Genetic Trends from a Selection Experiment in Broiler Breeder Lines Using Best Linear Unbiased Prediction Methodology.

1995 ◽  
Vol 32 (6) ◽  
pp. 379-386
Author(s):  
Gariba DANBARO ◽  
Kenji OYAMA ◽  
Fumio MUKAI ◽  
Soichi TSUJI ◽  
Tomonori TATEISHI ◽  
...  
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Selection Experiment ◽  
Linear Unbiased Prediction ◽  
Broiler Breeder ◽  
Best Linear Unbiased ◽  
Genetic Trends ◽  
Unbiased Prediction

GENETIC TRENDS FOR MILK AND FAT PRODUCTION IN DAIRY CATTLE

1979 ◽  
Vol 59 (1) ◽  
pp. 203-206 ◽  
Author(s):  
T. R. BATRA
Keyword(s):  
Dairy Cattle ◽  
Prediction Method ◽  
Weighted Average ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Sire Evaluation ◽  
Best Linear Unbiased ◽  
Jersey Cows ◽  
Genetic Trends ◽  
Unbiased Prediction

The Best Linear Unbiased Prediction method is used to evaluate dairy sires in Canada. Milk and fat production records of 2-yr-old Ayrshire, Guernsey, Holstein and Jersey cows calved from 1958 through 1975 were used in the sire evaluation done in November 1976. Genetic trends were estimated as twice the change in weighted average of sire proofs per year. Genetic trends for milk and fat production were 1.32 and 1.62 BCA for Ayrshire; 1.50 and.88 BCA for Guernsey;.72 and.80 BCA for Holstein; and.60 and.54 BCA for Jersey, respectively.

scholarly journals Correction: Weighted Genomic Best Linear Unbiased Prediction for Carcass Traits in Hanwoo Cattle. Genes 2019, 10, 1019

Genes ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1013
Author(s):  
Bryan Irvine Lopez ◽  
Seung-Hwan Lee ◽  
Jong-Eun Park ◽  
Dong-Hyun Shin ◽  
Jae-Don Oh ◽  
...  
Keyword(s):  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Hanwoo Cattle ◽  
Unbiased Prediction

The authors wish to make the following corrections to this paper [...]

A method for reducing inbreeding with Best Linear Unbiased Prediction

1993 ◽  
Vol 1993 ◽  
pp. 33-33
Author(s):  
B Grundy ◽  
WG Hill
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Breeding Value ◽  
Best Linear Unbiased Predictor ◽  
Linear Unbiased Prediction ◽  
Genetic Merit ◽  
Selection Decisions ◽  
Best Linear Unbiased ◽  
Estimated Breeding Value ◽  
Commercial Environment ◽  
Unbiased Prediction

An optimum way of selecting animals is through a prediction of their genetic merit (estimated breeding value, EBV), which can be achieved using a best linear unbiased predictor (BLUP) (Henderson, 1975). Selection decisions in a commercial environment, however, are rarely made solely on genetic merit but also on additional factors, an important example of which is to limit the accumulation of inbreeding. Comparison of rates of inbreeding under BLUP for a range of hentabilities highlights a trend of increasing inbreeding with decreasing heritability. It is therefore proposed that selection using a heritability which is artificially raised would yield lower rates of inbreeding than would otherwise be the case.

scholarly journals ​Best Linear Unbiased Prediction with Additive Main and Multiplicative Interaction for Stability Analysis of Barley Genotypes Evaluated in Coordinated Program

2021 ◽  
Author(s):  
Ajay Verma ◽  
R.P.S. Verma ◽  
J. Singh ◽  
L. Kumar ◽  
G.P. Singh
Keyword(s):  
Weighted Average ◽  
Best Linear Unbiased Prediction ◽  
High Yield ◽  
Linear Unbiased Prediction ◽  
Yield Performance ◽  
Multiplicative Interaction ◽  
Absolute Value ◽  
Best Linear Unbiased ◽  
Barley Genotypes ◽  
Unbiased Prediction

Background: Additive main and multiplicative interaction (AMMI) analysis had been exploited for multi environment trials for most of the crops. Usage of the best linear unbiased prediction (BLUP), along with AMMI tools, of the genotypes would improve the estimation of interaction effects. Methods: AMMI based measures of adaptability have been enriched with the incorporation of BLUP of genotypes by new Superiority index that allowed variable weights for stability and yield of genotypes. Result: Stability measure weighted average of absolute scores (WAASB) based on all significant interaction principal components ranked suitability of KB1754, RD3000, NDB1445 genotypes. Superiority index while weighting 0.65 and 0.35 for mean yield and stability arranged DWRB201, NDB1445, RD2552 as of stable high yield performance of barley genotypes. Corrected measure Modified AMMI Stability Value (MASV1) found RD2552, DWRB201, KB1762 and Modified AMMI Stability Value (MASV) ranked DWRB201, RD2552, KB1762. ASTAB measure achieved the desirable lower values for DWRB201 DWRB207, HUB268 genotypes. Biplot graphical analysis based on 60.7% of variation of the stability measures observed MASV1, ASTAB (AMMI based stability parameter), EV(Averages of the squared eigenvector values), SIPC (Sums of the absolute value of the IPC scores), Za (Absolute value of the relative contribution of IPCs to the interaction), W3, WAASB and MASV had been clubbed together. For the second year lower value of WAASB measure had observed for RD3016, KB1815 HUB273. Ranking of genotypes as per Superiority index found RD3017, RD2907, HUB274 as of stable high yield performance. Genotypes RD3017, RD2907 and NDB1173 pointed out by MASV1 while RD3017, RD2907, NDB1173 identified by MASV as the genotypes of choice. RD3017 NDB1173, RD2907 genotypes were selected as per values of ASTAB measure. Total of 71.8% of variation of the considered measures in biplot analysis expressed larger cluster comprised of AMMI based measures and a separate cluster of Superiority indexes as per mean, Geometric Adaptability Index (GAI) and HMGV also observed.

scholarly journals M-quantile Chambers-Dunstan Untuk Pendugaan Area Kecil

2021 ◽  
Vol 2021 (1) ◽  
pp. 80-89
Author(s):  
Aldi Rochman Nulkarim ◽  
Ika Yuni Wulansari
Keyword(s):  
Small Area ◽  
Random Effect ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

Metode Small Area Estimations (SAE) digunakan sebagai pendekatan yang reliabel dalam mengatasi kendala ketidakcukupan sampel pada survei sampel. BPS memproduksi statistik area kecil menggunakan metode SAE popular seperti Empirical Best Linear Unbiased Prediction dalam model Fay-Herriot (EBLUP-FH). Metode EBLUP-FH sebagai pendekatan parametrik memerlukan asumsi normalitas dan terbebas dari outliers pada kedua komponen random effect-nya. Namun, hal tersebut sulit dipenuhi karena seringkali data di lapangan berperilaku ekstrim. Metode SAE M-quantile Chambers-Dunstan (CD) merelaksasi asumsi parametrik dan robust dalam inferensi terhadap outliers. Penelitian ini mengkaji metode M-quantile CD dalam meningkatkan robustness pendugaan area kecil melalui penerapannya pada data riil untuk estimasi rata-rata pengeluaran rumah tangga per kapita tingkat kecamatan di DI Yogyakarta tahun 2018. Penelitian ini menggunakan data Susenas 2018 dan Podes 2018. Hasil implementasi pada data riil menunjukkan model M-quantile CD berhasil memperbaiki presisi EBLUP-FH. Dengan mengimplementasikan M-quantile CD diharapkan estimasi data berperilaku ekstrim lebih akurat untuk pengambilan kebijakan di daerah.

Sign in / Sign up

Export Citation Format

Share Document