The data collection phase of our study continues. We have completed tracking and DNA collection at seven ranches in New Mexico, Colorado, Nevada, Arizona and Wyoming. Currently, we have collected DNA samples and are tracking cows at the Fort Union Ranch in New Mexico and plan to track cows at the Squaw Valley Ranch in Nevada in the spring and summer of 2018. We have obtained high-density single nucleotide polymorphism (SNP) genotypes on 72 of the cows that have been tracked earlier in the study. We hope to get high-density SNP genotypes and adequate tracking data from additional cows to meet our goal of 200 cows before the end of the study.
In our previous SARE study (SW09-054), five quantitative trait loci (QTL) and underlying candidate genes (ACN9, FAM48A, GRM5, MAML3, and RUSC2) were found to be associated with grazing distribution traits in cattle. Using RNA sequencing technology and six tissue types from 10 Angus steers, we examined these candidate genes and identified additional 21 SNP that can be added to a previously develop 50-SNP panel for genotype associations with grazing distribution phenotypes. We also examined the repeatability of cattle spatial movement patterns during tracking and found that cattle used similar types of terrain during sampling period at most, but not all ranches. The lack of consistency may be due to cattle familiarity with study pastures and variability of topographical features. We also are examining the relationship between cow body size and terrain use. Preliminary results from suggest that larger cows may travel farther than smaller cows during the winter, but data from other ranches will be evaluated and analyses revised accordingly.
A. Validate previously identified genetic markers discovered from previous Western SARE funded research project (SW09-054) shown to be associated with cattle grazing distribution.
A1. Track at least 200 cows with GPS collars for at least 2 months at 7 or more mountainous ranches in New Mexico, Arizona, Colorado, and Nevada.
A2. Collect DNA samples from tracked cows.
A3. Analyze DNA from tracked cows using High Density single nucleotide polymorphism (SNP) chip (BovineSNPHD; ~770,000 genotypes) and determine if previously identified genetic markers are similarly associated with grazing distribution traits as found in the previous Western SARE study SW09-054.
B.Verify and enhance prototype DNA test(s) for identifying superior and inferior genotypes for grazing distribution.
B1. Analyze genetic markers used in a small prototype (180 SNP) panel with cows tracked in this project and compare associations to previously studied cattle herds.
B2. Add or replace genetic markers in the prototype SNP panel so that the most predictive SNP are used in final DNA test.
C. Develop procedures and end user tools for estimating molecular breeding values (MBV) for grazing distribution.
C1. Develop statistical procedures for estimating MBV for grazing distribution from cattle DNA samples.
C2. Automate download of genotypes from DNA service providing company into CSU Center for Genetic Evaluation of Livestock (CGEL) software for estimating MBV.
C3. Develop web-based software so producers can easily obtain the MBV for grazing distribution for each animal.
D. Provide training to ranchers and seedstock producers specifically designed to increase their knowledge and ability to utilize MBV to select cattle for improved grazing distribution.
D1. Show ranchers how to collect DNA samples from cattle and how to submit the samples for analysis.
D2. Teach ranchers how to obtain MBV from web-based systems for their cattle and utilize this information to select for improved grazing distribution.
D3. Show how ranchers can incorporate grazing distribution goals and objectives into their ranch-specific breeding program.
E. Estimate the potential for improvement of grazing distribution through MBV and genetic selection.
E1. Estimate the potential for improvement in grazing distribution through genetic selection under varying scenarios.
E2. Deliver this information to ranchers and land managers via multiple kinds of extension programs so they will know the potential for improving on-the-ground grazing use.
Our previous SARE study (SW09-054) showed that grazing distribution could be inherited and demonstrated the potential for practically applying selection to manipulate cattle grazing patterns. However, these results were based on 160 cows and additional research is needed before these exciting results can be used by ranchers. We propose to validate previously-identified genetic markers shown to be associated with cattle use of rugged terrain and areas far from water. We plan to verify and enhance prototype DNA test(s) that can be used to identify cows and bulls with superior and inferior grazing distribution genotypes. Potentially, these tests can be used to identify bulls that will sire daughters that use more rugged topography and travel farther water. These DNA tests could also be used to select (cull) cows with superior (inferior) genotypes. Such selections would not require expensive GPS tracking. Only a blood sample and DNA test would be needed at a cost of potentially less than $30 per animal. Data from DNA tests are complex so we will develop molecular breeding values (MBV) for producers to use in their cattle breeding selection programs. The MBV will be similar to routinely calculated Expected Progeny Differences (EPD’s) that ranchers regularly use to select bulls and replacement heifers.
During years 1 and 2, GPS tracking data and DNA samples will be collected at nine ranches to verify results of our previous research project (SARE SW09-054). The ranches are 1) Heartstone Angus (Dick and Erin Evans) near Silver City, NM; 2) Wilbanks Ranches (Aaron Wilbanks) near Mayhill, NM; 3) Ensz Ranch (Wesley Ensz) near Center, CO; 4) Silver Spur Ranches (Nick Wamsley) near Saratoga, WY; 5) Squaw Valley Ranch (Jessie Braatz) near Paradise Valley, NV; 6) Gund Ranch (Jon Wilker) near Austin, NV; 7) the OR O Ranch near Prescott, AZ, 8) NMSU College Ranch (aka Chihuahuan Desert Rangeland Research Center) near Las Cruces, NM; and, 9) Fort Union Ranch (James Stuart) near Watrous, NM. We have alternate/additional ranches if conflicts occur or if we have time to track additional cows: including the Rouse Ranch near Saratoga, WY, and Santa Rita Ranch near Green Valley, AZ. We will track 25 to 30 randomly-selected mature Angus cows at each ranch. Cows will be tracked in extensive pastures with rugged terrain using GPS collars recording positions at 10-minute intervals for 2 to 4 months at each ranch. Currently, NMSU has 28 GPS collars and has budgeted for 2 more collars for a total of 30 collars. Blood samples will be collected from all tracked cows. DNA will be stored until genotyping analyses using commercially available free-to-air (FTA) cards and also extracted from blood samples and frozen. GPS tracking data, digital elevation models, and geographical information software will be used to calculate indices of terrain use based on our previous study. Indices combine individual cow use of slope, elevation and distance from water so that cows can be ranked and compared at each ranch (contemporary group analyses).
All tracked cows will be genotyped using the Illumina Bovine SNPHD array, which evaluates approximately 770,000 genetic markers (i.e., single nucleotide polymorphisms; SNP). In addition, an 180 SNP panel will be developed including new markers from a transcriptome analysis and markers from the Illumina HD chip that were earlier associated with grazing distribution phenotypes (SARE SW09-054) . Samples will be genotyped using an allele discrimination platform by MALDI-TOF mass spectrometry (Sequenom MassARRAY(R)) (Neogen/GeneSeek Inc., Lincoln, NE) to perform association studies. Genotyping results and phenotypes will be processed and analyzed with SNP Variation Suite 7 software (SVS) from Golden Helix (Bozeman, Montana).
Marker-trait association analysis will be performed using a linear regression test under additive, dominant and recessive model assumptions. Statistical analyses will be performed using the genotype association and regression modules from SVS. A similar approach was successfully used by Dr. Medrano’s lab to analyze the association of tag SNP on BTA4 with milk production traits and tag SNP on the bovine STAT6 gene with carcass traits and in our previous SARE study (09—054). In brief, the adjusted phenotype is fitted to every encoded genotype. This linear regression will also be performed including SNP interactions using the SVS regression module. False discovery rate will be controlled according to the method of (Storey, 2002). The statistical models will include ranch and season so that comparisons and associations will be made across contemporary groups and adjusted for differences in terrain, ranch management and season. Results from the marker trait analyses (both SNPHD array and the 180 SNP panel) will be compared to the results from our previous study.
Statistical associations and animal pedigrees will be used by the CSU-Center for Genetic Evaluation of Livestock (CGEL) for estimation of MBV for whole genome selection. Genomic selection has moved to the forefront of discussion in the scientific discipline of breeding and genetics globally. Because of the great diversity in production systems and the traits of interest, using genotype information as a correlated trait to other production traits (growth, carcass, etc.) is a feasible approach to estimate MBV. The CGEL has helped to develop this approach for the Angus breed associations, which simply incorporates MBV into multi-trait genetic evaluation. Most importantly, for traits of grazing distribution, training and predicting analyses using the genotypes to estimate MBV are required. Therefore, we will train an analysis with the genotype and phenotype data on a base population and then predict the MBV from the genotypes of each individual in other populations. This procedure will produce breeding values of low accuracy; however, it is very effective for yielding breeding values that can be used rank animals having genotypes from most to least desirable. Therefore, MBV will be outputted for all animals and these numeric breeding values will be made available for ranchers to make selection decisions for herd improvement.
We plan to estimate the potential genetic progress that could be made in grazing distribution under alternative selection scenarios (e.g., high and low selection pressure) to evaluate the extent that grazing patterns could be manipulated. Tracking data will be summarized for various MBV. The contribution of hill climbers (favorable MBV), bottom dwellers (unfavorable MBV) and intermediates to forage removal in various parts of pastures (e.g., steep slopes or riparian areas) will be estimated. Time spent grazing by cattle in a part of a pasture multiplied by estimated forage intake rates should correspond to forage removal. Based on projections of genetic progress from selection, we can change the proportion of hill climbers, bottom dwellers and intermediates within a herd and then calculate corresponding changes in forage removal. This information will allow us to estimate the efficacy of selection for distribution under various scenarios, which will allow ranchers and land managers to compare genetic selection with other tools for manipulating grazing distribution
We have completed cattle tracking and DNA sample collection at 7 ranches. The ranches include: Wilbanks Ranch (Mayhill, NM); Heartstone Angus Rach (Silver City, NM), Chihuahuan Desert Rangeland Research Center (CDRRC, Las Cruces, NM); Gund Ranch (Austin, NV); Siliver Spur Ranch (Saratoga, WY), Ensz Ranch (Center, CO) and the ORO Ranch (Prescott, AZ). We obtained high density single nucleotide polymorphism (SNP) genotypes for 72 cows. The genotyped cows were tracked at the Willbanks, Heartstone Angus, Gund, CDRRC and Ensz Ranches. We encountered problems with the tracking collars earlier in the study. Some batteries in the Lotek collars did not last as long as expected, and their data will not be able to be used for most of the analyses. Also, the attachment apparatus of i-gotU tracking collars failed on several cows when first deployed, and the GPS receiver was lost. We believe that these problems have been solved and we will be able to successfully track more cows during the remainder of the study. Currently, we have collected DNA samples and are tracking 32 cows at the Fort Union Ranch (near Las Vegas, NM). We are planning on tracking and collecting DNA samples at the Squaw Valley Ranch near Winnemucca, NM and another ranch (to be determined) during the spring and summer 2018. This should allow us to meet our goal of successfully tracking and genotyping a total 200 cows.
Our previous study (SARE SW09-54) suggested that the traits used in indices to quantify grazing distribution are moderately heritable; therefore, genomic selection could be used to improve grazing distribution. Five quantitative trait loci (QTL) and underlying candidate genes (ACN9, FAM48A, GRM5, MAML3, and RUSC2) were found to be associated with grazing distribution traits in cattle. We examined these candidate genes and identified additional SNP that can be incorporated into a previously developed 50-SNP panel used for genotype associations with grazing distribution phenotypes. Sequencing of RNA (RNA-Seq) yielded 30 million reads (single-read) per sample from 6 tissues (aorta, LM muscle, lung, pulmonary artery, and right and left ventricle) collected as part of an altitude tolerance study of Angus cattle. These tissues were from 10 steers with outlying pulmonary arterial pressure observations and unique sires. Sequences were assembled to the annotated bovine reference genome (UMD3.1; release annotation 87) and analyzed using CLC Genomics Workbench (version 8.0). Variant detection was performed using two methods: (1) individual samples and (2) a pool of all samples. No variants were detected in GRM5, MAML3, and RUSC2; however, individual sample analysis identified 30 SNP within ACN9 and FAM48A, and pooled sample analysis identified 184 SNP within ACN9 and FAM48A. Twenty-one SNP were identified in both approaches. The Ensemble Variant Effect Predictor was used to determine the functional consequence of each SNP. Of the 21 SNP, 16 were intronic, four were exonic, and one was reported to be a downstream variant and a splice acceptor variant. The SNP discovered using RNASeq technology were compared to the exonic SNP in dbSNP within the 5 candidate genes. There were 1,663 exonic SNP in dbSNP in these genes. One synonymous SNP, located within ACN9 (rs382949979), was observed in both data from RNASeq and dbSNP. In summary, 21 SNP were discovered in two of the five candidate genes underlying QTL associated with grazing distribution. These SNP will be incorporated in a new genotyping panel to examine associations with the grazing distribution phenotypes.
We use tracking collars to monitor cattle movements and develop grazing distribution phenotypes. However, few studies have evaluated the consistency of cattle movements over time in rugged rangeland pastures. We examined the repeatability of cattle movement patterns at five locations in New Mexico and Arizona: the Chihuahuan Desert Rangeland Research Center (CDRRC), Evans Ranch, Wilbanks Ranch, Hartley Ranch and Todd Ranch. These data were obtained from this study and our previous SARE study (SW09-054). Eight to 19 randomly-selected cows from herds of 40 to 200 cows were tracked with GPS collars at each ranch. Cows were tracked at either 10- or 15-minute intervals. Terrain use was summarized by week. A repeated measures analysis was conducted on each ranch using the weekly average of slope use, elevation use, and distance from water as the dependent variables. Intraclass correlations of weekly averages of the three terrain use metrics were used to assess temporal consistency of grazing distribution traits. Week was a fixed effect and cow was a random effect. Intraclass correlations of terrain use by individual cows varied among ranches. The Wilbanks Ranch had the strongest intraclass correlations for slope, elevation, and distance to water of 0.60, 0.50, and 0.77, respectively. Intraclass correlations for elevation at the Hartley and Todd Ranches were strong, 0.61 and 0.71, respectively, but correlations for slope and distance to water were weak to moderate (0.18 to 0.30). In contrast, intraclass correlation at the CDRRC and Evans Ranch were weak (0.00 to 0.08). Our results suggest that consistency of terrain use by cattle can vary by location; however, these relationships are positive and moderate to strong at most ranches. Factors such as cattle familiarity with pastures and the nature of the terrain features may explain part of this variability, however additional analyses examining how temporal changes in terrain use affect this phenotype are planned.
Terrain use and grazing distribution traits in cattle are difficult to evaluate because movement patterns are temporally variable due to ever changing climatic and forage conditions. Terrain-use can be monitored with GPS collars, but the length of tracking is constrained by battery life and on-site grazing and management plans. Cattle tracking studies have varied from days to months. We evaluated data from our previous SARE study (SW09-054) to determine if shorter, 30 to 60 day, sampling durations were as effective as a 3 month tracking periods for characterizing slope and elevation use, and vertical and horizontal distance traveled from water of individual cows. Fifteen Limousin cows from a herd of 250 cows were tracked at 15-minute intervals for 92 days during late winter and early spring in a 9,065 ha pasture that included both gentle and rugged terrain. Terrain-use during 30- and 60-day periods at the beginning and end of the tracking were compared to the full 92-day period. Slope, elevation, and vertical and horizontal distance from water from the full sample period were regressed on values from the shorter data subsets and correlations were calculated between sampling periods for each pair of terrain use metrics. The 60-day periods showed strong agreement with the full 92-day period with correlation coefficients varying from 0.90 to 0.97. Correlations between 30-day periods and the 92-day period varied from 0.30 for distance to water during the early period to 0.90 to 0.93 for all traits during the late 30-day period. These preliminary analyses suggest that 2-month tracking periods are equivalent to 3-month tracking periods for identifying differences in terrain-use among beef cows. These findings help facilitate collection of grazing distribution phenotypes from larger number of cattle for genetic improvement.
Only a few studies have examined the impact of cow size on grazing distribution. Tracking data collected in this project at the CDRRC (Las Cruces, NM) was used to determine if there are relationships between cow body size and terrain use. Fifteen Brangus cows (2 to 13 years of age) were tracked at 10-minute intervals in a 2635-ha pasture with rugged terrain for 12 weeks during the winter prior to calving. After fitting the statistical model for age, residual correlations were used to examine the relationship between cow weight, body condition score, linear measures of cow size, and terrain use metrics. Larger cows with bigger heart girths and heavier weights used areas farther horizontally from water than smaller cows and the associated residual correlations were 0.52 and 0.64, respectively. Cows with greater hip heights and heavier weights used areas farter vertically from water (residual correlations of 0.85 and 0.53, respectively). Although the number of cows are limited, these preliminary results suggest that larger cows may be more willing to travel farther from water than smaller cows in extensive, rugged desert pastures during cool winter conditions.
Educational & Outreach Activities
We are currently in the data collection portion of the study. So it is too early to begin our planned outreach activities to producers. However, we have presented results from our previous Western SARE study (SW09-054) and a summary of this study at two international conferences: International Rangeland Congress in Saskatoon, Saskatchewan, Canada (July 2016) and the PA17 – The International Tri-Conference for Precision Agriculture in 2017 in Hamilton, New Zealand (October 2017). Results were also presented scientific conferences in the USA: Society for Range Management Annual Meeting in February 2016 and January 2017 (St George, UT) and American Society of Animal Science in July 2017 (Baltimore, MD). Six additional presentations based on this study will be given at the next annual meeting of the Society for Range Management in late January 2018 (Reno, NV).