Effect of THI on milk coagulation properties of Holstein-Friesian dairy cattle

ABSTRACT

The objective of this study was to evaluate the effect of temperature-humidity index (THI) on the milk coagulation properties of Holstein-Friesian dairy cattle from northeast part of Italy. A total of 592 individual milk samples from six dairy herds were evaluated. The milk coagulation properties traits analysed were milk rennet coagulation time and curd firmness, as well as the fat, protein, and casein contents, pH, milk aptitude to coagulate (IAC), and the somatic cell count. The THI was determined during the periods of sample collection. The THI results showed that values of up to 75 did not significantly change the IAC values; however, when the THI values were above 75, the IAC decreased significantly. The control of THI can be used to guarantee appropriate milk coagulation properties.

Key Words:
clotting; curd firmness; dairy cow; heat stress

Introduction

The milk coagulation properties (MCP) are an important characteristic of milk for cheese production (Summer et al., 2002; Cassandro et al., 2008). It is affected by various factors such as milk composition, breed, herd, year, and season. Several studies have investigated the relationship between MCP and milk quality traits both from the phenotypic and genetic points of view (Comin et al., 2008; Frederiksen et al., 2011; Toffanin et al., 2012; Tiezzi et al., 2015). In general, the main milk coagulation traits studied are the milk rennet coagulation time (RCT, min) and curd firmness (a₃₀, mm), but these two separate traits have been combined by Penasa et al. (2015) to give the aggregate index of milk aptitude to coagulate (IAC). The IAC was introduced as a new standardised trait to summarise RCT and a₃₀, both with the same importance (50%) in the index. The IAC could be adopted by the dairy industry as a global measure of MCP to reward or penalise milk for clotting characteristics in payment systems.

The milk composition can vary, since factors related to management, genetics, and the nutritional “status” can affect the basic milk constituents. The genetic changes influence the milk composition slowly, while changes related to the management and nutrition can provide changes faster (González, 2004). Heat stress, for example, negatively affects production and the milk composition of lactating dairy cows (Zimbelman et al., 2013). Fagan et al. (2010) evaluated the chemical composition of the milk from two herds with respect to the seasons and the best chemical composition and increased milk production were found in the winter and autumn. The temperature associated with moisture can have an even greater impact on these traits. In fact, the temperature-humidity index (THI) is a single value representing the combined effects of air temperature and humidity, associated with the level of thermal stress (Armstrong, 1994; Bohmanova et al., 2007). This index is widely used in hot areas all over the world to assess the effect of heat stress on dairy cows (Bouraoui et al., 2002; Brown-Brandl et al., 2003). Indeed, the heat stress is related to decreased milk production and an altered composition (Schneider et al., 1988; West et al., 2003; Bohmanova et al., 2007, Gantner, 2011). The objective of this study was to evaluate the effect of THI on the coagulation properties of the milk of Holstein-Friesian dairy cattle located in the northeastern part of Italy.

Material and Methods

We evaluated a total of 592 individual milk samples from Holstein-Friesian cows from six dairy herds located in the province of Venice (northeastern part of Italy), from July to November 2015. The samples were collected in the interval of two months, the first collection in the summer and the second collection in the fall. For only a farm, the collection interval was three months. The analyses were carried out in an official milk analysis laboratory with milk samples that were part of the milk recording system. The period between the collection and analysis were 36-72 h. All the samples arrived at the laboratory in refrigerated coolers with the addition of a preservative (Bronopol, Knoll Pharmaceuticals, Nottingham, UK) at a concentration of 250 μL 50 mL⁻¹ milk.

The milk samples were analysed for their fat, protein and casein contents, and pH using a MilkoScan FT6000 (Foss Electric A/S) and for the somatic cell count (SCC) using a Fossomatic 5000 (Foss Electric A/S). The values for SCC were log-transformed to give the somatic cell score (SCS) to achieve normality and homogeneity of the variances according to the following formula:

(1) $$\text{SCS}=3+{\log }_2\left(\text{SCC}/100,000\right)$$

The RCT and a₃₀ were predicted by MIRS using a Milko-Scan FT6000 (Foss Electric A/S) and the milk coagulation ability index (IAC) was calculated using the following formula according to Penasa et al. (2015):

(2) $$\text{IAC}=100+\left[\left({\text{a}}_{30}-{\text{mean}}_{\text{a}30}\right)/{\text{SD}}_{\text{a}30}\times 2.5\right]-\left[\left(\text{RCT}-{\text{mean}}_{\text{RCT}}\right)/{\text{SD}}_{\text{RCT}}\times 2.5\right]$$

To calculate the IAC, the mean and standard deviation of the experimental data were used.

The THI index was computed according to the following National Research Council formula (NRC, 1971):

(3) $$\text{THI}=\left(1.8\times \text{T}+32\right)-\left(0.55-0.0055\times \text{RH}\right)\times \left(1.8\times \text{T}-26\right),$$

in which T = temperature (°C) and RH = relative humidity (%).

The THI was calculated with the average values of environmental temperature and relative humidity. The average was obtained with the values of these parameters regarding the week of sampling. The data for the temperature and relative humidity were downloaded from the official web site (http://www.ilmeteo.it).

An ANOVA was applied to the IAC index data using the GLM procedure of SAS (Statistical Analysis System, version 9.3), according to the following linear model:

$${\text{y}}_{\text{ijklm}}=\mathrm{\mu }+{\mathrm{\alpha }}_{\text{i}}+{\mathrm{\beta }}_{\text{j}}+{\mathrm{\gamma }}_{\text{k}}+{\mathrm{\delta }}_1+{\mathrm{\epsilon }}_{\text{m}+}{\text{e}}_{\text{ijklm}},$$

in which y_ijklm is the dependent variable (IAC); μ the overall intercept of the model; α_i is fixed effect of the i-th herd of the cow sampled; class of β_j is the fixed effect of the j-th class of THI (j = <60; 60-65; 70-75; >75); γ_k is the fixed effect of the k-th class of fat content of milk; δ_l is the fixed effect of the l-th class of casein content of milk; ε_m is the fixed effect of the m-th class of somatic cells count content of milk; and e_ijklm is the random residual ~N (0, σ²_e).

Results and Discussion

The averages for the fat, protein, and casein percentages, pH, and SCS were 3.75, 3.35, and 2.59 g/100 g, and scores of 6.56 and 3.57, respectively (Table 1). These results are in agreement with those of Cassandro et al. (2008) and De Marchi et al. (2008), although the value for SCS was slightly higher.

Table 1
Descriptive statistics of milk composition and milk coagulation traits

Trait	N	Mean	SD (%)	Minimum	Maximum	Correlation with THI
Fat (g/100 g)	592	3.75	0.80	1.34	6.72	0.019
Protein (g/100 g)	592	3.35	0.44	2.3	5.12	–0.2852
Casein (g/100 g)	592	2.59	0.36	1.75	3.99	–0.3142
pH	592	6.56	0.07	6.30	6.79	–0.006
SCS1	592	3.57	2.35	–2.05	10.60	0.053
RCT (min)	592	27.54	6.67	8.62	56.95	0.2622
a30 (mm)	592	17.83	9.96	0.00	75.74	–0.2932
IAC	592	100.00	4.79	84.50	121.62	–0.2902
THI	592	67.59	10.50	54.04	81.53

• RCT - milk coagulation time; a₃₀ - curd firmness; IAC - milk aptitude to coagulate index; THI - temperature-humidity index; SCS - somatic cell score; SD - standard deviation.
• ¹
  SCS = [3 + log₂ (SCC/100,000)].
• ²
  P<0.001.

The average RCT value was 27.54 min, which is not close to the optimal value recommended for the renneting ability of milk (Zannoni and Annibaldi, 1981) or of the value of 16.69 min reported by Penasa et al. (2015) by MIRS. The average value for a₃₀ was 17.83 mm, which is a low value for the milk renneting ability (Zannoni and Annibaldi, 1981).

The temperature-humidity index can be used to determine the influence of heat stress on the productivity of dairy cows (Gantner et al., 2011). Armstrong (1994) classifies heat stress according to the THI variation as mild (72-78), moderate (79-89), and severe (90-99) and a THI below 72 characterizes an environment without heat stress. According to this author, the milk production is affected by heat stress when the THI values are higher than 72 (Armstrong, 1994), but according to Bernabucci et al. (2010), who made an extensive evaluation using over one million Italian Holstein lactation records, their results indicated that milk yield losses began at an average THI value of 68. However, heat stress could also cause changes in the milk composition, somatic cell counts (SCC), and mastitis frequency (Rodriguez et al., 1985; Du Preez et al., 1990); thus, these changes could also compromise the milk coagulation properties and, consequently, the IAC.

During the period of this study, the THI values (Table 1) were between 81.53 in July (moderate heat stress) and 54.04 in November (characterizes an environment without heat stress). The THI values were grouped into four ranges: less than 60, between 60 and 65, between 70 and 75, and finally greater than 75. Only in July was the THI value above 72, considered highly critical (Figure 1).

Figure 1
Average temperature, relative humidity, and THI variation.

The protein and casein contents were negatively associated with THI (−0.285, −0.314, respectively) (Table 1), the same observed by Bouraoui et al. (2002) for the milk protein percentage, which significantly decreased as a result of summer heat stress (2.96 vs. 2.88%), with THI values of 68 in the spring and 78 in the summer).

The RCT was positively correlated with THI and the correlation was negative for a₃₀ and IAC (0.262, −0.293, and −0.290, respectively (P<0.001) (Table 1).

The effects of herd, THI, fat (%), casein (%), and CSC on IAC were significant in explaining the variability of the trait (P<0.05) (Table 2). It can be seen that when the THI values were below 75, there were no large changes in the IAC value, but when the THI values were above 75, the IAC decreased significantly (Figure 2).

Table 2
Analysis of variance for milk coagulation ability index expressed on scale 100±5 scores

Effect	df	F	P-value
Herd	5	49.15	<.0001
THI	3	48.54	<.0001
Fat (%)	1	115.80	<.0001
Casein (%)	1	45.18	<.0001
SCS	1	84.13	<.0001
R2	0.52
RMSE	3.34

• THI - temperature-humidity index; SCS - somatic cell score; R² - coefficient of determination; RMSE - root mean square error.

Figure 2
Relationship between index of milk aptitude to coagulate (IAC) and temperature-humidity index (THI).

The results indicated an effect of THI on IAC. Indices of THI between 72 and 78 were considered mild by Armstrong (1994), but it was observed in this study that values above 75 negatively contributed to IAC.

Conclusions

Temperature-humidity index values below 75 do not cause significant changes in values of the index of milk aptitude to coagulate, but when the values of temperature-humidity index are above 75, the decrease in index of milk aptitude to coagulate is relevant. The control of temperature-humidity index can be used to guarantee appropriate milk coagulation properties.

Acknowledgments

To Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for the scholarship.

References

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Publication Dates

• Publication in this collection
  May 2017

History

• Received
  10 Aug 2016
• Accepted
  13 Feb 2017