Evaluation of Haemato‐Biochemical and Physiological Parameters of Different Dairy Breeds During the Dry and Wet Seasons in Central Ethiopia

ABSTRACT

Background

Haemato‐biochemical values are vital for assessing an animal's normal physiological condition. However, few studies have been conducted on various dairy cow breeds across different seasons in Ethiopia.

Objective

To evaluate the haemato‐biochemical and physiological parameters in different breeds of dairy cows during the dry and wet seasons in central Ethiopia.

Methods

A total of 90 dairy cows of different breeds and lactation stages were included in this study. Blood samples (∼10–15 mL) were collected aseptically from the jugular vein into EDTA tubes for haemato‐biochemical analysis, while plasma and serum were separated by collecting blood in heparin and non‐anticoagulant vials, respectively. Data on various parameters were statistically analysed using a two‐way analysis of variance.

Results

The findings indicated that the Borana breed had lower average values for physiological parameters such as rectal temperature, heart rate and respiration rate than cross‐breeds and Jersey breeds. The dry season showed higher averages for all physiological indicators than the wet season. Significant differences (p < 0.05) in physiological parameters were observed between seasons within the same lactation stages across three breeds under similar management practices. Levels of packed cell volume (PCV), haemoglobin (Hgb), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), red blood cell count (RBC) and white blood cell count (WBC) were measured. Compared to the dry season, most haematological parameters were significantly lower during the wet season, except for MCV. During both seasons, cross‐breeds and Jersey breeds exhibited higher levels of PCV, MCV, MCH and WBC than the Borana breed. PCV, MCV, MCH and RBC levels declined from early to late lactation in all breeds during both dry and wet seasons. Conversely, Hgb, MCHC and WBC levels increased from early to late lactation. The mean values for creatinine, urea and globulin were notably higher in the dry season than in the wet season. In contrast, total protein, glucose, cholesterol and albumin levels were significantly lower during the dry season. In addition, glucose and cholesterol levels increased from early to late lactation in all breeds under both conditions, while total protein, creatinine, urea and globulin levels decreased during this period.

Conclusion

Finally, the mean values of all haemato‐biochemical and physiological parameters fell within the reference ranges for healthy dairy cows, suggesting that these three breeds are well adapted to the climatic conditions of central Ethiopia.

The study found that Borana, Jersey and cross‐bred dairy cows in central Ethiopia are well‐adapted to the local climate, with haemato‐biochemical and physiological parameters remaining within normal reference ranges despite significant seasonal variations. During the dry season, the Borana breed displayed higher heat endurance while experiencing fewer physiological responses.

1. Introduction

Blood maintains physiological balance; therefore, any change in the cattle's body is reflected in blood parameters (Casella et al. 2012; Coroian et al. 2017). The biochemical profile of blood varies seasonally in response to environmental factors. It varies among animals due to several genetic and non‐genetic factors, including breeds, age, nutrition, sex and physiological status, such as pregnancy and lactation, and management systems such as housing and microclimate, which can all influence the amount of various blood parameters (Roubies et al. 2006; Aggarwal et al. 2016; de Oliveira et al. 2019; Purwar et al. 2019; Saeed et al. 2021; Setiawati et al. 2021). As a result, assessing haemato‐biochemical and physiological parameters is crucial as a reference for developing various livestock management strategies, selecting stress‐tolerant and adaptable animals and evaluating the overall health of animals (Das 2019; Barkakati and Kalita 2020; Loi et al. 2021). According to Nikhil et al. (2018), variations in haematological parameters are considered to be indicators of a livestock species' ability to adapt to heat stress. Haematological indicators reflect cows' adaptation to severe environmental conditions and other stressors (Coroian et al. 2017).

Furthermore, the haematological profile is one of the physiological variables that can provide vital information for disease diagnosis, assessing nutritional status, monitoring, prediction and livestock production and welfare, all of which can influence increasing livestock production and productivity (Otto et al. 2000; Ndlovu et al. 2007; Amle et al. 2014; Kaminski et al. 2014; Bezerra et al. 2017). Haemato‐biochemical values are critical for determining the normal physiological status of animals, as changes in these parameters are significant predictors of an animal's physiological condition (Mamun et al. 2013). However, very few studies have been conducted on the pure Borana breed, the Jersey breed and the 50% HF × Borana (cross‐breed). Determining physiological baseline values for various dairy cattle breeds is crucial for assessing animal status, nutrition, management practices and health (Šimpraga et al. 2013). These differences also hinder animal scientists' efforts to develop standardised tests for dairy cattle breeds.

Indigenous cattle play a crucial role in contributing to the national economy by supplying draught animal power, milk and manure. Cross‐breeds are more productive, but they droop under low inputs and harsh climates and are susceptible to tropical diseases. Wet and dry stress cause large variations in blood biochemical concentrations, affecting the animals' production performance (Ganaie et al. 2013).

Purebred Borana, Jersey and cross‐bred dairy cows are widely used in Ethiopia for milk production. Insights into haemato‐biochemical reference values for these dairy breeds could be highly important for adaptation and management strategies. There is currently no information on the haemato‐biochemical and physiological features of these dairy cow breeds. Therefore, an effort was made to evaluate the haemato‐biochemical and physiological characteristics of these breeds. Given the importance of haemato‐biochemical and physiological parameters in monitoring the nutrition and health status of dairy cows and the fact that environmental conditions are considered physiological stressors that affect the biological systems of dairy cattle, the current study aimed to evaluate the haemato‐biochemical and physiological parameters in different breeds of dairy cows in the dry and wet seasons in central Ethiopia. The reported results, which are the first in Ethiopia, could serve as reference data until a large‐scale study on a larger population of these breeds is conducted.

2. Materials and Methods

2.1. Description of the Study Area

The present study was carried out at the Holeta Agricultural Research Center (HARC) dairy farm in Ethiopia's central highlands. The HARC dairy farm was located 32 km west of Addis Ababa along the main road to Ambo at 9°0′ N latitude, 38°30′ E longitude and an elevation of 2400 m above sea level, whereas the Ada'a Berga dairy farm is located in the West Shewa Zone of Oromia Regional State, 64 km northwest of Addis Ababa, at 9°15′ N latitude, 38°25′ E longitude, with an elevation of 1166–3238 m above sea level. It is bordered on the south by Walmara, on the southwest by Ejerie, on the west by Meta Robi and on the north and east by the Muger River, which divides it from the North Shewa Zone. During the dry season, the study areas experienced hot, dry conditions (Gemechu et al. 2025).

2.2. Experimental Design

The experiment was conducted on dairy cattle of different breeds at the same lactation stage and parity at the HARC. The experiment was conducted in a 3 × 3 × 2 factorial arrangement of treatments (three breeds, three stages of lactation and two seasons). In this study, 45 dairy cows of different stages of lactation and breeds were used in each season (this study included 90 dairy cows from both seasons). Approximately 15 dairy cows of each breed (pure Borana, Jersey and cross‐breed) were selected for the evaluation of haemato‐biochemical and physiological parameters in each season based on their high economic values. The dairy cows under observation were in their third parity and were managed under the same systems on natural pasture and supplementary feed.

2.3. Animal Selection Criterion and Management

Healthy dairy cattle in good body condition and at different stages of lactation, breeds and the same parity were stratified and randomly selected. There was no history of diarrhoea in the previous seven days, nor were there any urogenital abnormalities during parturition. No medication was given in the previous seven days. There were no skin lesions/alopecia and no gastrointestinal or blood parasites, as assessed by faecal and blood smear examinations. All animals were regularly dewormed before the commencement of the study. This indicates that animals were treated with ivermectin and albendazole for external and internal parasite control, respectively. Overall, during the study period, all cows were similar in health status, parity and physiological phase, with only seasonal and breed variation. Dairy cow selection criteria excluded clinically sick animals detected by veterinary clinical examination.

The selected animals were evaluated in two different seasons. As shown in Table 1, the breed categories were pure Borana, Jersey and 50% HF × Borana (cross‐breed), and the two seasons were the dry and wet seasons. Based on the distribution of rainfall in the research areas, the seasons were divided into three categories in the highlands of Ethiopia: spring mid‐rainy season (February–May), summer rainy season (June–September) and dry season (October–January), which are referred to locally as Belg, Kiremt and Bega seasons (Diro et al. 2010). This study was conducted over the course of two seasons because these seasons represent the year's extreme and contrasting environmental and nutritional conditions. The first was the summer (wet) season, which lasted from June to September 2022 and was characterised by frequent rainfall. The second was the winter (dry) season, which lasted from October 2022 to February 2023. The selected animals were grouped according to Table 1 below.

TABLE 1.

Study group of animals under different stages of lactation in each season (n = 45).

	No. of animals
Group	Pure Borana	Cross‐breed	Jersey	Physiological phase
1	5	5	5	Early lactation
2	5	5	5	Mid lactation
3	5	5	5	Late lactation

Note: Borana lactation stage: Early lactation (between parturition and 60 days of lactation), mid lactation (between 60 and 120 days of lactation) and late lactation (between 120 and 180 days of lactation) and cross‐breed and Jersey lactation stage: Early lactation (between parturition and 100 days of lactation), mid lactation (between 100 and 200 days of lactation) and late lactation (between 200 and 305 days of lactation).

2.4. Data Collection Methods

2.4.1. Blood Sample Collection

Blood samples (∼10–15 mL) were collected aseptically from the jugular vein in an ethylenediaminetetraacetic acid (EDTA) vial for haemato‐biochemical examination. At the same time, plasma and serum were separated in heparin and anticoagulant‐free vials for laboratory analysis. In each season, three blood samples were collected on Days 1, 15 and 30 to track dynamic changes in blood parameters that may fluctuate in acute, subacute and more chronic phases, as described by Nikhil et al. (2018). Whole blood was collected between 10.00 AM and 11.00 AM by venipuncture of the jugular vein using vacutainers. A total of 270 blood samples (45 dairy cows in a season × 3 times of collection × 2 seasons) were collected from the jugular vein of cows. Of these, approximately 135 blood samples were collected in tubes containing EDTA as an anticoagulant for haematological analysis, while 135 blood samples were collected in serum separator tubes (SST) for biochemical analysis at the HARC Veterinary Laboratory.

The packed cell volume (PCV), haemoglobin (Hgb), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), red blood cell (RBC) and white blood cell (WBC) were determined from whole blood by an electronic cell counter (Hematology Analyzer, 2.5 Release, Germany). The plasma was separated from another aliquot of blood samples by centrifugation at 3500 rpm for 10 min at room temperature and stored at −20°C for the analysis of biochemical parameters (Nikhil et al. 2018).

Total protein was analysed from serum by the Biuret method, creatinine using a photometric colourimetric test for kinetic measurements (Jaffe‐reaction method), glucose using an enzymatic colourimetric test (GOD‐PAP method), urea using enzymatic methods (GLDH‐urease methods), albumin (BCG dye method), globulin concentrations (total protein minus albumin) and cholesterol level was determined using an enzymatic colourimetric method with lipid clearing factor (CHOD‐PAP method) using the reagents developed for each parameter test and the instrument HumaLyzer 3000, Germany. The blood or serum samples were analysed at the HARC Veterinary Laboratory within 24 hours of blood collection to assess various haematological and biochemical parameters.

2.4.2. Rectal Temperature, Respiration Rate and Heart Rate

Rectal temperature (RT) was measured by using a clinical veterinary thermometer inserted at the animal's rectum wall at a depth of approximately 3.0  inches (touching its bulb to the rectal wall) for 3 min (Kim et al. 2018). The respiration rate (RR) is one of the physiological responses used to measure the level of heat stress in cattle. The RR, expressed in breaths per minute (breaths/min), was recorded by counting flank movements (one outward and one inward movement counted as one respiration count) for over a minute (Leite et al. 2018).

Heart rate (HR) is expressed in the number of beats per minute, which was measured using a stethoscope placed on the left side of the cow's chest, behind the elbow and a stopwatch for 30 s, and multiplying the results by two to obtain the counts per minute, as described by Abera et al. (2021).

2.5. Data Analysis

The average blood samples for Days 1, 15 and 30 of each season were collected and compared among breeds and seasons. The Shapiro‒Wilk normality test was used to determine the normality of the variables. All variables were found to be normally distributed (p > 0.05). The collected dataset was statistically analysed using the general linear model (GLM) framework using SAS software version 9.4 and R software version 4.2.3. Breed, stage of lactation and season were fitted as independent variables, while physiological and haemato‐biochemical parameters were fitted as response variables. When the analysis of variance revealed a significant difference among the parameters, Tukey's honestly significant difference (HSD) test was used to compare means. p < 0.05 was considered statistically significant.

3. Results

3.1. Physiological Parameter Variability Between Breeds and Seasons

RT, HR and RR were significantly influenced by breed (p < 0.05). According to the present findings, the Borana breed had lower mean values for RT, HR and RR than the cross‐breeds and Jersey breeds. The mean RT, HR and RR of the cross‐bred cows were 38.8 ± 0.08, 67.7 ± 1.81 and 33.6 ± 0.80, respectively (Table 2). At the same time, the mean RT, HR and RR for the Jersey breed were 38.6 ± 0.08, 66.6 ± 2.02 and 33.2 ± 0.82, respectively.

TABLE 2.

Physiological parameters between breeds and seasons (mean ± SD).

Variables	RT (°C)	HR (bpm)	RR (breaths/min)
Breeds
Borana	38.4 ± 0.05b	57.0 ± 0.72b	29.0 ± 0.39b
Cross‐breed	38.8 ± 0.08a	67.7 ± 1.81a	33.6 ± 0.80a
Jersey	38.6 ± 0.08a	66.6 ± 2.02a	33.2 ± 0.82a
Seasons
Wet season	38.4 ± 0.04b	57.6 ± 0.65b	30.6 ± 0.51b
Dry season	38.8 ± 0.06a	70.0 ± 1.53a	33.3 ± 0.71a

Note: The different superscript letters (a and b) within the column indicate significant differences (p < 0.05).

Abbreviations: HR, heart rate; RR, respiration rate; RT, rectal temperature.

Seasonal variations in mean values for physiological parameters were statistically significant (p < 0.05). In the wet season, the mean RT, HR and RR were 38.4 ± 0.04, 57.6 ± 0.65 and 30.6 ± 0.51, respectively. The overall mean values for RT, HR and RR were 38.8 ± 0.06, 70.0 ± 1.53 and 33.3 ± 0.71, respectively, in the dry season (Table 3).

TABLE 3.

Seasonal effects on physiological parameters of different dairy cow breeds at various stages of lactation during the dry and wet seasons (mean ± SD).

Descriptions	RT		HR		RR
	Wet	Dry	Wet	Dry	Wet	Dry
Interaction of breeds and seasons
Borana	38.3 ± 0.08	38.4 ± 0.05B	56.9 ± 1.15B	57.2 ± 0.90B	29.0 ± 0.63B	29.2 ± 0.50B
Cross‐breed	38.5 ± 0.05b	39.0 ± 0.11Aa	59.0 ± 1.09Ab	76.4 ± 1.27Aa	32.0 ± 1.01Ab	35.2 ± 1.11Aa
Jersey	38.4 ± 0.10b	38.8 ± 0.09Aa	56.8 ± 1.14Bb	76.4 ± 1.39Aa	30.7 ± 0.85Bb	35.6 ± 1.10Aa
Interaction of breeds, seasons and stages of lactation
Early
Borana	38.6 ± 0.08	38.7 ± 0.05B	62.2 ± 0.64	61.7 ± 0.35B	32.0 ± 0.56Cb	31.4 ± 0.40Ca
Cross‐breed	38.7 ± 0.07b	39.3 ± 0.12Aa	62.5 ± 0.75b	81.7 ± 0.49Aa	36.0 ± 0.76Ab	40.0 ± 0.54Ba
Jersey	38.7 ± 0.06b	39.3 ± 0.09Aa	62.1 ± 0.16b	82.0 ± 0.35Aa	34.6 ± 0.32Bb	41.0 ± 0.51Aa
Mid
Borana	38.2 ± 0.17	38.3 ± 0.06B	56.1 ± 0.80Cb	55.4 ± 0.36Ba	28.0 ± 0.30C	28.2 ± 0.14C
Cross‐breed	38.5 ± 0.04b	39.0 ± 0.19Aa	60.9 ± 0.30Ab	76.7 ± 1.17Aa	30.0 ± 1.79Ab	34.0 ± 0.89Aa
Jersey	38.3 ± 0.22b	38.7 ± 0.05Aa	56.4 ± 0.60Bb	76.5 ± 1.31Aa	29.6 ± 1.15Bb	33.2 ± 1.15Ba
Late
Borana	38.2 ± 0.06	38.3 ± 0.05B	52.4 ± 0.73Cb	54.4 ± 0.45Ba	26.9 ± 0.42B	27.3 ± 0.28C
Cross‐breed	38.3 ± 0.03b	38.7 ± 0.20Aa	53.7 ± 0.90Ab	70.9 ± 0.84Aa	29.9 ± 0.88Ab	31.6 ± 1.60Aa
Jersey	38.2 ± 0.05	38.5 ± 0.07B	51.9 ± 0.63Bb	70.7 ± 1.51Aa	28.0 ± 0.60Ab	32.6 ± 0.26Ba

Note: The different superscript letters (A–C and a and b) indicate that there is a significant difference (p < 0.05) in the parameter within the column and row, respectively.

Abbreviations: HR, heart rate; RR, respiration rate; RT, rectal temperature.

3.2. Physiological Parameter Variability Among Seasons and Lactation Stages

Seasonal differences in physiological parameters for each dairy cow based on breed differences and lactation stages are displayed in Table 3. During the dry season, the RT increased by 0.1°C, 0.5°C and 0.4°C in the Borana, cross‐breeds and Jersey breeds, respectively, compared to the wet season. The HR increased by 0.3, 17.4 and 19.6 beats/min in the Borana, cross‐breeds and Jersey breeds, respectively, during the dry season compared to the wet season. Furthermore, the RR increased by 0.2, 3.2 and 4.9 breaths/min in the Borana, cross‐breeds and Jersey breeds, respectively, during the dry season compared to the wet season.

In addition, Table 3 showed that all physiological parameters during the dry season differed significantly (p < 0.05) among the three breeds. However, there were no significant differences (p > 0.05) between the cross‐breeds and Jersey breeds. In addition, there was no statistically significant difference (p > 0.05) in the RT, HR or RR of the Borana breed between the two seasons.

3.3. Haematological Parameter Variability Between Breeds and Seasons

All haematological parameters varied significantly (p < 0.05) across the three dairy breeds. However, there was no significant difference (p > 0.05) in PCV, MCV, MCH and MCHC between cross‐breeds and Jersey breeds (Table 4). According to the present findings, the Borana breed had lower mean values for PCV (32.2 ± 0.32), MCV (41.0 ± 1.02), MCH (13.9 ± 0.21) and WBC (4.4 ± 0.09) than the cross‐breeds and Jersey breeds. However, there were higher mean values for Hgb (11.0 ± 0.13), MCHC (34.1 ± 0.45) and RBC (8.0 ± 0.15). In the wet season, the mean PCV, Hgb, MCV, MCH, MCHC, RBC and WBC were 32.2 ± 0.30, 9.8 ± 0.14, 49.3 ± 1.00, 14.9 ± 0.18, 30.7 ± 0.58, 6.7 ± 0.13 and 5.2 ± 0.14, respectively.

TABLE 4.

Haematological parameter variability between breeds and seasons (mean ± SD).

Variables	PCV (%)	Hgb (g/dL)	MCV (fL)	MCH (pg)	MCHC (g/dL)	RBC (106/mcL)	WBC (103/mcL)
Breeds
Borana	32.2 ± 0.32b	11.0 ± 0.13a	41.0 ± 1.02b	13.9 ± 0.21b	34.1 ± 0.45a	8.0 ± 0.15a	4.4 ± 0.09c
Cross‐breed	34.7 ± 0.53a	10.6 ± 0.20b	50.9 ± 1.20a	15.4 ± 0.21a	30.5 ± 0.60b	6.9 ± 0.16b	6.1 ± 0.21b
Jersey	34.0 ± 0.50a	10.2 ± 0.22c	51.0 ± 0.91a	15.2 ± 0.17a	30.2 ± 0.71b	6.7 ± 0.15c	6.4 ± 0.23a
Seasons
Wet season	32.2 ± 0.30b	9.8 ± 0.14b	49.3 ± 1.00a	14.9 ± 0.18	30.7 ± 0.58b	6.7 ± 0.13b	5.2 ± 0.14b
Dry season	35.0 ± 0.40a	11.3 ± 0.09a	46.0 ± 1.15b	14.8 ± 0.20	32.6 ± 0.48a	7.7 ± 0.12a	6.1 ± 0.22a

Note: The different superscript letters (a–c) within the column indicate a significant difference (p < 0.05).

Abbreviations: Hgb, haemoglobin; MCH, mean corpuscular haemoglobin; MCHC, mean corpuscular haemoglobin concentration; MCV, mean corpuscular volume; PCV, packed cell volume; RBC, red blood cell count; WBC, white blood cell count.

3.4. Haematological Parameter Variability Among Breeds, Seasons and Lactation Stages

The haematological parameters of the three breeds during the dry and wet seasons differed significantly (p < 0.05), as shown in Table 5. However, there was no significant difference (p > 0.05) in PCV during the wet season and Hgb during the dry season between the three breeds. The blood samples collected from the three breeds within the dry season showed a significant difference (p > 0.05) in haematological parameters except Hgb levels. However, there was no significant variation (p > 0.05) in blood samples collected from cross‐breeds and Jersey breeds throughout the dry season, except for WBC.

TABLE 5.

Haematological variability between breeds and seasons (mean ± SD).

Parameters	Seasons	Borana breed	Cross‐breed	Jersey breed
PCV (%)	Wet	31.7 ± 0.50	32.9 ± 0.46B	32.1 ± 0.59B
	Dry	32.6 ± 0.39b	36.5 ± 0.70Aa	35.8 ± 0.45Aa
Hgb (g/dL)	Wet	10.5 ± 0.16Ba	9.8 ± 0.21Bb	9.2 ± 0.22Bc
	Dry	11.4 ± 0.12A	11.3 ± 0.19A	11.2 ± 0.15A
MCV (fL)	Wet	43.3 ± 1.54Ab	52.3 ± 1.51a	52.3 ± 1.03a
	Dry	38.7 ± 1.07Bb	49.5 ± 1.86a	49.8 ± 1.47a
MCH (pg)	Wet	14.2 ± 0.28b	15.4 ± 0.32a	15.0 ± 0.27ab
	Dry	13.5 ± 0.30b	15.3 ± 0.29a	15.5 ± 0.23a
MCHC (g/dL)	Wet	33.2 ± 0.74Ba	29.8 ± 0.76Bb	29.1 ± 1.19Bb
	Dry	35.1 ± 0.40Aa	31.3 ± 0.90Ab	31.3 ± 0.70Ab
RBC (106/mcL)	Wet	7.4 ± 0.18Ba	6.4 ± 0.19Bb	6.2 ± 0.17Bb
	Dry	8.5 ± 0.16Aa	7.4 ± 0.16Ab	7.2 ± 0.15Ab
WBC (103/mcL)	Wet	4.4 ± 0.11b	5.5 ± 0.24Ba	5.7 ± 0.24Ba
	Dry	4.5 ± 0.14c	6.7 ± 0.28Ab	7.2 ± 0.29Aa

Note: The different superscript letters (A and B and a–c) indicate that there is a significant difference (p < 0.05) in the parameter within the column and row, respectively.

Abbreviations: Hgb, haemoglobin; MCH, mean corpuscular haemoglobin; MCHC, mean corpuscular haemoglobin concentration; MCV, mean corpuscular volume; PCV, packed cell volume; RBC, red blood cell count; WBC, white blood cell count.

Significant differences (p < 0.05) in haematological parameters between breeds, seasons and within the same lactation phases are displayed in Table 6. However, blood samples collected from the Borana breed across seasons revealed no significant differences (p > 0.05) in PCV and WBC at any lactation stage or in Hgb, MCV, MCH and MCHC during the late stages of lactation. Similarly, the MCV and MCH of cross‐bred samples obtained at the early and mid‐lactation stages, as well as MCHC during the early lactation stage, showed no significant differences (p > 0.05). Blood samples from the Jersey breed showed no significant differences (p > 0.05) in MCV early lactation stages and MCH levels at early and late lactation stages between seasons.

TABLE 6.

Haematological variability between breeds, seasons and lactation stages (mean ± SD) in the study areas.

		Borana			Cross‐breed			Jersey
Parameters	Season	Early	Mid	Late	Early	Mid	Late	Early	Mid	Late
PCV (%)	Wet	33.0 ± 0.93a	32.1 ± 0.58b	30.1 ± 0.63c	32.3 ± 0.66Ba	34.3 ± 0.64Ba	32.0 ± 0.74Bb	34.0 ± 0.71Ba	32.8 ± 0.58Ba	29.6 ± 0.51Bb
	Dry	33.8 ± 0.66a	32.4 ± 0.51b	31.6 ± 0.51b	39.0 ± 0.45Aa	36.8 ± 0.37Ab	33.6 ± 1.03Ac	37.0 ± 0.52Aa	36.0 ± 0.63Aa	34.4 ± 0.75Ab
Hgb (g/dL)	Wet	9.9 ± 0.21Bb	10.9 ± 0.15Ba	10.7 ± 0.22a	8.9 ± 0.23Bc	9.9 ± 0.02Bb	10.5 ± 0.27Ba	8.4 ± 0.14Bc	9.1 ± 0.15Bb	10.3 ± 0.13Ba
	Dry	11.4 ± 0.29Aa	11.6 ± 0.19Aa	11.3 ± 0.17a	10.8 ± 0.25Ab	11.5 ± 0.22Aa	11.7 ± 0.38Aa	10.5 ± 0.12Ab	11.5 ± 0.20Aa	11.6 ± 0.20Aa
MCV (fL)	Wet	50.2 ± 1.60Aa	42.2 ± 1.34Ab	37.6 ± 0.60c	59.0 ± 0.66a	51.8 ± 0.92b	46.0 ± 1.15Ac	55.1 ± 1.88a	53.3 ± 0.72Aa	48.4 ± 1.05Ab
	Dry	43.8 ± 0.96Ba	36.9 ± 0.45Bb	35.2 ± 0.76b	57.9 ± 1.19a	48.7 ± 0.43b	41.9 ± 1.61Bc	56.1 ± 1.72a	49.0 ± 0.72Bb	44.5 ± 1.43Bc
MCH (pg)	Wet	15.0 ± 0.53Aa	14.3 ± 0.36Ab	13.3 ± 0.20c	16.3 ± 0.63a	15.0 ± 0.40b	15.0 ± 0.45Ab	15.6 ± 0.32a	14.3 ± 0.63Bc	15.2 ± 0.16b
	Dry	14.7 ± 0.51Ba	13.2 ± 0.16Bb	12.6 ± 0.26c	16.0 ± 0.54a	15.3 ± 0.32a	14.6 ± 0.47Bb	15.9 ± 0.40a	15.6 ± 0.33Aa	14.9 ± 0.42b
MCHC (g/dL)	Wet	30.0 ± 1.13Bb	34.0 ± 0.70Ba	35.5 ± 0.09a	27.6 ± 0.93c	29.0 ± 0.50Bb	32.8 ± 1.12Ba	24.7 ± 0.76Bc	27.7 ± 0.80Bb	34.8 ± 0.30Aa
	Dry	33.6 ± 0.93Ab	35.8 ± 0.05Aa	35.9 ± 0.05a	27.7 ± 0.83c	31.4 ± 0.90Ab	34.9 ± 0.70Aa	28.3 ± 0.44Ac	31.9 ± 0.80Ab	33.7 ± 0.76Ba
RBC (106/mcL)	Wet	6.6 ± 0.19Bb	7.6 ± 0.20Ba	8.0 ± 0.08Ba	5.5 ± 0.17Bc	6.6 ± 0.46Bb	7.0 ± 0.04Ba	5.4 ± 0.16Bc	6.4 ± 0.18Bb	6.8 ± 0.05Ba
	Dry	7.7 ± 0.16Ac	8.8 ± 0.04Ab	9.0 ± 0.06Aa	6.8 ± 0.20Ac	7.6 ± 0.02Ab	8.0 ± 0.13Aa	6.6 ± 0.21Ac	7.4 ± 0.09Ab	7.8 ± 0.15Aa
WBC (103/mcL)	Wet	4.9 ± 0.16a	4.2 ± 0.12b	4.1 ± 0.13b	6.6 ± 0.11Ba	5.4 ± 0.06Bb	4.5 ± 0.13Bc	6.7 ± 0.07Ba	5.6 ± 0.10Bb	4.6 ± 0.07Bc
	Dry	5.0 ± 0.18a	4.3 ± 0.27b	4.2 ± 0.04b	8.0 ± 0.07Aa	6.5 ± 0.12Ab	5.5 ± 0.07Ac	8.5 ± 0.07Aa	7.0 ± 0.11Ab	6.0 ± 0.23Ac

Note: The different superscript letters (A and B and a–c) indicate that there is a significant difference (p < 0.05) within the column and row, respectively.

Abbreviations: Hgb, haemoglobin; MCH, mean corpuscular haemoglobin; MCHC, mean corpuscular haemoglobin concentration; MCV, mean corpuscular volume; PCV, packed cell volume; RBC, red blood cell count; WBC, white blood cell count.

3.5. Biochemical Parameter Variability Between Breeds and Seasons

Biochemical indicators differed significantly (p < 0.05) across the three dairy breeds. However, total protein and creatinine levels were not significantly different between the cross‐breeds and Jersey breeds. Similarly, albumin levels did not differ considerably between the Borana and Jersey breeds (Table 7). According to the present findings, the Borana breed had lower mean values for creatinine (1.20 ± 0.07) and glucose (65.2 ± 1.54) levels than the cross‐breeds and Jersey breeds. However, it had higher mean values for total protein (7.0 ± 0.04), urea (17.1 ± 0.77), cholesterol (154.3 ± 2.96) and globulin (3.3 ± 0.04).

TABLE 7.

Biochemical parameter variability between breeds and seasons (mean ± SD).

Variables	Total protein (g/dL)	Creatinine (mg/dL)	Glucose (mg/dL)	Urea (mg/dL)	Cholesterol (mg/dL)	Albumin (g/dL)	Globulin (g/dL)
Breeds
Borana	7.0 ± 0.04a	1.2 ± 0.07b	65.2 ± 1.54c	17.1 ± 0.77a	154.3 ± 2.96a	3.7 ± 0.06b	3.3 ± 0.04a
Cross‐breed	6.8 ± 0.03b	1.7 ± 0.05a	83.8 ± 1.73a	14.0 ± 0.52c	116.4 ± 1.81c	3.8 ± 0.07a	3.1 ± 0.02c
Jersey	6.8 ± 0.03b	1.8 ± 0.05a	73.9 ± 1.53b	15.0 ± 0.51b	150.4 ± 2.63b	3.7 ± 0.06b	3.2 ± 0.03b
Seasons
Wet season	6.9 ± 0.03a	1.4 ± 0.05b	79.6 ± 1.54a	13.6 ± 0.41b	147.7 ± 3.38a	3.8 ± 0.05b	3.1 ± 0.02b
Dry season	6.8 ± 0.02b	1.7 ± 0.06a	69.0 ± 1.54b	17.1 ± 0.51a	133.1 ± 2.76b	3.6 ± 0.05a	3.2 ± 0.03a

Note: The different superscript letters (a–c) within the column indicate a significant difference (p < 0.05).

Significant seasonal differences (p < 0.05) in the mean values of biochemical parameters are presented in Table 7. During the wet season, the mean levels of total protein, creatinine, glucose, urea, cholesterol, albumin and globulin were 6.9 ± 0.03, 1.4 ± 0.05, 79.6 ± 1.54, 13.6 ± 0.41, 147.7 ± 3.38, 3.8 ± 0.05 and 3.1 ± 0.02, respectively.

3.6. Biochemical Parameter Variability Among Breeds, Seasons and Lactation Stages

Significant differences (p < 0.05) in biochemical parameters between the three breeds over the wet and dry seasons are displayed in Table 8. However, there was no significant seasonal difference (p > 0.05) in the total protein of the Jersey breed and globulin levels of Borana and cross‐breed. Blood samples collected from the three breeds during the dry and wet seasons differed significantly (p < 0.05) in biochemical parameters. However, there was no significant difference (p > 0.05) in total protein, creatinine, urea and globulin levels in blood samples collected from cross‐breeds and Jersey breeds within the wet season. Similarly, there was no significant difference (p > 0.05) in albumin levels in blood samples collected from the three breeds within the wet season. In the same way, there was no difference (p > 0.05) in cholesterol, albumin and globulin levels in blood samples collected from Borana and Jersey breeds within the dry season.

TABLE 8.

Biochemical parameter variability between breeds and seasons (mean ± SD).

Parameters	Seasons	Borana breed	Cross‐breed	Jersey breed
Total protein (g/dL)	Wet	7.0 ± 0.06Aa	6.9 ± 0.05Ab	6.9 ± 0.04b
	Dry	6.9 ± 0.05B	6.8 ± 0.04B	6.8 ± 0.03
Creatinine (mg/dL)	Wet	1.0 ± 0.06Bb	1.6 ± 0.05Ba	1.6 ± 0.05Ba
	Dry	1.4 ± 0.10Ab	1.9 ± 0.06Aa	1.9 ± 0.07Aa
Glucose (mg/dL)	Wet	71.2 ± 1.53Ac	89.6 ± 2.01Aa	77.9 ± 1.96Ab
	Dry	59.2 ± 1.54Bc	78.0 ± 1.89Ba	69.9 ± 1.90Bb
Urea (mg/dL)	Wet	15.2 ± 0.88Ba	12.4 ± 0.51Bb	13.2 ± 0.48Bb
	Dry	18.9 ± 1.08Aa	15.6 ± 0.71Ac	16.9 ± 0.60Ab
Cholesterol (mg/dL)	Wet	164.3 ± 3.27Aa	121.3 ± 2.34Ac	157.5 ± 3.90Ab
	Dry	144.3 ± 3.35Ba	111.5 ± 2.19Bb	143.4 ± 2.52Ba
Albumin (g/dL)	Wet	3.8 ± 0.07A	3.8 ± 0.06A	3.8 ± 0.08A
	Dry	3.6 ± 0.09Bab	3.7 ± 0.09Ba	3.5 ± 0.06Bb
Globulin (g/dL)	Wet	3.2 ± 0.05a	3.0 ± 0.03b	3.1 ± 0.03Bb
	Dry	3.3 ± 0.05a	3.1 ± 0.03b	3.2 ± 0.04Aa

Note: The different superscript letters (A and B and a–c) indicate that there is a significant difference (p < 0.05) within the column and row, respectively.

Significant variations (p < 0.05) in biochemical parameters between breeds, seasons and within the same lactation phases are shown in Table 9. However, blood samples from the three breeds showed no significant variation in total protein levels across lactation stages between seasons (p > 0.05). Similarly, albumin and globulin levels showed no significant variation (p > 0.05) at all stages of lactation in the three breeds, except for early lactation in the Jersey breed between seasons. Creatinine levels in the Borana breed and in the Jersey and cross‐bred breeds with mid and late lactation did not differ significantly between seasons (p > 0.05). In addition, there were no significant differences (p > 0.05) in urea levels at the mid‐stage for the Borana breed and at the late stage of lactation for the cross‐breed across seasons.

TABLE 9.

Biochemical parameter variability between breeds and seasons (mean ± SD) in the study areas.

		Borana			Cross‐breed			Jersey
Parameters	Season	Early	Mid	Late	Early	Mid	Late	Early	Mid	Late
Total protein (g/dL)	Wet	7.3 ± 0.06a	7.0 ± 0.04b	6.8 ± 0.05b	7.1 ± 0.07a	6.9 ± 0.04ab	6.7 ± 0.05b	7.0 ± 0.07a	6.8 ± 0.05ab	6.7 ± 0.05b
	Dry	7.1 ± 0.05a	6.8 ± 0.04b	6.7 ± 0.04b	6.9 ± 0.07	6.7 ± 0.04	6.7 ± 0.04	6.9 ± 0.04	6.7 ± 0.04	6.7 ± 0.04
Creatinine (mg/dL)	Wet	1.2 ± 0.07Ba	1.0 ± 0.07Bab	0.8 ± 0.07b	1.8 ± 0.02Aa	1.6 ± 0.05ab	1.4 ± 0.04b	1.8 ± 0.04Ba	1.6 ± 0.02ab	1.4 ± 0.04b
	Dry	1.8 ± 0.07Aa	1.4 ± 0.05Ab	1.0 ± 0.07c	2.1 ± 0.02Ba	1.8 ± 0.02b	1.6 ± 0.07b	2.2 ± 0.03Aa	1.8 ± 0.07b	1.6 ± 0.10b
Glucose (mg/dL)	Wet	64.8 ± 1.02Ac	70.8 ± 1.02Ab	78.0 ± 0.71Aa	80.2 ± 0.66Ac	90.2 ± 0.66Ab	98.4 ± 0.24Aa	69.0 ± 0.45Ac	78.0 ± 0.55Ab	86.8 ± 0.37Aa
	Dry	52.8 ± 1.02Bc	58.4 ± 0.51Bb	66.4 ± 0.51Ba	69.2 ± 0.37Bc	78.4 ± 0.51Bb	86.4 ± 0.51Ba	61.0 ± 0.45Bc	70.6 ± 0.51Bb	78.2 ± 0.49Ba
Urea (mg/dL)	Wet	18.4 ± 0.51Ba	16.2 ± 0.66a	11.0 ± 0.45Bb	14.2 ± 0.80Ba	12.4 ± 0.51Bab	10.6 ± 0.40b	15.0 ± 0.45Ba	13.4 ± 0.51Bab	11.2 ± 0.37Bb
	Dry	24.0 ± 0.32Aa	18.4 ± 0.51b	14.4 ± 0.51Ac	18.0 ± 0.71Aa	16.4 ± 0.51Aa	12.4 ± 0.51b	19.2 ± 0.37Aa	17.2 ± 0.58Aa	14.2 ± 0.37Ab
Cholesterol (mg/dL)	Wet	150.8 ± 1.02Ac	162.0 ± 0.71Ab	180.2 ± 0.66Aa	110.8 ± 0.37Ac	121.0 ± 0.71Ab	132.0 ± 0.71Aa	141.4 ± 0.60Ac	154.4 ± 0.51Ab	176.6 ± 0.40Aa
	Dry	130.4 ± 0.75Bc	141.8 ± 0.66Bb	160.6 ± 0.75Ba	101.2 ± 0.58Bc	112.4 ± 0.75Bb	121.0 ± 0.45Ba	132.2 ± 0.66Bc	143.0 ± 0.71Bb	155.0 ± 0.71Ba
Albumin (g/dL)	Wet	3.8 ± 0.03	3.7 ± 0.07	3.8 ± 0.07	3.8 ± 0.05	3.8 ± 0.05	3.8 ± 0.07	3.8 ± 0.07B	3.8 ± 0.05	3.7 ± 0.08
	Dry	3.6 ± 0.07	3.5 ± 0.04	3.7 ± 0.04	3.7 ± 0.07	3.6 ± 0.05	3.7 ± 0.03	3.5 ± 0.07Ab	3.5 ± 0.04b	3.7 ± 0.04a
Globulin (g/dL)	Wet	3.4 ± 0.04a	3.2 ± 0.05a	3.0 ± 0.02b	3.1 ± 0.05a	3.0 ± 0.04ab	2.9 ± 0.04b	3.2 ± 0.05B	3.1 ± 0.04	3.0 ± 0.04
	Dry	3.5 ± 0.03a	3.3 ± 0.02a	3.1 ± 0.02b	3.2 ± 0.03	3.1 ± 0.03	3.0 ± 0.04	3.4 ± 0.04Aa	3.2 ± 0.04b	3.1 ± 0.04b

Note: The different superscript letters (A and B and a–c) indicate that there is a significant difference (p < 0.05) within the column and row, respectively.

Creatinine levels in blood samples from the cross‐breeds and Jersey breeds showed no significant difference (p > 0.05) during mid and late lactation phases during the dry season. Similarly, during the dry season, there was no significant difference (p > 0.05) in urea levels between the early and mid‐stages of lactation in the cross‐breeds and Jersey breeds.

Blood samples from the three breeds obtained during the wet season differed significantly (p < 0.05) at all stages of lactation. Nevertheless, there was no significant change (p > 0.05) in total protein levels between the mid and late lactation stages for the Borana breed or between the early and mid lactation or mid and late lactation stages for the cross‐breeds and Jersey breeds during the wet season. Similarly, creatinine levels in blood samples collected from three breeds during the wet season did not change (p > 0.05) between early and mid lactation or between mid and late lactation.

4. Discussion

4.1. Physiological Parameter Variability Between Breeds and Seasons

Physiological parameters were significantly influenced by breed (p < 0.05). The current results showed that the Borana breed had lower mean values for HR, RR and RT than the Jersey and cross‐bred breeds. This may be because the Borana breed is better adapted to hot climates, exhibiting lower physiological responses than other breeds in both seasons, particularly during high‐temperature seasons. Therefore, it is reasonable to conclude that Borana breeds are well‐suited to tropical climates. The physiological characteristics of the three dairy cow breeds differed significantly (p < 0.05). However, there were no significant differences (p > 0.05) between Jersey and cross‐bred Jersey cattle in terms of RT, HR and RR.

Physiological parameter mean values showed statistically significant seasonal variations (p < 0.05). This indicated that the seasons had a significant (p < 0.05) impact on the RT, HR and RR of dairy cows of different breeds, particularly the cross‐breeds and Jersey breeds, during the dry season (Table 2). The present findings were lower than the early findings of Abera et al. (2021), who reported 38.9 ± 0.1, 80.3 ± 1.1 and 28.0 ± 0.4 mean RT, HR and RR of the Fogera cattle calves during the long‐wet season.

The dry season had a higher average of all physiological parameters than the wet season. This could be due to the body's response to higher ambient temperatures, attempts to cool down and maintain homeostasis and possible dehydration during the dry season. A similar finding was observed by Singh et al. (2019), who reported lower values of physiological parameters during the wet season. The lowering temperatures associated with rain during the wet season may reduce physiological parameters. The lower RR could be attributed to the cooler climate (Kumar et al. 2017). The present results were lower than the early findings of Abera et al. (2021), who reported 39 ± 0.1 and 86.0 ± 0.7 mean RT and HR of the Fogera cattle calves during the dry season. This disparity could be related to the claves' less‐developed thermoregulation mechanisms for heat dissipation, higher metabolic rates and their smaller size.

Overall, the mean values for all physiological indicators differed significantly (p < 0.05) across seasons and dairy cow breeds reared under the same management practices (Table 2). This suggests that the season has a significant impact on the normal physiology of dairy cows due to seasonal variations in temperature and relative humidity, feed availability and quality and genetic adaptations, all of which can have a substantial impact on dairy cows' physiological responses. According to Kumar et al. (2017), all physiological parameters may have been raised to regulate the ambient temperature. According to Kahn (2005), normal dairy cows have mean RTs of 38.0°C–39.3°C, HRs of 48–84 breaths/min and RRs of 26–50 breaths/min. Therefore, the means of all physiological parameters in this study fell within the ranges indicated by Kahn (2005) in the Merck Veterinary Manual.

4.2. Physiological Parameter Variability Among Seasons and Lactation Stages

The interaction of breeds and seasons had a significant influence (p < 0.05) on all dairy cows' physiological indicators (Table 3). The RT increased by 0.1°C, 0.5°C and 0.4°C in the Borana, cross‐breeds and Jersey breeds, respectively, compared to the rainy season. This physiological measure is the most reliable sign for objectively monitoring animal welfare in hot conditions (Silanikove 2000; Keim et al. 2002). Burfeind et al. (2012) found similar results, noting that the mean RT was considerably greater during the hot season than during the moderate period.

The overall mean HR recorded in this study agrees with Singh et al. (2019), who reported a decreased HR during the wet season compared to the dry season. The increased HR increases blood flow to the body's surface, facilitating heat loss (Marai et al. 2007; Kumar et al. 2017). According to Kumar et al. (2017), who studied the effects of seasons on the physiological parameters of Hariana and Sahiwal cows in both wet and dry seasons, the dry season's HR was higher than the wet season's HR, which is consistent with the current findings.

Furthermore, during the dry season, the Borana, cross‐breeds and Jersey breeds increased their breathing rates by 0.2, 3.2 and 4.9 breaths/min, respectively, compared to the rainy season. This could be because the ambient temperature was higher during the dry season than during the wet season. Singh et al. (2014) found that cows' RRs increased significantly during the hot and humid seasons due to higher ambient temperatures, findings consistent with the current study. Similarly, in line with the current findings, Sailo et al. (2017) studied Sahiwal and Karan Fries cows, which had significantly higher RRs and RTs in the dry season than in the wet season.

Generally, the animals' RR will rise to increase body cooling via respiratory evaporation. Changes in the RR are the animal's adaptive reaction to maintaining homeothermic equilibrium. An increase in the RR is the first reaction when animals are exposed to temperatures over the thermoneutral zone (Maurya et al. 2007), and it is able to remove excess heat while maintaining a stable deep body temperature (Silva et al. 2015). Dalcin et al. (2016) also reported that the RR was the best indicator of heat stress among several physiological indicators. In line with the current findings, Grewal and Aggarwal (2018) found a significant difference (p < 0.05) in RRs between the hot, humid and wet seasons.

Singh et al. (2019) found that the Hariana breed had lower mean HRs (71.23 ± 3.50) and RRs (27.84 ± 2.01) during the dry season than the cross‐breeds and Jersey breeds. However, during the dry season, the mean RT of the cross‐breeds and Jersey breeds was nearly comparable to that of the Hariana breed. The current study found a slightly higher RT and RR compared to Singh et al. (2019) values of 38.2 ± 0.50 and 25.54 ± 3.38 during the rainy season. However, Singh et al. (2019) also reported a higher HR (69.23 ± 2.42) during the rainy season. The HR of dairy cows exposed to high ambient temperatures increased from 64 to 81 pulses/min (Koubkova et al. 2002).

The RR, HR and RT of the Borana breed did not differ between the two seasons (Table 3). This indicates that the Borana breed is more adaptable to its current production place. However, seasons had a significant effect on the cross‐breeds and Jersey breeds' RT, HR and RR (p < 0.05). This could be attributed to their genetic adaptation levels, metabolic demands and adaptation mechanisms. In accordance with the present results, Mcmanus et al. (2014) found that purebred Bos taurus in Brazil had a considerably greater RR and RT than the Zebu and Holstein‐Zebu cross.

There were significant differences (p < 0.05) in physiological parameters among seasons within the same lactation stages of the three breeds (Table 3). However, there were no significant differences (p > 0.05) across seasons within the same lactation stages of Borana breeds, except for RR at the early stage and HR at the mid and late stages (p < 0.05). The RT, HR and RR of the three dairy cows were higher at an early stage of lactation, followed by the mid‐stage of lactation, and slightly reduced at the late stages of lactation during both seasons. This could be due to a combination of metabolic changes associated with lactation stages; the transition period involves hormonal changes and stress factors related to calving and other external environmental factors.

4.3. Haematological Parameter Variability Between Breeds and Seasons

The current results showed that the Borana breed had lower mean values for PCV, MCV, MCH and WBC than the Jersey and cross‐bred breeds. The mean values for RBC, MCHC and Hgb were nevertheless greater. This could be attributed to a combination of genetic adaptations to their environments and physiological responses to local conditions. Seasonal variations in mean values for haematological parameters were statistically significant (p < 0.05) except for MCH (Table 4). This indicated that the seasons had a significant impact on the haematological parameters of dairy cows of different breeds. However, this finding was in disagreement with the results observed by Aengwanich et al. (2011) and Hassan et al. (2013), who found that season did not affect haematological parameters of cross‐bred cattle in northeastern Thailand.

All haematological parameters, except MCV, had significantly lower mean values during the wet season than during the dry season. The findings of the current study also concur with those of El‐Harairy et al. (2010), who found that the mean Hgb value was higher in the hot season than in the wet season. This finding was also consistent with Abdalla et al. (2011), who observed a drop in PCV during the wet season, but El‐Harairy et al. (2010) revealed higher PCV values during the hot season. Mekroud et al. (2021) reported that the WBC was higher during the dry season than in other seasons (p < 0.05), which is consistent with the present findings. The significant decrease in MCV and MCH at high ambient temperatures seen in the current study suggests that cow adaptation is associated with a reduction in cellular oxygen requirements to reduce metabolic heat load, all of which can lead to lower MCV and MCH values.

Giri et al. (2017) and Dzavo et al. (2020) found higher MCHC in dairy cattle during the dry season, which is consistent with this finding. However, this finding disagrees with those reported by Casella et al. (2012) and Abera et al. (2021), who found that cows had a significant decline in RBC count with concurrent significant alterations in Hgb during the dry season. This finding was consistent with those reported by Naik et al. (2013), who performed a similar study on Punganur cattle and revealed considerably higher RBC values in the dry season compared to other seasons and age groups. Relatively higher levels of stress in the body may be linked to the increased RBC count during the dry season, requiring greater oxygen transport throughout the body. In contrast to current findings, higher levels of RBCs were found in the Gambia during the wet season, which could be related to reduced fluid intake due to low temperature and humidity (Ruzagira et al. 2014). However, Aengwanich et al. (2011) found no significant differences (p > 0.05) in RBC between the seasons in beef cattle.

Higher Hgb values were reported in the current study during the dry season, which is consistent with an earlier report by Shrikhande et al. (2008) and Kalyani et al. (2018) and contradicts the findings of those who reported lower Hgb values during the warm season compared to other seasons (Aengwanich et al. 2011; Ruzagira et al. 2014; Giri et al. 2017; Abera et al. 2021). The higher level of Hgb during the dry season could be attributed to Fe⁺'s higher overall binding capability (Purwar et al. 2019). Badawy et al. (2008), Babeker et al. (2013) and Hamad et al. (2017) found that MCV was lower in the wet season than in the hot season, which is inconsistent with the current findings. This rise may have resulted from a negative correlation between MCV and RBC count, which fell during the hot season.

The study found no statistically significant differences (p < 0.05) in MCH levels between wet and dry seasons among dairy cattle breeds (Table 4). This could imply that MCH is predominantly determined by the animal's physiological requirements for oxygen transport, which are less affected by short‐term environmental changes such as seasonal variations.

4.4. Haematological Parameter Variability Among Breeds, Seasons and Lactation Stages

Blood samples from three breeds collected during the wet season indicated a significant difference (p < 0.05) in Hgb, MCV, MCH, MCHC, RBC and WBC levels. However, there was no significant variation (p > 0.05) in MCV, MCH, MCHC, RBC and WBC levels in blood samples collected from cross‐breeds and Jersey breeds throughout the wet season. Similarly, there was no statistically significant difference (p > 0.05) in MCH levels in blood samples collected from Borana and Jersey breeds within the wet season. Cross‐breeds and Jersey breeds have higher levels of PCV, MCV, MCH and WBC during the dry and wet seasons than the Borana breed (Table 5). This could be attributed to the combination of genetic predispositions, physiological distinctions associated with their high‐producing features, and possibly varied responses to environmental variables.

Blood samples from the three breeds showed significant differences (p < 0.05) in Hgb, MCHC and RBC levels during the dry and wet seasons. Likewise, the blood samples from cross‐breeds and Jersey breeds showed a significant difference (p < 0.05) in PCV and WBC levels during dry and wet seasons. However, there was no significant difference (p > 0.05) in PCV, MCH and WBC levels in blood samples collected from Borana breeds during dry and wet seasons. Similarly, there was no significant difference (p > 0.05) in MCV and MCH in blood samples collected from cross‐breeds and Jersey breeds during the dry and wet seasons (Table 5), which indicates that cows can adapt to adverse environmental conditions without being stressed. This finding was consistent with that of Mekroud et al. (2021), who found a significant seasonal variation (p < 0.01) in WBC and no significant seasonal changes (p > 0.05) in MCV and MCH in Holstein dairy cows in the Constantine region of northeast Algeria. This study supports Abera et al. (2021) findings that seasonal variation had a significant impact on haematological parameters.

Similarly, in agreement with these findings, previous studies have reported non‐significant variation in MCV and MCH during the different seasons in dairy cattle (Farooq et al. 2017; Nikhil et al. 2018). When animals adjust to changing climatic conditions without stress, the levels of parameters such as MCV and MCH do not change much (Saeed et al. 2021).

The current study found that WBC values were higher during the dry season, which supported previous findings (Giri et al. 2016; Farooq et al. 2017). According to Lateef et al. (2014) and Mekroud et al. (2021), the dry season may have more white blood cells because high temperatures trigger the release of corticosteroids and epinephrine, which increases the amount of white blood cells. The WBC values increase as temperatures rise (Alameen and Abdalla 2012; Mazzullo et al. 2014). This result was also consistent with the report of Purwar et al. (2019), who found that the overall WBC count in local cattle was much lower during the wet season. However, Mirzadeh et al. (2010) and Abera et al. (2021) found significantly reduced WBC levels in all age groups of Iranian cattle and in Fogera cow calves during the dry season compared to other seasons, respectively.

All haematological parameters, with the exception of MCV and MCH for Borana and cross‐breed, had increased during the dry season compared to the wet season. However, the Borana breed experienced modest alterations in haematological parameters throughout the dry season (Table 5). Dairy cattle adapted to dry and hot areas typically exhibit minimal changes in haematological parameters when subjected to high temperatures and feed shortages (Dzavo et al. 2020). However, this finding contradicts Abera et al. (2021) findings, which showed lower MCV and MCH values during the wet season and lower MCHC values during the dry season. Higher MCHC levels in dairy cattle are typically reported during the warmer months (Babeker et al. 2013; Saeed et al. 2021), which is consistent with the current finding. During the warmer season, greater MCHC values imply that animals are more tolerant of their surroundings (Yehia et al. 2021; Astuti et al. 2022).

Significant (p < 0.05) variations in haematological parameters between breeds, seasons and within the same lactation stages are displayed in Table 6. Physiological status, management system, lactation stage and season all influence the haematological profile of clinically normal lactating cows (Farooq et al. 2017; Dzavo et al. 2020). As a consequence, all of the cows had similar health status, parity and physiological stages, with only seasonal and breed variations. PCV, MCV, MCH and WBC levels in the three dairy cow breeds decreased from early to late lactation in both the wet and dry seasons. However, dairy cows' Hgb, MCHC and RBC levels increased from early to late lactation. This could be the result of a combination of metabolic shifts, hormonal changes and physiological adaptations related to milk production.

According to Kahn (2005), normal dairy cows have a mean PCV of 24%–46%, Hgb of 8–15 g/dL, MCV of 40–60 fL, MCH of 11–17 pg, MCHC of 30–36 g/dL, RBC of 5–10 × 10⁶/mcL and WBC of 4–12 × 10³/mcL. Therefore, the mean values of all haematological parameters in this study were within the ranges of reference values for healthy dairy cows suggested by Kahn (2005) in the Merck Veterinary Manual, with the exception of the MCHC of cross‐breeds and Jersey breeds during the wet season. Haematological analysis values should be compared to standard value ranges to identify unusual physiological states in cattle (Winnicka 2008; George et al. 2010). A haematological and biochemical profile within normal physiological limits indicates good health and is strongly connected with milk production (Coroian et al. 2017).

4.5. Biochemical Parameter Variability Between Breeds and Seasons

The current research indicates that the mean creatinine and glucose levels of the Borana breed were lower than those of the cross‐breeds and Jersey breeds. However, they have higher mean values for globulin, cholesterol, urea and total protein (Table 7). This could be due to local breeds having less muscle mass and different activity patterns, resulting in lower creatinine production and having evolved to eat fewer energy‐rich diets, which may be able to maintain stable glucose levels with lower intakes. In agreement with current findings, Mapiye et al. (2010) found that Nguni cattle in South Africa had much lower creatinine levels than cross‐bred cattle, which they attributed to decreased muscle mass in Nguni cattle. Cross‐bred cows have low heat tolerance, which causes slow elimination of serum creatinine from the body (Sreedhar et al. 2013).

According to Kalyani et al. (2018), the globulin levels in various indigenous breeds were higher than those in exotic breeds, which is consistent with the current findings. Creatinine is a by‐product of muscle metabolism, and its levels vary depending on muscle mass and activity (Ávila et al. 2025). Creatinine synthesis is often proportional to body muscle mass since it is a breakdown product of creatine phosphate in muscle (Gross et al. 2005). A recent study found that muscle mass has a direct effect on creatinine excretion in the urine (Whittet et al. 2019). Kalyani et al. (2018) found that indigenous cow breeds had higher globulin levels and lower creatinine levels than exotic and cross‐bred cattle breeds, indicating that indigenous cattle had a higher disease resistance trait, which is consistent with the present finding.

The mean values for biochemical parameters show significant seasonal variations (p < 0.05) in Table 7. This suggests that seasons have a major impact on biochemical parameters in dairy cows of the three breeds. The temperature difference between wet and dry seasons may explain the seasonal effects on different biochemical parameters (Ephraim et al. 2020). Several studies have proven that seasonal variation has a significant impact on several metabolic parameters (Rasooli et al. 2004; Farooq et al. 2017). Such variations in biochemical parameters are caused by altered feed intake (Bhatta et al. 2014) and lowered metabolism (Marai et al. 2007). However, this finding contradicted the findings of Aengwanich et al. (2011) and Hassan et al. (2013), who found that season did not affect biochemical parameters.

The creatinine, urea and globulin mean values were significantly higher during the dry season than during the wet season. The total protein, glucose, cholesterol and albumin levels exhibited significantly lower mean values during the dry season than during the wet season. This could be attributable to a variety of reasons, including environmental conditions, reduced intake of energy and protein‐rich feed and animal physiological responses. In addition, variations in water intake and potential heat stress can impact blood volume and protein metabolism, influencing albumin and globulin levels. This result was in line with the findings of Rasooli et al. (2004), Farooq et al. (2017), Moreira et al. (2018), Dar et al. (2019) and Abera et al. (2021), who revealed that the dry season had higher creatinine and urea concentrations and lower mean values of total protein, cholesterol and glucose concentrations than the wet season. On the other hand, Okoruwa (2014) found that heat‐stressed West African dwarf goats had noticeably higher glucose levels. This could be because heat stress activates the hypothalamic–hypophyseal–adrenal axis, which in turn causes the production of cortisol and gluconeogenesis (Marai et al. 2007).

The higher creatinine levels in the dry season could be related to urea recycling, which is a response to restricted dietary protein intake, as seen by the lower total protein in the dry season compared to the wet season (Gross et al. 2005), and excess muscle catabolism for energy supply occurs as voluntary feed intake decreases throughout these seasons (Dar et al. 2019). The lower glucose concentration in the dry season compared to the wet season could be due to the animal's increased energy requirement to disperse more heat to maintain a normal body temperature, or it could be due to heat stress‐induced depressed dry matter intake (Bhatta et al. 2014), which is consistent with the current findings.

Low plasma cholesterol levels during the dry season could be attributed to heat stress‐induced faster fat metabolism (Alameen and Abdalla 2012) or enhanced lipid mobilisation by peripheral tissues (Abeni et al. 2007). However, this finding contradicts the report of Nikhil et al. (2018), who found no significant (p > 0.05) difference in total protein, albumin, globulin and albumin–globulin ratio concentrations between seasons in the hot and humid tropics. Contrary to recent studies, Mapfumo et al. (2017) revealed that the dry season had the greatest cholesterol concentration levels from Boran and Nguni cows reared in dry arid rangeland.

4.6. Biochemical Parameter Variability Among Breeds, Seasons and Lactation Stages

The biochemical parameters of the three breeds differed significantly (p < 0.05) throughout the wet and dry seasons (Table 8). The Jersey breed's total protein and Borana and cross‐bred globulin levels showed no significant seasonal change (p > 0.05). The total protein, creatinine and albumin levels of the three breeds were higher than those reported by Kalyani et al. (2018), who studied the blood chemistry values of Indian Zebu cattle breeds such as Gir, Deoni, Ongole and Tharparkar during the same dry season, but lower than those of the Kangayam, Sahiwal and Rathi breeds.

Cows from the three breeds exhibited greater globulin levels during the dry season. The increased globulin levels during the dry season could be attributed to a reaction to nutritional stress induced by the animals' inadequate intake of crude protein, rigorous liver or renal function from the natural rangeland during this time (Grünwaldt et al. 2005). The cross‐breeds and Jersey breeds had higher amounts of creatinine and glucose than the Borana breed during the dry and wet seasons. Mapfumo et al. (2017) found that cows from the Nguni herd had significantly higher (p < 0.05) creatinine and glucose levels in both wet and dry seasons compared with the Borana herd, which is consistent with the current findings. In contrast, the cross‐breeds and Jersey breeds showed lower levels of total protein, urea, cholesterol and globulin than the Borana breed. This could be because these breeds produce more milk and grow faster, which is associated with increased muscle mass and metabolic activity, resulting in higher creatinine levels.

In addition, the higher energy requirements for milk production and growth result in higher blood glucose levels. Mapfumo et al. (2017) found that in both the dry and wet seasons, Borana cows had the highest (p < 0.05) concentration levels of total cholesterol, which is consistent with current findings. Cross‐bred cattle have been shown to store fatty adipose tissue reserves and burn them in the form of cholesterol during periods of feed energy scarcity, such as the dry season (Berman 2011), resulting in such high concentrations. Glucose levels fall when body temperature and RR rise, as is typical during the hot summer months (Ndlovu et al. 2007), which is in line with the present finding.

There was no significant difference (p > 0.05) in the urea levels between the early and mid‐lactation stages of the Borana breed or between the early and mid‐lactation and mid‐and‐late lactation stages of the cross‐bred breeds and Jersey within the wet season. There was no significant difference (p > 0.05) in albumin levels between all lactation stages in the three breeds. This result was consistent with the findings of Kalyani et al. (2018), who found that albumin values did not differ significantly between breeds. Globulin levels in the early and mid‐stages of lactation for the Borana breed and cross‐breed, as well as in the mid and late stages of lactation for the cross‐breed, did not differ significantly (p > 0.05) within the wet season.

Glucose and cholesterol levels in three dairy cow breeds increased from early to late lactation in both wet and dry seasons. This could be because the transition from early to late lactation is characterised by a shift from high energy demands for milk production to a period of rest and replenishment of body reserves, resulting in changes in glucose, cholesterol and albumin levels. The current finding was supported by a study by Abd‐El Naser et al. (2014) and Hasan et al. (2021), who revealed that glucose, urea and cholesterol levels increased as lactation progressed from early to late stages. However, dairy cows' total protein, creatinine, urea and globulin levels declined from early to late lactation. This may be related to changes in metabolic demands, nutrition and hormonal influences at different stages of lactation. This finding also aligns with that of Patel et al. (2010), who observed an increasing trend in plasma total cholesterol concentrations from calving to subsequent postpartum weeks, suggesting that the initiation of ovarian activity and the establishment of estrus cyclicity postpartum were responsible.

In line with the current findings, Dębski et al. (2017), Moreira et al. (2018) and Mohammed et al. (2021) found that early lactation significantly reduced the total protein and blood glucose content of dairy cows because of increased lactose and fat requirements for milk production. In both seasons, cholesterol concentrations decreased during early lactation and gradually increased later (Moreira et al. 2018), which is consistent with the current findings. Similarly, Hussein et al. (2020) found that the serum glucose content of high‐yielding dairy cows decreased significantly in early and mid‐lactation cows. The reduced blood glucose level observed during the early stages of lactation could be attributed to the mammary gland utilising a considerable quantity of blood glucose for lactose production (Hasan et al. 2021).

Previous research (Cavestany et al. 2005; Abd‐El Naser et al. 2014) showed that serum total protein concentrations decreased significantly as lactation progressed. These declines may reflect maternal protein needs for milking and immunoglobulin production (Roubies et al. 2006; Mohri et al. 2007). Similarly, this result agrees with the findings of Thakur et al. (2024), who reported significant decreases in total protein, globulin and urea levels from early to late lactation. As milk production increases, the overall protein utilisation efficiency declines, resulting in increased nitrogen drainage in the form of urea through urine and milk (Roy et al. 2003).

Abd‐El Naser et al. (2014) and Thakur et al. (2024) found considerably decreased creatinine and cholesterol levels during early lactation, with the highest values in non‐lactating animals, which contradicts the current findings. Hussein et al. (2020) indicated that greater creatinine levels were detected in early lactating cows due to physiological adaptation, which is compatible with the current findings.

According to Kahn (2005), normal dairy cows have a mean total protein of 6.7–7.5 g/dL, creatinine of 0.5–2.2 mg/dL, glucose of 40–100 mg/dL, urea of 10–25 mg/dL, cholesterol of 86.4–147.9 mg/dL, albumin of 2.5–3.8 g/dL and globulin of 3.0–3.5 g/dL. Despite variations between seasons, breeds and lactation stages, the mean values of all biochemical indicators in this study fell within the reference levels for healthy dairy cows given by Kahn (2005) in the Merck Veterinary Manual. This suggests that the three breeds were better adapted to the climate conditions of central Ethiopia.

5. Conclusions

The seasonal variations in physiological statuses and haemato‐biochemical parameters of three dairy cattle breeds produced in the central Ethiopian climate were studied. The seasons had an impact on the haemato‐biochemical parameters of dairy cattle breeds. Although seasonal variation in these parameters was observed during the study, the variations remained within the reference range for healthy dairy cows, indicating that the three dairy cattle breeds are adaptable to the dry and wet seasons in central Ethiopia. However, the slight difference between wet and dry seasons suggests the need to consider amelioration strategies to counteract stress due to climate change in the future. Therefore, this study may be valuable for providing baseline information on the physiological and haemato‐biochemical parameters of dairy cattle to establish various dairy cow management strategies and assess physiological status. Future studies should be conducted in similar and dissimilar ecosystems, integrating numerous cross‐breeds and indigenous breeds, to identify the best‐adapting animals under different agroecological zones and production systems.

Author Contributions

Teshome Gemechu: writing – original draft, visualisation, software, methodology, investigation, formal analysis, data curation, conceptualisation. Mitiku Eshetu: writing – review and editing, validation, supervision, resources, methodology, funding acquisition. Yesihak Yusuf Mummed: writing – review and editing, validation, supervision, resources, project administration, methodology. Ulfina Galmessa: writing – review and editing, validation, supervision, methodology. Aemiro Kehaliew: writing – review and editing, validation, supervision, methodology.

Funding

This work was carried out with the support of Haramaya University under grant code number HURG_2021_01_03_99.

Ethics Statement

Animal experiments were carried out strictly in accordance with the relevant Guidelines for the Care and Use of Laboratory Animals. The Research and Ethics Committee of the School of Animal Science approved the protocol (Ref. AnSc/256/182/2022). The institution that owns the dairy cattle farm provided formal authorisation for this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The corresponding author may make the study's datasets public upon reasonable request.