scholarly journals Relationship between bull dam herd characteristics and bias in estimated breeding value of bull

1995 ◽  
Vol 4 (5-6) ◽  
pp. 463-472
Author(s):  
Pekka Uimari ◽  
Esa A. Mäntysaari
Keyword(s):  
Standard Deviation ◽  
Protein Production ◽  
The Other ◽  
Breeding Value ◽  
Data Set ◽  
Average Production ◽  
Final Data ◽  
Estimated Breeding Values ◽  
Lactation Record ◽  
Final Proof

The objective of the study was to relate estimated breeding values (EBVs) of the parents’ 305-days protein production and the bull dam herd-year characteristics to the empirical bias in pedigree indices (difference between the pedigree index and the final proof) of young bulls. Two animal model evaluations were carried out; one included records up to 1990 and the other up to spring 1992. The final data set included 242 bulls with pedigree indices, final proofs, parents’ EBVs, production and herd information (the size, the average production and the intraherd standard deviation) of the dams. The average empirical bias in pedigree indices was 13.6 kg. The correlation between the final proof of the bull and the EBVs of the bull sire or dam were 0.45 and 0.17, respectively. The low correlation with bull dam EBV indicates the unreliability of the bull dam EBVs. Size of the herd and the standard deviation of production in the herd when bull dam produced its third lactation were correlated with the empirical bias in pedigree index. Pedigree indices of the bulls coming from small herds with high intraherd standard deviation were more biased than those from the big herds with low intraherd standard deviation. The best bulls, when grouped according to their final proofs, were sons of the highest EBV sires. EBVs of bull dams did not differ in the highest and the lowest final proof groups, but the dams of the best bull group had a higher first lactation record than the dams of the other bull groups.

Repeatability and bias of estimated breeding values for dairy bulls and bull dams calculated from animal model evaluations

1993 ◽  
Vol 57 (2) ◽  
pp. 175-182 ◽  
Author(s):  
P. Uimari ◽  
E. A. Mäntysaari
Keyword(s):  
Animal Model ◽  
Dairy Cow ◽  
Relative Weight ◽  
Breeding Value ◽  
Potential Bias ◽  
Data Set ◽  
Breeding Values ◽  
Dairy Bulls ◽  
Estimated Breeding Values ◽  
Genetic Standard Deviation

AbstractAn animal model and an approximative method for calculating repeatabilities of estimated breeding values are used in Finnish dairy cow evaluation. Changes in estimated breeding values over time as daughters accumulate were studied. Special emphasis was given to the accuracy and potential bias in the pedigree indices of young sires. The data set used was the same as in the national evaluation and the traits investigated were protein yield and somatic cell count. The average repeatability in evaluation of bulls without daughters was 0·37. The empirical repeatability defined as a squared correlation between the pedigree index and the final sire proof was only 0·15. The reduction in the repeatability was attributed to the selection on pedigree index. The upward bias observed in pedigree indices was 5 kg (approx. 0·3 of genetic standard deviation). The bias was caused by the overestimation of bull dams' breeding value. Also the proofs of bull sires increased after the second crop of daughters. The correlation between the evaluations of the same sire calculated from two separate equal size daughter groups was 0·91 when the bull had 10 to 50 daughters and 0·87 with over 100 daughters. This illustrates how the relative weight of the pedigree decreases while more progeny information is accumulated in the evaluation.

Simple data processing procedures for seismic section noise reduction

Geophysics ◽  
1992 ◽  
Vol 57 (8) ◽  
pp. 1064-1067 ◽  
Author(s):  
Howoong Shon ◽  
Tokuo Yamamoto
Keyword(s):  
Standard Deviation ◽  
Data Processing ◽  
Noise Reduction ◽  
Data Transformation ◽  
The Other ◽  
Signal To Noise ◽  
Seismic Section ◽  
Data Set ◽  
Processing Steps ◽  
Optimum Threshold

Seismic traces are signals composed of information and noise. In essence, the part of the data set that is noise obscures the information and complicates the processing and interpretation of the seismic traces. Of the two procedures discussed, one is maximum standard deviation for an optimum threshold, and the other is the data transformation by variance. Either or both procedures can be used to enhance signal‐to‐noise (S/N) ratio. The procedures are data processing steps that are applied to each trace. Examples of successful application of the procedures are presented.

scholarly journals How to Create an Effective Thematic Map

2019 ◽  
Vol 76 (2) ◽  
pp. 258
Author(s):  
Doina VASILCA
Keyword(s):  
Standard Deviation ◽  
The Other ◽  
Thematic Maps ◽  
Huge Amount ◽  
Absolute Deviation ◽  
Data Set ◽  
Thematic Map ◽  
Equal Interval ◽  
Emergency Calls ◽  
Automatic Mapping

Considering the evolution of automatic mapping programs and the huge amount of available data, the key elements that go into creating thematic maps are presented so that those less familiar with map-making might be able to craft accurate, reliable and illustrative maps.Here, we used ArcGIS Pro in order to exemplify the process of creating thematic maps presenting correlations between the number of emergency/total 112 calls and the population of Romanian counties. These freely-available data were classified using Natural Brakes, Quantile, Equal Interval, Geometric Interval, Standard Deviation, Defined Interval and Manual Interval, so as to highlight their relevant aspects. The value of the goodness of absolute deviation fit was calculated for each method and each data set. The maps were then created using combined choropleth and proportional symbol methods. The ratio between the total number of 112 calls and the county populations, structured into five classes, has been represented using choropleth method. On the resulting map, emergency calls have been represented with proportional symbols. Furthermore, aspects related to the other map elements were presented all in one place, in order to create a thematic map that would be easy to understand and interpret.The findings of the present paper could be used by those who want to represent their own data in a very suggestive and reliable manner, without having had any cartographic training beforehand. They could also more easily interpret their datasets and be able to take the necessary steps in their own domains of activity.

scholarly journals Stability of estimated breeding values for average daily gain in Pannon White rabbits

2011 ◽  
Vol 56 (No. 8) ◽  
pp. 365-369 ◽  
Author(s):  
I. Nagy ◽  
J. Farkas ◽  
P. Gyovai ◽  
I. Radnai ◽  
Z. Szendrő
Keyword(s):  
Average Daily Gain ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
The Other ◽  
Breeding Value ◽  
Breeding Values ◽  
Spearman’S Rank Correlation ◽  
The Common ◽  
Estimated Breeding Values ◽  
Daily Gain

Stability of estimated breeding values for average daily gain (ADG) between 5 and 10 weeks of age was analysed for 47 242 Pannon White rabbits, reared in 7470 litters and born between 2000 and 2008. The dataset was divided into 5 successive 5-year periods: (1) 2000–2004, (2) 2001–2005, (3) 2002–2006, (4) 2003–2007, and (5) 2004–2008. Then, after selecting the appropriate part of the pedigree for these sub-datasets, genetic parameters and breeding values were estimated for ADG using REML and BLUP methods. In the applied models sex, year-month, animal and random litter effects were considered. Estimated heritabilities for all 5 periods from 1 to 5 were moderate and stable (0.28 ± 0.01, 0.28 ± 0.02, 0.29 ± 0.02, 0.27 ± 0.02, and 0.28 ± 0.02). Magnitudes of random litter effects were low and stable (0.14 ± 0.01, 0.15 ± 0.01, 0.15 ± 0.01, 0.16 ± 0.01, and 0.16 ± 0.01). After breeding value estimation the dataset of period 5 was merged pair-wise with the other periods 4, 3, 2 and 1 using an inner join. Thus only the common records of the datasets representing the periods 5-4, 5-3, 5-2, and 5-1 were included in the merged datasets. In these merged datasets each rabbit had two breeding values for ADG based on two different periods. Spearman's rank correlation coefficients were calculated between the breeding values based on the dataset of period 5 and the other periods. With the successive years the rank correlation coefficients decreased (0.989, 0.979, 0.965 and 0.924). The correlation coefficients between ranks remained moderately high, even when the proportion of the common rabbits in the merged datasets was low. However, a reasonable re-ranking occurred among the top animals. Rank correlations for the top 100 and 1000 animals varied from 0.41 to 0.55 and from 0.37 to 0.54, respectively, which could influence selection efficiency if the rolling base were used for genetic evaluation.

scholarly journals Implementation of an Economic Lifetime Net Merit for the Dual-Purpose German Black Pied Cattle Breed

Agriculture ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 41
Author(s):  
Saskia Meier ◽  
Danny Arends ◽  
Paula Korkuć ◽  
Sandra Kipp ◽  
Dierck Segelke ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Dairy Cattle ◽  
Milk Yield ◽  
Cattle Breed ◽  
Breeding Value ◽  
Dual Purpose ◽  
Breeding Values ◽  
Fitness Traits ◽  
Milk Performance ◽  
Estimated Breeding Values

Recently, a Total Merit Index (RZ€) has been developed for German Holstein dairy cattle on the basis of margin in Euro. Our aim was to adjust this lifetime net merit for the dual-purpose German Black Pied cattle breed (DSN) accounting for beef production in addition to milk performance and fitness traits. We used the estimated breeding values of DSN sires and developed a breeding value for carcass weight and quality. Furthermore, we adjusted the German Holstein marginal profits per standard deviation, which are used to calculate the estimated breeding values, to DSN-specific values. The DSN Net Merit is the sum of the three sub-indices DSN Net Milk, DSN Net Fitness, and DSN Net Beef, which contribute to the DSN Net Merit with 52.84%, 43.43%, and 3.73%, respectively. The DSN Net Merit that was calculated for 33 DSN sires ranged between EUR −1114 and +709. The DSN Net Merit strongly correlates with the Total Merit Index. The implementation of the DSN Net Merit is useful for selection and mating decisions. Especially, the sub-index DSN Net Beef, which does not correlate with existing breeding values, can be used to maintain the dual-purpose character of DSN while modestly improving milk yield. The approach can be easily adapted to other dual-purpose breeds.

Joint estimation of (co) variance components and breeding values for mean and dispersion of days from calving to first service in Holstein cow

2017 ◽  
Vol 57 (4) ◽  
pp. 760 ◽  
Author(s):  
Heydar Ghiasi ◽  
Majbritt Felleki
Keyword(s):  
Standard Deviation ◽  
Genetic Correlation ◽  
Additive Genetic Variance ◽  
Joint Estimation ◽  
Breeding Value ◽  
Genetic Trend ◽  
Breeding Values ◽  
The Mean ◽  
Estimated Breeding Values ◽  
Genetic Standard Deviation

The present study explored the possibility of selection for uniformity of days from calving to first service (DFS) in dairy cattle. A double hierarchical generalised linear model with an iterative reweighted least-squares algorithm was used to estimate covariance components for the mean and dispersion of DFS. Data included the records of 27 113 Iranian Holstein cows (parity, 1–6) in 15 herds from 1981 to 2007. The estimated additive genetic variance for the mean and dispersion were 32.25 and 0.0139; both of these values had low standard errors. The genetic standard deviation for dispersion of DFS was 0.117, indicating that decreasing the estimated breeding value of dispersion by one genetic standard deviation can increase the uniformity by 12%. A strong positive genetic correlation (0.689) was obtained between the mean and dispersion of DFS. This genetic correlation is favourable since one of the aims of breeding is to simultaneously decrease the mean and increase the uniformity of DFS. The Spearman rank correlations between estimated breeding values in the mean and dispersion for sires with a different number of daughter observations were 0.907. In the studied population, the genetic trend in the mean of DFS was significant and favourable (–0.063 days/year), but the genetic trend in the dispersion of DFS was not significantly different from zero. The results obtained in the present study indicated that the mean and uniformity of DFS can simultaneously be improved in dairy cows.

The Determinants of Capital Adequacy Ratio: The Case of the Vietnamese Banking System in the Period 2011-2015

2017 ◽  
Vol 33 (2) ◽  
Author(s):  
Ngoc Anh Nguyen
Keyword(s):  
Banking System ◽  
Precious Metals ◽  
Capital Adequacy ◽  
The Other ◽  
Total Asset ◽  
Data Set ◽  
Hand Size ◽  
Net Interest Margin ◽  
Capital Adequacy Ratio ◽  
Positive Effect

The analysis of a data set of observation for Vietnamese banks in period from 2011 - 2015 shows how Capital Adequacy Ratio (CAR) is influenced by selected factors: asset of the bank SIZE, loans in total asset LOA, leverage LEV, net interest margin NIM, loans lost reserve LLR, Cash and Precious Metals in total asset LIQ. Results indicate based on data that NIM, LIQ have significant effect on CAR. On the other hand, SIZE and LEV do not appear to have significant effect on CAR. Variables NIM, LIQ have positive effect on CAR, while variables LLR and LOA are negatively related with CAR.

scholarly journals Measurement of the D → K−π+π+π− and D → K−π+π0 coherence factors and average strong-phase differences in quantum-correlated $$ D\overline{D} $$ decays

2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
M. Ablikim ◽  
◽  
M. N. Achasov ◽  
P. Adlarson ◽  
S. Ahmed ◽  
...  
Keyword(s):  
The Other ◽  
Unitarity Triangle ◽  
Quantum Superposition ◽  
Final State ◽  
Data Set ◽  
Strong Phase ◽  
Signal Decay ◽  
Belle Ii ◽  
Coherence Factors ◽  
Phase Differences

Abstract The decays D → K−π+π+π− and D → K−π+π0 are studied in a sample of quantum-correlated $$ D\overline{D} $$ D D ¯ pairs produced through the process e+e− → ψ(3770) → $$ D\overline{D} $$ D D ¯ , exploiting a data set collected by the BESIII experiment that corresponds to an integrated luminosity of 2.93 fb−1. Here D indicates a quantum superposition of a D0 and a $$ {\overline{D}}^0 $$ D ¯ 0 meson. By reconstructing one neutral charm meson in a signal decay, and the other in the same or a different final state, observables are measured that contain information on the coherence factors and average strong-phase differences of each of the signal modes. These parameters are critical inputs in the measurement of the angle γ of the Unitarity Triangle in B− → DK− decays at the LHCb and Belle II experiments. The coherence factors are determined to be RK3π = $$ {0.52}_{-0.10}^{+0.12} $$ 0.52 − 0.10 + 0.12 and $$ {R}_{K{\pi \pi}^0} $$ R K ππ 0 = 0.78 ± 0.04, with values for the average strong-phase differences that are $$ {\delta}_D^{K3\pi }=\left({167}_{-19}^{+31}\right){}^{\circ} $$ δ D K 3 π = 167 − 19 + 31 ° and $$ {\delta}_D^{K{\pi \pi}^0}=\left({196}_{-15}^{+14}\right){}^{\circ} $$ δ D K ππ 0 = 196 − 15 + 14 ° , where the uncertainties include both statistical and systematic contributions. The analysis is re-performed in four bins of the phase-space of the D → K−π+π+π− to yield results that will allow for a more sensitive measurement of γ with this mode, to which the BESIII inputs will contribute an uncertainty of around 6°.

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

scholarly journals 45 A permutation test for validation of genomic estimated breeding values

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 8-9
Author(s):  
Zahra Karimi ◽  
Brian Sullivan ◽  
Mohsen Jafarikia
Keyword(s):  
Permutation Test ◽  
Breeding Value ◽  
Small Data ◽  
Data Sets ◽  
Type I ◽  
Future Performance ◽  
Small Data Sets ◽  
Estimated Breeding Value ◽  
Estimated Breeding Values ◽  
Top 40

Abstract Previous studies have shown that the accuracy of Genomic Estimated Breeding Value (GEBV) as a predictor of future performance is higher than the traditional Estimated Breeding Value (EBV). The purpose of this study was to estimate the potential advantage of selection on GEBV for litter size (LS) compared to selection on EBV in the Canadian swine dam line breeds. The study included 236 Landrace and 210 Yorkshire gilts born in 2017 which had their first farrowing after 2017. GEBV and EBV for LS were calculated with data that was available at the end of 2017 (GEBV2017 and EBV2017, respectively). De-regressed EBV for LS in July 2019 (dEBV2019) was used as an adjusted phenotype. The average dEBV2019 for the top 40% of sows based on GEBV2017 was compared to the average dEBV2019 for the top 40% of sows based on EBV2017. The standard error of the estimated difference for each breed was estimated by comparing the average dEBV2019 for repeated random samples of two sets of 40% of the gilts. In comparison to the top 40% ranked based on EBV2017, ranking based on GEBV2017 resulted in an extra 0.45 (±0.29) and 0.37 (±0.25) piglets born per litter in Landrace and Yorkshire replacement gilts, respectively. The estimated Type I errors of the GEBV2017 gain over EBV2017 were 6% and 7% in Landrace and Yorkshire, respectively. Considering selection of both replacement boars and replacement gilts using GEBV instead of EBV can translate into increased annual genetic gain of 0.3 extra piglets per litter, which would more than double the rate of gain observed from typical EBV based selection. The permutation test for validation used in this study appears effective with relatively small data sets and could be applied to other traits, other species and other prediction methods.

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