, and BTA25) genome-wide SNPs significantly associated with UB, DFR, and UD, respectively. There were no significant SNPs capturing more than 0.5% of the total additive genetic variance, highlighting the highly polygenic nature of these traits. The candidate genes overlapping with these genomic regions were previously reported to have biological functions that included but were not limited to transcription regulation, DNA repair mechanisms, inflammatory responses, epithelial differentiation processes, nutrient transport, extracellular matrix stabilization, cell division, and immune regulation. The strongest candidate genes associated with udder conformation traits are AFM, AFP, CREBBP, NLRP12, SP3, EBF2, B4GALT1, HDAC9, CDK13, and KDM2A. Furthermore, many QTL known to be associated with various milk production, reproduction, and health traits overlapped with the significant SNPs identified in this study. In summary, we identified significant genetic markers, candidate genes, and genomic regions associated with these traits, offering insights into the genetic architecture underlying udder conformation in Holstein cattle and its implications for dairy production.