J. Anim Sci. 2008. 86:592-601. doi:10.2527/jas.2007-0257
© 2008 American Society of Animal Science
The correlation of chemical and physical corn kernel traits with growth performance and carcass characteristics in pigs1
S. M. Moore*,
K. J. Stalder*,
D. C. Beitz*,
C. H. Stahl*,
W. A. Fithian
and
K. Bregendahl*,2
* Department of Animal Science, Iowa State University, Ames 50011; and
Golden Harvest Seeds Inc., Waterloo, NE 68069
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Abstract
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Corn kernel composition may affect its nutritive value and, thus, pig growth performance and carcass characteristics. The objective of this study was to determine the influence of the chemical and physical traits of corn kernels from different hybrids on the growth performance and carcass characteristics of pigs. A total of 288 crossbred pigs were grown in a 3-phase program from 21 kg of BW until slaughter at 113 kg of BW with 12 pens (4 pigs/pen) per dietary treatment. Target BW for each phase were 20 to 40 kg (grower 1), 40 to 80 kg (grower 2), and 80 to 120 kg (finisher). In each phase, diets were formulated to be marginally deficient in Lys, TSAA, Ca, Na, and nonphytate P to improve the likelihood of detecting differences in performance due to corn hybrid. Each of 6 corn hybrids represented a wide range of kernel chemical and physical traits and was substituted for corn in a common diet formulation on an equal weight basis to make the 6 dietary treatments. Physical and chemical composition of the kernels were analyzed and correlated with performance measures by multivariate ANOVA. Kernel density was correlated with i.m. fat (IMF) content in LM (r = –.35, P < 0.05). Stenvert grinding time was correlated (P < 0.05) with ADG during the grower 1 phase (r = 0.26), ADFI during the grower 2 phase (r = 0.27), final BW (r = 0.27), and IMF (r = –0.36). The amylose content of the cornstarch was correlated (P < 0.05) with ADG during the grower 2 phase (r = –0.28) and with BW at the end of the grower 2 phase (r = –0.27). The NDF content of the kernels was correlated (P < 0.05) with ADG during the finisher phase (r = –0.30), final BW (r = –0.33), and number of days to market (r = 0.31). The ADF content of the kernels was correlated (P < 0.05) with ADG during the grower 1 phase (r = –0.26), final BW (r = –0.26), and IMF (r = 0.31). The correlations of performance measure variation with individual kernel hybrid physical and chemical traits were statistically significant yet not large enough to base corn hybrid selection for feeding pigs on any single kernel chemical or physical trait.
Key Words: carcass quality • corn kernel trait • growth performance • swine
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INTRODUCTION
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In Midwestern swine diets, corn is a major component, supplying primarily energy, as well as protein and minerals, at a reasonable cost (USDA, 2006
). Typically, nutritionists formulate diets for swine based on reference values for the nutritive content of corn and other ingredients (NRC, 1998). Yet, nutrient variability in dietary ingredients results in growth performance changes in pigs (Fairbairn et al., 1999; Stein et al., 2006). Bulk corn grain typically is analyzed for moisture content, foreign particulates, and test weight. Little focus is given to physical or chemical traits beyond those because of the large quantity of grain requiring analysis (USDA, 2004). However, increasing specific nutrient content, such as nonphytate P in corn grain, results in improved performance measures in pigs (Veum et al., 2001; Hastad et al., 2005), and different corn hybrids have different nutrient digestibility and ME values in pigs (Spencer et al., 2000; Lampe et al., 2006). Studies in pigs have compared individual kernel traits or chemical composition and their effects on growth performance (Camp et al., 2003; Hastad et al., 2005; Lampe et al., 2006). In addition to variability in chemical composition, there are also physical differences among corn grain hybrids such as kernel density, hardness, and resistance to grinding that may affect livestock performance. For example, kernel hardness traits are negatively correlated with feed conversion and ruminal propionate concentrations in cattle (Jaeger et al., 2006). However, little, if any, peer-reviewed literature exists that comprehensively investigates the effects of chemical and physical kernel traits of corn hybrids and their contribution to changes in growth performance in pigs. The objective of this study was to determine the relationship between physical and chemical traits among several commercially available corn hybrids and growth performance and carcass quality in pigs.
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MATERIALS AND METHODS
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Corn Growth Conditions and Hybrid Kernel Traits
The 6 corn hybrids used in this study were commercially available varieties (Golden Harvest Seeds Inc., Waterloo, NE), representing a wide range of kernel chemical and physical traits (Tables 1 and 2). The 6 hybrids were planted on April 27, 2004, in the same field in Webster County, Iowa, and harvested on November 22, 2004. All cultivation practices (including fertilization rates and chemical application) were identical among the 6 hybrids. Each variety was planted in 60 rows, with the middle 36 rows of each hybrid used in the study to minimize the use of cross-pollinated corn. After harvest, approximately 19,000 kg of each corn hybrid was transported individually to Ames, Iowa, dried, and stored in separate gravity-flow grain bins.
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Table 1. Kernel physical traits from 6 corn hybrids used in evaluation of the effect of corn characteristics on growth performance in pigs (as-fed basis)1
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Table 2. Kernel chemical traits from 6 corn hybrids used in evaluation of the effect of corn characteristics on growth performance in pigs (as-fed basis)1
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Before chemical analyses, representative samples of each corn hybrid were ground through a 1-mm screen. The moisture content of each hybrid was determined by drying at 135°C for 2 h (Table 1
). Total N content was determined using the micro-Kjeldahl method on a distilling unit (Kjeltech 1028, US Tecator Inc., Herndon, PA), and the CP content was calculated as Kjeldahl N x 6.25. Individual AA content for each hybrid was determined by ion exchange chromatography (Experiment Station Chemical Laboratories, University of Missouri, Columbia). The contents of Na and Ca in the kernels were determined by inductively coupled plasma optical emission spectroscopy (Experiment Station Chemical Laboratories, University of Missouri). The total P content of each hybrid was determined gravimetrically (Experiment Station Chemical Laboratories, University of Missouri). The lipid content was determined as ether extract using a lipid extraction apparatus (Goldfisch Apparatus, Laboratory Construction Co., Kansas City, MO). Corn kernel fatty acid composition was determined after chloroform-methanol extraction of methyl esterified fatty acids using a gas chromatograph (Varian Model 3350 gas chromatograph, Varian Inc., Palo Alto, CA). Neutral detergent fiber and ADF contents were determined using a fiber digestion apparatus (Ankom 200 Fiber Digester, Ankom Technology, Macedon, NY).
Proportions of amylose and amylopectin present in starch isolated from each hybrid were determined by selective amylopectin precipitation and colorimetric glucose determination. Briefly, amylose and total starch samples were prepared from each corn hybrid according to the instructions of the manufacturer using a commercial kit (K-AMYL, Megazyme International Ireland Inc., Bray, Ireland). Amylose and total starch samples were enzymatically hydrolyzed to glucose monomers with either β-amylase, which digests only amylose, or a combination of β-amylase and amyloglucosi-dase, which digest both amylose and amylopectin (i.e., total starch). The resultant samples from either amylose alone or total starch were analyzed for glucose monomer content according to the instructions of the manufacturer in a commercial glucose assay kit (GAGO20-1KT, Sigma Aldrich Inc., St. Louis, MO), with the exception of reaction volumes, which were decreased to one-fifth of the reaction volumes in the protocol to facilitate the use of a microplate spectrophotometer (Powerwave HT, BioTek Instruments Inc., Winooski, VT). The percentage of amylose in the starch was calculated from the ratio of glucose in the amylose sample and in the total starch sample; the amylopectin content was calculated as the difference between the total starch and amylose contents.
A representative sample of kernels from each corn hybrid was aspirated to remove foreign matter and broken kernel particles using an aspirator (Kice 6DT4, Kice Industries Inc., Wichita, KS). Aspirated samples were stored in closed containers at –20°C until analyses of physical traits. The test weight of each hybrid was determined in triplicate using a test weight apparatus and computerized grain scale (Seedburo Model 8800, Seedburo Equipment, Chicago, IL). The 1,000-kernel weight was measured by counting 200 kernels in a magnetic parts feeder (Syntron, EB-00, FMC Corporation, Philadelphia, PA) equipped with a seed counter (Seedburo Equipment), then multiplying the weight of the 200 kernels by 5. The kernel density was determined in triplicate using a pycnometer (Micrometrics AccuPyc 1330, Micrometrics, Norcross, GA). The Stenvert hardness (i.e., Stenvert grinding time, Stenvert grinding resistance, hammer mill speed at maximal grinding power, and the percentage of hard and soft endosperm) was determined at the University of Nebraska (Lincoln) in a Stenvert grinding apparatus (Micro Hammer Mill V, Glen Mills Inc., Maywood, NJ) equipped with a 2-mm screen at 360 rpm (Pomeranz, 1985). The particle size of representative samples of the ground corn used in the diets was determined with National Institutes of Standards and Technology-approved US Standard testing sieves at Kansas State University (Manhattan), according to the methods of Baker and Hermann (2002).
Experimental Diets
A 3-phase feeding program was used, and within each period, the target pig BW were 20 to 40 kg (grower 1), 40 to 80 kg (grower 2), and 80 to 120 kg (finisher). Diets were formulated using NRC (1998) published nutrient values for all ingredients (including corn). All diets were formulated to contain amounts of Lys, TSAA, Ca, Na, and nonphytate P between 10 and 15% less than that recommended by the NRC (1998) to improve the likelihood that differences among corn kernel traits would elicit detectable changes in performance. The dietary treatments were created by individually substituting the 6 corn hybrids at an equal rate to a common diet formulation; thus, the only difference among the experimental diets was the corn hybrid used. Because the experiment was carried out over 9 mo, dietary treatments were mixed fresh approximately every 3 wk to prevent feed spoilage or a loss of vitamin activity. Immediately before mixing the dietary treatments, all corn was ground in a hammer mill equipped with a 7.94-mm screen, and all diets were fed in meal form.
Animals and Management
All procedures relating to the use of live animals in this study were approved by the Iowa State University Institutional Animal Care and Use Committee.
A total of 288 crossbred barrows (Pig Improvement Corporation, Lexington, KY) with an initial BW of 21.4 ± 3.1 kg were used in this experiment. The barrows were from 2 genetic pools, consisting of a total of 202 PIC337 x Camborough-22 barrows and 86 PIC337 x (PIC337 x Camborough-22) barrows. The experimental unit was the pen containing 4 pigs, with 12 replications per dietary treatment. Because of the availability of pigs and the facility, all the pigs in the study could not be allotted at the same time. Instead, the pigs were started in 7 periods, in which 2 periods consisted of 1 group and the remaining periods consisted of 2 groups of 6 pens per group. When 2 groups (i.e., 12 pens) were initiated, the pigs were sorted by BW into 2 groups (heavier and lighter), and they, as well as pigs for other periods, were assigned accordingly to pens to minimize the CV among initial average pen BW, to prevent lit-termates being allotted to the same pen and to maintain equal representation of pig genetic makeup among pens within a group.
Growth and Carcass Data Collection
Pigs were housed in 1.8 x 2.0 m (grower 1 phase) or 1.8 x 2.7 m (grower 2 and finisher phases) partially slatted-floor pens in a completely enclosed facility. Each pen was equipped with a 2-hole feeder and 1 nipple drinker. Pigs were allowed free access to feed and water throughout the experiment. Initial pig BW were recorded, and BW were monitored at least every 2 wk thereafter to determine the timing of the dietary transitions and slaughter. Dietary transitions were made on a block basis, when the mean BW of all pigs in the block, irrespective of diet, was within 2.5 kg of the target BW for each phase. Feed consumption (measured as feed disappearance) and BW were recorded at the beginning and end of each growth phase and at slaughter, and ADG, ADFI, and G:F were calculated.
One week before slaughter, LM i.m. fat (IMF) percentage, LM area (LMA), and backfat thickness (BF10) were evaluated at the 10th rib on all pigs by ultrasonic measurement. The evaluations were performed by a National Swine Improvement Federation-certified technician with an ultrasound machine (Aloka 500V SSD, Corometrics Medical Systems Inc., Wallingford, CT). A minimum of 4 longitudinal images taken 7 cm lateral to the midline across the 10th to 13th ribs were collected and visually assessed for acceptability by a certified technician. Predicted IMF was calculated based on the method of Newcom et al. (2002), whereas LMA and BF10 were calculated according to the NPPC (2000).
When the block mean BW was 120 kg, pigs with BW between 105 and 137 kg were transported to a commercial slaughter facility (Hormel Foods, Austin, MN). After slaughter, carcass weights were recorded, and carcasses were stored at 4°C and sampled for carcass traits within 24 h of slaughter by Iowa State University personnel. Ultimate pH was measured on the face of the LM at the 10th rib using a pH star probe (SFK Ltd, Hvidovre, Denmark) calibrated using 2 buffers (pH 4.0 and 7.0) before each carcass measurement. Pork color was evaluated in the Hunter L*a*b* color space on the face of the LM at the 10th rib using a Minolta CR-310 (Minolta Camera Co. Ltd., Osaka, Japan) with a 50-mm-diam. aperture, D65 illuminant, and calibrated to the white calibration plate. Individual pig percentage of fat-free body mass, rate of lean gain, and lean gain efficiency were calculated using ultrasonic measures, live BW, and carcass weight (NPPC, 2000).
Statistical Analyses
The 6 dietary treatments were assigned to pens according to a randomized complete block design, with location within the barn and date of pig allotment as blocking criteria. The experimental unit was the pen. To determine the effects of corn hybrids on growth performance and carcass quality, data were subjected to ANOVA using the GLM procedure (SAS Inst. Inc., Cary, NC). Fixed effects in the analysis model were hybrid and block. For BF10, LMA, and carcass lean weight, the pig BW at slaughter was used as a covariate. When the effect of hybrid was significant, means were separated using Fisher’s protected LSD (Snedecor and Cochran, 1980).
To determine the principal component effects of analyzed corn kernel traits on performance measures, data were subjected to the multivariate ANOVA procedure within the GLM procedure of SAS (Bray and Maxwell, 1982). The fixed effect in the multivariate ANOVA model was block. For BF10, LM, and carcass lean weight, the pen mean BW at slaughter was used as a covariate.
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RESULTS AND DISCUSSION
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Differences existed in both chemical and physical kernel traits among corn hybrids (Tables 1 and 2). These differences were attributed to genetic differences among the hybrids tested, because the corn was grown in the same field under the same growing conditions in the same growing season. Moreover, within corn hybrids, chemical and physical traits are relatively consistent compared with the variation among different hybrids (Reynolds et al., 2005). Nutrient contents for the hybrids used in this trial were 10 to 20% less than values used for dietary formulation (NRC, 1998; Tables 1, 2, and 3). However, gross chemical and physical traits of the 6 corn hybrids tested in this study were representative of values for test weight, lipid content, CP content, and kernel density when compared with the reported test weight (0.740 kg/L), lipid content (3.6%), CP (7.3%), and kernel density (1.26 g/cm3) means for corn grown in Webster County, Iowa, in 2004 (Rippke, 2005). Corn is typically sold as a commodity and is valued by bulk density, moisture content, and absence of foreign particulates. Test weight, the most common bulk density measure used for corn in the United States, is a measure of the weight of grain per bushel, whereas 1,000-kernel weight is strictly the weight of 1,000 kernels, and there can be positive or negative correlations between test weight, 1,000-kernel weight, and kernel density (Thompson and Goodman, 2006). Therefore, in addition to bulk grain density, individual kernel density was measured directly in the current study, and test weight and kernel density were positively correlated with each other (r = 0.61, P < 0.01; data not shown).
There are 2 types of starch present in corn kernels: amylose, a linear chain of glucose molecules, and amylopectin, a branched chain of glucose molecules. The proportion of each starch type in corn kernels can influence the physical characteristics of the kernels, as well as the digestibility of the starch and other nutrients (Hibberd et al., 1982). Corn endosperm hardness, also referred to as vitreousness, has been reported to be positively correlated with amylopectin content in the endosperm and is measured by evaluating absolute (not bulk) density or Stenvert hardness of the kernels or physically dissecting the kernels (Correa et al., 2002). Because the branched structure of amylopectin allows more sites for enzymatic attachment and hydrolysis (Mazur and Nakatani, 1993), the digestibility and ME content of the starch increases with increasing amylopectin content (Mazur and Nakatani, 1993; Shelton et al., 2004). Thus, corn containing a relatively greater proportion of amylopectin should be relatively harder and have a greater ME content (Hastad et al., 2005), and growth performance could improve when compared with corn containing less amylopectin (Camp et al., 2003). Even though bulk grain density can be used as a measure of vitreousness (Li et al., 1996), in this study, the amylopectin content, absolute density, and Stenvert hardness measures of each corn hybrid were measured to obtain accurate assessments of the hardness and starch type present in the kernels. Stenvert hardness measures were grouped to indicate a general hardness or vitreousness of the corn kernels; a greater Stenvert percentage of hard endosperm, greater grinding resistance, and longer time to grind are all indicative of a harder kernel (Li et al., 1996). Unless otherwise stated, kernel hardness in this discussion will refer to the collective Stenvert measures, rather than any 1 Stenvert measure, because kernel hardness is a description of the various Stenvert measures in combination (Pomeranz, 1985; Li et al., 1996).
In the current study, there were corn hybrid effects on growth performance but not carcass quality (Tables 4 and 5). The corn hybrid included in the diet affected BW at the end of the grower 1 (P < 0.01) and grower 2 (P < 0.05) phases. The corn hybrid included in the diet also affected ADG during the grower 2 (P < 0.01) and finisher (P < 0.01) phases. Furthermore, the corn hybrid fed to the pigs affected G:F during the finisher phase (P < 0.01), number of days to market (P < 0.01), and overall lean growth efficiency (P < 0.01).
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Table 4. Pig growth performance resulting from corn hybrid present as the dietary treatment during 3 growth phases and the overall experiment evaluating the effect of corn characteristics on growth performance in pigs1,2
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Table 5. Pig carcass quality resulting from corn hybrid present as the dietary treatment during 3 growth phases and the overall experiment evaluating the effect of corn characteristics on growth performance in pigs1,2
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The influence of individual kernel traits on growth performance and carcass quality was determined through principal component analysis. The kernel density and the Stenvert time to grind, both indicators of kernel hardness, were the only physical kernel traits correlated with growth performance and carcass in the current study (Table 6). The kernel density of the hybrid fed to the pigs was negatively correlated with LM IMF percentage (r = –.35, P < 0.05). Furthermore, LM IMF percentage was negatively correlated with Stenvert time to grind (r = –0.36, P < 0.05) and positively correlated with ADF content of the corn kernels (r = 0.31, P < 0.05). Because the IMF percentage in pork loin is related to flavor and marbling (Newcom et al., 2005; Schwab et al., 2006), any influence of kernel traits on IMF could also influence consumer acceptability of pork. There is evidence that the type of starch present in the diet has an effect on ADFI, energy availability, and utilization of other dietary nutrients (Noblet and Perez, 1993; Le Goff and Noblet, 2001). The increased Stenvert grinding time and absolute density of the kernels are measures that indicate an increased vitreous-ness and, hence, relatively greater proportion of amylopectin in the corn. Therefore, the greater amylopectin content in the cornstarch observed in the current study may have resulted in relatively greater ME content because of the branched structure of amylopectin (Mazur and Nakatani, 1993; Lu, 1999).
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Table 6. Correlations between corn kernel traits from different hybrids present in dietary treatments and pig growth performance and carcass quality characteristics during 3 pig growth phases and the overall experiment evaluating the effect of corn characteristics on performance in pigs1,2
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The ADF content of the corn kernels also may have affected carbohydrate availability. Fiber is a complex dietary component that may have prevented the pigs from completely digesting starch, because fiber can encapsulate the starch, which results in limiting the ability of digestive enzymes to access and break down the starch (Lenis et al., 1996; Gilani et al., 2005). Furthermore, some types of fiber increase viscosity of the digesta when exposed to water in the digestive tract, which in turn can impede mixing and the contact among the feed components, digestive enzymes, and the intestinal wall (Fontaine et al., 2003). Increasing dietary glucose availability has a protein-sparing effect and can decrease protein degradation (Fulks et al., 1975; Fuller et al., 1977), and, therefore, a greater content of amylopectin in cornstarch might have increased the protein and lipid contents of the muscle. However, all of the kernel traits affecting IMF must be viewed together to evaluate the final biological result. In this study, there was no effect of the specific corn hybrid fed to the pigs on estimated IMF content in the LM (Table 5), even though individual kernel traits correlated with IMF (Table 6). The relatively greater amylopectin content in the cornstarch (i.e., more readily available dietary glucose) should have resulted in greater IMF as mentioned before. However, the greater kernel amylopectin content may have been counteracted by a reduction in glucose availability caused by the increased ADF content of the kernels, resulting in no net change in IMF deposition. Furthermore, the ADF content of the corn kernels correlated negatively with Stenvert hardness (r = –0.38, P < 0.01; data not shown), which could have been indicative of decreased digestibility and, thus, resulted in decreased G:F. Because the absolute difference in growth performance measures among treatments was relatively small, results from this study agree with previous reports that the type and availability of dietary starch had no effect, or at least an unclear effect, on carcass quality in pigs (Beech et al., 1991; Camp et al., 2003).
The hardness of the kernels, indicated by Stenvert grinding time, was positively correlated with ADG in the grower 1 phase (r = 0.26, P < 0.05), ADFI during the grower 2 phase (r = 0.27, P < 0.05), and BW at the end of the finisher phase (r = 0.27, P < 0.05). However, even though Stenvert grinding time was positively correlated with final BW, there was no correlation between Stenvert grinding time and the overall ADG or G:F. The Stenvert grinding time was negatively correlated with the amylase-to-amylopectin ratio in the cornstarch (r = –0.25, P < 0.05; data not shown). That indicates that the harder kernel may have contained a greater content of amylopectin in the cornstarch, which in turn resulted in an increased energy availability from the corn (Mazur and Nakatani, 1993; Lu, 1999; Camp et al., 2003). This increased energy availability could, at least, partly explain the increased ADG in the grower 1 phase, but, if so, a decreased ADFI and increased G:F would have been expected, because the pigs consume feed to meet their energy needs (Henry, 1985; Pettigrew and Moser, 1991; Ellis and Augspurger, 2001). Thus, the observed increase in ADG in the grower 1 phase is unlikely to have been a result of an increased amylopectin content in the cornstarch. The divergent correlations of kernel hardness with performance indicated that the effects of any single kernel trait can only partially explain the variation in growth performance and that the effects of all the kernel traits must be evaluated in combination to determine the contribution of a specific hybrid to biological growth or carcass trait variability.
The NDF content of the corn kernels was negatively correlated (P < 0.05) with final BW (r = –0.33) and ADG (r = 0.30) during the finisher phase and was positively correlated with number of days to market (r = 0.31, P < 0.05). Furthermore, the ADF content of the corn kernels was correlated negatively with final BW (r = –0.26, P < 0.05). Fiber is likely correlated with decreased performance because of its antinutritive properties previously discussed. Given equivalent ADFI, diets containing a relatively greater percentage of dietary fiber would result in relatively less energy and nutrients available for growth, and, therefore, pigs would grow slower and take longer to reach market weight. There were effects of corn hybrid on ADG in the grower 2 and finisher phases as well as number of days to market, likely because of the combination of fiber content of the kernels and amylopectin content of the cornstarch.
Several studies have shown that a greater amylopectin content in cornstarch or greater glucose availability show little or no improvement in growth performance (Wahlstrom et al., 1977; Camp et al., 2003; Lampe et al., 2006). Moreover, Camp et al. (2003) reported no effect of dietary starch type or availability on carcass quality traits in pigs, a result similar to that observed in the current study, in which corn hybrid had small, but statistically significant, effects on growth performance and no effects on carcass quality. In this study, statistically significant correlations between corn kernel traits from different corn hybrids and performance measures were detected. However, the contribution of any 1 physical or chemical trait was limited in its influence on performance, and, frequently, the performance effects would not be large enough for a producer to make decisions to include a particular hybrid in swine diets based on any single kernel trait. The complex interactions of many individual kernel traits combined to elicit growth performance changes. Therefore, determining which trait has a larger contribution to the growth performance is difficult without looking at the contribution of every principal component in concert. Taking individual trait contributions into account becomes critical when traits have competing effects, such as amylopectin content in the cornstarch and NDF content of the kernels. Furthermore, only limited importance can be attributed to any correlation between principal components and performance measures when they are relatively weak. For instance, in the current study, the efficiency of lean gain in pigs was affected by hybrid, yet efficiency of lean gain did not correlate with any of the kernel chemical or physical traits evaluated. However, it may be practical to select corn hybrids based on a combination of traits, which will elicit improved performance when fed to grower-finisher pigs. As an example, final BW correlated with Stenvert grinding time (r = 0.27), NDF content of the kernels (r = –0.33), and ADF content of the kernels (r = –0.26). Adding the proportion of variation in final BW accounted for by the 3 traits (i.e., their r2) indicates that selecting a relatively harder corn kernel with relatively decreased ADF and NDF contents will account for up to 24% of the variation seen in final BW. Because of the limited practical contribution of any corn hybrid trait to differences in performance variables, corn producers and swine producers should not base decisions to include a specific hybrid in diets based on any 1 physical or chemical trait. Producers should consider a combination of traits that will enhance growth performance only if it is cost effective to do so. However, selection of hybrids based on kernel traits would require the need to store grain with varying chemical or physical traits in separate, identity preserved areas, and most elevators may fail to capture added value from the multiple segmented subsets of grain. If a combination of several beneficial traits cannot be obtained, it is still prudent to purchase and sell corn on the basis of test weight, moisture content, and foreign particulate as normal. This does not imply, however, that pig producers should not formulate diets taking into consideration the nutrient content of the corn (e.g., digestible Lys and ME) and meeting the dietary needs of the animals through proper diet formulation. Because swine nutritionists can analyze and balance diets effectively based on current analytical techniques, corn producers should be more concerned with yield and other desirable agronomic traits rather than any 1 physical or chemical trait resulting from a given corn hybrid.
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Footnotes
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1 We would like to thank Golden Harvest Seeds Inc. (Waterloo, NE) for financial support of this project. Additionally, we would like to recognize Feed Energy Company (Des Moines, IA) for donating feed ingredients. The help of the staff at the Iowa State University Swine Nutrition and Management Research Farm and in the laboratory of K. Bregendahl is appreciated greatly. 
2 Corresponding author: kristjan{at}iastate.edu
Received for publication May 7, 2007.
Accepted for publication December 3, 2007.
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Abstract
INTRODUCTION
