Perspective Chapter: Tracing the Growth of the Domestic Pig

Abstract

The phenomenon of growth can be viewed as the key basis of pig production and has therefore long been the subject of intensive research. Growth is the result of a series of biological processes in the animal and it is not surprising that an immense amount of research has been carried out by scientists from various disciplines. Since growth is influenced by genetic and environmental factors and their interaction, most of the studies mentioned here deal with these aspects. In addition, this review deals with the different methods for the description of growth and the application of some growth models in various studies in pigs. It is concluded that understanding the relationships between the factors that influence growth and developing accurate models are essential for implementing strategies to better utilize growth potential of pigs.

1. Introduction

Organismic growth is the quintessential expression of life, which encompasses all processes by which elements and compounds are transformed over time into new living mass-biomass [1]. It is clearly one of the most important characteristics of living beings. The changes occurring during growth are realized quantitatively and qualitatively in different ways. Hammond [2] distinguished that “as an animal grows up, two things happen: (i) it increases in weight until it reaches adult size; this we call growth, and (ii) it changes its body shape and form, and its various functions and capacities come into full play; this we call development.” This postulate encompasses the two most common understandings of growth in animal science: on the one hand, the increase in body size per unit of time and, on the other hand, the formation of new structures and the change in the proportion of body components (morphogenesis), i.e., development. In this respect, an increase in size, length, girth, weight or other physical appearance may be considered growth. However, if the change occurs at an organizational level or as the formation of new structures and alteration of the proportions of body parts, this may be considered as development. Developmental studies require laborious work involving serial dissection trials on genetically identical animals kept under typical environmental conditions and fed the same diet [2, 3]. Besides this, several non-invasive techniques are used nowadays to provide such information without the need for slaughter of the investigated animals (see chapter: Tracing the Inside of Pigs Non-invasively: Recent Developments). These studies revealed the sequence of events during the growth of the major tissues from conception to maturity and established the order of maturation for each tissue, described as waves of growth in Figure 1. By the time all tissues have reached mature size, daily feed intake may have declined from its peak and often fluctuates seasonally [4]. In both the prenatal and postnatal phases, the growth of the animals is subject to various influences.

Figure 1.

Waves of growth: 1 = nerve tissue; 2 = bone; 3 = muscle; 4 = fat; 5 = daily feed intake [4].

The growth of the modern domestic pig is faster, more intensive and less understood than the growth of its wild relative. The reason for this is that once the pig has reached the peak of its “productive” life, it is usually culled from the population. During the long selection work to improve the economic traits of pigs, the genes that lead to increased growth were regularly used to obtain larger muscles at younger ages. Accordingly, the genetic variants that have better growth characteristics and are therefore selected for the parents have negative effects on the overall health and longevity of their offspring. This is called incidental trade-off. Species-specific maximum expected lifetime for a domestic pig is 27 years; its fertile age ends at 6, while its effective lifespan is reported to be 1.8 years of age, which makes up about 7% of their potential lifespan [5].

The remarkable growth rate of farming pigs until they reach their final slaughter weight was achieved by the implementation of research results in the field of genetics and selection combined with the studies on their nutritional requirements involving other environmental factors such as housing and others.

2. Factors influencing the growth of animals

The main factors affecting the growth of animals are listed in Table 1 [6]. These factors vary during the different stages of growth, particularly prenatal and postnatal growth, the latter of which can be further subdivided into pre-weaning and post-weaning growth. Common factors influencing prenatal and postnatal growth of pigs are related to the sow’s genotype, nutrition, age, parity and the number and size of embryos in the uterus (litter size).

Prenatal	Postnatal
	Before weaning	After the weaning
Genotype; Hormonal status; Genotype of the dam; Nutrition of the dam; Litter size; Age of the dam; Other environmental factors (temperature, humidity, hygiene, management, etc.)	Genotype; Hormonal status; Genotype of the dam; Nutrition of the dam; Litter size, weight at birth; Age of the dam; Milk production of the dam; Other environmental factors (temperature, humidity, hygiene, management, etc.)	Genotype; Hormonal status; Genotype of the animal; Nutrition of the animal; Sex; Weight at weaning; Other environmental factors (temperature, humidity, hygiene, management, etc.)

Table 1.

Important factors influencing the growth of animals [6].

Along with these, hormonal status plays a significant role in the regulation of prenatal growth in pigs. Some of the hormones that have been studied in relation to prenatal growth of pigs are cortisol, insulin, leptin and thyroid hormones. Cortisol is a stress hormone that can cross the placenta and affect the development of the fetal brain, immune system and metabolism. Insulin is a hormone that regulates blood glucose levels and influences fetal growth and body composition. Leptin is a hormone that is produced by adipose tissue and regulates appetite, energy expenditure and fat deposition. Thyroid hormones are involved in the regulation of basal metabolic rate, thermogenesis and tissue differentiation [7]. The hormonal status of the dam and the fetus can interact and influence each other, resulting in different outcomes for the prenatal growth of pigs. For example, high cortisol levels in the sow can reduce placental blood flow and nutrient delivery to the fetus, leading to intrauterine growth retardation [8]. On the other hand, high insulin levels in the fetus can stimulate fetal growth and increase birth weight, but they also increase the risk of metabolic disorders later in life [9]. Therefore, hormonal status is important not only for the prenatal growth of pigs but also for their postnatal health and performance.

To be precise, the above factors can influence the survival, health performance, growth and development of piglets before and after birth. After the weaning, genotype, nutrition and sex of the fatteners become major factors that influence pig growth. The influence of sex has become very interesting in that respect due to the voluntary initiative aimed at stopping the surgical castration of male pigs in the European Union. Following this initiative, research began to flourish on the effects of different castration methods (surgical, immunological or no castration) and sex (male or female) on the growth performance, carcass composition and meat quality of pigs. Entire male pigs have been found to have a better feed conversion ratio, lower fat deposition and a higher lean meat percentage than surgically castrated and immunocastrated pigs. However, they also have a higher risk of boar taint, i.e., an unpleasant odor and taste in cooked pork caused by the accumulation of androstenone and skatole in the adipose tissue. Female pigs, on the other hand, have intermediate growth performance, carcass composition and meat quality between entire males and surgically castrated pigs. Surgically castrated pigs have lower growth rates, higher feed conversion ratio and higher fat deposition than entire males and immunocastrated pigs. Immunocastrated pigs have similar growth rate and feed efficiency as entire males before the second vaccination and similar carcass composition and meat quality as surgically castrated pigs after the second vaccination [10].

Undoubtedly, animal growth is subject to many biological processes. These processes are greatly influenced by genetic and environmental factors. The genotype of an animal determines its potential, while the environment influences the stage at which genetic potential is expressed.

2.1 Genetic studies on pig growth

The genetic status of pigs and its influence on growth and other production traits has been extensively studied over the past decades, leading to tremendous advances in methodology and improvements in the traits studied. Initially, genetic influences were studied based on phenotypic analyses of data across families in which a particular gene with a large effect (major gene) segregates. A good example of major genes is the so-called “halothane” or malignant hyperthermia syndrome (MHS) gene which encodes the sarcoplasmic reticulum calcium release channel or type 1 ryanodine receptor (Ryr1) and the RN—(Rendement Napole) or PRKGA3 gene, encoding the γ3 subunit of adenosine monophosphate-activated protein kinase (AMPK), a protein that plays a key role in energy metabolism in skeletal muscle; both important for carcass and meat quality traits. These were originally determined using segregation analysis without DNA marker information. Later, DNA markers were used to locate genes responsible for qualitative traits such as coat color, as well as genes with significant effects on quantitative traits such as growth rate, intramuscular fat (IMF), etc. In this sense, a number of growth-related genes involved in the regulation of growth rate, feed intake, stimulation of anabolic processes, regulation of growth hormones, energy balance, control of embryonic muscle fiber development and its postnatal hypertrophic growth have been studied. Good examples are the studies on melanocortin-4 receptor (MC4R), insulin-like growth factor I (IGF1), pituitary-specific transcription factor-1 (PIT1), leptin (LEP), myostatin (MSTN), growth hormone 1 (GH1), MyoD gene family and others [11, 12]. The common approach used in this research is called quantitative trait locus (QTL) mapping. Compared to this method, the candidate gene approach has proven to be relatively simple. In this approach, polymorphisms in candidate genes are used to detect associations between different populations. When these associations are identified, the resulting marker can potentially be used in breeding programs. DNA-level information together with phenotypic data, can increase the accuracy of selection, which in this case is called marker-assisted selection (MAS) scheme [13, 14, 15, 16].

After the turn of the millennium, genomics has advanced at a rapid pace. The genetic architecture of traits related to growth was found to be complex and controlled by many interacting genes. However, the statistical tools used for genomic analyses have evolved significantly in the form of sophisticated software that can process large amounts of data. The combination of dramatically improved computing power and a new generation of analytical technologies led to great genetic improvement in domestic animals. Since the first successful genome-wide association (GWA) study in humans by Klein [17], this method has become very popular in livestock research. When performed with care, genome-wide association study (GWAS) has proven to be an accurate method for identifying genes associated with different phenotypes in livestock and a powerful tool for elucidating complex traits including growth [18, 19]. In this sense, the genome-wide association study (GWAS) has been widely used in the study of genes responsible for growth traits, resulting in more than 600 QTLs associated with average daily gain (ADG) and age at 100 kg live weight (AGE), as reported by Tang et al. [20]. In the same study, the authors found 27 newly discovered genome-wide significant single nucleotide polymorphisms (SNPs) and identified nine new QTLs that can be used in pig breeding. This information could be used in genomic selection to improve ADG and AGE and achieve better outcomes in pig production.

When analyzing such complex traits as growth, the gene-by-gene approach soon proved limited compared to the gene network approach based on SNP-by-SNP analysis of co-associations, interactions and molecular pathways [21]. For example, Zhao et al. [22] constructed a map of the regulatory signaling flow network to investigate key genes involved in muscle development and their interactions (Figure 2). In this way, 763 and 909 potential direct interactions between differentially expressed genes were identified between Landrace (LR) lean and Lantang (LT) obese pigs, respectively. Such an approach can be a major step forward in improving meat production in terms of quantity and quality [23].

Figure 2.

The interaction network of differentially expressed genes (DEGs) regulating myogenesis in (a) Landrace (LR) and (b) Lantang (LT) pig breeds. Yellow and blue dots represent DE and non-DE genes, respectively. Straight lines represent interaction associations between genes. Solid and dashed lines represent direct and indirect interactions, respectively. The value and diameter of the lines represent the size of the interaction [22].

2.2 Environmental influences on the growth of pigs

Economic efficiency in pig production is highly influenced by the environment in which pigs grow up, as the genetic potential of animal growth can be limited by environmental factors, especially nutrition. In addition to nutrition, stocking density, condition management, social stress, environmental temperature and the health status of the pigs also play an important role in the growth of the animals. On the other hand, environmental enrichment, such as larger pens and feeding areas, bedding or specially enriched feed, can have a positive effect on pig growth [24].

Holck et al. [25] have shown that pigs kept under commercial conditions are unlikely to reach their maximum protein intake potential, even when they have ad libitum access to high-quality, nutrient-rich feed. They reported significant differences in most indicators of growth potential between pigs housed in two different environments (confined and unconfined). The results of this study are presented in Table 2.

Growth traits	Commercial	Optimal	Optimal/commercial; %	P value
Daily gain, kg/d	0.73	1.03	142	<0.001
Days to 118 kg	192	160	83	<0.001
Daily lean gain, g/d	240	342	142	<0.001
Daily fat gain, g/d	240	353	148	<0.001
Daily feed intake, kg/d	2.45	3.00	122	<0.05
Feed/gain	3.29	2.85	87	<0.05
Feed/lean gain	10.39	9.30	90	n.s.
Backfat, cm	2.48	2.74	112	n.s.

Table 2.

Comparison of commercial versus optimal conditions [25].

The principle that governs an animal’s response to changes in diet is that the animal tends to adapt to environmental and nutritional changes in such a way that the vital functional relationships between the essential body components are maintained or that the proportions are altered in such a way that the animal has the best chance of survival and successful reproduction. When nutrition is limited, tissues, organs and body components have different priorities for available nutrients depending on the order of their development and their functional priority. This is one of the axioms of the well-known “Hammond School,” as shown in Figure 1. In addition, when an animal is kept on a sub-maintenance diet for a long period, various tissues and body components are used for energy and protein to continue life processes in reverse order of maturity. Fat is metabolized first, followed by muscle and bone, and these tissues are broken down first in the regions where they mature the latest. In such a case, growth falls below genetic potential, but when food becomes plentiful again, growth rates accelerate and exceed those of comparable animals that are fed abundantly all the time (Figure 3). This phenomenon is known as “compensatory growth” and has its biological and economic value. It was described early [27] and presented as a “repair curve” that takes into account the maintenance of homeostasis as the most important process underlying the whole phenomenon [28]. The term itself was first proposed by Bohman [29], who used it in beef cattle, but later on, it became broadly used in other species. In pigs, complete and incomplete compensatory growth can be observed. Complete compensatory growth can be defined as an increased growth rate in pigs that were previously kept on a restricted diet when compared to the pigs fed without restriction in feeding and both groups reach similar body weight at the same age. In the case of incomplete compensatory growth, the increase in the growth rate of restrictively fed pigs is not sufficient to achieve approximate body weight at the same age. According to some research, this is likely to happen due to the short lifespan of the modern growing-finishing pigs, which makes this strategy questionable to some extent. Moreover, due to the fact that if applied during the intensive development stage, full recovery is not possible. It was also shown that the magnitude of compensatory growth is affected by the duration of restriction and recovery [30].

Figure 3.

Difference in growth curves constructed for pigs kept on restricted and non-restricted diets [26].

It is well-known that one of the most important conditions for optimal growth of pigs is fulfilling their nutrient requirements for tissue maintenance and growth. Some results have shown that 34% of the daily energy intake is for tissue maintenance [31]. Feed efficiency is defined as the ratio between average daily weight gain and average daily feed consumption at different stages of production [32]. In practice, this measure represents the efficiency of converting feed into weight gain. High feed efficiency and a high proportion of daily nutrient intake are directly related to growth. Changes in lean and fat tissue growth are under the influence of feed intake. Physical feed intake capacity is the most important limiting factor for growth in pigs. Supplementation with exogenous feed enzymes can increase feed efficiency in pigs and have a positive effect on growth in fattening pigs [33].

The nutritional requirements of pigs change throughout the different stages of production, which often leads to inaccurate feeding levels so that pigs often receive more nutrients than they actually need [34]. Precision feeding is an innovative approach where animals are fed the optimal amount of balanced feed in a timely manner. This feeding strategy has a positive impact on production efficiency and environmental footprint. The implementation of this feeding strategy can significantly reduce production costs (>8%), protein and phosphorous intake (25%), excretion (40%) and greenhouse gas emissions (6%) by increasing individual nutrient efficiency [35].

With access to the automatic feeding system for pigs, precision feeding has a positive effect on growth composition when nutrient requirements are dynamic and modulates pig growth [35, 36].

Factors of the physical environment such as housing conditions (feeding space, floor area or group size) and their optimal balance play an important role in the growth of pigs. Studies show that pigs kept in individual pens have better growth performance due to higher feed intake and lower physical activity; however, this is not allowed in the EU and some other countries due to the regulations for the welfare of intensively reared pigs. According to Peden et al. [37], keeping pigs in large groups but with limited space can lead to the occurrence of aggressiveness and specific feeding behavior, resulting in lower feed intake. Mkwanazi et al. [38] found that housing design affects the growth rates of pigs. Pigs raised in an enriched environment had lower stress levels and better feed conversion than pigs housed in conventional pens. Numerous studies have shown that a reduction in floor space negatively affects average daily feed intake and growth rate [39, 40, 41]. Similarly, average daily gain and body weight improved when the feeding area was increased during the growing and finishing phases [42]. Laskoski et al. [43] showed a tendency for a linear improvement in average daily gain during the weaning phase when the number of piglets per feeding place decreased.

Ambient temperature plays an important role in pig production because newborn piglets are susceptible to hypothermia. According to Stephens [44], piglets become lethargic and less effective in competing for colostrum intake when body temperature deviates by 2°C from normal. This results in a lower average daily gain and a lower growth rate in the post-weaning period. Both lower and higher ambient temperatures than optimal for a given category affect feed intake and consequently reduce the growth rate of pigs. According to Taljaard [45], a 1°C decrease in ambient temperature increases feed intake by 1–1.5%. Kingma et al. [46] defined the thermoneutral zone as the temperature controlled only by dry heat loss. Temperatures below the thermoneutral zone reduce growth rate and have a negative impact on bone and carcass characteristics [45]. Temperatures above 20°C in combination with low air velocity also have the same negative effect on pig growth [47]. Mayorga et al. [48] found that heat stress in pig production reduces feed efficiency and growth rate, resulting in lower carcass quality. Under heat stress, pigs consume less feed and achieve lower average daily gains. Pigs with a higher body weight are more sensitive to heat stress than pigs with a lower body weight. Some of the strategies to alleviate heat stress in pigs include reducing the amount of fiber in the diet and increasing the fat content in the diet, as well as selecting pig genotypes that can adapt to a warmer environment. Supplementing the low crude protein diet with lysine, tryptophan and threonine reduces heat production without negative effects on growth performance and carcass finishing [49].

3. Modeling the growth of pigs

The models applied to animal growth can be divided into dynamic or temporal models, which describe the relationship between growth and age, and relative or allometric models, in which the growth of a particular tissue or body part is related to the live/carcass weight of the animal in question.

Temporal growth is usually described by models consisting of non-linear functions with a sigmoidal shape and a fixed inflection point, e.g., Gompertz, logistic and von Bertalanffy, and those with a flexible inflection point such as the Richards model with a variable inflection point determined by the shape parameter (which combines all the above growth functions into one). The Richards function is an extended form of the von Bertalanffy growth function generalized by derived rate parameters in the context of its constraints [50]. The main feature of a suitable growth function is that it summarizes the observed data in a set of parameters with biological significance. One of the two dominant strands in growth research is based on this assertion. It is called the “biological” stream and looks for a model with a biological basis and biologically meaningful parameters, no matter how crude. The models are usually based on deterministic differential equations that describe the dynamics of growth. There are numerous examples in the literature of the use of biologically based models based on deterministic differential equations. Table 3 shows a list of growth functions and desired criteria for predictors of potential growth, with weight (W) used throughout as a measure of size [50].

Model	W = f(t)	No. of parameters	Can be expressed as “rate is a function of state,” only †	Continuous growth	Asymptote	$\mathrm{dW}/\mathrm{d}t\to 0$ at W $\to 0$ and A	Point of inflection	Monotonic decrease in relative growth state
Gompertz function	$A.exp\left(-exp\left(-G_0-\left(k.t\right)\right)\right)$	3	✓	✓	✓	✓	✓	✓
Robertson’s function	$A/1+\left(\left(A-W\circ \right)/W\circ \right)\left(\mathit{exp}-\left(A.k.t\right)\right)$	3	✓	✓	✓	✓	✓	✓
Hill’s function	$\left(\mathrm{W}\circ {\mathrm{k}}^{\mathrm{n}}+A.t^n\right)/(k^n+t^n$)	3	⨯	✓	✓	✓	✓	✓
Brody’s function	t ≤ t* $W_{\circ .}exp\left(c.t\right)$; t ≥ t* $A^{\circ }\left[1-exp\left(-k\left(\mathrm{t}-\mathrm{t}\ast \right)\right)\right]$	3	✓	⨯	✓	✓	✓	⨯
Von Bertalanffy’s growth model	$\left\{n/k\right.-\left(\frac{n}{k}-W_{\circ }^{\left(1-m\right)}\right)e{-}^{\left(1-m\right)k.t}\}{}^{1/\left(1-m\right)}$	4	✓	✓	✓	✓	✓	✓
Janoschek’s function	$A-\left(A-W\right).exp(-k.t^p$)	4	⨯	✓	✓	✓	✓	✓
Richard’s function	$W_{0.}A/{\left(W_0^n+\left(A^n-W_0^n\right)exp\left(-\left(k.t\right)\right)\right)}^{1/n}$	4	✓	✓	✓	✓	✓	✓
Park’s model	$A.\left(1+a.exp\left(-b.t\right)+c.exp\left(-d.t\right)\right)$	5	✓	✓	✓	✓	✓	✓
Moore’s model	$A.{\left(1+exp\left(\frac{-p_{n\bullet }{\mathit{log}}_e\left(t-3\bullet 5\right)}{A^{0.27}}\right)\right)}^{-1/0.27}$	Varies with ‡ pn	⨯	✓	✓	✓	Varies with ‡ pn	Possible
Black et al. nutrient utilization model	$\frac{\mathit{dw}}{\mathit{dt}}=k\left(\frac{A-W}{A}\right){\left(W+b\right)}^c$	4	✓	✓	✓	✓	✓	✓
Bridges et al. growth of body composition model	$W_0+A.\left(1-exp\left(-\left(m.t^a\right)\right)\right)$	4	✓	✓	✓	✓	✓	✓
France et al. simple flexible growth function	$A-\left(A-W_0\right)\mathit{exp}⌊-k\left(t-T\right)+2c\left(\mathit{vt}-\mathit{vT}\right)⌋$	5	⨯	✓	✓	✓	✓	⨯
Wan et al. new flexible growth function	$A-\left(\frac{1}{b}+c.exp\left(k.t\right)\right)$	4	⨯	✓	✓	✓	✓	✓
Generalized Michaelis-Menton equation	$\left(W_0{k}^c+A.t^c\right)/(k^c+t^c$)	4	⨯	✓	✓	✓	✓	✓

Table 3.

Growth functions for the description of growth dynamics—Temporal growth [50].

Where in all cases W₀ is the final weight of the animal (kg); A is the animal’s mature weight (kg); k is a parameter used in that particular model and t is time (days); Other parameters are constants individual to the function; †Time is not seen as part of state; ‡p_n is a polynomial extension.

On the other hand, the so-called “statistical” strand uses a different methodology in growth analyses, usually polynomials, which allows for an independent structure without interest in inherent biology [51]. Indeed, high-order polynomials have been found to have a reasonably good fit for pig data [52, 53, 54, 55, 56], but these stochastic models have no asymptote, while the parameters of the model have no biological significance. Such approaches are used to fit polynomial growth curves to sets of internal, arbitrarily correlated data, to test linear hypotheses about the regression coefficients, and to derive confidence intervals for the growth curves. Its most common application is to describe differential growth, growth of different tissues and the changing composition of the carcass, often expressed as relative growth, e.g., related to live/carcass weight or growth of a particular tissue within a particular body part, e.g., ham, loin, shoulder, etc. [57].

The foundation for this approach was laid by Huxley [58], who proposed a model in which the relative proportions of an animal are determined by overall weight. This approach later became known as the general theory of relative growth based on the concept of allometry. Initially, it was criticized for various reasons [59], but later more complex models were developed to give a better description of these relationships during pig growth. For a comprehensive overview of the equations used in the analysis of body composition changes in pigs during growth and development (Table 4), see the work by Wagner et al. [53].

Model	Equation
Linear-quadratic	Y = b0 + b1X + b2X^2
Allometric	$\mathrm{Y}={\mathrm{bX}}^{{\mathrm{b}}_1}$
Quadratic-allometric	$\mathrm{Y}={\mathrm{bX}}^{{\mathrm{b}}_1+{\mathrm{b}}_2{log}_{10}\mathrm{x}}$
Augmented-allometric	$\mathrm{Y}={\mathrm{bX}}^{{\mathrm{b}}_1}{\left(\mathrm{c}-\mathrm{X}\right)}^{{\mathrm{b}}_2}$
Exponential function I	$\mathrm{Y}={\mathrm{e}}^{{\mathrm{b}}_0+{\mathrm{b}}_1\mathrm{x}+{\mathrm{b}}_2{\mathrm{x}}_2+{\mathrm{b}}_3{\mathrm{x}}_3}$
Exponential function II	$\mathrm{Y}=\mathrm{c}\left(1-{\mathrm{e}}^{{\mathrm{b}}_0+{\mathrm{b}}_1\mathrm{x}+{\mathrm{b}}_2{\mathrm{x}}_2}\right)$

Table 4.

Equations used in the study of changes in body composition of pigs during growth [54].

Nevertheless, all models are only an approximation of reality and should be evaluated on the basis of their empirical performance. According to Whittemore [60], models generally fall into two categories: real models and simulation models. A good example of the former is the use of an animal in research on human nutritional needs, known as the animal model. The latter is based on computation through the use of algorithms involving mathematical functions, logical operators and textual instructions. The empirical performance of pigs can be measured as qualitative and quantitative changes in body composition or its components, such as lean body protein, lipid, moisture and ash, as shown by Halas [61]. In that study, the growth of empty body weight (EBW) was presented as the sum of the component growth curves. The author demonstrated that the proposed model could effectively predict the qualitative response of pigs to wide-ranging variation of nutrient composition, which enabled the development of feeding strategies for optimization of pig production. According to De Lange et al. [62], the criteria for the effective use of a predictive growth model are adequate accuracy and good flexibility; in addition, the model should be easy to understand and easy to use. This means that the model should be reasonably accurate in its predictions, that the range of conditions under which the model is accurate should be consistent with its intended application, that a clearly defined protocol is developed on how to obtain the inputs and parameters of the model; that the correct use of the model for the systematic evaluation of alternative management and feeding strategies is clearly stated and that the model is user-friendly and relatively inexpensive.

The gain in lean and fat tissue in the carcass or primal cuts is often measured as it determines the economic value of pig carcasses. Entire pricing systems have been developed to estimate the carcass value of pigs [63, 64, 65]. When using these systems, the carcass value is related to the amount of lean in the primal cuts. Knowing the composition of the carcass during growth can optimize important decisions for pig producers and help them increase profit. Landgraf et al. [66] stated that the economic impact of knowledge of growth characteristics and related changes in body composition is valuable for predicting carcass value development during growth. It is also suitable for optimizing nutrient supply during the fattening period, setting selection targets for optimal body tissue development, refining alternative methods for determining optimal slaughter weight and providing parameters for describing the growth of pigs and their body components. Growth models that can be used to improve the efficiency of the entire pig production system should be able to provide an answer to these questions. The widespread use of Industry 4.0 technologies has enabled the collection of extensive biological and environmental data that can be used with recent computational advances based on machine learning techniques. These techniques are able to process a large amount of data and resolve the complicated interactions between different environmental conditions and growth measurements [67].

To determine the optimal slaughter weight or age, it is important to describe the growth of the pig with a model that allows accurate prediction. Kušec et al. [68] evaluated the use of both allometric and temporal models in the analysis of live weight (LW), muscle and fat growth of pigs of different MHS genotypes (NN and Nn) kept under two different feeding systems (intensive vs. restricted). The muscle and fat volume data were obtained using magnetic resonance imaging (MRI). The curves for live weight and analyzed tissues resulting from the temporal model are shown in Figures 4–6.

Figure 4.

Live weight growth curves of two MHS genotypes (NN and Nn) of the pigs kept on the intensive and restrictive feeding regime [68].

Figure 5.

Muscle growth curves of two MHS genotypes (NN and Nn) of the pigs kept on the intensive and restrictive feeding regime [68].

Figure 6.

Fat growth curves of two MHS genotypes (NN and Nn) of the pigs kept on the intensive and restrictive feeding regime [68].

Using the allometric function, no significant effect of MHS genotype (NN vs. Nn) on muscle growth was found, but analysis of muscle growth using the asymmetric S-function showed significant differences in the coefficients related to the progressive growth phase. Although the allometric equation and the asymmetric S-function showed similar growth patterns for the tissues studied, the latter proved to be more informative and provided a basis for important decisions in pig fattening. The use of the asymmetric S-function as a generalization of the Richards equation allowed predictions to be made not only within the intervals of the data sets obtained but also along the time scale based on the initial condition, i.e., the first live weight measurement.

In another study by Kušec et al. [69], using the same model, it was found that the difference between the predicted and actual time needed to reach a live weight of 100 kg in pigs kept under intensive and restricted feeding conditions was 7 days or less in 88% and 79% of the cases, respectively, which can be accepted as fairly accurate.

4. Conclusions

In pigs, growth is considered as the material base of production. Modern breeding techniques combined with improved feeding and a good understanding of environmental conditions in pig production have raised the growth rate to impressive levels. Recent advances in genomics and other omics disciplines have greatly improved our knowledge of the number and role of different interrelated genes and their interaction with the environment in regulating pig growth. The studies of the nutritional needs of pigs at different stages of growth have enabled further improvements in the growth characteristics of modern pigs. Dramatically improved computational power and further development of statistical tools and mathematical models have also contributed to increasing growth efficiency in modern pig production.

The multitude of influences on the various aspects of growth and the methods used to collect and analyze the data make this vast field of research interesting to scientists from diverse backgrounds. Their work led to the development of the domestic pig as we know it today, with superior growth rates, daily gains, feed efficiency and other growth-related traits. Alongside this, the state-of-the-art models developed over the last few decades have enabled effective decision-making processes for optimal pig production. Understanding the relationships between the factors that influence pig growth and developing appropriate models are crucial for implementing strategies that enable better use of their growth potential.

Conflict of interest

The authors declare no conflict of interest.