Sex selection techniques and their implications.

Sex selection techniques provide economic benefits to dairy and beef herd management. Therefore, the development of such techniques has attracted the attention of reproductive biologists. There have been numerous studies concerning the development of sex selection techniques. As the sex of the offspring is determined by certain chromosomes, namely the X and Y chromosomes in mammals, most studies have focused on sperm sexing, attempting to separate the X and Y chromosome-bearing sperm based on differences in deoxyribonucleic acid (DNA) content, head size/volume, motility, and immunological specificity. However, most of these methods have failed to show reproducibility. Only the flow cytometric method has been confirmed to be accurate and reliable thus far. More than three decades have passed since this technique was first developed. The sexed semen produced with the method is currently commercialized and widely used for artificial insemination (AI) in cattle around the world. Recently, however, another technique based on differences in DNA contents using a fluidics device was developed by a commercial company. Studies focused on immunological approaches and the modification of sperm motility have also described the successful separation of the two types of sperm. Therefore, the present review evaluated the available sex selection techniques and their implications.

Simulating Beef Cattle Herd Productivity with Varying Cow Liveweight and Fixed Feed Supply

The liveweight of New Zealand beef cows has increased in recent decades due to selection for higher growth rates. Published data suggest that the efficiency of beef cow production decreases with increasing cow liveweight. Changes in beef herd size, feed demand, production, and cash operating surplus (COS) were simulated with average mature cow liveweight varied to 450, 500, 550, and 600 kg. With total annual beef feed demand fixed at the same level, in all scenarios cow numbers and numbers of weaned calves decreased with increasing cow liveweight. When the model was run with consistent efficiency of calf production across the mature cow liveweights (scenario A), heavier cows were more profitable. However, using published efficiency data (scenarios B and C), herds of heavier cows were less profitable. The likely most realistic scenario for New Zealand hill country farms (scenario B) had COS decrease from New Zealand Dollars (NZD) 456/ha with a herd of 450 kg cows to NZD 424/ha with 600 kg cows. Reductions in COS were relatively small, which may not deter farmers from breeding heavier cows for higher calf growth rates. However, the results of this analysis combined with indirect potential economic impacts suggest that the heaviest cows may not be optimal for New Zealand hill country conditions.

PSV-16 Influence of the late gestation maternal rumen microbiome on the calf meconium and early rumen microbiome

Understanding the development of the calf rumen microbiome is important in developing manipulation strategies to improve efficiency as the animal ages. We hypothesized that the cow maternal microbiome would influence the colonization of the calf rumen microbiome. Our objective was to relate the microbiomes of the cow rumen fluid (RFC) to the calf meconium (M) and calf rumen fluid (RFN) at twenty-eight days of age. Mature, multiparous Angus crossbred cows (n = 10) from the University of Wyoming beef herd were used in this study. Rumen fluid was collected from the cows prior to parturition. Immediately following parturition, meconium was collected from the calf and at 28 days post-parturition, rumen fluid was collected from the calves. Microbial DNA was isolated using a lysis buffer and mechanical bead-beating procedure and purified using the QIAamp DNA Stool Mini Kit (Qiagen). Amplicon sequencing of the 16S rRNA V4 region was completed on the MiSeq and analyzed with QIIME2. Both alpha and beta diversity were evaluated by sample type and day. Richness and evenness differed by sample type. The greatest richness and evenness was in RFC (q < 0.01) followed by RFN and M, which did not differ from each other (q ³ 0.5). Bray-Curtis and Jaccard beta diversity differed by each sample type (q < 0.01). These data indicate that the M and RFN do not differ in number and distribution of features, but the samples are compositionally different. Additionally, the RFC differed in both alpha and beta diversity from both calf samples. These profiles can be used to develop hypotheses for the pathway of colonization in the early gut yet still reflect the vast differences in the developmental stage between the cow rumen microbiome and the early calf gastrointestinal microbiome.

Animal-level factors associated with the achievement of desirable specifications in Irish beef carcasses graded using the EUROP classification system

Beef carcasses in Europe are classified on measures of carcass weight, conformation, and fat cover. These measurements provide the basis for payment to producers, with financial penalties for carcasses that do not conform to desirable characteristics. The objective of the present study was to identify animal-level factors associated with the achievement of a desirable carcass weight, conformation score, fat score, and age at harvest, as stipulated by Irish beef processors in accordance with the EUROP carcass classification system. The stipulated specifications were a EUROP conformation score ≥O=, a carcass weight between 270 and 380 kg, a EUROP fat score between 2+ and 4=, and an age at harvest ≤ 30 mo. In the present study, 59% of cattle failed to achieve at least one of these desired specifications. The logit of the probability of achieving the desired specifications was estimated using multivariable logistic regression and carcass data from 4,717,989 cattle finished and harvested in Ireland between the years 2003 and 2017. In comparison to beef-origin carcasses and after accounting for breed differences, the likelihood of dairy-origin carcasses achieving the desired age, conformation, fat, and weight specifications was 0.97, 0.88, 1.14, and 1.05, respectively. In comparison to heifer carcasses, the odds ratio (OR) of bull and steer carcasses simultaneously achieving all of the desired specifications (i.e. the overall specification) was 0.35 and 0.95, respectively. Additionally, after accounting for breed differences, heifers from the dairy herd were half as likely as heifers from the beef herd to achieve the overall specification, whereas the odds of dairy-origin bulls (OR = 3.46) and steers (OR = 2.41) achieving the overall specification was greater than that of their respective beef-origin counterparts. Finally, cattle with a greater breed proportion of Angus were most likely to achieve the overall specification. Results from the present study could provide a deeper understanding as to why animals fail to achieve desirable carcass specifications and could be implemented into the management decisions made on farm to ensure that the supply of beef carcasses that achieve the desired metrics is maximized.

Canadian Regional Agriculture Model: PMP Applied to the Beef Sector in Canada

This paper describes the Positive Mathematical Programming (PMP), the method for calibrating models of agricultural livestock production and resource use by a nonlinear total cost function. The PMP method is applied to agricultural sectoral models to study changes in policy and market signals. The Canadian Regional Agriculture Model (CRAM) is a regional, multi-sectoral, comparative static, partial equilibrium, mathematical programming model developed and maintained by Agriculture and Agri-Food Canada (AAFC) since mid-eighties. The PMP process converts a linear model using flexibility constraints into a nonlinear model in the absence of the flexibility constraints. A component of CRAM is the beef sector. The elements of the set of total cost curves are defined as quadratic function in terms of the number of cows and calves in the beef production activities. The marginal cost curves were then approximated using the shadow values from linear programming solution with linear curves. Once the flexibility constraints were removed, the model automatically calibrates to the base year production levels. The results from four scenarios indicated the beef sector of CRAM could predict the impact of the scenarios on the size of beef herd. In Scenario 1 where cash costs were increased by 10 percent, the breeding herd size decreased from 3.73 percent in New Brunswick to 0.0 percent in Ontario and Quebec. In Scenario 2 where barley costs were decreased by 10 percent, the breeding herd size increased from 0 percent for British Columbia, Alberta, Ontario, Quebec, Prince Edward Island and Nova Scotia to 1.93 percent for New Brunswick. In Scenario 3 where carcass weight per beef cow could be increased by 10 percent, the increase in beef herd size ranged from 0 percent for Ontario and Quebec to 2.56 percent for New Brunswick. In Scenario 4 where world beef prices were increased by 10 percent increase in beef herd size ranged from 4.48 percent for Manitoba to 25.78 percent for New Brunswick.

Canadian Regional Agriculture Model: Positive Mathematical Programming Applied to the Beef Sector in Canada

This paper describes the Positive Mathematical Programming (PMP), the method for calibrating models of agricultural livestock production and resource use using a nonlinear total cost function. The PMP method is applied to agricultural sectoral models to study changes in policy and market signals. The Canadian Regional Agriculture Model (CRAM) is a regional, multi-sectoral, comparative static, partial equilibrium, mathematical programming model developed and maintained by Agriculture and Agri-Food Canada (AAFC) since mid-eighties. The PMP process converts a linear model using flexibility constraints into a nonlinear model in the absence of the flexibility constraints. A component of CRAM is the beef sector. The elements of the set of total cost curves are defined as quadratic function in terms of the number of cows and calves in the beef production activities. The marginal cost curves are then approximated using the shadow values from linear programming solution with linear curves. Once the flexibility constraints were removed, the model automatically calibrates to the base year production levels. The results from four scenarios indicated the beef sector of CRAM could predict the impact of the scenarios on the size of beef herd. In Scenario 1 where cash costs were increased by 10 percent, the breeding herd size decreased from 3.73 percent in New Brunswick to 0.0 percent in Ontario and Quebec. In Scenario 2 where barley costs were decreased by 10 percent, the breeding herd size increased from 0 percent for British Columbia, Alberta, Ontario, Quebec, Prince Edward Island and Nova Scotia to 1.93 percent for New Brunswick. In Scenario 3 where carcass weight per beef cow could be increased by 10 percent, the increase in beef herd size ranged from 0 percent for Ontario and Quebec to 2.56 percent for New Brunswick. In Scenario 4 where world beef prices were increased by 10 percent increase in beef herd size ranged from 4.48 percent for Manitoba to 25.78 percent for New Brunswick.

Canadian Regional Agriculture Model: Positive Mathematical Programming Applied to the Beef Sector in Canada

This paper describes the Positive Mathematical Programming (PMP), the method for calibrating models of agricultural livestock production and resource use using a nonlinear total cost function. The PMP method is applied to agricultural sectoral models to study changes in policy and market signals. The Canadian Regional Agriculture Model (CRAM) is a regional, multi-sectoral, comparative static, partial equilibrium, mathematical programming model developed and maintained by Agriculture and Agri-Food Canada (AAFC) since mid-eighties. The PMP process converts a linear model using flexibility constraints into a nonlinear model in the absence of the flexibility constraints. A component of CRAM is the beef sector. The elements of the set of total cost curves are defined as quadratic function in terms of the number of cows and calves in the beef production activities. The marginal cost curves are then approximated using the shadow values from linear programming solution with linear curves. Once the flexibility constraints were removed, the model automatically calibrates to the base year production levels. The results from four scenarios indicated the beef sector of CRAM could predict the impact of the scenarios on the size of beef herd. In Scenario 1 where cash costs were increased by 10 percent, the breeding herd size decreased from 3.73 percent in New Brunswick to 0.0 percent in Ontario and Quebec. In Scenario 2 where barley costs were decreased by 10 percent, the breeding herd size increased from 0 percent for British Columbia, Alberta, Ontario, Quebec, Prince Edward Island and Nova Scotia to 1.93 percent for New Brunswick. In Scenario 3 where carcass weight per beef cow could be increased by 10 percent, the increase in beef herd size ranged from 0 percent for Ontario and Quebec to 2.56 percent for New Brunswick. In Scenario 4 where world beef prices were increased by 10 percent increase in beef herd size ranged from 4.48 percent for Manitoba to 25.78 percent for New Brunswick.