Possibilities of Using “BUGA” Named Software for Breeding Value Estimation of Anatolian Water Buffalo Population of Istanbul

Yıl 2023, Cilt: 63 Sayı: 2, 80 - 86, 31.12.2023

Kemal Yazgan , Mehmet İhsan Soysal

https://doi.org/10.46897/livestockstudies.1416498

Öz

In this study, variance components and breeding value estimations of Anatolian water buffaloes were estimated from the data obtained within the scope of community based water buffalo improvement program of Anatolian water buffalo population of İstanbul. The "BUGA" named software, which was started to be developed with the support of Harran University Scientific Research Project Unit (HUBAP) for use in the estimation of (co)variance components and breeding value were used in this study. The BUGA software used in the research can predict genetic and environmental effects together with variance components as Best Linear Unbiased Prediction (BLUP) with the Restricted Maximum Log likelihood (REML) method. Unlike its counterparts of another software of foreign origin, it has a visual interface. The language of use is Turkish. The dataset includes records such as city, district, village, genealogy information, date of birth, gender, calving date, lactation parity, number of test day milk yield, lactation milk yield and previous calving date of 442 Anatolian buffaloes raised in this population. The records were analysed using the repeatability model with BUGA software and the additive genetic and permanent environmental effects of all animals in the dataset were estimated. Random environment variance, genotypic variance and permanent environment variance values were estimated as 107112.60 kg2, 20045.22 kg2 and 1259.50 kg2, respectively, while heritability (h2) and degree of repeatability (r) were determined as 0.1561 and 0.1659, respectively. The results obtained as an example of case study applied for the possibility of usage the BUGA software in the estimation of genetic parameters and breeding values showed that BUGA software can be easily used in farm recording conditions with the advantage of friendly user features.

Anahtar Kelimeler

(co)variance components, average ınformation algorithm, expectation-maximization algorithm, computer software

Kaynakça

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