Description of the growth curve for Angus pasture-fed cows under extensive systems

Comparative Transcriptome Analyses of Leg Muscle during Early Growth between Geese (Anser cygnoides) Breeds Differing in Body Size Characteristics

Goose is an important poultry commonly raised for meat. The early growth performance of geese significantly influences their market weight and slaughter weight, affecting the poultry industry's economic benefits. To identify the growth surge between the Shitou goose and the Wuzong goose, we collected the early growth body traits from 0 to 12 weeks. In addition, we investigated the transcriptomic changes in leg muscles at the high growth speed period to reveal the difference between the two geese breeds. We also estimated the growth curve parameters under three models, including the logistic, von Bertalanffy, and Gompertz models. The results showed that except for body length and keel length, the best-fitting model between the body weight and body size of the Shitou and Wuzong was the logistic model. The growth turning points of Shitou and Wuzong were 5.954 and 4.944 weeks, respectively, and the turning point of their body weight was 1459.01 g and 478.54 g, respectively. Growth surge occurred at 2-9 weeks in Shitou goose and at 1-7 weeks in Wuzong goose. The body size traits of the Shitou goose and Wuzong goose showed a trend of rapid growth in the early stage and slow growth in the later stage, and the Shitou goose growth was higher than the Wuzong goose. For transcriptome sequencing, a total of 87 differentially expressed genes (DEGs) were identified with a fold change ≥ 2 and a false discovery rate < 0.05. Many DEGs have a potential function for growth, such as CXCL12, SSTR4, FABP5, SLC2A1, MYLK4, and EIF4E3. KEGG pathway analysis identified that some DEGs were significantly enriched in the calcium signaling pathway, which may promote muscle growth. The gene-gene interaction network of DEGs was mainly related to the transmission of cell signals and substances, hematological system development, and functions. This study can provide theoretical guidance for the production and breeding management of the Shitou goose and Wuzong goose and help reveal the genetic mechanisms underlying diverse body sizes between two goose breeds.

Relationships between body- and udder-related type traits with somatic cell counts and potential use for an early selection method for water buffaloes (Bubalus bubalis)

Water buffalo milk is a reliable source of high-quality nutrients; however, the susceptibility of mastitis in buffaloes must be taken into consideration. An animal with somatic cell count (SCC) of greater than 250,000 cells/mL is reported to be likely to have mastitis which has serious adverse effects on animal health, reproduction, milk yield, and milk quality. Type traits (TTs) of water buffalo can affect SCC in animal milk to some extent, but few reports on the correlation between SCC and TTs are available. In this study, a total of 1908 records collected from 678 water buffaloes were investigated. The general linear model was used to identify factors associated with phenotypic variation of the somatic cell score (SCS) trait, including parity, lactation length, calving year, and calving season as fixed effects. Using PROC CORR analysis method, taking calving year and lactation length as covariates, the correlation co-efficient between TT and SCS was obtained. Our results showed that correlation co-efficients between the 45 TTs with SCS ranged from 0.003 to 0.443 (degree of correlation). The correlation between udder traits and SCS was greater than that between body structure traits and SCS. Among udder traits, distance between teats (including front and rear teat distance [r = 0.308], front teat distance [r = 0.211], and teat crossing distance [r = 0.412]) and teat circumference (r = 0.443) had the highest correlation with SCS, followed by the leg traits including rear leg height (r = -0.354) and hock bend angle (r = -0.170). Animal with high rear legs (>48 cm) and short teat crossing distance (<17 cm), and narrow teat circumference (<11 cm) exhibited low SCS. Using four nonlinear models (Von Bertalanffy, Brody, Logistic, and Gompertz), the optimal growth curves of the TTs highly correlated with the SCS (rear leg height and teat crossing distance) were fitted, and the correction co-efficients of these two TTs rear leg height and teat crossing distance of animal from young age (2 mo old) to first lactation (35 mo old) were attained for establishment of early selection method for water buffaloes with low SCS. This study provides theoretical support for early selection of low-SCS water buffaloes and lays a foundation for improving milk quality and promoting healthy development of water buffalo's dairy industry.

Evaluation of non-invasive bioforensic techniques for determining the age of hot-iron brand burn scars in cattle

Hot-iron branding is a traditional form of permanent cattle identification in the United States. There is a need for science-based determination of cattle brand age. Near infrared reflectance spectroscopy (NIRS) has been used to obtain information about animal tissues and healing processes. Height-width allometry and NIRS were applied to hot-iron cattle brand scars to determine if either or both of these methods can be used to non-invasively establish the interval sincethe application of hot-iron cattle brands. Length and width of a brand routinely applied to calves (~30–60 d old) were established and then the same measurements were recorded on 378 calfhood branded cattle of known age ranging from 0.5 to > 6.5 yr-of-age. Brand width and height increased over the original measurements by > 100% between calfhood application and 2.5 yr-of-age (P < 0.001). Brand size did not change dramatically between 2.5 and > 6.5 yr, however, both width and height were (P < 0.05) greater at maturity than at weaning. Near infrared spectra were collected from a) branded skin b) non-clipped (hair), non-branded skin, and c) hair clipped, non-branded skin on Bos taurus cross calves. Individual trial calibrations yielded high R² and low SE of calibration values as well as similar cross validation performance (P < 0.001). Numerically lower but still strong performance (P < 0.001) resulted from combined data set calibrations. Cross-trial prediction of brand age was unsuccessful. One single year calibration underpredicted (P < 0.001) brand age of an independent validation set by 2.83 d, and another single year calibration underpredicted (P < 0.001) the same validation set by 9.91 d. When combined, these two datasets resulted in a calibration that overpredicted brand age in the validation set by 6.9 d (P < 0.02). Discriminant analyses for identification of skin surface type yielded success rates of 90% for branded, 99% for non-clipped, non-branded, and 96% for clipped, non-branded (P < 0.01). Discriminant analyses were also performed on samples grouped into a) less than 33 d, b) 141–153 d, and c) 169 d categories. All group membership identifications were successful at greater than 90% (P < 0.01). Preliminary results indicate that brand size could be used to indicate brand age and that NIRS can predict brand age as well as discriminate between broad brand age groups in cattle. More work will need to be done before these techniques can be used in real-world forensic applications.

Genetic parameters for body weight from birth to calving and associations between weights with test-day, health, and female fertility traits

A data set including 57,868 records for calf birth weight (CABW) and 9,462 records for weight at first insemination (IBW) were used for the estimation of direct and maternal genetic effects in Holstein Friesian dairy cattle. Furthermore, CABW and IBW were correlated with test-day production records and health traits in first-lactation cows, and with nonreturn rates in heifers. Health traits considered overall disease categories from the International Committee for Animal Recording diagnosis key, including the general disease status, diarrhea, respiratory diseases, mastitis, claw disorders, female fertility disorders, and metabolic disorders. For single-trait measurements of CABW and IBW, animal models with maternal genetic effects were applied. The direct heritability was 0.47 for CABW and 0.20 for IBW, and the direct genetic correlation between CABW and IBW was 0.31. A moderate maternal heritability (0.19) was identified for CABW, but the maternal genetic effect was close to zero for IBW. The correlation between direct and maternal genetic effects was antagonistic for CABW (-0.39) and for IBW (-0.24). In bivariate animal models, only weak genetic and phenotypic correlations were identified between CABW and IBW with either test-day production or health traits in early lactation. Apart from metabolic disorders, there was a general tendency for increasing disease susceptibilities with increasing CABW. The genetic correlation between IBW and nonreturn rates in heifers after 56 d and after 90 d was slightly positive (0.18), but close to zero when correlating nonreturn rates with CABW. For the longitudinal BW structure from birth to the age of 24 mo, random regression models with the time-dependent covariate "age in months" were applied. Evaluation criteria (Bayesian information criterion and residual variances) suggested Legendre polynomials of order 3 to modeling the longitudinal body weight (BW) structure. Direct heritabilities around birth and insemination dates were slightly larger than estimates for CABW and IBW from the single-trait models, but maternal heritabilities were exactly the same from both models. Genetic correlations between BW were close to 1 for neighboring age classes, but decreased with increasing time spans. The genetic correlation between BW at d 0 (birth date) and at 24 mo was even negative (-0.20). Random regression model estimates confirmed the antagonistic relationship between direct and maternal genetic effects, especially during calfhood. This study based on a large data set in dairy cattle confirmed genetic parameters and (co)variance components for BW as identified in beef cattle populations. However, BW records from an early stage of life were inappropriate early predictors for dairy cow health and productivity.

How to improve breeding value prediction for feed conversion ratio in the case of incomplete longitudinal body weights

With the development of automatic self-feeders, repeated measurements of feed intake are becoming easier in an increasing number of species. However, the corresponding BW are not always recorded, and these missing values complicate the longitudinal analysis of the feed conversion ratio (FCR). Our aim was to evaluate the impact of missing BW data on estimations of the genetic parameters of FCR and ways to improve the estimations. On the basis of the missing BW profile in French Large White pigs (male pigs weighed weekly, females and castrated males weighed monthly), we compared 2 different ways of predicting missing BW, 1 using a Gompertz model and 1 using a linear interpolation. For the first part of the study, we used 17,398 weekly records of BW and feed intake recorded over 16 consecutive weeks in 1,222 growing male pigs. We performed a simulation study on this data set to mimic missing BW values according to the pattern of weekly proportions of incomplete BW data in females and castrated males. The FCR was then computed for each week using observed data (obser_FCR), data with missing BW (miss_FCR), data with BW predicted using a Gompertz model (Gomp_FCR), and data with BW predicted by linear interpolation (interp_FCR). Heritability (h) was estimated, and the EBV was predicted for each repeated FCR using a random regression model. In the second part of the study, the full data set (males with their complete BW records, castrated males and females with missing BW) was analyzed using the same methods (miss_FCR, Gomp_FCR, and interp_FCR). Results of the simulation study showed that h were overestimated in the case of missing BW and that predicting BW using a linear interpolation provided a more accurate estimation of h and of EBV than a Gompertz model. Over 100 simulations, the correlation between obser_EBV and interp_EBV, Gomp_EBV, and miss_EBV was 0.93 ± 0.02, 0.91 ± 0.01, and 0.79 ± 0.04, respectively. The heritabilities obtained with the full data set were quite similar for miss_FCR, Gomp_FCR, and interp_FCR. In conclusion, when the proportion of missing BW is high, genetic parameters of FCR are not well estimated. In French Large White pigs, in the growing period extending from d 65 to 168, prediction of missing BW using a Gompertz growth model slightly improved the estimations, but the linear interpolation improved the estimation to a greater extent. This result is due to the linear rather than sigmoidal increase in BW over the study period.

Transcriptomic profile of leg muscle during early growth in chicken

The early growth pattern, especially the age of peak growth, of broilers affects the time to market and slaughter weight, which in turn affect the profitability of the poultry industry. However, the underlying mechanisms regulating chicken growth and development have rarely been studied. This study aimed to identify candidate genes involved in chicken growth and investigated the potential regulatory mechanisms of early growth in chicken. RNA sequencing was applied to compare the transcriptomes of chicken muscle tissues at three developmental stages during early growth. In total, 978 differentially expressed genes (DEGs) (fold change ≥ 2; false discovery rate < 0.05) were detected by pairwise comparison. Functional analysis showed that the DEGs are mainly involved in the processes of cell growth, muscle development, and cellular activities (such as junction, migration, assembly, differentiation, and proliferation). Many of the DEGs are well known to be related to chicken growth, such as MYOD1, GH, IGF2BP2, IGFBP3, SMYD1, CEBPB, FGF2, and IGFBP5. KEGG pathway analysis identified that the DEGs were significantly enriched in five pathways (P < 0.1) related to growth and development: extracellular matrix–receptor interaction, focal adhesion, tight junction, insulin signaling pathway, and regulation of the actin cytoskeleton. A total of 42 DEGs assigned to these pathways are potential candidate genes inducing the difference in growth among the three developmental stages, such as MYH10, FGF2, FGF16, FN1, CFL2, MAPK9, IRS1, PHKA1, PHKB, and PHKG1. Thus, our study identified a series of genes and several pathways that may participate in the regulation of early growth in chicken. These results should serve as an important resource revealing the molecular basis of chicken growth and development.

Comparison on accuracy of different nonlinear models in predicting growth of Podolica bulls

Animal growth does not follow a linear pattern, being explained mathematically by functions that have parameters with biological meaning. These parameters are used to estimate the expected weight of animals at specific ages. Several nonlinear models have been used to describe growth. This study was carried out to estimate the parameters of logistic, Gompertz, Richards and von Bertalanffy growth curve models in a sample of Podolica young bulls to determine the goodness of fit. Animals were weighed every 3 months from birth to 810 days of age. The results indicate that all the growth models used were easily fitted to the observed data with Gompertz and logistic functions presenting less computational difficulty in terms of number of iterations to achieve convergence. Moreover, logistic and Richards equations provided the best overall fit being useful to describe the growth of Podolica bulls. Considering that the literature lacks information on growth curves in Podolica breed, the study of a mathematical model for growth describing the developmental pattern of a specific population within a peculiar environment is a useful tool to improve Podolica breed production.