Lysine requirements for pigs

Can the growth and carcass characteristics of market hogs be manipulated by nutrition?

Published on:
Author/s :
2965 0 Statistics
Share :


Pork producers market their pigs to pork processors that provide premiums and discounts based on predicted carcass lean yield.  Recently, some marketing grids have been developed that provide maximum payment for pigs of a specific predicted lean yield percentage.  The gilts of some of the leanest, most feed efficient genetic populations are too lean and may be discounted 5 to 6 percent relative to pigs in the most ideal carcass lean yield percentage class.  The accurate estimation of lysine requirements (g lysine:Mcal ME or NE) is crucial towards targeting the desired predicted lean yield and feed efficiency.  For a population of pigs, the pigs´responses in protein accretion to increased dietary lysine concentrations are linear- curvilinear- plateau in nature due to pig to pig variation in their requirements.  A slight decrease in dietary lysine concentrations can target specific predicted carcass lean yield percentages.  However, too great of reductions in dietary lysine concentrations decrease Body Weight (BW) growth and feed efficiency. Pork producers must evaluate management alternatives such as use of alternative genetic populations of pigs, increased market weights and use of ractopamine.  The future of pork production will focus on the combined effects of genetics, nutrition, management and marketing strategies. 


The pork industry is driven by consumer demand for consistent quality lean pork products.  To meet current and future consumer demand, the pork industry must continue to improve the efficiency of lean pork production.  The primary approaches for improving the efficiency of pork production are through genetic, nutritional, and management changes.

Each commercial producer must first decide which genetics to use in their production facility.  After making the key genetic decisions, each pork producer must consider cost-effective nutritional and management changes to optimize the expression of genetic potential of their pigs.  A number of alternative management decisions must be evaluated which are influenced by production costs (i.e., feed, facility, labor, and interest costs) and the marketing system including premiums and discounts for carcass weight and predicted lean yield percentage.

Economic components of lean growth efficiency

Feed is the largest single cost in producing pork. Commercial producers should be interested in the lean growth potential of their pigs, since pigs with high lean growth rates are more efficient in converting feed to both lean and BW gain.  These relationships exist because the energetic cost of carcass fat tissue growth is approximately four times greater than muscle growth.  At 100 kg BW, the marginal growth of fat tissue is approximately 85% lipid, 3% protein, and 12% water.  The marginal growth of muscle tissue is approximately 76% water, 21% protein, and 3% lipid.  Pigs that have a higher percent lean at market weight have deposited a higher ratio of lean to fat in their carcasses (Table 1).  Also high lean gain pigs are later maturing with greater percent water and decreased percent lipid in their muscle and fat tissues at the same BW (Wiseman et al., 2007; Schinckel et al., 2008a).

Selection of increased lean growth has been successful.  Pigs with high lean growth rates have greater BW and lean tissue gain per unit feed intake and have substantially reduced cost per kilogram of lean gain (Schinckel et al., 2008b).   Modern high lean pigs are more feed efficient and maintain their greater protein accretion rates and carcass lean growth rates to heavier BW´s (Figure 1).  Selection for lean efficiency has reduced feed intake. High lean gain, low feed intake pigs require diets with greater concentrations of lysine and other essential amino-acids (Schinckel, 1994; Schinckel and de Lange, 1996; NCR, 1998).  


Alternative methods to estimate the lysine requirements for pigs.

The lysine to energy ratio requirements of pigs is largely determined by the pig´s compositional growth at each specific range of BW.  The greater the ratio of lean muscle gain to fat tissue growth (or protein to lipid accretion), the greater the lysine required per unit energy intake.  The underlying issue with the evaluation of lysine requirements is the fact feeding a population of pigs which have variation in their rates of BW growth, changes in body composition (protein and lipid), carcass composition (muscle and fat tissue) , and feed intakes which produces variation in their lysine requirements.  For a population of pigs, as daily lysine intakes increase, the mean protein accretion rates of the pigs´ increases in a linear fashion until some of the pigs´ lysine requirements are met. This occurs at a protein accretion rate of approximately 85 % of the population mean.  As lysine intakes increase, the percentage of pigs whose lysine requirements are met increases. This produces a curvilinear response of protein accretion to the increased lysine intakes (Figure 2).  As the lysine intakes increase further, essentially all the pig´s lysine requirements have been met and the protein accretion rates achieve a plateau (Moughan et al., 1989; Gahl et al., 1994; Schinckel, 1994).  The response in protein accretion can be modeled as a linear - curvilinear - plateau response to increased daily lysine intakes (Gahl et al., 1994) or a decreased efficiency in which SID lysine is used for protein accretion (Moughan, 1989; Schinckel, 1994).  

The marginal efficiency in which absorbed available lysine is used for protein accretion is 0.75 (Mohn et al., 2000).  Thus the population´s lysine requirement could be estimated by using population mean protein accretion, the digestibility of the lysine in the feedstuffs and an efficiency of utilization of SID lysine of .75.  This requirement would be correct for the average pig in the population and would result in the protein deposition of all pigs with above average protein accretion rates (approximately 50%) to not be achieved.  In other words, feeding to the mean requirement of a population of pigs will not result in the overall maximal population protein accretion to be achieved.  To take this into account, Moughan (1989) estimated an efficiency of .63 should be used if the maximal population protein accretion rates were to be achieved. Schinckel (1994) using additional data from Gahl et al., (1994), estimated an efficiency value of .652 should be used. This would result in pigs being fed at 115 % of their population mean requirement to achieve each pig´s lysine requirement and achieve the maximal population mean protein rates.  These results were reproduced by Brossard et al. (2009) who used a stochastic model to predict the response of a population of pigs to diets with different lysine concentrations. Brossard et al. (2009) found that a close to maximum response could be achieved when pigs were fed at 110% of their population mean requirement.

There are several approaches that can be used to estimate lysine requirements.  The first is to conduct large scale swine nutrition experiments in which a large number of pigs are fed diets with a range of lysine: calorie ratios.  The pig´s responses in ADG, feed efficiency (gain:feed) and predicted carcass value are used to evaluate the optimal dietary concentration at different specific BW ranges (De la Llata et al., 2007; Main et al., 2008).  The optimal lysine concentration is one that maximizes income over feed costs per head (De la Llata, et al., 2007;Main et al., 2008) or daily income above feed costs (Li, 2003; Canchi et al., 2010).  Feed intake data must be accurately collected.  With increased feed costs, feed efficiency is the major component of the income over feed costs for the pigs of each dietary lysine concentration.  Some trials have included hundreds even thousands of pigs.  Commercial producers with common genetics and production systems should consider cooperative research trials to estimate the lysine requirements for their pigs.

The second approach is to develop lean growth curves and feed intake curves for pigs reared under commercial conditions (Schinckel and de Lange, 1996; Schinckel et al., 2001).  For establishing pig growth curves a random sample of 40 pigs of each sex should be weighed and scanned every 3 weeks and at the time of marketing for a total of at least 5 and preferably 6 points over the grow-finish period.  The best approach is to serially weigh the same set of pigs and fit the growth functions to a mixed nonlinear model to reduce the impact of potential biases (Craig and Schinckel, 2001).  Feed intakes relative to BW can be estimated directly or indirectly based on the estimated dietary energy requirements required for maintenance, protein accretion and lipid accretion (Schinckel and Delange, 1996).  The best approach is to obtain an initial estimate of fat-free lean gain based on days from approximately 20-25 kg to market weight and their predicted carcass fat free lean percentage.  The pigs used for the actual serial measurements should be fed diets approximately 20 -25 % greater lysine concentration so that the pigs full "operational " protein accretion rates can be achieved.  If the pigs are under fed in lysine or any other nutrient then the limiting nutrient with prevent the pigs from achieving their maximal lean gain (protein accretion) and the estimates of lysine requirements will be less than their actual values.

The third option is to estimate the fat-free lean and using the NRC model (NRC, 1998; based on data of Schinckel et al., 1996).  The NRC model estimates the protein accretion rates and daily lysine requirements (g/d) as a function of fat-free lean gain from 25 to 125 kg BW.  One key assumption is that the prediction equations for fat-free lean are accurate and predict the carcass composition of pigs of different sexes and genetic populations with minimal biases.  If the carcass measurements are taken accurately, nearly complete dissection of muscle and fat tissues is completed, and the chemical (lipid) analyses of the dissected lean and fat tissue are done accurately- then the resulting equations will be accurate.  Accurate equations for fat-free lean percentage should have Residual Standard Deviation (RSD) of 2.6 % or less and account for greater than 90 % of the actual genetic population variation in fat-free lean percentage (Schinckel et al., 2010).  Unfortunately currently widely used prediction equations have substantially greater RSD´s (3.74 to 4.32 %, Johnson et al., 2004) and account for a relatively small percentage of the true variation in carcass lean percentage.  Using the current US equations, the same set of pigs measured at nearly identical locations with different measurement devices have drastically different estimates of FFL gain and daily lysine requirements (Schinckel et al., 2010). When the carcass measurements are taken with greater levels of measurement errors the equations developed will include false quadratic and cross-product terms even to the extent that pigs with greatly different (i.e., 27 and 44 mm) backfat thickness have identical predicted fat-free lean percentage (Johnson et al., 2004; Schinckel et al., 2007; 2010).  When evaluated out of sample using accurate prediction equations for fat-free lean (Orcutt et al., 2000, RSD= 2.35 %), the NRC model predicted and observed lysine requirements were in close agreement (Wei and Zimmerman, 2003). 

The fourth option is use equations developed by Purdue and Kansas State University (KSU, 1998) which predict the grams of lysine required per Mcal of ME for barrows and gilts from their predicted fat-free lean at 120 kg BW.  The equation for barrows is g lysine/ Mcal ME = 0.0366 BW - 0.3799 FFL% + .00012637 BW2 + 0.006052 FFL%2- 0.0013845 BW x FFL% + 8.68.  The equation for gilts is g lysine/ Mcal ME = 0.04189 BW - 0.3369 FFL% + .00010207 BW2 + 0.00578 FFL%2- 0.001529 BW x FFL% + 7.046. 

These methods of estimating lysine requirements may result in different estimates of nutrient requirements (Hauschild et al., 2010).  The researchers used a stochastic model and compared the results of factorial or empirical methods to estimate the lysine: NE ratios of pigs from 25 to 105 kg BW.  Their results demonstrated that the two methods commonly provide different nutrient recommendations (Hauschild et al., 2010).  One underlying important concept is that pork producers are feeding a population of pigs with different nutrient requirements and the population response to different dietary nutrient concentrations is not the same as an estimate of the population mean nutrient requirements.

The effects of feeding marginally lysine deficient diets

As the pigs are fed diets with decreased lysine per Mcal of energy the pigs´ compositional growth will be altered towards decreased protein accretion and increased lipid accretion (Stahly et al., 1988; Schinckel et al., 2003: Brossard et al., 2009). The diets ratio of lysine to dietary energy sets an upper limit on the pigs´ratio of protein accretion to lipid accretion. As the dietary concentration of lysine is reduced from a high level (110 to 115 % of the population mean requirement) -the reduction of the dietary lysine on the pigs composition growth increases as the concentration decreases as the compositional growth of a greater percentage of the pigs is affected. 

This response is shown by a trial in which 1200 gilts (trial 1, 27-120 kg BW ) and 1200 barrows (trial 2 , 34 to 120 kg BW) were phase fed a series of diets from 27 to 120 kg (De La Llata et al., 2007).  The dietary treatments were arranged as a factorial with four levels of lysine for each phase and two levels of added dietary fat (0 or 6 % added) with the diets formulated to have similar lysine:Mcal ME ratios for the 0 and 6% fat added diets.  Pigs were reared under commercial conditions with 25 pigs per pen and 0.67 m2 per pig.  Lysine:calorie ratios (g lysine/Mcal ME) for trial 1 were 2.96, 3.26, 3.56 and 3.86 in phase 1; 2.25, 2.50, 2.75 and 3.0 in phase 2; 1.64 , 1.84, 2.04 and 2.24 in phase 3; and 1.12, 1.32 1.52 and 1.72 in phase 4. In trial 2, the lysine:calorie ratios were 2.41, 2.71 , 3.01 and 3.31 in phase 1; 1.75, 2.0, 2.25 and 2.5 in phase 2;  1.38, 1.58, 1.78, and 1.98 in phase 3; and 1.02, 1.22, 1.42 and 1.62 in phase 4.

The results of De La Llata et al., (2007) are shown in Tables 2 and 3.  Overall ADG, gain:feed, loin depth, and predicted carcass lean% decreased as the lysine:calorie ratios decreased. Backfat depths increased as the dietary concentrations of lysine decreased.  Both daily feed intakes and daily ME intakes were not affected by the dietary lysine concentrations.  A very slight reduction in lysine concentration from the greatest level to second greatest lysine concentrations had little impact to reduce ADG or gain:feed and increased backfat by approximately one mm. Further decreases in dietary lysine concentrations produced increasing greater reductions in feed efficiency, and predicted carcass lean percentage. 

The stochastic simulation model of Brossard et al. (2009) found similar results (Table 4).  As the lysine fed was reduced from 110 to 100 and then to 90 % of the population mean requirement, the pigs ADG was reduced (1.068 to 1.05 to 0.988 kg/d with a ten phase feeding program), feed:gain increased (2.23 to 2.27 and 2.41 ),  protein mass at slaughter (110 kg BW) decreased (17.1, 17.0 and 16.8 kg at 110 kg BW) and increased lipid mass increased (25.4 , 25.9 and 27.9 kg).  These results indicate that at a precise feeding at approximately 100 % of the population´s mean requirement, the lean percentage of the pigs could be reduced slightly with only small reductions in feed efficiency and ADG. 

The model of Brossard et al. (2009) assumed that daily feed intakes would not be affected by the dietary lysine concentration.  Some researchers have found that pigs fed diets with reduced lysine concentrations had increased daily feed intakes (Ferguson et al., 2001).  This response in daily feed intake is not consistent (De La Llata et al., 2007; Main et al., 2008).  Genetic lines or sire families within a genetic population that increase their daily feed intakes slightly (3 to 4%) in response to the reduced dietary lysine concentrations will be better able to achieve greater lean growth rates and feed efficiency.

As discussed by Brossard et al. (2009), the feeding of diets at 90 and 100% of the population mean requirement limits the ratio of protein:lipid deposited in a percentage of the pigs.  Feeding pigs at 100% of the mean requirement will limit the body compositional growth and resulting carcass leanness of approximately 50% of the pigs.  Based on their stochastic results, the feeding of diets with reduced concentrations of lysine should result in reduced variation in carcass composition and increased variation in BW growth (Brossard et al., 2009).  The increased variation in BW growth is produced as the BW growth of the lighter pigs, which have the greater than average dietary lysine requirements, is reduced to a greater extent than the other pigs.  The extent to which the lightest pigs are fed diets less than their requirements increases as dietary concentrations of lysine are reduced at each diet change.  Sorting of pigs based on BW at weaning or the time of exiting from the nursery into light and heavy weight groups could drastically reduce the under feeding of the lighter pigs and in theory reduce the overall variation in BW growth (Schinckel et al., 2009b).

Impact of the marketing grids

Most pork processors have developed a marketing grid which provides premiums and discounts for carcass weight and leanness.  In theory, grids are designed to represent the true carcass value after accounting for processing costs.  When pigs were more variable in backfat and lean percentage, there were close relationships between predicted carcass lean percentage and carcass value after accounting for processing costs.  When pigs were more variable in backfat and lean percentage, there were close relationships between predicted carcass lean percentage and carcass value (Akridge et al., 1992) especially when trimmed hams and loins were marketed.  However, pigs have become leaner and less variable in carcass lean percentage.  In recent years, the relative value of the belly has increased and there is an antagonistic relationship between carcass leanness and the belly weight relative to carcass weight (Marcoux et al., 2007).  This has resulted in poorer relationships between market carcass value and measures of carcass lean yield (Marcoux et al., 2007).

The current Quebec commercial pork grading grid provides the greatest payment for carcasses of lean yield percent classes 3 and 4 with 57.7 to 61.8% lean yield (Table 5).  The increased carcass value to have a lean yield 3 (59.6 to 61.8% lean yield) in comparison to yield grade 2 (61.8 to 64.3 %) is 5% with carcasses with the most ideal carcass weights of 90 to 104.9 kg.  It is not apparent as to why the leaner class 2 pigs are less valuable that the class 3 pigs in the Quebec marketing grid.  In the United States light weight lean pigs do not receive additional lean premiums as their bellies produce bacon which is too thin to receive the highest grade. 


If the percentage of pigs are in the yield grade 2 class is too great, then feeding diets with slightly reduced lysine levels is an alternative.  The challenge is to identify the dietary lysine concentrations that increase the percentage of pigs currently in lean yield class 2 to class 3 with the smallest reductions in feed efficiency and ADG.  In some cases, increased market weights may also increase the percentage of pigs in class 3 versus class 2.  The rates of increased backfat and loin muscle depth relative to BW and carcass weight must be modeled (Schinckel et al., 2009a).  By reducing the lysine concentration of the final diets, the rate of fat accretion and corresponding rate of increase in backfat depth (mm/kg BW or carcass weight) will increase.  A combination of feeding diets with slightly reduced lysine concentrations and increased market weights will increase the percentage of pigs achieving lean yield grade 3.  The increase in carcass value (5 to 6 %) must be greater than the increased feed and facility costs.

The mean predicted backfat and loin muscle depths were modeled for a lean genetic population of pigs.  The mean predicted backfat and loin depths used in the Québec lean yield equation (Figure 5).  At 61.8% lean yield about 50% of the pigs will be in yield class 2 and 50 % in yield class 3.  This occurs at 108 kg BW for the barrows and 122 kg BW for the gilts.  The maximal percentage of pigs should be in yield class 3 at a mean predicted lean yield of 60.7%.  This is achieved at 111.1 kg BW for the barrows and at 150 kg BW for the gilts.  It should be noted that the standard deviation in predicted lean yield percentage is approximately 1.5 % for the gilts and 1.2 % for the barrows based on standard deviation of 3 mm for backfat depth in the gilts and 4 mm for the barrows (Schinckel et al 2009).  With these standard deviations in the backfat and loin muscle depth measurements at the same BW, it is impossible for all pigs to be yield class 2.  Since the value index values are greater for lean yield class 4 carcasses than lean yield class 2, then the maximal mean index values will be obtained when the mean lean yield index is less than 60.7 % - approximately 60.3 % for weight group 6 pigs and slightly less for weight class 5 pigs.  

With mixed sex feeding, decreasing the dietary lysine concentration will reduce the carcass lean percentage, ADG, and feed efficiency of the gilts to much greater extent than the barrows.  Reduction in the dietary lysine concentrations will especially decrease the ADG of the lightest gilts, which have the greatest lysine requirements, and may increase the overall variation in BW growth (Brossard et al., 2009). 

Alternative terminal sire lines and sires within line should be considered which result in the greatest daily profit above feed costs when pigs are sold on the marketing grid.  Terminal sire lines which produce gilts with slightly greater daily feed intakes may result in smaller decreases in ADG and feed efficiency when diets with reduced lysine concentrations are fed.  Higher feed intake gilts, gilts that have slightly increased feed intakes when fed diets with lysine concentrations below their requirements, will have increased BW and carcass lean growth in comparison to gilts whose daily feed intakes do not increase when fed diets with reduced lysine concentrations.

Feeding diets with decreased lysine concentrations will also increase the intramuscular content of the loin and ham muscles (Pettigrew and Esnaola, 2001: Bidner et al., 2003; Teye et al., 2006).  Teye et al. (2006) also found the feeding of low protein diets improved pork muscle tenderness and juiciness scores and altered the fatty acid profiles of the loin muscle.  To fully evaluate the impact of feeding diets with reduced lysine concentrations, the overall impacts on actual carcass value, pork muscle composition and quality should also be evaluated.

Development of Stochastic Growth Models

Most pork producers summarize the means of production unit specific measurements of animal performance and market prices received.  The average performance of pigs is important.  However, it is becoming increasingly apparent that the variability in the growth rates of pigs is also extremely important to the economic costs and returns of both the pork producer and processor (King, 1999).  Pork processors have the objective to market lean pork products, which are uniform in weight and composition in order to receive premium pricing for their product. 

The optimization of pork production systems requires knowledge of the between pig variation in BW and carcass composition in order to develop the most beneficial management and nutritional strategies (Pomar et al., 2003; Schinckel et al., 2003a, 2009b).  Mixed model nonlinear analyses account for the underlying variance-covariance structure of serial BW data (Craig and Schinckel, 2001) and can be used to develop stochastic pig growth models which to predict the variation in BW and body composition (Schinckel et al., 2003a).  Stochastic models better reflect curvilinear responses of population mean performance to dietary treatments (Pomar et al., 2003; Hauschild et al., 2010) and reproduce the variation in the pigs´ lysine requirements (Figures 3 and 4, Schinckel et al., 2009b).  Systems analyses toward the optimal combined use of genetics, nutrition, marketing strategies, pig management strategies and use of ractopamine require of the use require the use of stochastic models (Li, 2003; Pomar et al., 2003; Schinckel et al., 2003a, 2009b).

Using a Stochastic Model to Evaluate Swine Production Management with Ractopamine

The economically optimal use of ractopamine (RAC) had been examined using a growth model for a single pig with average growth properties.  In reality, however, pigs are produced in groups, where, with All-in/All-0ut (AIAO) management, the group size equals the barn capacity.  The turn-over of the barn depends on the marketing day of the last batch, not the pig with the average growth rate.

A bio-economic model was developed based on a stochastic growth model (Schinckel et al., 2003a), which incorporated the economic optimization principles of livestock replacement, swine growth under limited dietary lysine intake, and compositional growth response to RAC (Schinckel et al., 2003b).  This stochastic bio-economic model was used to derive the optimal production and marketing decisions for grow-finish swine production with RAC, which include both dietary lysine management and RAC management (Li, 2003).

The optimal management was derived for four payment schemes, simulating producers with various marketing channels and market structures.  They were: 1) carcass payment with discounts on underweight and overweight carcasses; 2) carcass merit payment system adopted from Hormel´s Carcass Lean Value Program; 3) lean to fat price ratio of 2:1, with discounts on underweight and overweight carcasses; and 4) lean to fat price ratio of 4:1, with discounts on underweight and overweight carcasses. 

The net returns from using RAC increased from payment scheme 1 to 4, with greater lean to fat price ratios resulting in greater net returns.  The optimal RAC concentration ranged from 4.5 to 8.6 g/ton, whichincreased from payment scheme 1 to 4.  Because greater RAC concentrations require greater dietary lysine concentrations, the optimal dietary lysine concentrations in the second and third diets varied considerably under each payment scheme.  Ractopamine feeding durations for the last batch of marketed pigs varied slightly across payment schemes, ranging from 26 to 30 days and averaging at 28 days for all four payment schemes.

Ractopamine had greater economic values under tight marketing schedules than when pigs were marketed under the optimal marketing age or under loose schedules.  With extremely tight schedules, the dietary concentration of RAC should be increased to a large degree, while with loose schedules, the RAC concentration should be decreased slightly.

With the current marketing commercial grid, pork producers with lean pigs with a high percentage in class 2 should consider using the lower concentration of RAC and feed levels of lysine that allow the majority of the RAC response to increase carcass weight and feed efficiency without the maximal decrease in backfat (Schinckel et al., 2003b; Apple et al., 2004; Webster et al., 2007).  Feeding high energy fat added diets with RAC may increase carcass weights while reducing the impact of RAC to reduce backfat thickness (Apple et al., 2004; Schinckel, unpublished data).  Dietary lysine levels that allow for the majority of the increased carcass lean growth (but not maximal carcass leanness) will produce pork with slightly greater intramuscular fat content similar to pigs not fed RAC  (Apple et al., 2004; Weber et al., 2006; Webster et al., 2007).

Producers with pigs in lean yield class 4 and especially lean yield class 5 should consider feeding greater concentrations of RAC and dietary lysine concentrations to increase the predicted lean yield of their pigs.  Also, step up programs (i.e., 18 d at 5 ppm and 17 at 10 PPM) will produce a greater RAC response (Schinckel et al., 2006, Canchi et al., 2010).  It should also be realized that most prediction equations for measures of carcass lean and fat tissue content only predict approximately 50% of the actual changes in carcass composition produced by RAC (Schinckel et al., 2003b). 

Future of modeling and systems analysis.

The process of producing pigs and the evaluation of alternative decisions is quite complex.  Our goal has been to model parts of the pork production system for the evaluation of optimization procedures.  Optimization as defined by Webster´s New Collegiate Dictionary is "the process of making something as perfect, effective or functional as possible."  The Oxford English Dictionary defines optimization as "the actions or process of making the best of something."  The future of pork production will focus on the combined effects of genetics, nutrition, management and marketing strategies.  The optimization of pork production systems requires the use of stochastic models. The impact of pork marketing grids on the optimization of pork production systems and cost of variation requires further study.  

Literature Cited

Akridge, J. T., B. W. Brorson,  L. D.  Whipker, J. C. Forrest, C, H. Kuei, and A. P. Schinckel. 1992.  Evaluation of alternative techniques to determine carcass value. J. Anim. Sci. 70:18-28.

Apple, J. K., C. V. Maxwell, D. C. Brown, K. G. Fresian, R. E. Musser, Z. B. Johnson and T. A. Armstrong.  2004.  Effects of dietary lysine and energy density on performance and carcass characteristics of finishing fed ractopamine. J. Anim. Sci. 82:3277-3287.

Bidner, B. S., M. Ellis, D. P. White, S. N. Carr, and F. K. McKeith.  2004.  Influence of dietary lysine level, pre-slaughter fasting, and rendement napole genotype on fresh pork quality.  Meat. Sci. 68:53-60.

Brossard, L., J. Y. Dourmad, J. Rivest and J van Milgen.  2009.  Modelling the variation in performance of a population of growing pig as affected by lysine supply and feeding strategy.  Animal 3(8); 1114-1123.

Boys, K., N. Li, P. V. Preckel, A. P. Schinckel, and K. A. Foster.  2006.  Economic replacement of a heterogeneous herd.  A. J. of Agric. Econ. 89(1):24-35.

Canchi, D., N. Li, K. Foster, P.V. Preckel, A. Schinckel, and B. Richert.  2010.  Optimal Control of Desensitizing Inputs: The Case of Paylean.  Amer. J. Agr. Econ. 92(1): 56-69.

Craig, B. A. and A. P. Schinckel.  2001. Nonlinear mixed effects model for animal growth. Prof. Anim. Scientist. 17:256-260.

De Lange, C. F. M., B. J. Marty, S. Birkett, P. Morel, and B. Szkotnicki.  2001.  Application of pig growth models in commercial pork production.  Can. J. Anim. Sci. 81:1.

De La Llata, S. S. Dritz, M. D. Tokach, and R. D. Goodband.  2007. Effects of increasing lysine to calorie ratio and added fat for growing-finishing pigs reared in a commercial environment: Growth performance and carcass characteristics.  Prof. Anim. Sci. 23:417-428.

Ferguson, N. S., G. Lavers, and R. M. Gous.  2001. The effect of stocking density on the responses of growing pigs to dietary lysine.  Anim. Sci. 73:459-469.

Gahl, M. J., T. D. Crenshaw and N. J. Benevenga.  1995. Diminishing returns in weight, nitrogen and lysine gain of pigs fed six levels of lysine from three supplemental sources. J. Anim. Sci. 73:3177-3187.

Hicks, C. , A. P. Schinckel, J. C. Forrest, J. T. Akridge, J. R. Wagner and W. Chen.  1998.  Biases associated with genotype and sex in prediction of fat-free lean mass and carcass value. J. Anim. Sci.  76:2221-2234.

Hauschild, L., C. Pomar, P. A. Lovatto.  2010. Systematic comparison of the empirical and factorial methods used to estimate the nutrient requirements of growing pigs.  Animal. 4(5):714-723.

Johnson, R. K., E. P. Berg, R. Goodwin, J. W. Mabry, R. K. Miller, O. W. Robison, H. Sellers, and M. D. Tokach.  2004.  Evaluation of procedures to predict fat-free lean in swine carcasses.  J. Anim. Sci. 82:2428-2441

King, R. H. 1999. A review-Nutritional constraints to pig performance and pig variability.  In Manipulating Pig Production VII.  P. D. Cranwell (Ed.). p. 245.  Aust. Pig Sci. Assoc., Werribee, Victoria 3030, Australia.

KSU. 1998. Methods to calculate lysine requirements based on genotype and environment.  Kansas State Agric. Exper. Stat. Swine Update.  Vol 20 (4).

Li, N.  2003.  Economic analysis of optimal production and marketing management strategies for swine production operations with Paylean.  Ph.D Thesis, Department of Agric. Econ., Purdue University, West Lafayette, IN.

Main, R. G., S. S. Dritz, M. D. Tockach, R. D. Goodband and J. L. Nelssen. 2008. Determining the optimal lysine:calorie ratio for barrows and gilts in a commercial finishing facility.  J. Amin. Sci. 86:2190-2207.

Marcoux, M. C. Pomar, L. Faucitano, and C. Brodeur. 2007.  The relationship between different pork carcass lean yield definitions and the market carcass value. Meat Sci. 75:94-102.

Mohn, S., A. M. Gillis, P. J. Moughan, and C. F. M. de Lange. 2000. Influence of dietary lysine and energy intakes on body protein deposition and lysine utilization in the growing pigs.  J. Anim. Sci. 78:1510-1519.

Moughan, P. J. 1989.  Simulation of the daily partitioning of lysine in the 50 kg livewieght pig- A factorial approach to estimating amino acid requirements for growth and maintenance.  Res. and Dev. In Agric. 6:7-14.

National Research Council (NRC).  1998.  Nutrient Requirements of Swine, 10th Revised Edition.  National Academic Press, Washington, DC.

Orcutt, M. W., J. C. Forrest, M. D. Judge, A. P. Schinckel, and C. H. Kuei.  1990.  Practical means for estimating pork carcass composition.  J. Anim. Sci. 68:3987-3997

Pettigrew J. E., and M. A. Esnaola.  2001.  Swine nutrition and pork quality: A review.  J. Anim. Sci. (E. Suppl.):E316-E342.

Pomar, C., I. Kyriazakis, G. C. Emmans, and P. W. Knap.  2003.  Modeling stochasticity:  Dealing with populations rather than individual pigs.  J. Anim. Sci. 81(E. Suppl. 2):E178-E186.

Schinckel, A. P. 1994. Nutrient requirements of modern pig genotypes. In: Recent Advances in Animal Nutrition. P.C. Garnsworthy and D.J.A. Cole, Ed. Univ. of Nottingham Press, Nottingham, U.K. p.133.

Schinckel, A.P., P.V. Preckel and M.E. Einstein.  1996.  Prediction of daily protein accretion rates of pigs from estimates of fat-free lean gain between 20 and 120 kilograms liveweight.  J. Anim. Sci. 74:498-503

Schinckel, A. P., and C. F. M. de Lange.  1996.  Characterization of growth parameters needed as inputs for pig growth models.  J. Anim. Sci. 74:2021-2036.

Schinckel, A. P., J. W. Smith, M. D. Tokach, S. S. Dritz, M. Einstein, J. L. Nelssen, and R. D. Goodband.  2002.  Two on-farm data collection methods to determine dynamics of swine compositional growth and dietary lysine requirements estimates.  J. Anim. Sci.  80:1419-1432.

Schinckel, A. P., N. Li, P. V. Preckel, M. E. Einstein, and D. Miller.  2003a.  Development of a stochastic pig compositional growth model.  Prof. Anim. Sci. 19:255-260.

Schinckel, A. P., N. Li, P. V. Preckel, and M. E. Einstein.  2003b.  Development of a model to describe the compositional growth and dietary lysine requirements of pigs fed ractopamine.  J. of Anim. Sci. 81:1106-1119.

Schinckel, A. P. , N. Li, B. T. Richert, P. V. Preckel, K. Foster and M. E. Einstein. 2006. Development of model to describe the compositional growth and dietary lysine requirements of pigs fed increasing dietary concentrations of ractopamine. Prof. Anim. Sci. 22:438-449.

Schinckel, A. P., D. C. Mahan, T. G. Wiseman and M. E. Einstien.  2008a. Impact of alternative energy systems on the estimated feed intake requirements of pigs with varying lean and fat tissue growth rates when fed corn and soybean meal based diets.  Prof. Anim. Sci.  24:198-207.

Schinckel, A. P., M. E. Einstein, K. Foster, and B. A. Craig.  2007.  Evaluation of the impact of errors in the measurement of backfat depth on the prediction of fat-free lean mass.  J. Anim. Sci. 85:2031- 2042.

Schinckel, A. P., T. G. Wiseman, D. C. Mahan, J. C. Peters, N. D. Fastinger, S. Ching, Y. Y. Kim, and M. E. Einstein.  2008b.  Growth of protein, moisture, lipid and ash of two genetic lines of barrows and gilts from 20 to 125 kilograms body weight.  J. Anim. Sci. 86:460-471.

Schinckel, A.P., M. E. Einstein, S. Jungst, C. Booher, and S. Newman.  2009a. Evaluation of the Growth of Backfat Depth, Loin Depth, and Carcass Weight for Different Sire and Dam Lines. Prof. Anim. Sci. 25:325-344.

Schinckel, A. P., M. E. Einstein, S. Jungst, C. Booher, T. S. Stewart, and S. Newman.  2009b.  Development of a stochastic model of pig growth to evaluate the impact of birth and twenty-one day body weight and potential sorting strategies on the body composition growth and lysine requirements of pigs.  Prof. Anim. Sci.  25:663-688.

Schinckel, A. P.,  J. R. Wagner, J. C. Forrest and M. E. Einstein. 2010.  Evaluation of the Prediction of Alternative Measures of Carcass Composition by Three Optical Probes. J. Anim. Sci. 88:767-794.

Stahly, T. S., G. L. Cromwell, and D. Terhune. 1988.  Responses of pigs from high and low lean growth genotypes to dietary lysine levels.  J. Anim. Sci. 66(Supp. 1):137.

Teye, G. A., P. R. Sheard, F. M. Whittington, G. R. Nute, A. Stewart, and J. D. Wood.  2006.  Influence of dietary oils and protein level on pork quality.  1.  Effects on muscle fatty acid composition, carcass, meat, and easting quality.

Weber, T. E., B. T. Richert, M. A. Belury, Y. Gu, K. Enright and A. P. Schinckel.  2006.  Evaluation of the effects of dietary fat, conjugated linoleic acid, and ractopamine on growth performance, pork quality, and fatty acid profiles in genetically lean gilts.  J. Anim. Sci. 84:720-732.

Webster, M. J., R. D. Goodband, M. D. Tockach, J. L. Nelssen, S. S. Dritz, J. A. Unruh, K. R. Brown, D. E. Real, J. M. Derouchey, J. C. Woodworth, C. N. Groesbeck, and T. A. Marsteller. 2007.  Interactive effects between ractopamine hydrochloride and dietary lysine on finishing pig growth performance, carcass characteristics, pork quality and tissue accretion.  Prof. Anim. Sci. 23:597-611.

Wei, R., and D. R. Zimmerman.  2003.  An evaluation of the NRC (1998) growth model in estimating lysine requirements of barrows with a lean growth rate of 348 g/d.  J. Anim. Sci.  81:1772-1780.

Wiseman, T. G., D. C. Mahan, J. C. Peters, N. D. Fastinger, S. Ching, and Y. Y. Kim.  2007.  Tissue weights and body composition of two genetic lines of barrows and gilts from twenty to one hundred twenty-five kilograms of body weight.  J. Anim. Sci. 85:1825-183. 

This presentation was given at the 47th annual Eastern Nutrition Conference, May 11-12, 2011 in Montreal, Quebec. thanks the author and the organizing committee for this contribution. 

Would you like to discuss about this topic: Can the growth and carcass characteristics of market hogs be manipulated by nutrition??
Engormix reserves the right to delete and/or modify comments. See more details

Comments that contain the following items won´t be published:

  • Repeated spelling mistakes.
  • Advertisements, Web sites and/or e-mail addresses.
  • Questions or answers not relevant to the topic discussed in the Forum.
You need to be part of Engormix to post a comment on this discussion
Post a comment
Professional Services
Jon Bergstrom Jon Bergstrom
Marshall, Missouri, Estados Unidos de América
Scott Webster Scott Webster
Madison, Wisconsin, Estados Unidos de América
John Craggs John Craggs
Altoona, Iowa, Estados Unidos de América
Copyright © 1999-2017 Engormix - All Rights Reserved