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Revista Colombiana de Ciencias Pecuarias

Print version ISSN 0120-0690On-line version ISSN 2256-2958

Rev Colom Cienc Pecua vol.37 no.2 Medellín Apr./June 2024  Epub May 05, 2025

https://doi.org/10.17533/udea.rccp.v37n2a3 

Original research articles

Effects of non-genetic factors on milk yield and chemical composition of milk from Holstein-Friesian cows

Efectos de factores no genéticos en la composición química y producción de leche en vacas Holstein-Friesian

Efeitos de fatores não genéticos na composição química e na produção de leite em vacas da raça Holandesa-Frísia

1University Muş Alparslan, Department of Animal Production and Technologies, Muş, Türkiye.


Resumen

Antecedentes:

Para determinar con precisión el desempeño de las vacas en términos de producción y componentes lacteos, es necesario conocer la cantidad y dirección de los factores ambientales. Aunque existen muchos estudios sobre los factores ambientales que afectan la producción de leche, no hay suficiente información sobre los efectos y la dirección de los factores ambientales en la composición de la leche.

Objetivo:

Determinar los efectos de factores no genéticos, tales como temporada de parto, orden de lactancia, etapa de lactancia, edad, tamaño del rebaño sobre la producción de leche, la composición química de la leche cruda y el recuento de células somáticas (SCC) en vacas Holstein-Friesian.

Métodos:

El material del estudio estuvo compuesto por 15.354 muestras de leche cruda de 5.118 vacas Holstein-Friesian en 276 granjas lecheras en Turquía. El análisis de datos se realizó utilizando la función de modelo lineal general (GLM) del programa estadístico SPSS.

Resultados:

Los valores medios de grasa (F), proteína (P), materia seca (DM), lactosa (L), urea (U), Log10SCC de la leche fueron 3,74 ± 0,01, 3,19 ± 0,01, 11,36 ± 0,03, 4,32 ± 0,01%, 21,57 ± 0,28 mg/dL, 5.244 ± 0,01 células/mL, respectivamente. La producción máxima de leche (PMY), producción de leche de lactancia (LMY), producción de leche a los 305 días (305-d MY) y los valores de SCC fueron 33,7 ± 0,13, 8.538,33 ± 89,64, 6.479,42 ± 168,96 kg, y 224.164,34 ± 4.402,79 células/mL, respectivamente.

Conclusiones:

Se recomienda tomar medidas para mejorar el contenido de proteína, materia seca y urea de la leche en las granjas lecheras de Turquía e investigar en detalle la relación entre contenido de urea en leche cruda, mastitis subclínica y características reproductivas.

Palabras clave: composición de la leche; correlación fenotípica; factores no genéticos; Holstein-Friesian; producción de leche; recuento de células somáticas; vaca

Abstract

Background:

It is necessary to determine the extent and direction of environmental factors to accurately assess cow performance in terms of milk yield and milk components. Although many studies have explored environmental factors affecting milk yield, there is not enough information about the effects and direction of environmental factors on milk composition.

Objective:

To determine the effects of non-genetic factors, such as calving season, lactation number, lactation stage, animal age, and herd size on milk yield, chemical composition of raw milk, and Somatic Cell Count (SCC) in Holstein-Friesian cows.

Methods:

Data were obtained from 15,354 raw milk samples of 5,118 Holstein-Friesian cows at 276 dairy farms in Türkiye. The data analysis was performed using the General Linear Model (GLM) feature of the SPSS statistics program.

Results:

Mean fat (F), protein (P), dry matter (DM), lactose (L), urea (U), and Log10SCC values of milk were 3.74 ± 0.01, 3.19 ± 0.01, 11.36 ± 0.03, 4.32 ± 0.01%, 21.57 ± 0.28 mg/dL, and 5.244 ± 0.01 cells/mL, respectively. Peak milk yield (PMY), lactation milk yield (LMY), 305-day milk yield (305-d MY), and SCC values were 33.7 ± 0.13, 8,538.33 ± 89.64 kg, 6,479.42 ± 168.96 kg, and 224,164.34 ± 4,402.79 cells/mL, respectively.

Conclusion:

Dairy farms in Türkiye should improve protein, dry matter, and urea contents in milk and investigate in detail the relationship between raw milk urea, subclinical mastitis, and reproductive features.

Keywords: cow; Holstein-Friesian; milk composition; milk yield; non-genetic factors; phenotypic correlation; somatic cell count

Resumo

Antecedentes:

Para determinar com precisão o desempenho das vacas em termos de produção de leite e componentes do leite, é necessário conhecer a quantidade e a direção dos fatores ambientais. Embora existam muitos estudos sobre fatores ambientais que afetam a produção de leite, não há informações suficientes sobre os efeitos e a direção dos fatores ambientais na composição do leite.

Objetivo:

Determinar os efeitos de fatores não genéticos como estação de parto, ordem de lactação, estágio de lactação, idade, tamaño de la manada na produção de leite, composição química do leite cru e contagem de células somáticas (SCC) em vacas da raça Holandês-Frísia.

Métodos:

O material do estudo foi composto por 15.354 amostras de leite cru de 5.118 vacas da raça Holandesa-Frísia em 276 fazendas leiteiras na Turquia. A análise dos dados foi realizada utilizando o recurso General Linear Model (GLM) do programa estatístico SPSS.

Resultados:

Os valores médios de gordura (F), proteína (P), matéria seca (DM), lactose (L), uréia (U) e Log10SCC do leite de vaca foram encontrados como 3,74 ± 0,01, 3,19 ± 0,01, 11,36 ± 0,03, 4,32 ± 0,01%, 21,57 ± 0,28 mg/dL e 5.244 ± 0,01 células/mL, respectivamente. Pico de produção de leite (PMY), produção de leite de lactação (LMY), produção de leite em 305 dias (305-d MY) e valores de SCC foram determinados como 33,7 ± 0,13, 8.538,33 ± 89,64, 6.479,42 ± 168,96 kg e 224.164,34 ± 4.402,79 células/mL, respectivamente.

Conclusões:

Recomenda-se tomar medidas para melhorar o teor de proteína, matéria seca e uréia do leite em fazendas leiteiras na Turquia e investigar em detalhes a relação entre o teor de uréia do leite cru, mastite subclínica e características reprodutivas.

Palavras-chave: composição do leite; contagem de células somáticas; correlação fenotípica; fatores não genéticos; Holstein-Frísia; produção de leite; vaca

Introduction

Milk is composed of water, protein, amino acids, vitamins, lipids, fatty acids, and minerals. It is affected by factors such as breed or genetic group, milk production, stage of lactation, parity, feeding, and season of calving. Knowledge on the relative effects of genetic and environmental factors affecting milk components allows for changes in milk composition (Simões et al. 2014; Boro et al. 2016). Milk yield, milk chemical composition, and somatic cell count (SCC) can be affected by multiple genetic and non-genetic interrelated factors, such as parity, stage of lactation, calving season, herd, and calving year (Erdem et al., 2007; Atasever & Stadnik, 2015; Bertocchi et al., 2014; Sobczuk-Szul et al., 2015; Boujenane, 2021).Practices that help breeders gain information on how to obtain quality raw milk and improve milk quality for milk products (cheese, yogurt, cream, etc.) in different regions of Türkiye are also needed (Şahin & Yıldırım, 2012). The SCC in cow’s milk should be less than 200,000 cells/mL. When this number exceeds 200,000 cells/mL the udder lobe is most likely infected (Querengasser et al., 2002). In addition, the SCC in milk is an indicator of both resistance and sensitivity of animals to mastitis, which can be used to monitor the level or formation of subclinical mastitis in herds or individual animals (Malik et al., 2018). Milk Urea Nitrogen (MUN) is not in the protein structure and represents total nitrogen in milk. Urea passes into the milk from the secretory cells of the mammary glands and indicates the amount of degradable protein in the rumen. The MUN value is determined directly by the amount of urea in milk. MUN values between 10 and 14 mg/dL are considered normal. Daily dry matter and protein consumption affect MUN concentration in milk. While MUN values in milk below 10 mg/dL indicate insufficient dry matter and protein consumption, MUN values above 14 mg/dL indicate the opposite (Keser et al., 2019).The purpose of this study was to determine the effects of non-genetic factors (calving season, lactation number, lactation stage, and animal age) on milk yield, chemical composition of raw milk, and SCC in Holstein-Friesian cows.

Materials and Methods

Data were obtained from 15,354 raw milk samples of 5,118 Holstein-Friesian cows from 276 dairy cattle farms in Türkiye. An average of three raw milk samples per cow was used. Based on EU standards, raw milk samples were taken from each cow 3 times a year to determine the SCC (Anonymous, 2006). Raw milk samples were taken equally from the beginning to the end of the milking process using a special sampling tool (Izmirbirlik Süt Numune Alma Aparatı, Izmir, Türkiye). The raw milk sampler consists of two parts: a pipe system in a spiral structure that separates the samples from the milk output, and a 500 mL container for collecting milk samples (Figure 1).

Figure 1 Raw milk sampler (Izmirbirlik Süt Numune Alma Aparatı, Izmir, Türkiye) 

SCC and chemical components (fat, protein, dry matter, lactose, and urea) of the collected raw milk were analyzed using the milk analyzer (Bentley Combi FTS, Maroeuil, France) (Figure 2). This analyzer was suitable and met the requirements of the International Committee for Animal Recording standards (ICAR, 2017).

The Bentley FTS, which represents the latest technology for automated milk analysis, can analyze 400 samples per hour. This piece of equipment was engineered in accordance with Bentley Instruments’ rigorous design principles and provides precise and accurate measurements. It uses a Fourier Transform Spectrometer (FTIR) to analyze the milk composition, including dry matter, fat, protein, lactose, urea, and SCC. After the first stirring, the milk is drawn from a sample vial and delivered to the measurement module. The sampling, sequencing, and identification of the sample vials are performed using the auto sampler. No chemicals are used in the analysis (Figure 2).

The Bentley FTS meets the standards set by the International Dairy Federation (IDF), International Committee of Animal Recording (ICAR), and Association of Official Agricultural Chemists (AOAC) (BENTLEY, 2023).

Figure 2 Bentley milk analyzer (Bentley Combi, FTS, Maroeuil, France). 

The following data were collected for each animal sampled: fat, dry matter, lactose, protein, SCC, animal age, number of milking days, the highest daily milk yield, lactation milk yield, 305-day milk yield, season in which samples were taken, and lactation number. This information was obtained from the herd-book system of the Cattle Breeders’ Association of Türkiye.

Milk yield and milk components

In this study, the effects of calving season, lactation number, lactation stage, and animal age on fat (F), protein (P), dry matter (DM), lactose (L), urea (U), SCC, lactation milk yield (LMY), 305-day milk yield (305-d MY), and peak-day milk yield (PDMY) were investigated (Tables 1, 2 and 3).

Seasons were grouped into the following four classes: 1) Winter (December, January, February), 2) Spring (March, April, May), 3) Summer (June, July, August), and 4) Fall (September, October, November). Regarding lactation number, cows were categorized as 1 through 7 and above. Animal age was classified in months, as follows: 24-36, 37-48, 49-60, 61-72, 73-84, 85-96, and 97 and above. Herd size was grouped into the following five classes: <51, 51-100, 101-500, 501-1000, and >1000 animals.

Lactation stage was divided into six groups, as follows: Lactation-I (<46 days), early Lactation-II (46-90 days), mid-Lactation (91-180 days), late lactation-I (181-270 days), and late Lactation-II (>270 days) (Table 4).

Statistical analysis

Data analysis was performed with the SPSS statistics program (SPSS 25.0; 2021). Analysis of variance (ANOVA) was used for data analysis. The statistical model evaluated the effect of calving season, lactation number, animal age, and herd size on milk yield, milk components, and SCC. Since repeated milk samples were taken randomly on different lactation days, the effect of repeated measurements was included in the error variance. The following statistical model was used:

Yijkl=µ+ ai +bj +ck+dl+eijkl

Where;

µ = Overall Mean.

ai = Effect of ith season at calving (1-4).

bj = Effect of jth lactation number (1-7).

ck = Effect of kth animal age (1-7).

dl = Effect of lth herd size (1-5).

eijkl = Random error.

Duncan’s multiple range test (p<0.05) was used to compare the mean values of groups. Correlations among milk yield and milk components were also calculated with the SPSS program (SPSS 25.0, 2021).

Results

The mean standard deviation and median results of F, P, DM, L, U, SCC, PDMY, LMY, and 305-d MY are provided in Table 1.

Table 1 Descriptive statistics for milk yield, raw milk components, and SCC. 

Parameter Unit N X ± SE SD Median
F % 1,490 3.74 ± 0.01 0.56 3.68
P % 1,490 3.19 ± 0.01 0.31 3.16
DM % 1,490 11.36 ± 0.03 1.02 11.33
L % 1,490 4.32 ± 0.01 0.35 4.30
U mg/dL 1,133 21.57 ± 0.28 9.43 19.00
PDMY kg 5,118 33.70 ± 0.14 9.64 33.00
LMY kg 974 8,538.33 ± 89.64 2,797.43 8,526.50
305-d MY kg/305 974 6,479.42 ± 168.96 5,273.01 7,666.50
SCC cells/mL 1,490 224,164.34 ± 4,401.80 169,911.59 174,250.00

N: Sample size, X: Least square mean, SE: Standard error, SD: Standard deviation, F: Fat, P: Protein, DM: Dry matter, L: Lactose, U: Urea, PDMY: Peak-day milk yield, LMY: Lactation milk yield, 305-d MY: 305-day milk yield, SCC: Somatic cell count.

Although the effect of calving season on P and DM was not statistically significant (p>0.05), the effect of calving season on L (p<0.05), F (p<0.01), and SCC (p<0.01) was significant. The effect of lactation number on F (p>0.05) was not significant, while its effect on P, DM, L, and SCC was significant (p<0.01) (Table 2). The effect of animal age and herd size on F, P, DM, L, and SCC was significant (p<0.01) (Table 2).

The effect of calving season and herd size on LMY, 305-d MY, Urea, and PDMY was significant (p<0.01). Although the effect of lactation number on LMY and 305-d MY was not significant (p>0.05), the effects on U (p<0.05) and PDMY (p<0.01) were significant. In addition, the effect of animal age on 305-d MY (p<0.05), LMY, U, and PDMY (p<0.01) were significant (Table 3).

Table 2 Least square means of raw milk components according to factors. 

F P DM L SCC
Factors N X ± SE X ± SE X ± SE X ± SE X ± SE
Herd size (head) ** ** ** ** **
<51 473 3.60 ± 0.02ab 3.13 ± 0.01b 11.20 ± 0.05b 4.25 ± 0.02a 249,611.07 ± 6,940.80bc
51-100 254 3.69 ± 0.03bc 3.23 ± 0.02c 11.25 ± 0.06b 4.26 ± 0.02a 275,973.03 ± 9,937.08c
101-500 380 3.76 ± 0.03c 3.23 ± 0.01c 11.75 ± 0.05c 4.43 ± 0.18a 225,614.92 ± 8,632.51b
501-1000 187 3.56 ± 0.05a 3.40 ± 0.03d 11.91 ± 0.07c 4.51 ± 0.03b 162,564.17 ± 18,677.82a
>1000 196 4.29 ± 0.03d 2.98 ± 0.01a 10.64 ± 0.06a 4.21 ± 0.02c 151,573.98 ± 2,748.76a
Calving season ** NS NS * **
Winter 286 3.76 ± 0.03b 3.16 ± 0.02a 11.32 ± 0.06 a 4.29 ± 0.02a 239,304.23 ± 9,162.12bc
Spring 233 3.58 ± 0.03a 3.20 ± 0.02 a 11.48 ± 0.06 a 4.36 ± 0.02b 216,238.24 ± 11,072.04ab
Summer 463 3.64 ± 0.02a 3.19 ± 0.01 a 11.33 ± 0.05 a 4.31 ± 0.02ab 245,631.99 ± 9,167.40c
Autumn 508 3.89 ± 0.03c 3.19 ± 0.01 a 11.37 ± 0.05 a 4.33 ± 0.02ab 199,711.00 ± 6,518.58a
Lactation number NS ** ** ** **
1 279 3.72 ± 0.04 a 3.31 ± 0.02c 11.78 ± 0.06c 4.51 ± 0.02d 185,468.32 ± 13,235.68a
2 367 3.69 ± 0.03 a 3.18 ± 0.02ab 11.39 ± 0.05b 4.35 ± 0.02c 210,707.06 ± 7,898.99ab
3 400 3.80 ± 0.03 a 3.17 ± 0.01ab 11.30 ± 0.05ab 4.28 ± 0.02abc 226,615.05 ± 7,546.44abc
4 213 3.70 ± 0.04 a 3.10 ± 0.02a 11.04 ± 0.06a 4.20 ± 0.02a 250,654.18 ± 10,716.77bcd
5 133 3.74 ± 0.05 a 3.13 ± 0.03a 11.15 ± 0.09ab 4.24 ± 0.03ab 278,076.54 ± 15,401.94d
6 55 3.88 ± 0.08 a 3.16 ± 0.04ab 11.36 ± 0.13b 4.30 ± 0.04bc 228,090.91 ± 14,978.54abc
7+ 43 3.77 ± 0.09 a 3.22 ± 0.04b 11.29 ± 0.15ab 4.24 ± 0.05ab 264,302.33 ± 21,308.18cd
Animal age (months) ** ** ** ** **
24-36 31 3.76 ± 0.13b 3.43 ± 0.06c 12.20 ± 0.17d 4.64 ± 0.07d 274,451.61 ± 55,247.65b
37-48 277 3.60 ± 0.03a 3.31 ± 0.02b 11.71 ± 0.06c 4.49 ± 0.02c 176,780.36 ± 13,119.01a
49-60 229 3.70 ± 0.03ab 3.17 ± 0.02a 11.49 ± 0.06bc 4.37 ± 0.02bc 214,898.38 ± 8,934.86ab
61-72 365 3.84 ± 0.03b 3.16 ± 0.02a 11.30 ± 0.06ab 4.29 ± 0.02ab 216,177.26 ± 75,59.11ab
73-84 236 3.73 ± 0.03ab 3.14 ± 0.02a 11.14 ± 0.06a 4.22 ± 0.02a 249,793.81 ± 10,483.25bc
85-96 163 3.73 ± 0.05ab 3.11 ± 0.02a 11.10 ± 0.08a 4.21 ± 0.02a 247,558.22 ± 11,371.27bc
97≤ 189 3.79 ± 0.05b 3.16 ± 0.02a 11.20 ± 0.07a 4.24 ± 0.02a 260,049.77 ± 4,404.63bc

NS: Not significant (p>0.05), *: Significant at the level of p<0.05, **: Significant at the level of p<0.01.

Different superscript letters (a, b, c, d) within the same column indicate significant difference between means.

N: Sample size, SD: Standard deviation, X: Least square mean, SE: Standard error, F: Fat (%), P: Protein (%), DM: Dry matter (%), L: Lactose (%), U: Urea (mg/dL), SCC: Somatic cell count (cells/mL).

Table 3 Least square means for milk yield characteristics and urea according to factors. 

LMY 305-d MY U PDMY
Factors N X ± SE X ± SE N X ± SE N X ± SE
Herd size (head) 974 ** ** 1,133 ** 5,118 **
<51 121 7,025.08 ± 189.91b 6,409.88 ± 400.80a 387 19.28 ± 0.40a 601 28.48 ± 0.29a
51-100 65 6,835.49 ± 256.47ab 5,550.38 ± 507.32a 206 19.74 ± 0.56a 292 29.91 ± 0.38b
101-500 252 8,856.00 ± 179.54b 6,895.01 ± 330.04b 260 18,87 ± 0.49a 751 37.00 ± 0.39d
501-1000 233 10,025.80 ± 207.28c 8,646.33 ± 372.05c 86 23.26 ± 1.60b 1,291 35.91 ± 0.30d
>1000 303 8,099.88 ± 123.28a 6,694.55 ± 278.94b 194 30.97 ± 0.42c 2,183 33.21 ± 0.18c
Calving Season 974 ** ** 1,133 ** 5,118 **
Winter 169 7,897.63 ± 238.71a 8,099.88 ± 123.28a 225 20.85 ± 0.55b 1,392 34.72 ± 0.24c
Spring 89 7,958.00 ± 297.82a 7,805.09 ± 645.36c 169 19.00 ± 0.53a 990 33.76 ± 0.31b
Summer 258 8,288.53 ± 157.59a 7,051.88 ± 330.45bc 351 20.83 ± 0.52b 1,246 32.03 ± 0.25a
Autumn 458 9,028.22 ± 128.01b 6,367.45 ± 243.22b 388 23.78 ± 0.52c 1,490 34.11 ± 0.27bc
Lactation Number 974 NS NS 1,133 * 5,106 **
1 399 8,616.64 ± 116.57 a 6,818.04 ± 258.74 a 180 18.64 ± 0.47a 1,843 31.73 ± 0.21b
2 212 8,372.86 ± 200.14 a 6,437.45 ± 367.64 a 259 22.76 ± 0.61b 1,318 34.76 ± 0.25cd
3 229 8,798.81 ± 225.71 a 6,221.88 ± 372.27 a 322 22.33 ± 0.56b 1,078 35.95 ± 0.33d
4 84 8,346.35 ± 331.50 a 6,361.38 ± 541.44 a 175 21.41 ± 0.75ab 499 34.79 ± 0.45cd
5 29 7,756.93 ± 323.61 a 5,278.45 ± 840.34 a 112 21.53 ± 0.83ab 218 32.33 ± 0.62b
6 11 8,567.55 ± 759.31 a 5,964.36 ± 1,440.28 a 45 21.98 ± 1.52b 87 32.82 ± 0.89bc
7≤. 10 6,802.8 ± 412.61 a 4,796.90 ± 1,336.07 a 40 21.29 ± 1.60ab 63 29.44 ± 0.87a
Animal Age (months) 974 ** * 1,133 ** 5,112 **
24-36 54 8,653.34 ± 402.36b 6,534.51 ± 597.89b 14 16.61 ± 1.14a 593 31.67 ± 0.32a
37-48 312 8,578.20 ± 124.36b 6,905.61 ± 389.71b 151 19.00 ± 0.52ab 1,371 33.29 ± 0.25b
49-60 165 8,625.95 ± 237.12b 6,874.28 ± 439.37b 177 20.27 ± 0.63bc 948 33.63 ± 0.32b
61-72 212 9,007.64 ± 212.01b 6,574.12 ± 431.80b 283 23.61 ± 0.61c 899 36.20 ± 0.34c
73-84 120 8,599.79 ± 297.49b 6,220.31 ± 523.49b 199 22.57 ± 0.71bc 611 35.35 ± 0.42c
85-96 60 7,065.30 ± 380.29a 3,832.65 ± 552.25a 143 21.51 ± 0.88bc 327 32.10 ± 0.49a
97≤ 51 7,497.41 ± 218.68a 6,054.20 ± 615.96b 166 20.97 ± 0.70bc 363 31.31 ± 0.46a

NS: Not significant (p>0.05), *: Significant at the level of p<0.05. **: Significant at the level of p<0.01.

Different superscript letters (a, b, c, d) within the same column indicate significant difference between means.

N: Sample size, X: Least square mean, SE: Standard error, LMY: Lactation milk yield (kg), 305-d MY: 305-day milk yield (kg), PDMY: Peak-day milk yield (kg), U: Urea (mg/dL).

Although the F component had the highest value in the mid-lactation stage (91-180 days), it showed the lowest in the second late lactation stage (270 days≥). Difference between lactation stages in terms of the F component was statistically significant (p<0.01). However, while the P component presented the highest value within the first late lactation stage (181-270 days), it showed the lowest value during mid-lactation (91-180 days). The difference between lactation stages in terms of the P component was significant (p<0.05) (Table 4).

For DM and L components, the highest values were observed within the first stages of late lactation (181-270 days) and the first stages of early lactation (<45 days), respectively. However, the lowest values were determined in the second early lactation (46-90 days) and mid-lactation (91-180 days) stages for the DM component as well as in the second late lactation stage for the L component. Differences between lactation stages were not statistically significant for DM (p=0.065) nor L components (p=0.111) (Table 4).

The U component had the highest value in the middle stage of lactation (91-180 days) although the lowest value was seen in Log10SCC within the same stage. However, during the first late lactation stage (181-270 days), Log10SCC had the highest value, while the U component had the lowest value. Differences between lactation stages for Log10SCC and U components were statistically significant (p<0.01) (Table 4).

Table 4 Least square means of raw milk components according to lactation stage. 

F P DM L Log10SCC U
Parameters N X ± SE X ± SE X ± SE X ± SE X ± SE X ± SE
Lactation Stage ** * NS NS ** **
1st Early (≤ 45) 91 3.67 ± 0.06ab 3.20 ± 0.03b 11.49 ± 0.11a 4.37 ± 0.04a 5.22 ± 0.04a 21.43 ± 1.32a
2nd Early (46-90) 188 3.76 ± 0.04b 3.17 ± 0.02ab 11.29 ± 0.07a 4.34 ± 0.03a 5.24 ± 0.02ab 23.72 ± 0.82b
Mid (91-180) 346 3.93 ± 0.03c 3.13 ± 0.02a 11.29 ± 0.06a 4.32 ± 0.02a 5.18 ± 0.02a 25.31 ± 0.62b
1st Late (181-270) 285 3.78 ± 0.03b 3.22 ± 0.02b 11.50 ± 0.06a 4.36 ± 0.02a 5.28 ± 0.02b 19.27 ± 0.57a
2nd Late (270≥) 580 3.62 ± 0.02a 3.20 ± 0.01b 11.35 ± 0.04a 4.30 ± 0.01a 5.27 ± 0.01b 19.67 ± 0.39a
Overall 1,490 3.74 ± 0.01 3.19 ± 0.01 11.36 ± 0.03 4.32 ± 0.01 5.24 ± 0.01 21.57 ± 0.28

NS: Not significant (p>0.05), *: Significant at the level of p<0.05, **: Significant at the level of p<0.01.

Different superscript letters (a, b, c, d) within the same column indicate significant difference between means.

N: Sample size, X: Least square mean, SE: Standard error, F: Fat (%), P: Protein (%), DM: Dry matter (%), L: Lactose (%), U: Urea (mg/dL), Log10SCC: Value based on log10 for somatic cell count.

A positive, significant (p<0.01) and strong relationship between DM and L content was observed in the present study. Additionally, there was a positive, significant (p<0.01) and moderate relationship between P and L contents between 305-d-MY and PDMY, and between DM and P contents. However, significant and negative correlations were found between SCC and all the traits, except for the P component. The direction of the relationship between SCC and P was positive, whereas it was negative with the other traits (PDMY, F, DM, L, U) (Table 5).

Table 5 Phenotypic correlations between milk yield, milk components, and SCC. 

Characteristic PDMY F P DM L U SCC LMY
PDMY 1
F 0.078** 1
P 0.191** 0.051* 1
DM 0.232** 0.379** 0.678** 1
L 0.301** 0.182** 0.585** 0.841** 1
U 0.131** 0.256** 0.094** -0.147** -0.136** 1
SCC -0.207** -0.127** 0.096** -0.064* -0.143** -0.104** 1
LMY 0.340** -0.063 0.110* 0.084 0.149** 0.050 0.046 1
305-d MY 0.648** 0.046 0.218** 0.247** 0.289** 0.153** -0.043 0.443**

*: Significant at the level of p<0.05, **: Significant at the level of p<0.01.

F: Fat, P: Protein, DM: Dry matter, L: Lactose, U: Urea, SCC: Somatic cell count, PDMY: Peak-day milk yield, LMY: Lactation milk yield, 305-d MY: 305-day milk yield.

r<0.3 none or very weak, 0.3<r<0.5 weak, 0.5<r<0.7 moderate, and 0.7 < r strong correlations.

Discussion

The mean F was 3.74 ± 0.01% (Table 1). In previous studies, Hanus et al. (2010), Czajkowska et al. (2014), Suàrez et al. (2016), and Kul et al. (2019) found mean F was 4.06, 3.73, 4.17, and 3.39, respectively. Önal et al. (2021) reported that the lowest F by season was 3.44 ± 0.058% in autumn and 3.72 ± 0.048% in summer. Visentin et al. (2018) found that milk yield averaged 22.74 kg/d and mean F was 4.03 ± 0.61%. On the other hand, El-Tarabany et al. (2018) reported that F was 3.44% for Holstein-Friesian cows, while Boujenane (2021) reported that average F was 3.54 ± 0.76%. Marshall et al. (2020) also found that F was 5.12 and 6.52%, in early and late lactation periods, respectively. In addition, while F in raw milk was affected by herd size, calving season, and animal age, it was not affected by lactation number. The F component in raw milk increased for <51, 51-100, and 101-500 herd sizes (3.60 ± 0.02, 3.69 ± 0.03, 3.76 ± 0.03, respectively). A decrease in F was observed for herd sizes of 501-1,000 head (3.56 ± 0.05%). The highest F component was obtained for herd size greater than 1,000 heads (4.29 ± 0.03%).

The lowest F occurred during spring (3.58 ± 0.03%) and increased during summer, autumn, and winter (3.64 ± 0.02, 3.89 ± 0.03, and 3.76 ± 0.03%, respectively). This difference is likely related to sufficiency of roughage stocks in the farms. The values obtained for summer and autumn differ from the results (3.72 ± 0.06, and 3.44 ± 0.06%, respectively) reported by Önal et al. (2021).

The P component was 3.19 ± 0.01% (Table 1). In a similar study, mean P was 3.28%, ranging from 3.19 to 3.33% (Aydin et al., 2010). In other studies, P was 3.43, 3.53, 3.66, and 3.37% (Suàrez et al., 2016; Visentin et al., 2018; El-Tarabany et al., 2018; and Czajkowska et al., 2014; respectively). In addition, Marshall et al. (2020) and Boujenane (2021) reported that mean P was 3.02 ± 0.34%. Sarıalioğlu and Laçin (2021) reported that P in family dairy farms and modern dairy farms was 3.49 ± 0.07 and 3.45 ± 0.01%, respectively. Önal et al. (2021) reported that the highest P for winter was 3.46 ± 0.031.

The P component in raw milk increased as herd size increased to 1,000. For herd sizes greater than 1,000, the P level in milk decreased. Accordingly, P fluctuated depending on lactation number and animal age, rather than steady increasing or decreasing.

Önal et al. (2021) reported that the highest milk DM (13.50% ± 0.103) was observed during spring. Suàrez et al. (2016), El-Tarabany et al. (2018), and Czajkowska et al. (2014) found 13.16, 12.80, and 12.61% DM, respectively. In contrast, Boujenane (2021) reported that mean DM was 8.72 ± 0.36%. Another study found DM in family and modern dairy farms to be 9.64 ± 0.21 and 9.52 ± 0.05%, respectively (Sarialioğlu and Laçin, 2021). Changes in DM in terms of herd size, lactation number, and animal age was consistent with changes in protein rates. Therefore, fluctuations in DM may have been due to differences in feeding levels among farms, which is similar to protein rate.

The mean L component of milk was 4.32 ± 0.01% (Table 1). Ayaşan et al. (2011) found L between 4.15 ± 0.06 and 4.34 ± 0.06%. Flipejova and Kovacik (2009) reported that milk L ranged from 4.02 to 4.99 with a mean value of 4.59, and El-Tarabany et al. (2018) found mean L was 4.94%. In addition, Czajkowska et al. (2014) found it to be 4.89 ± 0.21%, and Boujenane (2021) reported that mean L was 4.89 ± 0.24%. Moreover, Marshall et al. (2020) found L in the early and late lactation periods to be 5.04 and 4.81%, respectively. It is known that L is not markedly affected by feeding. In terms of herd size, the L trend was similar to that of P and DM. The L in milk decreased as animal age increased. In terms of seasons, the lowest (4.29 ± 0.02%) and highest (4.36 ± 0.02%) L percentages were observed in winter and spring, respectively.

The mean SCC value (224,164.32 ± 4,401.80 cells/mL) was lower than that reported by Flipejova and Kovacik (2009), and Suàrez et al. (2016) (1,525,400 and 523,207 cells/mL, respectively), but it was in line with the value observed by Gürbulak et al. (2009) (226,800 ± 4,200 cells/mL).

Eyduran et al. (2005) reported that lactation number and months had an effect on SCC in milk from Holstein-Friesian cows, and mean SCC for August and November was 1,311,761 ± 239,631 and 732,810 ± 146,264 cells/mL, respectively.

Böcekli (2015) assessed the effect of SCC on milk yield, reporting that <200,000, 201,000-500,000, and >501,000 cells/mL had a significant effect on milk yield, with 28.75, 27.48, and 26.78 kg, respectively.

In a similar study, the highest SCC values occurred during the summer months (Aytekin and Boztepe, 2014). In a study conducted by Önal et al. (2021), it was shown that lactation number and season affected SCC. The authors found that the highest SCC occurred during the 4th lactation (928.30 ± 117.93 x103 cell/mL) and milk with the lowest SCC occurred during the 1st lactation (356.47 ± 50.55 x103 cell/mL). They also showed that SCC values descended from 1,003.88 ± 83.53, 877.63 ± 97.43, 575.81 ± 63.97, and 212.36 ± 17.94 x103 cell/mL for winter, spring, autumn, and summer, respectively (Önal et al., 2021). Sarialioğlu and Laçin (2021) also reported that mean SCC in milk samples were 4.23±0.19 and 3.79±0.16 Log10 for family and modern dairy farms, respectively.

The SCC decreased with increasing herd size. This result is thought to be related to investments in modernization and automation. The highest seasonal SCC values were observed in the summer and winter seasons, respectively. This result might be caused by high temperatures during the summer and high humidity in winter, related with unfavorable barn conditions.

When examining herd records and feed profile of dairy farms, the nitrogen value of milk (U) is used as the standard method since it provides a practical approach for measurement and evaluation (Roy et al., 2011). In the present study, the U value was 21.57±0.28 mg/dL. In addition, U was significantly affected by lactation stage (Table 4), calving season, and lactation number (p<0.01), but it was not affected by animal age (Table 3).

Milk urea nitrogen varies according to several factors. If milk protein is 3.0 and 3.2%, then milk urea nitrogen varies between 12 and 16 mg/dL; since as P increases, urea nitrogen decreases. This is because more nitrogen consumption is used for milk protein (Abdouli et al. 2008).

Depatie (2000) reported that SCC did not affect milk urea nitrogen. On the other hand, Kwai-Hang et al. (1985) stated that increased SCC increased milk urea nitrogen. Other studies have reported that milk urea nitrogen is low in milk with excess SCC. In those studies, milk urea concentration had a positive relationship with milk yield and a negative relationship with milk F levels (Faust et al., 1997).

Abdouli et al. (2008) reported that milk urea nitrogen of cows bred under Mediterranean conditions was 30.39 mg/dL, while this value was 20.43-32.49, 11.15, 12.7-13.9, 20.64, and 11.75 mg/dL (Frank and Swensson, 2002; Arunvipas et al., 2008; Meeske et al., 2009; Czajkowska et al., 2014; and Zhang et al., 2018; respectively).Marshall et al. (2020) also found that U during the early and late lactation periods was 18.60 and 16.10 mg/dL, respectively. In contrast, Boujenane (2021) found mean U was 17.6 ± 8.17 mg/dL.

The overall mean value (21.57 ± 0.28 mg/dL) obtained in the present study was above the accepted upper limit for milk urea nitrogen (14 mg/dL). The mean U values were high for herds with 501-1,000 and >1,000 heads (23.26 ± 1.60 and 30.97 ± 0.42 mg/dL, respectively). This might be due to the use of high protein mixed feeds for obtaining high milk yields per cow.

Mean PDMY, LMY, and 305-day MY values in the present study were 33.70 ± 0.14, 8,538.33 ± 89.64, and 6,479.42 ± 168.96 kg, respectively. The effects of calving season on PDMY, LMY, and 305-day MY were significant (p<0.01; Table 3). The effect of lactation number on PDMY was not significant, while its effect on LMY and 305-day MY was significant (p<0.01; Table 3). The effect of cow age on PDMY and LMY was also significant (p<0.01; Table 3).

In the present study, LMY was 8,538.33 ± 89.64 kg. In previous studies, LMY means were 5,929 ± 23, 7,700.02 ± 99.17, 4,716.1 ± 243, 3,032.41 ± 66.78, 5,720.00 ± 43.6, and 4,726.12 kg (Bakır and Kaygısız, 2013; Yıldırım et al., 2018; Gamaniel et al., 2019; Kidane et al., 2019; McClearn et al., 2020; and Sanad et al., 2021; respectively). Thus, the present study found higher LMY compared to all the mentioned studies.

In the present study, the 305-day MY value was 6,479.42 ± 168.96 kg. In similar studies conducted in Holstein-Friesian cows this value was 5,523 ± 27, 8,246 ± 1,194.6, 9,435 ± 156.12, 7,923.28 ± 80.92, 6,197.88 ± 1,681.35, and 8,369.72 kg (Bakır and Kaygısız, 2013; Van Eetvelde et al., 2017; Duru, 2018; Yıldırım et al., 2018; Tutkun and Yener, 2018; and Habib et al., 2020; respectively). Although LMY in the present study was higher than values reported by Bakır and Kaygısız (2013) and Tutkun and Yener (2018), it was lower than the values found by Van Eetvelde et al. (2017), Duru (2018) and Yıldırım et al. (2018). Since the mean of the lactation period is different for each herd, the 305-day MY is used instead of LMY to compare milk yield among herds. Accordingly, the differences observed for 305-day MY are thought to be due to herd genetics, environment in which they were raised, and different feeding plans.

In the present study, mean PDMY (33.70 ± 0.14 kg in Holstein-Friesian cows) was lower than those found by Sönmez et al. (2018; 35.00 ± 0.50) and Castaño et al. (2020; 39.77), but higher than values reported by Serkan et al. (2013; 30.81 ± 0.83), Yılmaz and Kaygısız (2000; 21.5 ± 0.60), Abosaq et al. (2017; 22.79) and Ghavi and Zadeh (2019; 31.31). In terms of milk yield, the 305-d MY was used as a basis for comparison since lactation periods showed variation among cows. Accordingly, milk yield in herd size between 101 and 1,000 heads was higher than in herds below 100 heads and above 1,000 heads. In terms of season, the lowest 305-d MY was found throughout autumn, and the highest during winter. This situation is associated with increase in winter and spring calving and increased roughage and concentrate feed based on Türkiye climate. Although there were fluctuations in the 305-d MY values of lactation number and age groups, a decreasing trend was observed in the 305-d MY due to increased lactation number and age.

While F and U ratios increased during early lactation (1-90 days), the P ratio decreased. During mid-lactation (days 91-180) F and U ratios reached their highest values, while the P ratio saw its lowest levels. For late lactation (>181 days) the F and U ratios decreased, while the P ratio increased. Due to the use of body fat reserves throughout the early lactation period and the increase in the amount of feed according to increased milk yield, the fat rate increased until the end of the mid-lactation period.

Unlike fat, protein is not markedly affected by feeding, but has a negative relationship with milk yield. For this reason, protein is at the lowest level during mid-lactation when milk yield is at the highest. However, protein in early and late lactation stages is higher than in the mid-lactation period.

Although SCC decreased during mid-lactation, it increased in early and late lactation. This might result from the increase in epithelial cell loss of with as lactation period progresses and mastitis during the dry period before early lactation.

The U component in milk was at the highest level in mid-lactation and it was lower during the early and late lactation periods. This is due to increased offer of concentrated feed as milk yield increases, as well as change in protein and energy content of the feed. The U levels were very high during all lactation stages considering that accepted U in raw milk is 10-14 mg/dL.

In conclusion, the effects of calving season on 305-day MY, LMY, PDMY, U, L, F, and SCC; the effect of lactation number on PDMY, P, DM, L, and SCC; and the effect of animal age and herd size on LMY, 305-day-old MY, PDMY, and all milk components were statistically significant.

Although dairy farmers in this study are conscious of milk yield and milk quality, they nevertheless need to take measures to improve P, DM, and U components of milk. In addition, based on these results, detailed research should be conducted on subclinical mastitis as well as the relationship between MUN and reproduction in dairy farms in Türkiye.

Acknowledgement

I would like to acknowledge The Cattle Breeders’ Association of Türkiye (CBAT) for authorizing the use of the data presented in this study (Decision of the Board of Directors No. 2019/10; 21.08.2019).

References

Abdouli H, Rekik B, Haddad-Boubaker A. Non-nutritional factors associated with milk urea concentrations under Mediterranean conditions. World Journal of Agriculture Science 2008; 4:183-188. [ Links ]

Abosaq FM, Zahran SM, Khattab AS, Zeweil HS, Sallam SM. Improving reproductivity and productivity traits using selection indices in Friesian cows. J Adv Agric Res 2017; 7:110-121. [ Links ]

Anonymous. Specific hygiene rules for food of animal origin. Publications Office of the European Union; 2006, 1-10. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2006:320:0001:0010:EN:PDFLinks ]

Arunvipas P, VanLeeuwen JA, Dohoo IR, Keefe GP, Burton SA, Lissemore KD. Relationships among milk urea-nitrogen, dietary parameters, and fecal nitrogen in commercial dairy herds. Can J Vet Res 2008; 72(5):449-53. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2568051/Links ]

Atasever S, Erdem H. Association between subclinical mastitis markers and body condition scores of Holstein cows in the Black Sea region. Turk J Anim Vet Adv 2009; 8:476-480. [ Links ]

Atasever S, Stádník L. Factors affecting daily milk yield, fat and protein percentage, and somatic cell count in primiparous Holstein cows. Indian J Anim Res 2015; 49:313-316. https://doi.org/10.5958/0976-0555.2015.00048.5 [ Links ]

Ayaşan T, Hızlı H, Yazgan E, Kara U, Gök K. The effect of somatic cell count on milk urea nitrogen and milk composition. Kafkas Univ Vet Fac J 2011; 17:659-662. [ Links ]

Aydin S, Donder E, Akin OK, Sahpaz F, Kendir Y, Alnema MM. Fat-free milk as a therapeutic approach for constipation and the effect on serum motilin and ghrelin levels. Nutrition 2010; 26:981-985. https://doi.org/10.1016/j.nut.2009.11.023 [ Links ]

Aytekin I, Boztepe S. Somatic cell count, importance and effect factors in dairy cattle. Turkish Journal of Agriculture - Food Science and Technology 2014; 2(3):112-121. https://doi.org/10.24925/turjaf.v2i3.112-121.66 [ Links ]

Bakır G, Kaygısız A. Milk yield characteristics of Holstein cows and the effect of calving month on milk yield. KSU J Nat Sci 2013; 16:1-7. [ Links ]

BENTLEY. Product overview for Bentley combi FTS. Bentley Instruments, Inc. Peavey Rd Chaska; 2010. https://bentleyinstruments.com/products/combination-systems/nexgenLinks ]

Bertocchi L, Vitali A, Lacetera N, Nardone N, Varisco GG, Bernabucci U. Seasonal variations in the composition of Holstein cow's milk and temperature-humidity index relationship. Animal 2014; 8:667-674. https://doi.org/10.1017/S1751731114000032 [ Links ]

Biswajit R, Brahma B, Ghosh S, Pankaj PK, Mandal G. Evaluation of milk urea concentration as useful indicator for dairy herd management: A review. Asian J Anim Vet Adv 2011; 6:1-19. https://doi.org/10.3923/ajava.2011.1.19 [ Links ]

Boro P, Nah BC, Prakash C, Madkar A, Kumar N, Kumari A, Channa GP. Genetic and non-genetic factors affecting milk composition in dairy cows. International Journal of Advanced Biological Research 2016; 6(2):170-74. [ Links ]

Boujenane I. Non-genetic effects on daily milk yield and components of Holstein cows in Morocco. Trop Anim Health Prod 2021; 53:224. https://doi.org/10.1007/s11250-021-02663-w [ Links ]

Böcekli H. Investigating the factors affecting the somatic cell count (SCC) in Holstein cows. M.Sc. Thesis. Istanbul University; 2015, 1-47. http://nek.istanbul.edu.tr:4444/ekos/TEZ/ET001121.pdfLinks ]

Czajkowska A, Sitkowska B, Piwczynski D, Wojcik P, Mroczkowski S. Genetic and environmental determinants of the urea level in cow's milk. Arch Anim Breed 2014; 58:65-72. https://doi.org/10.5194/aab-58-65-2015 [ Links ]

Depatie C. Nutritional, managerial, physiological, and environmental factors affecting milk urea nitrogen in Quebec Holstein cows: a field trial. M.Sc. Thesis. McGill University. Montreal, Canada; 2000, 1-84. https://escholarship.mcgill.ca/concern/theses/p8418q022?locale=enLinks ]

Dominguez-Castaño P, Toro Ospina AM, El Faro L, Augusto J, Silva V. Genetic principal components for reproductive and productive traits in Holstein cows reared under tropical conditions. Trop Anim Health Prod 2021; 53:193. https://doi.org/10.1007/s11250-021-02639-w [ Links ]

Duru S. Determination of starting level of heat stress on daily milk yield in Holstein cows in Bursa city of Türkiye. Ankara Univ Vet Fak J 2018; 65:193-198. https://doi.org/10.1501/Vetfak_0000002846 [ Links ]

El-Tarabany MS, El-Tarabany AA, Emara SS. Impact of crossbreeding Holstein and Brown Swiss cows on milk yield, composition, and fatty acid profiles in subtropics. Trop Anim Health Prod 2018; 50:845-850. https://doi.org/10.1007/s11250-017-1506-2 [ Links ]

Erdem H, Atasever S, Kul E. Some environmental factors affecting somatic cell count of Holstein cows. J Appl Anim Res 2007; 32:173-176. https://doi.org/10.1080/09712119.2007.9706871 [ Links ]

Eyduran E, Özdemir T, Yazgan K, Keskin S. Siyah alaca inek sütündeki somatik hücre sayısına laktasyon sırası ve dönemin etkisi. YYU Vet Fak Derg 2005; 16(1):61-65. [ Links ]

Faust MA, Kimler LH, Funk R. Effects of laboratories for milk urea nitrogen and other milk components. J Dairy Sci 1997; 80:206. [ Links ]

Flipejova T, Kovacik J. Evaluation of selected biochemical parameters in blood plasma, urine and milk of dairy cows during the lactation period. Slovak J Anim Sci 2009; 42:8-12. [ Links ]

Frank B, Swensson C. Relationship between content of crude protein in rations for dairy cows and milk yield, concentration of urea in milk and ammonia emissions. J Dairy Sci 2002; 85:1829-1838. https://doi.org/10.3168/jds.S0022-0302(02)74257-4 [ Links ]

Gamaniel IB, Egahi JO, Addass PA. Effect year of calving and parity on the productive performance of Holstein Friesian cows in Vom Nigeria. Asian Journal of Research in Animal and Veterinary Sciences 2019; 4(2):1-8. [ Links ]

Ghavi N, Zadeh H. Application of non-linear mathematical models to describe effect of twinning on the lactation curve features in Holstein cows. Research in Veterinary Science 2019; 122:111-117. https://doi.org/10.1016/j.rvsc.2018.11.017 [ Links ]

Gürbulak K, Canoğlu E, Abay M, Atabay Ö, Bekyürek T. Determination of subclinical mastitis in dairy cows by different methods. Kafkas Univ Vet Fak Dergisi 2009; 15:765-770. [ Links ]

Habib A, Gouda G, Shemeis A, El-Sayed M. Expected impact of selection for milk yield on reproductive performance traits in Holstein Friesian cows under Egyptian conditions. Egyptian J Anim Prod 2020; 57(1):25-31. https://doi.org/10.21608/ejap.2020.92771 [ Links ]

Hanus O, Frelich J, Tomaska M, Vyletelova M, Gencurova V, Kucera J, Trinacty J. The analysis of relationships between chemical composition, physical, technological and health indicators and freezing point in raw cow milk. Czech J Anim Sci 2010; 55(1):11-29. [ Links ]

International Committee of Animal Recording (ICAR). The global standard for livestock data. Guidelines. ICAR, MJ Utrecht, The Netherlands; 2017, 1-14. https://www.icar.org/Guidelines/05-Conformation-Recording.pdfLinks ]

Keser O, Alp M, Kutay CY, Demirel G, Kocabağ N. The evaluation of different feeding methods with regard to milk urea nitrogene and milk composition in dairy cattles. Lalahan Hay Araşt Enst Derg 2017; 57(2):83-87. [ Links ]

Keskin I, Tozluca A. Describing of different mathematical models for lactation curve and estimation of control interval in dairy cattle. Selcuk Journal of Agriculture and Food Sciences 2004; 18:11-19. [ Links ]

Kidane AB, Delesa KE, Mummed YY, Tadesse M. Reproductive and productive performance of Holstein Friesian and crossbreed dairy cattle at large, medium and small scale dairy farms in ethiopia. Int J Adv Res Biol Sci 2019; 6:15-29. [ Links ]

Marshall CJ, Beck MR, Garrett K, Barrell GK, Al-Marashdeh O, Gregorini P. Grazing dairy cows with low milk urea nitrogen breeding values excrete less urinary urea nitrogen. Science of the Total Environment 2020; 739:139994. https://doi.org/10.1016/j.scitotenv.2020.139994 [ Links ]

Kul E, Şahin A, Atasever S, Uğurlutepe E, Soydaner M. The effects of somatic cell count on milk yield and milk composition in Holstein cows. Vet Arhiv 2019; 89:143-154. https://doi.org/10.24099/vet.arhiv.0168 [ Links ]

Kwai-Hang KF, Hayes JF, Moxley JE, Monardes HG. Percentages of protein and nonprotein nitrogen with varying fat and somatic cells in bovine milk. J Dairy Sci 1985; 68:1257-1262. https://doi.org/10.3168/jds.S0022-0302(85)80954-1 [ Links ]

Malik TA, Mohini M, Mir SH, Ganaie BA, Singh D, Varun TK, Thakur S. Somatic cells in relation to udder health and milk quality-a review. J Anim Health Prod 2018; 6:18-26. http://dx.doi.org/10.17582/journal.jahp/2018/6.1.18.26Links ]

McClearn B, Delaby L, Gilliland TJ, Guy C, Dineen M, Buckley F, McCarthy B. An assessment of the production, reproduction, and functional traits of Holstein-Friesian, Jersey×Holstein-Friesian, and Norwegian Red×(Jersey×Holstein-Friesian) cows in pasture-based systems. J Dairy Sci 2020; 103:5200-5214. https://doi.org/10.3168/jds.2019-17476 [ Links ]

Meeske R, Botha PR, Van der Merwe GD, Greyling JF, Hopkins C, Marais JP. Milk production potential of two ryegrass cultivars with different total non-structural carbohydrate contents. South Africa J Anim Sci 2009; 39:15-21. https://doi.org/10.4314/sajas.v39i1.43541 [ Links ]

Önal AR, Özkan M, Tuna YT. The effects of season and lactation number on the composition and quality of Holstein cattle raw milk. Journal of Tekirdag Agricultural Faculty 2021; 18(2):368-374. https://doi.org/10.33462/jotaf.831567 [ Links ]

Querengasser J, Geishauser T, Querengasser K, Fehlings K, Bruckmaier R. Investigations of milk quality from teats with milk flow disorders. J Dairy Sci 2002; 10:2582-2588. https://doi.org/10.3168/jds.S0022-0302(02)74342-7 [ Links ]

Sanad SS, Gharib MG, Ali MAE, Farag AM. Prediction of milk production of Holstein cattle using principal component analysis. J of Animal and Poultry Production Mansoura Univ 2021; 12:1-5. https://doi.org/10.21608/jappmu.2021.149198 [ Links ]

Sarıalioğlu FS, Laçin E. Effects of business structure and management on milk quality. Journal of the Institute of Science and Technology 2021; 11(1):807-818. https://doi.org/10.21597/jist.793731 [ Links ]

Serkan E, Kalender H, Çelik O. Effect of parity and reproductive status on peak milk yield and some reproductive traits of Holstein cows. Journal of Lalahan Livestock Research Institute 2013; 53:17-27. [ Links ]

Simões MG, Portal RE, Rabelo JG, Ferreira CLLF. Seasonal variations affect the physicochemical composition of buffalo milk and artisanal cheeses produced in Marajó Island (Pa, Brazil). Advance Journal of Food Science and Technology 2014; 6(1):81-91. http://dx.doi.org/10.19026/ajfst.6.3035Links ]

Sobczuk-Szul M, Wielgosz-Groth Z, Nogalski Z, Mochol M, Rzemieniewski A, Pogorzelska-Przybylek P. Changes in the fatty acid profile of cow's milk with different somatic cell counts during lactation. Vet Med Zoot 2015; 69:52-57. [ Links ]

Sönmez Z, Özdemir M, Bayram B, Aksakal V. Relationships between GH/AluI polymorphism and some performance traits in Holstein cows. Turkish Journal of Agriculture-Food Science and Technology 2018; 6:602-606. https://doi.org/10.24925/turjaf.v6i5.602-606.1838 [ Links ]

SPSS. IBM SPSS Statictics for Windows, version 25.0. New York: IBM Corp 440; 2021. [ Links ]

Suàrez GJ, Pomarez JV, Rangel AC, Rodrìguez VR, Angulo LM, Garay OV. Raw milk quality in Northwestern Colombia. Rev Colomb Cienc Pecu 2016; 29:210-217. https://doi.org/10.17533/udea.rccp.325013 [ Links ]

Şahin A, Yıldırım A. Somatic cell count and raw milk composition in water buffaloes. Journal of Agricultural Faculty of Gaziosmanpaşa University (JAFAG) 2012; 29(2):43-48. [ Links ]

Tutkun M, Yener SM. Estimates of the trends components in the milk yield of Holstein Friesian cows. Animal Science 2018; 61(1):23-30. https://animalsciencejournal.usamv.ro/pdf/2018/issue_1/Art4.pdfLinks ]

Van Eetvelde M, Kamal MM, Vandaele L, Opsomer G. Season of birth is associated with first-lactation milk yield in Holstein Friesian cattle. Animal 2017; 11:2252-2259. https://doi.org/10.1017/S1751731117001021 [ Links ]

Visentin G, Penasa M, Giovanni N, Cassandro M, Massimo DM. Phenotypic characterization of major mineral composition predicted by mid-infrared spectroscopy in cow milk. Italian Journal of Animal Science 2018; 17(3):549-556. https://doi.org/10.1080/1828051X.2017.1398055 [ Links ]

Yıldırım F, Özdemir S, Yıldız A. Some milk yield traits and related gene expressions of Holstein cattle raised in Koçaş Agricultural Enterprise KSU J Agric Nat 2018; 21:353-362. https://doi.org/10.18016/ksudobil.333580 [ Links ]

Yılmaz İ, Kaygısız A. Lactation curve traits of Holstein cattle's. Journal of Agricultural Sci 2000; 6:1-10. https://doi.org/10.1501/Tarimbil_0000000988 [ Links ]

Zhang H, Wang M, Jiang H, Cui Y, Xia H, Ni W, Li M, Karrow NA, Yang Z, Mao Y. Factors affecting the milk urea nitrogen concentration in Chinese Holstein cows. Anim Biol 2018; 68:193-211. https://doi.org/10.1163/15707563-17000099 [ Links ]

Funding This study was conducted with contributions from the Cattle Breeders’ Association of Turkey (CBAT)

4Use of artificial intelligence (AI) No AI or AI-assisted technologies were used during the preparation of this work.

5To cite this article Şahin O. Effects of non-genetic factors on milk yield and chemical composition of milk from Holstein-Friesian cows. Rev Colomb Cienc Pecu 2024; 37(2):73-87 DOI: https://doi.org/10.17533/udea.rccp.v37n2a3

Recibido: 12 de Noviembre de 2022; Aprobado: 20 de Septiembre de 2023

*Corresponding author. Department of Animal Production and Technologies, Muş Alparlan University, 49250, Muş, Türkiye. Phone:+905458757765. E-mail: o.sahin@alparslan.edu.tr

Conflicts of interest The author declares he has no conflicts of interest with regard to the work presented in this report.

Author contributions Study design, literature review, data analysis, and manuscript writing were all conducted by OŞ.

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