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  • Estimating Genetic Parameters using a Random Regression Model
    53-55
    Views:
    113

    One of the most important part of the genetic evaluation using a random regression model is the estimation of variance components. This is the topic of many papers because the large computational costs. We can use restricted maximum likelihood (REML), Gibbs sampling and ℜ method for the estimation of genetic parameters. The variance components are necessary to calculate the heritabilities and repeatabilities.
    The aim of our paper is to estimate the variance components using a random regression repeatability model from test day data set of Hungarian Holstein-Friesian dairy cows and to analyse the change of additive genetic and permanent environmental variance, heritability and repeatability over lactation.

  • Estimation of direct and maternal genetic parameters for weaning weight in Hungarian Simmental cattle
    17-22
    Views:
    155

    The aim of the current research was to estimate variance components and genetic parameters of weaning weight in Hungarian Simmental cattle. Weaning weight records were obtained from the Association of Hungarian Simmental Breeders. The dataset comprised of 44,278 animals born from 1975 to 2020. The data was analyzed using the restricted maximum likelihood methodology of the Wombat software. We fitted a total of six models to the weaning weight data of Hungarian Simmental cattle. Models ranged from a simple model with animals as the only random effect to a model that had maternal environmental effects as additional random effects as well as direct maternal genetic covariance. Fixed effects in the model comprised of herd, birth year, calving order and sex. Likelihood ratio test was used to determine the best fit model for the data. Results indicated that allowing for direct-maternal genetic covariance increases the direct and maternal effect dramatically. The best fit model had direct and maternal genetic effects as the only random effect with non-zero direct-maternal genetic correlation. Direct heritability, maternal heritability and direct maternal correlation of the best fit model was 0.57, 0.16 and -0.78 respectively. The result indicates that problem of (co-)sampling variation occurs when attempting to partition additive genetic variance into direct and maternal components.

  • Study of drought stress correlation on yield and yield components of maize cultivars (Zea mays L.)
    67-73
    Views:
    186

    This article was investigated to study the correlation and analysis of drought stress regression on maize cultivars' yield and components. The variance analysis results showed a significant difference between drought stress levels in terms of plant height, total dry weight and number of seeds per row, the total weight of cob, grain yield, harvest index, stem diameter, and cob weight with protective leave. Also, there was a significant difference in ear weight without protective leaves, ear diameter, ear length, plant weight, 100-seed weight, and seed per ear on hybrid treatments. There were statistically significant differences between cultivars in plant height, leaf area, ear diameter, ear length, number of seeds per row, number of seeds per ear, the total weight of cob wood, 100-seed weight, harvest index, plant dry weight. The results of the correlation of traits for the mean levels of drought stress showed a positive and significant correlation between plant yield and plant height, seed per row, ear length and weight of 5 pieces of wood and also with a total weight of cob wood, ear weight with bark showed the highest correlation. There is a significant correlation between leaf area and stem diameter, plant weight, total dry weight at the probability level of 0.05. Correlation coefficients between traits in non-stress conditions showed a positive and significant correlation between grain yield and height, ear length and grain in the row, which was a significant increase compared to stress conditions. The correlation of traits under full stress conditions also showed that the correlation coefficient between cob length trait and positive height was positive and significant. From the study of correlation coefficients between maize traits in non-stress conditions, it can be concluded that the most important components of grain yield are cob length and grain per row. While the correlation coefficients under moisture stress conditions show that the grain trait in the row has a positive and significant correlation with yield, under stress conditions in the cob stage did not show any traits with correlation yield.

  • Evaluation of Hungarian Sporthorse mare performance tests
    83-87
    Views:
    93

    Results of the Hungarian Sporthorse mare performance tests were evaluated. Data from the period of 1993-2009 were used, covering
    scores of 618 3-year-old and 310 4-year-old mares, 109 of them were tested at both ages. Seventeen traits were scored on the tests, which
    covered ten conformational, three free jumping performance and four movement analyses traits, respectively. Breeding value estimation was
    based on BLUP animal model. Test year, age and owner were included in the model as fixed effects. Variance components were estimated
    with VCE-6 software package. Heritabilities ranged from 0.32 (frame) to 0.50 (saddle region) for conformation traits, from 0.39 (jumping
    style) to 0.49 (jumping ability and jumping skill) for free jumping traits and from 0.20 (walk) to 0.48 (canter) for movement analysis traits.
    Breeding value indexes were constructed for each trait group. Conformation index was computed based on the weighted scores of the
    breeding values of conformational traits. The conformational score scales were used as weightings. Free jumping and movement indexes
    contain the proper breeding values with equal weights. A total index was also constructed using conformation index, two times the free
    jumping index and two times the movement index. Each breeding values and breeding value indexes were presented with the mean 100 and
    standard deviation of 20 for the easier understanding.

  • Analysis of show-jumping results with different measure variables
    77-81
    Views:
    131

    The aim of this paper is to estimate heritabilities and to  compare different fitted models for Hungarian Sporthorse showjumping results. Our analysis is based on the show-jumping results between 1996 and 2004. The repeatability animal model for the evaluation of the test results included the fixed effects of gender, breeder, rider, age, year of competition, type of competition, height of fence and number of starters. Variance and covariance components were estimated with VCE-5 software package. Fitting of the models were evaluated with log-likelihood values and Akaike’s information criterion (AIC). Heritability was low in all cases.
    The lowest goodness-of-fit model was height of fence-error score and the best-fitting genetic model based on AIC was model using cotangent transformation.

  • Evaluation of Hungarian show-jumping results using different measurement variables
    81-85
    Views:
    167

    The aim of the study was to compare different fitted models for show-jumping results of sporthorses and to estimate heritability and repeatability value. Show-jumping competition results collected between 1996 and 2011 were analyzed. The database contained 358 342 starts of 10 199 horses. Identity number, name and gender of the horse, rider, competition year, the level and location of the competition and placing were recorded in the database. To measure performance of horses, placing, number of starters and competition level were used. Competitions were categorized into five groups based on their difficulty level. The used repeatability animal model included fixed effects for age, gender, competition place, year of competition (and competition level in case of non-weighted measurement variables), and random effects for rider, animal and permanent environment effect. Variance components were estimated with VCE-6 software package. The goodness-of-fit of the models was low and moderate. Heritability and repeatability values were low for each measurement variables. The best goodness-of-fit model the weighted square root of placing resulted the highest heritability and repeatability value h2=0.074 and R=0.296.

  • The parameters of homemade jams after storage
    39-42
    Views:
    150

    Nowadays producing jams is considered a traditional way of plum processing in Hungary. Plum jams without added sugar can be stored

    This research aimed to analytically determine which quality parameters of the jams and to what extent they change during storage. Furthermore, this study intended to find out whether a concious consumer can presume any difference between varieties or the year of harvest, or wheather a one-time customer should suspect differences in quality parameters of the different products. This study focused on those differences or alterations in the parameters that occur in the jams made from several different plum varieties produced in different years.

    I analyzed the classic chemical parameters (dry matter content and ash content) and physiologically important nutritional components (phenolic and flavonoids antioxidants and vitamin C). In this research I used jams which were produced from 6 varieties grown in 2009 (President, Tophit, Bluefre, Elena, Presenta, Stanley), 4 varieties from 2010 (President, Bluefre, Elena, Presenta) and 6 varieties from 2011 (President, Tophit, Bluefre, Elena, Presenta, Stanley). Jams were produced with traditional technology in cauldrons without added sugar. Jars were placed into a relatively dark and cool place and were stored there until the analysis.

    Having regard to the results, when consumers choose between the different products they also choose quality since the processed plum variety, and the year of production/processing determine the nutritional value of the specific product. This could be used for market positioning and promotion of the product, however further research is needed to gain more information from the differences that derive from the varieties, year of harvest or other factors. This way fruit and jam producers could turn these informations into market advantage.