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METABOLISM AND NUTRITION |
* Department of Animal Science, Iowa State University, Ames, IA 50011; and Golden Harvest Seeds Inc., Waterloo, NE 68069
1 Corresponding author: kristjan{at}iastate.edu
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Key Words: broiler • corn kernel trait growth performance • egg production • laying hen
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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, 2, and 3). 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 hybrid 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 21 metric tons of each corn hybrid was transported individually to Ames, Iowa, dried, and stored in separate gravity-flow grain bins.
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). Total N content was determined by the micro-Kjeldahl method according to AOAC method 960.52 (AOAC International, 1995) on a Kjeltech 1028 distilling unit (US Tecator Inc., Herndon, PA), and the CP content was calculated as Kjeldahl N x 6.25 according to AOAC method 979.09 (AOAC International, 1995). Kernel contents of individual amino acids, Na, Ca, and P were determined at a contract laboratory (Experiment Station Chemical Laboratories, University of Missouri, Columbia, MO). Contents of individual amino acids for each hybrid were determined by ion-exchange chromatography with AOAC methods 982.30E [(a), (b), and (c); AOAC International, 1995]. Contents of Na and Ca in the kernels were determined by inductively coupled plasma optical emission spectroscopy with AOAC methods 968.08C, D (b) and 935.13A (a) (AOAC International, 1995). The total P content of each hybrid was determined gravimetrically with AOAC methods 966.01 (plant tissue), and 946.06 (AOAC International, 1995). The corn kernel lipid content was determined as ether extract by using a Goldfisch lipid extraction apparatus (Laboratory Construction Co., Kansas City, MO). Lipids were extracted from the corn kernels by using a 2:1 vol/vol mixture of chloroform-methanol and dried under N2. Fatty acid methyl esters were prepared from 10 mg of extracted lipid by using acetyl chloride (Christie, 1993) and quantified with a gas chromatograph (Varian model 3350 gas chromatograph, Varian Inc., Palo Alto, CA) equipped with a 100-m 2560 fused-silica capillary column with a 0.25-mm inner diameter and 0.2 µm film thickness (Supelco, Bellefonte, PA). The split-splitless injector was set to a 25 mL/min split, and the column temperature was set to 70°C for 4 min, increased to 175°C by 15°C/min and held for 27 min, followed by an increase in temperature to 215°C by 4°C/min and held for 28 min, for a total run time of 77 min. The injector and detector temperatures were set to 220°C. Individual fatty acids were identified by the retention time of purified lipid standards (GLC458 and C:11, Nu-Chek Prep, Elysian, MN). Relative fatty acid content for individual fatty acids was calculated as the ratio of an individual fatty acid peak area to the total fatty acid pool peak area. Neutral detergent fiber (NDF) and acid detergent fiber (ADF) contents were determined by using an Ankom 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 (Yun and Matheson, 1993; Gibson et al., 1996). Briefly, amylose and total starch samples were prepared from each corn hybrid according to the manufacturer’s instructions in a commercial kit (K-Amyl, Megazyme International Ireland Inc., Bray, UK). Amylose and total starch samples were hydrolyzed enzymatically to glucose monomers with either β-amylase, which digests only amylose, or a combination of β-amylase and amyloglucosidase, which digest both the amylose and amylopectin present (i.e., total starch). The resultant samples from either amylose alone or total starch were analyzed for glucose monomer content according to the manufacturer’s instructions 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 protocol reaction volumes 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 with an aspirator (Kice 6DT4, Kice Industries Inc., Wichita, KS) to remove foreign matter and broken kernel particles. Aspirated samples were stored in closed containers at –20°C until analyses of physical traits. Because there can be positive or negative correlations among bulk density (i.e., test weight and 1,000-kernel weight) and absolute density (i.e., kernel density; Thompson and Goodman, 2006), all were measured and correlated with poultry performance changes. The test weight of each hybrid was determined by using a test weight apparatus and computer 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. Although 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 in addition to bulk grain density to obtain accurate assessments of the hardness and starch type present in the kernels. Stenvert hardness measures were grouped to indicate the general hardness or vitreousness of the corn kernels; a higher Stenvert percentage of hard endosperm, higher grinding resistance, and longer time to grind are all indicative of a harder kernel (Li et al., 1996). Thus, for practical purposes, hardness in this manuscript will refer to the collective Stenvert measures, rather than any one Stenvert measure. The kernel density was determined in triplicate by 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 percentage of hard and soft endosperm) were determined at the University of Nebraska (Lincoln, NE) 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 USA Standard testing sieves at Kansas State University (Manhattan, KS) according to Baker and Hermann (2002).
Experimental Diets
In the broiler chicken experiment, a single diet was formulated for each of the starter (0 to 2 wk of age), grower (2 to 4 wk of age), and finisher (4 to 6 wk of age) phases by using NRC (1994) published nutrient values for all ingredients (including corn). Within each phase, the diet was formulated to contain amounts of Lys, TSAA, Ca, nonphytate P, and Na at 10 to 15% less than that recommended by the NRC (1994) to improve the likelihood of detecting a response to the differing traits among the corn hybrids (Table 4). Similarly, for laying hens, a single diet was formulated to include concentrations of Lys and TSAA 10 to 15% less than that recommended by the NRC (1994). In the laying hen experiment, the performance measure end points were almost entirely egg related, and because corn contributes relatively little Ca and nonphytate P to a laying hen diet, it was expected that no detectable differences in eggshell formation would result from the differences in Ca and P contents among the 6 corn hybrids (Boling et al., 2000; Douglas et al., 2000; Jang et al., 2003). Thus, to prevent potential loss of data from broken or soft-shelled eggs stemming from a Ca- and P-deficient diet, the laying hen diet was formulated to contain 100% or greater of the NRC (1994)-recommended contents of Ca, nonphytate P, and vitamin D. Within a phase, corn- and soybean-based diets containing equal amounts of 1 of 6 corn hybrids were formulated (Table 4). Therefore, the only difference between diets within a growth phase was the corn hybrid used. Immediately before mixing of the dietary treatments, all corn was ground in a hammer mill equipped with a 7.94-mm (20/64-in.) screen, and all diets were fed in mash form for both the broiler chicken and laying hen experiments.
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Broiler Chickens.
A total of 720 one-day-old Ross x Ross 308 male broiler chicks (Welp Hatchery, Bancroft, IA) were allotted to floor pens (10 chicks per pen, 1,486 cm2/chick) in a randomized complete block design. The location within the barn served as the blocking criterion, and pen was the experimental unit. The trial was carried out in 2 identical 6-wk experiments, with 360 chicks per experiment and 1 wk between the 2 experiments. The chicks in the first experiment were provided with fresh pine shavings bedding, which was reused in the second experiment. The chickens were allowed free access to feed and water throughout the experiment. Broilers were provided 23:1 L:D for 0 to 7 d of age, 20:4 L:D for 8 to 28 d of age, and 23:1 L:D thereafter.
Pen BW were recorded at 0, 2, and 4 wk of age, whereas individual BW were recorded at 6 wk of age. Feed consumption, measured as feed disappearance, was recorded at 2, 4, and 6 wk of age. Feed utilization was calculated as grams of BW gained per kilogram of feed consumed. Flock uniformity was determined at 6 wk of age as the BW CV in each pen.
Laying Hens.
A total of 240 fifty-two-week-old Hy-Line W-36 laying hens, obtained from a commercial facility, were allotted to wire-bottomed cages (Chore-Time Inc., Milford, IN) in a randomized complete block design (2 hens/cage, 619 cm2/hen). The location within the barn served as a blocking criterion, and cage was the experimental unit. After transport, the hens were allowed a 1-wk acclimatization period, after which data were collected from 53 to 67 wk of age. During the acclimatization period, hens were fed a common diet (19.8% CP, 4.2% Ca, 0.48% nonphytate P, 2,900 kcal/kg of MEn) formulated to meet or exceed the nutrient contents recommended by the NRC (1994). The hens were allowed free access to feed and water throughout the experiment and were supplied 16:8 L:D during the entire experiment.
Egg production was recorded daily during the 14-wk-long experiment. Weights from all eggs collected in a 24-h period were recorded weekly, and egg mass was calculated as egg weight x egg production. Feed consumption, measured as feed disappearance, was recorded weekly, and feed utilization was calculated as grams of egg mass produced per kilogram of feed consumed. Egg specific gravity, a measure of eggshell thickness (Holder and Bradford, 1979), was determined during wk 14 of the experiment (i.e., birds at 67 wk of age). Specific gravity was determined on eggs collected over a 24-h period and equilibrated to 22°C for 24 h before placing eggs sequentially in 13 saline solutions of 1.058 to 1.082 g/cm3 (with 0.002 g/cm3 increments between solutions). During wk 14 of the experiment (i.e., birds at 67 wk of age), eggs from a 24-h period were collected and whole yolks were isolated from the egg contents and stored under N2. The fatty acid profile of isolated egg yolks was determined by gas chromatography as previously described for the corn analysis portion of this study. The BW of the hens was recorded at 53 and 67 wk of age.
Statistical Analyses
The experimental design of the broiler chicken and laying hen experiments was a randomized complete block design with 6 dietary treatments. In the broiler chicken trial, there were 2 experiments with 6 replicates in each experiment, and the experimental unit was 10 broilers in a pen. In the laying hen experiment, there were 20 replications, and the experimental unit was a cage containing 2 hens. To determine the main effects of corn hybrids on performance measures, data were subjected to ANOVA analysis by using the GLM procedure of SAS 9.1 (SAS Institute, Cary, NC). Fixed effects in the broiler chicken model included hybrid, experiment, block, block within experiment, and hybrid x experiment interaction. Pen within hybrid was included as a random effect in the model. Because there were no significant hybrid x experiment interactions for the variables measured, data for both broiler experiments were pooled. Similarly, the fixed effects used in the ANOVA model to evaluate the laying hen data included hybrid and block. Again, cage within hybrid was used as a random effect, and initial BW was included in the laying hen model as a linear covariate to remove variation because of differences present in initial hen BW among the dietary treatments. When the main effect of hybrid was significant, means were separated by using Fisher’s protected least significant difference (Snedecor and Cochran, 1980). The multivariate ANOVA (MANOVA) procedure within the GLM procedure of SAS was used to determine the principal component effects of all analyzed corn kernel traits on observed differences for all performance measures (Bray and Maxwell, 1982). Factors in the broiler chicken MANOVA model were experiment, block within experiment, and pen within hybrid as a random variable. The factors in the laying hen MANOVA model were block, cage within hybrid as a random variable, and initial BW as a linear covariate. In all comparisons, P 
0.05 was considered significant.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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, 2, and 3). In this study, test weight and kernel density were positively correlated with each other (P < 0.01, r = 0.61). In addition, Stenvert hardness was positively correlated with Stenvert time to grind (P < 0.05, r = 0.76). Furthermore, the ADF content of the corn kernels correlated negatively with Stenvert hardness (P < 0.01, r = –0.38).
Kernel Trait and Broiler Chicken Growth Performance Correlations
During the Starter Phase, corn hybrid influenced average daily BW gain (ADG), feed consumption, feed utilization, and BW at 2 wk of age (Table 5). When the principal components analysis was performed, Stenvert grinding time of the corn kernels was positively correlated with feed utilization, and explained 10% of the variation seen in feed utilization (P = 0.01, r = 0.31). The kernel test weight was positively correlated with feed consumption (P < 0.05, r = 0.26) and explained 7% of the observed variation in feed consumption (Table 6). No other corn kernel physical traits correlated significantly with growth performance.
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). In contrast, the Lys content of the corn kernels was positively correlated with feed utilization in the finisher phase (P < 0.05, r = 0.28). The ADF content in the corn kernels was also negatively correlated with starter phase feed utilization (P = 0.05, r = –0.05; Table 6).
The lipid content in the corn was positively correlated (P < 0.05) with feed consumption during the finisher phase (r = 0.29) and the entire experiment (r = 0.27) in broilers (Table 6). Lipid content also was negatively correlated with feed utilization during the finisher phase (r = –0.27) and the overall phases (r = –0.25) in broilers.
Kernel Trait and Laying Hen Performance Correlations
In the week before introducing the treatment diets, no difference in egg production was detected (Table 7). In laying hens from 53 to 67 wk of age, corn hybrid affected feed consumption, egg production, egg mass, feed utilization, and egg specific gravity (Table 7). The egg production was correlated significantly with the corn test weight (r = 0.22), 1,000-kernel weight (r = –0.21 to –0.34), and Stenvert hardness (r = 0.20 to 0.33) for various end points (Table 8). Egg specific gravity was negatively correlated with ADF content of the kernels (r = –0.29), amylose content of the cornstarch (r = –0.31), and the ratio of amylose to amylopectin in the cornstarch (r = –0.32).
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), and the ADF content of the corn kernels was negatively correlated with egg production (P < 0.05, r = –0.25), egg mass (P < 0.05, r = –0.21), and egg specific gravity (P = 0.01, r = –0.29). In the current study, no significant correlations were observed between egg yolk fatty acid content and corn kernel fatty acid content by principal component analysis. |
DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Two types of starch are 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 of the kernels, by evaluating Stenvert hardness, or by physically dissecting the kernels (Correa et al., 2002). Because the branched structure of amylopectin allows more sites for enzymatic attachment and hydrolysis, the digestibility and ME content of the starch increases with increasing amylopectin content (Lu, 1999; Batal and Parsons, 2004). Thus, corn containing a relatively higher proportion of amylopectin should be relatively harder and have a higher ME content (Lu, 1999), and growth and production performance measures could improve when compared with corn containing less amylopectin.
Kernel Trait and Broiler Chicken Growth Performance Correlations
There is evidence that the type of starch present in the diet has an impact on feed consumption, energy availability, and utilization of other dietary nutrients, with kernels containing a higher proportion of amylopectin in the starch resulting in improved production performance (Moran, 1982; Weurding et al., 2001a,b). The increased Stenvert grinding time of the kernels is one measure indicating an increased vitreousness, and thus a relatively higher proportion of amylopectin in the corn. The higher amylopectin content in the cornstarch may have resulted in relatively higher ME because of the branched structure of amylopectin, and thus more energy for growth (Lu, 1999; Batal and Parsons, 2004). The improved broiler growth, even though not statistically correlated with the hardness of the corn, resulted in improved feed utilization, which may have been explained by the increased ME content of the corn kernels. There was a positive correlation between kernel test weight and feed consumption; test weight was negatively correlated with amylopectin content, so the energy availability attributable to starch type may have influenced the feed consumption. Test weight does not directly measure the starch type present in the kernels or the energy availability of the ground corn, so the amylopectin content in relation to the test weight might have been a factor in the observed feed consumption differences. The reasons for the positive correlation observed between test weight, feed consumption, and feed utilization are therefore unclear. Further, because the correlation between kernel test weight and feed consumption was relatively weak (P < 0.05, r = 0.26), the interaction of many principal components likely influenced feed consumption to a larger degree than did test weight alone.
Unexpectedly, in the broiler chicken starter phase, the Lys and Met contents of the corn kernels did not affect ADG of the broilers. We expected that differences in the chemical traits of the corn would affect growth performance because the diets were formulated to contain a concentration of essential amino acids lower than those recommended by NRC (1994). A dietary treatment containing corn with a relatively higher Lys content or Lys availability would thus have a relatively smaller Lys deficiency than other dietary treatments, resulting in improved growth performance compared with broilers consuming the other corn (Hickling et al., 1990; Acar et al., 1991). Perhaps the energy availability resulting from the vitreousness or amylopectin content of the corn kernels may have played a greater role in feed consumption and feed utilization than the content of available amino acids in the kernels. Further, because correlations between amino acid contents and feed consumption and feed utilization were weak, the influence of other principal components may have influenced feed consumption to a greater degree than did the content of available amino acids alone. The correlations between amino acid content and feed consumption during the starter phase were consistent with previous reports of decreased consumption of diets with amino acid contents below those required for optimal growth and feed efficiency (Mack et al., 1999; Baker et al., 2002; Eits et al., 2005). Eits et al. (2005) suggested that energy and protein contents in unbalanced diets would interact to decrease feed consumption by an as yet unknown mechanism. However, because the Lys and Met contents of the kernels each contributed 8% to the variation in feed consumption, and Lys and Met contributed 17 and 11%, respectively, to the variation in feed utilization of starter phase broilers (Table 6), it is unclear how energy availability—because of the vitreousness of the corn, as proposed earlier—could influence feed consumption and feed utilization to a greater degree than specific amino acid content.
Because the correlations of Met and Lys with feed consumption and feed utilization reversed with age in this study, physical characteristics may be considered when formulating diets for younger birds with relatively more immature digestive tracts. In finisher phase broiler chickens and in the overall experiment from 0 to 6 wk of age, amino acid content was positively correlated with feed utilization, which is in agreement with previous studies (Acar et al., 1991; Baker et al., 2002; Dean et al., 2006). Because the major proportion of BW gain and feed consumption occurred during the finisher phase, overall growth performance correlations were similar for the finisher phase and for the entire experiment.
Fiber content of the kernels may have prevented the chickens from completely digesting starch, and thus could have caused the observed negative correlations between ADF content and feed utilization in starter phase broilers. Fiber can encapsulate the starch in grains, which in turn limits the ability of digestive enzymes to access and break down the starch (Sklan et al., 2003; Gilani et al., 2005). Further, some types of fiber increase viscosity of the digesta when exposed to water in the digestive tract. The increased viscosity in turn impedes mixing and the contact among the feed components, digestive enzymes, and the intestinal wall from which nutrients are absorbed (Fontaine et al., 2003).
The lower feed utilization in broiler chickens fed diets containing relatively more lipids was contrary to the improved feed utilization observed in broilers fed supplemental fat (Latour et al., 1994; Lu, 1999). In the present study, chickens consuming diets with relatively higher lipid content consumed more feed; therefore, energy availability from lipids in the corn was not the only factor that determined feed consumption. Starch type present in the corn or the influence of fiber encapsulation, as described earlier, could have affected energy use by the broilers, and, in fact, there was a negative correlation between lipid content in the corn kernels and percentage of amylopectin present in the cornstarch (P < 0.01, r = –0.30). However, the amylopectin content present in the corn kernel starch was not correlated with any growth performance measures; thus, the reasons for the correlation of lipid content and feed consumption are not entirely clear.
In younger broiler chickens, physical characteristics of the corn and their contribution to nutrient digestibility played a larger role than did nutrient content of the corn kernels on the growth performance measures. However, in older broilers with a more mature digestive system, the nutrient content of the corn played a larger role than did the corresponding physical characteristics.
Kernel Trait and Laying Hen Performance Correlations
Corn kernel density, because of its positive correlation with ground corn particle size (data not shown), contributed to changes in laying hen performance measures, possibly because of the grinding characteristics of the corn. Even though all corn was ground through the same hammer mill screen, denser kernels resulted in a larger particle size and also possibly contained more amylopectin than amylase, and thus more digestible energy for use in egg production (Lu, 1999; Batal and Parsons, 2004). In general, feed particles larger than 1.2 mm might decrease egg production performance (Nir et al., 1994). However, ground corn particle size correlated with only feed utilization in laying hens, likely because the particle sizes of the ground corn used in this study were within or near the 700- to 900-µm range considered optimum for laying hen performance (Leeson and Summers, 2005).
Egg specific gravity was correlated with ADF and amylose content in the corn kernels. However, because the Ca and P contents of the diets were formulated to meet or exceed NRC (1994) recommendations, and because the effects of ADF and starch type present in the corn kernels have no direct apparent effect on eggshell strength, the causes of these relationships are unclear at this time.
As discussed for broilers, fiber content would affect energy availability, and thus should have been correlated positively with feed consumption. However, because fiber content and feed consumption were negatively correlated, NDF content was not the major contributing factor to feed consumption in laying hens. No other principal components showed significant effects on feed consumption, which was likely because feed consumption variation was small among the hybrids (only approximately 4 g/hen per day). The negative correlation between ADF content of the corn and egg production performance was expected and was attributed to the lower nutrient density of the diet and potentially lower nutrient digestibility, as previously discussed for broiler chickens.
Manipulation of dietary fatty acid composition affects the fatty acid composition of egg yolks (Leeson et al., 1998; Blank et al., 2002). However, because of the addition of crude soybean oil to the diets and also because of the relatively low corn lipid content, corn oil contributed less than 30% of the total fat in any diet (Table 4). Combined with the small variation of total lipids in the corn kernels, the differences in the corn kernel fatty acid composition were sufficiently small, such that their influence on egg yolk lipid content was not detectable.
When the correlations of test weight and Stenvert hardness measures with laying hen performance were analyzed, their magnitude of contribution to performance measures in laying hens from 53 to 67 wk of age was similar to the magnitude of contributions from chemical traits of the corn hybrids. In contrast, in younger broiler chickens, physical characteristics of the corn and their contribution to nutrient digestibility played a larger role than did nutrient content of the corn kernels on the growth performance measures. However, in older broilers with a more mature digestive system, the nutrient content of the corn played a larger role than the corresponding physical characteristics. Thus, physical characteristics such as Stenvert hardness or kernel density that are readily measurable can be used to evaluate the potential for performance gains when selecting corn hybrids for inclusion in poultry diets, particularly by producers who grow their own corn or purchase corn under a contract agreement.
In both experiments, correlations between corn kernel traits from different corn hybrids and performance measures were detected. However, the contribution of any one physical or chemical trait was limited in its influence on performance, and frequently, the performance effects would not be large enough to influence purchasing decisions of a particular hybrid for poultry diets. The complex interactions of many individual kernel traits combine to elicit growth and production performance changes, and determining which has a larger contribution to the variations is difficult without looking at the contribution of every principal component in concert, especially when traits have competing effects. In broiler chickens, for example, the negative correlations of kernel Lys and Met content with feed consumption and feed utilization reversed with age, which indicates that kernel characteristics can have opposite effects (or have a comparatively reduced effect) on chickens of different ages. Further, only limited relevance can be assigned to any correlation between principal components and performance measures when they are relatively weak. Therefore, corn producers and poultry producers should not base decisions to grow or include a specific corn hybrid in diets based on any one physical or chemical trait. However, it may be practical to select corn based on a combination of traits that will elicit improved performance. As an example, in laying hens, egg production was positively correlated with kernel test weight (r = 0.22), Stenvert hardness (r = 0.22), and Stenvert grinding time (r = 0.21). However, egg production was negatively correlated with 1,000-kernel weight (r = –0.25) and ADF content (r = –0.25). Adding the individual contributions of kernel traits to performance measure variations (i.e., their r2) suggests that selecting a harder corn with a relatively higher test weight can account for up to 12% of the possible increase seen in egg production (Table 8), whereas selecting a relatively lower 1,000-kernel-weight hybrid containing a relatively lower content of ADF can account for a further 12% of the increase. However, at most, with these hybrids, selecting for those 4 traits (i.e., relatively harder, higher test weight, lower 1,000-kernel weight, and lower ADF content) may account for only up to 24% of the differences in egg production rates, corresponding to an improvement from, say, 77 to 79%.
Corn and poultry producers may consider a combination of traits that will enhance growth performance if it is cost effective to do so. However, selection of hybrids based on kernel trait combinations 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 multiply 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. Moreover, corn producers should be more concerned with yield and other desirable agronomic traits rather than any one physical or chemical trait resulting from a given corn hybrid.
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ACKNOWLEDGMENTS |
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Received for publication May 7, 2007. Accepted for publication January 5, 2008.
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REFERENCES |
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|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
|---|
AOAC. 1995. Official Methods of Analysis of AOAC International. 16th ed. AOAC Int., Gaithersburg, MD.
Baker, D. H., A. B. Batal, T. M. Parr, N. R. Augspurger, and C. M. Parsons. 2002. Ideal ratio (relative to lysine) of tryptophan, threonine, isoleucine, and valine for chicks during the second and third weeks posthatch. Poult. Sci. 81:485–494.
Baker, S., and T. L. Hermann. 2002. Evaluating particle size. Publ. MF-2051. Dept. Grain Sci. and Ind., Kansas State Univ., Manhattan, KS.
Batal, A. B., and C. M. Parsons. 2004. Utilization of various carbohydrate sources as affected by age in the chick. Poult. Sci. 83:1140–1147.
Blank, C., M. A. Neumann, M. Makrides, and R. A. Gibson. 2002. Optimizing DHA levels in piglets by lowering the linoleic acid to alpha-linolenic acid ratio. J. Lipid Res. 43:1537–1543.
Boling, S. D., M. W. Douglas, M. L. Johnson, X. Wang, C. M. Parsons, K. W. Koelkebeck, and R. A. Zimmerman. 2000. The effects of dietary available phosphorus levels and phytase on performance of young and older laying hens. Poult. Sci. 79:224–230.
Bray, J. H., and S. E. Maxwell. 1982. Analyzing and interpreting significant MANOVAs. Rev. Educ. Res. 52:340–367.[CrossRef]
Christie, W. W. 1993. Preparation of ester derivatives of fatty acids for chromatographic analysis. Pages 69–111 in Advances in Lipid Methodology—Two. W. W. Christie, ed. The Oily Press, Dundee, UK.
Correa, C. E., R. D. Shaver, M. N. Pereira, J. G. Lauer, and K. Kohn. 2002. Relationship between corn vitreousness and ruminal in situ starch degradability. J. Dairy Sci. 85:3008–3012.
Dean, D. W., T. D. Bidner, and L. L. Southern. 2006. Glycine supplementation to low protein, amino acid-supplemented diets supports optimal performance of broiler chicks. Poult. Sci. 85:288–296.
Douglas, M. W., C. M. Peter, S. D. Boling, C. M. Parsons, and D. H. Baker. 2000. Nutritional evaluation of low phytate and high protein corns. Poult. Sci. 79:1586–1591.
Eits, R. M., R. P. Kwakkel, M. W. Verstegen, and L. A. den Hartog. 2005. Dietary balanced protein in broiler chickens. 1. A flexible and practical tool to predict dose-response curves. Br. Poult. Sci. 46:300–309.[CrossRef][Web of Science][Medline]
Fontaine, A. S., S. Bout, Y. Barriáere, and W. Vermerris. 2003. Variation in cell wall composition among forage maize (Zea mays L.) inbred lines and its impact on digestibility: Analysis of neutral detergent fiber composition by pyrolysis-gas chromatography-mass spectrometry. J. Agric. Food Chem. 51:8080–8087.[CrossRef][Web of Science][Medline]
Gibson, T. S., V. A. Solah, and B. V. McCleary. 1996. A procedure to measure amylose in cereal starches and flours with Con A. J. Cereal Sci. 25:111–119.[CrossRef]
Gilani, G. S., K. A. Cockell, and E. Sepehr. 2005. Effects of antinutritional factors on protein digestibility and amino acid availability in foods. J. AOAC Int. 88:967–987.[Web of Science][Medline]
Hibberd, C., D. Wagner, R. Schemm, E. Mitchell, D. Weibel, and R. Hintz. 1982. Digestibility characteristics of isolated starch from sorghum and corn grain. J. Anim. Sci. 55:1490–1497.
Hickling, D., W. Guenter, and M. Jackson. 1990. The effects of dietary lysine and methionine on broiler chicken performance and breast meat yield. Can. J. Anim. Sci. 70:673–678.
Holder, D. P., and M. V. Bradford. 1979. Relationship of specific gravity of chicken eggs to number of cracked eggs and percent shell. Poult. Sci. 58:250–251.[Web of Science]
Jaeger, S. L., M. K. Luebbe, C. N. Macken, G. E. Erickson, T. J. Klopfenstein, W. A. Fithian, and D. S. Jackson. 2006. Influence of corn hybrid traits on digestibility and the efficiency of gain in feedlot cattle. J. Anim. Sci. 84:1790–1800.
Jang, D. A., J. G. Fadel, K. C. Klasing, A. J. Mireles Jr., R. A. Ernst, K. A. Young, A. Cook, and V. Raboy. 2003. Evaluation of low-phytate corn and barley on broiler chick performance. Poult. Sci. 82:1914–1924.
Latour, M. A., E. D. Peebles, C. R. Boyle, and J. D. Brake. 1994. The effects of dietary fat on growth performance, carcass composition, and feed efficiency in the broiler chick. Poult. Sci. 73:1362–1369.[Web of Science][Medline]
Leeson, S., L. Caston, and T. MacLaurin. 1998. Organoleptic evaluation of eggs produced by laying hens fed diets containing graded levels of flaxseed and vitamin E. Poult. Sci. 77:1436–1440.
Leeson, S., and J. D. Summers. 2005. Commercial Poultry Nutrition. 3rd ed. Univ. Books, Guelph, Ontario, Canada.
Li, P. X., A. K. Hardacre, H. Campanella, and K. J. Kirkpatrick. 1996. Determination of endosperm characteristics of 38 corn hybrids using the Stenvert hardness test. Cereal Chem. 73:466–471.
Li, Y. C., D. R. Ledoux, T. L. Veum, V. Raboy, and D. S. Ertl. 2000. Effects of low phytic acid corn on phosphorus utilization, performance, and bone mineralization in broiler chicks. Poult. Sci. 79:1444–1450.
Lu, Z. 1999. Influence of different cultivars of corn on nutrient digestibility and metabolizable energy value of broiler chicken diets. MS Thesis. Iowa State Univ., Ames.
Mack, S., D. Bercovici, G. De Groote, B. Leclercq, M. Lippens, M. Pack, J. B. Schutte, and S. van Cauwenberghe. 1999. Ideal amino acid profile and dietary lysine specification for broiler chickens of 20 to 40 days of age. Br. Poult. Sci. 40:257–265.[CrossRef][Web of Science][Medline]
Moran, E. T., Jr. 1982. Starch digestion in fowl. Poult. Sci. 61:1257–1267.[Web of Science][Medline]
Nir, I., G. Shefet, and Y. Aaroni. 1994. Effect of particle size on performance. 1. Corn. Poult. Sci. 73:45–49.
NRC. 1994. Nutrient Requirements of Poultry. 9th rev. ed. Natl. Acad. Press, Washington, DC.
Perez-Prat, E., and M. M. van Lookeren Campagne. 2002. Hybrid seed production and the challenge of propagating male-sterile plants. Trends Plant Sci. 7:199–203.[CrossRef][Web of Science][Medline]
Pomeranz. 1985. Determination of maize hardness by the Stenvert hardness tester. Cereal Chem. 62:108–112.
Reynolds, T. L., M. A. Nemeth, K. C. Glenn, W. P. Ridley, and J. D. Astwood. 2005. Natural variability of metabolites in maize grain: Differences due to genetic background. J. Agric. Food Chem. 53:10061–10067.[CrossRef][Web of Science][Medline]
Rippke, G. 2005. 2004 Strip plots: Webster-Iowa Central Community College. http://www.extension.iastate.edu/NR/rdonlyres/2E507671-F686-47C1-8DE7-52AF126BCAAF/0/04nonrrcniccc.pdf Accessed July 5, 2007.
Sklan, D., A. Smirnov, and I. Plavnik. 2003. The effect of dietary fibre on the small intestines and apparent digestion in the turkey. Br. Poult. Sci. 44:735–740.[CrossRef][Web of Science][Medline]
Snedecor, G. W., and W. G. Cochran. 1980. Statistical Methods. 7th ed. Iowa State Univ. Press, Ames.
Thompson, D. L., and M. M. Goodman. 2006. Increasing kernel density for two inbred lines of maize. Crop Sci. 46:2179–2182.
USDA. 2004. Grain Inspection Handbook—Book II: Grain Grading Procedures. Federal Grain Inspection Serv., USDA, Washington, DC.
USDA. 2006. Feed situation and outlook yearbook. Handbook FDS-2006. Econ. Res. Serv., USDA, Washington, DC.
Weurding, R. E., A. Veldman, W. A. Veen, P. J. van der Aar, and M. W. Verstegen. 2001a. In vitro starch digestion correlates well with rate and extent of starch digestion in broiler chickens. J. Nutr. 131:2336–2342.
Weurding, R. E., A. Veldman, W. A. Veen, P. J. van der Aar, and M. W. Verstegen. 2001b. Starch digestion rate in the small intestine of broiler chickens differs among feedstuffs. J. Nutr. 131:2329–2335.
Whitt, S. R., L. M. Wilson, M. I. Tenaillon, B. S. Gaut, and E. S. Buckler. 2002. Genetic diversity and selection in the maize starch pathway. Proc. Natl. Acad. Sci. USA 99:12959–12962.
Yun, S. H., and N. K. Matheson. 1993. Structures of the amylopectins of waxy, normal, amylose-extender, and wx:Ae genotypes and of the phytoglycogen of maize. Carbohydr. Res. 243:307–321.[CrossRef][Web of Science][Medline]
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