computer simulation model of swine production ...

Computer simulation model of swine production systems: II. Modeling body composition and weight of female pigs, fetal development, milk production, and growth of suckling pigs C. Pomar, D. L. Harris and F. Minvielle J ANIM SCI 1991, 69:1489-1502.

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COMPUTER SIMULATION MODEL OF SWINE PRODUCTION SYSTEMS: II. MODELING BODY COMPOSITION AND WEIGHT OF FEMALE PIGS, FETAL DEVELOPMENT, MILK PRODUCTION, AND GROWTH OF SUCKLING PIGS' Chdido Porn&,

Dewey L. Harris3 and Francis Minvielle4

UniversitC L a d , Quebec, Canada, GlK-7P4 and

US. Department of Agriculture, ARS, Clay Center, NE 68933-0166 ABSTRACT

Theoretical concepts and relationships used to develop a deterministic computer simulation model of female pigs during their reproductive life are described. The model predicts, in a continuous form, body composition and weight of female pigs, fetal development, sow milk yield, and growth of suckling pigs according to genotype, diet, and management conditions. The model simulates growth of adult female pigs. Dietary nutrients are used first for maintenance and second for fetal growth or milk production. Any surplus is retained in the body. Energy and protein body reserves are mobilized when a nutrient deficit occurs during gestation or lactation. However, the rates of body protein and fat accretion as well as the favprotein ratio are limited by boundaries, the values of which depend on the nutritional and physiological status of the pig. The model's ability to simulate sow body weight changes and composition, fetal growth, milk production, and suckling pig's growth is illustrated. Key Words: Reproduction, Models, Simulation, Sows, Pregnancy, Lactation J. Anim. Sci.

1991.

693489-1502

ment, nutrition, genetics, economics, etc.) that act during both the growth period and the Swine production efficiency i s the result of reproductive life of the pig. The complexity many distinct but interacting factors (environ- within and among these factors cannot be fully comprehended in a quantitative and dynamic fashion by either the human mind or by traditional research (Baldwin, 1976; Koong et h e authors wish to acknowledge the many suggestions concerning model elements from numerous discus- ai., 1976; Whittemore, 1986). Therefore, syssions with scientists at the Roman L. Hruska U.S. Meat tems analysis techniques by the means of Animal Research Center at Clay Center, NE. These simulation modeling are proposed as an include G. L. Bennett, R. K. Christenson, I. J. Ford, K.E. essential part of the scientific method (WhitteGregory, K. A. Leymaster, h4. D. MacNeil, R R. Maurer, W. G. Pond and L. D. Young, and also P. Savoie from more, 1986) because they allow an analysis of Agriculture Canada at Lennoxville (Quebec) Canada. The the whole system and the interactions of its efforts of I. M. Dzakuma in developing the figures is also components (Koong et al., 1976). appreciated, as well as those of Sherry Kluver for typing This paper describes a deterministic lifethe manuscript. TEis study was p d y supported by a &rant cycle model that includes and extends the from CORPAQ-Agriculture. Qucbec. 2Present address: Station de Recherche, Agricdme young pig growth model of Pomar et al. Canada, C. P. 90,Lennoxville, Qu&ec, Canada, JIM-123. (1991). The combined model simulates the SSDA-ARS, Roman L. Hruska US Meat Anim. Res. overall individual pig's life. The objective of Center, Clay Center, NE. this study was to develop a swine model that 4Present address: INRA-CNRZ, Labomtoire de Gh.6- would be more mechanistic than previous ones tique Factorielle, 78350, Jouy-en-Josas, Prance. (Men and Stewart, 1983; Tess et al., 1983; 'Dept. of Zootechnie, B A A . Black et al., 1986; Pettigrew et al., 1986; Received November 20, 1989. Singh, 1986) in order to ensure a reliable Accepted October 16, 1990. Introduction

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response into a wide range of combined nutritional, genetic, and managerial situations. This goal required a model that integrates fundamental biological processes regulating the accretion of body protein as well as energy and protein metabolism during growth of female pigs, gestation, and lactation based on theoretical concepts for the independent estimation of lipid and protein accretions or losses. The model should also incorporate a large number of factors affecting the efficiency of swine production systems. Among these factors, genotypic and nutritional effects and their interactions with growth, body composition, and the productivity of the sow were required. Theoretical concepts and relationships presented here constitute the second part of the framework for a complete life-cycle model of pigs.

TABLE 1. ABBREVLATIONS AND ACRONYMS USED IN THE TEXT _______~~

Symbol

Meaning

A B C DE e

constant constant constant Digestible energy i n W d Natural logarithm base Energy requirements for maiatenance Ideal protein intake Intrinsic potential for protein accretion Total body mass of lipids Body fat mass of the fetus and suckling pigs Fat accretion rate of the fetus and suckling pigs Body lipid accretion rate constant Total body protein precursor mass: approximationfor DNA, Protein precursor accretion rate Total body protein mass Body protein mass of the fetus and suckling Pigs Protein accretion rate of the fetus and suckling Pigs Body protein accretion rate Time Total litter weight at birth Total litter weight Average daily milk yield

EM IPI

m LT LTP LTpr LTr

M

PP PPr FT PTP

m Model Description

PTr t

Strategy The simulation model described here predicts body composition and weight of female pigs during the overall reproductive life, along with fetal development, milk production, and suckling pig growth. Genotype parameters, diet composition, reproductive performance, and management alternatives are model input variables. Rate variables are expressed on a daily basis, energy is in megajoules (MJ) and mass is in kilograms (kg) when not specified in the text. The general strategy used to model growth and composition of adult animals and nursing pigs is based on the one previously reported (Pomar et al., 1991) with extensions to include the additional physiological processes.

TBW TLW y(t)

except lysine). Total body protein precursor mass (PP) used in this model is an approximation of the total empty body deoxyribonucleic acid (Pomar et al., 1991). The intrhsic potential for protein accretion (IPTr) is the maximal amount of protein that an animal can retain in a day when there is no external (mainly nutritional) limitation. The terms PPr, PTr, and LTr represent, respectively, the amounts of PP, PT, and LT mass retained in each integration step. The estimation of IPTr for adult animals is done in the same way as for young pigs. Under conditions of normal growth and when extrinsic factors, such as nutrition, are not limiting Body Composition and Weight the retention of body protein, adult pigs can of Adult Female Pigs retain as much protein as IPTr allows. When As for young animals (Pomar et al., (1991), intrinsic or extrinsic factors limit protein empty BW of an adult sow is assumed to be accretion under conditions of protein gain. PPr 95% of its total body live weight and is is restricted in the same proportion as PTr defined as the algebraic sum of its main (Pomar et al., 1991). However, at the end of chemical components, which include the total gestation sows generally lose weight (if the body mass of protein 0, lipids (LT), water, products of conception are excluded). Weight and ash. These and other abbreviations are loss may also occur during lactation. Under presented in Table 1. It is assumed in the situations of body protein losses, that is, when model that all diets are palatable and ade- PTr is negative, PP mass decreases proportionquately balanced in all known nutrients (in- ally to the losses of IT. This mechanism cluding minerals, vitamins, and all amino acids allows the simulation of body tissue losses

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limits are imposed to avoid situations that do not seem to occur normally or are not Energy Protein consistent with our understanding. Thus, changes in body weight are in relation to changes of PT and(or) LT mass and, generally, these changes are in the same direction at the First same time. However, this hypothesis will ,.. ....+ Maintenance +."."...< probably not be valid in near steady-state situations because numerous anabolic and i catabolic processes are occurring simultai j neously (Close and Fowler, 1985). In fact, fiestation Fuller et al. (1976) and Close et al. (1978) observed that, even when BW gain is close to ..........) zero an animal may deposit as much as 25% of its intrinsic potential for protein accretion ..".."""........ ".: i (IlpTr), with offsetting losses in other components, mainly LT. Whittemore et al. (1981) also observed that young pigs of 5 kg live BW in steady-state condition can lose as much as 50 g of lipids daily and retain an equivalent priority amount of water. Therefore, simulated PTr and Last : LTr are allowed to have opposite algebraic Figure 1. Protein and nonprotein energy flow. Solid signs only when both protein and fat retention lined path represent metabolic processes and dotted lines are close to zero (Figure 2). represent catabolic associated processes. It is difficult to determine from the literature a value for the maximal amount of PT that a sow can retain while losing lipids from its body. Also, the amount of LT that a sow can during periods of heavy nutritional demands. It also allows depleted sows to express slight retain while losing body protein is not well compensatory protein growth to recover from understood. Because these body changes are gestation and lactation body protein losses. probably related to the sow's nutritional status, Lysine concentration in the diet is used to we arbitrarily assumed that 10% of the calculate the ideal protein intake (IPI) for maximal daily fat losses, that is, .06% of the gestating and lactating sows according to the sow LT mass, can be retained or lost when PTr ARC (1981) optimal lysine diet concentra- is zero (see Figure 2 for a diagram of these tions. Protein and energy (EM) requirements boundaries). The hypotheses involved are for maintenance, as well as the energy and basically intuitive and more data are needed to protein efficiencies for growth and the energy evaluate accurately the dynamics of body and protein losses in urine are calculated for composition changes when sows are near adult animals as * previously discussed for steady-state weight condition and to ascertain the presence and magnitude of limits to the young pigs (F'omar et al., 1991). After energy and protein requirements for processes involved, as well as the degree of maintenance have been satisfied, the remaining genetic control on these limits. available M E and ideal protein intake (PI) As for growing pigs, the minimum LTr/PTr may be used for either fetal growth or milk boundary for sows and gestating gilts is production (Figure 1). The surplus, if any, is expected to be determined by the genotype. retained in the body. Open and gestating However, this assumed minimum L T r m r females generally gain weight because they are ratio during the growth period is taken as the either still growing or are recovering from inverse of the slope of the upper boundary of lactation body losses or both. The composition the upper right quadrant (FTr and LTr 2 0) of this gain has not been adequately studied. during the sow's reproductive life and 10% of Nevertheless, it is assumed that the amount of the maximal daily fat losses with zero PTr is PTr and LTr retained depends on the final taken as the intercept Figure 2). This slope is balance between requirements and ingested represented as .4 in Figure 2. The slight nutrients available for growth. However, some adjustment to the intercept of the minimal LTr/

-uI

i

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PTr boundaq relative to the young animal model (Pomar et al., 1991) is implemented to allow small changes in body composition near the steady-state weight conditions as observed in real situations. When there is violation of this boundary (upper left quadrant of Figure 2), there is a deficit of ME available for LTr. An equivalent situation is reached when there is a surplus of ideal protein intake (PI) for PT retention (PI > PTr), and, in both cases, a fraction of the P I is deaminated as proposed for growing pigs. The boundary at the top in the upper right quadrant (positive PTr), (Figure 2) is the P T r , or genetic potential for protein accretion. The amount of LT retainWday by a sow (right boundary in the upper right quadrant of Figure 2) is function of the energy surplus it receives. All these boundaries are dynamic and they are

evaluated at each integration step. Thus, as the sow gets older, P T r decreases because body protein synthesis approaches a plateau and protein degradation increases with the increases in body protein mass. At the limit, old sows allowed ad libitum access to feed will reach their adult protein mass and steady-state body weight. In these circumstances, energy and protein intake are only used to satisfy maintenance requirements. At the end of gestation or during lactation, sows generally lose weight. Under these circumstances, body fat and protein stores are used to overcome the nutritional deficit. The composition of the body weight loss is difficult to predict because of many different factors such as feed intake, body composition of the sow, body composition of the fetuses, and so on. However, protein or fat reserves are

: Total body mass o f lipid P T r : Body protein accretion r a t e L T r : Body lipid accretion r a t e

Genetic potential for protein accretion

PTr

t

......- . __. . ............ ... .--._ .:-.- --- ...____ .--...

r = .4*PTr

-a

Maximal Daily fat losses ( . 6 % o f LT)

LTr

--. LTr

= 20 *PTr

...

-a 8 = 10% o f maximal daily fat

..... :;qKB:--

- ........................................... .. ... .- . . .

losses

Maximal Daily

+ protein losses ( .6%ofPT)

Figure 2. Rotein (IT) and fat (LTr) accretion and loss during gestation and lactation,

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seldom catabolized alone. Bowland (1967) and Tess et al. (1983) assumed that the protein/fat ratio was constant at 1/20 for lactation weight losses. A more realistic approach seems feasible, which assumes that the amount of lipids and protein catabolized is related to needs associated with the energy and protein deficit. As for body protein gain, some boundaries are assumed so as to avoid seemingly unrealistic situations (lower left quadrant of Figure 2). The nature of these boundaries is to represent 1) the metabolic capacity of the sow to catabolize protein and fat tissues (maximal daily fat and protein losses), 2) the minimal protein that should be catabolized from the body per unit of catabolized fat (upper boundary in lower left quadrant with a slope of 1/20}, and 3) the minimal fat that should be catabolized per unit of catabolized protein (lowest boundary in lower left quadrant). Little information is available for the maximal amounts of PT and LT mass that can be catabolized, as well as for the slope of these boundaries. Black et al. (1986), quoting Greenhalgh et al. (1980), King (1982), and King and Dunkin (1986), quoting Greenhalgh et al. (1980), King (1982), and King and Dunkin (1986) assumed that .6 to .8% of the total body protein could be lost daily without reduction of milk production. Our model assumes that .6%of LT and PT can be lost daily before fetal growth or milk yield is reduced. It is also assumed that the slope of the upper boundary for weight losses is 20, accepting the value of Bowland (1967) and Tess et al. (1983). Because no estimates were found in the literature for the slope of the lower boundary, this is determined in the model by connecting, by a straight line, the point of maximal PTr daily losses (-.6% of PT mass) and maximal daily LTr losses (-.6% of LT mass) with the point LTr = .1 LT and pTr = 0 (lower left quadrant of Figure 2). When energy deficit is too high in relation to these boundaries, extra P I is deaminated, as described for similar situations in growing pigs and adult females retaining IT.Under some nutrient requirements and diet composition, required FT losses may be too large in relation to LT losses. Under these circumstances, control of feed intake and(or) total sow production would possibly be implicated in the stabilization of the amount of protein and fat catabolized. However, these mechanisms are uncertain, and the model assumes that addi-

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tional LT is degraded and lost as heat. Only when maximal protein or fat losses are reached will milk production be reduced. Fetal Growth Although other approaches are also available (Verstegen et al., 1987), the factorial method is used to estimate total energy requirements during pregnancy because this method has been proven adequate (Vanschoubroek and Van Spaendonck, 1973). To represent the increase of maintenance energy requirements (EM) with the progress of gestation (Verstegen et al., 1971), ARC (1981) proposed to increase the basic requirements of the sow by 1 H/kg of metabolic weight, per day, and for each day after the 40th d of gestation. In our model, EM is calculated separately for the sow and each fetus with the same relationship as used for growing pigs (Pomar et al., 1991). These values are then added together to estimate the total EM requirements at each specific integration step. Although the higher heat production observed during late pregnancy may not justify the use of the same EM predictor throughout the sow reproductive cycle (Verstegen et al., 1987). other results do not show differences in EM requirements between pregnant and nonpregnant sows (Noblet and Close, 1980; WalachJaniak et al., 1986). A similar approach is used in the model to evaluate the total protein requirements for maintenance during pregnancy. Total birth weight (TBW) is calculated as follows: TBW = 1.1(litter size born alive) (fetal birth weight), where the additional 10% accounts for stillbirths, reabsorbed fetuses (Tess et al., 1983), and conception products. Litter size and fetal birth weight are model input variables that depend on genotype, parity, estrus at breeding, and preceding lactation length. The following equation of Pomeroy (1960) allows the prediction of the total weight of the litter (TLW throughout pregnancy: TLW = .1 (.2447t -4.06)3 (TBW/1396), where t is the day of gestation. The first

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derivative of this equation is used in our model to estimate the basic daily fetal growth. Moustgaard (1962) showed that fetal chemical composition is not constant throughout pregnancy. However, fetal mass is small compared with the whole sow mass until late pregnancy. Therefore, for simplicity, basic fetal mass is assumed to contain 11% protein and 1% fat, which is within the range of values observed at birth by Pomeroy (1960), Wood and Groves (1965), Curtis et al., (1967), and Okai et al. (1977). From results of Curtis et al. (1967) and Okai et al. (1977), fetal ash content is taken as 3.9% of the input litter weight. Feed intake level during gestation slightly affects the size and chemical composition of the litter (Vanschoubroek and Van Spaendonck, 1973; Walach-Janiak et al., 1986). Basic protein retention in the fetus (PTPr= .11 x fetal growth rate) represents the maximal potential for protein accretion. It is only under severe protein restriction that FTPr should be decreased. Nevertheless, this situation is unlikely to be reached because it would be the result of very poor protein ingestion and very poor sow body condition. In fact, results of Pond et al. (1968) suggest that severe protein restriction during pregnancy affects the dam’s body condition rather than the offspring development. Therefore, we assumed that the small but real effect of the sow intake on fetal weight (Vanschoubroek and Van Spaendonck, 1973) will focus on body fat rather than on protein mass. Thus, the 1% fat assumed for fetal weight gain represents the minimum fat retained on a daily basis. Despite the limited information available, we assume also that extra fat can be retained by the fetuses only when the sow is also gaining fat. Thus, 5% of the energy used for the sow lipid retention is directed toward fetal fat deposition (LTPr). Results of Vanschoubroek and Van Spaendonck (1973) indicate that an increase in pig birth weight reaches a plateau with high intakes. This plateau is simulated in the model, limiting the extra fat retained by the fetus to five times the basic fat retention. On the other hand, situations in which energy and protein available for fetal retention are under the minimal potential are not included in the model. Such conditions are seldom observed in well-managed swine production units. Very few estimates exist for the energetic efficiency of fetal growth. The values range from .2 (Hovell et al., 1977) to .8 (ARC,

1981). As proposed by Close et al. (1985) and adopted by Black et al. (1986), the efficiency of ME utilization for fetal growth is assumed to be .6. This efficiency is increased to .8 when the energy used for fetal growth comes from the sow body reserves. Values for IPI efficiency for fetal growth are scarce in the literature, but the real value is probably between .8 and 1.0. If we assume that this coefficient reflects the fetal protein accretion as a propodon of the protein available, and that some protein goes into other conception products, a value of .8 seems reasonable. During the fetal protein retention process, losses of body protein also can take place in the sow. When the protein used for fetal protein retention comes from the sow body protein reserves, protein efficiency is assumed to be .95. Milk Production Few data are available to accurately predict milk yield curves for the sow throughout

lactation. This results from the difficulty of obtaining a satisfactory and easy estimate of the sow’s milk yield and from the differences between methods. For these reasons, cow milk yield information was used in our model to characterize the shape of the sow milk yield curve. This was done even though these two curves are not necessarily similar and different sows possibly have different milk curve patterns (Salmon-Legagneur, 1958). One of the most popular models to describe dairy cow milk yield is the one proposed by Wood (1969): y(t) = AtBe(-Ct), where y(t) is the average daily milk yield in the week t, e is the base of the natural logarithms, and A, B, and C are positive parameters defining the lactation curve. Dhanoa (1981) showed that the reparameterization of Wood’s model by making B = M x C (where M is the time between calving and the peak milk yield) has better mathematical properties because the correlations between nonlinear parameters are reduced. Both forms of Wood‘smodel can be used in a continuous form, but both are inaccurate when t is small. Black et al. (1986) suggested the transformation t’ = t + 10 in Wood’s model to allow for an intercept different from zero. In the present model, Dhanoa’s equation was fitted to sow

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milk yield data presented by Elsley (1971). The model was standardized for a peak milk yield of one unit. The transformation t’ = t + 25 was adopted because it decreases the residuals from regression and allows y(t = 0) to be greater than zero. A nonlinear least squares minimization procedure (SAS, 1985) was used for this purpose and solutions were A = 33.26 x 106, M = 2.46, and C = 66.45 x lW3. Initial daily milk yield is 70% of the maximal yield and the peak milk yield is reached at the end of the 4th wk of lactation. Potential milk production for gilts is assumed to be 74% of the sow milk yield potential (Elsley, 1971). As observed by Salmon-Legegneur (1965), no difference between second and subsequent lactations is included in the model. Breed differences in milk yield have been observed in sows (Allen and Lasley. 1960). However, because insufficient data are available to characterize the milk yield curves of all the breeds, daily milk production is calculated in the model as the product of the standardized milk yield (maximal value of 1) and the peak milk yield. Peak milk yield is an input variable supplied by the user according to the genotype of the simulated sow. However, this value is termed udder potential because it represents the sow mi& yield only when there is no nutritional or other factor limiting the synthesis of milk. Colostrum composition is considerably different from that of milk (Penin, 1955; Bowland, 1967; Fahmy, 1972; Brent et aL, 1973) and gradual changes in milk composition occur during the first days of lactation (Penin, 1954, 1955; Brent et al., 1973). The energy content in sow milk is relatively constant throughout lactation (Brent et al., 1973). Therefore, it is taken to be 5.0 M.l/kg according to the data of Brent et al. (1973) and Klaver et al. (1981). Milk protein concentration is generally high at parturition but declines rapidly during the first 24 h. The decline then slows and a minimum occurs at about the 16th d after farrowing (Penin, 1954, 1955; Brent et al., 1973). However, changes in milk protein content between the 2nd and 5th wk after parturition are relatively small (Perrin, 1954; Pond et al., 1962; Brent et al., 1973). Based on the data of Pond et al. (1962), Bowland (1967), Elsley (1971), Fahmy (1972), Brent et al. (1973), and Klaver et al. (1981), milk protein concentration is assumed to be 5.6%.

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Sow feeding level does not have a large effect on milk yield in early lactation (O’Grady et al., 1973; Klaver et al., 1981). However, O’Grady et al. (1973) observed that sows with low energy intakes had lower milk yields in late lactation, this effect increasing with the number of parities. They also argued that sows with low energy intakes could not maintain later milk yields by using their body reserves as at the beginning of the fist lactation. These results agree with those of Klaver et al. (1981). who noted that body condition of the sow seems to be the primary factor influencing milk production. Therefore, we assume that the sow energy or protein intake only affects milk yield in relation to the body reserve condition of the sow. Similarly, milk composition is not greatly affected by energy intake (O’Grady et al., 1973). but small effects have been observed on milk protein concentration (O’Grady et al., 1973; Greenhalgh et al., 1980). Here, milk composition is also assumed to be independent of the amount of nutrients ingested. Klaver et al. (1981) observed that sows with poor body condition, but fed at high levels, restricted the use of their body tissues for milk production. On the other hand, sows that generally gain more weight during gestation tend to use more of their body reserves during lactation (Greenhalgh et al., 1980). The most important factor associated with weight losses during lactation is probably the body condition of the sow. Thus, sows in good condition can rely on their reserves to replace the nutrient deficit occunring during the periods of high milk production. In contrast, thin sows depend mainly on their feed intake. These mechanisms are simulated in the model because potential losses of lean and fat tissues are expressed as a function of their own masses. Indeed, fat sows will tend to lose more weight during lactation because they will be able to supply body nutrients more easily than thin sows. Influence of preceding lactations on the subsequent ones is also simulated through this mechanism. Simulated daily milk yield is less than the calculated udder potential when 1) pig milk requirements are lower than this potential or when 2) dietary nutrients and body reserves cannot satisfy all the sow nutrient requirements. Therefore, the effect of litter size on milk production observed by Salmon-Legagneur (1965) and Elsley (1971) is simulated in this way. Small litters will usually nor require

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all the sow udder potential, whereas large litters will demand it all earlier and during longer periods. Efficiency of ME utilization for milk production is assumed to be .70 according to De Lange et al. (1980),whose estimate is close to the recommended value of ARC (1981). Because the composition of weight changes of the sow during lactation cannot be measured accurately, body energy efficiency for milk production is not well known. However, as observed for other productions, body energy reserves are probably used more efficiently for milk energy production than dietary energy. Thus, an efficiency of .SO seems reasonable, which is close to the value presented by ARC (1981) and Tess et al. (1983). Also, as proposed for growth, ideal protein available for production cannot be utilized totally in the milk. Few values have been published, and the model assumes that 80% of the available ideal protein intake can be utilized in milk. This value is increased to 90% when the synthesized milk protein is produced from the body protein reserves.

model needs to specify the age at which creep feed will be available in the pen. However, under the age of 14 d, intake of solids by pigs is assumed to be negligible (NRC,1987). Large litters and depleted sows will lead to higher creep feed consumption as a result of higher nutrient requirements and lower milk production, respectively. Under these circumstances, maximal daily creep feed intake is limited in the model to tbree times the proposed average creep feed intake relationship for suckling pigs (NRC, 1987). This equation predicts the average digestible energy intake (DE) between 14 and 35 d of age and has the following form: DE (MJ/kg) = .0469t-.6347,

where t is the age of the pig in days. Milk GE is assumed to be 97% digestible (Klaver et al., 1981; Tess et al., 1983). In the same way, milk protein digestibility is taken as 95%. Total DE and protein are the sum of milk and creep feed digestible nutrients. The P I in creep feed is calculated as for growing pigs Pig Growth During Lactation I available is the result of adding and total P P I in creep feed and in milk. This latter As observed by Fahmy and Bernard (1970), no weight differences are assumed between calculation assumes that 95% of the digestible protein of milk is retained in the pig's body. male and female pigs during fetal development Next, PTFV and LTPr are calculated as was and preweaning growth. A similar assumption done for body protein (PTr) and fat (LTr) is made for the body composition of preweanaccretion rates in growing pigs. During periods ing pigs. of restricted intake, the model allows LTPr to Based on the preweaning growth curve decrease down to .25 times PTFV. This value is developed by Robison (1976) and on body composition data of Manners and McCrea lower than any observed LTr/PTr ratio for (1963) and Wood and Groves (1965),Tess et growing pigs. It is generally accepted that the al. (1983)proposed to predict protein (PTP) proportion of fat in the suckling pig gain is and fat (LTP) preweaning pig mass from birth low and it increases as the pig gets older. to 56 d of age as follows: Ad libitum Feed Intake FT ' P = .1595+ .019Ot+ .00032t2 of Lactating Sows LTP = .0145+ .0225t = .00045t2 Lactating sows are generally allowed ad where t is the age of the pigs in days. The first libitum access to feed, whereas gilt and sow derivatives of these equations are used in the feeding is restricted when females are open model to predict the maximal PTP (PTPr) and and during gestation. Feed intake during LTP (LTPr) accretion rates during lactation. lactation is low immediately after farrowing We assume that suckling pigs consume but rapidly increases as lactation proceeds. in milk to satisfy maintenance energy and protein NRC (1987), DE intake for a lactation length requirements and for protein and lipid growth. of 28 d was predicted by: When the sow cannot supply all the required DE (MJ/d) = 56.07 + 2.49t - .072t2, milk, suckling pigs will tend to eat creep feed to compensate for the deficit in energy (Greenhalgh et al., 1980). The user of the where t is the lactation day. This relationship

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does not consider the effect of sow parity on lactation DE intake. It is assumed in the model that DE intake during lactation is directly related to the sow protein mass. Assuming a sow mean body protein mass (IT) of approximately 27 kg, DE intake can be calculated by multiplying the prediction from the above equation by pT127. Reproduction-Cycle Model Evaluation

Model verification was performed by checking for both mathematical and logical consistency throughout the whole model development process as previously described for the growing individual animal model (Pomar et al., 1991). Model validation might be achieved by comparing performance of the model (predicted) to real system performance measurements (actual). However, comparisons between predicted and actual composition of body weight or weight gain cannot be fully made without the precise information that will allow characterization of the biological material and the reproduction of the experimental conditions. For example, genetic characterization of simulated animals requires body chemical composition data at several points of the growth period and life cycle. Because such data are often not available, these comparisons are not feasible at this point for the reproduction-cycle model evaluation. Simulated results in the reproduction cycle are strongly dependent on the genetic characterization of the simulated animals and on many environmental and management conditions. Appropriately designed experiments seem necessary in many cases for full validation of detailed portions of the model. Nevertheless, the model can be evaluated in several ways to judge its robustness and suitability. First, because the model is based on fundamental concepts, description of these provides a first step toward model verification. Second, model results can be evaluated for their reasonableness and ability to represent specific production situations. For this evaluation. protein mass at maturity for the simulated sows was assumed to be equivalent to the basic genotype (35.5 kg; Pomar et al., 1991). Temperature and seasonal effects are not accounted for in this model and protein digestibility is assumed to be 75%. To represent a realistic scenario, the experimental

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procedure described by Whittemore et al. (1980) was replicated with the model. However, because of lack of adequate information (observed feed intake and many other experimental conditions) required to effectively represent the trial genotype, comparisons between experimental and simulated results are inadequate and therefore they are not presented. Gilts were allowed ad libitum access to feed with standard diets until 93 kg live BW, at which point the evaluation exercise starts. Gilts were fed 1.8 kg daily between 93 kg BW and first parturition. Sows were simulated during their first two reproductive cycles. All sows were given a common nutritional regimen containing 12.8 MJkg of DE, 154 g/kg of CP and 7 g/kg of lysine. During the second pregnancy, 2.3 kg/d of feed was given to the sows. Simulated sows were fed based on an ascending scale starting at 1.4 kg. The ration was increased by .45 kg daily up to a maximum of 2 kg, plus .5 kg/(d-pig) in the litter. Thus, a sow with a litter of 10 pigs would be fed 1.4 kg on the 1st d (parturition), with an increase of .45 kg/d until total fed would reach 7.0 kg/d (13 d later). First mating is simulated at 116 kg BW and at 12 d after weaning in the second mating. Simulated litter size and weight are those obtained in the experiment. Pig mortality during lactation is simulated as proposed by Tess et aL (1983) from the overall experimental pig viability, which was estimated as 88.5%. Simulated pigs are weaned at 35 d of age. Detailed results (Figures 3 to 5) show that, during half of the first and most of the second gestation, dietary protein intake satisfies requirements for both the fetus and the sow. Therefore, only small amounts of body protein reserves of the sow are mobilized to satisfy fetal requirements (Figure 3B). Because body protein losses during the simulated lactations are significantly lower than body protein increases during both gestations, the sow shows a net gain in body protein for the total period. However, this is not the case for fat reserves. Early in gestation, energy intake fulfills the overall energy requirements for only a few days. Afterward, the sow’s fat reserves are mobilized. This fat mobilization is less in the second gestation. Thus, simulated body fat mass decreases over the experiment. During both gestations, part of the protein

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ingested by the sow is deaminated to increase (or decrease) fat accretion (or losses). This Occurrence is controlbed by the boundaries imposed by the PTr/LTr slope (Figure 2). For this reason, the slopes for protein and fat accretion shown in Figures 3B and 3C change when retention becomes negative as the accretion rate reaches the upper boundary. Also, the rapid increase in nutrient require

ments of the fetuses as the end of gestation approaches seem to be responsible for the deceleration of gain of body components observed in the sow. At the beginning of both simulated lactations, energy and protein reserves rapidly decrease. Then, feed intake rapidly increases with the specified feeding program and, therefore, body losses decrease and the sow

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can even gain weight for a short period of time (Figures 3 and 4). However, milk energy and protein requirements soon increase and the sow has to once again mobilize her body reserves to satisfy the increasing milk requirements of the growing litter (Figure 6). Milk yield reductions at the end of both lactations lead to the decrease in the sow’s body losses. For only 2 d at the beginning of the first gestation and approximately 5 wk during the

second gestation, the protein accretion rate of the sow reaches its maximal potential. Discussion

This evaluation exercise demonstrates the ability of the model to simulate sow body weight gains and losses, body composition, fetal growth, milk production, and suckling pig growth. The main difficulty encountered in

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representing real situations is to properly characterize the simulated genotype. The basis for modeling growth responses in this model was the experiment of Walstra (1980), based on pigs of Dutch Landrace breeding in a minimal disease condition between 1970 and 1974. To be more reliable, the model must be calibrated to represent other breeds and crossbred genotypes under various environmental conditions. In fact, part of the differences observed between simulated and real animals are certainly due to the lack of genetic calibration. Also, body protein mass plays an indirect, but important, role in the determination of energy and protein requirements for maintenance. Maintenance requirements increase as pigs get heavier and older, and they represent the most important energetic need in adult females. Research is needed to estimate with precision maintenance requirements in adult animals and the main factors that may affect them. More precise estimates of these requirements should lead to more accurate simulation of the energetic metabolism of adult females. The simulation model proposed herein is generally more mechanistic than others previously presented and is the first to use fundamental concepts to simulate protein growth potential of adult females. The proposed model is also the first to separately estimate energy and protein gain or loss during the reproductive cycle, with boundaries depending on the nutritional and physiological status of the animal. Because of the incorpora-

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tion of these mechanisms, this model can give a more accurate representation of real systems. Therefore, the model is expected to respond more reliably to a wide range of combined nutritional, genetic, and managerial situations, if these situations are adequately described in quantitative terms. Implications This computer simulation model extends the basis for predicting the growth and body composition of pigs to include the gains and losses in protein and lipid components of open, gestating, and lactating sows. The predictions incorporate the interaction of performance potentials, due to genetics and other factors, with the amount and nutritional characteristics of diets fed during these periods. This model facilitates planning the feeding program to maintain adequate body reserves to support gestation and lactation needs without excessive fatness. More precise control of feeding programs can allow maximum reproduction and lactation without excess costs.

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Llterature Clted

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