Birth and weaning weights of 556 Friesian calves by 41 sires out of 318 different dams over a 11 years period were obtained from a herd of Friesian in Sakha Experimental Farm, Ministry of Agriculture, Egypt were used. The records were analyzed by Multiple Trait Likelihood Method (MTDFREML) by using a repeatability animal model (BOLDMAN et al., 1995). Convergence was attained after 699 iterations. The fixed effects included in the model were season and year of calving, parity and sex and the random effects were direct and maternal genetic, permanent maternal environmental and error. Direct heritability estimates for birth weight (BW) and weaning weight (WW) are 0.28 and 0.13, respectively, while, maternal heritability estimates for the same traits are 0.14 and 0.06, respectively. Repeatability estimates are 0.75 and 0.15 for BW and WW, respectively. Phenotypic and genetic correlations are 0.89 and 0.80, respectively. Estimates of calve breeding values ranged from -3.12 to 4.11 kg for BW and ranged from -4.10 to 5.11 kg for WW. Sire breeding values ranged from -3.40 to 2.99 kg for BW and ranged from -2.50 to 4.47 kg for WW. Dam breeding values ranged from -6.80 to 5.54 kg for BW and ranged from -6.10 to 6.39 kg for WW.

A total of 2095 lactation records of Holstein Friesian cattle kept at Dalla Farm in Egypt during the period from 1988 to 1992 were used in this study. Those data were used to estimate variances from direct and maternal genetic effects. The data was analyzed by using Multiple Traits Derivative Free Restricted Maximum Likelihood (MTDFREML) according to BOLDMAN et al. (1995) using repeatability Animal Model. Two models were used: Model 1 includes month of calving, year of calving, parity as fixed effects, days open and days dry as covariates and direct genetic, maternal genetic, covariance between direct and maternal genetic, permanent environmental and residual as random effects; Model 2 is similar to Model 1, but excluding additive maternal and covariance between additive direct and maternal effects. Estimates of heritability for 305 day milk yield (305 dMY) were 0.22 and 0.23, for Model 1 and Model 2, respectively. Heritability estimates for age at first calving (AFC) were 0.77 and 0.82 for Model 1 and model 2, respectively. The removal of additive maternal genetic effects and covariance between direct and maternal genetic effects from the model increased estimates for heritability of additive genetic effects by 0.01 and 0.05 for 305 dMY and AFC, respectively. Then, the additive maternal genetic effect and covariance between direct and maternal genetic effects do not seem to make important contributions to the phenotypic variance for milk yield and age at first calving, and these effects are probably not important for genetic evaluations.

A total 1481 first lactation records of local born Friesian heifers were collected from Sakha Farm, Animal Production Research Institue, Ministry of Agriculture, during the period from 1980 to 1993. A linear mixed model was used to study the fixed effects of month and year of calving, age at first calving as a covariate and the random effect of sire on productive traits (i.e., 90 day milk yield (90dMY), 305 day milk yield (305dMY), total milk yield (TMY), lactation period (LP) and dry period (DP)). The effects of the same factors on reproductive traits (i.e., days open (DO) and calving interval (CI)) were also studied. Least squares means of 90 dMY, 305 dMY, TMY, LP, DP, DO and CI were 959 kg, 3252 kg, 3709 kg, 367 d, 65 d, 145d and 426d, respectively. A least squares analysis of variance showed significant effect of month of calving on 90 dMY, 305 dMY, TMY and LP (p<0.05 or p<0.01). Year of calving had a significant effect on all traits studied (p<0.05 or p<0.01). Including age at first calving (AFC) as a polynomial regression of the second degree in the model yielded significant (p<0.05 or p<0.01) partial linear regression coefficients of 90 dMY, 305dMY, TMY and DP on AFC while the quadratic term was significantly only for 305dMY. Sire of the heifers had a significant effect on all productive traits. Heritability estimates for 90dMY, 305dMY, TMY, LP, DP, DO and CI were 0.30 ± 0.08, 0.30 ± 0.08, 0.15 ± 0.06, 0.10±0.06, 0.09±0.06, 0.05±0.06, 0.05±0.06, respectively. In addition, genetic and phenotypic correlations between different traits studied are calculated and tabulated. Negative genetic correlations between each of DP, DO and CI and 90dMY, 305dMY and TMY concluded that selection against dry period and days open will increase milk yield. Therefore, a reduction of DP and DO are the desirable goal of dairymen.

ÖzetGelişen toplum yapısı ile birlikte gerçek hayatta yaşanılan sorunlar ve olaylara bakış açıları da değişmektedir. İnsanlar sorunlarını sahip oldukları sözel ve sayısal verileri kullanarak çözmekte ve bunun için çeşitli yöntemlerden yararlanmaktadırlar. Matematiksel yöntemler insanlara kesinlik içeren durumlarda sorunların çözümlenmesinde sayısal verileri analiz ederek yardımcı olurken, belirsizlik içeren durumlarda yetersiz kalabilmektedir. Son yıllarda kalite değerlendirilmesi gibi belirsizlik içeren durumlarda ortaya çıkan problemlerin çözümünde sıklıkla kullanılan bulanık mantık, yapay zeka yöntemlerinden bir tanesidir. Klasik mantık teorisine göre daha esnek bir yapıya sahip olan bulanık mantık teorisi, olayları nesnelere "0" ve "1" arasında atadığı doğruluk dereceleri ile açıklamakta böylece sözel ve sayısal veriler arasında bir bağ oluşturmaktadır. Bu çalışmada, çiğ süt örneklerinin kalite sınıflarına ayrılmasını amaçlayan bulanık mantık tabanlı bir karar destek sistemi geliştirilmiştir. Sistemin girdileri çiğ süt örneklerine ilişkin toplam bakteri sayısı, somatik hücre sayısı ve protein miktarlarının ölçülen değerleridir. Tasarlanan bulanık sistemin çıktısı ise çiğ süt kalite değerlendirmesi şeklindedir. Yapılan analizin başarısını belirlemek amacıyla uzman kararları ile karşılaştırma yapılmış ve sistemin %80 değerinde başarılı olduğu görülmüştür. Sistemin modellenmesi Matlab (sürüm R2010b) programı kullanılarak yapılmıştır. Anahtar sözcükler: Bulanık mantık, Karar destek sistemi, Çiğ süt kalitesi Fuzzy Logic Approach in the Evaluation of Raw Milk Quality SummaryThe problems that faced with in real life and perspective of the events change with developing structure of society. The people in the face of problem use a variety of methods with their verbal and numerical data to find solution. Mathematical methods that including precision are sufficient in the analyses of numerical data while the modeling of verbal data may be insufficient in case of uncertainty. In recent years, fuzzy logic is one of the artificial intelligence methods that used in solution of the problems which are rosed from quality evaluation situations that consists of uncertainty cases. The fuzzy logic theory that has more flexible structure than the theory of classical logic, describe the events with degree of accuracy which is between "0" and "1" appointed to object. Fuzzy logic-based decision support system offers to people a more realistic and objective perspective in decision making. In this study, fuzzy logic base decision support system which aims to classify raw milk samples in quality has been developed. System inputs are; bacteria count for milk samples, somatic cell count and values for measured protein amounts. Designed fuzzy logic output is consist of raw milk quality value measurement; in order to calculate the success of the analysis, results have been compared to specialist's decisions and due to the comparison, it noticed that the system has 80% success rate. Modeling of the system has been made via Matlab (version R2...

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