RESEARCH ARTICLE

 

Do haematological profiles of cows in drought prone areas differ with conformation?

 

Takunda Dzavo (Dzavo, T)

Bindura University of Science Education, Faculty of Agriculture and Environmental Science, Dept. Animal Science, Private Bag 1020, Bindura, Zimbabwe.

Titus J. Zindove (Zindove, TJ)

Fiji National University, School of Animal and Veterinary Sciences, Dept. Animal Science, P.O Box 7222, Nasinu, Fiji.

Marshall Dhliwayo (Dhliwayo, M)

Bindura University of Science Education, Faculty of Agriculture and Environmental Science, Dept. Animal Science, Private Bag 1020, Bindura, Zimbabwe.

Michael Chimonyo (Chimonyo, M)

University of Kwa-Zulu Natal, School of Agriculture, Earth and Environmental Science, Animal and Poultry Science, P. Bag X01, Scottsville 3209, Pietermaritzburg, South Africa.

Musavenga T. Tivapasi (Tivapasi, MT)

University of Zimbabwe, Dept. Veterinary Science, P.O. Box MP 167, Mount Pleasant, Harare, Zimbabwe.

 

Abstract

Aim of study: Severe and frequent droughts have resulted in loss of thousands of rangeland cattle worldwide. The objectives of the current study were to assess the reduction in dimensionality of seven conformation traits and to determine the relationships between extracted constructs and haematological parameters associated with drought resilience in beef cows.

Area of study: Muzarabani district, Zimbabwe.

Material and methods: Fifty multiparous Mashona cows kept on natural rangelands were used. The collinearity of seven conformation traits of the cows was reduced using principal component analysis. The relationships between the principal components and hematological profiles of the cows were subsequently determined using regression analysis.

Main results: First extracted principal component described body capacity (body depth, flank circumference, chest girth). The second component described the frame size (stature and body length) of the cows and the third component was comprised of sheath height and dewlap size. Cows characterised by deep bodies, large flanks and chest girths had low percent haematocrit (HCT), mean capsular haemoglobin concentration (MCHC) and red cell distribution (RDW) (p<0.05). Small-framed cows were associated with low mean platelet volume (MPV), HCT, MCHC and RDW levels in blood (p<0.05). As principal component 3 of conformation traits increased, white blood cell count, mean corpuscular volume, RDW and MPV decreased (p<0.05).

Research highlights: Small-framed cows with large thoracic capacities, large dewlaps and belly bottoms far away from the ground surface are able to maintain haematological normalcy under rangelands in drought prone areas.

Additional key words: body conformation; haemoconcentration; season; coat colour

Abbreviations used:   BCS (body condition score); BD (body depth); BL (body length); DL (dewlap size); EOS (eosinophil); FBC (full blood count); FC (flank circumference); HCT (percent haematocrit); HG (heart girth); HGb (haemoglobin); LYMP (lymphocyte count); MCH (mean capsular haemoglobin); MCHC (mean capsular haemoglobin concentration); MCV (mean cell volume); MON (monocytes); MPV (mean platelet volume); NEUT (neutrophils); PCV (packed cell volume); PLT (platelet count); RBC (red blood cell count); RDW (red cell distribution); SH (sheath height); ST (stature); WBC (white blood cell count).

Authors’ contributions: Obtained funding: TJZ. Collected data: TD, TJZ and MT. Performed data analyses and drafted the manuscript: TJZ and TD. Corrected and edited manuscript: TJZ, MC and MD. All authors read and approved the final manuscript

Citation: Dzavo, T; Zindove, TJ; Dhliwayo, M; Chimonyo, M; Tivapasi, MT (2020). Do haematological profiles of cows in drought prone areas differ with conformation? Spanish Journal of Agricultural Research, Volume 18, Issue 2, e0604..https://doi.org/10.5424/ sjar/2020182-16029

Received: 15 Nov2019. Accepted: 24 Jun 2020

Copyright © 2020 INIA. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC-by 4.0) License.

Funding agencies/institutions Project/Grant
Bindura University of Science Education Research and Postgraduate Centre, Zimbabwe RBGA/07/2016

Competing interests: The authors have declared that no competing interests exist.

Correspondence should be addressed to Titus J. Zindove: zindovetj@gmail.com


 

CONTENTS

Abstract

Introduction

Material and methods

Results

Discussion

Acknowledgements

References

IntroductionTop

 Cattle play important food security and socio-cultural roles in both developed and developing regions (Nyamushamba et al., 2017). Most of these cattle, especially in sub-tropical regions, are exposed to harsh production environments that negatively affect their productivity. Droughts are one of the major threats to cattle production in sub-tropical regions (Scasta et al., 2015a). Cattle, due to their high maintenance requirements, are vulnerable to water and feed shortage during drought periods (Sejian et al., 2016). Owing to frequent and intense droughts, cattle in sub-tropical regions are prone to hydric stress, immunosuppression, nutritional stress and, potentially, death (Dzavo et al., 2019).

 Some cattle breeds, usually those native to drought stricken regions, have been reported to be resilient and/or resistant to diseases and parasites, hydric stress and nutritional tress (Mwai et al., 2015). One such breed is the Mashona breed, a popular beef breed in the sub-Sahara which is native to Zimbabwe. These breeds have, however, been diluted by fast growing genotypes which have been gaining popularity in most communal setups (Zindove et al., 2015). Crossing adapted native genes with fast growing genotypes has resulted in nondescript herds with altered adaptability to harsh environmental conditions (Scasta et al., 2015a; Dzavo et al., 2019). In the wake of frequent droughts and heat waves, it is important to quantify animal-specific drought mitigation strategies. Commonly reported strategies to mitigate drought effects on cattle include sprinkling and shed provision to cool the cattle, supplementation during times of feed shortage and use of borehole water for cattle to drink (Dzavo et al., 2019). Genetic selection for drought resilience in cattle has received limited attention (Scasta et al., 2015a). The advantage of genetic selection for resilience, in contrast to management improvements, is that it is cumulative and affects all subsequent generations of cattle. There is need to select for improved robustness in cattle herds prone to droughts.

 Genetic variation in resilience to drought components such as nutritional and hydric stress has been demonstrated in cattle (Takeda et al., 2018; Osei-Amponsah et al., 2019) implying that selection for drought resilience in cattle is possible. The genetic architecture underlying resilience to drought in cattle is, however, poorly understood (Osei-Amponsah et al., 2019). Use of molecular tools in selection for resilience to drought is, therefore, difficult. Phenotypic selection methods maximize selection gain based on available data. Published estimates of the heritability of feed conversion efficiency in beef cattle ranges from 0.20 to 0.35 (Menezes et al., 2018; Takeda et al., 2018) whilst those for water intake efficiency as defined by water to weight gain ratio ranges from 0.12 to 0.47 (Menezes et al., 2018; Ahlberg et al., 2019). The high he ritabilities for feed conversion and water intake efficiency indicate that phenotypic selection to improve resilience to nutritional and hydric stress which are proxies of drought resilience in beef cattle is feasible. Optimizing drought resilience in beef cattle, however, still remains one of the most significant challenges of extensive beef producers in drought prone areas (Dzavo et al., 2019). This could be due to the fact that drought resilience is a very complex trait consisting of different resilience types. The challenge is to weigh the value of the various measures of drought resilience to pinpoint the most suitable and appropriate measure. It is difficult to define, record and select for all the components of drought resilience. Measurement and recording of the component traits linked to drought resilience is costly, laborious and time consuming (Dzavo et al., 2019).

 Haematological blood tests are primarily used as indicators of hydric stress, nutritional stress and to detect diseases in cattle (Brucka-Jastrzębska et al., 2007). The haematological parameters are, thus, an important tool that helps beef and dairy cattle producers to monitor the health of their cattle. To identify abnormal physiological situations in cattle, values from blood analysis are compared with ranges of standard values (Cozzi et al., 2011; Roland et al., 2014). Despite the potential usefulness, reference values for haematological values in beef cattle are presented in literature less frequently. A few studies on reference values for haematological values in beef cows used records from open cows (Doornenbal et al., 1988). The most popular blood diagnostic test for health status in cattle is the blood count determination, which includes erythrocyte, leukocyte and thrombocyte counts, as well as haemoglobin content, haematocrit value and red blood cell parameters (Singh et al., 2013). These parameters are also influenced by hydric and nutritional stress in cattle (Radkowska & Herbut, 2014). Changes in haematological parameters are, thus, possible proxies to predict potential resilience of cattle to hydric, nutritional and pathological stresses in cattle. Drought-resilient cattle are expected to manifest the least changes in haematological parameters when subjected to extreme temperatures, feed shortages and pathological stress (Radkowska & Herbut, 2014). The use of haematological parameters as indictors for drought resilience in cattle is very limited possibly because recording of the haematological parameters requires blood sample collection and analyses which is time-consuming, expensive and labour intensive. Use of conformation traits as indirect indicators of drought resilience in cattle could be an option to improving drought resilience in herds in drought prone regions. There is no data on the relationship between conformation traits with drought resilience as indicated by haematological profiles of cattle.

 Conformation traits describe measurements for a range of visual characteristics of an animal and are related to its anatomical and skeletal appearance (Zindove et al., 2015). There are suggestions that conformation of animals is related to their functional traits such as health, reproduction and survival (Berry et al., 2004; Zindove et al., 2015). In their study on phenotypic and genetic relationships between the conformation traits such as body depth, body length, stature, flank circumference and chest girth and reproductive performance of cattle, Zindove et al. (2015) concluded that small-framed cows are more fertile than their large framed counterparts due to adaptability to harsh environmental conditions such as extreme temperatures. Likewise, Forabosco et al. (2004) reported that small-framed cows have greater longevity and stability, and are more productive than the large-framed counterparts under continental climates. There is, however, no data on the relationship between conformation traits and adaptability to harsh environmental conditions.

 There is need to determine the relationships between conformation traits and haematological profiles before basing selection decisions on conformation with the intention of breeding for improved drought resilience in cattle. Since there are no proper recording programmes yet for haematological profiles and conformation traits,hence a dearth of pedigree information, determination of genetic relationships is difficult. Determining the phenotypic relationships among conformation and haematological profiles can lay the foundation for future exploration of genetic correlations involved. Once the relationship between conformation traits and haematological profiles is established, drought resilient cattle can be bred based on conformation. Using body conformation to select for drought resilience can be very simple and effective. Conformation traits can be recorded and selected for at an early stage and, thus, early selection can be facilitated. Farmers who do not keep records can also indirectly select for drought resilience using conformation traits through visual appraisal.

 There is a wide range of conformation traits which can be potentially used in selection for adaptability in cattle and most of the traits are highly correlated (Zindove et al., 2015). Conformations traits of beef cattle have been reported to be strongly correlated with Pearson correlations ranging from 0.5 to 0.81 amongst body depth, stature, chest circumference, body length and flank circumference being reported (Parés-Casanova & Mwaanga, 2013; Zindove et al., 2015). The strong correlations between conformation traits suggest that there is redundancy. Given the collinearity and apparent redundancy between the conformation traits, it is likely that all the conformation traits which are considered during selection of cows do not really measure different constructs. If conformation traits are to be used in selection, there is need to combine them into constructs that describe conformation of the cattle so as to do away with redundancy and collinearity. Identifying dependencies between conformation traits also helps to reduce chances of over- or underestimation of target drought resilience traits during indirect selection using conformation of the cattle. Collinearity of predictor variables can cause wrong identification of relevant predictors and it can be removed using principal component analysis (PCA) (Dormann et al., 2013). After grouping them into principal components, relationship between the conformation traits and indicators of drought resilience can be analysed without redundancy and collinearity. The objectives of the current study were to assess the reduction in redundancy and collinearity of seven conformation traits using PCA and to determine the relationships between extracted principal components and haematological parameters associated with drought resilience in beef cattle.

Material and methodsTop

 Animal handling adhered to guidelines by the Zimbabwe Scientific Animal Act, 1963, subsection 2 of section 4, License Number L624.

 Study site

 The study was conducted in Muzarabani district of Mashonaland Central province, Zimbabwe. The district is located at 310 05’ E and 160 25’ S and lies at an altitude of 500 m above sea level (Musiyiwa et al., 2014). The area experiences a semi-arid climate characterized by two distinct seasons; the hot-wet season which occurs from September to April and a cool-dry season during the rest of the year (Katanha & Masocha, 2014). The land surface is largely dry and barren in the cool-dry season. During the hot-wet season, the area is predominated by pasture species unpalatable to domestic livestock. The area is characterised by extremely high temperatures with highest mean monthly maximum temperature of 37 ˚C in February (Mugandani et al., 2012). Rainfall is erratic, averaging 600 mm per annum (Chanza & de Wit, 2014). Frequent droughts are common during the hot-wet season (Musiyiwa et al., 2014). The vegetation type of the area is mainly Mopani bush veld and foothill wooded grasslands (Chanza & de Wit, 2014).

 

 Sampling and conformation traits measurement

 A total of 10 farmers with at least 20 multiparous Mashona cows participated in the study. The farmers were selected using the snow-ball sampling technique (Zindove& Chimonyo, 2015). The cows were kept on communal natural rangelands. A trained individual measured conformation traits between November 2017 and June 2018. Measurements were taken on dipping days between 1000 h in the morning and 1500 h in the afternoon once in each season; hot-wet season and cool-dry season. A trained individual identified 5 Mashona cows from eachof the farmers’ herd. The pregnancy status of each cow was then determined by the veterinarian. Only open cows were used for the study. A total of 50 cows from parity 3 to 8 were used. The cows were certified as clinically healthy by a veterinarian. The breed score and body condition score (BCS) of each of the identified cows were then determined. The breed score was determined using a 1 – 9 score based on nine physical characteristics of Mashona cattle (Assan, 2012). The following characteristics were considered: 1: medium frame (stature <170 cm), 2: less developed dewlap and umbilical fold, 3: long tail that almost touches the ground, 4: small rump drooping towards tail, 5: small and prominent eyes, 6: almost non-existent cervico-thoracic hump, 7: small ears with a sharp apex, 8: small udders and teats, 9: short, straight, close and glossy hair. Each cow was given a score corresponding to the number of descriptive characteristics it possessed. For Mashona cattle to be registered with Mashona Cattle Society of Zimbabwe, they should meet at least five of the nine physical characteristics in addition to other minimum performance requirements (Hall, 1998). Assan (2012) reported that Mashona cows that meet at least five of the physical characteristics have the same growth and reproductive characteristics. A cow was, therefore, considered for this study only if it met at least f ive of the nine characteristics. The five-point European system was used to determine the BCS, where a score of 1 is emaciated and a score of 5 is very fat (Scott, 2017). The cows were held in a cattle crush and restrained in a head bail for measurement and were ear tagged for identification.

 Before taking the measurements, each farmer was interviewed on number of calvings of each cow and the coat colour was recorded. The measurements taken are described in Table 1. All conformation measurements were taken using a plastic tape measure. Dentition was used to determine the age of each cow. Cows with visible incisors were categorised as young whilst those with broken or gummy mouth were categorised as old (Raines et al., 2008).

 

 Collection of blood and analyses

 Blood samples were collected from the jugular vein on a single day during hot-wet (November 2017) and cooldry season (June 2018) by a veterinarian. The blood samples were collected in the morning (10:00 h) and afternoon (15:00 h) for each cow. The cows were withheld using a crush pen. Ethylene diamine tetra-acetate (EDTA)-coated vacutainer tubes were used for blood collection. The tubes were stored in cooler boxes with ice blocks and transported to the Diagnopath laboratories, Harare, Zimbabwe, for analyses.

 A mindray BC5150 (Shenzhen, P.R. China) automatic cell counter was used to perform full blood count (FBC), lymphocyte count (LYMP), white blood cell count (WBC), red blood cell count (RBC), haemoglobin (HGb) concentration, percent haematocrit (HCT), mean cell volume (MCV), mean capsular haemoglobin (MCH), mean capsular haemoglobin concentration (MCHC), platelet count (PLT), red cell distribution (RDW), mean platelet volume (MPV), neutrophils (NEUT) count, monocytes (MON) count and eosinophil (EOS) count).

Reagents used in the analyses were a diluent, DIFF lyse and LH lyse and probe cleanser. The lysing agent destroys red blood cells and at the same time shrinks the leukocytes cell membrane and cytoplasm for easier determination of the leukocytes. The full blood cell analysis was divided into three main groups (i.e., leukocytes; erythrocytes and thrombocytes). The WBC was measured by the Mindray BC5150 automatic cell counter within 24 h of blood collection. The flow cytometry + tri-angle laser scatter + chemical dye method was done for WBC 5-part differential analysis and WBC counting. In the flow cytometry method used, a fluorescent chemical dye was applied, then the blood films were analysed under a triangle laser scatter beam as described by Furie et al. (2011). Peroxidase reagents were used to distinguish between peroxidase-positive cells, such as neutrophils, eosinophil and monocytes.

 Erythrocytes that included RBC, HCT and red blood cell indices such as MCV, MCH, MCHC and RDW were analysed through the improved DC impedance method. In the improved DC impedance method, sample tubes containing EDTA anti-coagulated blood were diluted automatically with an isotonic solution prior to analysis. The diluent was forced through an aperture as cells which passed one after the other through the capillary opening. Electrodes on each side of the aperture which had direct current passed through these electrodes. The passing cells would then produce a change in the direct current resistance and thus an electronic signal which is proportionate to its volume. The cells were then identified based on their size and represented in a volume distribution curve, which was defined by the sum of impulses within a certain size distribution. The HCT and packed cell volume (PCV) was determined indirectly from the average size and number of RBCs through the coulter impedance principle as described by Wennecke (2004). The other red blood cell indices (i.e., MCV, MCH, MCHC and RDW) were then automatically determined by the Mindray BC5150 analyser. The refractive index measurements made it possible to distinguish platelets from particles of similar size, thus providing an accurate platelet count (Harris et al., 2005).

 The HGb was analysed using the Mindray BC5150 machine and the cyanide free reagent method was employed. The principle behind this method as described by Chakravarthy et al. (2012) is based on the conversion of all haemoglobin and haemoglobin derivatives, in the presence of a non-ionic detergent, into a stable end product, which shows an absorption maximum at 575 nm. The reaction mixture was then automatically analysed to determine HGb concentration in g/dl.

 

 Statistical analyses

 All data were analysed using SAS (2012). The effect of breed score on conformation traits was determined using PROC GLM. The effects of parity of cow, season, coat colour, age of cow, time of the day and BCS on LYMP, WBC, RBC, HGb, HCT, MCV, MCH, MCHC, PLT, RDW, MPV, NEUT, MON and EOS were determined using PROC GLM for repeated measures assuming fixed models with all possible first-order interactions. First-order autoregressive correlation (AR (1)) was fitted to the model. The model for the final analysis was obtained after eliminating interactions that were not significant (p>0.05).

 The PCA was used to organise conformation traits into relatively homogeneous groups called principal components. The matrix of partial correlations, Kaiser Statistic for sampling adequacy was used to determine the degree of interrelations between variables and adequacy for use in principal component analyses. Principal components were chosen based on Kaiser’s eigenvalue rule which states than only principal components with eigenvalues greater than one are considered (Cattell, 1966). Principal components were rotated using varimax rotation. The principal components weights of greater than 0.55 were considered to indicate a significant correlation between traits and principal components (Cattell, 1966).

To cater for differences in scales of measurements of the conformation traits, each trait was scored on a scale of 1 to 9, inclusive, according to extremes of the direct measurements for analysis purposes. For example, for body length, a score of 1 meant the cow was among those with the longest bodies and a score of 9 meant the cow was among those with the shortest bodies in the sample. The PROC REG (SAS, 2012) was then used to test whether the relationships between haematological parameters and principal components extracted from conformation traits were linear, quadratic or exponential.

 

Table 1. Description of the conformation measurements taken on Mashona cows

BIF: Beef Improvement Federation

ResultsTop

Effect of season, time of the day, coat colour and breed score on haematological properties

The effects of breed score on conformation traits in Mashona cows are shown in Table 2. Breed score had no effect on BCS, heart girth, sheath height and dewlap size (p>0.05). Cows with a breed score of 5 were shortest with widest flanks, longest and deepest bodies (p<0.05). Significant levels of parity, season, coat colour, time of the day and body condition score on hematological parameters are shown in Table 3. Season and time of the day had significant effects on WBC, HGb, HCT, MCH, MCHC, MPV, NEUT, LYMP and EOS blood levels. Breed score affected WBC, RBC and HCT whilst BCS affected WBC, RBC, MCV and EOS (p<0.05). Coat colour had significant effect on HGb, MCV and MPV. Season and time of the day had an interaction on HCT, MCV and MCH (p<0.05). Season and coat colour had an interaction on MCH, MON and EOS (p<0.05). The WBC, HGb, HCT, MCH, MCHC, MPV, NUET, LYMP and EOS levels in blood were high during the hot-wet season and during the morning (p<0.05; Table 4). Black colored cows had higher HGb, MCV and MPV than their brown counterparts (p<0.05). The RBC, MCV and EOS generally decreased as BCS increased (p<0.05; Table 4). Variations of MCH, MCV and HCT with day time during the cool-dry and hot-wet season are shown in Fig. 1. Du-ring the cool-dry season, MCH, MCV and HCT levels in blood were the same throughout the day (p>0.05). During the hot-wet season, cows had high MCH, MCV and HCT levels in blood in the afternoon (p<0.05). Seasonal variations in MCH, MON and EOS levels in blood of black and brown cows are shown in Fig. 2. Du-ring the cool-dry season, MCH, MCV and HCT levels in blood were the same in back and brown cows (p>0.05). Black colored cows had high MCH, MON and EOS levels in their blood during the hot-wet season (p<0.05).

 

Table 2. Effects of breed score on body condition score (BCS), body depth (BD), stature (ST), body length (BL), flank circumference (FC) heart girth (HG), sheath height (SH) and dewlap size (DL) in Mashona cows (n = 200).

Values of in a column with different superscript significantly differ (p<0.05)

 

Principal components extracted from conforma-tion traits of Mashona cows

Principal component pattern coefficients of the vari-max rotated components and aggregated confirmation trait groups using PCA are shown in Table 5. About 53.79 % of the overall variability of the conformation traits of the cows concentrated in three extracted principal components. Principal component 1, 2 and 3 accounted for 28.82 %, 14.55 % and 10.42 % of the total variance, respectively. Body depth, flank circumference and chest girth had significant principal component weights in prin-cipal component 1 (PC1; principal component wei-ghts>0.55). Principal component 2 (PC2) was comprised of stature and body length whilst principal component 3 (PC3) was comprised of sheath height and dewlap size (principal component weights>0.55). Body depth had the highest communality (0.72) and sheath height had the least (0.5).

 

 

Table 3. Significant levels for effects of parity, season, coat colour, age, time of the day and body condition score on haematological properties of Mashona cows (n = 200)

WBC= white blood cell count; RBC= red blood cell count; HGb= haemoglobin (g/dl); HCT= haematocrit (%); MCV= mean cor-puscular volume; MCH= mean corpuscular haemoglobin; MCHC= mean corpuscular haemoglobin concentration (g/dl); PLT= pla-telet count; RDW= red cell distribution (%); MPV= mean platelet volume; NEUT= neutrophil count; LYMP= lymphocytes count; MON= monocytes count; EOS= eosinophil count and BS= breed score; BCS= body condition score and CC= coat colour. *p<0.05; **p<0.01; NS: not significant (p>0.05)

 

Tabla 4. Least square means (±SE) of season, time of the day coat colour and age on haematological parameters of Mashona cows (n = 200).

WBC= white blood cell count; RBC= red blood cell count; HGb= haemoglobin (g/dl); HCT= haematocrit (%); MCV= mean corpus-cular volume; MCH= mean corpuscular haemoglobin; MCHC= mean corpuscular haemoglobin concentration (g/dl); PLT= platelet count; RDW= red cell distribution (%); MPV= mean platelet volume; NEUT= neutrophil count; LYMP= lymphocytes count; MON= monocytes count; EOS= eosinophil count and BS= breed score; BCS= body condition score and CC= coat colour. Values of each parameter in a column with different superscripts differ (p<0.05)

 

Tabla 4. Continued

WBC= white blood cell count; RBC= red blood cell count; HGb= haemoglobin (g/dl); HCT= haematocrit (%); MCV= mean corpus-cular volume; MCH= mean corpuscular haemoglobin; MCHC= mean corpuscular haemoglobin concentration (g/dl); PLT= platelet count; RDW= red cell distribution (%); MPV= mean platelet volume; NEUT= neutrophil count; LYMP= lymphocytes count; MON= monocytes count; EOS= eosinophil count and BS= breed score; BCS= body condition score and CC= coat colour. Values of each parameter in a column with different superscripts differ (p<0.05)

 

Relationships between principal components ex-tracted from conformation traits and hematologi-cal parameters of the cows

Relationships between principal components extracted from conformation traits and haematological parameters of the cows are shown in Table 6. The PC1 had negative linear and quadratic relationships with RDW (p<0.05). As the PC1 of conformation traits increased HCT, MCHC and RDW decreased quadratically (p<0.05). Cows cha-racterised by deep bodies, large flanks and chest girths tended to show low MCHC, HCT and RDW levels in blood (p<0.05). The PC2 of conformation traits had signi-ficant positive linear and quadratic relationships with MPV, HCT, MCHC and RDW levels in blood (p<0.05). Small-framed cows were associated with low MPV, HCT, MCHC and RDW levels in blood (p<0.05). As PC3 of conformation traits increased, WBC, MCV, RDW and MPV decreased (p<0.05).

DiscussionTop

Determination of haematological parameters of cattle is important in establishing their metabolic, health, nutri-tional and physiological status. The interpretation of hae-matological parameters hinges on the reference values de-termined using healthy cattle under similar environmental conditions (Doornenbal et al., 1988; Alberghina et al., 2011; Cozzi et al., 2011). When identifying cattle with hematologic abnormalities, it is important to use reference range generated from a group of healthy cattle with similar physiological characteristics (Roland et al., 2014). Published haematological reference values on beef cows used records from open cows (Doornenbal et al., 1988) and, thus, to establish an accurate comparison, this study used records on open cows.

Although the WBC, HGb, HCT, MCH, MCHC, MPV, NUET, LYMP and EOS levels in blood were high during the hot-wet season, they dropped back to the range previously reported in cattle under semi-arid conditions (Mazzullo et al., 2014; Roland et al., 2014). Although the period between October and February is classified as the hot-wet season is semi-arid areas, it is now characterised by high temperatures, low rainfall, dry spells and frequent droughts due to climate change over the past decade (Descheemaeker et al., 2018; Dzavo et al., 2019). High WBC, LYMP, EOS and NEUT during the hot-wet season indicate increased immune activity. Heat stress coupled with nutritional and hydric stress during hot and dry periods can result in sick cattle (Broucek et al., 2009; Ajeet & Meena, 2011; Giri et al., 2017). The same reason can be attributed to high MPV during the hot-wet season. In agreement with our findings, previous studies have reported health problems in cattle exposed to high environmental temperatures, hydric and nutritional stress (Khan et al., 2011; Kamal et al., 2016; Giri et al., 2017). The observation that HGb and HCT levels in blood of the cows were high during the hot-wet season suggests possible haemoconcentration due to reduced plasma volume as a result of reduction of plasma water due to low water intake coupled with increased evaporative cooling (Lamp et al., 2015). The same reasons could explain observed high HCT levels during the afternoon in the hot-wet season. Similary, Obitsu et al. (2011) reported an increase in HGb and HCT levels in dairy cows during the hot seasons. High MCH and MCHC levels during the hot-wet season could be attributed to high HGb. High MCH can be due to normal sized cells with high HGb concentrations (Lamp et al., 2015). Higher MCH and MCHC in cattle during the hot-wet season were also reported by Giri et al. (2017).

 Black cows feel the effects of heat earlier and more than brown and white coloured cows (Atrian & Shahryar, 2012). Black cows are also more vulnerable to hydric stress than their light coloured counterparts because they have high water requirements due to high susceptibility to heat stress (Brown-Brandl, 2018). It can be inferred that black coloured cows had high MCH, MON and EOS during the hot-wet season than their brown coloured counterparts because they were heat stressed and dehydrated. Heat stress and dehydration triggers immune response as indicated by high EOS levels (Giri et al., 2017). The MCH and MON are among the primary indicators of heat and hydric stress in cattle (Pandey et al., 2017). The same reasons could be attributed to high HGb in black cows during the hot-wet season observed herein. Haemo-concentration, resulting in high HGb levels, is more pronounced in dark coloured cattle when environmental temperatures are high (Caulfield et al., 2014). High MCV in black cows during the hot-wet season could be due to increase in cellular oxygen requirements as a result of increased respiration and perspiration (Mazzullo et al., 2014). Increased MCV in black cows during the how-wet season may also indicate vitamin B12 deficiency (Mazzullo et al., 2014). Nutrient deficiencies in cattle under natural rangelands can be exacerbated by thermal stress which reduces feed intake (Nardone et al., 2010). Cows with low BCS had high RBC, MCV and EOS probably because of heat, hydric and/or nutritional stress. Similarly, Mazzullo et al. (2014) reported high levels of haematological parameters such as EOS and MCV in thin and emaciated cattle.

 Classifying cattle for conformation can be an important tool in cattle breeding and can complement use of records which is expensive, time consuming and labour intensive. Reducing collinearity of conformation traits by grouping them into principal components can reduce chances of over and/or underestimation of the adaptive potential of the cows during indirect selection. Our findings that three components were extracted tallies with Zindove et al. (2015) who extracted two factors describing the conformation of Nguni cows in South Africa. Differences in the number of extracted components could be due to differences in number of variables used. The components of PC1, body depth, flank circumference and chest girth, represents body capacity of the cows (Zindove et al., 2015). In agreement with our inferences, Parés-Casanova & Mwaanga (2013) highlighted that body depth and flank circumference are strong indicators of cattle’s rumen capacities. Body stature and body length which were components of PC2 describes the frame size cows (Pundir et al., 2011). Although sheath height and dewlap size were grouped together in PC3, there is dearth of literature describing them as conformation traits in cattle. The two traits are, however, very important adaptation traits in cattle under extreme temperatures and hydric stress (Hansen, 2004; Zindove et al., 2015).

 The results of the present study suggest that there is a negative relationship between body capacity of cows and MCHC and RDW values and a positive relationship between frame size of cattle and HCT, MCHC, MPV and RDW. Similar to our findings, the HCT and MPV values of Muturu cattle, a small-framed breed, were reported to be generally lower than Bunaji cattle a large-framed breed in Nigeria (Ode et al., 2017). It is however important to note that, unlike our study, the differences reported by Ode et al. (2017) were between breeds. Since both breeds are considered adapted to environmental conditions of the study area (Adebambo, 2001), the differences in the haematological parameters of the two breeds were largely attributed to their morphological differences rather than genetics (Ode et al., 2017). No direct analysis of relationship between haematological profiles and morphology in the subjects was performed and, therefore, it was a tentative explanation of the observed differences. To our knowledge, no similar studies have been performed within cattle breeds. The observed decrease in MCHC and RDW as the body capacities of cows increased and decrease in HCT, MCHC, MPV and RDW as frame size decreased may possibly play a role in the ability of small-framed cattle with large body capacities to strive in stressful conditions such as drought.

 The observation that MCHC and RDW decreased as PC1 increased is an indication that cows with large body and rumen capacities are less likely to experience nutritional deficiencies and health problems under stressful conditions such as drought. Scasta et al. (2015b) and Zindove et al. (2015) argued that large body and rumen capacities helps cattle in drought prone areas to thoroughly digest low quality forage efficiently due to potentially longer passage rates and, thus, meet their nutritional requirements. Deep bodies coupled with large flanks and chest girths are associated with plenty space for the rumen and digestive system (visceral mass) hence affects the food ingestion, digestion and assimilation capacity of a cow which in turn influences nutritional and health status of the cows (Dubey et al., 2012). Cows with small body capacities are more likely to struggle to meet nutritional requirements during times of feed shortages such as drought periods or when grazed on rangelands with pastures of poor nutritional value (Zindove et al., 2015). It can, thus, be inferred that cows with deep wide bodies highly suits areas that are experience frequent and severe droughts.

 Decrease in HCT as cows’ bodies, flanks and chests increased in size was unexpected. Large body capacity, as denoted by deep bodies, wide flanks and chests, is associated with plenty space for the rumen and digestive system and this, in turn, affects fermentation rates (Dubey et al., 2012). As the body capacity increases the gut capacity also increases and, thus, the cows can consume large amounts of dry matter and fermentation rates are expected to be high (Hansen, 2004). High fermentation rates are associated with increased metabolic heat (Kumar et al., 2016). Thus, contrary our findings, cows characterised by deep bodies in addition to large flanks and chest girths are expected to have high HCT since they produce more heat during fermentation which causes haemoconcentration under hot and dry environments (Phillips, 2018). Haemoconcentration results in high HCT levels in blood (Sreedhar et al., 2013). The unexpected low HCT in cows with large body capacities could be because of the interactions between chest girth and heat dissipation by the cows. Cattle with large chest girths are able to inhale and exhale large air volumes thereby losing much heat to the environment through the vapour released in the breath (Brown-Brandl, 2018). This implies that low HCT in cows with large flanks and chests might be due to better ability to disperse heat and not due to reduced heat production.

 The positive relationship between PC2 (body length and stature) and HCT, MCHC, MPV and RDW maybe a reflection on the interactions between nutritional status, health status and frame size in cattle kept under drought prone areas. High HCT, MCHC, MPV and RDW levels in cattle are an indication of nutritional and health problems such as iron-deficiency anaemia, immune mediated thrombocytopenia and haemo-concentration (Roland et al., 2014). As PC2 increased, hence frame size, HCT, MCHC, MPV and RDW in cattle increased. This implies that small-framed cows are more adapted to heat and nutritional stress compared to their large-framed counterparts. This can be attributed the facts that the rate of heat transfer between cows and the environment is proportional to the body surface area (Alfonzo et al., 2016). The large the body surface area the higher the amount of heat the cows absorb from the environment when environmental temperatures are high (Alfonzo et al., 2016). Scasta et al. (2015a) suggested that small-framed cattle have less heat tolerance than their large-framed counterparts because of differences in metabolic rate. Metabolic heat production in cows is a function the size of the internal surfaces of their digestive organs. The larger the frame size, the larger the size of the internal surfaces of their digestive organs and, thus, the more metabolic heat is produced (Gillooly et al., 2001). The observed low adaptation to heat stress in large-framed cows as indicated by high HCT, MCHC and MPV levels could, therefore, be due to high metabolic heat production by these animals. Basing on our findings, low heat absorption from the environment coupled with low metabolic rate and, thus, reduced heat production helps to maintain heat balance in small-framed cows. Small-framed cattle are, therefore, suitable for hot environments.

 The finding that large-framed cows had high RDW levels could be an indication that they were nutritionally stressed. High RDW levels in blood of cattle are mainly caused by poor nutrition (Roland et al., 2014). In agreement to our findings Zindove et al. (2015) reported that, unlike their small-framed counterparts, large-framed cows cannot meet their nutritional and water requirements under natural rangelands in drought prone areas. This can be attributed that small-framed animals have lesser maintenance requirements than large-framed animals hence they can easily meat their nutritional requirements (Jurado et al., 2015). Use of small-framed beef cattle with low feed requirements is, therefore, advised in drought prone areas. The relationship between PC3 (sheath height and dewlap size) and WBC, MCH, RDW and MCV may be largely be a result of the interaction between heat resilience by the cows, health status and haematological profiles. High WBC, MCH, RDW and MCV all can indicate unhealthy and/or heat stressed cattle. Observed decrease in WBC, MCH, RDW and MCV as sheath height and dewlap size increased was expected. As the distance between the ground and the bottom of the cows’ bellies increases, the cows are insulated from the ground when it’s hot (Brown-Brandl, 2018). There are suggestions that the dewlap is responsible for dissipating heat in livestock (Bro-Jørgensen, 2016). As the surface area of the dewlap increases, the more heat is dissipated when environment temperatures are high (Bro-Jørgensen, 2016).

 It can be concluded that the cows’ hematological prof iles were significantly affected by season and time of day with high WBC, HGb, HCT, MCH, MCHCH, MPV, NUET, LYMP and EOS found in cows during the afternoons of the hot-wet season. Black cows had more immuno- response related hematological profiles during the hot-wet season. The elevation of HCT, MCHC and RDW in cows occurred as the body depth, flank circumference and chest girth decreased, indicating a greater capacity of cows characterised by deep bodies, large flanks and chest girths to withstand hydric, heat and nutritional stress in drought prone areas. Increase in HCT, MCHC, RDW and MPV as frame size of cows increased a greater capacity meet nutritional and water requirements by small-framed cows under dry rangelands. Decrease in dewlap size and sheath height is causes decrease in WBC, MCH, RDW and MCV in cows kept under rangelands in drought prone areas. It is likely that small-framed cows with large thoracic capacities, large dewlaps and belly bottoms far away from the ground surface are able to maintain haematological normalcy under natural rangelands in drought prone areas. It is, however, important to note that bred cows are important to the productivity and profitability of the cow herd. There is, therefore, need to establish reference hematological values for pregnant beef cows and explore more on the relationship between these conformation traits and hematological profiles of pregnant cows.

 

Figure 1. Mean cell haemoglobin (a), mean corpuscular volume (b) and haematocrit levels (c) in Mashona cows during the morning and afternoon of cool-dry and hot-wet seasons

 

Figure 2. Mean cell haemoglobin (a), monocytes (b) and eosinophil levels (c) of black and brown Mashona cows during the cool-dry and hot-wet season

 

Table 5. Eigenvalues and share of total variance and principal component loadings after rotation of conformation traits of Mashona cows during hot-wet and cool-dry season

 

 

Table 6. Regression coefficients (±SE) of principal components (PC) extracted from conformation traits on hematological parame-ters in Mashona cow cows

BD = body depth; BS = body stature; BL = body length; FC = flank circumference; HG = heart girth; NH = navel height; DL = dewlap size. **p<0.01; *p<0.05; nsp>0.05.

 

 

AcknowledgementsTop

 

We wish to thank the farmers in Muzarabani district (Zimbabwe) and extension officers for their cooperation and support during data collection.

ReferencesTop

Adebambo OA, 2001. The Muturu: a rare sacred breed of cattle in Nigeria. Anim Gen Res Info 31: 27-36. https://doi.org/10.1017/S1014233900001450
Ahlberg CM, Allwardt K, Broocks A, Bruno K, Taylor A, Mcphillips L, Krehbiel CR, Calvo-Lorenzo M, Richards CJ, Place SE, Desilva U, 2019. Characterization of water intake and water efficiency in beef cattle. J Anim Sci 97 (12): 4770-4782. https://doi.org/10.1093/jas/skz354
Alberghina D, Giannetto C, Vazzana I, Ferrantelli V, Piccione G, 2011. Reference intervals for total protein concentration, serum protein fractionsand albumin/globulin ratios in clinically healthy dairy cows. J Vet Diagn Invest 23 (1): 111-114. https://doi.org/10.1177/104063871102300119
Ajeet K, Meena K, 2011. Effect of heat stress in tropical livestock and different strategies for its amelioration. J Stress Physiol Biochem 7: 45-54.
Alfonzo EPM, Barbosa da Silva MVG, dos Santos Daltro D, Stumpf MT, Dalcin VC, Kolling G, Fischer V, McManus CM, 2016. Relationship between physical attributes and heat stress in dairy cattle from different genetic groups. Int J Biometeorol 60 (2): 245-253. https://doi.org/10.1007/s00484-015-1021-y
Assan N, 2012. Genetic improvement and utilization of indigenous cattle breeds for beef production in Zimbabwe: past, present and future prospects: A review. Sci J Agric 1 (1): 1-7.
Atrian P, Shahryar HA, 2012. Heat stress in dairy cows (A Review). Res Zool 2 (4): 31-37.
Berry DP, Buckley F, Dillon P, Evans RD, Veerkamp RF, 2004. Genetic relationships among linear type traits, milk yield, body weight, fertility and somatic cell count in primiparous dairy cows. Irish J Agr Food Res 43: 161-176.
BIF, 2010. Guidelines for uniform beef improvement programs. Beef Improvement Federation. https://beefimprovement.org/wp-content/uploads/2013/07/BIFGuidelinesFinal_updated0916.pdf 
Bro-Jørgensen J, 2016. Evolution of the ungulate dewlap: Thermoregulation rather than sexual selection or predator deterrence? Front Zool 13 (1): 1-7. https://doi.org/10.1186/s12983-016-0165-x
Broucek J, Kisac P, Uhrincat M, 2009. Effect of hot temperatures on the haematological parameters, health and performance of calves. Int J Biometeorol 53: 201-208. https://doi.org/10.1007/s00484-008-0204-1
Brown-Brandl TM, 2018. Understanding heat stress in beef cattle. R Bras Zootec 47: 1-9. https://doi.org/10.1590/rbz4720160414
Brucka-Jastrzȩbska E, Kawczuga D, Brzezińska M, Orowicz W, Lidwin-Kaźmierkiewicz M, 2007. Dependence of hematological parameters in Simmental breed cattle on physiological conditions. Med Weter 63: 1583-1586.
Cattell RB, 1966. The scree-test for the number of factors. Multivariate Behav Res 1: 140-161. https://doi.org/10.1207/s15327906mbr0102_10
Caulfield MP, Cambridge H, Foster SF, Mcgreevy PD, 2014. Heat stress: a major contributor to poor animal welfare associates with long-haul live export voyages. Vet J 199 (2), 223-228. https://doi.org/10.1016/j.tvjl.2013.09.018
Chakravarthy A, Muniyappa C, Kandakoor S, Bhattacharya A, Dhanabala K, Gurunatha K, 2012. Bio efficacy of inorganic nanoparticles CdS, nano-Ag and nano-TiO2 against Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae). Curr Biot 6: 271-281.
Chanza N, de Wit A, 2014. Harnessing indigenous knowledge for enhancing CBA and resilience in Muzarabani dryland, Zimbabwe. East and Southern Africa CBA & Resilience Learning Conference, ILRI Campus, Addis Ababa, Ethiopia, 1-4 Sept. 2014.
Cozzi G, Ravarotto L, Gottardo F, Stefani AL, Contiero B, Moro L, Dalvit P, 2011. Reference values for blood parameters in Holstein dairy cows: Effects of parity, stage of lactation and season of production. J Dairy Sci 94 (8): 3895-3901. https://doi.org/10.3168/jds.2010-3687
Descheemaeker K, Zijlstra M, Masikati P, Crespo O, Tui SHK, 2018. Effects of climate change and adaptation on the livestock component of mixed farming systems: A modelling study from semi-arid Zimbabwe. Agric Sys 159: 282-295. https://doi.org/10.1016/j.agsy.2017.05.004
Doornenbal H, Tong AK, Murray NL, 1988. Reference values of blood parameters in beef cattle of different ages and stages of lactation. Can J Vet Res 52 (1): 99-105
Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, Marquéz JRG, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36 (1): 27-46. https://doi.org/10.1111/j.1600-0587.2012.07348.x
Dubey A, Mishra S, Khune V, Gupta PK, Sahu BK, Nandanwar AK, 2012. Improving linear type traits to improve production sustainability and longevity in purebred Sahiwal cattle. J Agr Sci Tech 2: 636-639.
Dzavo T, Zindove T J, Dhliwayo M, Chimonyo M, 2019. Effects of drought on cattle production in sub-tropical environments. Trop Anim Health Pro 51: 669-675. https://doi.org/10.1007/s11250-018-1741-1
Forabosco F, Groen AF, Bozzi R, Van Arendonk JAM, Filippini F, Boettcher P, Bijma P, 2004. Phenotypic relationships between longevity, type traits, and production in Chianina beef cattle. J Anim Sci 82 (6): 1572-1580. https://doi.org/10.2527/2004.8261572x
Furie R, Petri M, Zamani O, Cervera R, Wallace DJ, Tegzova D, Schwarting A, Merrill JT, Chatham WW, Stohl W, 2011. A Phase III, randomized, placebo-controlled study of Belimumab, a monoclonal antibody that inhibits B lymphocyte stimulator, in patients with systemic lupus erythematosus. Arthritis Rheum 63 (12): 3918-3930. https://doi.org/10.1002/art.30613
Gillooly JF, Brown JH, West GB, Savage VM, Charnov EL, 2001. Effects of size and temperature on metabolic rate. Sci J 293: 2248-2251. https://doi.org/10.1126/science.1061967
Giri A, Bharti VK, Kalia S, Ravindran V, Ranjan P, Kundan TR, Kumar B, 2017. Seasonal changes in haematological and biochemical profile of dairy cows in high altitude cold desert. Indian J Anim Sci 87 (6): 723-727.
Hall SJ, 1998. Traditional livestock in semi-arid north eastern Zimbabwe: Mashona cattle. Trop Anim Health Pro 30 (6): 351-360.
Hansen PJ, 2004. Physiological and cellular adaptations of zebu cattle to thermal stress. Anim Reprod Sci 82: 349-360. https://doi.org/10.1016/j.anireprosci.2004.04.011
Harris GP, Haygarth PM, Condron LM, Heathwaite AL, Turner BL, 2005. The phosphorous transfer continuum: Linking source to impact with an interdisciplinary and multi- scaled approach. Sci Total Environ 344 (3): 5-14. https://doi.org/10.1016/j.scitotenv.2005.02.001
Jurado NV, Tanner AE, Blevins SR, Rich J, Mayes RW, Fiske D, Swecker Jr WS, Lewis RM, 2015. Feed intake and diet selection in Angus-cross heifers of two frame sizes at two stages of growth. J Anim Sci 93 (4): 1565-1572. https://doi.org/10.2527/jas.2014-8453
Kamal R, Dutt T, Patel M, Dey A, Chandran P C, Bharti PK, Barari SK, 2016. Behavioural, biochemical and hormonal responses of heat-stressed crossbred calves to different shade materials. J Appl Anim Res 44 (1): 347-354. https://doi.org/10.1080/09712119.2015.1074076
Katanha A, Masocha V, 2014. Schistosomiasis an issue in flood prone area of Dambakurima Ward 1, Muzarabani District of Zimbabwe. Int J Sci Res 3: 646-650.
Khan IA, Khan A, Hussain A, Riaz A, Aziz A, 2011. Hemato-biochemical alterations in crossbred cattle affected with bovine theileriosis in semi-arid zone. Pak Vet J 31 (2): 137-140.
Khan MA, Khan MS, and Waheed A, 2018. Morphological measurements and their heritabilities for Sahiwal cattle in Pakistan. J Anim Plant Sc 28 (2): 431-440.
Kumar S, Singh SV, Pandey P, Kumar N, Hooda OK, 2016. Estimation of metabolic heat production and methane emission in Sahiwal and Karan Fries heifers under different feeding regimes. Vet World 9 (5): 496-500. https://doi.org/10.14202/vetworld.2016.496-500
Lamp O, Derno M, Otten W, Mielenz M, Kuhla B, 2015. Metabolic heat stress adaption in transition cows: Differences in macronutrient oxidation between late-gestating and early-lactating German Holstein dairy cows. PLOS ONE 10: e0125264. https://doi.org/10.1371/journal.pone.0125264
Mazzullo G, Rifici C, Caccamo G, Rizzo M, Piccione G, 2014. Effect of different environmental conditions on some haematological parameters in cow. Ann Anim Sci 14 (4): 947-954. https://doi.org/10.2478/aoas-2014-0049
Menezes GDO, Gomes RDC, Ribas MN, Torres Jr RDA, Fernandes Jr JA, Pereira GM, Fávero R, da Silva LOC, 2018. Genetic and phenotypic parameters for feed and water efficiency in Senepol cattle. In: Cooperation, networking and global interactions in the animal production sector; Bryant J et al. (eds.). ICAR, Rome, Italy. https://www.alice.cnptia.embrapa.br/alice/bitstream/doc/1102269/1/Geneticandphenotypicparameters.pdf
Mugandani R, Wutal M, Makarau A, Chipindu B, 2012. Re-classification of agro-ecological regions of Zimbabwe in conformity with climate variability and change. Afr Crop Sci J 20 (2): 361-369.
Musiyiwa K, Filho WL, Harris D, Nyamangara J, 2014. Implications of climate variability and change for smallholder crop production in different areas of Zimbabwe. Res J Environ Earth Sci 6 (8): 394-401. https://doi.org/10.19026/rjees.6.5249
Mwai O, Hanotte O, Kwon Y, Cho S, 2015. African indigenous cattle: unique genetic resources in a rapidly changing world. Asian-Australas J Anim Sci 28 (7): 911-921. https://doi.org/10.5713/ajas.15.0002R
Nardone A, Ronchi B, Lacetera N, Ranieri MS, Bernabucci U, 2010. Effects of climate changes on animal production and sustainability of livestock systems. Livest Sci 130 (1-3): 57-69. https://doi.org/10.1016/j.livsci.2010.02.011
Nyamushamba GB, Mapiye C, Tada O, Halimani TE, Muchenje V, 2017. Conservation of indigenous cattle genetic resources in Southern Africa's smallholder areas: Turning threats into opportunities-A review. Asian-Australas J Anim 30 (5): 603-621. https://doi.org/10.5713/ajas.16.0024
Obitsu T, Kamiya M, Kamiya Y, Tanaka M, Sugino T, Taniguchi K, 2011. Effects of high ambient temperature on urea‐nitrogen recycling in lactating dairy cows. Anim Sci J 82 (4): 531-553. https://doi.org/10.1111/j.1740-0929.2011.00880.x
Ode SA, Adamu M, Saror DI, 2017. Haematology and some serum biochemistry of apparently healthy Muturu and Bunaji breeds of cattle in Benue State, Nigeria. Comp Clin Path 26 (1): 233-236. https://doi.org/10.1007/s00580-016-2375-1
Osei-Amponsah R, Chauhan SS, Leury BJ, Cheng L, Cullen B, Clarke IJ, Dunshea FR, 2019. Genetic selection for thermotolerance in ruminants. Animals 9 (11): 948 -966. https://doi.org/10.3390/ani9110948
Pandey P, Hooda OK, Kumar S, 2017. Impact of heat stress and hypercapnia on physiological, haematological and behavioral profile of Tharparkar and Karan Fries heifers. Vet World 10 (9): 1146-1155. https://doi.org/10.14202/vetworld.2017.1149-1155
Parés-Casanova PM, Mwaanga ES, 2013. Factor analysis of biometric traits of Tonga cattle for body conformation characterization. Global J Multidiscip Appl Sci 1 (2): 41-46. https://doi.org/10.13070/rs.en.1.809
Phillips CJ, 2018. Principles of cattle production. CSIRO Publ, Clayton South, Australia. 203 pp. https://doi.org/10.1079/9781786392701.0000
Pundir RK, Singh PK, Singh KP, Dangi PS, 2011. Factor analysis of biometric traits of Kankrej cows to explain body conformation. As-Australas J Anim Sci 24: 449-445. https://doi.org/10.5713/ajas.2011.10341
Radkowska I, Herbut E, 2014. Hematological and biochemical blood parameters in dairy cows depending on the management system. Anim Sci Pap Rep 32 (4): 317-325.
Raines CR, Dikemen ME, Unruh JA, Knock RC, 2008. Predicting cattle age from eye lens weight and nitrogen content dentition and United States Department of Agriculture maturity score. J Anim Sci 86 (12): 3557-3567. https://doi.org/10.2527/jas.2007-0445
Roland L, Drillich M, Iwersen M, 2014: Hematology as a diagnostic tool in bovine medicine. J Vet Diagn Invest 26 (5): 592-598. https://doi.org/10.1177/1040638714546490
SAS Inst., 2012. Base SAS® 9.3 procedures guide. SAS Institute, Cary, NC, USA.
Scasta JD, Lalman DL, Henderson L, 2015a. Drought mitigation for grazing operations: matching the animal to the environment. Rangelands 38 (4): 204-210. https://doi.org/10.1016/j.rala.2016.06.006
Scasta JD, Henderson L, Smith T, 2015b. Drought effect on weaning weight and efficiency relative to cow size in semiarid rangeland. J Anim Sci 93 (12): 5829-5839. https://doi.org/10.2527/jas.2015-9172
Scott P, 2017. Condition score (BCS) in beef herds. National Animal Disease Information Service (NADIS). https://www.nadis.org.uk/disease-a-z/cattle/condition-score-bcs-in-beef-herds/#:~:text=Body%20condition%20scoring%20(BCS)%20is,energy%20reserves%20in%20the%20cow
Sejian V, Gaughan J, Bhatta R, Naqvi M, 2016. Impact of climate change on livestock productivity. FAO food and nutrition series02. http://www.feedipedia.org/content/impact-climate-change-livestock-productivity
Singh SV, Gupta S, Singh PK, Singh AV, Sohal JS, Kumar N, Kumar A, Chaubey KK, Singh B, 2013. Therapeutic management of clinical Bovine Johne's disease using goat based 'indigenous vaccine'in native Hariana Cattle: case reports. Adv Anim Vet Sci 1 (1): 23-28.
Sreedhar S, Rao KS, Suresh J, Moorthy PR, Reddy VP, 2013. Changes in haematocrit and some serum biochemical profile of Sahiwal and Jersey× Sahiwal cows in tropical environments. Vet Arh 83 (2): 171-187.
Taiwo BBA, Olarin ODD, Aluko FA, 2010. Breed and environmental factors affecting body measurements of beef cattle in Yewa, Nigeria. Agric J 5: 211-214. https://doi.org/10.3923/aj.2010.211.214
Takeda M, Uemoto Y, Inoue K, Ogino A, Nozaki T, Kurogi K, Yasumori T, Satoh M, 2018. Evaluation of feed efficiency traits for genetic improvement in Japanese Black cattle. J Anim Sci 96 (3): 797-805. https://doi.org/10.1093/jas/skx054
Wennecke G, 2004. Haematocrit - A review of different analytical methods. Radiometer Medical ApS. https://acutecaretesting.org/-/media/acutecaretesting/files/pdf/hematocrit--a-review-of-different-analytical-methods.pdf
Zindove TJ, Chimonyo M, 2015. Comparison of trait preferences of Nguni farmers located in semi-arid and sub-humid environments. Trop Anim Health Pro 47: 607-611. https://doi.org/10.1007/s11250-014-0750-y
Zindove TJ, Chimonyo M, Nephawe KA, 2015. Relationship between linear type and fertility traits in Nguni cows. Anim 9 (6): 944-951. https://doi.org/10.1017/S1751731114003231