Duration: 10/01/2019 to 09/30/2024

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Statement of Issues and Justification

The states in the Southeastern U.S. from the east coast to Texas and Oklahoma are home to a large portion of the nation’s cowherd (40%; USDA, 2014) and contribute a comparable proportion of the calves in the chain of beef production. This region, to varying degrees, has distinct issues facing beef producers: 1) cattle that are adapted to the local heat, humidity, and poor forage quality are not as productive infertility, growth, and carcass traits; 2) cattle that usually excel in performance infertility, growth, and carcass traits lack necessary adaptation to the regional conditions to exhibit high levels of performance. It is common throughout the region to use the Brahman breed in purebred and crossbreeding situations to provide adaptation and utilize heterosis for fertility traits. A probable consequence of changing global climate is that temperate regions are likely to become warmer (Field et al., 2014). This would extend the adaptation/productivity issues further to the U.S. North. Another important consideration for the regional beef production is that calves that are born and weaned in the South are generally transported to the Great Plains for growth on winter pasture and in feedlot feeding conditions prior to slaughter; these Southern-born calves are not adapted to winter conditions on the Great Plains.

Strategies for addressing these issues should focus on tools for improvement of fertility and production traits in adapted cattle, such as Brahman, and on enhancing the adaptability of Bos taurus breeds that have some well-known advantages in the same traits. These strategies would directly impact these priority areas of the Southern Association of Agricultural Experiment Station Directors (SAAESD, 2018): Goal 1 “An agricultural system that is highly competitive in the global economy,” particularly #1 “Integrated and sustainable agricultural production systems” and #9 “Health and well-being of food animals”; Goal 5 “Enhanced economic opportunity and quality of life for Americans,” #3 “Risk management and assessment in agricultural systems.” This project would indirectly impact Goals 2-4, as this proposed project fits into these particularly well, as increased productivity in tropical and subtropical regions of the world is critical to increased global food security. Most funded beef cattle work in the U.S. does not address production in tropical or subtropical areas. University-based beef cattle research in the South has been drastically curtailed, including reduction of beef cow research populations. It is important to address these issues; otherwise, there will be an inefficient use of the huge forage resources in the Southern Region that are not suitable for human food crop production. A large group of producers will have minimal research support for activities important to their productive and economic well-being.
The limited resource situations of individual research groups and universities make it desirable to approach research and problem resolution from the perspective of a regional team of researchers and small university research herds of cattle. Genetic evaluation requires more sample size than individual research groups can provide. Adaptation and reproduction should be addressed in sub-environments of the region, for example, the Gulf Coast low-quality forage region and the challenging Upper South fescue environment, as knowledge acquired in one area likely will have a distinct application in another. It is appropriate to investigate and interact with researchers in the states that receive Southern-born calves, such as Kansas and Oklahoma, as team focus is then extended to problems and issues across the chain of beef production. Combining the extension resources of the different states has great potential to augment the impact of research conducted.

Related, Current and Previous Work

Accomplishments from the current project (S-1064) include: 1) assessment of measures of tick count, accumulation of udder conformation measurements as well as development of an improved method of quantifying eye pigment characteristics, 2) establishment of a large dataset of detailed phenotypes of reproduction and adaptation traits from different environments with pedigree information and banked DNA; this will lead to an economic assessment of ERTs for the next cycle, 3) establishment of the degree of additive genetic control (heritability) for winter hair coat shedding and association to cow production performance. These accomplishments will be foundations for objectives in the proposed project and specific activities under those objectives will expand the investigation across breed types and locations. Collaborators have banked sources of DNA for all project animals, and our intent is to use that DNA in the proposed work and leverage these resources to obtain funding for genomics work that will complement these efforts.

Genetics of Characteristics of Soundness in Cattle.

Bovine ocular squamous cell carcinoma is a skin cancer occurring on the eyelids and/or eyeball (also nictitating membrane and caruncle) of cattle, most common in Herefords, frequently known as cancer eye (Anderson et al., 1957). Production losses in Hereford cattle due to bovine ocular squamous cell carcinoma (commonly called cancer eye) are a major concern in the beef industry. Anderson et al. (1957) and Anderson (1960, 1963, 1991) reported the lower incidence of bovine ocular squamous cell carcinoma in white faced cattle with increased pigmentation around the eyes, and the heritability of pigmented eyelids is high (> 0.40). Updated characterization of eye pigmentation in Hereford straightbreds and crossbreds would facilitate identification of possible genomic regions controlling eye pigmentation. Brow prominence or cranial shape may also offer protection from solar-induced eye cancer in those cattle.

Newborn calves need to nurse unassisted, particularly in range conditions where assisting those calves may not be feasible. Dam udder type is one factor that affects the calfs ability to nurse. Calves had difficulty nursing when the dams have poor udder attachment or teat sizes of either extreme (Wythe, 1970; Edwards, 1982; Ventorp and Michanek, 1992). Poor udder quality resulted in delayed consumption of colostrum, which was important for immunity. Therefore, calf mortality rates were higher when dams had large teats and pendulous udder suspension (Frisch, 1982). Thus, improving udder quality can be beneficial to producers through reducing the amount of labor associated with assisting calves to nurse and increasing the number of calves weaned per cow, an important measure of efficiency.

Udder quality is one of many factors considered by producers when culling cows from the herd. Poor udder quality in aged cows, defined by large teats, pendulous udder suspension, or mastitis, is often one of the primary reasons that they leave production (Greer et al., 1980; Frisch, 1982; Kersey DeNise et al., 1987, Riley et al., 2001). Two breed types that have a reputation for development of udder problems include Brahman (Riley et al., 2001) and Hereford (Kersey DeNise et al., 1987; Bradford et al., 2015) and their crosses. Genomic association for variability in udder traits of beef cows was identified on BTA 5 (Tolleson et al., 2017). Those phenotypes, although repeated records on cows, were, however, observations only at the time of calving (Riley et al., 2001; Tolleson et al., 2017). Apparently, there have been no efforts to genetically or genomically characterize such traits across a single lactation. Udder quality deteriorates with age; therefore, any improvements could correspondingly increase productive life and decrease the pressure and costs associated with replacement heifers.

Differences in skull dimensions may offer health or other advantages to some cattle or groups of cattle.  Particularly, prominent brow structure may offer sufficient protection to cattle (especially of Hereford background) against solar-induced eye cancer (D. McCullers, Crooked Lake Ranch, Frostproof, FL, personal communication).  Although not to the extent of other species of domestic animals (e.g., dogs, Drake, 2011), there is substantial variation in cranial shape in cattle and is an important breed classification variable (Felius et al., 2011).  Genetic control of such variation is unknown.

Leg and Foot Soundness.

Structural soundness is important in every phase of the cattle industry. The ability to move involves the legs, pasterns as well as the structure of feet in cattle. Indicator traits of lameness (i.e. hoof health and morphological conformation scores) can be used to improve the accuracy of selection and subsequent genetic gain in dairy cattle (Ring et al., 2018). Heritabilities of the most commonly analyzed claw disorders based on data from routine claw trimming were generally low with ranges of linear model estimates from 0.01 to 0.14, and threshold model estimates from 0.06 to 0.39 (Heringstad et al., 2017). Estimated genetic correlations among claw disorders were found among sole hemorrhage, sole ulcer and white line disease and between digital/interdigital dermatitis and heel horn erosion (Heringstad et al., 2017). In Nellore cattle, visual scores for feet and legs were assigned at yearling and 2 to 5 months later. Vargas et al. (2017) reported heritabilities for the visual scores at yearling (0.18) and scores taken later (0.39). In a study using the same population of cattle, Vargas et al. (2018) conducted a genome-wide association study (GWAS) and found feet and leg structure evaluated as a binary trait to have large effects associated with locations on chromosomes 1, 2, 6, 7, 8, 10 and 14 and together explained 8.96% of additive genetic variance. They also evaluated feet and leg problems as a categorical score to assess the overall quality of feet and legs and found large effects on chromosomes 1, 7, 10, 11, 18, 20, 22, 28, and 29 and that explained 8.98% of additive genetic variance. Both management and genetic selection can be used to improve foot and claw health, but only genetic improvement provides permanent gains (Heringstad et al., 2017). Heringstad et al. (2017) also stated that it is important to establish recording systems that use common trait definitions and recording standards to ensure that evaluations are based on high-quality data and that claw health traits must be added to total merit indices with sufficient economic weight to enable genetic improvement.

Genetic research in eyelid pigmentation in whiteface crosses, udder conformation, and foot and leg soundness is warranted in modern cattle and for production systems where less artificial inputs are desired, due to economic and environmental sustainability considerations. Improved characterization of these adaptation type traits and their economic impacts are needed for producers in the Southern region to make more informed decisions regarding production and profitability. These types of traits are the focus priority for Objective 1.

Systems Approach to Analyzing Novel Economic Relevant Traits (ERT).

Several economic evaluations at the herd level (McGrann, 2003; Dhuyvetter and Langemeier, 2010; FINBIN, 2012) have indicated large potential for profitability among cow-calf producers in various regions of the U.S. Many breeding and production decisions are made without formal economic analyses. Preconditioning of calves for feedlot conditions is widely recommended to many producers, and, calves that are documented to be preconditioned (weaned, received recommended vaccinations, trained to eat from feed bunk, etc.) typically receive $3 to $8 per 100 lb ($0.07 to $0.17 per kg) price premium. However, studies (Peterson et al., 1989; Pritchard and Mendez, 1990) have indicated that preconditioning may not be justified when feeding costs are high. Evaluations regarding economic indicator traits such as net present value have been utilized for breeding cows (e.g., Meek et al., 1999; Mathews and Short, 2001), but these economic evaluations have not included genetic information nor have they been targeted toward situations or cow types typical in the Southern U.S.

Animals used in this project belong to research/experimental stations and thus are often measured numerous times for different variables. These rich data sets provide a unique opportunity to dissect traits that have complex interactions at the genetic and environmental level. Several traits including yield, meat quality, fertility and longevity as well as specific exposure factors (e.g., production environment) are being routinely collected by the different stations participating in this project. Some of the collected phenotypes are heterogeneous due to the lack of a clear biological definition. For example, calving rate is a composite trait that is influenced by the ability of the cow to get pregnant and its ability to maintain the pregnancy. Thus, two cows that failed to calve could be due to two different biological processes that are not often accounted for in breed or national genetic evaluations due to lack of information.

The depth of our data will provide answers to some of these confounding factors. Additionally and in order to optimize the use of data generated at the different locations, we will create a central database. Emphasis on economic-based evaluations on multiple production traits will be included in all project objectives. Cow-calf producers need and desire economic as well as genetic evaluations for more efficient and sustainable breeding and production systems.

Documentation of genetic components and development of thermotolerance measurements pertaining to heat tolerance adaptive traits.

Heat stress is a common problem in ruminant production throughout the tropics and the southern U.S. Methods used to alleviate heat stress have ranged from the minimum of just changing management practices to providing shade and incorporating sprinklers and fans in barns for dairy cattle. The latter option can be costly in terms of materials, equipment and resources while the former can be accomplished in a less expensive manner. Another, albeit slower method, is to use genetic selection for tolerance to the high heat and humidity found throughout much of the Caribbean.

Senepol cattle are very well adapted to the tropical environment. Temperate livestock breeds introduced to tropical conditions often have a difficult time adapting to the constant heat stress of the environment. When Holstein cattle were mixed with Siboney de Cuba cattle (5/8 Holstein X 3/8 Cuban Zebu) in Cuba, the newly introduced Holstein cows spent less time grazing and more time seeking shade than Siboney de Cuba cows and had higher rectal temperatures during the hottest time of day (Langbein and Nichelmann, 1993). Based on these results the authors suggested that the two different genotypes should be kept in separate groups to avoid decreased grazing time by the adapted cattle because they were influenced by the behavior of the non-adapted cattle. Water intake is critical for grazing livestock to maintain homeothermy as well as general survival.

Rectal temperatures of Senepol cattle under heat stress were often 0.5º C lower than Angus and Hereford cattle (Hammond et al., 1996). Crosses of Senepol with Angus and Hereford were subsequently found to be similar in heat tolerance to Senepol (Hammond et al., 1996). Observation that calves of Senepol ? Angus cows generally possessed either the short, sleek hair of the Senepol or normal, longer hair, along with other evidence led to the conclusion that a major gene for hair type was dominant in mode of inheritance (Olson et al., 2003). The slick-haired phenotype is visually dramatic and usually easy to differentiate from normal-haired individuals of temperate Bos taurus ancestry. The hair weights of calves with slick hair (0.74 g) were much less than those of calves scored as normal-haired (2.41 g). Comparisons of the heat tolerance of Charolais-sired, slick- and normal-haired 25% Senepol cattle of the same breed composition showed that slick-haired animals were able to maintain rectal temperatures approximately 0.5º C lower than those of normal-haired animals when under heat stress. Also, respiration rates of normal-haired animals were higher than those of slick-haired animals.

Sweating is another method that livestock use to cool themselves and regulate core body temperature. One method to measure sweating rate uses a hand-held closed-chamber VapoMeter (Delfin Technologies Ltd. Kuopio, Finland). This instrument measures evaporative heat loss and can detect differences between shaved and unshaved surfaces (Gebremedhin et al., 2007). Gebremedhin et al. (2009) reported that sweating rates were higher for cows with black coats compared to cows with white coats and there are breed differences as well.
Physiological status can also impact core body temperature. We have reported that pregnant hair sheep ewes had a lower range of body temperature throughout the day compared to non-pregnant ewes and speculate that it may be a fetal protection mechanism (Godfrey et al., 2017). In support of the need for thermal control during pregnancy, studies in dairy cattle have shown that heat stress can reduce fertility and embryonic survival (Dunlap and Vincent, 1971; Putney et al., 1988; Ealy et al., 1993). In cattle, the deleterious effects of maternal heat stress decline as pregnancy proceeds (Ealy et al., 1993), which may reflect acquisition of thermal resistance by the embryo as it progresses to the blastocyst stage (Edwards and Hansen, 1997). 

Objectives

1. Estimate genetic variation associated with animal health and structural soundness using classical animal breeding and genomic techniques to facilitate sustainable beef cattle production systems
   
2. Systems approach to analyzing novel ERTs associated with female production including longevity, fertility and meat quality database creation
   
3. Documentation of genetic components and development of thermotolerance measurements pertaining to heat tolerance adaptive traits in sustainable beef cattle production systems.
   

Methods

Activity 1. DNA Collection and Storage

DNA samples will be collected from pedigreed populations of cattle from various units participating in this project across all objectives. A variety of sources of DNA may be obtained, such as whole blood harvested in purple top tubes. This blood can then be transferred to DNA cards for storage at room temperature or to cryotubes and stored in a –80°C freezer at each location until testing is determined. Data on each animal will include individual, sire, and dam identification, breed or breed type, and location. A catalogue of information including phenotypic data and DNA samples from the different locations will be assembled and updated annually.

Objective 1. Estimate genetic variation associated with structural soundness using classical animal breeding and genomic techniques to facilitate sustainable beef cattle production systems.

Activities by sub-objective:

Objective 1.1 Eye and facial pigmentation

We will use photographs and digital quantification software to determine proportion of eyelid with pigmentation. Each animal will have one photo to identify the animal (primarily have used tag or brand), one of full face straight on to clarify markings, one of eye straight across on left side, one of eye aiming up (to characterize the eyelid under the upper eyelashes) on the left side, one of eye straight across on right side, and one of eye aiming up on the right side.
Quantifications of pigmentation will be conducted using procedures developed by Davis et al. (2015). Briefly, these include image modification with Adobe Photoshop Elements 2.0 (Adobe Systems Incorporated, San Jose, CA), including cropping and conversion to 8-bit grayscale images. Image J software (Java, Bethesda, MD) will be used to accommodate a 255-pixel background and quantify images in terms of x- and y-axis coordinates in pixels. Microsoft Excel (Microscoft Corporation, Redmond, WA) will be used, at least in preliminary stages, for identification of pigmentation pixel peaks and area delineation. There may be consolidation of these steps into an analysis pipeline using code developed in the course of the project. Alternative quantification software will actively be sought.

Multiple locations will contribute to this objective. Evaluated breed types will include 1) Hereford, 2) Hereford-Bos taurus crosses, and 3) Hereford-Bos indicus crosses (including Braford in this category even though it is recognized as a distinct breed). The target number of animals in each breed type category is 2,000. Although initially the intent of this objective is to evaluate animals with Hereford ancestry, it may be possible to expand to animals of Simmental ancestry and such decisions will be considered during the course of this project. Dependent variables will be analyzed using linear mixed models in ASReml (Gilmour et al., 2009). Investigated fixed effects will include parameterizations of animal age, location, breed type, sex, and interactions. Year will be a random effect. Dependent variables are expected to be distributed normally; if they are not, appropriate link functions (log, logit, probit, etc.) will be applied to data for analyses and means comparisons.

An important component of this objective is to bank sources of DNA for future use in genome-wide association analyses.

Objective 1.2 Udder conformation

Teats and udder will be evaluated using descriptive scoring guidelines according to the Beef Improvement Federation (BIF, 2018). Teat size will be scored on a 1 to 9 scale (Figure 1, BIF, 2018) such that low scores from one to three will indicate large, distended teats. High scores from 7 to 9 will indicate very small teats. Teat size scores from 4 to 6 will indicate intermediate or moderate size.

Udder suspension scores will be assigned to assess the strength of udder attachment (Figure 1, BIF, 2018). Low scores from 1 to 3 indicate udders that were pendulous or broken down. High scores, from 7 to 9, indicate udders with strong attachment held to varying degrees close to the body cavity.

Udder scores will be recorded for each cow 3 separate times annually: 1) at calving, 2) mid-lactation, and 3) 1 wk after weaning. Scores will not be recorded, kept, or analyzed for cows that were not lactating, that is, they were open or lost a calf. Scores will be recorded on Brahman or Brahman-cross cows (Florida, Mississippi, and Texas) and Hereford or Hereford-cross cows (Arkansas, Mississippi, South Carolina, Texas). The American Hereford Association (AHA) currently has a genetic evaluation for teat/udder conformation.

Objective 1.3 Foot Structure

Foot scores (AAA, 2018) will be obtained for Angus purebred and crossbred calves at weaning at 5 to 7 mo of age and subsequently on yearling females that are kept for breeding. We will score calves for the attributes shown in Figure 1 (AAA, 2018) within confinement such as a scale and separately when walking outside of the scale. Key comparisons will be breed type (Angus vs. crossbred Angus), weaning age vs. yearling heifer, and within-scale vs. external assignment of scores. The AAA currently has a genetic evaluation for feet/leg structure.

We will assess correspondence of foot scores with relevant growth traits pre-weaning, and post-weaning and reproductive performance of yearling heifers (successful parturition per exposure to bulls or to artificial insemination for breeding) using regression coefficient estimates and(or) correlation coefficients.

Objective 1.4 Skull conformation

Differences in skull dimensions may offer health or other advantages to some cattle or groups of cattle. We will evaluate 3-dimensional cameras for potential use in quantification of such dimensions. We plan to propose this as high-risk/novel idea research for USDA funding opportunities within the year.

Objective 2. Systems approach to analyzing novel ERTs associated with female production including longevity, fertility and meat quality database creation.

Activities:

A general spreadsheet template for data collection will be created. Data collection in all stations will follow the general format of the template. Every time new data is collected, the excel file will be updated and a copy will be sent to the database manager. An R-script (To be developed by R. Rekaya and others) will be used to harness the new data (by comparison with previous stored files). The new information will be added to the general master data file. The master file as well as the excel files provided by the different stations will be available for download (as csv or tab delimited text files) from a secure server.

Collected phenotypes will be a mixture of continuous (e.g., growth) and discrete (e.g., fertility) traits. Some confounding may exist between breed and location for some stations. Furthermore, systematic and random missingness is likely to occur for some traits. To face these challenges, sophisticated statistical tools will be employed. Mixed linear and threshold (latent variable) models will be used within frequentist and Bayesian frameworks. Existing tools such as ASReml 3.0 (Gilmour et al., 2009) and BLUPF90 suit (Misztal et al., 2002) will be used when appropriate. However, in the presence of multiple binary traits or/and a mixture between discrete and continuous responses, more specialized software will be used. Tools developed by the Georgia group (Rekaya et al., 2013; Chang et al., 2017) will be used in those cases.

The following data will be collected for heifers and cows: (1) Breed of cow, (2) Sire ID/sire breed and dam ID/dam breed of cow, (3) cow birth date, (4) Mating information (natural or artificial insemination; single or multiple sires; number of cows per bull; season or insemination date(s), (5) Predominant forage in pastures (fescue 0 = no; 1 = yes), (6) Sire/sire breed of calf, (7) Cow:bull ratio, (8) Body condition score (date and stage of production), (9) Palpation status (0 = non-pregnant; 1= pregnant), (10) Calving status (0 = no; 1 = yes), (11) Weaning status ( 0= no; 1 = yes), (12) Calving date (calving season, spring or fall), (13) Calving difficulty (1 = normal; 2 = easy pull; 3 = hard pull; 4 = caesarian section; 5 = abnormal presentation, note the abnormal presentation of calf), (14) Calf vigor issues (1 = normal; 2 = weak but nursed without assistance; 3 = weak and assisted to nurse; add any notes), (15) Calf birth weight, (16) Calf weaning date, (17) Calf weaning weight, (18) Cow temperament at calving, (19) Date of death and reason/notes for cow or her calf, and (20) Date of culling and reason/notes for cow and/or her calf leaving herd.

Economic analyses will be conducted to evaluate the value of traits measured from the cows and resulting calves. The economic value of selected traits will be analyzed using net present value methods. Simulation methods will be used to incorporate market risk and uncertainty into the analyses. These methods will allow quantification of economic impacts for numerous production considerations at the cow-calf level and assist in development of decision tools to aid in economic-based decision making; many breeding and genetic recommendations have not been formally evaluated economically.

Net present value (NPV), the present value of the revenue minus costs over the investment period, is a common tool for analysis of the profitability of an investment. Cows are genuine investments, as revenues are generated from the calves sold and the salvage value of the cow.

NPV=?_(i=1)^t?(REV-COST)/?(1+i)?^t

NPV= net present value
REV= cash inflows
COST = cash outflows
i= discount rate
t = (cow age in years – 2)

The NPV for each project year will be estimated for individual cows. The effect of increased longevity will be evaluated in terms of the difference in NPV. Stochastic simulation techniques, using Simetar (Richardson et al., 2008) will be used to account for variability in steer and heifer weaning weight and the weaning rate based on the variability observed in the cow data. Simulation provides the opportunity to make probabilistic estimates of alternative strategies based on the estimated distributions of economic returns.

The basic cow-calf model will account for revenue and the cost associated with each cow in the herd. Revenue will be determined as products of the probability a cow would wean a steer with the stochastic weight of a steer and the stochastic price of a steer for that particular year. This value will then be added to the dollar amount generated by multiplying the probability of the cow having a heifer calf by the heifer price and stochastic heifer weight. Maintenance costs (e.g., $600, but revised in accordance with current conditions) will be deducted for each year that a cow is in the herd. The profit (loss) dollar amount will then be discounted to present value. This basic model will be altered relative to the different types of traits in the different objectives of the proposed project.

Changes in dollar value across the project duration will be modeled, as well as the differences in reproductive rate and performance traits (e.g., weaning weight) for the different breeds utilized. Price data will be stochastically simulated.
The participating institutions have the practical and theoretical expertise for the successful implementation of this objective and we do not foresee any major problem.

Objective 3. Documentation of genetic components and development of thermotolerance measurements pertaining to heat tolerance adaptive traits in sustainable beef cattle production systems.
Activities:

Objective 3.1 Body Temperature and Sweating Rate

Multiparous cows will be utilized each year. Core body temperature will be measured for 96-hr periods during each trimester of pregnancy and during the postpartum period, prior to re-breeding. Temperature data loggers (Water Temperature Pro v2 Data Logger or TidbiT v2 Water Temperature Data Logger; Onset Computer Corp., Bourne, MA) attached to blank CIDR devices will be used to measure vaginal temperature (VT). Ambient conditions (temperature, relative humidity, solar radiation and wind speed) will be monitored using a weather station (Vantage Pro 2, Davis Instruments Corporation, Hayward, CA). Temperature-humidity index will be calculated using the formula THI = (0.8 x T) + [(RH/100) x (T - 14.4)] + 46.4, where T is the temperature (°C) and RH is the relative humidity (NOAA, 1976). Heat load index (HLI) will be calculated as proposed by Gaughan et al. (2010) for temperatures above 25º C: HLI = 8.62 + 0.38 × RH + 1.55 × BG – 0.5 × WS + e((2.4– WS)), with WS = wind speed (m/s) and e = base of the natural logarithm.

Sweating rate (SR) will be evaluated using a VapoMeter (Delfin Technologies Ltd. Kuopio, Finland). On 3 d during each 96-hr evaluation period cows will be monitored for SR in the sun and shade and in the morning and afternoon. Cows will be allowed to adapt to the sun or shade conditions for 20 minutes prior to having SR measured. Measurements will be taken over the shoulder, the ribs and the flank on the left side of the animal. Respiration rate (RR) will be evaluated at these times by counting flank movements for 15 seconds and adjusting to breaths/minute (BPM).
Data will be analyzed using stage of pregnancy (1st, 2nd, 3rd trimester and non-pregnant) as the main effects in the model.

Objective 3.2 Hair Shedding

Cows, calves, and yearlings will be evaluated each spring starting in March and assessed every 28 days until July for hair coat characteristics. At each evaluation animals will be visually inspected for amount of winter hair shed and shedding pattern. Hair shedding will be evaluated using a numerical scoring system. A score of 1 = completely shed or slick (100% shed); 2 = 75% shed; 3 = 50% shed; 4 = 25% shed; and 5 = 0% shed or full winter coat. For shedding pattern a score of 1 = slick, shedding complete; 2 = animal has shed off to below the middle of the rib cage; 3 = slick strip covers the full topline and the back of the hindquarters; 4 = a completely slick strip down the topline of the animal; and 5 = no evidence of shedding, even down the topline.

A subset of animals showing the extreme phenotypes will be evaluated for differences in body temperatures through rectal and thermal images if possible. Respiration rates will also be recorded prior to animals entering the chute and in the shade.

Cow data will be collected for assessment of the influence of shedding type on production characters. These will include breed, breed type, and pedigree information on each animal for genetic analysis. Cow performance data will include cow weights, body condition scores, reproductive records and performance of their calves from birth to weaning. In order to appropriately build models, other information will be collected including forage type, calving season, internal parasite control, and type of mineral supplement.

Analyses will be conducted using SAS (SAS Inst. Inc., Cary, NC) and ASReml (Gilmour et al., 2009). Because they are categorical rather than normally-distributed, hair scores will be analyzed using logistic regression procedures after application of appropriate link functions, such as a logit or probit function. Results will be presented after application of inverse of the link function used.

Measurement of Progress and Results

Outputs

• Several stations are collecting the same set of phenotypes. Although individually each station has a limited data set for robust inferences, sharing data through the proposed database will provide a much needed tool to optimize the use of resources. Furthermore, it will substantially increase the statistical power and it will allow the investigation of relevant scientific questions including breed/founder effects and genotype by environment interactions. The richness of the phenotyping of the animals used in this project will undoubtedly lead to a better understanding of the genetic basis of important beef traits, especially fertility and longevity. Even using genomic information, we are still facing major problems to improve these traits due to their low heritability which is in part the result of the heterogeneity of the collected phenotypes. It is likely that new measures of genetic assessment and economic values for breeding beef cows may be developed through meta-analyses.
• It is proposed that differences in vaginal temperature will be detected at different stages of the production cycle in the cows. Even though Senepol cattle are well adapted high heat and humidity they may also display characteristics in response to the ambient conditions that vary across stages of the production cycle.

Outcomes or Projected Impacts

• Characterization of various forms of soundness and its impact on animal health and productivity in cow herds
• Improved characterization of and improvement in cow herd fertility and adaptation to regional climatic and nutritional aspects.
• Increased beef cow longevity, which would lead to increased economic efficiency
• Improvements in health and adaptation traits would not only allow for increased fertility in cows herds but also allow for less reliance on artificial inputs (insecticide, pharmaceuticals, purchased feeds, etc.) for improved environmental and economic sustainability.
• More precise information regarding beef cattle breeding systems in the Southern Region will allow producers to tailor breeding and management decisions for improved production efficiency and therefore improved sustainability. It is likely that producers will become more familiar with consideration and incorporation of economic considerations based on corresponding breeding and management strategies, with increased emphasis on assessment and utilization of adaptation concepts.

Milestones

(2020):There are no specific milestones required for one objective to be completed before another one begins or is completed. However, successful completion of each objective relies on annual communication among participants and annual data collection and reporting.

(2021):Annual communication among participants and annual data collection and reporting.

(2022):Annual communication among participants and annual data collection and reporting. Analyses of preliminary pooled datasets will be emphasized (such as after first 2 or 3 years of project) to aid in publication development and the timeline for final joint analyses (as opposed to only conducting joint analyses after all years of data are collected).

(2023):Annual communication among participants and annual data collection and reporting.

(2024):Annual communication among participants and annual data collection and reporting. Final joint analyses.

Projected Participation

View Appendix E: Participation

Outreach Plan

Traditional publication outlets will continue to be utilized and include scientific abstracts, peer-reviewed journal articles and proceedings papers. Presentations will be made to scientists and graduate students at scientific meetings such as the American Society of Animal Science sectional and national meetings. Information will be made available to producers through state and university field days and short courses. Many participants will also be able to reach large numbers of producers on national levels through committee assignments and invited presentations associated with National Cattlemen’s Beef Association, the Beef Improvement Federation as well as state-level cattle industry groups. Publications and presentations will be audience-appropriate. Some committee members are Extension scientists and have expertise in conveyance and translation of scientific results to customers of beef production research.

Organization/Governance

The standard form of governance as described in Guidelines for Multistate Research Activities will be employed. Objective coordinators will be: 1) Dr. David G. Riley, Texas A&M University, 2) Dr. Romdhane Rekaya, University of Georgia, 3) Dr. Bob Godfrey, University of the Virgin Islands. Participants of each location provide guidance for joint analyses and associated publications.

Literature Cited

American Angus Association (AAA). 2018. Foot score guidelines. http://www.angus.org/performance/footscore/footscorebrochure.pdf. Accessed 7 December 2018.

Anderson, D. E. 1960. Studies on bovine ocular squamous carcinoma (Cancer Eye) V. Genetic
Aspects. J. Hered. 51:51-58.

Anderson, D. E. 1963. Genetic aspects of cancer with special reference to cancer of the eye in
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