Research Project: Identifying and Mitigating Factors that Limit Beef Production Efficiency

Location: Livestock and Range Research Laboratory

2025 Annual Report

Objectives
Objective 1. Determine the limiting nutrients for efficient growth of beef calves and reproduction in beef females grazing native forages at different stages of maturity. Sub-objective 1A: Determine effects of autumn/winter utilization (i.e., dormant) rangeland forage utilization on heifer development, and subsequent reproductive performance. Sub-objective 1B: Develop management strategies to improve rangeland cattle production and ecological stability through effective use of rangeland forage and supplementation of young cows. Sub-objective 1C: Identify better strategies for extensive rangeland livestock operations to prepare for seasonal and/or extended droughts through strategic supplementation of mature cows that optimize milk constituents and improve calf gain. Sub-objective 1D: Evaluation of livestock nutrition models for predicting weight gains/losses and body condition for livestock under supplemental and precision feeding. Objective 2. Determine the limitations of efficient embryonic development involving successful placentation and implantation to mitigate embryonic losses that decrease reproductive efficiency in cattle. Sub-objective 2A: Determine the physiological role of estradiol in endometrial function, conceptus growth, and gene expression that contribute to increased pregnancy success in cattle. Sub-objective 2B: Determine specific nutritional impacts on ovum fertility and early embryonic development in beef heifers that contribute to pregnancy success. Sub-objective 2C: Determine the effect of fertilization by suboptimal sperm on embryonic mortality in beef cattle. Objective 3. Optimize selection and assign breeding to maintain genetic variation (limit inbreeding) in Line 1 Hereford population. Sub-objective 3A: Utilize recombination rate to increase genetic variation and mitigate the accumulation of inbreeding. Sub-objective 3B: Evaluate the effects of selection on runs of homozygosity on inbreeding depression and performance of Line 1 Hereford. Objective 4. Determine G x E (genetic/genomic x environmental/management) interactions and the effect of heterozygosity on the composite trait of lifetime production efficiency in order to enable management practices that favor desired outcomes. Sub-objective 4A: Determine effects of dry lot heifer development, and subsequent reproductive performance from dams developed on different nutritional planes and evaluate the genetic variation and the existence and extent of genotype by nutritional environment interaction in heifer development. Sub-objective 4B: Determine differences in respiration gas fluxes throughout a production year from weaning to 3 years of age by cattle from dams developed on different planes of nutrition. Sub-objective 4C: Determine effects of variation among adult cows that experienced in utero nutrient restriction on embryonic survival of genetically similar embryos and their performance as calves.

Approach
Feed consumption, genetic selection and reproductive efficiency, are primary determinants of beef production efficiency. Our overarching goal is to better define these variables and develop strategies and technologies to alleviate their limitations to beef production efficiency. Sufficient nutrient intake resulting in adequate body energy stores are believed essential for reproduction. Thus, producers are challenged to match nutritional environment, which is subject to seasonal and annual variation, and various genotypes to obtain sustainable reproduction and female retention rates. Our approach is, of necessity, multi-disciplinary, involving both basic and applied aspects of genetics, nutrition, and physiology in a semi-arid grazing production system. This plan brings to fruition ongoing research and establishes investigations of genetic by environmental interactions as well as nutritional and physiological mechanisms limiting reproductive success. Four distinct cattle populations (an intercross of Charolaise (25%), Red Angus (50%) and Tarentaise (25%) herd, Line 1 Hereford herd, Precision Livestock Hereford-Angus herd, and Physiology Hereford-Angus herd) will be used to facilitate assessment of genetic, strategic nutritional and physiological factors affecting productivity. Distinct nutritional environments differing in provision of strategic supplements to cattle grazing forage will be tested to challenge the nutrition-reproduction interface to reveal roles of genetic, physiological, and management factors influencing feed utilization and animal productivity. Identification of genetic, nutritional, and physiological mechanisms that limit or contribute to beef production efficiency will facilitate early in life selection and management of replacement animals that are most fit for rangeland environments. This research will result in the establishment of evidence-based selection, development and management protocols that provide producers options for addressing industry needs and dealing with climate and environmental variability.

Progress Report
Objective 1, Sub-objective 1A: Growth and reproductive success among heifers receiving strategic supplementation of key amino acids during development were recorded and supplement intake was measured on an individual animal basis. Objective 1, Sub-objective 1B: Data collection continued from cows that were traditionally managed or whose performance was continually measured using precision measurement technologies for management decisions. GPS collars were used to collect animal locations of both traditional and precision managed pastures for 3 months during both the growing season and dormant season. Unmanned aerial vehicle (UAV) imagery and pasture biomass were collected before and after grazing for the two herd types. UAV imagery was processed to estimate vegetation height and volume and regressions were conducted to explore relationships between pasture biomass and UAV-derived vegetation measurements. Near infrared reflectance spectrometry (NIRS) readings were collected from quadrats in pastures prior to biomass collection and after oven drying of samples. A NIRS calibration equation was developed to predict forage crude protein from oven-dried biomass. Objective 1, Sub-objective 1C: Milk constituents, rumen microbiome, and calf growth data have been collected from cows receiving strategic supplementation before and after calving to determine the ability of calcium propionate and rumen protected fatty acid supplements to improve performance. Objective 1, Sub-objective 1D: Scripting to download and process precision scale and feeder data from the vendor’s application programming interface (API) were completed. This script and scripts for downloading weather station and forecast data were merged into a platform to analyze ruminant nutrition models for predicting weight gain/losses and supplemental feed needs on an individual animal and herd basis. Three ruminant nutrition models are being tested that vary in complexity of data requirements and outputs. Data from the past two years were input into the models and outputs are being evaluated to assess model capabilities for predicting livestock performance. GPS collar data has been analyzed to evaluate movement, grazing locations, and landscape utilization in traditional versus precision management during the growing and dormant seasons. UAV and satellite imagery have been processed for combining with GPS location data to examine spectral and terrain qualities of livestock grazing locations. Objective 2, Sub-objective 2A: Reproductive tissues have been collected, transcriptomics conducted, and data analyses have been completed. Manuscripts are undergoing co-author input. Objective 2, Sub-objective 2B: Targeted nutrient supplementation and embryo collection has been delayed awaiting bioinformatics analyses of transcriptomics to lead these investigations. Objective 2, Sub-objective 2C: Laboratory analyses of bovine sperm fertility biomarkers (especially negative biomarkers of fertility) have identified the nanopurification cocktail containing magnetic particles coated with three specific lectins identified for removal of sub-fertile sperm from bull ejaculates before freezing to enhance fertility of semen doses for a heterospermic field trial in dairy cows. A field trial is planned for Fall 2026 because of the decreased fertility associated with heat stress in dairy cows. Two years of data have been collected, and a third year initiated, in a field trial involving artificial insemination (AI) and natural service in which in vitro semen biomarker evaluation of ejaculates and offspring parentage will be evaluated to identify additional sperm fertility biomarkers. Objective 3, Sub-objective 3A: A computer program has been developed, phenotypes collected, and manuscript published that estimated the recombination rate of genes associated with important production traits in cattle. The Line 1 Hereford population has greater inbreeding and lower recombination rate than a composite population (CGC). Further, the average genetic recombination distance was 1.34 centi-Morgans per Mega-base (cM/Mb) for the composite cattle and 1.36 cM/Mb for Line 1 cattle. Another study evaluated a composite Sanaga beef cattle breed called Mashona, which is known for its resistance to pests and heat. The study used high-density single nucleotide markers to assess the genetic composition of U.S. Mashona to see a better understanding of their population history, detect selection signatures and estimate genetic diversity that could be beneficial to the U.S. cattle industry. Genomic analysis of Mashona demonstrated that the average proportion of Bos taurus genetics in Mashona was 0.81, with individual proportions ranging from 0.77 to 0.84. Examining regions with high autozygosity or differentiated from other breeds revealed several selection signatures in the Mashona population associated with production traits and adaptability. A region on chromosome 6 contained genes associated with horn fly resistance. Additional regions contained genes and quantitative trait loci (QTLs) associated with calving ease, and increased reproduction and maternal traits. Mashona’s unique level of genetic diversity makes it a good candidate breed for beef cattle crossbreeding in hot and humid areas of the United States. However, due to its relatively small population size, breeders will need to balance existing genetic diversity and selection for important traits. Objective 4, Sub-objective 4A: Growth and performance data has not been collected from heifers as described within the project plan because of strict culling that occurred within this herd of cattle to meet Montana Agriculture Experiment Station requirements. There are no more cattle in this herd that will allow evaluation of heifers as proposed, but we have begun a retrospective study using previously compiled heifer data from this same population that will allow us to make strong inferences on genotype by environment interactions affecting longevity. Objective 4, Sub-objective 4B: Daily respiration (methane, carbon dioxide, and oxygen) and production efficiency has been collected from heifers, two-year-olds, and three-year-olds with different epigenetic inputs. Results have revealed that daily methane and carbon dioxide respiration data must be collected for 45 and 28 days, respectively, to obtain emission values with 80% accuracy in cattle. Objective 4, Sub-objective 4C: There are no longer any cows remaining that had experienced in-utero nutrient restrictions available for study due to strict culling of older cows by Montana Agriculture Experiment Station, and thus, this subobjective is not attainable. In another study, placentation efficiency during pregnancy in cattle is being evaluated by comparing in-vitro derived embryos, AI or natural service to produce pregnancies. We have collected gestational data and placental tissues in efforts to understand mechanisms associated with longer gestation and large offspring syndrome that can occur following in-vitro fertilization, a procedure that has surpassed the conventional superovulation and embryo flushing for embryo transfer in the beef seedstock industry and “beef on dairy” calf production.

Accomplishments
1. Early identification of cattle pregnancy status and embryonic mortality. The gold standard for pregnancy detection in cattle is trans-rectal ultrasound evaluation of the uterus, but the earliest this can consistently be used to detect pregnancy is day 30 of gestation. Fertilization is successful in 91% of matings in cattle but embryonic survival until day 30 of gestation is rarely greater than 70%, indicating that at least 20% of pregnancies are lost after fertilization and before day 30 of gestation. ARS researchers at Miles City, Montana, and their collaborators demonstrated that pregnancy could be detected as early as day 18 of gestation by measuring interferon tau, the embryonic-produced maternal recognition of pregnancy signal to the dam, from cervical swab samples. This is a significant advancement that will aid in characterizing causes of embryonic loss during placental attachment, which occurs gradually beginning around day 18 of gestation. This early pregnancy detection should be more beneficial for dairy producers, who handle cows multiple times daily and predominantly use artificial insemination because it will allow an approximately 10 day earlier re-insemination of cows that are not pregnant. Dairy cows often experience much greater pregnancy loss (~50%) between fertilization and day 30 of gestation, which emphasizes the value of this novel method of pregnancy detection.

2. Improved fertility assessment of sperm from beef bulls. Currently, fertility can be assessed in beef bulls by veterinarians using the Breeding Soundness Examination (BSE). However, the BSE has a pass/fail outcome that is very subjective and is more valuable in identifying infertile bulls. One of the more consistent laboratory measures of bull fertility has been sustained motility of sperm post-thaw (for example 3 hours post-thaw) for frozen straws of semen or long-term motility in fresh ejaculates. Both methods are labor and time intensive. A more common approach recently developed has been the use of the fluorescent dye JC-1 to evaluate mitochondrial energy potential in sperm to use as a measure of sustained motility. ARS researchers in Miles City, Montana, identified that a problem with this method is that a majority of dead sperm also fluoresce. Thus, ARS researchers incorporated a viability component (dye) to more accurately assess sustained motility in semen from livestock species. This improved assay will assist in the development of a bull fertility index which will quantitatively assess fertility in bulls and allow management decisions of how to use the more fertile bulls within an operation. Use of more fertile bulls within an operation should improve overall herd fertility and may even allow selection for increased fertility in livestock.

3. Machine learning evaluated for the genomic prediction of growth traits in a composite beef cattle population. Genomic selection is widely used in the livestock industry, yet existing models for predicting genomic breeding values often remain suboptimal. Machine learning models provide an opportunity to enhance prediction accuracy due to their flexibility to accommodate both linear and non-linear relationships. ARS researchers at Miles City, Montana, evaluated four machine learning models, Random Forest, Support Vector Machine, Convolutional Neural Networks and Multi-Layer Perceptron for predicting genomic values related to birth weight (BW), weaning weight (WW), and yearling weight (YW), in comparison with conventional models, GBLUP (Genomic Best Linear Unbiased Prediction), Bayes A and Bayes B. The results showed that the GBLUP model achieved the highest prediction accuracy for both BW and YW, whereas the Random Forest model exhibited superior prediction accuracy for WW. Additionally, GBLUP outperformed the other models in terms of model fit. Overall, the GBLUP model had superior prediction accuracy and model fit compared to the machine learning models tested. The machine learning models showed promising results which warrants further research in refining these models.

4. Improved calf growth and reproductive performance with maternal injectable mineral. In beef herds, the calf is the primary source of profit for the producer. Consequently, the growth and reproductive efficiency of both male and female calves is critical to the success of the operation. Most of the previous research has investigated the role of trace minerals in calf growth to weaning, calf health, and feedlot growth and performance. Little is known about how mineral supplementation treatments to cows during pregnancy affects growth and reproductive performance of their calves, but a growing body of evidence suggests it may improve both traits. Ovarian and testicular development begins during gestation and studies have begun to investigate the effects of cow nutrition and mineral supplement on reproductive measures in their offspring. Obviously, in order for mineral supplementation to affect subsequent calf development, the cows would need to consume mineral supplements on a regular basis. ARS scientists at Miles City, Montana, demonstrated that a single injection of trace minerals to cows during gestation tended to improve the weaning weights of calves and increased the sperm producing capability of bull calves at 14 months of age. The 6-pound increase in weaning weight as a result of in-utero mineral supplementation would be worth at least $20 more profit per calf in today’s economy. Bulls whose dams received injectable trace mineral during pregnancy had increased body weights and scrotal circumference at 14 months of age and improved sperm quality compared to bulls born to dams that did not receive injectable mineral during pregnancy. Because these benefits directly impact the growth and fertility of bull offspring, producers should consider providing injectable mineral to their pregnant cows to improve their operation’s income.

5. Genetic characterization and diversity of Mashona cattle within the United States beef industry would benefit beef cattle crossbreeding in hot and humid areas. Genomic evaluation of the African composite Sanaga beef cattle breed, Mashona, revealed genetic markers associated with important traits in beef cattle. This breed is known for its resistance to pests and heat. Recently, several United States producers in hot and humid areas of the country started using this breed in crossbreeding programs and reported increased resistance to heat stress and improvement in maternal traits. ARS researchers at Miles City, Montana, used high-density single nucleotide markers to assess the genetic composition of U.S. Mashona to seek a better understanding of their population history, detect selection signatures, and determine if this breed contains unique genetic diversity that could be beneficial to the U.S. cattle industry. The results showed that the average proportion of Bos taurus genetics in Mashona was 0.81, with individual proportions ranging from 0.77 to 0.84. Examining regions with high autozygosity or differentiated from other breeds revealed several selection signatures in the Mashona population associated with production traits and adaptability. A region on chromosome 6 contained genes associated with horn fly resistance. Additional regions contained genes and quantitative trait loci (QTLs) associated with calving ease, increased reproduction and maternal traits. Mashona’s unique level of genetic diversity makes it a good candidate breed for beef cattle crossbreeding in hot and humid areas of the United States. However, due to its relatively small population size, breeders will need to balance existing genetic diversity and selection for important traits.

6. Drone-derived estimates of pasture forage provide accurate measures of livestock feed availability. In recent years, monitoring and management of rangelands using drones has increased due to the ability of drones to capture images at very high resolutions (< 1 inch), which could be helpful in estimating forage available for livestock grazing. ARS researchers in Miles City, Montana, collaborated with scientists from Texas A&M University-Kingsville to compare conventional methods of forage estimation (vegetation clipping and double sampling techniques) to drone-based estimation to assess accuracy and time efficiency. Six forage estimation approaches were tested on South Texas rangelands including: 1) vegetation clipping; 2) double sampling (i.e., clipping some plots and visual estimation of production in others); 3) drone estimates flown from an altitude of 50-m above ground level (AGL) combined with double sampling; 4) drone estimates from 100 m AGL with double sampling; 5) drone estimates from 50-m AGL with vegetation clipping; and 6) drone estimates from 100 AGL with vegetation clipping. Statistical analyses indicated that all six methods were similar in their ability to estimate forage biomass. Variability in drone-based forage estimates at the pasture scale decreased with lower flight altitudes (which increased image resolution) and with increasing numbers of clipped samples. Vegetation clipping combined with drone sampling was found to be the most time- efficient method for pasture biomass estimation. Results indicate that producers and land managers could benefit by using drones to quantify forage biomass over larger areas and potentially less accessible terrain.

7. New artificial intelligence (AI) forecasts offer better long-term livestock forage predictions. The ability to accurately predict future rangeland forage conditions can be helpful for livestock decision making in the face of drought or for other conditions that may affect stocking rates. Many existing forecasting tools can only forecast a single future period. The ability to forecast multiple dates into the future would potentially increase usefulness for assessing future risks. ARS researchers in Miles City, Montana, collaborated with researchers from Texas A&M AgriLife Research to compare two new artificial intelligence (AI) methods for predicting future forage conditions (from 1 month to 12 months) with current AI methods that can predict single and multiple dates in the future. Results indicated that methods predicting single dates were more accurate for short-term forecasts (1 month). For medium to longer term forecasts (3 to 12 months), both single and multiple-date forecasts were useful. Although predicting long-term forage biomass (12 months) remains challenging, two new AI forecasting methods would help producers make better risk management and supplemental feed decisions, especially when facing a drought.

Review Publications
Hay, E.A., Ling, A.S. 2025. Genomic evaluation of recombination in small highly inbred beef cattle populations. Animal Research and One Health. https://doi.org/10.1002/aro2.103.
Zhu, B., Wang, T., Niu, Q., Wang, Z., Hay, E.A., Xu, L., Chen, Y., Zhang, L., Gao, X., Gao, H., Cao, Y., Xhao, Y., Li, J., Xu, L. 2025. Multiple strategies association revealed functional candidate FASN gene for fatty acid composition in cattle. Communications Biology. 8. Article 208. https://doi.org/10.1038/s42003-025-07604-z.
Hay, E.A. 2024. Machine learning for genomic prediction of growth traits in a composite beef cattle population. Animals. 14(20). Article 3014. https://doi.org/10.3390/ani14203014.
Ling, A.S., Hay, E.A., Lozada-Soto, E.A., Hayes, E., Browning, R., Blackburn, H.D. 2025. Out of Africa: Genetic characterization and diversity of Mashona cattle in the United States. Journal of Animal Science. https://doi.org/10.1093/jas/skaf045.
Zezeski, A.L., Hamilton, L.E., Geary, T.W. 2025. Improved evaluation of mitochondrial membrane potential in bovine spermatozoa using JC-1 with flow cytometry. Biology of Reproduction. https://doi.org/10.1093/biolre/ioaf081.
Bishop, J.V., Guzeloglu, A., Scheller, T., Docheff, J., Gonzalez-Berrios, C.L., Van Campen, H., Nett, T.M., Zezeski, A.L., Geary, T.W., Thatcher, W.W., Hansen, T.R. 2025. Early identification of bovine pregnancy status and embryonic mortality. Biology of Reproduction. 112(5):981-995. https://doi.org/10.1093/biolre/ioaf066.
Brenner, M.A., Marques, R.S., Posbergh, C.J., Zezeski, A.L., Geary, T.W., McCoski, S.R. 2025. Maternal injectable mineral effects on male and female offspring growth and reproductive parameters. Animals. 15. Article 330. https://doi.org/10.3390/ani15030330.
Reinhart, K.O., Rinella, M.J., Waterman, R.C., Sanni Worogo, H.S., Vermeire, L.T. 2024. Carbon sequestration uncertainty: is grazing-induced soil organic carbon accrual offset by inorganic carbon loss? Soil & Tillage Research. 46(2). Article RJ24006. https://doi.org/10.1071/RJ24006.
MacNeil, M., Waterman, R.C. 2025. Repeatability of carbon dioxide and methane emissions and oxygen consumption by forage-consuming beef heifers. Animal. 19(4). Article 101469. https://doi.org/10.1016/j.animal.2025.101469.
Noa-Yarasca, E., Osorio Leyton, J.M., Angerer, J.P. 2024. Extending multi-output methods for long-term aboveground biomass time series forecasting using convolutional neural networks. Machine Learning and Knowledge Extraction. 6(3):1633-1652. https://doi.org/10.3390/make6030079.
McGranahan, D.A., Angerer, J.P. 2025. Evaluating an attempt to restore summer fire in the Northern Great Plains. Environmental Management. 75:1656-1664. https://doi.org/10.1007/s00267-025-02209-y.
Page, M.T., Perotto-Baldivieso, H.L., Ortega-S, J., Tanner, E.P., Angerer, J.P., Combs, R.C., Johnston, B.K., Ramirez, M., Camacho, A.M., Dimaggio, A.M., Daniels, W.D., Kimmet, A. 2025. Developing large-scale pasture approaches to quantify forage mass in rangelands using drones. Rangeland Ecology and Management. 100:111-120. https://doi.org/10.1016/j.rama.2025.03.005.