Titus J. Zindove- Faculty of Agriculture and Life Sciences, Lincoln University, Christchurch, New Zealand
As the demand for pasture-finished beef continues to grow, the importance of sustainably producing cattle under pasture-based systems has become increasingly evident. The arid and semi-arid environments are very variable and volatile, making cattle robustness an increasingly valuable attribute. The current trend in genetic selection in pasture-based cattle production systems has focused on adaptation among other traits but ignoring the importance of robustness. Robustness is a difficult phenotype to characterise because it is a complex trait composed of multiple components, including dynamic elements such as the rates of response to, and recovery from, environmental perturbations. Further, measuring robustness’ component traits is time-consuming, expensive, and labour intensive. To implement sound and effective selection procedures for robustness in beef cattle, simplified alternative strategies are a necessity. The use of highly heritable and easy to measure conformation traits is one possible alternative pending ascertainment of the relationship between conformation traits and robustness of beef cattle. Indirect selection for robust beef cattle using conformation traits may help to produce environmentally friendly beef cattle that are resilient and able to cope with environmental variations. This review deciphers robustness and conformation of beef cattle and their potential complementarity in selection for resilience to harsh environmental conditions.
1 Introduction
The global beef cattle industry is very heterogeneous in nature, consisting of more than 250 breeds and their crosses under a diversity of environments (Phillips, 2018). Although the industry is characterised by different production systems, the cattle are mostly kept under extensive range conditions, especially in arid and semi-arid areas (Drouillard, 2018). In the quest for meeting market demands, there has been a tendency to use large framed fast-growing breeds by beef producers. The Nellore, Hereford, Angus and Simmental breeds, for example, are currently some of the common cattle breeds registered in United States and Brazil who are among the world’s top beef producers (Espigolan et al., 2013; McManus et al., 2016; Drouillard, 2018; Bem et al., 2024). Some of these breeds have also been rapidly displacing native breeds in developing regions such as Asia and Africa (Zindove et al., 2015). Most of large-framed fast-growing breeds, however, struggle to thrive with high temperatures, poor nutrition and/or high parasite burdens under natural rangelands in arid and semi-arid areas (Bowen et al., 2018; Matope et al., 2020).
While characterised by limited crop production, semi-arid and arid landscapes have extensive rangelands which are highly suitable for beef production (Begizew, 2021). Based on the Köppen climate classification, arid regions are defined as areas characterised by evapotranspiration exceeding precipitation by a big margin whilst semi-arid areas are those that receive precipitation slightly lower than the evapotranspiration (Chen and Chen, 2013). These definitions reflect on the common challenges in cattle production in these regions such as erratic rainfall which constrains forage quantity and quality. Water scarcity coupled with frequent droughts, high ambient temperatures and heat waves results in high incidence of heat stress, low feed intake, reduced growth rates and reproductive efficiency among beef herds (Dzavo et al., 2019). Under these extensive beef production systems, optimal performance is a function of the forage quality and physiological status of the cattle (DelCurto et al., 2000). Natural rangelands in arid and semi-arid areas do not provide adequate nutrition to enable large-framed and fast-growing cattle breeds to express their genetic potential (Dzavo et al., 2019). Pasture quantity and quality in the arid and semi-arid areas are especially compromised during the dry seasons and drought years when both dry matter availability and nutrient content of natural pastures are very low (Bowen et al., 2018). As a result of increased competition for conventional feed ingredients and increased demand for naturally produced beef (Soren et al., 2018), there is a need for more efficient grass-based beef production systems. High costs of conventional feedlot rations coupled with increased demand of beef from grass fed cattle have resulted in most beef producers in developing regions such as Africa and Asia finishing their slaughter cattle on natural grazing with little or no supplementation (Bester et al., 2003).
Managing and optimizing performance of fast-growing breeds remains one of the most significant challenges of extensive beef producers in arid and semi-arid areas (Delaby et al., 2018). The foundation of beef production is in the breeding herd. The desire of beef farmers in arid and semi-arid areas is to select cattle that have unique genetic attributes such as adaptation and resilience to drought, heat, diseases, high reproductive efficiency, and ability to utilise low-quality forages (Zindove et al., 2015; Delaby et al., 2018). The ability of the cattle to meet these requirements is due to heredity and environment. While environmental stressors in arid and semi-arid areas are regarded as routine, there is realization that the stressors are becoming more and more complex (Matope et al., 2020). New stressors are emerging, and the existing ones are becoming more frequent and intense. Global warming has changed the temperature ranges and vegetation species in arid and semi-arid areas over the past decade (Soren et al., 2018). Climate change has also resulted in the emergence of new disease syndromes for cattle and change in the prevalence of existing diseases, particularly those spread by external parasites in semi-arid areas (Lata et al., 2018; Nyangiwe et al., 2018; Caminade et al., 2019).
Due to the ever-changing environment in the arid and semi-arid areas, cattle that had adapted to the routine challenges are subjected to new environmental stressors. For example, despite being considered as one of the most adapted breeds to arid and semi-arid conditions, offtake of Nguni cattle in the sub-Saharan Africa is low (Musemwa et al., 2010; Zindove et al., 2015). Low offtake for the Nguni cattle has been attributed to low fertility and high mortality rates because of more severe and frequent droughts in sub-Saharan Africa (Collins-Lusweti, 2000; Mapiye et al., 2009). Calving intervals of nearly one year in Nguni herds under extensive systems were reported by Collins-Lusweti (2000) compared to two years reported a decade later (Nqeno et al., 2008; Maciel et al., 2013). Abortions and stillbirths have been reported to be increasingly getting common among Nguni herds in communal production systems (Zindove and Chimonyo, 2015). These reports imply that the cattle herds are adapted to the routine harsh environmental conditions but not robust. That is, although they have become best fitted to harsh environmental conditions that they have been routinely subjected to over many years, they lack the ability thrive under novel and/or exacerbated challenges. Adaptability has dominated research programmes on coping with harsh environmental conditions in arid and semi-arid subtopics (Zindove et al., 2015; Reverter et al., 2017; Engle et al., 2018). Robustness of beef cattle has, so far, not received due attention in literature. For optimum production under harsh environmental conditions, cattle herds must be adaptable by being robust. Adaptation without robustness, which maybe the current state of most native breeds dominated beef herds in semi-arid areas, is not always good as it can leave the cattle vulnerable to unknown or unanticipated environmental shocks. For example, occasional severe snowfall events in semi-arid areas (Shang et al., 2012) might cause cold stress in cattle which have become adapted to heat stress resulting in reduced productivity. Under performance of beef cattle under arid and semi-arid natural rangelands is not entirely dependent on management practices but is also heavily influenced by genetic variation (Rust and Groeneveld, 2002).
The success of direct selection for common traits of economic importance in beef such as calving interval, yearling weight and age at first calving has been reported to be slow due to their low heritabilities ranging between 0.01 and 0.15 (Van der Westhuizen et al., 2001; Norris et al., 2004). Long generation intervals are also a challenge to genetic improvement for production and reproductive traits through selection (Zindove et al., 2015). Use of conformation traits as indirect indicators of robustness of cattle could be an option to reducing generation interval since it allows early selection. Conformation traits describe measurements for a range of characteristics describing the shape and structure of an animal (Berry et al., 2004). Making selection and culling decisions basing on conformation traits can be easier, faster and cheaper for farmers compared to use of other traits such reproductive and growth traits. Zindove et al. (2015) argued that the physical characteristics of cattle can play an important role in selection for adaptability in beef cattle. In recent years, beef cattle improvement has not paid as much attention to shape and structure as indicators of adaptation and/or robustness. Against this background, the objective of this narrative review is to synthesise scientific evidence on the potential genetic selection for robustness of beef cattle under challenging environmental conditions. Considering their relevance to arid and semi-arid conditions, the review will cover both Bos indicus and Bos Taurus beef breeds, and their crosses. Traits which can be used as determinants of cattle robustness such as heat tolerance, fertility, diseases resistance and feed conversion efficiency will be covered. The review goes on to consider the challenges of genetic selection for such traits and the possibility of indirect selection using conformation traits which are more heritable and easier to measure.
2 Literature selection criteria and synthesis
This review is a narrative synthesis of empirical data on importance of genetic selection for robustness in beef cattle and evidence on the potential use of body conformation traits as practical proxies for indirect selection for robustness in beef cattle. Academic research databases Science direct, Scopus, Web of Science and Google Scholar were selected as the primary sources to locate literature deciphering the concept of robustness, genetic selection for body conformation in cattle and the association between conformation traits and robustness indicators in cattle. The following limiting criteria were used: i) Language – English; ii) Date of publication – publications from 1990 to 2025; iii) Topic of published work – keywords: cattle robustness, canalization in cattle, semi-arid areas, arid areas, extensive cattle production, cattle plasticity, cattle conformation traits, cattle liner type traits, heat tolerance, cattle adaptation, heat tolerance, feed efficiency and harsh environmental conditions; iv) Type of publication – peer-reviewed journal articles, academic books and postgraduate theses and dissertations. The reviewer searched the literature by title and/or abstract and selected eligible sources which reported on robustness, genetic parameters, genetic and/or phenotypic associations or relationships between conformation traits and indicators of robustness under harsh environmental conditions in beef cattle. Studies on dairy cattle under grazing systems were also considered if the reported findings were deemed applicable to beef production. The literature search was conducted between 10 February and 31 March 2025.
3 Robustness in beef cattle
3.1 Definition of robustness, related concepts and its use in beef production
In literature, the ideas of adaptation and robustness of cattle are somewhat not clearly separated. There are also other closely related terms like plasticity and canalisation which are used in overlapping ways in animal genetics. It is important to understand these terms, how they relate to each other and their importance in animal genetics. Of these concepts, it is probably canalisation and robustness which are commonly used interchangeably whilst the are not exactly the same. Canalisation refers to the evolvement of the genetic architecture of a trait such that the appearance of uncommon individuals in a population is rare (Hallgrimsson et al., 2019). This allows a stable phenotype for a specific trait in a population. Thus, canalisation is measured at population level. Adaptation involves non-genetic short-term adjustments to environmental stressors by individual animals and genetic changes which happens across many generations to fit in a specific environment (Casey, 2023; Akinsola et al., 2024). Animals, thus, get adapted to challenges that they have been routinely subjected to for some time. Robustness refers to an individual animal’s ability to maintain its reproducing and surviving capabilities amid routine, unpredictable and novel environmental constraints (Klopcic et al., 2009; Urruty et al., 2016; Nel et al., 2023). So, robustness can be measured at both individual and population level. Robustness, thus, encompasses both canalisation and adaptation. Plasticity on the other hand quantifies how an animal’s phenotype changes in response to changes in the environment (Silva Neto et al., 2024). Animals exhibiting high plasticity are characterised by significant changes in performance as the environment changes whilst low plasticity is when the animals are less affected by environmental changes. Robust animals are not too sensitive to environmental changes hence they have low plasticity. What is common amongst these for concepts is that they relate to the sensitivity of the animals to the environment but what differs is the type and scale of environmental disturbance they address. While canalisation, plasticity and adaptation are regarded as proxies of resilience of cattle (Silva Neto et al., 2024), it is robustness which can be the most comprehensive proxy of resilience as it encompasses all the other concepts in addition to the animal’s ability to sustain productivity across novel environmental challenges. Conceptual relationships between robustness of cattle, canalisation, plasticity, adaptation and resilience are shown in Figure 1.
Figure 1. Conceptual framework deciphering the robustness in cattle and related concepts.
Despite its importance, especially in dynamic environments such as semi-arid and arid areas, genetic selection for robustness of beef cattle has been limited due to the complexity of the trait and difficulties in quantifying it. Genetic selection programmes in grass-based cattle production systems have mainly focused on adaptation among other traits with little, if any, emphasis on robustness. Silva Neto et al. (2024)l and Santana et al. (2025) suggested that the sensitivity of beef cattle to the environment can be quantified through estimating trait stability and changes across different environments using reaction norm models (RNM). Multi trait indices can also be used to capture the variance in the performance of cattle across different environments (Rodrigues et al., 2025). While both RNM and multi-trait indices can quantify the sensitivity of individual cattle to different environments, they do not distinguish between the effect of routine and novel environmental stressors. So, whilst these methods ca effectively quantify phenotypic stability across environmental gradients in beef cattle, it not clear if they capture the full construct of robustness.
Change in extremity and frequency of routine challenges could be the reason why even with adapted breeds, herd productivity of extensive beef producers in arid and semi-arid areas is low. For example, due to on-going efforts of restocking the adapted Nguni cattle, the breeds now predominate South Africa’s beef cattle population (van der Westhuizen et al., 2019). Despite large populations of adapted breeds, the productivity of beef herds in Southern Africa’s arid and semi-arid areas fluctuate in response to variation in environmental conditions (Dzavo et al., 2019). Robustness of beef cattle needs to be explored to safeguard beef production in changing climate.
Several management plans have been on the ground to counter the effects of climate change on extensive beef production. Basically, farmers and researchers have been concentrating on short term strategies to improve productivity of beef cattle under natural rangelands. These include supplementary feeding, shed provision, water harvesting, nutritional management and destocking (Dzavo et al., 2019). Unfortunately, these strategies may be unsustainable, costly, and not applicable to all production systems due to high feed costs and/or losses involved (Matope et al., 2020). It is more practical to provide artificial sheds and sprinkle cattle with water when the cattle are under confinement. Use of conventional feeds is a challenge under free range systems where resource-poor farmers rely mostly on natural pastures (Matope et al., 2020). Integration of beef cattle production with forestry trees which provide shade has been used successfully in reducing heat stress in beef cattle in Brazil (Lopes et al., 2016). Trees in some semi-arid and arid regions such as sub-Saharan Africa, however, shade off their leaves during dry periods and, thus, cattle on natural rangelands are exposed to radiant heat during grazing (Kiage, 2013).
3.2 Why beef cattle in arid and semi-arid areas should be robust not just adapted
Beef production in arid and semi-arid areas is increasingly threatened by new and exacerbated environmental conditions. Phenomena such as droughts and heat waves are becoming more frequent and more intense, the expansion of diseases and parasites such as tsetse fly, east coast fever and ticks into new zones as a result of shifts in rainfall and vegetation patterns (Kipkorir, 2024). Emerging diseases, diseases crossing their traditional boundaries and prolonged intense droughts have been reported to be the major causes of cattle losses in sub-Saharan and central African regions whose herds are dominated by adapted native genes (Gifford-Gonzalez, 2000; Tongue and Ngapagna, 2019). In addition to adaptation where the animals that are best fitted to their historical environments, it is important that the cattle be able to strive under novel conditions in order to maintain optimum performance. Robust cattle would be able to maintain productivity in the wake of the new environmental conditions where cattle that are only adapted would falter.
With adapted but not robust cattle, new and/or exacerbated climatic conditions would reinforce the need for adjustment of breeding objectives and re-ranking of animals in beef herds. Cattle which rank highly for adaptive traits may perform poorly under novel and exacerbated stressors resulting in miscalculations in selection and replacement decisions (Rauw and Gomez-Raya, 2015). Therefore, in addition to buffering productivity and welfare of cattle under volatile and uncertain environments, selection for robust animals also aligns breeding objectives with unexpected environmental conditions ensuring the cattle herds remains productive and sustainable, especially under arid and semi-arid conditions. Nielsen et al. (2014) emphasised the importance of selecting beef cattle under extensive systems for robustness to ensure the breeding objectives remain consistent over a long time.
4 Genetic strategies to improve robustness in beef cattle
4.1 Phenotypic selection for robustness in beef cattle
Traditionally, crossbreeding among breeds that complement each other has been used to harness adaptability and high productivity (Wilson, 2018). For example, crossbreds and synthetic breeds such as the Bonsmara and Brangus predominate southern African arid and semi-arid regions because of their superior adaptation to harsh environments and high productivity (Wilson, 2018). Although crossbreeding has brought success in complementing breeds adaptive and productive traits, performance of crossbred beef herds under natural rangelands in African arid and semi-arid regions is low in the wake of phenomena such as severe droughts and heat waves (Wilson, 2018). Scasta et al. (2016) suggested that there is considerable variation in sensitivity to environmental variation among crossbred cattle herds. This implies that direct selection for traits such as disease resistance and heat tolerance that are related to robustness among purebred and crossbred herds could be an option to improve sensitivity to environmental stressors.
There have been studies on genetic selection for robustness indicators such as disease resistance and heat tolerance in beef cattle (Klopcic et al., 2009; Rauw and Gomez-Raya, 2015; Delaby et al., 2018). Genetic variation in resistance to diseases and parasites has been demonstrated in cattle (Bishop and Woolliams, 2014). This implies that selection for diseases and parasites resistance and resilience in cattle is possible since they vary between breeds and between individuals of the same breed. Ayres et al. (2015) reported heritable variation in tick counts of grazing Nellore cattle and their crosses with Herefords indicating differences in tick sensitivity across different genotypes. Similarly, using RNM, Mota et al. (2016) reported strong genotype x environment interactions for tick resistance causing re-ranking of Hereford and Braford cattle in different environments, suggesting moderate to high sensitivity to parasitic pressure. Phenotypic selection for sensitivity of beef cattle to environmental variations has also been explored using traits such as weight gain and heat tolerance (Carvalheiro et al., 2019; Johnson et al., 2025). Powerful tools such as the RNM have been used to describe how weight gain and heat tolerance in beef (Bradford et al., 2016; Carvalheiro et al., 2019) and dairy cattle (Nguyen et al., 2016; Ravagnolo and Misztal, 2000) changes across a range of environments. Using the RNM, Bradford et al. (2016) and Nguyen et al. (2016) concluded that cattle with insignificant decline in production as the heat load increased were considered to be robust. It is, however, not clear if these animals had been routinely exposed to such heat loads before the experiment.
In arid and semi-arid areas, heat stress in reported as one of the most critical factors with direct influence on feed and reproductive efficiency of grazing cattle (Sigdel et al., 2020; Neto et al., 2025). There is empirical evidence that the variance components of feed efficiency and reproductive performance traits of grazing cattle can be altered by heat stress resulting the reduction of the overall phenotypic performance of the cattle and changing their estimated breeding values (EBVs). For example, Sigdel et al. (2020) demonstrated significant genotype x environment interaction for reproductive performance of dairy cattle under hot conditions, with reduced fertility and changes in EBVs as a result of heat stress. This showed differences in sensitivity to heat stress among individual cattle. Similarity, NRM applied to grazing Nellore cattle under tropical conditions showed that heat stress results in significant re-ranking of cattle for feed efficiency, with the cattle being categorised into robust, plastic and highly plastic (Neto et al., 2025). These studies clearly demonstrate that selection for cattle that maintain both reproductive and feed efficiency across different heat loads can improve the resilience of pasture-based cattle systems in hot environments.
4.2 Genomic selection for robustness in beef cattle
There is very little, if any, data on genomic regions associated with sensitivity of beef cattle to both routine and novel harsh environmental conditions. In dairy cattle, the RNM has been used to test the change in the extend and direction of the effects different Single Nucleotide polymorphisms (SNPs) on milk production across different temperatures, humidity and feeding levels (Hayes et al., 2009; Lillehammer et al., 2009). Hayes et al. (2009) concluded that the gene NCBI XM_865508, located chromosome 9, can be used to select dairy bulls which produce daughters which can maintain high milk production at both high and low levels of feeding. Lillehammer et al. (2009) suggested that there are two clusters of SNPs on bos taurus autosome (BTA) 14 that could be selection candidates for fat percentage sensitivity to environmental changes in dairy cattle. Candidate genes located on BTA29 such as fibroblast growth factor 4 were reported to underpin milk production sensitivity to temperature humidity index (THI) in dairy cattle (Hayes et al., 2009). There is need to study similar associations in beef cattle. Although these studies suggest that the reported SNPs could facilitate marker-assisted selection for robustness in cattle, it remains unclear whether the associations were identified in animals routinely exposed to both low and high levels of feeding and withing the specific THI range studied. If so, the SNPs may be associated with adaptation but not necessarily robustness, since robustness involves the animal maintaining production levels under both routine environments and novel or exacerbated harsh conditions.
4.3 Limitations and methodological challenges to selection robustness in beef cattle
Despite the quest to include it in breeding goals, the practical application of robustness in beef selection programs is limited by several challenges. The main challenge is the fact that in addition to the cattle’s ability to perform well under routine harsh environmental conditions, robustness involves the ability of the animals to maintain performance for determinant traits such as heat tolerance, reproductive performance and feed conversion efficiency under novel and often unforeseen environmental conditions (Arndt et al., 2022). This makes it a complex trait to measure and record. Carvalheiro et al. (2019) suggested that the complexity, inconsistent expression and difficulties in quantification of environmental factors influencing cattle robustness introduces noise into predation models such RNM, compromising their reliability. In highly variable environments such as arid and semi-arid regions, where novel environments are common (Matope et al., 2023), the prediction models may not be able to effectively capture the full scope of the environmental stressors that can impact productivity of the cattle.
Robustness of beef cattle in determined by many traits covering but not limited to heat tolerance, fertility, diseases resistance and feed conversion efficiency. While measurements of these traits under routine harsh conditions, hence determination of their heritabilities is well established, quantifying cattle’s ability to maintain specific phenotypes under novel environments is far more difficult since it requires recording traits across a wide range of environments. As a result, many current studies on genomic and phenotypic selection for environmental sensitivity of cattle are not fully representative of robustness. Furthermore, although data in beef cattle is scant, a few studies that have been conducted on the physiological traits which are considered as standard indicators for heat tolerance such as rectal temperature and respiration rate in dairy cows indicated that they are lowly heritable with heritabilities ranging between 0.04 and 0.17 as shown in Table 1 (Dikmen et al., 2012; Luo et al., 2021; Freitas et al., 2023). Very few, if any, studies have been conducted on the heritability of triiodothyronine (T3) and Thyroxine (T4) levels as indicators of heat sensitivity in cattle. These traits are also difficult to measure and, thus, their use in large-scale selection programs and low-input systems is still limited (Carabaño et al., 2019).
Table 1. Summary of heritability estimates (h2) for determinants traits of robustness under arid and semi-arid conditions.
Although studies have been published on genes and genetic markers associated with robustness indicators such as resistance to diseases and parasites, and heat tolerance in cattle (Biegelmeyer et al., 2015; Baena et al., 2018; Moré et al., 2019) and environmental sensitivity in dairy cattle (Hayes et al., 2009; Lillehammer et al., 2009), work which specifies genomic regions associated sensitivity to both routine and novel environmental challenges in cattle is scanty. There are also nuances which are still poorly understood (Bishop and Woolliams, 2014; Twomey et al., 2019). An example of such nuances is when Brotherstone et al. (2010) showed a strong pattern of increasing estimated heritabilities for bovine tuberculosis related survival of cattle as prevalence of mortality increased despite genetic control of bovine tuberculosis resistance being reported to be strong and consistent by Bermingham et al. (2009). Previous studies have also shown that in some cattle populations the resistance locus for diseases is additive whereas in others it is dominant (Bishop and Woolliams, 2014). Use of molecular tools in selection for robustness in cattle is, therefore, still marred by some discrepancies. Identification of genetic markers that truly capture the genetic basis of cattle’s ability to maintain specific production levels under both routine environments and completely new or exacerbated harsh conditions is a necessity. A viable strategy to indirectly select for robustness in beef cattle can be a huge breakthrough. Genetic selection for robustness in beef cattle using the highly heritable and easy to measure conformation traits is one possible strategy (Dzavo et al., 2020; Matope et al., 2023).
5 Conformation traits as a selection tool for robustness in beef cattle
Various conformation traits and the genetic control of the traits are discussed below with the goal of exploring on their use for sustained genetic improvement for robustness in beef cattle.
5.1 Conformation traits and their heritabilities in beef cattle
Conformation traits have received little attention in beef cattle breeding despite their potential usefulness in improving productivity. Although there is not enough empirical evidence yet, Zindove and Chimonyo (2015) suggested that morphological adaptations form the foundation to robustness of cattle under harsh and highly variable environmental conditions. Conformation traits which have been commonly studied in beef cattle in semi-arid and arid areas include body depth, flank circumference, heart girth circumference, stature, sheath height, dewlap size and length of cattle (Zink et al., 2011; Zindove et al., 2015; Dzavo et al., 2020).
Current beef cattle breeding programs focus primarily on reproductive and production traits such as calving interval, calving ease, age at first calving, growth and carcass traits, rather than on conformation traits (Zindove et al., 2015; Krupová et al., 2025). Consequently, data on conformation traits in beef cattle is limited. Recently, some work has been done to document conformation traits in beef cattle, especially in arid and semi-arid areas (Zindove et al., 2015; Dzhulamanov et al., 2019; Kamprasert et al., 2019; Dzavo et al., 2020), although less has been done to incorporate them in beef breeding programs. The mean values for commonly reported conformation traits in beef cattle under arid and semi-arid areas are shown in Table 2.
Table 2. Mean values of commonly used conformation traits in mature cows of different breeds found is semi-arid and arid areas.
Frame size scoring in beef is usually based on stature of the animal (Beef Improvement Federation, 2010). Stature is measured as the height of the cow at her hips (Mwacharo et al., 2006). As shown in Table 2, stature varies widely with breed with values between 119 and 135 cm being reported. Bonsmara and Nguni cows are among the shortest breeds whilst Brahmans are among the tallest. The standard deviation of stature in Table 2 ranged from 1 to 10% of the means, implying that, in some herds, there are sizable differences in the stature of cattle within the same breed.
Body depth is defined as the distance between the top of the spine and the bottom of the deepest point of the rear rib (Dubey et al., 2012). It has been suggested that the body depth of a cow is an indicator of its body capacity (Zindove et al., 2015). Cows with deep bodies in addition to wide, well-sprung ribs are said to have a large body capacity (Hansen et al., 1999). There is limited data on the depth of bodies in beef breeds. Mean values ranging 65 to 104 have been reported in Sahiwal, Nguni, Mashona and Aberdeen Angus cows with Nguni cows having the deepest bodies (Table 2). No literature on means values of body depth in Brahman, Simmental, Hereford and Bonsmara cows was found.
Flank circumference refers to the distance around the body taken just in front of the hook bones, immediately after the udder (Taiwo et al., 2010). Although it has been reported to be an indicator of body capacity of cattle, it is not usually recorded in beef cattle (Zindove et al., 2015). Only two studies reporting on mean values of flank circumference in Nguni and Mashona cattle were found (Zindove et al., 2015; Dzavo et al., 2020). Heart girth circumference refers to the total distance around of the animal’s heart girth (Zindove et al., 2015). A large girth and a long body are needed in cattle as maximum space is desired for adequate heart, lung and gland capability (Berry et al., 2004). Taiwo et al. (2010) defined body length as the distance from the middle dip in vertebrate between the shoulder blades to back of rum Heart and lung capacity are important traits in beef cattle under natural rangelands especially in hot environments (Dzavo et al., 2020) and, as a result, data on heart girth in beef cattle is commonly available as shown in Table 2. Of the breeds in Table 2, the Simmental and Angus, which are Bos taurus, had the largest heart girths and longest bodies. Swahiwal, Nguni and Mashona cows, which are Bos indicus breeds, had smaller heart girths than the Bos Taurus breeds except the Brahman.
Dewlaps, defined as loose skin folds hanging from the neck of ungulate species, are postulated to be associated with thermoregulation ability in cattle (Dzavo et al., 2020). Sheath height is defined as the distance from the level ground to the navel of cattle (Zindove et al., 2015). To date, very few studies have explored on dewlap size and sheath height and their function in beef cattle despite them being a potential indicator of adaptability and robustness. With the standard deviation ranging from 20 to 30% of the means (Table 2), findings from the few studies conducted imply that there are sizable differences in the maximum width of dewlaps of cattle of the same breed (Khan et al., 2018; Dzavo et al., 2020).
Heritability estimates for important conformation traits in beef cattle are listed in Table 3. In general, heritability estimates of conformation traits in cattle are high (Kamprasert et al., 2019; Khan et al., 2018). This suggests that many of these traits are largely influenced by the genetic make-up of the cattle with little influence from the environment. The reports of the high heritabilities of conformation traits have led to the conclusion that a large proportion of the observed variation is attributable to existing additive genetic variation, suggesting that these traits are amenable to effective selection (Mazza et al., 2014). Moderate heritability has, however, been reported for flank circumference in beef cattle (Kirschten, 2001). There are limited studies on the heritability of flank circumference in beef cattle and, thus, it is not clear if the moderate heritability is due to environmental effects and/or unexplained additive and non-additive genetic effects. There are also discrepancies on the heritability of body length with low (Vesela et al., 2005), medium (Kirschten, 2001) and high (Kamprasert et al., 2019) heritabilities being reported. No reports on the heritability of sheath height were found.
Table 3. Summary of heritability(h2) estimates for commonly used conformation traits in beef cattle.
5.2 Genomic regions associated with conformation traits in beef cattle
Although conformation traits can be successfully incorporated in beef cattle breeding programs based on phenotype, identifying the genes and/or genomic regions underpinning these traits can add an additional layer of insight that enables genomic selection. In general, data on genetic architecture of conformation traits in cattle is scanty. Conformation traits such as stature, heart girth and body length are reported to be controlled by many regions in the genome (Bouwman et al., 2018; Doyle et al., 2020). Although the conformation traits are influenced by many genes, due to limited influence by the environment (Khan et al., 2018), their complexity might be better than that of production and health traits which are largely influenced by the environment in addition to many genes (Berry et al., 2011; Filipčík et al., 2020). Although there is limited, if any, published data, studies by Sousa Junior et al. (2024) and Amorim et al. (2025) imply that, because of the strong influence of skeletal and morphological development, cattle conformation traits are canalised during the animal’s growth and, thus, exhibit low sensitivity to environmental changes, especially after skeletal maturity.
A few studies have been published on polymorphisms associated with some of the conformation traits in beef cattle. Doyle et al. (2020) found regions associated with stature on beef and dairy breeds’ autosomes BTA 5, and 6. Of the 514 DNA variants reported to affect stature in cattle, gene variants located in QTLs near the NCAPG and LCORL genes found on BTA6 were reported to be the lead variants accounting for up to 0.6% of the genetic variation (Doyle et al., 2020). Similarly, An et al. (2019) reported loci on BTA5, BTA6 and BTA14 to be associated with stature in Chinese beef cattle. Bouwman et al. (2018) also reported that out of the accountable 14% of the genetic differences in stature within beef cattle breeds, 1.3% was caused by NCAPG and LCORL. Although there are variations in reported genes controlling stature in beef cattle, genomic regions concentrated on BTA6 are commonly reported indicating their potential in genomic evaluations for stature.
High degree of genetic correlation has been reported between flank circumference, heart girth and body length in beef cattle (Chen et al., 2018, Chen et al., 2020) indicating that they might be under the control of linked or pleiotropic genes. Chen et al. (2018) and Chen et al. (2020) identified a total of 66 genes associated with flank circumference, body length and heart girth of which genes such as ANGEL2, SPATA22, SCN5A, BOLA-DRB3, FADS2 and ENSBTAG00000046327 were regarded as having major effects on the three traits. This might imply that flank circumference, body length and heart girth share some biological functions. ENSBTAG00000037537 and CTU1, found on BTA6 have been reported to be associated with body depth in Canadian Holstein cattle (Abo-Ismail et al., 2017; Long et al., 2024). The availability of data on genomic regions affecting stature, flank circumference, body length, body depth and heart girth allows for genomic selection for the traits beef cattle. However, there is a need for more data and critical synthesis of the reported findings in order to come up with more precise results and conclusions on the genomic regions affecting the traits in beef cattle.
Sheath height and dewlap size are presented in literature less frequently. Little has been done to evaluate the genetic architecture of the two traits in cattle. To my knowledge, there is no data on genes associated with sheath height and dewlap size in cattle. Although there is limited, if any, data on genetic correlations between body depth and other conformation traits such as body length, stature, flank circumference and heart girth in beef cattle, a low value of 0.15 was reported between body depth and stature in dairy cows by Němcová et al. (2011) suggesting minimum or absence of pleiotropic effects. This implies that genomic regions affecting body depth might not be similar to those affecting stature. There is a need for multi-trait genome wide association studies to identify genomic regions controlling body depth, sheath height and dewlap size in beef cattle.
6 Relationship between conformation traits and performance of beef cattle
Relationships between the conformation traits such body depth, stature, body length, flank circumference and heart girth and determinants of robustness such as reproductive performance have been reported in beef cattle kept under natural rangelands (Dubey et al., 2012; Zindove et al., 2015). Table 4 shows correlation coefficients between conformation traits and determinants of robustness in different cattle populations. Zindove et al. (2015) reported phenotypic correlation coefficients ranging from -0.2 to 0.4 among conformation traits such as frame size and body capacity and fertility indicators in grazing beef cattle. As a result, conformation traits are used as early predictors of reproductive traits such as longevity, calving interval and age at first calving in beef cows (Gutierrez and Goyache, 2001; Larroque and Ducrocq, 2001; Zindove et al., 2015; Shin et al., 2021; Hindman et al., 2022). The existence of moderate genetic correlations between these conformation traits and reproductive traits in cows, ranging from -0.5 to 0.6 (Berry et al., 2004), justifies their potential use in predicting reproductive performance of beef cattle. It is, however, not clear whether the conformation traits influence indicators of robustness such as digestive efficiency and heat tolerance which are inferred to be the underlying causes of the association between the conformation traits and reproductive performance.
Table 4. Summary of significant (P < 0.05) phenotypic and genetic correlations between conformation traits and determinants of robustness in cattle.
It has been suggested that the association between reproductive performance of beef cows and body depth under harsh environmental conditions is based on the interactions between body depth, abdominal cavity capacity, digestion efficiency and nutritional status (Zindove et al., 2015). Dubey et al. (2012) hypothesised that large abdominal cavities may provide more space for the digestive system, which in turn could influence feed intake, digestion and assimilation capacity of cattle. In support of this, Farias et al. (2018) argued that cows with deep bodies provide lots of room for the rumen to expand and digest large amounts of high fibre diets along with plenty of water. Although there is no supporting data, Zindove et al. (2015) postulated that cows with deeper bodies, hence large abdominal capacity, are capable of using low quality forage efficiently due to potentially longer passage rates and consequently more thorough digestion compared to those with shallow bodies. This implies that such cows can efficiently digest both low quality and high-quality diets and, thus, can cope with variability in nutritional quality of pastures. These suggestions, however, remain to be confirmed.
In addition to digestion, literature suggests that body conformation traits influence feed efficiency and reproductive performance of cattle grazing in semi-arid and arid conditions. Zindove et al. (2015) and Farias et al. (2018) postulated that the body capacity of grazing cows can be linked to their high forage intake and efficient nutrient utilisation under natural rangelands characterised by low quality pastures. Contrarily, although it is associated with growth potential, large frame size results in high maintenance energy requirements which may reduce feed efficiency if not complemented with high productivity. For instance, a study by Şentürklü et al. (2021) found out that small-framed beef cattle grazing under low quality pastures were more efficient than their large-framed counterparts. In a study under extensive systems, Nasca et al. (2015) reported that large-framed cattle gained more weight whilst incurring higher feed costs associated with supplementation. Reinforcing the trade-off between frame size and feed efficiency of cattle grazing on low quality pastures, Freetly et al. (2020) and Long et al. (2024) reported that feed intake, a highly heritable trait in grazing cows, is strongly associated body size. This agreed with the study by Şentürklü et al. (2021) who found out that that grazing large-framed steers had higher weight gain than their small-framed counterparts but lower cost per Kg gain. These studies demonstrate how variation in frame size and body capacity influence the balance between forage intake, maintenance requirements and nutrient utilisation in beef cattle grazing on low quality pastures. Ultimately, this has an influence on the reproductive performance of the cattle, especially in semi-arid and arid areas. It is, however, important to note that the association between frame size and the efficiency of cattle depends on the production system and management practices (Mosher et al., 2021).
Scientific reports have emphasised on the combined effect of body length, flank circumference and heart girth on reproductive efficiency in cows (Zindove et al., 2015; Zindove and Chimonyo, 2015). Using factor analysis, Zindove et al. (2015) grouped body length, flank circumference and heart girth into a distinct factor which they suggested represented body capacity. The same authors argued that the relationship between conformation traits and fertility in beef cattle might be because of the interaction between flank circumference, heart girth, body length, body capacity and nutritional status of cows during pregnancy. During late gestation, cows reduce dry matter intake as a consequence of constraints in rumen fill and digestion (French, 2006; Salin et al., 2017). There are suggestions that cows with small body capacities as denoted by flank circumference, heart girth and body length do not have enough room for the rumen to expand or for the foetus to be carried comfortably without displacing other organs (Reynolds et al., 2004). A study by Pereira et al. (2020) found that gravid uterus displaces rumen volume resulting in reduced feed intake, which can cause longer anoestrous periods after parturition. The nutritional status of the cow at the time that she calves influences when she returns to oestrus hence calving interval (Ciccioli et al., 2003). Flack circumference, heart girth and body length may therefore have a combined effect on body capacity, which in turn has been suggested to influence prepartum feed intake hence nutritional status of cows and, subsequently, fertility. Whether these three traits have individual effects on fertility of beef cattle under harsh and highly variable environmental conditions remains unknown. No data was found on the relationship between flank circumference, heart girth and body length and health or growth traits in beef cattle.
Data on the link between flank circumference, heart girth circumference, and body length and heat tolerance of beef cattle is also scanty. The implication that thoracic cavity size, metabolic heat production and heat dissipation by cattle are interrelated (Brown-Brandl, 2018) underpins the hypothesis that there is an association between flank circumference, heart girth circumference and body length and heat tolerance traits such as respiratory rate, heart rate, respiratory rate, T3 and T4 concentration in blood. Dzavo et al. (2020) suggested that thoracic cavity size limits lung expansion during inspiration. There is need for empirical evidence to validate this suggestion.
The rate of heat transfer between cows and the environment is proportional to the body surface area along with other factors like humidity, ambient temperature and coat characteristics (Alfonzo et al., 2016; Sejian et al., 2018). The larger the body surface area the higher the potential of heat exchange between the cows and the environment when environmental temperatures are high (Alfonzo et al., 2016). Scasta et al. (2016) suggested that small-framed cattle are highly likely to be more heat tolerant than their large-framed counterparts, possibly due to differences in metabolic rates. Metabolic heat production in cows is influenced by the internal surfaces of their digestive organs. Large frame sizes are usually associated with large internal surfaces of digestive organs in cattle which may result in high metabolic heat production (Gillooly et al., 2001). There is, however, no data on the relationship between frame size or stature and physiological parameters associated with heat and cold stress in cattle. There is need to determine the relationships between stature and physiological parameters such as heart rate, rectal temperature, respiratory rate, T4 and T3 concentration in blood before basing selection decisions on stature with the intention of enhancing heat and cold resilience in beef cattle.
7 Factors affecting conformation traits in beef cows
Based on the growing evidence that body conformation traits such as stature, chest circumference and body depth are associated with key aspects of robustness of cattle such heat tolerance, feed efficiency and reproductive performance of cattle grazing in natural rangelands, there are suggestions that the conformation traits can be practical proxies for indirect selection for robustness in beef cattle. For example, as highlighted in the previous sections, Zindove et al. (2015), Nasca et al. (2015) and Şentürklü et al. (2021) demonstrated that variations body frame size and body capacity are associated with variation in fertility, heat tolerance and feed efficiency under extensive systems. If the conformation traits are to be successfully incorporated into breeding programs, it is necessary to have an in-depth understanding of the factors that underpin their expression.
Consideration of both genetic and environmental influences of conformation traits is essential for accurate estimation of breeding values. Although there is evidence that a substantial proportion of the phenotypic variation in confirmation traits in cattle is attributable to heritable genetic effects, environmental factors also have significant contribution (Marinov et al., 2015). While there is limited information on the environmental factors affecting body conformation in beef cattle, available data suggest that they are important. For example, Taiwo et al. (2010) and Marinov et al. (2015) reported that factors such as parity, herd, age at classification, physiological status and breed influence conformation traits in cattle, emphasizing the need to adjust for these factors when estimating breeding values.
There are suggestions that long term adaptation to specific environment has played a role in shaping the conformation of cattle. Climatic variation across different geographical areas has resulted in the emergence of distinct cattle ecotypes withing specific breeds. For example, environmental adaptation has resulted in differences in body size and shape between Nguni cattle ecotypes such as the Venda, Swazi, Makatini, Kapriv and Pedi (Bester et al., 2003). Such adaptive divergence is a clear illustration of the interaction of environmental factors such as climate and geographical location with genetics to influence the body conformation of cattle.
8 Future perspectives and applicability of indirect selection for robust beef cattle using conformation traits
The use of body conformation traits in cattle breeding programs is gaining ground because the traits can be assessed on-farm with ease and at a low cost (Nye et al., 2020). The use of trained classifiers, cheap and easier technologies such as 3D imaging is becoming more and more common in cattle production (Nye et al., 2020; Alempijevic et al., 2025).Considering that both body conformation and robustness of cattle are multidimensional biological constructs as described by Zindove et al. (2015) and Klopcic et al. (2009), on-farm applicability of indirect selection for robust beef cattle using conformation traits will depend on the ability of farmers to reduce the dimensionality of the confirmation data into meaningful latent constructs which can mapped onto robust outcomes. Machine learning and artificial intelligence (AI) have been used successfully in predicting performance of both beef and dairy cattle based on raw images and 3D features (Li et al., 2024; Nilchuen et al., 2025). Practical and powerful demonstrations which have been successfully implemented include the use of udder conformation principal component analysis-based indices to find quantitative trait loci (QTLs) for mastitis in dairy cattle and the combined use of machine learning and 3D imaging of cattle to predict liveweight and body condition score on-farm (Miles et al., 2021; Lewis et al., 2025). Although there are limited, if any, studies on using machine learning and 3D imaging to predict robustness yet, the previous studies on other traits show that it’s possible to use cattle images or 3D point clouds to automatically extract low dimensional constructs of confirmation predictors. Computer technology can then be used to learn the non-linear combinations of confirmation traits that best predict robust proxies and produce surrogate latent traits that breeding programs can record and incorporate into their selection indexes.
While the potential benefits of using conformation traits to indirectly select for cattle robustness have been discussed, it is important to highlight potential drawbacks. One such drawback is the potential adverse correlations between body confirmation traits and other traits of economic importance. For example, whilst large body capacity in cows might be associated with high feed intake and heat tolerance, it has also been reported to be positively correlated with high birth weight which can possibility increase incidence of calving problems unless the cows have large pelvic areas (Yang et al., 2023). Rowan (2022) argued that large body capacity in cows is generally associated with reduced feed efficiency, especially when grazing on low quality forages with little to no supplementation. Furthermore, genetic selection for some body conformation traits such as stature maybe have adverse correlations with carcass characteristics including yield and quality. Although Barro et al. (2023) demonstrated that selection for small frame in beef cattle may result in increased muscularity, Khatri and Huff-Lonergan (2025) found that small-framed cattle were associated with low carcass yield, which compromises carcass value. This highlights the importance of striking a balance on the traits on economic importance in the selection program through use of multiple trait selecting indexes to cater of adverse correlations.
Even though genetic evaluation models can correct of evaluator effects, within and between evaluator inconsistency in scoring of body conformation traits might still result in significant bias. Veerkamp et al. (2002) reported substantial within- and between-classifier variability in scoring and genetic parameters of body conformation traits in cattle. In addition to this, the paucity of validated genomic regions directly associating body conformation traits with robustness in beef cattle is a scientific gap which might constrain the reliability of indirect selection for robust cattle using body conformation traits. The fact that both body conformation traits and robustness are influenced by many genetic, environmental, physiological and management factors that are dynamic over time may risk misevaluation of cattle. Changes in these factors over time in an animal’s life might result in the weakening or strengthening of the relationship between the conformation traits and robustness of the cattle resulting in possible miscalculation and incorrect ranking of animals.
9 Summary
Beef cattle breeding programs routinely select for productive traits such as reproductive and growth performance. Less effort has been dedicated on reducing sensitivity to environmental variation despite the fact that, in the semi-arid and arid regions, cattle are frequently exposed to challenging environmental conditions which are difficult to control. Robustness, the driving force in grass-based beef production systems in semi-arid and arid areas and, thus, production of cattle with reduced sensitivity to environmental variations is critical. Defined as the ability on animals to maintain steady performance across routine, unpredictable and novel environmental challenges, robustness in closely related but not the same as plasticity, canalization and adaptation. Robustness is a complex multifactorial trait which is difficult to measure and record. Current approaches to quantify robustness in cattle include the use of statistical methods such as NRM to estimate how performance and estimated breeding values of cattle change across different environments. While these approaches have provided valuable insights on the variation in sensitivity of cattle to the environment, such as distinguishing between plastic and highly plastic cattle under heat stress, they do not clarify on sensitivity to routine and/or novel environmental stressors. To implement sound and effective selection procedures for robustness in beef cattle, there is need to come up with simplified alternative strategies. Scientific studies on conformation traits in beef cattle have reported strong phenotypic correlations with physiological functions such as feed efficiency, reproductive performance and heat tolerance which are relevant for robustness. Furthermore, studies on cattle grazing under semi-arid conditions have shown that traits associated with the body capacity of cattle are correlated with fertility. Although data on common genomic regions underpinning both conformation traits and robustness are scant, genetic correlations between conformation traits and reproductive performance of dairy cattle under extensive systems ranging from -0.5 to 0.6 have been reported. These findings substantiate the potential role of conformation traits as practical and easy-to-measure proxies for cattle. Genetic studies show that conformation traits, such as body depth, stature, flank circumference, heart girth and body length have moderate to high heritabilities, ranging from 0.2 to 0.7. This supports their suitability as selection candidates. Empirical evidence suggest that conformation traits can be incorporated into breeding objectives alongside reproduction and production traits as a practical way of enhancing robustness of grazing beef cattle.
Author contributions
TZ: Conceptualization, Writing – review & editing, Writing – original draft.
Funding
The authors declared that financial support was not received for this work and/or its publication.
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References
Abo-Ismail M. K., Brito L. F., Miller S., Sargolzaei M., Grossi D. A., Moore S. S., et al. (2017). Genome-wide association studies and genomic prediction of breeding values for calving performance and body conformation traits in Holstein cattle. Genet. Selection Evol. 49, 82. doi: 10.1186/s12711-017-0356-8
Akinsola O. M., Musa A. A., Muansangi L., Singh S., Mukherjee S., and Mukherjee A. (2024). Genomic insights into adaptation and inbreeding among Sub-Saharan African cattle from pastoral and agropastoral systems. Front. Genet. 15, 1430291. doi: 10.3389/fgene.2024.1430291
Alempijevic A., Vidal-Calleja T., Falque R., Walmsley B., and McPhee M. (2025). 3D imaging for on-farm estimation of live cattle traits and carcass weight prediction. Meat Sci. 225, 109810. doi: 10.1016/j.meatsci.2025.109810
Alfonzo E. P. M., da Silva M. V. G. B., dos Santos Daltro D., Stumpf M. T., Dalcin V. C., Kolling G., et al. (2016). Relationship between physical attributes and heat stress in dairy cattle from different genetic groups. Int. J. Biometeorology 60, 245–253. doi: 10.1007/s00484-015-1021-y
Amorim S. T., Retallick K. J., Garcia A., Ibañez-Escriche N., and Morota G. (2025). Genetic heterogeneity of residual variance for foot score traits in american angus cattle. J. Anim. Breed. Genet. 143, 68–78. doi: 10.1111/jbg.12949
An B., Xia J., Chang T., Wang X., Xu L., Zhang L., et al. (2019). Genome-wide association study reveals candidate genes associated with body measurement traits in Chinese Wagyu beef cattle. Anim. Genet. 50, 386–390. doi: 10.1111/age.12805
Arndt S. S., Goerlich V. C., and van der Staay F. J. (2022). A dynamic concept of animal welfare: The role of appetitive and adverse internal and external factors and the animal’s ability to adapt to them. Front. Anim. Sci. 3, 908513. doi: 10.3389/fanim.2022.908513
Ayres D. R., Pereira R. J., Boligon A. A., Baldi F., Roso V. M., and Albuquerque L. G. (2015). Genetic parameters and investigation of genotype× environment interactions in Nellore× Hereford crossbred for resistance to cattle ticks in different regions of Brazil. J. Appl. Genet. 56, p107–p113. doi: 10.1007/s13353-014-0238-5
Baena M. M., Tizioto P. C., Meirelles S. L. C., and Regitano L. C. D. A. (2018). HSF1 and HSPA6 as functional candidate genes associated with heat tolerance in Angus cattle. Rev. Bras. Zootecnia 47, e20160390. doi: 10.1590/rbz4720160390
Barro A. G., Marestone B. S., dos S E. R., Ferreira G. A., Vero J. G., Terto D. K., et al. (2023). Genetic parameters for frame size and carcass traits in Nellore cattle. Trop. Anim. Health production 55, 71. doi: 10.1007/s11250-023-03464-z
Beef Improvement Federation (BIF) (2010). Available online at: http://www.beefimprovement.org/PDFs/guidelines/Guidelines-9th-Edition.pdf (Accessed February 11, 2023).
Begizew G. (2021). Agricultural production system in arid and semi-arid regions. Int. J. Agric. Sci. Food Technol. 7, 234–244. doi: 10.17352/2455-815X.000113
Bem R. D., Benfica L. F., Silva D. A., Carrara E. R., Brito L. F., Mulim H. A., et al. (2024). Assessing different metrics of pedigree and genomic inbreeding and inbreeding effect on growth, fertility, and feed efficiency traits in a closed-herd Nellore cattle population. BMC Genomics 25, 738. doi: 10.1186/s12864-024-10641-3
Bene S., Nagy B., Nagy L., Kiss J. P., Polgár J. P., and Szabo F. (2007). Comparison of body measurements of beef cows of different breeds. Arch. Anim. Breed. 50, 363–373. doi: 10.5194/aab-50-363-2007
Bermingham M. L., More S. J., Good M., Cromie A. R., Higgins I. M., Brotherstone S., et al. (2009). Genetics of tuberculosis in Irish Holstein–Friesian dairy herds. J. Dairy Sci. 92, 3447–3456. doi: 10.3168/jds.2008-1848
Berry D. P., Bermingham M. L., Good M., and More S. J. (2011). Genetics of animal health and disease in cattle. Irish Veterinary J. 64, 5. doi: 10.1186/2046-0481-64-5
Berry D. P., Buckley F., Dillon P., Evans R. D., and Veerkamp R. F. (2004). Genetic relationships among linear type traits, milk yield, body weight, fertility and somatic cell count in primiparous dairy cows. Irish J. Agric. Food Res. 43, 161–176.
Bester J., Matjuda E., Rust J. M., and Fourie H. J. (2003). “The Nguni: a case study,” in FAO Community-based management of animal genetic resources – proceedings of the workshop held in Mbabane, Swaziland 7–11 May 2001 (UNDP, GTZ, CTA, FAO, Rome), 45–68.
Biegelmeyer P., Nizoli L. Q., Da Silva S. S., dos Santos T. R. B., Dionello N. J. L., Gulias-Gomes C. C., et al. (2015). Bovine genetic resistance effects on biological traits of Rhipicephalus (Boophilus) microplus. Veterinary Parasitol. 208, 231–237. doi: 10.1016/j.vetpar.2015.01.010
Bishop S. C. and Woolliams J. A. (2014). Genomics and disease resistance studies in livestock. Livestock Sci. 166, 190–198. doi: 10.1016/j.livsci.2014.04.034
Bouwman A. C., Daetwyler H. D., Chamberlain A. J., Ponce C. H., Sargolzaei M., Schenkel F. S., et al. (2018). Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nat. Genet. 50, 362–367. doi: 10.1038/s41588-018-0056-5
Bowen M. K., Chudleigh F., Buck S., and Hopkins K. (2018). Productivity and profitability of forage options for beef production in the subtropics of northern Australia. Anim. Production Sci. 58, 332–342. doi: 10.1071/AN16180
Bradford H. L., Fragomeni B. O., Bertrand J. K., Lourenco D. A. L., and Misztal I. (2016). Genetic evaluations for growth heat tolerance in Angus cattle. J. Anim. Sci. 94, 4143–4150. doi: 10.2527/jas.2016-0707
Brotherstone S., White I. M. S., Coffey M., Downs S., Mitchell A., Clifton-Hadley R., et al. (2010). Evidence of genetic resistance of cattle to infection with Mycobacterium bovis. J. Dairy Sci. 93, 1234–1242. doi: 10.3168/jds.2009-2609
Brown-Brandl T. M. (2018). Understanding heat stress in beef cattle. Rev. Bras. Zootecnia 47, e20160414. doi: 10.1590/rbz4720160414
Caminade C., McIntyre K. M., and Jones A. E. (2019). Impact of recent and future climate change on vector-borne diseases. Ann. New York Acad. Sci. 1436, 157–173. doi: 10.1111/nyas.13950
Carabaño M. J., Ramón M., Menéndez-Buxadera A., Molina A., and Díaz C. (2019). Selecting for heat tolerance. Anim. Front. 9, 62–68. doi: 10.1093/af/vfy033
Carvalheiro R., Costilla R., Neves H. H., Albuquerque L. G., Moore S., and Hayes B. J. (2019). Unraveling genetic sensitivity of beef cattle to environmental variation under tropical conditions. Genet. Selection Evol. 51, 1–14. doi: 10.1186/s12711-019-0470-x
Casey N. H. (2023). Livestock adaptation to climate. Anim. Front. 13, p3–p5. doi: 10.1093/af/vfad048
Chen D. and Chen H. W. (2013). Using the Köppen classification to quantify climate variation and change: An example for 1901–2010. Environ. Dev. 6, 69–79. doi: 10.1016/j.envdev.2013.03.007
Chen Y., Du M., Liu Y., Gao X., Zhang W., Xu L., et al. (2018). Whole-exome sequencing identified rare variants associated with body length and girth in cattle. BioRxiv, 287474. doi: 10.1101/287474
Chen Q., Huang B., Zhan J., Wang J., Qu K., Zhang F., et al. (2020). Whole-genome analyses identify loci and selective signals associated with body size in cattle. J. Anim. Sci. 98, 1–8. doi: 10.1093/jas/skaa068
Ciccioli N. H., Wettemann R., Spicer L. J., Lents C. A., White F. J., and Keisler D. H. (2003). Influence of body condition at calving and postpartum nutrition on endocrine function and reproductive performance of primiparous beef cows. J. Anim. Sci. 81, 3107–3120. doi: 10.2527/2003.81123107x
Collins-Lusweti E. (2000). Performance of Nguni, Afrikander and Bonsmara cattle under drought conditions in the North West Province of Southern Africa. South Afr. J. Anim. Sci. 30, 33–33. doi: 10.4314/sajas.v30i1.3872
Czerniawska-Piątkowska E., Szewczuk M., Chociłowicz E., and Konstancik N. (2012). Comparison of Limousin and Simmental primiparous cows based on the variability of age at first calving, body weight and the analysis of their growth and development. Electronic J. Polish Agric. Universities 15, 7.
Delaby L., Buckley F., McHugh N., and Blanc F. (2018). “Robust animals for grass based production systems. Sustainable meat and milk production from grasslands,” in Proc. 27th general meeting of the european grassland federation (European Grassland Federation, Zürich, Switzerland, Cork, Ireland), 389–400.
DelCurto T., Hess B. W., Huston J. E., and Olson K. C. (2000). Optimum supplementation strategies for beef cattle consuming low-quality roughages in the western United States. J. Anim. Sci. 77, 1–16. doi: 10.2527/jas2000.77E-Suppl1v
Dikmen S., Cole J. B., Null D. J., and Hansen P. (2012). Heritability of rectal temperature and genetic correlations with production and reproduction traits in dairy cattle. J. Dairy Sci. 95, 3401–3405. doi: 10.3168/jds.2011-4306
Donoghue K. A., Rekaya R., Bertrand J. K., and Misztal I. (2004). Threshold-linear analysis of measures of fertility in artificial insemination data and days to calving in beef cattle. J. Anim. Sci. 82, 987–993. doi: 10.2527/2004.824987x
Doyle J. L., Berry D. P., Veerkamp R. F., Carthy T. R., Walsh S. W., Evans R. D., et al. (2020). Genomic regions associated with skeletal type traits in beef and dairy cattle are common to regions associated with carcass traits, feed intake and calving difficulty. Front. Genet. 11, 20. doi: 10.3389/fgene.2020.00020
Drouillard J. S. (2018). Current situation and future trends for beef production in the United States of America - A review. Asian-Australasian J. Anim. Sci. 31, 1007–1016. doi: 10.5713/ajas.18.0428
Dubey A., Mishra S., Khune V., Gupta P. K., Sahu B. K., and Nandanwar A. K. (2012). Improving linear type traits to improve production sustainability and longevity in purebred Sahiwal cattle. J. Agric. Sci. Technol. 2, 636–639.
Dzavo T., Zindove T. J., Dhliwayo M., and Chimonyo M. (2019). Effects of drought on cattle production in sub-tropical environments. Trop. Anim. Health Production 51, 669–675. doi: 10.1007/s11250-018-1741-1
Dzavo T., Zindove T. J., Dhliwayo M., Chimonyo M., and Tivapasi M. T. (2020). Do haematological profiles of cows in drought prone areas differ with conformation? Spanish J. Agric. Res. 18, 12. doi: 10.5424/sjar/2020182-16029
Dzhulamanov K. M., Gerasimov N. P., Ruchay A. N., Kolpakov V. I., and Dzhulamanov E. B. (2019). The assessment of morphological features in Hereford cattle. In IOP Conf. Series: Earth Environ. Sci. 341, 012062. doi: 10.1088/1755-1315/341/1/012062
Engle B. N., Corbet N. J., Allen J. M., Laing A. R., Fordyce G., McGowan M. R., et al. (2018). Multivariate genomic predictions for age at puberty in tropically adapted beef heifers. J. Anim. Sci. 97, 90–100. doi: 10.1093/jas/sky428
Espigolan R., Baldi F., Boligon A. A., Souza F. R., Gordo D. G., Tonussi R. L., et al. (2013). Study of whole genome linkage disequilibrium in Nellore cattle. BMC Genomics 14, 305. doi: 10.1186/1471-2164-14-305
Farias G. D., Cerdótes L., Restle J., Pascoal L. L., Costa P. T., Ferreira O. G. L., et al. (2018). Body size and its effects on productive efficiency of cows with predominant Nellore genetic composition. Acta Scientiarum Anim. Sci. 40, e42532. doi: 10.4025/actascianimsci.v40i1.42532
Filipčík R., Falta D., Kopec T., Chládek G., Večeřa M., and Rečková Z. (2020). Environmental factors and genetic parameters of beef traits in fleckvieh cattle using field and station testing. Animals 10, 2159–2169. doi: 10.3390/ani10112159
Freetly H. C., Kuehn L. A., Thallman R. M., and Snelling W. M. (2020). Heritability and genetic correlations of feed intake, body weight gain, residual gain, and residual feed intake of beef cattle as heifers and cows. J. Anim. Sci. 98, skz394. doi: 10.1093/jas/skz394
Freitas P. H. F., Johnson J. S., Wen H., Jacob M., Maskal F. T., Maltecca C., et al. (2023). Genetic parameters for automatically-measured vaginal temperature, respiration efficiency, and other thermotolerance indicators measured on lactating sows under heat stress conditions. Genet. Selection Evol. 55, 65. doi: 10.1186/s12711-023-00842-x
French P. D. (2006). Dry matter intake and blood parameters of nonlactating Holstein and Jersey cows in late gestation. J. Dairy Sci. 89, 1057–1061. doi: 10.3168/jds.S0022-0302(06)72173-7
Gifford-Gonzalez D. (2000). Animal disease challenges to the emergence of pastoralism in sub-Saharan Africa. Afr. Archaeological Rev. 17, 95–139. doi: 10.1023/A:1006601020217
Gillooly J. F., Brown J. H., West G. B., Savage V. M., and Charnov E. L. (2001). Effects of size and temperature on metabolic rate. Science 293, 2248–2251. doi: 10.1126/science.1061967
Gutierrez J. P. and Goyache F. (2001). Estimation of genetic parameters of type traits in Asturiana de los Valles beef cattle breed. J. Anim. Breed. Genet. 119, 93–100. doi: 10.1046/j.1439-0388.2002.00324.x
Hallgrimsson B., Green R. M., Katz D. C., Fish J. L., Bernier F., Roseman C. C., et al. (2019). The developmental genetics of canalization. Semin. Cell Dev. Biol. 88, 67–79. doi: 10.1016/j.semcdb.2018.05.019
Hansen L. B., Cole J. B., Marx G. D., and Seykora A. J. (1999). Productive life and reasons for disposal of Holstein cows selected for large versus small body size. J. Dairy Sci. 82, 795–801. doi: 10.3168/jds.S0022-0302(99)75298-7
Hayes B. J., Bowman J., Chamberlain A. J., Savin K., van Tassell C., Sonstegard T. S., et al. (2009). A validated genome wide association study to breed cattle adapted to an environment altered by climate change. PloS One 4, e6676. doi: 10.1371/journal.pone.0006676
Hindman M. S., Huedepohl B., Dewell G. A., Brick T. A., Silva G. S., and Engelken T. J. (2022). Physical traits and reproductive measurements associated with early conception in beef replacement heifers. Animals 12, 1910. doi: 10.3390/ani12151910
Johnson C., Richeson J. T., Rudd J., Salak-Johnson J. L., Lalman D. L., Biggs R., et al. (2025). Does selection of beef cattle for production traits impact post-weaning immunological phenotype and robustness traits. J. Anim. Sci. 103, 3–3. doi: 10.1093/jas/skaf170.003
Kamprasert N., Duijvesteijn N., and van der Werf J. H. J. (2019). Estimation of genetic parameters for BW and body measurements in Brahman cattle. Animal 13, 1576–1582. doi: 10.1017/S1751731118003348
Khan M. A. and Khan M. S. (2016). The heritability estimates of linear type traits in Sahiwal cows. J. Anim. Plant Sci. 26, 25–33.
Khan M., Khan M., and Waheed A. (2018). Morphological measurements and their heritabilities for Sahiwal cattle in Pakistan. J. Anim. Plant Sci. 28, 431–440.
Khatri Y. and Huff-Lonergan E. (2025). Eating quality of ribeye steaks from young bulls and steers is comparable: A study examining the effect of sex, frame size, and feed time on palatability attributes. Food Sci. Nutr. 13, e70359. doi: 10.1002/fsn3.70359
Kiage L. M. (2013). Perspectives on the assumed causes of land degradation in the rangelands of Sub-Saharan Africa. Prog. Phys. Geogr. 37, 664–684. doi: 10.1177/0309133313492543
Kipkorir K. K. (2024). Incidences and impacts of climate-induced vector borne diseases in arid and semi-arid regions of Kenya. J. Aquat. Terrestrial Ecosyst. 2, 48–61. doi: 10.13140/RG.2.2.21305.38248
Kirschten D. P. (2001). Genetic parameters of linear type traits for beef cattle and their correlation with production of Simmental cows (Montana, USA: Doctoral dissertation, Montana State University-Bozeman, College of Agriculture).
Klopcic M., Reents R., Philipsson J., and Kuipers A. (2009). Breeding for robustness in cattle (The Netherlands: Wageningen Academic Publisher).
Krupová Z., Krupa E., Brzáková M., Veselá Z., and Malát K. (2025). Beef breeding systems and preferences for breeding objective traits. Animals 15, 2175. doi: 10.3390/ani15152175
Larroque H. and Ducrocq V. (2001). Relationships between type and longevity in the Holstein breed. Genet. Selection Evol. 33, 39–59. doi: 10.1186/1297-9686-33-1-39
Lata K., Das G., Verma R., and Baghel R. P. S. (2018). Impact of climate variability on occurrence and distribution of vector and vector-borne parasitic diseases. J. Entomology Zoology Stud. 6, 1388–1393.
Lewis R., Kostermans T., Brovold J. W., Laique T., and Ocepek M. (2025). Automated body condition scoring in dairy cows using 2D imaging and deep learning. AgriEngineering 7, 241. doi: 10.3390/agriengineering7070241
Li D., Yan G., Li F., Lin H., Jiao H., Han H., et al. (2024). Optimized machine learning models for predicting core body temperature in dairy cows: enhancing accuracy and interpretability for practical livestock management. Animals 14, 2724. doi: 10.3390/ani14182724
Lillehammer M., Hayes B. J., Meuwissen T. H. E., and Goddard M. E. (2009). Gene by environment interactions for production traits in Australian dairy cattle. J. Dairy Sci. 92, 4008–4017. doi: 10.3168/jds.2008-1484
Long M., Wang B., Yang Z., and Lu X. (2024). Genome-wide association study as an efficacious approach to discover candidate genes associated with body linear type traits in dairy cattle. Animals 14, 2181. doi: 10.3390/ani14152181
Lopes L. B., Eckstein C., Pina D. S., and Carnevalli R. A. (2016). The influence of trees on the thermal environment and behaviour of grazing heifers in Brazilian Midwest. Trop. Anim. Health Production 48, 755–761. doi: 10.1007/s11250-016-1021-x
Luo H., Li X., Hu L., Xu W., Chu Q., Liu A., et al. (2021). Genomic analyses and biological validation of candidate genes for rectal temperature as an indicator of heat stress in Holstein cattle. J. Dairy Sci. 104, 4441–4451. doi: 10.3168/jds.2020-18725
Maciel S., Amimo J., Martins M., Okeyo A. M., Scholtz M. M., and Neser F. W. C. (2013). Feedlot performance of the Nguni ecotypes in southern Mozambique. Livestock Res. Rural Dev. 25, 6.
Magnabosco C. D. U., Ojala M., De Los Reyes A., Sainz R. D., Fernandes A., and Famula T. R. (2002). Estimates of environmental effects and genetic parameters for body measurements and weight in Brahman cattle raised in Mexico. J. Anim. Breed. Genet. 119, 221–228. doi: 10.1046/j.1439-0388.2002.00339.x
Maiwashe A. N., Bradfield M. J., Theron H. E., and Van Wyk J. B. (2002). Genetic parameter estimates for body measurements and growth traits in South African Bonsmara cattle. Livestock Production Sci. 75, 293–300. doi: 10.1016/S0301-6226(01)00324-4
Mapiye C., Chimonyo M., Dzama K., Raats J. G., and Mapekula M. (2009). Opportunities for improving Nguni cattle production in the smallholder farming systems of South Africa. Livestock Sci. 124, 196–204. doi: 10.1016/j.livsci.2009.01.013
Marinov I., Penev T., and Gergovska Z. (2015). Factors affecting linear type traits in black-and-white cows. Int. J. Curr. Microbiol. Appl. Sci. 4, 374–383.
Martínez-Velázquez G., Gregory K. E., Bennett G. L., and Van Vleck L. D. (2003). Genetic relationships between scrotal circumference and female reproductive traits. J. Anim. Sci. 81, 395–401. doi: 10.2527/2003.812395x
Matope A., Zindove T. J., Dhliwayo M., and Chimonyo M. (2020). Mitigating the effects of drought on cattle production in communal rangelands of Zimbabwe. Trop. Anim. Health Production 52, 1–10. doi: 10.1007/s11250-019-02020-y
Matope A., Zindove T., Dhliwayo M., Chimonyo M., and Tivapasi M. (2023). Heat tolerance in Mashona beef cows in semi-arid rangelands: does conformation matter? Acta Agric. Slov. 119, 1–11. doi: 10.14720/aas.2023.119.1.2259
Mazza S., Guzzo N., Sartori C., Berry D. P., and Mantovani R. (2014). Genetic parameters for linear type traits in the Rendena dual-purpose breed. J. Anim. Breed. Genet. 131, 27–35. doi: 10.1111/jbg.12049
McManus C., Barcellos J. O., Formenton B. K., Hermuche P. M., de Carvalho O. A. Jr., Guimarães R., et al. (2016). Dynamics of cattle production in Brazil. PloS One 11, e0147138. doi: 10.1371/journal.pone.0147138
Meyer K., Hammond K., Parnell F., Mackinnon M. J., and Sivarajasingam S. (1990). Estimates of heritability and repeatability for reproductive traits in Australian beef cattle. Livestock Production Sci. 25, 15–30. doi: 10.1016/0301-6226(90)90038-8
Miles A. M., Posbergh C. J., and Huson H. J. (2021). Direct phenotyping and principal component analysis of type traits implicate novel QTL in bovine mastitis through genome-wide association. Animals 11, 1147. doi: 10.3390/ani11041147
Moré D. D., Cardoso F. F., Mudadu M. A., Malagó-Jr W., Gulias-Gomes C. C., Sollero B. P., et al. (2019). Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin. BMC Genomics 20, 1–20. doi: 10.1186/s12864-019-6360-3
Morris C. A. (2007). A review of genetic resistance to disease in Bos taurus cattle. Veterinary J. 174, 481–491. doi: 10.1016/j.tvjl.2006.09.006
Morris C. A., Green R. S., Cullen N. G., and Hickey S. M. (2003). Genetic and phenotypic relationships among faecal egg count, anti-nematode antibody level and live weight in Angus cattle. Anim. Sci. 76, 167–174. doi: 10.1017/S135772980005342X
Mosher M. K., Olorunkoya S., Bhowmik N., Ringwall K. A., Hanna L. L. H., and Swanson K. C. (2021). “Effects of cow size on measures of efficiency in lactating multiparous crossbred beef cattle,” in 2021 north dakota livestock research report (Fargo, ND, USA: North Dakota State University) 32.
Mota R. R., Lopes S., Tempelman R. J., Silva F. F., Aguilar I., Gomes C. C. G., et al. (2016). Genome-enabled prediction for tick resistance in Hereford and Braford beef cattle via reaction norm models. J. Anim. Sci. 94, 1834–1843. doi: 10.2527/jas.2015-0194
Musemwa L., Mushunje A., Chimonyo M., and Mapiye C. (2010). Low cattle market off-take rates in communal production systems of South Africa: Causes and mitigation strategies. Journal of Sustainable Development in Africa. 12, 209–226.
Mwacharo J. M., Okeyo A. M., Kamande G. K., and Rege J. E. O. (2006). The small East African shorthorn zebu cows in Kenya. I: Linear body measurements. Trop. Anim. Health Production 38, 65–74. doi: 10.1007/s11250-006-4266-y
Nasca J. A., Feldkamp C. R., Arroquy J. I., and Colombatto D. (2015). Efficiency and stability in subtropical beef cattle grazing systems in the northwest of Argentina. Agric. Syst. 133, 85–96. doi: 10.1016/j.agsy.2014.10.014
Nel C. L., van der Werf J. H., Rauw W. M., and Cloete S. W. (2023). Challenges and strategies for genetic selection of sheep better adapted to harsh environments. Anim. Front. 13, 43–52. doi: 10.1093/af/vfad055
Němcová E., Štípková M., and Zavadilová L. (2011). Genetic parameters for linear type traits in Czech Holstein cattle. Czech J. Anim. Sci. 56, 157–162. doi: 10.17221/1435-CJAS
Neto J. S., Brito L. F., Mota L. F. M., Silva M. R. G., Rodrigues G. R. D., and Baldi F. (2025). Exploring the impact of heat stress on feed efficiency in tropical beef cattle using genomic reaction norm models. animal 19, 101612. doi: 10.1016/j.animal.2025.101612
Nguyen T. T. T., Bowman P. J., Haile-Mariam M., Pryce J. E., and Hayes B. J. (2016). Genomic selection for tolerance to heat stress in Australian dairy cattle. J. Dairy Sci. 99, 2849–2862. doi: 10.3168/jds.2015-9685
Nielsen H. M., Amer R., and Byrne T. J. (2014). Approaches to formulating practical breeding objectives for animal production systems. Acta Agriculturae Scandinavica Section A—Animal Sci. 64, 2–12. doi: 10.1080/09064702.2013.827237
Nilchuen, Suwanasopee T., and Koonawootrittriron S. (2025). Integrating deep learning and mobile imaging for assessment of automated conformational indices and weight prediction in Brahman cattle. Smart Agric. Technol. 12, 101079. doi: 10.1016/j.atech.2025.101079
Norris D., Banga C., Benyi K., and Sithole B. C. (2004). Estimation of genetic parameters and variance components for growth traits of Nguni cattle in Limpopo Province, South Africa. Trop. Anim. Health Production 36, 801–806. doi: 10.1023/B:TROP.0000045966.59590.96
Nqeno N. (2008). Reproductive performance of cows in sweet and sour veld types under communal production systems in the eastern cape province of South Africa (South Africa: MSc thesis, University of Fort Hare).
Nyangiwe N., Yawa M., and Muchenje V. (2018). Driving forces for changes in geographic range of cattle ticks (Acari: Ixodidae) in Africa: A review. South Afr. J. Anim. Sci. 48, 829–841. doi: 10.4314/sajas.v48i5.4
Nye J., Zingaretti L. M., and Pérez-Enciso M. (2020). Estimating conformational traits in dairy cattle with DeepAPS: a two-step deep learning automated phenotyping and segmentation approach. Front. Genet. 11, 513. doi: 10.3389/fgene.2020.00513
Ojo A. O., Mulim H. A., Campos G. S., Junqueira V. S., Lemenager R., Schoonmaker J., et al. (2024). Exploring feed efficiency in beef cattle: from data collection to genetic and nutritional modeling. Animals 14, 3633. doi: 10.3390/ani14243633
Olasege B. S., Tahir M. S., Gouveia G. C., Kour J., Porto-Neto L. R., Hayes B. J., et al. (2021). Genetic parameter estimates for male and female fertility traits using genomic data to improve fertility in Australian beef cattle. Anim. Production Science. 61, 1863–1872. doi: 10.1071/AN21097
Olson C. A., Li C., Block H., McKeown L., and Basarab J. A. (2021). Phenotypic and genetic correlations of beef replacement heifer feeding behaviour, feed intake and feed efficiency with cow performance and lifetime productivity. J. Anim. Breed. Genet. 138, 300–313. doi: 10.1111/jbg.12522
Pereira J. M. V., Marcondes M. I., de Campos Valadares Caton F. S.J., Sguizzato A. L. L., and Silva A. L. (2020). Digestive parameters during gestation of Holstein heifers: Digestion during pregnancy. Livestock Sci. 242, 104325. doi: 10.1016/j.livsci.2020.104325
Rauw W. M. and Gomez-Raya L. (2015). Genotype by environment interaction and breeding for robustness in livestock. Front. Genet. 6, 310. doi: 10.3389/fgene.2015.00310
Ravagnolo O. and Misztal I. (2000). Genetic component of heat stress in dairy cattle, parameter estimation. J. Dairy Sci. 83, 2126–2130. doi: 10.3168/jds.S0022-0302(00)75095-8
Reverter L. R., Forte M. R. S., Kasarapu P., De Cara M. A. R., Burrow H. M., and Lehnert S. A. (2017). Genomic inbreeding depression for climatic adaptation of tropical beef cattle. J. Anim. Sci. 95, 3809–3821. doi: 10.2527/jas.2017.1643
Reynolds C. K., Dürst B., Lupoli B., Humphries D. J., and Beever D. E. (2004). Visceral tissue mass and rumen volume in dairy cows during the transition from late gestation to early lactation. J. dairy Sci. 87, 961–971. doi: 10.3168/jds.S0022-0302(04)73240-3
Rodrigues G. R., Valente J., Rezende V. T., Araújo L. F., Silva Neto J. B., Mota L. F., et al. (2025). Genetic parameters of resilience indicators across growth in beef heifers and their associations with weight, reproduction, calf performance and pre-weaning survival. J. Anim. Breed. Genet. 143, 79–91. doi: 10.1111/jbg.70001
Rowan T. N. (2022). Invited Review: Genetic decision tools for increasing cow efficiency and sustainability in forage-based beef systems. Appl. Anim. Sci. 38, 660–670. doi: 10.15232/aas.2022-02306
Rust T. and Groeneveld E. (2002). Variance component estimation of female fertility traits in two indigenous and two European beef cattle breeds of South Africa. South Afr. J. Anim. Sci. 32, 23–29. doi: 10.4314/sajas.v32i1.3787
Şentürklü S., Landblom D., Paisley S., Wachenheim C., and Maddock R. (2021). Frame score, grazing and delayed feedlot entry effect on performance and economics of beef steers from small-and large-framed cows in an integrated crop-livestock system. Animals 11, 3270. doi: 10.3390/ani1111327
Salin S., Vanhatalo A., Elo K., Taponen J., Boston R. C., and Kokkonen T. (2017). Effects of dietary energy allowance and decline in dry matter intake during the dry period on responses to glucose and insulin in transition dairy cows. J. Dairy Sci. 100, 5266–5280. doi: 10.3168/jds.2016-11871
Santana M. L., Bignardi A. B., Rodrigues G. R., Cyrillo J. N., Brito L. F., and Mercadante M. E. (2025). Genotype-by-environment interaction for yearling weight of Nellore cattle in pasture and feedlot conditions using a “double” genomic reaction norm model. J. Anim. Sci. 103, skaf169. doi: 10.1093/jas/skaf169
Scasta J. D., Lalman D. L., and Henderson L. (2016). Drought mitigation for grazing operations: matching the animal to the environment. Rangelands 38, 204–210. doi: 10.1016/j.rala.2016.06.006
Sejian V., Bhatta R., Gaughan J. B., Dunshea F. R., and Lacetera N. (2018). Adaptation of animals to heat stress. Animal 12, s431–s444. doi: 10.1017/S1751731118001945
Shang Z. H., Gibb M. J., and Long R. J. (2012). Effect of snow disasters on livestock farming in some rangeland regions of China and mitigation strategies–a review. Rangeland J. 34, 89–101. doi: 10.1071/RJ11052
Shin S., Lee J., and Do C. (2021). Genetic relationship of age at first calving with conformation traits and calving interval in Hanwoo cows. J. Anim. Sci. Technol. 63, 740. doi: 10.5187/jast.2021.e73
Sigdel A., Liu L., Abdollahi-Arpanahi R., Aguilar I., and Peñagaricano F. (2020). Genetic dissection of reproductive performance of dairy cows under heat stress. Anim. Genet. 51, 511–520. doi: 10.1111/age.12943
Silva Neto J. B., Mota L. F., Londoño-Gil M., Schmidt I., Rodrigues G. R., Ligori V. A., et al. (2024). Genotype-by-environment interactions in beef and dairy cattle populations: a review of methodologies and perspectives on research and applications. Anim. Genet. 55, p871–p892. doi: 10.1111/age.13483
Soren S., Singh S. V., Borah S., Kumar A., and Dwivedi D. K. (2018). Global warming: impact, adaptation and ameliorative measures of semen quality under tropical climatic conditions in crossbred bulls. J. Anim. Res. 8, 731. doi: 10.30954/2277-940X.10.2018.1
Sousa Junior L. B., Pinto L. F. B., Cruz V. A., Oliveira Junior G. A., Oliveira H. R., Chud T. S., et al. (2024). Genome-wide association and functional genomic analyses for body conformation traits in North American Holstein cattle. Front. Genet. 15, 1478788. doi: 10.3389/fgene.2024.1478788
Taiwo B. B. A., Olaniran O. D. D., and Aluko F. A. (2010). Breed and environmental factors affecting body measurements of beef cattle in Yewa, Nigeria. Agric. J. 3, 211–214. doi: 10.3923/aj.2010.211.214
Tongue L. K. and Ngapagna A. N. (2019). “Emerging vector-borne diseases in central africa: A threat to animal production and human health,” in Current topics in the epidemiology of vector-borne diseases (London, UK: IntechOpen).
Twomey A. J., Berry D. P., Evans R. D., Doherty M. L., Graham D. A., and Purfield D. C. (2019). Genome-wide association study of endo-parasite phenotypes using imputed whole-genome sequence data in dairy and beef cattle. Genet. Selection Evol. 51, 15. doi: 10.1186/s12711-019-0457-7
Urruty N., Tailliez-Lefebvre D., and Huyghe C. (2016). Stability, robustness, vulnerability and resilience of agricultural systems. A review. Agron. Sustain. Dev. 36, p1–15. doi: 10.1007/s13593-015-0347-5
van der Westhuizen L., MacNeil M., and Scholtz M. (2019). The genetic diversity (and sturdiness) of South Africa’s landrace cattle breeds: research and training. Red Meat/Rooivleis 10, 82–87.
Van der Westhuizen R. R., Schoeman S. J., Jordaan G. F., and Van Wyk J. B. (2001). Genetic parameters for reproductive traits in a beef cattle herd estimated using multitrait analysis. South Afr. J. Anim. Sci. 31, 41–48. doi: 10.4314/sajas.v31i1.3846
Van Nieuwenhuizen G. E. (2017). The relationship between pelvis size and age, breed, gender, body measurements and reproductive status of South African beef cattle (The Netherlands: Master’s thesis). Utrecht University).
Veerkamp R. F., Gerritsen C. L. M., Koenen E. P. C., Hamoen A., and De Jong G. (2002). Evaluation of classifiers that score linear type traits and body condition score using common sires J. Dairy Sci. 85, 976–983. doi: 10.3168/jds.S0022-0302(02)74157-X
Vesela Z., Pribyl J., Safus P., Vostry L., Seba K., and Stolc L. (2005). Breeding value for type traits in beef cattle in the Czech Republic. Czech J. Anim. Sci. 9, 385–393.
Wilson R. T. (2018). Crossbreeding of cattle in africa. J. Agric. Environ. Sci. 7, 16–31. doi: 10.15640/jaes.v7n1a3
Yang J., Zhang Z., Lu X., and Yang Z. (2023). Effect of dam body conformations on birth traits of calves in chinese holsteins. Animals 13, 2253. doi: 10.3390/ani13142253
Zindove T. J. and Chimonyo M. (2015). Are calving interval, abortions, incidence of stillbirths and pre-weaning losses in Nguni cows associated with linear type traits? Anim. Reprod. Sci. 160, 49–56. doi: 10.1016/j.anireprosci.2015.07.002
Zindove T. J., Chimonyo M., and Nephawe K. A. (2015). Relationship between linear type and fertility traits in Nguni cows. Animal 9, 944–995. doi: 10.1017/S1751731114003231
Keywords: adaptation, genetic selection, climate change, arid areas, semi-arid areas, beef cattle, robustness, body conformation
Citation: Zindove TJ (2026) Body conformation and robustness as potential frontiers in pasture-based beef cattle breeding: a narrative review. Front. Anim. Sci. 6:1637995. doi: 10.3389/fanim.2025.1637995
Received: 30 May 2025; Accepted: 11 December 2025; Revised: 17 October 2025;
Published: 09 January 2026.
Edited by:
Alexandre Rossetto Garcia, Brazilian Agricultural Research Corporation (EMBRAPA), BrazilReviewed by:
Joao B. Silva Neto, São Paulo State University, BrazilIsabella De Carvalho, São Paulo State University, Brazil
Tawheed Ahmad Shafi, Maharashtra Animal and Fishery Sciences University, India
Copyright © 2026 Zindove. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Titus J. Zindove, VGl0dXMuWmluZG92ZUBsaW5jb2xuLmFjLm56