ISSN : 1302-7050
Namık Kemal Üniversitesi
Tekirdağ Ziraat Fakültesi Dergisi Journal of Tekirdag Agricultural Faculty
An International Journal of all Subjects of Agriculture
Cilt / Volume: 12 Sayı / Number: 3 Yıl / Year: 2015
Sahibi / Owner
Namık Kemal Üniversitesi Ziraat Fakültesi Adına On Behalf of Namık Kemal University Agricultural Faculty
Prof.Dr. Ahmet İSTANBULLUOĞLU Dekan / Dean
Editörler Kurulu / Editorial Board Başkan / Editor in Chief
Prof.Dr. Türkan AKTAŞ
Ziraat Fakültesi Biyosistem Mühendisliği Bölümü Department Biosystem Engineering, Agricultural Faculty
taktas@nku.edu.tr
Üyeler / Members Prof.Dr. M. İhsan SOYSAL Zootekni / Animal Science
Prof.Dr. Servet VARIŞ Bahçe Bitkileri / Horticulture Prof.Dr. Temel GENÇTAN Tarla Bitkileri / Field Crops
Prof.Dr. Sezen ARAT Tarımsal Biyoteknoloji / Agricultural Biotechnology
Prof.Dr. Aydın ADİLOĞLU Toprak Bilimi ve Bitki Besleme / Soil Science and Plant Nutrition Prof.Dr. Fatih KONUKCU Biyosistem Mühendisliği / Biosystem Engineering
Doç.Dr. İlker H. ÇELEN Biyosistem Mühendisliği / Biosystem Engineering Doç.Dr. Ömer AZABAĞAOĞLU Tarım Ekonomisi / Agricultural Economics
Doç.Dr. Mustafa MİRİK Bitki Koruma / Plant Protection Doç.Dr. Ümit GEÇGEL Gıda Mühendisliği / Food Engineering Yrd.Doç.Dr. Harun HURMA Tarım Ekonomisi / Agricultural Economics
Araş.Gör. Eray ÖNLER Biyosistem Mühendisliği / Biosystem Engineering İndeksler / Indexing and abstracting
CABI tarafından full-text olarak indekslenmektedir/ Included in CABI
DOAJ tarafından full-text olarak indekslenmektedir / Included in DOAJ
EBSCO tarafından full-text olarak indekslenmektedir / Included in EBSCO
FAO AGRIS Veri Tabanında İndekslenmektedir / Indexed by FAO AGRIS Database
INDEX COPERNICUS tarafından full-text olarak indekslenmektedir / Included in INDEX COPERNICUS
TUBİTAK-ULAKBİM Tarım, Veteriner ve Biyoloji Bilimleri Veri Tabanı (TVBBVT) Tarafından taranmaktadır / Indexed by TUBİTAK- ULAKBİM Agriculture, Veterinary and Biological Sciences Database
Yazışma Adresi / Corresponding Address
Tekirdağ Ziraat Fakültesi Dergisi NKÜ Ziraat Fakültesi 59030 TEKİRDAĞ E-mail: ziraatdergi@nku.edu.tr
Web adresi: http://jotaf.nku.edu.tr Tel: +90 282 250 20 00
ISSN: 1302–7050
Danışmanlar Kurulu /Advisory Board Bahçe Bitkileri / Horticulture
Prof. Dr. Ayşe GÜL Ege Üniv., Ziraat Fak., İzmir Prof. Dr. İsmail GÜVENÇ Kilis 7 Aralık Üniv., Ziraat Fak., Kilis
Prof. Dr. Zeki KARA Selçuk Üniv., Ziraat Fak., Konya Prof. Dr. Jim HANCOCK Michigan State University,USA
Bitki Koruma / Plant Protection
Prof. Dr. Cem ÖZKAN Ankara Üniv., Ziraat Fak., Ankara Prof. Dr. Yeşim AYSAN Çukurova Üniv., Ziraat Fak., Adana Prof. Dr. Ivanka LECHAVA Agricultural University, Plovdiv-Bulgaria
Dr. Emil POCSAI Plant Protection Soil Conser. Service, Velence-Hungary Biyosistem Mühendisliği / Biosystem Engineering
Prof. Bryan M. JENKINS U.C. Davis, USA
Prof. Hristo I. BELOEV University of Ruse, Bulgaria Prof. Dr. Simon BLACKMORE The Royal Vet.&Agr. Univ. Denmark
Prof. Dr. Hamdi BİLGEN Ege Üniv.Ziraat Fak. İzmir Prof. Dr. Ali İhsan ACAR Ankara Üniv. Ziraat Fak. Ankara Prof. Dr. Ömer ANAPALI Atatürk Üniv., Ziraat Fak. Erzurum Prof. Dr. Christos BABAJIMOPOULOS Aristotle Univ. Greece
Dr. Arie NADLER Ministry Agr. ARO, Israel Gıda Mühendisliği / Food Engineering
Prof.Dr.Evgenia BEZIRTZOGLOU Democritus University of Thrace/Greece Assoc.Prof.Dr.Nermina SPAHO University of Sarajevo/Bosnia and Herzegovina
Prof. Dr. Kadir HALKMAN Ankara Üniv., Mühendislik Fak., Ankara Prof. Dr. Atilla YETİŞEMİYEN Ankara Üniv., Ziraat Fak., Ankara
Tarımsal Biyoteknoloji / Agricultural Biotechnology
Prof. Dr.İskender TİRYAKİ Çanakkale Üniv., Ziraat Fak., Çanakkale Prof. Dr. Khalid Mahmood KHAWAR Ankara Üniv., Ziraat Fak., Ankara
Prof.Dr. Mehmet KURAN Ondokuz Mayıs Üniv., Ziraat Fak., Samsun Doç.Dr.Tuğrul GİRAY University of Puerto Rico, USA
Doç.Dr.Kemal KARABAĞ Akdeniz Üniv., Ziraat Fak., Antalya
Doç. Dr. İsmail AKYOL Kahramanmaraş Sütçü İmam Üniv., Ziraat Fak., Kahramanmaraş Tarla Bitkileri / Field Crops
Prof. Dr. Esvet AÇIKGÖZ Uludağ Üniv., Ziraat Fak., Bursa Prof. Dr. Özer KOLSARICI Ankara Üniv., Ziraat Fak., Adana
Dr. Nurettin TAHSİN Agriculture University, Plovdiv-Bulgaria Prof. Dr. Murat ÖZGEN Ankara Üniv., Ziraat Fak., Ankara Doç. Dr. Christina YANCHEVA Agriculture University, Plovdiv-Bulgaria
Tarım Ekonomisi / Agricultural Economics
Prof. Dr. Faruk EMEKSİZ Çukurova Üniv., Ziraat Fak., Adana Prof. Dr. Hasan VURAL Uludağ Üniv., Ziraat Fak., Bursa Prof. Dr. Gamze SANER Ege Üniv., Ziraat Fak., İzmir
Prof. Dr. Alberto POMPO El Colegio de la Frontera Norte, Meksika Prof. Dr. Şule IŞIN Ege Üniv., Ziraat Fak., İzmir
Toprak Bilimi ve Bitki Besleme Bölümü / Soil Sciences And Plant Nutrition Prof. Dr. M. Rüştü KARAMAN Yüksek İhtisas Üniv., Ankara
Prof. Dr. Metin TURAN Yeditepe Üniv., Müh. ve Mimarlık Fak. İstanbul Prof. Dr. Aydın GÜNEŞ Ankara Üniv., Ziraat Fak., Ankara
Prof. Dr. Hayriye İBRİKÇİ Çukurova Üniv., Ziraat Fak., Adana Doç. Dr. Josef GORRES The University of Vermont, USA Doç. Dr. Pasguale STEDUTO FAO Water Division Italy
Zootekni / Animal Science Prof. Dr. Andreas GEORGOIDUS Aristotle Univ., Greece
Prof. Dr. Ignacy MISZTAL Breeding and Genetics Universit of Georgia, USA Prof. Dr. Kristaq KUME Center for Agricultural Technology Transfer, Albania
Dr. Brian KINGHORN The Ins. of Genetics and Bioinf. Univ. of New England, Australia Prof. Dr. Ivan STANKOV Trakia University, Depart. of Animal Science, Bulgaria
Prof. Dr. Muhlis KOCA Atatürk Üniv., Ziraat Fak., Erzurum Prof. Dr. Gürsel DELLAL Ankara Üniv., Ziraat Fak., Ankara
Prof. Dr. Naci TÜZEMEN Kastamonu Üniv., Mühendislik Mimarlık Fak., Kastamonu Prof. Dr. Zlatko JANJEČİĆ University of Zagreb, Agriculture Faculty, Hırvatistan
Prof. Dr. Horia GROSU Univ. of Agricultural Sciences and Vet. Medicine Bucharest,Romanya
Tekirdag Ziraat Fakültesi Dergisi / Journal of Tekirdag Agricultural Faculty 2015 12(3)
İ Ç İ N D E K İ L E R / C O N T E N T S H. Arda, İ. Atılgan Helvacıoğlu, Ç. Meriç, C. Tokatlı
İpsala İlçesi Sulama Sularında Bazı Ağır Metal İçeriklerinin Araştırılması
Investigation on the Heavy Metal Contents in Irrigation Water of İpsala District ... 1-7 A. Semerci, O. Parlakay, A. Duran Çelik
Süt Sığırcılığı Yapan İşletmelerin Ekonomik Analizi: Hatay İli Örneği
Economic Analysis of Dairy Farms: The Case of Hatay Province ... 8-17 T. Gümüş, İ. Alper Bursa
Eritme Peynirinde Bazı Patojen Bakteriler Üzerine Farklı Baharatların İnhibisyon Etkisi
The inhibition effect of different spices on some pathogen bacteria in processed cheese ... 18-26 R. Olgun, T. Yılmaz
Kentsel Yeşil Alanlarda Vandalizm ve Olası Tasarım Çözümleri: Antalya Kenti Örneği
Vandalism and Possible Design Solutions in Urban Green Areas: The Case of Antalya ... 27-39 G. Ertemli, N. Demirbaş
Competitiveness of The Turkish Dried Fruit Sector
Türk Kurutulmuş Meyve Sektörünün Rekabetçiliği ... 40-46 Ş. Çelik, H. İnci, T. Şengül, B. Söğüt
Diskriminant Analizi ile Bıldırcın Yumurtalarında Bazı Kalite Özellikleri ile Tüy Rengi Arasındaki İlişkinin İncelenmesi Investigation by Discriminant Analysis of the Relationship Between Plumage Color in Some Quality Characteristics
and Quail Eggs. ... 47-56 M.I. Soysal, E.K. Gürcan, S. Genç, M. Aksel
The Comparison of Growth Curve with Different Models in Anatolian Buffalo
Mandalarda Büyüme Eğrisinin Farklı Büyüme Modelleri ile Karşılaştırılması... 57-61 N. Büyüktosun, F. Tan
Farklı Özelliklerdeki Polietilen Malzemelerin Paket Silajlarda Kullanımı ve Yem Kalitesi Üzerine Etkileri
Effects on Forage Quality and Use in Vaccumed Silage Bags of Different Polyethylene Materials ... 62-67 D. Demiroğlu, Y. Memlük
Sivas Kentsel Gelişim Alanının Kentin Peyzaj Özelliklerine Göre Değerlendirilmesi
Evaluation of Sivas Urban Development Space by The City’s Landscape Features ... 68-81 N. Öner, H.H. Tok, M.T. Sağlam
Merlot Üzüm Çeşidinde Yaprak Gübresi Uygulamasının Verim ve Şıra Kalitesi Üzerine Etkisi
Effects on The Yield and Quality of Grape Juice in Merlot Grape Varieties Foliar Fertilizer Application ... 82-99 B. Karakaya Aytin, A. B. Korkut
Edirne Merkez İlçe Kentsel Sit Alanı Sınırları İçerisindeki Açık ve Yeşil Alan Varlığının İrdelenmesi
Investigation Open and Green Areas Existence in The Boundaries of Protected Area of Edirne City ... 100-108
A. Aybek, S. Üçok, M. Ali İspir, M. Emin Bilgili
Türkiye’de Kullanılabilir Hayvansal Gübre ve Tahıl Sap Atıklarının Biyogaz ve Enerji Potansiyelinin Belirlenerek Sayısal Haritalarının Oluşturulması
Digital Mapping and Determination of Biogas Energy Potential of Usable Animal Manure and Cereal Straw Wastes in
Turkey ... 109-120
57
The Comparison of Growth Curve with Different Models in Anatolian
Buffalo
M.I. Soysal1,* E.K. Gurcan1 S. Genc2 M. Aksel3
1Namık Kemal University, Faculty of Agriculture, Department of Animal Science, Tekirdag, Turkey misoysal@nku.edu.tr
2Ahi Evran University, Faculty of Agriculture, Department of Agricultural Biotechnology, Kırsehir, Turkey
3Istanbul Water Buffalo Breeders Association, Istanbul, Turkey
The aim of this study is determining the relationship between body weight and age on growth curve models and selecting the best fitted model. The study was conducted in live weight records maintained a total of 54 male and female buffalo heads for a year. Richards, Gompertz, Logistic and Von Bertalanffy models are used as growth model in recent study. The model parameters were calculated and the comparisons among the models are materialized according to goodness of fit criteria (R2, R2adj, MSE, AIC and BIC). As a result, all models were indicated that high and similar goodness of fit criteria. Richards and Von Bertalanffy models are the most appropriate (R2=0,996; R2adj
=0,993; MSE=62,71; AIC=42,47; BIC=27,29) for female animal and (R2=0,998; R2d =0,998; HKO=18,51; AIC=30,51;
BIC=12,65) male animal, respectively.
Key words: Anatolian Buffalo, Growth Curve, Growth Curve Models
Mandalarda Büyüme Eğrisinin Farklı Büyüme Modelleri ile Karşılaştırılması
Araştırmanın amacı, mandalarda canlı ağırlık ile yaş ilişkisinin farklı büyüme modelleri kullanarak belirlenmesi ve en uygun modelin seçilmesidir. Çalışma bir yıl süre ile canlı ağırlık kayıtları tutulan toplam 54 baş dişi ve erkek mandalarda yürütülmüştür. Modellemede kullanılan modeller ise Richards, Gompertz, Logistic ve Von Bertalanffy modelleri olmuştur. Her bir model için model parametreleri hesaplanmış, modeller arasındaki karşılaştırmalar belirleme katsayısı (R2), düzeltilmiş belirleme katsayısı (Rd), hata kareler ortalaması (HKO), Akaike (AIC) ve Schwarz Bayesyan (BIC) uyum kriterlerine göre yapılmıştır. Sonuç olarak tüm modellerde yüksek bir uyum olmakla birlikte en yüksek dişi mandalarda Richards modeli (R2=0,996; R2d =0,993; HKO=62,71; AIC=42,47; BIC=27,29) ve erkek mandalarda ise VonBertalanffy modeli (R2=0,998; R2d =0,998; HKO=18,51; AIC=30,51; BIC=12,65) bulunmuştur.
Anahtar Kelimeler: Anadolu Mandası, Büyüme Eğrisi, Büyüme Eğrisi Modelleri
Introduction
In recent years, although there is an increase trend on buffalo breeding in Turkey and the breeding is continuing under elemental conditions. Especially it is gratifying that studies in animals’ records being kept as they never done before. When examining statistic data in our country, end of 2013, total buffalo number was 117591, female buffalo number was 51940 (TUIK 2014). In the world, there are 200 million heads buffalo. 83,5% of this buffalo population is breeding in South East Asia countries (Borghese 2013). Buffaloes, rather than cattle, are much more endurable to diseases, bad maintenance and breeding conditions. Concurrently, they are able to benefit from rough feeds which are cheap, high-rated cellulose and less quality ( Kreul and Sarican 1993, Soysal 2009, Sahin et al. 2013).
The growth concept starts with fertilization of egg cell in mother’s abdomen and continues until
mature age. For this reason, the growth, considered in two phase as before birth and after birth. Increasing live weight consubstuntiated with growth animal is under genetic and environmental effects as it is in other characteristics. The model used in growth of modelling and with the parameter in this model, animal’s health, age of using at breed in and ideal cutting age could be estimated (Akbas 1995).
With meat yield in buffaloes, the growth curve which is change of growth according to time may be evaluated as an important evaluation criteria in breeding studies to be held about meat. Growth curve’s determination with nonlinear mathematic models has an important role in animal breeding also on determination of food matter requirements. In setting down of growth curve, Brody, Logistic, Gompertz, Richards and Von
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Bertalanffy models are used widely (Akbas 1995, Sengul and Kiraz 2005).
Madad et al. (2013) are researched some environmental factors effecting various yield characteristics in Azeri Buffaloes. They found calving year and calving season are important for yield characteristic. Sekerden (2010) is researched various body measures and growth characteristic for 0-12 month Anatolian and Anatolian x Italian crossbreed buffaloes and various environmental effects on these characteristics. As to all characteristics they stated that crossbreed genotype has growing faster. They determined that on 12 month age, live weight averages are 182.3 kg in males and 160.4 in females (Akbulut et al. 1994).
In many studies, it is found that gender factor has significant effects on growth. Especially, growth is much quicker in male animals than females (Salama and Schalles, 1992, Sekerden et al, 1997).
Sahin et al. (2014) executed changing of Anatolion Buffalo’s live weight’s changing according to time with non-linear models. They used, Logistic, Richards, Gompertz and Brody models, in study.
They found that while coefficients of determination fo Logistic, Brody, Gompertz, and Richards for male animals are 0.94, 0.93, 0.95 and 0.97, respectively, in female animals they are respectively, 0.96, 0.92, 0.96 and 0.98. As a result, they specified that Richards model is best identifying model for both gender. In this materialized study, by benefiting from buffaloes’
known as Anatolian Buffalo monthly recording weight yield records, it is studied that modelling of growth curve with different models according to birth to 12 month age and specifying the best fit model between used models.
Material and Method
The animal material consisted of 54 heads animal which scaled live weight of 12 month age period raised in a special company in Istanbul, animal material of this study. Numbers of the animals’
are 23 females and 31 males. Live weights recorded monthly from birth to 12 month with 100 g sensible weigh bridge. Animals raised intensively without letting out to meadow.
Breeding is realized inside with breast milk, first 15 days and then next 6 month, the breeding consist of 18% protein feed with breast milk and after 6 month it continued by ablactating and feeding (compact feed, grass and razmol). Breast milk had to be given 10% of live weight.
Models used in modelling are Richards, Gompertz, Logistic and Von Bertalanffy (Richards, 1959).
Model parameters calculated for each model and comparison between models were performed according to fit criteria of determination of coefficient (R2), adjusted determination of coefficient (R2d), mean squares of error (MSE), Akaike (AIC), Schwarz Bayes (BIC) (Akaike, 1974, Schwarz, 1978, Narinc et al. 2010). Functions forms of used models stated in Table 1 as R2=1- (SSE/SST), R2d=R2-(k-1/n-k)(1-R2), MSE=SSE/(n-k), AIC=n.ln(SSE/n)+2k and BIC= n.ln(SSE/n)+k.ln(n).
Ranked among in this model, it is showed that A:
mature live weight, B: rate of gained weight after birth to mature weight, Yt: weight at the age of t, t: animal’s age at the time of scaling, k: intrinsic growth rate, m: at the time inflection point when the curve turn into increasing to decreasing.
Modelling and parameter estimations belonging to models are materialized on Statistica package software(Statistica1994).
Table 1. Non-linear models used to estimate growth curve
Models Expression
Richards Wt=A.(1-B.exp(-k.t))m
Gompertz Wt=A.exp(-B.exp(-k.t))
Logistic Wt=A.(1+B.exp(-k.t))-1
Von Bertalanffy Wt=A.(1-B.exp(-k.t))3
Wt: Body weight in at age t; A,B, k and m: parameters of model; t:age (day)
59
Results and Discussion
Parameter estimations and fit criteria for models used in this study are presented at Table 2 and 3, according to animal gender. A parameters, according to female and male animals, is found highest at Richard model. In all models, A parameters presenting mature weight of male gender are found higher than females. When examining coefficients of determination Gompertz, Logistic, Von Bertalanffy and Richard models are found as 0.994, 0.993, 0.994 and 0.996, respectively. As to male animals it is found 0.998 for all models.
As to mean squares of error for female animals, Gompertz, Logistic, Von Bertalanffy and Richards models are 80.74, 100.7, 73.99 and 62.71, respectively. As to male animals’ all models they are 20.16, 27.36, 18.51, 21.10, respectively.
Mountly weight recorded animals, observed weights according to gender and estimated weights existing in models are given at Table 4.
In represented study there is a high fitting between all models and highest fitting in females is Richard model and in males it is Von Bertalanffy model. In similar studies Sahin et al. (2014) found coefficient of determination in buffalo for Logistic, Brody, Gompertz and Richard for male animals as 0.94, 0.93, 0.95 and 0.97,respectively and as to female animals it is 0.96, 0.92, 0.96, 0.98, respectively. Richard model is defined as best describing model for both gender. Fundora et al.
(2006) found Logistic model as much fitting in water buffaloes and Araujo et al. (2012) found Logistic and Gompertz model as much fitting in Murrah breeds. In another study, non-linear mathematical functions used as Brody, Gompertz, Richards, Bertalanffy and Verhulst. These models were compared in several buffalo production systems in Colombia. As a result growth was better described by the curves by Brody and Gompertz (Agudelo-Gomez et al. 2009). Observed daily weights and estimated weights’ ,according to models, changing as to time presented at Figure 1 and 2.
Table 2. The mean and standart error of growth curve parameters
Gender Model A B k m
Female
Gompertz 123,5±7,14 1,26±0,18 0,010±0,003 ---
Logistic 121±6,73 2,24±0,60 0,019±0,05 ---
Von Bertalanffy 124,7±7,44 0,35±0,04 0,012±0,003 --- Richards 152,4±61,31 -2,31±1,45 0,003±0,006 28,80±9,18
Male
Gompertz 160,9±12,32 1,54±0,08 0,007±0,001 --- Logistic 148,5±8,83 3,09±0,35 0,011±0,001 --- Von Bertalanffy 168,7±15,13 0,41±0,01 0,005±0,001 ---
Richards 208,7±119,9 -1,06±1,09 0,002±0,004 31,80±4,61
Table 3. The goodness of fit criteria based on different models.
Gender Model R2 Radj2 MSE AIC BIC
Female
Gompertz 0,994 0,991 80,74 48,18 30,33
Logistic 0,993 0,988 100,7 50,83 32,98
VonBertalanffy 0,994 0,991 73,99 47,14 29,28
Richards 0,996 0,993 62,71 42,47 27,29
Male
Gompertz 0,998 0,998 20,16 31,53 13,68
Logistic 0,998 0,997 27,36 35,20 17,34
VonBertalanffy 0,998 0,998 18,51 30,51 12,65
Richards 0,998 0,997 21,10 29,40 14,22
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Table 4.Observed and predicted weight values for each models
Gender
Age(day)
Observed Weight Values (kg)
Predicted Weight Values(kg)
Gompertz
Logistic
Von Bertalanffy
Richards
Female
1 32 35,51 37,74 34,63 30,51
24 45 50,02 49,91 50,19 54,15
50 78 65,87 64,75 66,31 69,29
109 92 93,74 94,33 93,46 90,71
173 108 110,314 111,66 109,78 105,43
233 110 117,59 117,86 117,43 115,19
280 116 120,38 119,70 120,59 121,11
359 134 122,42 120,71 123,14 128,78
Male
1 32 34,56 36,56 33,85 32,32
36 52 48,40 48,24 48,64 49,61
78 63 65,88 64,41 66,47 67,83
151 99 94,39 93,88 94,45 94,33
216 115 114,85 115,71 114,48 113,51
255 118 124,55 125,42 124,22 123,39
289 132 131,54 131,84 131,43 131,15
342 143 140,07 138,75 140,58 141,83
Figure 1. Observed and predicted growth curve of models for body weight of female.
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Figure 2. Observed and predicted growth curve of models for body weight of male.
Conclusion
As a result, all models are indicated that high and similar goodness of fit criteria. Richards and Von Bertalanffy models are the most appropriate (R2=0,996; R2adj =0,993; MSE=62,71; AIC=42,47;
BIC=27,29) for female animal and (R2=0,998; R2d
=0,998; HKO=18,51; AIC=30,51; BIC=12,65) male animal, respectively.
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