This study was carried out for two purposes: comparing performances of Regression Tree and Automatic Linear Modeling and determining optimum sample size for these methods under different experimental conditions. A comprehensive Monte Carlo Simulation Study was designed for these purposes. Results of simulation study showed that percentage of explained variation estimates of both Regression Tree and Automatic Linear Modeling was influenced by sample size, number of variables, and structure of variance-covariance matrix. Automatic Linear Modeling had higher performance than Regression Tree under all experimental conditions. It was concluded that the Regression Tree required much larger samples to make stable estimates when comparing to Automatic Linear Modeling.

This study presents the first insights to the genetic diversity and structure of the Turkish donkey populations. The primary objectives were to detect the main structural features of Turkish donkeys by microsatellite markers. A panel of 17 microsatellite markers was applied for genotyping 314 donkeys from 16 locations of Turkey. One hundred and forty-two alleles were identified and the number of alleles per locus ranged from 4 to 12. The highest number of alleles was observed in AHT05 (12) and the lowest in ASB02 and HTG06 (4), while ASB17 was monomorphic. The mean H_O in the Turkish donkey was estimated to be 0.677, while mean H_E was 0.675. The polymorphic information content (PIC) was calculated for each locus and ranged from 0.36 (locus ASB02) to 0.98 (locus AHT05), which has the highest number of alleles per locus in the present study. The average PIC in our populations was 0.696. The average coefficient of gene differentiation (G_ST) over the 17 loci was 0.020 ± 0.037 (p < 0.01). The G_ST values for single loci ranged from −0.004 for LEX54 to 0.162 for COR082. Nei’s gene diversity index (H_t) for loci ranged from 0.445 (ASB02) to 0.890 (AHT05), with an average of 0.696. A Bayesian clustering method, the Structure software, was used for clustering algorithms of multi-locus genotypes to identify the population structure and the pattern of admixture within the populations. When the number of ancestral populations varied from K = 1 to 20, the largest change in the log of the likelihood function (ΔK) was when K = 2. The results for K = 2 indicate a clear separation between Clade I (KIR, CAT, KAR, MAR, SAN) and Clade II (MAL, MER, TOK, KAS, KUT, KON, ISP, ANT, MUG, AYD and KAH) populations.

In this study, to analyze the mtDNA D-loop region and the origin of the maternal lineages of 16 different donkey populations, and to assess the domestication of Turkish indigenous donkeys in seven geographical regions, we investigated the DNA sequences of the D-loop region of 315 indigenous donkeys from Turkey. A total of 54 haplotypes, resulting from 35 polymorphic regions (27 parsimoniously informative and 6 singleton sites), were defined. Twenty-eight of these haplotypes are unique (51.85%), and 26 are shared among different Turkish indigenous donkey populations. The most frequent haplotype was Hap 1 (45.71%), followed by two haplotypes (Hap 4, 15.55% and Hap 7, 5.39%). The breed genetic diversity, evaluated by the haplotype diversity (H_D) and nucleotide diversity (π_D), for the Turkish donkey populations ranged from 0.533 ± 0.180 (Tekirdağ–Malkara, MAL) to 0.933 ± 0.122 (Aydin, AYD), and from 0.01196 ± 0.0026 (Antalya, ANT) to 0.02101 ± 0.0041 (Aydin, AYD), respectively. We observed moderate-to-high levels of haplotype diversity and moderate nucleotide diversity, indicating plentiful genetic diversity in all of the Turkish indigenous donkey populations. Phylogenetic analysis (NJT) and median-joining network analysis established that all haplotypes were distinctly grouped into two major haplogroups. The results of AMOVA analyses, based on geographic structuring of Turkish native donkey populations, highlighted that the majority of the observed variance is due to differences among samples within populations. The observed differences between groups were found to be statistically significant. Comparison among Turkish indigenous donkey mtDNA D-loop regions and haplotypes, and different countries’ donkey breeds and wild asses, identified two clades and which is named Somali (Clade IV) and Nubian (Clade V) lineages. The results can be used to understand the origin of Turkish donkey populations clearly, and to resolve the phylogenetic relationship among all of the different regions.

Bu çalışmada, Kırşehir ili Damızlık Sığır Yetiştiricileri Birliği’ne üye 389 işletmedeki Siyah Alaca sığırlara ait 2007-2017 yılları arasında tutulan 4589 adet süt verim kaydı kullanılmıştır. Süt verimi özelliklerinden 305 gün süt verimi (305 GSV), laktasyon süresi (LS) ve kuruda kalma süresine (KKS) ve bunlar üzerine buzağılama yılı, laktasyon sırası, buzağılama mevsimi, işletme faktörünü ve buzağılama yaşının etkileri belirlenmiştir. Çalışmada 305 GSV, LS ve KKS ortalamaları sırasıyla 7350,5±30,70 kg, 398,2±1,61 gün ve 62,5±17,02 gün olarak hesaplanmıştır. Buzağılama yılı, laktasyon sırası, buzağılama mevsimi ve işletme büyüklüğünün 305 GSV ve LS üzerine etkileri istatistik olarak önemlidir (p<0,01). KKS üzerine buzağılama yılının ve işletme büyüklüğününün etkisi önemli (p0,05) olarak tespit edilmiştir. Çalışmada doğum yıllarına göre 305 GSV’nin fenotipik yönelim 62,6 kg olarak bulunmuştur. Sonuç olarak bu çalışma ile Kırşehir ilinde yetiştirilen Siyah Alaca sığırların süt verim özellikleri belirlenmiştir. Yıllar itibari ile 305 GSV bakımından istatistik olarak önemli farklılıklar olduğu tespit edilmiştir.

In this study, performances of Multiple Linear Regression and Automatic Linear Modelling are compared for different sample sizes and number of predictors. A comprehensive Monte Carlo simulation study was carried out for this purpose. Random numbers generated from multivariate normal distribution by using RNMVN function of IMSL library of Microsoft FORTRAN Developer Studio composed the material of this study. Results of the simulation study showed that the sample size and the number of predictors are the main factors that lead to produce different results. Although both methods gave very similar results especially when studied with large sample sizes (n≥100), the Automatic linear modelling is preferred for analyzing data sets due to its simplicity in analyzing data and interpreting the results, ability to present results visually and providing more detailed information especially studying large complex data sets. It will be beneficial to use the Automatic linear modelling especially in analyzing massive and complex data sets for the purposes of investigating the relationships between one continuous dependent and 10 or more predictors and determine the factors that affect the response or target variable. At the same time, it will also be possible to evaluate the effect of each predictor with a more detailed response.