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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES VETERINARY ACADEMY
FACULTY OF ANIMAL SCIENCE DEPARTMENT OF ANIMAL NUTRITION
Fransisca Risny Oktavia
Influence of Winter Season Climate Variability on Dairy Milk - Adaptation Review of Various Breeds
Žiemos sezono klimato pokyčių įtaka pienui – įvairių veislių adaptacijos apžvalga
Master Thesis
The supervisor
Asist. Kristina de Witte
Kaunas 2021
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THE WORK WAS DONE IN THE DEPARTMENT OF ANIMAL NUTRITION CONFIRMATION OF THE INDEPENDENCE OF DONE WORK
I confirm that the presented Master Theses “Influence of winter season climate variability on dairy milk - adaptation review of various breeds”
1. Has been done by me.
2. Has not been used in any other Lithuanian or foreign university.
3. I have not used any other sources not indicated in the work and I present the complete list of the used literature.
Fransisca Risny Oktavia
(date) (author’s name, surname) (signature)
CONFIRMATION ABOUT RESPONSIBILITY FOR CORRECTNESS OF THE ENGLISH LANGUAGE IN THE DONE WORK
I confirm the correctness of the English language in the done work.
Milda Sirtautaite
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CONCLUSION OF THE SUPERVISOR REGARDING DEFENCE OF THE MASTER THESIS
Kristina de Vitte
(date) (supervisor’s name, surname) (signature)
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THE MASTER THESES HAVE BEEN APPROVED IN THE DEPARTMENT/CLINIC
(date of approbation) (name, surname of the manager of department/clinic) (signature) Reviewers of the Master Thesis
1)
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2)
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Evaluation of defence commission of the Master Thesis:
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4 Table of content
Abstract ...6
Santrauka...7
Abbreviations...8
Introduction...9
1 Literature review...11
1.1 Climate Variability...11
1.1.1 Climate variability on livestock...11
1.1.2 Winter anomaly...12
1.1.3 Climate Variability and its Relationship to Climate Change...13
1.2 Food Safety in Milk production………..….14
1.2.1 Milk Quality……….14
1.2.2 Milk composition………...15
1.2.2.1 Milk Protein……….…15
1.2.2.2 Milk fat………16
1.2.2.3 Milk Lactose………16
1.2.2.4 Milk Urea………17
1.3 Seasonal Influences on Milk Yield………...17
1.4 Seasonal Influences on Milk Protein and Fat………18
1.5 Seasonal Influence on Milk Lactose……….18
1.6 Seasonal Influence on SCC………...18
1.7 Seasonal Influences on Milk Urea……….19
1.8 Temperature influence on Milk Traits………...19
1.9 Relative Humidity influence on Milk Traits………..20
1.10 Cattle Breed Influence on Milk Traits……….20
2 Materials and Methods………21
2.1 Winter Data Collection……….21
2.1.1 Monthly Temperature conditions………21
2.2 Animals and Housing………22
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2.3 Milking Procedure and Milking Equipment………..22
2.4 Milk Sampling and Sample Treatment………..23
2.5 Winter Season Pattern Study………...…………..24
2.6 Statistical Analysis………24
3 Research Results….……….26
3.1 Climate Variability in Lithuania………....26
3.2 Performance Comparison among different cow breeds in winter 2018-2019…...27
3.3 Performance Comparison among different cow breeds in winter 2019-2020…...30
3.4 Influence of winter climate variability on Milk Quality Indicator……… and composition……….31
3.5 Correlations of Temperature and Relative Humidity with Milk Traits………….32
4 Discussion of results……….………..35
Conclusions………37
Suggestions/Recommendations………..38
Acknowledgement………..39
References………..40
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Influence of winter season climate variability on dairy milk − adaptation review of various breeds
Fransisca Risny Oktavia
ABSTRACT
The purpose of this study was to analyse the effect of the warmest winter in Europe in 2019- 2020 on milk quality and milk composition, as well as to investigate the influence of winter 2018-2019 and winter 2019-2020 on milk quality and composition on different cow breeds. 30 dairy cows were randomly chosen in this research according to the breeds which fulfilled the pre-set criteria for inclusion in the research. To be included in the experiment, the cows from three different breeds (Swedish Red, Simmental and Holstein) needed to be milked during the winter of 2019-2020. This research has been conducted for the period December 2019 – May 2020. Analysis of Variance (ANOVA) was used to compare means among the variables. In addition, bivariate correlations among the variables were evaluated by Pearson’s linear correlation coefficient to determine the influence of environmental conditions. The result of this study showed that Swedish Red, Simmental and Holstein dairy cows are well adapted toward warm winter. Besides, the warmest winter season in 2019-2020 has negative influence on milk, which can be seen mainly by the increase of milk urea correlated with the temperature increase.
Key words: Swedish Red, Simmental, Holstein, climate variability, winter
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Žiemos sezono klimato pokyčių įtaka pienui – įvairių veislių adaptacijos apžvalga
Fransisca Risny Oktavia
SANTRAUKA
Šio tyrimo tikslas buvo išanalizuoti šilčiausios 2019–2020 metų žiemos Europoje poveikį pieno kokybei ir pieno sudėčiai, taip pat ištirti 2018–2019 metų žiemos ir 2019–2020 metų žiemos įtaką skirting karvių veislių pieno kokybei ir sudėčiai. Šiame tyrime atsitiktinai pasirinkta 30 melžiamų karvių pagal veisles, kurios atitiko iš anksto nustatytus įtraukimo į tyrimą kriterijus.
Kad būtų įtrauktas į eksperimentą, trijų skirtingų veislių (švedų raudonųjų, Simmentalių ir Holšteinų) karves reikėjo melžti 2019–2020 metų žiemą. Šie tyrimai buvo atliekami nuo 2019 m. gruodžio mėn. iki 2020 m. gegužės mėn. Variacijų analizė (ANOVA) buvo naudojama norint palyginti kintamųjų vidurkius. Be to, dviejų kintamųjų koreliacijos buvo įvertintos pagal Pearsono tiesinį koreliacijos koeficientą, siekiant nustatyti aplinkos sąlygų įtaką. Šio tyrimo rezultatas parodė, kad švedų raudonosios, Simmentalio ir Holšteino melžiamos karvės yra gerai prisitaikiusios šiltai žiemai. Be to, šilčiausias žiemos sezonas 2019–2020 m. turi neigiamos įtakos pienui, tai daugiausia galima pastebėti dėl išaugusio pieno karbamido, susijusio su temperatūros padidėjimu.
Raktiniai žodžiai: Švedų raudonasis, Simmentalis, Holšteinas, klimato kaita, žiema
8 Abbreviations
SCC - Somatic Cell Count
C3S - Copernicus Climate Change Service DNA - Deoxyribonucleic Acid
ECMWF - European Centre for Medium-Range Weather Forecasts CO2 - Carbon Dioxide
MUN - Milk Urea Nitrogen NEFA - Nonesterified Fatty Acid GLM- General Linear Model ANOVA- Analysis of Variance SE- Standard Errors
IPCC- Intergovernmental Panel on Climate Change AR4- Fourth Assessment Report (AR4)
9 Introduction
Milk is one of the most important dietary products, which contains nearly all the nutrients necessary to sustain life [1]. Milk has been an important source of protein for human consumption for ages. It also provides all the major nutrients like fat, carbohydrates and proteins [2].
The dairy produce industry is becoming one of the major backbones of the development of world economies. The growing demand for the dairy products due to population and nutritional awareness has resulted in greater production [3]. Dairy farming is one of the leading farming branches in Lithuania. 1363.01 thousand tonnes of cow milk were produced in Lithuania in 2018. Ministry of Agriculture asserts that this is, on average, 7000 kg of milk produced by one cow, which is 1.5% more than it was in 2017. Increased milk production emphasises the need to establish high quality production facilities [4].
Somatic cell count has been used as a significant indicator in monitoring milk quality.
Season is one of the factors that influence the levels of somatic cell count (SCC) of milk. [5,6].
An elevated SCC in milk has a negative influence on the quality of raw milk [7]. Meanwhile, chemical composition of milk varies greatly as a consequence of numerous factors such as breed of animal, climate and season [8]. In addition, environmental factors such as temperature and relative humidity often limit the performance of dairy cows [9].
For example, Copernicus Climate Change Service (C3S) [10] claims that the period from December 2019 to February 2020 resulted in the warmest winter record in Europe. However, the number of studies on the influences of climate anomaly on milk is still limited, despite Europe experiencing exceptionally warm winters in recent years.
In order to analyse the effect of the warmest winter in Europe in 2019-2020 on milk quality and milk composition, further research is needed. In addition, the influence of winter
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2018-2019 and winter 2019-2020 on milk quality and composition on different cow breeds should be investigated as well. The main objectives of this research study were to determine breeds that have the best adaptation ability towards warm winter, as well as determining the associations of two different winters (2018-2019 and 2019-2020) with SCC and its impact on milk composition and quality. Besides, it is predicted that the warmest winter season in 2019- 2020 has negative impact on milk.
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1 Literature review 1.1 Climate Variability
Climate variability is defined as seasonal, interannual or long-term variations in temperature and precipitation around an average condition defined over several decades. It can be quantified by satellite data and used to estimate the likely impact on agricultural and economic system [11].
1.1.1 Climate variability on livestock
Climate variability in ecosystems plays an important role on fodder, cow’s physiological adaptation abilities, ruminal fermentation, cow nutrition, husbandry systems, and influences on DNA integrity. The quality and quantity of milk compounds is generally a result of complex interactions of variables. These are not fixed, and they can change throughout the year depending on environmental conditions and climate variability [12]. As a result of thermal challenges associated with climate change and -variability, normal animal behavioural, immunological, and physiological functions are all potentially impacted. Changing climatic circumstances is not a new phenomenon. Even today, global environmental conditions vary considerably. However, the rate at which environmental conditions change, the extent to which animals are exposed to extreme conditions and the inability of animals to adequately adapt to those environmental changes are always a concern. Within limits, domestic livestock can cope with most gradual thermal difficulties. However, lack of prior conditioning to rapidly changing or adverse weather events most often results in catastrophic deaths in domestic livestock, and losses of productivity in surviving animals [13].
12 1.1.2 Winter anomaly
The Copernicus Climate Change Service (C3S) [10], implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission, reported persistent mild weather over Europe as (Fig 1.1), particularly in the North and East. The last winter was 3.4°C warmer than the average winter for the period of 1981-2010. The mean temperature for the mentioned 30-year period was almost 1.4°C higher than that of the previous warmest winter in 2015/16 [10].
Fig 1.1 Surface air temperature anomaly for the boreal winter from December 2019 to February 2020 relative to the average for 1981-2010.
The extreme warmth of the winter 2019/20 (Fig 1.2) combines the C3S ERA5 data for 1979- 2020 with publicly available data from up to six other providers covering the winters from 1850/51 onwards. Fig 1.2 shows that winter 2019/20 is more than 2°C warmer than every winter prior to 1975 [14].
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Fig 1.2 Monthly European-average surface air temperature for winter (December to February).
1.1.3 Climate Variability and its Relationship to Climate Change
Climate change is impacting changes in climate variability and in the frequency, intensity, spatial extent, duration, and timing of extreme weather and climate events [15]. Climate change and changing climate variability in the future may result in changes in climate variability and the frequency of extreme events may have substantial impacts on the prevalence and distribution of pests, weeds, crop and livestock diseases [16]. The impacts of climate change on livestock production factors are presented in Fig. 1.3. Temperature affects most of the critical factors for livestock production, such as water availability, animal production, reproduction and health. Meanwhile, forage quantity and quality are affected by a combination of increases in temperature, CO2 and variation in precipitation. Livestock diseases are mainly affected by an increase in temperature and variation in precipitation [17].
14 Fig 1.3 Impact of Climate Change 1 on Livestock.
1.2 Food Safety in Milk production
The quality of food products, primarily milk and dairy commodities, is a main concern in food safety and market economy [18]. Milk, as well as dairy products, due to their unique composition and properties, is a great medium for the growth of various spoilage and pathogenic microorganisms. Besides, the major determinant that influences quality and safety of dairy products is the quality of raw milk [19,20].
1.2.1 Milk Quality
The somatic cell count (SCC) is the most widely accepted criteria that is used to quantify milk quality because milk that is infected with mastitis generally has a higher SCC than milk
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from uninfected cows [21]. The European Economic Union Regulation 92/46 requires that the milk whose SCC is more than 400.000 cell/ml cannot be used as raw milk. The same regulation concluded that this type of milk was not suitable for human consumption as of 1998 [22]. A higher than 200.000/ml SCC is a mastitis indicator. In this case, the majority of somatic cells are neutrophiles. The organism reacts with a defence mechanism against irritating agents with an increased cell count (especially polimorphonuclear leukocytes), so their increased number in any of the udder quarters shows disrupted secretion [23].
1.2.2 Milk composition
Generally, milk is composed of 87.7 % water, 3.3% protein, 3.4% fats, 4.9% lactose and 0.7% mineral [24]. However, the chemical composition of milk varies greatly as a consequence of numerous factors such as species, breed of animal, climate, season, lactation etc. [8]. Milk composition may also influence the susceptibility to infection. Differences in protein, fat, lactose, and urea concentrations may influence bacterial growth in milk by changing the local environment [25].
1.2.2.1 Milk protein
Proteins in bovine milk can be classified into caseins (αS1-, αS2-, β-, and κ-CN) and whey proteins (α-LA and β-LG). Examination of genetic variation of the major milk proteins is of interest due to the documented correlation with compositional and technological traits [26].
Recently, the production of milk protein in high-yielding dairy cows has received more emphasis as component pricing based on units of fat and protein has become more established in the dairy factories. The composition of milk and milk proteins is influenced by many factors.
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However, it is not known if milk protein composition varies in cows. Furthermore, feeding plays an important role in milk protein composition [27].
1.2.2.2 Milk fat
The lipid component of milk is very complex chemically and exists as a unique emulsion, in the form of spherical droplets, commonly known as fat globules [28]. Milk fat globules are the natural colloidal assemblies secreted by the mammary epithelial cells to provide lipids and other bioactive molecules in the gastrointestinal tract of newborns [29]. In bovine milk, lipids represent approximately 3.5-5.2% of the total milk composition and are predominantly composed of triglycerides, accounting for more than 98% of the total milk lipids. The rest of the milk lipids (∼2%) are subdivided into various smaller classes, specifically the diacylglycerols (diglycerides), monoacylglycerols (monoglycerides), free fatty acids, phospholipids and cholesterol. Milk fat is an important carrier of lipid-soluble constituents, such as carotenoids, liposoluble vitamins (A, D, E and K) and several volatile flavour compounds [28].
1.2.2.3 Milk Lactose
Lactose is the main carbohydrate in mammals’ milk, and it is responsible for the osmotic equilibrium between blood and alveolar lumen in the mammary gland. It is the major bovine milk solid, and its synthesis and concentration in milk are affected mainly by udder health and the cow’s energy balance and metabolism [30]. A decrease in lactose percentage of milk leads to reduce in milk yield due to lactose plays an active role for transmission of water to the mammary gland [31]. Moreover, milk lactose concentration increases slightly as production increases and declines slowly at the end of lactation along with production [32].
17 1.2.2.4 Milk Urea
Urea as a part of the non-protein fraction of nitrogen in milk and represents the final product of protein metabolism in the rumen of ruminants. Urea molecules regulate concentration of body fluids and serum by passive diffusion, and milk urea levels are positively and highly correlated [33]. Thus, urea content can be determined in the bloodstream and in milk. Milk urea nitrogen (MUN) can be used as a tool to monitor protein feeding efficiency and dietary protein-energy ratio in dairy cows. Apart from feeding, milk urea content can be affected by some other factors, such as season, milk yield, stage of lactation, etc. [34,35].
1.3 Seasonal Influences on Milk Yield
Throughout several decades, the selection of high-yielding dairy breeds of cattle has directed towards improving genetic predisposition for higher production of milk and quantity of consumed feed. According to Scholtz [36], despite the success achieved, the main environmental factors affecting dairy cow production are ambient temperature, humidity, solar radiation and wind. Most studies of the heat stress in livestock have concentrated mainly on temperature and relative humidity because data of the amount of thermal radiation received by animal, wind speed, and rainfall are not generally available. High temperature decreases feed intake in order for dairy cows to reduce digestive heat production, whereas sweating and water intake increase. It is known that during heat stress, reduction in feed intake and milk yield and negative energy balance occur [9].
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1.4 Seasonal Influences on Milk Protein and Fat
Hu et al. [37] suggested that heat shock induced the stress responses of bovine mammary epithelial cells, with a strong effect on the secretion of protein and fat concentration in milk and increased the tolerance of the cell itself. Thus, heat stress has direct (tissue hyperthermia) and indirect (reduced feed intake) effects, both of which may affect milk protein synthesis in the mammary gland. Furthermore, the metabolic alterations triggered by hot conditions are also implied.
During the cold season, water intake was reduced, and fat concentration increased. This might let them use up to their body reserves of fat and protein, which negatively affected the percentages of these ingredients in the milk [38].
1.5 Seasonal Influence on Milk Lactose
The seasonal influence on milk lactose can be explained by the availability of forage. The decrease of lactose during summer is largely due to increased intake of grass and reduces the energy forage. Winter hay intake restores lactose to normal [39,40].
1.6 Seasonal Influence on SCC
Researchers have shown that the incidence of clinical mastitis (udder infections with visible signs) is higher in the summer months in hot and humid environments when the number of pathogens is higher. Cows under stress, high temperatures and excess moisture are more susceptible to infections, having a greater number of SCC in raw milk [41]. It is known that SCC increases during summer months and decreases during winter months. Higher SCC is probably caused by increased contamination of the udder during months of elevated temperature and humidity and by decreased time devoted to cows by producers. A similar
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pattern is observed in this study with significantly less healthy cows during lactation in summer and fall than in winter and spring, when the proportion of newly infected and chronically infected cows during lactation is significantly higher [42].
1.7 Seasonal Influences on Milk Urea
Rajala-Schultz and Savile [43] claimed that heat stress influences the milk urea nitrogen concentration in summer because of a lower dry matter intake and higher requirement for energy needed for thermoregulation. The urea concentration in milk tended to be higher during the summer and early fall. High milk urea concentrations at high temperatures in the summer months can be caused by heat stress, which reduces dairy matter intakes. That, in turn, intensifies the degradation of protein in the rumen. Other nutritional problems in summer could be lower water intake, especially during grazing, and mowburn of silage, when cows are kept indoors during the entire year. The correlation between milk urea and temperature in cooler seasons has not been explained. However, month and temperature are connected with the period of grazing and silage preparation, as well as quality of stored forage [44].
1.8 Temperature influence on Milk Traits
Animals suffer heat stress when the core body temperature exceeds the range specified for normal activity because of a total heat load greater than the capacity for heat dissipation.
Besides, high temperature stress affects production and reproduction performance by decreasing antioxidant enzyme activity, which increases oxidative damage in the tissues, and by changing carbohydrate, lipid, and protein metabolism. In response to reduced feed intake and consequent negative energy balance, insulin levels decrease, allowing for adipose lipolysis and increased circulating Nonesterified Fatty Acid (NEFA). On the other hand, heat-stressed
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cows, despite inadequate nutrient intake, exhibit increased basal insulin levels lack of increase of NEFA levels, and increased plasma urea nitrogen [45,46]. In addition, cold stress has impact on the amount of daily milk yield. As well, state high‑performing dairy milk cows are very sensible to cold stress in relation to milk production [47,48].
1.9 Relative Humidity influence on Milk Traits
Increase of relative humidity to 90% induces additional decrease of milk yield for 31, 25, and 17% of normal yield, respectively [49]. Moreover, relative humidity influenced the variability of the basic composition of cow milk. Although, only the fat content, pH and titrable acidity of goat milk were significantly correlated with relative humidity [50].
1.10 Cattle Breed Influence on Milk Traits
In selecting cows for higher milk yields and milk quality, it is important to understand how these traits are affected by the bovine genome. The major milk proteins exhibit genetic polymorphism and these genetic variants can serve as markers for milk composition, milk production traits, and technological properties of milk [51]. Besides, various methods have been employed to identify significant genetic markers for milk production [52]. In addition, previous studies showed varying susceptibility of cow breeds to the increase in somatic cell count. Elevation of somatic cell count (SCC) produced a decrease in major albumins, i.e.
alphaLA and beta-LG. SCC growth caused a significant rise in immunoactive proteins (lactoferrin and lysozyme), as well as bovine serum albumin (BSA) [53,54].
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2 Materials and Methods
The research was performed at a private dairy farm in Kedainiai, Lithuania and state- owned enterprise “Pieno tyrimai”. The research work was carried out from 2018-12-01 till 2019-03-01 and from 2019-12-01 till 2020-03-01 at the Lithuanian University of Health Sciences, Kaunas. The meteorological data were obtained from the Lithuanian Hydrometeorological Service.
2.1 Winter Data Collection
In order to evaluate the role of warmer winters in Lithuania, monthly meteorological data (average temperature and the amount of precipitation) during the research period were obtained from the Lithuanian Hydrometeorological Service (www.meteo.lt) archive from 2018-12-01 until 2020-03-01. Average daily temperature and relative humidity data were obtained from Kaunas Airport Weather Station database through tutiempo.net.
2.1.1 Monthly Temperature conditions
The mean maximum and minimum temperatures for the three winter months of the examined years (2018 and 2019) are tabulated as follows (Table 2.1).
Table 2.1 Monthly Temperature conditions.
Month Average (℃) Minimum (℃) Maximum (℃)
December 2018 -2,3 -14 +3
January 2019 -5,1 -21 +2
February 2019 +2,4 -5,1 +9
December 2019 +2,6 +1,5 +8
January 2020 +1,2 -6,6 +5,5
February 2020 +2,4 -5,1 +8,8
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2.2 Animals and Housing
For the thesis, 10 crossbreed cows of each breed were selected: Holstein, Simmental and Swedish Red. All cows were fed with high quality feed using a total mix ration system according to the amount of crude protein and requirements of metabolizable energy. All cows were clinically healthy, with no clinical signs either in the milk or at udder and cow level. The cows were kept in a loose open type housing barn equipped with boxes and a feeding path. Milk quality and composition from dairy cows were analysed as part of monthly milk control. Cows are fed twice a day during milking with this feed: 20 kg of maize, 11 kg of grass silage, 3 kg of cold-pressed rapeseed cake, 4.5 kg of crushed triticale, 1 kg molasses, 100 g of chalk, 200 g of minerals. In late lactation: 16 kg of corn, 15 kg of grass silage, 2 kg of rapeseed, 3.5 kg of crushed triticale, 1 kg of molasses, 160 g of minerals. 30 dairy cows (10 Holstein, 10 Simmental and 10 Swedish Red) were randomly chosen in this research according to the breeds, which fulfilled the pre-set criteria for inclusion in the research. To be included in the experiment, the cows from three different breeds needed to be milked during the winter of 2019-2020 (Table 2.2).
Table 2.2 Data on research groups.
Group Season Breed Number of cows (n)
1 Winter 2018-2019 & 2019-2020 Swedish red 10 2 Winter 2018-2019 & 2019-2020 Simmental 10 3 Winter 2018-2019 & 2019-2020 Holstein 10
2.3 Milking Procedure and Milking Equipment
The cows in the open type housing barn were milked with herringbone milking parlour type. The milking equipment was operated by the Westfalia Germany system, which collects
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and checks all the data coming from the milking parlours. 10 cows are driven into the site at one time and stop one after another. Cows are milked in accordance with the milking rules, which provide for the following sequence of milking procedures:
• Milking begins at the time as scheduled. Milkers wear necessary equipment, such as gloves and waterproof aprons.
• Wiping the teats and udder with proper clean cloth, which has been soaked in antiseptic liquid.
• The first jets of milk are suppressed with close monitoring for any changes in the milk.
The milk is separated, and the state of the cow is evaluated by a veterinarian.
• Within 2 min. milkers are fitted from the beginning of the milking procedures. Towards the finishing of milking, the milkers switch off automatically.
• The teats are examined and sprayed with an antiseptic liquid.
• Towards the finishing of milking, all the 10 cows in a row are released from the milking parlour in one time.
2.4 Milk Sampling and Sample Treatment
Milk samples were taken from every cow during one milking session. Milk analyses were taken once per month, during morning and evening milking. The quantity of milk was checked and recorded during both milking. Samples of milk were taken in turns: one month they were collected in the morning, and the next month they were collected in the evening. Indicators of milk were assessed using reports of a herd of cattle from 2018-2020. Contents of the milk composition (fat, protein, lactose, and urea) and the standard of milk quality (SCC) in the milk were estimated by SE “Pieno tyrimai”. LactoScopeFTIR, the mid-infrared detector was used to analyse contents of the milk (fat, protein, lactose and urea amounts). Somascope detector helped to evaluate SCC, which uses the flow cytometry principle. In the thesis the quantitative and
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qualitative productivity indicators are being compared of different cow breeds, which are as follows:
• Average amount of milk (kg);
• Average milk protein (%);
• Average milk fat content (%);
• Average lactose content in milk (%);
• Average number of somatic cells (thousand / ml);
• Average urea content in milk (mg / 100 ml).
2.5 Winter Season Pattern Study
The winter sample was examined by utilizing 180 composition data units of milk, which were recorded in 30 dairy cows. The months of December, January and February were defined as winter month. The seasonal variation and breed adaptation performances in milk quantity and quality were evaluated by means of a GLM (General Linear Model), where milk yield, fat, protein, lactose, SCC and urea were set as dependent variables, while breed, season, temperature and relative humidity as independent variables.
2.6 Statistical Analysis
The primary information collected will be systematized, grouped, analysed, and presented in the Microsoft Excel 20 program. In order to be informed on different factors, seasons, breeds and interactions between them on milk traits variability, factorial Analysis of Variance (ANOVA) was used to compare means among the variables in the conditions specified (Tabachnick and Fidell 2007). In addition, bivariate correlations among the variables were evaluated by Pearson’s linear correlation coefficient to determine the influence of
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environmental conditions. A linear regression analysis was also conducted to determine the regression coefficients, statistical significance of regression model (t-value), and proportion of milk traits (dependent) contributed by independent variables (temperature and relative humidity) derived from the multiple correlation coefficient (Adjusted R2). The criterion for statistical significance was p <0.05. The values are presented as the mean ± standard deviation in Figures 4.1, and as the standard error in the tables. The data were analysed, using Microsoft Excel 365.
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3 Research Results 3.1 Climate Variability in Lithuania
Variability of the temperature and precipitation recorded in the recent winter (2019-2020) was compared to that of the former winter (2018-2019) (Table 3.1). The analysis highlighted an increase of temperature and precipitation in 2019-2020.
Table 3.1. Winter Temperature and Precipitation.
Month Mean Temperature (℃) Mean Precipitation (mm)
December 2018 -2,3 60
January 2019 -5,1 52,5
February 2019 +2,4 40
December 2019 +2,6 52
January 2020 +1,2 59
February 2020 +2,4 62
According to the Figure 3.1, it is clear that mean weather temperature in winter has increased significantly. December 2018 was colder and had more precipitation than December 2019 and January 2019 was colder and drier than January 2020, but February 2019 and February 2020 were almost the same, only precipitation in February 2020 was higher than in 2019.
Fig 3.1 Winter Temperature and Precipitation.
-20 0 20 40 60 80
December January February
WINTER SEASONS
Mean Temperature (°C) 2019 Mean Temperature (°C) 2020 Mean Precipitation (mm) 2019 Mean Precipitation (mm) 2020
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3.2 Performance Comparison among different cow breeds in winter 2018-2019
The summarized data of milk productive parameters on Swedish Red, Simmental and Holstein are presented in Table 3.2. The detailed evaluation of the effect of breeds adaptative ability in milk production yield revealed that there was significant difference on milk yield between Swedish red and Holstein (p= 0.00001), as well as between Simmental and Holstein (p= 0.013).
Protein parameter revealed that there was a statistically significant difference between Swedish red and Holstein (p=0.007), also between Simmental and Holstein (p= 0.00001). Meanwhile, lactose parameter revealed that there was a statistically significant difference not only between the Swedish red and Simmental (p= 0.00001) but also Swedish Red and Holstein (p= 0.0008). Moreover, urea parameter revealed that there was a significant difference only between Swedish Red and Holstein (p=0.016), according to ANOVA test (Appendix 2).
Table 3.2 Summarized data (expressed as mean±SE) values of milk quality indicator and composition in winter 2019. Different superscript letters within the same column indicate statistically significant differences between breeds (p<0.05).
Breeds SCC (t./ml) Milk (L) Fat % Protein % Lactose% Urea (mg) Swedish
Red
163.633±17.
8
25.153±0.
8a
4.9067±0.1 3
3.608±0.0 7a
4.2873±0.0
4a 24.9±0.5a Simment
al 120.866±20.
1
22.556±0.
7a 4.695±0.11 3.498±0.0 4a
4.5013±0.0 2b
24.7±0.6
a
Holstein 192.733±27.
06
19.86±0.7
b
4.8686±0.1 0
3.891±0.0 6b
4.4643±0.0 3b
23.03±0.
5b
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Figure 3.2 emphasises on the average milk yield content during winter 2018-2019. It shows that Swedish Red cows produced more milk than Simmental and Holstein. Holstein performed the worst during this period.
Fig 3.2 Average milk yield content during winter 2018-2019.
However, average protein content was the highest in Holstein cows’ milk even though this breed produced less milk during winter 2018-2019. Whereas, protein content in Simmental cows’ milk was the lowest, according to Figure 3.3.
Fig 3.3 Average protein content during winter 2018-2019.
0 5 10 15 20 25 30
1
L
MILK YIELD
swedish red simmental holstein
3 3.2 3.4 3.6 3.8 4
1
%
PROTEIN
swedish red simmental holstein
29
Figure 3.4 shows that the average lactose content during winter months of 2018-2019 was the highest in Simmental cows’ milk. Holstein cows produced similar amount, but Swedish Red cows produced significantly less lactose in their milk.
Fig 3.4 Average lactose content during winter 2018-2019.
In Figure 3.5 it is seen that more urea was found in Swedish Red and Simmental cows’ milk.
The same amount of urea was discovered in milk of both breeds. On the other hand, Holstein cows’ milk had much less urea.
Fig 3.5 Average urea content during winter 2018- 2019.
4.1 4.2 4.3 4.4 4.5 4.6
1
%
LACTOSE
swedish red simmental holstein
20 22 24 26
MG
UREA
swedish red simmental holstein
30
3.3 Performance Comparison among different cow breeds in winter 2019-2020
The summarized data of milk productive parameters on Swedish Red, Simmental and Holstein are presented in Table 3.3. According to ANOVA test towards milk quality indicators and composition in 2019-2020, there is a significant difference between Swedish Red and Holstein breed on milk yield trait (p=0.002) also between Simmental and Holstein on milk protein trait (p= 0.00006) (Appendix 3).
Table 3.3 Summarized data (expressed as mean±SE) values of milk quality indicator and composition in winter 2019-2020. Different superscript letters within the same column indicate statistically significant differences between breeds (p<0.05).
Breeds SCC (t./ml) Milk (L) Fat % Protein % Lactose% Urea (mg) Swedish
Red 163.267±32 24.37±0.92a 4.858±0.21 3.546±0.06
a
4.347±0.0 3
27.56±0.
9 Simment
al 125.936±22.
9
21.846±0.6 3 b
4.8176±0.1 4
3.438±0.03
a
4.515±0.0
2 27.9±0.9 Holstein
152.1±21.8 20.663±0.6 9b
4.7406±0.1 4
3.767±0.06
b
4.435±0.0 7
25.13±1.
2
According to the data in Figure 3.6, average milk yield production during winter 2019-2020 was again the highest from Swedish Red cows.
Fig 3.6 Average milk yield during winter 2019-2020.
0 5 10 15 20 25 30
1
L
MILK YIELD
swedish red simmental holstein
31
However, Holstein cows were the main producers of protein in their milk during winter 2019- 2020. Swedish Red and Simmental cows produced similar amount of protein in this period (Figure 3.7).
Fig 3.7 Average protein content during winter 2019-2020.
3.4 Influence of winter climate variability on Milk Quality Indicator and composition The summarized data of milk quality indicator and composition parameters on winter 2018-2019 and winter 2019-2020 are presented in Table 3.4. The detailed evaluation of the effect of winter variability in milk production yield revealed that there was significant difference between urea parameters (Appendix 4).
Table 3.4 Summarized data (expressed as mean±SE) values of milk quality indicator and composition in winters 2018-2019 and 2019-2020. Different superscript letters within the same column indicate statistically significant differences between years (p<0.05).
Seasons SCC.(t./ml) Milk (L) Fat % Protein % Lactose% Urea (mg) Winter
2018- 2019
159.07±13 22.523±0.49 4.823±0.06 3.665±0.4 4.417±0.02 24.21±0.3a
Winter 2019-
2020
146.84±15 22.293±0.46 4.805±0.09 3.584±0.36 4.432±0.029 26.86±0.6b
0 1 2 3 4 5
1
%
PROTEIN
swedish red simmental holstein
32
Comparing winters of 2018-2019 and 2019-2020, it is clear that average urea content in milk was significantly increased in winter 2019-2020 (Figure 3.8).
Fig 3.8 Average Urea content during winter 2018-2019 and winter 2019-2020.
3.5 Correlations of Temperature and Relative Humidity with Milk Traits
Table 3.5.1 shows that significant correlation is evident in winter 2018-2019 between temperature and relative humidity on milk protein traits. In addition, the significant correlation between relative humidity on milk protein traits was negative. On the other hand, in winter 2019-2020, significant positive correlation was detected between temperature and milk fat (r = 0.383, p=0.0001). Besides, in winter 2019-2020, relative humidity showed significant negative correlations with milk yield, milk fat and milk urea, respectively.
The linear regression established that in winter 2018-2019, both temperature and relative humidity could significantly predict milk protein traits variability, which accounted 4%
(adjusted R2) for temperature predictor, while 5% (adjusted R2) for relative humidity predictor.
On the other hand, temperature could significantly (p = 0.0001) affect milk fat traits and accounted for 13% of milk traits variability in winter 2020. Moreover, relative humidity
22 23 24 25 26 27 28
MG
UREA
winter 2018-2019 winter 2019-2020
33
contributed to milk traits variability in milk yield, milk fat, and milk urea, respectively. Milk yield accounted for 9% of the variability (p = 0.002) and milk fat accounted for 5% of milk traits variability (p = 0.015), while milk urea accounted for 7% of milk traits variability (p = 0.004) (Table 3.5.2).
Table.3.5. 1 Pearson’s linear correlations of temperature and relative humidity vs. milk parameters (milk yield, protein, lactose, SCC and urea), winters 2018-2019 and 2019-2020.
Traits Winter 2018-2019 Winter 2019-2020
Temperature Relative Humidity
Temperature Relative Humidity Milk
Yield(n:90)
r -0.124 0.153 0.510 -0.317
p 0.243 0.147 2.732 0.002*
Fat (n:90) r 0.0132 -0.0607 0.383 -0.254
p 0.901 0.569 0.0001* 0.015*
Protein (n:90) r 0.222 -0.243 -0.062 -0.006
p 0.034* 0.02* 0.560 0.954
Lactose (n:90) r -0.146 0.094 -0.117 0.078
p 0.167 0.375 0.270 0.459
Somatic cell (n:90)
r 0.012 -0.671 0.063 0.016
p 0.910 0.529 0.554 0.878
Urea (n:90) r 0.197 -0.014 0.562 -0.296
p 0.853 0.888 7.99 0.004*
*Significant correlation (p<0.05).
Table 3.5.2 Linear Regression of Temperature and Relative Humidity with milk traits (milk yield, protein, lactose, SCC and urea), winters 2018-2019 and 2019-2020.
season milk
trait predictor Multiple
R R2 Adjusted
R2 SE Sig.
Winter 2018-
2019
Milk yield
temperature 0.124 0.015 0.004 4.758 0.243 Relative
Humidity
0.153 0.236 0.012 4.738 0.147 fat temperature 0.013 0.0001 -0.011 0.660 0.901
Relative Humidity
0.0607 0.0036 -0.007 0.659 0.569 protein temperature 0.222 0.049 0.038 0.380 0.034*
Relative Humidity
0.243 0.059 0.048 0.378 0.020*
temperature 0.146 0.021 0.010 0.193 0.167
34 lactose Relative
Humidity
0.094 0.008 -0.002 0.194 0.375 SCC
temperature 0.012 0.0001 -0.011 125.5 0.910 Relative
Humidity 0.067 0.004 -0.0068 125.2 0.529 urea
temperature 0.019 0.0003 -0.010 3.209 0.853 Relative
Humidity 0.0149 0.0002 -0.011 3.210 0.888
2020
Milk yield
temperature 0.510 0.260 0.252 3.877 2.732
Winter
2019- 2020
Relative
Humidity 0.317 0.100 0.090 4.275 0.002*
fat
temperature 0.383 0.147 0.137 0.871 0.0001*
Relative
Humidity 0.254 0.064 0.054 0.913 0.015*
protein
temperature 0.0621 0.003 -0.007 0.349 0.560 Relative
Humidity
0.006 0.00003 -0.011 0.3501 0.954
lactose
temperature 0.117 0.013 0.002 0.285 0.270 Relative
Humidity 0.078 0.006 -0.005 0.286 0.459 SCC
temperature 0.063 0.003 -0.007 144.69 0.554 Relative
Humidity
0.016 0.0002 -0.011 144.9 0.878 urea temperature 0.562 0.316 0.308 5.05 7.989
Relative Humidity
0.296 0.087 0.077 5.834 0.004*
Multiple R: the Correlation Coefficient that measures the strength of a linear relationship between two variables; R2: squared multiple correlation coefficient; Adjusted R2: the R square adjusted for the number of independent variable in the model; SE: standard errors of the regression coefficients; Sig: two-sided observed significance levels (p) for the t-statistics.
35
4 Discussion of results
Climate change is affecting climate variability and frequency, intensity, extent, duration, and timing of extreme weather and climate events, which may impact on food chain caused by changing temperatures and precipitations [16,55]. The present study was aimed to investigate changes of temperature and precipitation in Lithuania. The analysis highlighted a warming trend and an increase of precipitation during winter 2019-2020, as projected by The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) climate model, according to which temperature increases are slightly higher in northern Europe. The warming in northern Europe is likely to be the largest in winter with increases in precipitation (up to 16%) [56,57].
Analytical data result with Anova (p<0.05) indicates that Swedish Red cows recorded significantly higher milk yield in both winter seasons compared with Holstein cows and the highest average amounts of the milk yield. This can be explained because milk production has been one of the most important traits for selection of bulls in Scandinavia [58]. Besides, the highest average amounts according to the milk fat (%) in both winter seasons were also determined in the Swedish Red cows. As milk fat is the main raw material in the manufacture of cream and butter. Thus, a higher fat content will increase the yields of butter and cream and affect the consistency of cheese [59]. On the other hand, significantly higher lactose content in Simmental cows compared to Swedish Red cows in winter 2018-2019 and no significant difference on milk lactose in winter 2019-2020 were not expected, since the synthesis of lactose is the driving force of milk production in the udder and its concentration regulated by osmosis [60].
36
In this research, the content of milk urea was significantly influenced by the warming winter. An increase of milk urea content was significantly visible in winter 2019-2020 compared to winter 2018-2019. Czajkowska, et al. [61] emphasized that milk urea increased with increasing temperatures, regardless of the season. The similar was reported by Rzewuska and Strabel [44], explaining that it is not easy to understand the mechanism underlying the increase of milk urea correlated with the temperature increase, however temperature relates to quality of stored forage. In addition, concentration of milk urea nitrogen in dairy cows could be used as an indicator to usher for nutritional strategies and to help reduce nitrogen emissions to the environment [62]. Therefore, ammonia emission generally increased upon an increase in adjusted milk urea levels [63].
The environmental variables in this study (temperature and relative humidity) were chosen primarily because (1) they were thought to be of importance for milk traits of dairy cattle, (2) they were available, and (3) they have a reasonable variation within the country.
Temperature could significantly predict milk protein in winter 2018-2019 and milk fat in winter 2019-2020. As Bertocchi et al. [6] confirms, temperature starts to increase but are not very high, indicate an initial reduction in fat and protein content during a period that is not considered hot for dairy cows. On the other hand, very little research has been conducted about the relationship between relative humidity and milk traits. In this research, significant negative correlation was determined between relative humidity with milk fat, milk protein and milk urea respectively during winter 2018-2019, also with milk urea during winter 2019-2020. This result shows that relative humidity had significant effects on milk composition. In contrast, Zhu et al.[64]
reported that high relative humidity had little effect on milk composition of Guanzhong dairy goats. Eventhough, it is widely accepted that environmental variables including temperature and humidity play an essential role in the lactation performance in lactating animals [65,66].
37
Conclusions
Based on the results, the highest average amounts according to the milk yield (L) and fat (%) in both winter seasons were determined in the Swedish Red cows. In addition, the warmest winter season in 2019-2020 has negative influence on dairy milk, which can be seen mainly by the increase of milk urea correlated with the temperature increase.
In addition to that, it is seen from this research that Swedish Red, Simmental and Holstein cow breeds could be the option for the dairy farmers, because these breeds are adaptive to climate variability, which can be seen by similar outcomes between winters 2018-2019 and 2019-2020 on majority of milk traits. More specifically, Swedish Red cows could also be the option for dairy farmers, which focused on milk fat‐based products. Farmers should also take climate variability into consideration for fodder storage management in order to reduce the negative influence of warming climate, which cause more urea to appear in milk.
38
Suggestions/Recommendations
This thesis focused on milk quality and milk composition on Swedish red, Simmental and Holstein dairy cows. Further studies are needed to evaluate the effect of warm winter on other breeds to compare environmental sensitivity. Besides, in order to analyse the effect of warm winter more thoroughly, more indoor parameters, which is suitable for in-line analysis, such as indoor temperature and light condition is needed.
39
Acknowledgement
First of all, I would like to express my gratitude to Kristina De Witte for being my supervisor. Kristina’s support, help with milk sampling, information about the cows and input on results have been of great value to me. I could not have done this without her.
Also, I would like to thank to prof. Makra Lazlo, a meteorologist, who I met in Hungary during ERASMUS+. He helped me a lot with usage of climate data and its analysis.
Also, Milda Sirtautaite for being such a great friend who took of her spare time to proofread this thesis.
Finally, I would like to devote my last acknowledgement to my parents for their love and support, without which I would not have even made it this far.
THANK YOU!
40 References
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