Goat Milk Nutritional Quality Software-Automatized Individual Curve Model Fitting, Shape Parameters Calculation and Bayesian Flexibility Criteria Comparison

Mathematical modeling of egg production curve in a multivariety endangered hen breed

This study aimed to compare the egg laying performance of the four varieties (white, franciscan, black, and partridge) of a Spanish endangered Utrerana hen breed. A flock of 60 Utrerana hens (15 hen/variety) were individually housed to enable daily egg traceability. Compartmental, Gamma, linear hyperbolic, logistic curvilinear, McNally, Narushin-Takma, and quadratic logarithmic nonlinear regression functions were fitted. Goodness-of-fit (coefficient of determination (R²)) and flexibility criteria (mean squared error (MSE), Akaike information criteria (AIC), corrected Akaike information criteria (AICc), Bayesian information criteria (BIC)) were evaluated to identify the best-fitting function to model for egg production curve. Best-fitting values were reported by the six-parameter Narushin-Takma model for white (R² = 0.828), franciscan (R² = 0.888), and black (R² = 0.899) varieties. By contrast, quadratic logarithmic was reported to be the best-fitting model for partridge Utrerana hen egg production curve (R² = 0.917). The characterization of the laying cycle of endangered avian breeds varieties permits tailoring productive strategies which may ensure animal welfare at the same that they boost economic potentialities, enabling the productive model to better simultaneously fulfill animal needs and human demands. As a result, breed diversity may act as the motor element to improve economic profitability, but in turn may also ensure the conservation of the local genetic resources from which desirable products, such as the egg, are obtained.

The Study of Growth and Performance in Local Chicken Breeds and Varieties: A Review of Methods and Scientific Transference

A review of the scientific advances in the study of the growth and performance in native chicken breeds and varieties over the past 20 years was performed. Understanding the growth patterns of native breeds can only be achieved if the constraints characterizing these populations are considered and treated accordingly. Contextually, the determination of researchers to use the same research methods and study designs applied in international commercial poultry populations conditions the accuracy of the model, variability capturing ability, and the observational or predictive performance when the data of the local population are fitted. Highly skewed sex ratios favouring females, an inappropriate census imbalance compensation and a lack of population structure render models that are regularly deemed effective as invalid to issue solid and sound conclusions. The wider the breed diversity is in a country, the higher the scientific attention paid to these populations. A detailed discussion of the most appropriate models and underlying reasons for their suitability and the reasons preventing the use of others in these populations is provided. Furthermore, the factors conditioning the scientific reception and impact of related publications used to transfer these results to the broad scientific public were evaluated to serve as guidance for the maximization of the success and dissemination of local breed information.

Exploration of Three Incidence Trend Prediction Models Based on the Number of Diagnosed Pneumoconiosis Cases in China From 2000 to 2019

OBJECTIVE

To predict the future incidence trend of pneumoconiosis in China, and to evaluate three predictive models.

METHODS

We selected pneumoconiosis cases (2000-2019) to fit Generalized Additive Model (GAM), Curve Fitting Method, and GM (1,1) Model, chosen average fitting relative error, relative error of prediction, and coefficient of determination to evaluate models.

RESULTS

Chinese incidence trend of pneumoconiosis would decrease in the future. Predicted value of GAM (14,566) and Curve Fitting Method (15,781) in 2019 was close to the actual value (15,898). Relative error of prediction of GAM and Curve Fitting Method was -8.38% and -0.73%, respectively.

CONCLUSIONS

The government needs to strengthen prevention and control since pneumoconiosis cases might remain huge in the future. Besides, we advise that GAM and Curve Fitting Method can be used to predict Chinese incidence trend of pneumoconiosis.

Evaluation of Different Test-Day Milk Recording Protocols by Wood's Model Application for the Estimation of Dairy Goat Milk and Milk Constituent Yield

Simple Summary

Considering the seasonal trend of milk production due to application of estrus synchronization treatments in autumn or in spring season, as well as to meet meat production and market requirements for religious holidays, one may find it useful to simplify the milk and milk constituent yield quantification during the entire period of lactation. The present work aimed to verify the application of Wood’s model to a different number of test-day milk recording protocols in order to reduce the frequency of National Breeding Association test-day milking record protocol. Wood’s model fit well on goat milk production, showing high R² values. Moreover, the milk yield, fat-corrected milk yield, fat yield, and protein yield estimation applying Wood’s model to four recordings (first, second, fourth, and sixth month records) showed results superimposable to International Committee of Animal Recording (ICAR) application and Wood’s model application on test-day records every 15 days.

Abstract

Goats have important social and economic roles in many countries because of their ability to survive and be productive in marginal areas. The overarching aim of this study was to compare the application of Wood’s model to different test-day milk recording protocols for estimation of total milk, fat, and protein yield in dairy goats. A total of 465 goats were used (Garganica, 78; Girgentana, 81; Jonica, 76; Maltese, 77; Red Mediterranean, 76; Saanen, 77). Milk yield was recorded every 15 days throughout lactation of 210 days, for a total of 14 collection days, during both morning and afternoon milking sessions. Milk samples were collected and analyzed for protein and fat. The fat-corrected milk was standardized at 35g fat/kg of milk. Wood models showed high R² values, and thus good fitting, in all the considered breeds. Wood model applied to first, second, fourth, and sixth month recordings (C) and ICAR estimation showed total milk yield very close to Wood’s model applied to all 14 recordings (A) (p > 0.38). Differently, Wood’s model applied to the first, second, third, and fourth month recording (B) estimation showed great differences (p < 0.01). This could be applied for farms that had the necessity to synchronize flock groups for kidding in order to produce kid meat. In farms that apply the estrus induction and/or synchronization for kidding, it would be possible to perform only four test-day milk recordings and to apply the Wood’s model on them in order to obtain the estimation of total milk, fat, and protein yield during lactation for animals inscribed, or to be inscribed, to the genealogical book.

Bayesian Linear Regression Modelling for Sperm Quality Parameters Using Age, Body Weight, Testicular Morphometry, and Combined Biometric Indices in Donkeys

Simple Summary

The prediction of sperm output and other reproductive traits based on testicular biometry is an important tool in the reproductive management of stallions. Nevertheless, corresponding research in donkeys remains scarce. Several donkey breeds in Europe face a compromising threat of extinction, which has been accelerated by the low renovation of populations and their inbreeding levels. Although research on female reproductive physiology has made crucial advances, much less is known about the physiology of the male. In the present work, two Bayesian models were built to predict for sperm output and quality parameters in donkeys. Models included combinations of age as a covariate and biometric and testicular measurements as independent factors. Results evidenced that the goodness-of-fit was similar for both models—hence, the combination of biometry and testicular factors presented improved predictive power. The application of these models may assist in the process of making decisions in respect to the reproductive/biological, clinical, and selection handling of the animals.

Abstract

The aim of the present study is to define and compare the predictive power of two different Bayesian models for donkey sperm quality after the evaluation of linear and combined testicular biometry indices and their relationship with age and body weight (BW). Testicular morphometry was ultrasonographically obtained from 23 donkeys (six juveniles and 17 adults), while 40 ejaculates from eight mature donkeys were analyzed for sperm output and quality assessment. Bayesian linear regression analyses were considered to build two statistical models using gel-free volume, concentration, total sperm number, motility, total motile sperm, and morphology as dependent variables. Predictive model 1 comprised the covariate of age and the independent factors testicular measurements (length, height and width), while model 2 included the covariate of age and the factors of BW, testicular volume, and gonadosomatic ratio. Although goodness-of-fit was similar, the combination of predictors in model 1 evidenced higher likelihood to predict gel-free volume (mL), concentration (×10⁶/mL), and motility (%). Alternatively, the combination of predictors in model 2 evidenced higher predictive power for total sperm number (×10⁹), morphologically normal spermatozoa (%), and total motile sperm count (×10⁹). The application of the present models may be useful to gather relevant information that could be used hereafter for assisted reproductive technologies.

Conditioning Factors of Linearized Wood's Function Lactation Curve Shape Parameters, Milk Yield, Fat and Protein Content in Murciano-Granadina Primiparous Does

Simple Summary

Strategies aiming to improve milk yield and quality are essential to maximizing profitability and dairy goat chain efficiency, which in turn optimizes the commercialization of the products derived. A total of 137,927 official controls were traced and recorded from 22,932 Murciano-Granadina primiparous goats between 1996 and 2016. In this regard, the effects of conditioning factors related to kidding (such as type, year and season) and farm were evaluated to detect the phenotypic sources of variation of milk yield, fat content, protein content and curve shape parameters in Murciano-Granadina primiparous goats to provide useful information for dairy goat early selection.

Abstract

A total of 137,927 controls of 22,932 Murciano-Granadina first lactation goats (measured between 1996–2016) were evaluated to determine the influence of the number of kids, season, year and farm on total milk yield, daily milk yield, lactation length, total production of fat and protein and percentages of fat and protein. All factors analyzed had a significant effect on the variables studied, except for the influence of the number of kids on the percentages of fat and protein, where the variation was very small. Goats with two offspring produced nearly 15% more milk, fat and protein per lactation compared to goats with simple kids. Kiddings occurring in summer–autumn resulted in average milk, fat and protein yields nearly 14, 19 and 23% higher when compared to winter–spring kiddings. Lactation curves were evaluated to determine the effects of the number of kids and season, using the linearized version of the model of Wood in random regression analyses. Peak Yield increased by about 0.3 kg per additional offspring at kidding, but persistence was higher in goats with single offspring. The kidding season significantly influenced the lactation curve shape. Hence summer-kidding goats were more productive, and peak occurred earlier, while a higher persistence was observed in goats kidding during autumn.