EFFECT OF CALVING INTERVAL ON MILK YIELD IN JERSEY COWS UNDER CONDITIONS OF SMALL FAMILY FARMS IN ALBANIA

Effect of calving interval on 305 day milk yield for first three lactations was studied in order to increase efficiency of selection schemes and to more efficiently manage Jersey cows that have been raised on small scale family farms in Albania. Results obtained by processing data of 1476 cows, managed in 935 small scale family farms, pointed out that current calving interval significantly affects on milk production for first three lactations (p<0.05). Preceding calving interval affected 305 day milk yield (p<0.05) in second lactation only. Linear regression model accounted for 20-25 % of the total variance of 305 day milk yield. Extension of calving interval over 420, 430, 450 days for first, second and third lactations respectively, did not increase milk production when converted to 305 day lactation. Stochastic relations between calving interval and calving age and month are moderated. Values of Pierson’s correlation coefficients ranged 0.38 to 0.69. Adjustment of milk production in order to reduce effect of calving interval on total phenotypic variance of milk yield is valid for first lactation only. Adjustment of 305 day milk yield for second and third lactations in order to reduce effects of factors “calving age and month” brings about, at the same time, elimination of calving interval effect

Küçük Ölçekli İşletme Koşulları Altındaki Jersey İneklerinde İlk Üç Laktasyonda Üçyüzbeş Günlük Süt Verimi Üzerine Araştırmalar

Data of 935 first lactations, 607 second lactations and 432 third lactations obtained by 1476 Jersey cows that are managed in small scale family farms, under conditions of low input production system were analyzed in order to study effects of factors: age and month of calving on variance of 305-day milk yield. Analyze of variance carried out according to a GML with fixed factors showed statistical effects of these factors – age on calving (CA): first lactation (P<0.05), second lactation (P<0.05), month of calving (CS): first lactation (P<0,001), second lactation (P<0.01), third lactation (P<0.05) and herd (H): first, second and third lactations (P<0.001). Average growth of 305-day milk yield, in second lactation was 198 kg more than first lactation, and 271 kg more in third lactation compared to second lactation. To make possible the comparison of milk yield obtained by cows in different physiological conditions, it is necessary that 305-day milk yield be adjusted in order to reduce effects of calving age and month.Düşük gelirli üretim sistemi koşulları altındaki küçük ölçekli aile işletmelerinde yetiştirilen 1476 Jersey ineğinden elde edilen 935 birinci 607 ikinci 432 üçüncü laktasyon kaydı yaş,buzağılama ayı faktörlerinin 305 günlük laktasyon verimi değişkenliğindeki etkilerini incelemek üzere analiz edilmiştir. Sabit faktörlü GML modeli ile yürütülen varyans analizi sonucunda bu faktörlerden buzağılama yaşının(CA),ilk (P<0.05), ikinci laktasyon üzerine (P<0.05);buzağılama ayının ilk laktasyon (P<0,001), ikinci laktasyon (P<0.01), üçüncü laktasyon (P<0.05) üzerine ve Sürü faktörünün (H): ilk ikinci üçüncü laktasyon üzerine etkili olduğu (P<0.001) anlaşılmıştır. Üçyüzbeş günlük süt verim miktarındaki ortalama büyüme ikinci laktasyonda birinci laktasyondan 198 kg daha fazla üçüncü laktasyonda ise ikinci laktasyona göre 271 kg daha fazladır.Farklı fizyolojik koşullardaki ineklerden elde edilen süt verim miktarlarını karşılaştırmayı mümkün kılmak bakımından 305 günlük süt verimlerinin buzağılama yaş ve ayı nın etkilerini azaltmak için düzeltilmesi gerekir

Arnavutluk’ta Küçük Ölçekli Aile İşletmesi Koşullarındaki Jersey İneklerinde 305 Günlük Süt Verim Özellikleri Üzerine Bir Araştırma-II. 305 Günlük Süt Verimi İçin Düzeltme Faktörleri

Data of 935 first lactations, 607 second lactations and 432 third lactations obtained by 1476 Jersey cows that are managed in small scale family farms, under conditions of low input production system, were analyzed in order to study effects of factors: calving age and season on variance of 305-day milk yield and their effect adjustment. ML (Maximum Likelihood) Method was used to obtain unbiased prediction for adjustment factors, as it is able to take into account and estimate not only differences “between cows” but also “within cow”. Analysis of variance carried out according to mixed linear regression model indicates that all factors included in this model show statistically significant effects (P<0,001) on the variance of 305 day milk yield. Mixed linear model with “age of calving x month of calving” increased significance (P<0.05) of this model in explanation of total phenotypic variance of milk yield for first three lactations only by 0,72 %. This situation doesn?t justify the use of joint multiplicative factors for calving age and month.Yetersiz girdili üretim sistemleri koşullarında, küçük ölçekli aile işletmelerinde yetiştirilen 1476 Jersey ineğinden elde edilen 935 birinci ,607 ikinci,432 üçüncü laktasyon sırasındaki verim kayıtlarında buzağılama yaşı ve mevsiminin 305 günlük süt verimi ve düzeltme etki faktörleri değişkenliğindeki rolü incelenmiştir. Düzeltme faktörlerinin sapmasız tahminlerini elde etmek konusunda yanlızca “inekler arası “farklılığı değilde fakat ayni zamanda “ inekler içi” farlılığıda dikkate aldığından Maksimim olabilirlik meteodu kullanılmıştır. Karışık Doğrusal Regresyon metoduna göre yürütülen varyans analizi; modele dahil edilen tüm tüm faktörlerin 305 günlük süt verimi değişkenliği üzerine etkilerinin istatistik olarak önemli olduğunu göstermiştir(P<0,001). “Buzağılama yaşı x Buzağılama ayı “ögesini içeren karışık doğrusal model de bu unsurun ilk üç laktasyon için süt verimindeki toplam varyasyondaki açıkladığı kısmın yanlızca % 0.72 olduğu ve önemli olduğu(P<0.05) gözlenmiştir.Bu durum buzağılama yaşı ve ayına göre çoklu düzeltme faktörleri için birleşik carpım faktörleri kullanımının doğru olmadığını belirtir

Einstien-Multidimensional Extrapolation methods

In this paper, we present a new framework for the recent multidimensional extrapolation methods: Tensor Global Minimal Polynomial (TG-MPE) and Tensor Global Reduced Rank Extrapolation (TG-RRE) methods. We develop a new approach to the one presented in \cite{17}. The proposed framework highlights, in addition their polynomial feature, the connection of TG-MPE and TG-RRE with nonlinear Krylov subspace methods. A unified algorithm is proposed for their implemention. Theoretical results are given and some numerical experiments on linear and nonlinear problems are considered to confirm the performance of the proposed algorithms

Leiomyoblastome gastrique: à propos de trois cas

Le leiomyoblastome gastrique est une tumeur musculaire rare qui touche essentiellement l'adulte. Son développement est souvent exogastrique.Le diagnostic positif repose sur l'histologie et le traitement est basé sur la chirurgie. Nous rapportons trois cas de leiomyoblastome gastrique colligés dans le service de chirurgie générale au 5ème Hôpital Militaire. L'âge  moyen des patients est de 47 ans; le motif de consultation était représenté par une hémorragie digestive et l'imagerie médicale a posé le diagnostic de masse tumorale dans tous les cas. Le traitement chirurgicalconsistait en une gastrectomie partielle et le compte rendu   anatomopathologique a confirmé le leiomyoblastome gastrique dans les trois cas. Le siège de la tumeur a été posé par la fibroscopie oeso  gastroduodénale, le traitement était chirurgical et les suites post  opératoires étaient simples avec un contrôle par des fibroscopies  répétitives sans aucun signe de récidive. Le leiomyoblastome gastrique est une tumeur rare. L'écho endoscopie joue un rôle primordial dans le  diagnostic positif ainsi que dans l'évaluation de l'extension pariétale de ces tumeurs. Le traitement est essentiellement chirurgical

A Novel Approach for Education Indoor Air Quality Management using Wireless Sensor Networks

Abstract: Learning environments are a very important component in the educational system, and have a major role in improving learners&apos; performance. However changes in Indoor Environment Quality (IEQ) factors such as temperature, CO2 level, and noise in addition to the number of students per class can harm learners&apos; health and decrease their knowledge acquisition capacities. Due to the number of studies that showed how IEQ improvement leads to students&apos; performance increase, this paper present the implementation, design and results of a WSN based IEQ monitoring system for the sake of students&apos; performance improvement and decision making accuracy increase