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Predicting Metabolizable Energy of Normal Corn from its Chemical Composition in Adult Pekin Ducks
Research Note Predicting Metabolizable Energy of Normal Corn from its Chemical Composition in Adult Pekin Ducks F. Zhao,1 H. F. Zhang, S. S. Hou, and Z. Y. Zhang The State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100094, China crude fiber (r = −0.905), ADF (r = −0.915), and NDF (r = −0.95) contents, and moderately correlated with gross energy (GE; r = −0.55) content in corn calibration samples. In contrast, no significant correlations were found for CP, ether extract, and ash contents. According to the stepwise regression analysis, both NDF and GE were found to be useful for the ME prediction models. Because the maximum absolute difference between the in vivo ME determinations and the predicted ME values was 61 kcal/kg, it was concluded that, for White Pekin ducks, the latter could be used to predict the ME content of corn with acceptable accuracy.
Key words: duck, corn, metabolizable energy, prediction model 2008 Poultry Science 87:1603–1608 doi:10.3382/ps.2007-00494
INTRODUCTION In 2005, the total yield of Chinese corn was >130,000,000 t. Corn is the principal energy source for ducks, comprising >40% by weight of the duck diet in China. To produce an accurate evaluation of the ME content of corn for poultry diet formulation, a considerable number of studies have been conducted to predict the ME content of corn based on its physical characteristics or chemical composition. Using the adult rooster as a test animal, many researchers have shown that the ME content of corn was correlated with its bulk density or chemical composition (Conner et al., 1976; Leeson et al., 1977; Mollah and Annison, 1981; Dale, 1994; NRC, 1994; Lessire et al., 2003). These results also indicated that the ME value of corn could be predicted, but few studies have been reported with ducks. In general, models that are based on chemical composition and used to predict ingredient ME value are more accurate than models based upon physical characteristics of the test ingredient. However, there has been no uniform model predicting the ME content of corn for birds based
©2008 Poultry Science Association Inc. Received December 4, 2007. Accepted April 9, 2008. 1 Corresponding author: [email protected]
on the chemical composition. Several factors can affect the accuracy of ME prediction models, which subsequently influences their successful use. One such factor is the sample size for regression analysis; another is the representativeness of samples for the feedstuff as a whole. In some experiments aimed at predicting the ME content of raw materials, more than 25 samples were included in the regression analysis (Dale, 1994; Lessire et al., 2003). However, in other studies, the number of samples was less than 15 (Mollah and Annison, 1981). Prediction models from smaller sample sizes may have greater R2 and less residual standard deviation (RSD), but may not be as accurate as other models developed with a greater number of samples (Carre´, 1990). On the other hand, the range of ME and chemical composition contents of samples obviously affect the accuracy of the prediction model. For example, low variation in the ME and chemical composition contents of calibration sample set might provide an incorrect prediction model (Carre´, 1990). Because the ME from corn contributes more than 40% of the total dietary ME content in diets typically fed to ducks in China, research to establish a model for predicting the AME and TME contents of corn for ducks should improve the accuracy of calculating ME in diet formulations for ducks. Therefore, this study utilized a series of calibration samples comprising corn and corn plus corn gluten meal, corn hulls, corn germ, and corn starch to establish an ME prediction model for White Pekin ducks.
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ABSTRACT Two experiments were conducted to establish an ME content prediction model for normal corn for ducks based on the grain’s chemical composition. In Experiment 1, observed linear relationships between the determined ME content of 30 corn calibration samples and proximate nutrients, acid detergent fiber (ADF), and neutral detergent fiber (NDF) were used to develop an ME prediction model. In Experiment 2, 6 samples of corn selected at random from the primary corn-growing regions of China were used for testing the accuracy of ME prediction models. The results indicated that the AME, AMEn, TME, and TMEn were negatively correlated with
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Table 1. Composition and nutrient content of the corn-soybean mealbased diet fed during the wash-out period between ME determinations Item
m) in a temperature-controlled room (25°C) and provided with 12 h of light daily.
%
Experimental Design 70.87 23.37 1.42 0.30 1.23 1.70 0.07 0.04 1.00 89.68 16.79 3.07 4.12 5.66 2,950 0.82 0.32 0.90 0.60
1 Supplied per kilogram of diet: vitamin A (retinyl acetate), 2,500 IU; vitamin D3, 400 IU; vitamin E (DL-α-tocopheryl acetate), 10 IU; vitamin K3, 0.50 mg; thiamin, 1.80 mg; riboflavin, 4 mg; pyridoxineⴢHCl, 3 mg; vitamin B12 (cobalamin), 7 g; D-Ca pantothenate, 11 mg; nicotinic acid, 55 mg; folate, 0.50 mg; D-biotin, 0.12 mg; choline chloride, 750 mg; copper (CuSO4ⴢ5H2O), 8 mg; iron (FeSO4ⴢ7H2O), 80 mg; zinc (ZnSO4), 40 mg; manganese (MnSO4ⴢH2O), 60 mg; selenium (Na2SeO3), 0.15 mg; iodine (KI), 0.35 mg. 2 Calculated value according to the AME of roosters.
MATERIALS AND METHODS Duck ME Assay All procedures were approved by the animal care and welfare committee of Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing. The method of ME determination was similar to the TME bioassay described by Sibbald (1976) and partly modified to account for the difference in digestive physiology between rooster and duck as shown by studies in our lab (Fan, 2003). The modifications to Sibbald’s bioassay include feed withdrawal of all birds for 36 h before feeding test samples, use of 60 g of feedstuff for force feeding, and a 36-h period of excreta collection. In a 14-d wash-out period between ME trials, water and a corn-soybean mealbased diet (Table 1) were available for ad libitum consumption. Endogenous energy losses were determined using 4 replicates of 3 ducks per replicate during each ME trial. Four kilograms of each sample were made and ground through a 2-mm screen before pelleting. Pellets, 4 mm in diameter and 6 mm long, were prepared by regulating the ratio of water to feedstuff with a laboratory nonsteam press pellet mill, and were then air-dried until the water content was <14% before force feeding. A stainless steel funnel with a narrow stem (40 cm long and 1.0 cm inner diameter) was used for force feeding. The collection method of excreta was in accordance with that described by Adeola et al. (1997). In each ME trial, ducks were placed in individual cages (0.45 m × 0.38 m × 0.51
Experiment 1. The objective of this experiment was to determine the relationship between ME and the chemical composition of 30 corn calibration samples to develop a prediction model for ME that could be utilized for the formulation of diets for White Pekin ducks. The corn calibration samples were made by combining different percentages of corn, corn gluten meal, corn hulls, corn germ, and corn starch (Table 2) to provide a wide distribution of proximate nutrient compositions that spanned the range of values previously observed for 427 samples of Chinese normal corn, excluding high-oil corn. Separate ME trials were conducted under similar conditions from October 2005 to January 2006 to measure the ME contents of the each of the 30 corn calibration samples. One hundred and twenty 18-wk-old White Pekin drakes of similar weight (3.8 to 4.0 kg) provided by the Waterfowl Research Center of Chinese Academy of Agricultural Sciences (Beijing) were selected and randomly divided into 10 groups of 12 birds each. Each group contained 4 replicates of 3 ducks per replicate. One of the 10 groups was used for the determination of endogenous losses and each of the 9 remaining groups was used to determine the ME content of 1 calibration sample. After the ME determinations of the first 9 samples (numbers 1 to 9 in Table 2) were conducted, there was a 14-d wash-out period in which ducks were provided with free access to water and a corn-soybean meal-based diet (Table 1) formulated to meet the National Research Council (1994) requirements. Then, the same 120 ducks were randomly reassigned into 10 groups of 12 birds (4 replicates of 3 ducks) to determine endogenous losses and the ME of samples 10 to 18 (Table 2), followed by a 14-d wash-out period. Subsequently, the same 120 ducks were again randomly reassigned into 10 groups of 12 birds (4 replicates of 3 ducks) to determine endogenous losses and the ME of samples 19 to 27 (Table 2), followed by a 14-d wash-out period. Finally, 48 of the same 120 ducks were randomly selected and assigned into 4 groups of 12 birds (4 replicates of 3 ducks) to determine endogenous losses and the ME of samples 28 to 30 (Table 2). Experiment 2. The objective of this experiment was to test the suitability of various models to predict the ME content of a corn sample based on its chemical composition. Six corn cultivars were randomly selected from the main growing areas of China (Table 3) to test the accuracy of ME prediction model established in Experiment 1. The ME contents of each of the 6 corn cultivars were determined in vivo using 4 replicates of three 18-wk-old White Pekin ducks (3.8 to 4.0 kg) each per sample.
Chemical Analysis After completion of excreta collection in each ME trial, all excreta samples were dried at 65°C for 48 h, then re-
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Ingredient Corn Soybean meal Soybean oil Sodium chloride Limestone Calcium phosphate DL-Methionine LysineⴢHCl Vitamin-mineral premix1 Analyzed nutrient content DM CP Crude fiber Ether extract Ash Calculated nutrient content ME,2 kcal/kg Lys Met Calcium Total phosphorus
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RESEARCH NOTE Table 2. Composition of calibration samples, Experiment 1 Analyzed nutrients on DM basis1
Ingredients, % Calibration sample
Corn gluten meal
Corn hull
Corn germ
Corn starch
42.24 45.08 54.08 66.19 68.35 71.08 74.42 74.61 75.65 76.72 76.91 77.94 78.21 79.24 81.00 82.25 83.02 83.03 83.51 83.52 84.90 85.15 85.15 85.36 85.53 86.63 86.74 88.50 91.62 100.00
8.01 6.86 6.00 0.26 3.33 3.52 0.28 3.15 — 0.09 2.20 — 4.92 2.02 0.87 4.38 — 0.92 3.28 1.27 0.18 — 1.11 2.01 0.50 — 1.58 — 2.09 —
9.78 20.15 20.80 15.44 — 8.15 24.41 11.87 10.87 22.67 15.13 20.77 16.17 12.82 17.76 11.40 9.30 15.17 12.54 13.68 14.86 6.68 9.00 9.58 12.77 9.20 11.60 7.41 4.19 —
— — — — 0.27 — 0.89 — 1.16 — — 0.21 0.70 — 0.37 — 2.64 0.88 0.67 1.53 0.06 1.49 — 3.05 1.20 0.35 0.08 2.85 2.10 —
39.97 27.91 19.12 18.11 28.05 17.25 — 10.37 12.32 0.52 5.76 1.08 — 5.92 — 1.97 5.04 — — — — 6.68 4.74 — — 3.82 — 1.24 — —
CF
CP
Ether extract
1.9 3.1 3.5 2.9 1.1 2.2 3.5 2.3 2.4 3.4 2.8 3.7 3.5 2.8 3.5 2.5 2.5 3.4 2.9 2.9 3.3 2.2 2.4 2.8 2.7 2.4 2.6 2.3 2.0 1.6 2.7 0.6
10.8 11.3 11.5 8.5 8.9 10 9.9 10.6 8.7 9.8 10.7 9.7 13 10.5 10.2 12.6 9.2 10.4 11.9 10.8 10 9 9.5 10.8 10.2 9.2 10.9 9.1 10.5 9.3 10.3 1.1
2.3 3.0 3.4 3.1 2.4 3.0 4.5 3.2 3.7 4.2 3.6 4.3 4.6 3.5 4.3 3.8 4.8 4.2 3.9 4.4 3.8 4.3 3.2 5.3 4.3 3.8 3.3 5.0 4.8 3.6 3.9 0.7
Ash
ADF
NDF
GE
AME
0.8 0.9 1.0 1.1 1.1 1.1 1.1 1.1 1.1 1.2 1.1 1.2 1.2 1.2 1.4 1.2 1.5 1.1 1.3 1.1 1.1 1.3 1.3 1.2 1.3 1.4 1.2 1.4 1.3 1.3 1.2 0.2
2.9 5.1 5.4 4.1 1.8 3.5 6.8 4.0 3.8 6.4 4.5 6.2 5.6 4.3 5.7 4.2 4.2 5.5 4.4 4.9 5.2 3.3 3.9 4.6 4.3 4.1 4.2 3.8 3.4 2.5 4.4 1.1
11.1 16.9 18.4 15.4 6.0 11.8 21.8 13.9 13.6 21.1 15.8 20.4 16.6 15.3 18.3 14.5 14.7 17.5 15.1 16.6 16.5 12.4 13.2 14.5 15.0 14.0 14.7 12.6 11.9 9.4 15.0 3.3
4,493 4,571 4,621 4,488 4,458 4,518 4,642 4,558 4,517 4,590 4,582 4,599 4,664 4,584 4,623 4,598 4,608 4,603 4,643 4,610 4,587 4,541 4,489 4,654 4,582 4,506 4,595 4,579 4,592 4,489 4,573 55
3,610 3,419 3,316 3,417 3,851 3,560 3,214 3,509 3,482 3,253 3,473 3,274 3,415 3,468 3,448 3,480 3,570 3,412 3,500 3,436 3,451 3,556 3,462 3,506 3,429 3,514 3,425 3,561 3,669 3,712 3,480 130
% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Mean SD
AMEn
TME
TMEn
3,873 3,679 3,575 3,683 4,111 3,820 3,477 3,773 3,743 3,515 3,739 3,534 3,679 3,729 3,708 3,739 3,828 3,681 3,761 3,702 3,718 3,818 3,723 3,767 3,689 3,776 3,686 3,820 3,932 3,977 3,742 130
3,711 3,515 3,414 3,543 3,952 3,665 3,338 3,623 3,605 3,368 3,572 3,396 3,504 3,569 3,559 3,578 3,680 3,525 3,598 3,545 3,562 3,678 3,579 3,635 3,538 3,630 3,550 3,680 3,774 3,816 3,590 128
kcal/kg 3,535 3,342 3,241 3,365 3,779 3,492 3,162 3,447 3,431 3,193 3,395 3,222 3,327 3,395 3,386 3,405 3,508 3,346 3,424 3,368 3,384 3,503 3,405 3,460 3,365 3,455 3,376 3,507 3,598 3,639 3,415 128
1
Mean of 3 determinations per sample. CF = crude fiber; ADF = acid detergent fiber; NDF = neutral detergent fiber; GE = gross energy.
equilibrated with air for 24 h and ground through a 0.5mm screen before analysis. The feedstuff and excreta were analyzed for DM (method 934.01), CP (method 954.01), crude fiber (CF; method 962.09), ether extract (method 920.39), ash (method 942.05), and nitrogen (method 955.04) using procedures of the AOAC (1990). Energy contents of feedstuff and excreta were determined by using a Parr 1281 automatic adiabatic calorimeter (Parr Instrument Co., Moline, IL). The ADF and NDF contents
of feedstuffs were determined according to the procedure described by Van Soest (1963) and Van Soest et al. (1991), respectively. The AME, AMEn, TME, and TMEn of the samples were calculated according to the procedure described by Adeola et al. (1997).
Statistical Analysis Possible relationships between chemical composition and ME content were analyzed with correlation and step-
Table 3. Growth location, variety, and nutrients of test corn samples, Experiment 2 Test corn samples Item Growth location Variety Color Nutrient,1 DM basis GE, kcal/kg CP, g/kg Ether extract, g/kg CF, g/kg Ash, g/kg NDF, g/kg ADF, g/kg
1
2
3
4
5
6
Heilongjiang Baidan 9 White
Shanxi Nongda 108 Yellow
Hebei Haideng 3 Yellow
Shandong Yedan 981 White
Shaanxi Zhengdan 958 Yellow
Xingjiang Dika 656 Yellow
4,491 85 39 16 22 94 33
4,546 85 45 15 12 85 27
4,541 94 41 13 14 65 25
4,524 102 37 15 16 79 25
4,508 86 39 13 13 63 23
4,520 81 36 11 12 61 22
1 Mean of 3 determinations per sample. CF = crude fiber; ADF = acid detergent fiber; NDF = neutral detergent fiber; GE = gross energy.
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Corn
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Table 4. Correlation coefficients between chemical composition2 and ME of calibration samples, Experiment 1
CP Ether extract Ash ADF NDF GE AME AMEn TME TMEn
CF
CP
Ether extract
Ash
ADF
NDF
GE
AME
AMEn
TME
0.35 0.38 −0.08 0.96 0.95 0.71 −0.90 −0.91 −0.90 −0.91
0.05 −0.26 0.31 0.26 0.62 −0.23 −0.29 −0.23 −0.29
0.60 0.43 0.38 0.69 −0.26 −0.24 −0.26 −0.24
−0.05 −0.07 0.16 0.15 0.18 0.15 0.17
0.98 0.72 −0.91 −0.92 −0.91 −0.92
0.67 −0.95 −0.95 −0.95 −0.95
−0.54 −0.56 −0.54 −0.55
0.99 0.99 0.99
0.99 0.99
0.99
Correlation whose absolute value is more than 0.38 is significantly different from zero at P < 0.05. CF = crude fiber; ADF = acid detergent fiber; NDF = neutral detergent fiber; GE = gross energy.
1 2
RESULTS AND DISCUSSION As planned, the chemical composition of calibration samples varied to a great extent in Experiment 1 (Table 1). The mean gross energy (GE) content was 4,573 kcal/ kg of DM, and ranged from 4,458 to 4,664 kcal/kg of DM, which was similar to that in a previous study by Lessire et al. (2003). The mean CP content was 103 g/kg of DM with a range from 85 to 130 g/kg of DM. The mean CF, ADF, and NDF contents were 27, 44, and 150 g/kg of DM with ranges from 11 to 37, 18 to 68, and 60 to 218 g/ kg of DM, respectively. The mean ether extract content was 39 g/kg of DM and ranged from 23 to 53 g/kg of DM. The mean ash was 12 g/kg of DM and ranged from 8 to 15 g/kg of DM. The values of these nutrients from corn published in Chinese Feed Database (CFD, 2005) were in the range obtained in our experiment. The mean AMEn was 3,415 kcal/kg of DM, which was similar to the value of 3,503 kcal/kg of DM in Pekin ducks reported by King et al. (1997). The CFD (2005) and NRC (1994) AMEn values for corn in cockerel were 3,740 and 3,764 kcal/kg of DM, respectively, which is in the range of 3,162 to 3,779 kcal/kg obtained for calibration samples in our experiment. However, the mean AMEn of 30 sam-
ples in our study was less than that of 37 corn samples in adult cockerels reported by Lessire et al. (2003), which might be due in part to the high-oil corn used. In our study, the variation of GE in calibration samples was less than that of the 4 ME measures, which suggested that the difference in chemical composition could affect the availability of energy of corn in ducks. This phenomenon was in accordance with a study with cockerels reported by Dale (1994). The range of chemical composition and ME contents in 30 calibration samples was greater than that of 37 corn samples observed by Lessire et al. (2003), Leeson et al. (1993), Dale (1994), and Dale and Jackson (1994). This result was advantageous for establishing a ME prediction model according to the results of Carre´ (1990). The correlation coefficients between AME and AMEn, TME, and TMEn of calibration samples were significantly high (0.99; P < 0.05; Table 4). These results were in accordance with the findings of Sibbald (1982), Lessire et al. (2003), and Francesch et al. (2002). Our study also indicated that nitrogen-corrected values were also proportional to the AME or TME value in the duck feedstuffs. Therefore, the 4 ME measures (AME, AMEn, TME, and TMEn) in the calibration samples had almost the same relationship with their chemical composition. Correlation analyses (Table 4) showed that the AME, AMEn, TME, and TMEn were highly negatively correlated with CF, ADF, and NDF contents, and moderately correlated with GE content (average of the 4 ME r values were −0.905, −0.915, −0.95, and −0.55, respectively) in the calibration samples. In contrast, no significant correlations were found with CP, ether extract, and ash content. The GE content was also correlated positively with CF, ADF,
Table 5. Equations of prediction of the ME (kcal/kg of DM) values of corn according to neutral detergent fiber (NDF) and gross energy (GE) contents (%, DM basis), Experiment 1 ME
Equation
AME AMEn TME TMEn
2,299.1 2,509.8 2,606.0 2,708.2
1
– – – –
41.6 40.4 41.4 40.3
× × × ×
Residual standard deviation.
NDF NDF NDF NDF
+ + + +
0.394 0.330 0.384 0.325
× × × ×
GE GE GE GE
R2
RSD,1 kcal/kg
P-value
0.9181 0.9200 0.9154 0.9188
38.6 37.6 39.2 37.8
<0.0001 <0.0001 <0.0001 <0.0001
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wise regression analysis using the CORR and REG procedures of SAS (SAS Institute, 1990). The correlation and regression coefficients were considered different from zero at P < 0.05. Residual standard deviation was used for measuring the goodness-of-fit of linear models. The smaller was RSD, the better fitting was the model. This was done by the procedure described by Kaps and Lamberson (2004).
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0.3724 0.5285 4,034 59 0.2504
4,026 77 3,701 57 0.7509
3,713 72 3,770 58 3,766 78
The observed ME values were determined with the in vivo method; the predicted ME values were calculated according to the neutral detergent fiber and gross energy contents of corn.
1
3,886 71
3,877 56
5 −32 19 11 33 17 3,789 3,843 3,922 3,860 3,919 3,931 3,794 3,811 3,941 3,871 3,952 3,948 −19 −61 5 14 20 −8 3,941 4,000 4,081 4,016 4,076 4,089 3,922 3,939 4,086 4,030 4,096 4,081 9 −30 22 14 35 21 3,612 3,667 3,746 3,684 3,743 3,755 3,621 3,637 3,768 3,698 3,778 3,776 −16 −58 9 18 22 −3 3,662 3,679 3,827 3,771 3,835 3,823
1 2 3 4 5 6 Statistics Mean SD P-value
3,678 3,738 3,817 3,752 3,812 3,825
Predicted Predicted Predicted Observed Test corn
Difference
Observed Predicted
Difference
Observed
TME, kcal/kg AMEn, kcal/kg AME, kcal/kg
NDF, CP, and ether extract contents (average of r values were 0.71, 0.72, 0.67, 0.62, and 0.69, respectively). However, the ME content was negatively correlated with GE content in our study, which did not agree with the results of Lessire et al. (2003). In those chemical compositions that were significantly correlated with the ME of calibration samples, any 2 of NDF, CF and ADF had highly significant correlations between themselves (r ≥ 0.95). Correlation between the ME and NDF contents was greatest among those 3 fiber contents, which indicated that the effect of CF and ADF contents on the ME content of calibration samples could be explained by NDF content. Therefore, the present results indicated that the ME content of corn calibration samples in adult ducks might be largely dependent on NDF and GE content. Similar observations have also been reported on the ME content of corn in cockerels (Lessire et al., 2003). Using correlations and stepwise regression analysis, the equations to predict ME content of corn in ducks were established according to the significant linear relationship between ME, NDF, and GE content (Table 5). The equations based on NDF and GE contents for predicting AME, AMEn, TME, and TMEn contents had high accuracy with R2 > 0.90 and RSD <40 kcal/kg, which indicated that only less than 10% of the observed variation in the ME content of corn calibration samples resulted from factors other than NDF and GE content. This suggested that the accuracy of the prediction model for ME was close to that observed in vivo during a classic metabolizable energy experiment, which was in accordance with the previous results of Lessire et al. (2003) and Dale (1994), who used cockerels as the test animal. To test the suitability of these models (Table 5) to predict the ME content of a normal corn sample, the ME content of 6 samples of corn was measured by both the in vivo method and prediction models. Our results showed that the chemical compositions (Table 3) and ME content determined by using the in vivo method of 6 samples were all in the range of the 30 corn calibration samples (Table 2). The maximum absolute difference between ME determined by the in vivo method and the prediction model was 61 kcal/kg (Table 6). The fluctuation of ME measurements among 4 replications with the in vivo method in one sample of corn ranged from 31 to 178 kcal/kg. This suggested that the accuracy of prediction models for ME was close to that obtained in vivo. Therefore, the prediction models established from 30 corn calibration samples as described in this article can be used to predict the ME content of corn for ducks with acceptable accuracy.
ACKNOWLEDGMENTS We gratefully acknowledge the financial support of Basic Science Research Program (ywf-td-4), State Science and Technique Support Project (2006BAD12B01–1), and the State Commonwealth Research Project (2005DIB4J033) in China. We also wish to thank W. Huang, L. Zhao, Y. W. Dong, Q. J. Wang, X. H. Jiang, M. Zhao,
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Table 6. Comparison of ME contents in corn determined by using the in vivo method and prediction model,1 Experiment 2
Difference
Observed
TMEn, kcal/kg
Difference
RESEARCH NOTE
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and R. P. Wang (Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing) for their help in force feeding and excreta samples collection.
REFERENCES
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Adeola, O., D. Ragland, and D. King. 1997. Feeding and excreta collection techniques in metabolizable energy assays for ducks. Poult. Sci. 76:728–732. Association of Official Analytical Chemists (AOAC). 1990. Official Methods of Analysis. Association of Official Analytical Chemists, Washington, DC. Carre´, B. 1990. Predicting the dietary energy value of poultry feeds. Pages 283–300 in Feedstuff Evaluation. J. Wiseman, and D. J. A. Cole, ed. Butterworths, London, UK. Chinese Feed Database (CFD). 2005. Tables of Feed Composition and Nutritive Values for Poultry in China. The Center of Chinese Feed Database Information, Beijing. http://www. Chinafeeddata.org.cn Accessed May 2007. (in Chinese) Connor, J. K., A. R. Neill, and K. M. Barram. 1976. The metabolizable energy content for the chicken of maize and sorghum grain hybrids grown at several geographical regions. Aust. J. Exp. Agric. Anim. Husb. 16:699–703. Dale, N. 1994. Relationship between bushel weight, metabolizable energy, and protein content of corn from an adverse growing season. J. Appl. Poult. Res. 3:83–86. Dale, N., and D. Jackson. 1994. True metabolizable energy of corn fractions. J. Appl. Poult. Res. 3:179–183. Fan, H. P. 2003. Comparative study of the digestion of feed nutrients between cockerel and drake. MS thesis. Chinese Academy of Agricultural Sciences, Beijing. Francesch, M., K. Bernard, and J. M. McNab. 2002. Comparison of two direct bioassays using 3-week-old broilers to measure
the metabolisable energy of diets containing cereals high in fibre: Differences between true and apparent metabolisable energy values. Br. Poult. Sci. 43:580–587. Kaps, M., and W. Lamberson. 2004. Biostatistics for Animal Science. CABI Publishing, Cambridge, UK. King, D., D. Raglang, and O. Adeola. 1997. Apparent and true metabolizable energy values of feedstuffs for ducks. Poult. Sci. 76:1418–1423. Leeson, S., J. D. Summers, and T. R. Daynard. 1977. The effect of kernel maturity at harvest as measured by moisture content, on the metabolizable energy value of corn. Poult. Sci. 56:154–156. Leeson, S., A. Yersin, and L. Volker. 1993. Nutritive values of 1992 corn crop. J. Appl. Poult. Res. 2:208–213. Lessire, M., J. M. Hallouis, B. Barrier-Guillot, M. Champion, and N. Femenias. 2003. Prediction of the metabolisable energy value of maize in adult cockerel. Br. Poult. Sci. 44:813–814. Mollah, Y., and E. F. Annison. 1981. Prediction of metabolisable energy of wheat, maize and sorghum in poultry diets from chemical compositon. Proc. Nutr. Soc. Aust. 6:137. National Research Council. 1994. Nutrient Requirements of Poultry. 9th rev. ed. Natl. Acad. Press, Washington, DC. SAS Institute. 1990. SAS/STAT User’s Guide: Statistics. SAS Institute Inc., Cary, NC. Sibbald, I. R. 1976. A bioassay for true metabolizable energy in feedingstuffs. Poult. Sci. 55:303–308. Sibbald, I. R. 1982. Measurement of bioavailable energy in poultry feeding stuffs: A review. Can. J. Anim. Sci. 62:983–1048. Van Soest, P. J. 1963. Use of detergents in the analysis of fibrous feeds. II. A rapid method for the determination of fiber and lignin. J. Am. Off. Anal. Chem. 46:829–835. Van Soest, P. J., J. B. Robertson, and B. A. Lewis. 1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74:3583–3597.
Key words: duck, corn, metabolizable energy, prediction model 2008 Poultry Science 87:1603–1608 doi:10.3382/ps.2007-00494
INTRODUCTION In 2005, the total yield of Chinese corn was >130,000,000 t. Corn is the principal energy source for ducks, comprising >40% by weight of the duck diet in China. To produce an accurate evaluation of the ME content of corn for poultry diet formulation, a considerable number of studies have been conducted to predict the ME content of corn based on its physical characteristics or chemical composition. Using the adult rooster as a test animal, many researchers have shown that the ME content of corn was correlated with its bulk density or chemical composition (Conner et al., 1976; Leeson et al., 1977; Mollah and Annison, 1981; Dale, 1994; NRC, 1994; Lessire et al., 2003). These results also indicated that the ME value of corn could be predicted, but few studies have been reported with ducks. In general, models that are based on chemical composition and used to predict ingredient ME value are more accurate than models based upon physical characteristics of the test ingredient. However, there has been no uniform model predicting the ME content of corn for birds based
©2008 Poultry Science Association Inc. Received December 4, 2007. Accepted April 9, 2008. 1 Corresponding author: [email protected]
on the chemical composition. Several factors can affect the accuracy of ME prediction models, which subsequently influences their successful use. One such factor is the sample size for regression analysis; another is the representativeness of samples for the feedstuff as a whole. In some experiments aimed at predicting the ME content of raw materials, more than 25 samples were included in the regression analysis (Dale, 1994; Lessire et al., 2003). However, in other studies, the number of samples was less than 15 (Mollah and Annison, 1981). Prediction models from smaller sample sizes may have greater R2 and less residual standard deviation (RSD), but may not be as accurate as other models developed with a greater number of samples (Carre´, 1990). On the other hand, the range of ME and chemical composition contents of samples obviously affect the accuracy of the prediction model. For example, low variation in the ME and chemical composition contents of calibration sample set might provide an incorrect prediction model (Carre´, 1990). Because the ME from corn contributes more than 40% of the total dietary ME content in diets typically fed to ducks in China, research to establish a model for predicting the AME and TME contents of corn for ducks should improve the accuracy of calculating ME in diet formulations for ducks. Therefore, this study utilized a series of calibration samples comprising corn and corn plus corn gluten meal, corn hulls, corn germ, and corn starch to establish an ME prediction model for White Pekin ducks.
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ABSTRACT Two experiments were conducted to establish an ME content prediction model for normal corn for ducks based on the grain’s chemical composition. In Experiment 1, observed linear relationships between the determined ME content of 30 corn calibration samples and proximate nutrients, acid detergent fiber (ADF), and neutral detergent fiber (NDF) were used to develop an ME prediction model. In Experiment 2, 6 samples of corn selected at random from the primary corn-growing regions of China were used for testing the accuracy of ME prediction models. The results indicated that the AME, AMEn, TME, and TMEn were negatively correlated with
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Table 1. Composition and nutrient content of the corn-soybean mealbased diet fed during the wash-out period between ME determinations Item
m) in a temperature-controlled room (25°C) and provided with 12 h of light daily.
%
Experimental Design 70.87 23.37 1.42 0.30 1.23 1.70 0.07 0.04 1.00 89.68 16.79 3.07 4.12 5.66 2,950 0.82 0.32 0.90 0.60
1 Supplied per kilogram of diet: vitamin A (retinyl acetate), 2,500 IU; vitamin D3, 400 IU; vitamin E (DL-α-tocopheryl acetate), 10 IU; vitamin K3, 0.50 mg; thiamin, 1.80 mg; riboflavin, 4 mg; pyridoxineⴢHCl, 3 mg; vitamin B12 (cobalamin), 7 g; D-Ca pantothenate, 11 mg; nicotinic acid, 55 mg; folate, 0.50 mg; D-biotin, 0.12 mg; choline chloride, 750 mg; copper (CuSO4ⴢ5H2O), 8 mg; iron (FeSO4ⴢ7H2O), 80 mg; zinc (ZnSO4), 40 mg; manganese (MnSO4ⴢH2O), 60 mg; selenium (Na2SeO3), 0.15 mg; iodine (KI), 0.35 mg. 2 Calculated value according to the AME of roosters.
MATERIALS AND METHODS Duck ME Assay All procedures were approved by the animal care and welfare committee of Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing. The method of ME determination was similar to the TME bioassay described by Sibbald (1976) and partly modified to account for the difference in digestive physiology between rooster and duck as shown by studies in our lab (Fan, 2003). The modifications to Sibbald’s bioassay include feed withdrawal of all birds for 36 h before feeding test samples, use of 60 g of feedstuff for force feeding, and a 36-h period of excreta collection. In a 14-d wash-out period between ME trials, water and a corn-soybean mealbased diet (Table 1) were available for ad libitum consumption. Endogenous energy losses were determined using 4 replicates of 3 ducks per replicate during each ME trial. Four kilograms of each sample were made and ground through a 2-mm screen before pelleting. Pellets, 4 mm in diameter and 6 mm long, were prepared by regulating the ratio of water to feedstuff with a laboratory nonsteam press pellet mill, and were then air-dried until the water content was <14% before force feeding. A stainless steel funnel with a narrow stem (40 cm long and 1.0 cm inner diameter) was used for force feeding. The collection method of excreta was in accordance with that described by Adeola et al. (1997). In each ME trial, ducks were placed in individual cages (0.45 m × 0.38 m × 0.51
Experiment 1. The objective of this experiment was to determine the relationship between ME and the chemical composition of 30 corn calibration samples to develop a prediction model for ME that could be utilized for the formulation of diets for White Pekin ducks. The corn calibration samples were made by combining different percentages of corn, corn gluten meal, corn hulls, corn germ, and corn starch (Table 2) to provide a wide distribution of proximate nutrient compositions that spanned the range of values previously observed for 427 samples of Chinese normal corn, excluding high-oil corn. Separate ME trials were conducted under similar conditions from October 2005 to January 2006 to measure the ME contents of the each of the 30 corn calibration samples. One hundred and twenty 18-wk-old White Pekin drakes of similar weight (3.8 to 4.0 kg) provided by the Waterfowl Research Center of Chinese Academy of Agricultural Sciences (Beijing) were selected and randomly divided into 10 groups of 12 birds each. Each group contained 4 replicates of 3 ducks per replicate. One of the 10 groups was used for the determination of endogenous losses and each of the 9 remaining groups was used to determine the ME content of 1 calibration sample. After the ME determinations of the first 9 samples (numbers 1 to 9 in Table 2) were conducted, there was a 14-d wash-out period in which ducks were provided with free access to water and a corn-soybean meal-based diet (Table 1) formulated to meet the National Research Council (1994) requirements. Then, the same 120 ducks were randomly reassigned into 10 groups of 12 birds (4 replicates of 3 ducks) to determine endogenous losses and the ME of samples 10 to 18 (Table 2), followed by a 14-d wash-out period. Subsequently, the same 120 ducks were again randomly reassigned into 10 groups of 12 birds (4 replicates of 3 ducks) to determine endogenous losses and the ME of samples 19 to 27 (Table 2), followed by a 14-d wash-out period. Finally, 48 of the same 120 ducks were randomly selected and assigned into 4 groups of 12 birds (4 replicates of 3 ducks) to determine endogenous losses and the ME of samples 28 to 30 (Table 2). Experiment 2. The objective of this experiment was to test the suitability of various models to predict the ME content of a corn sample based on its chemical composition. Six corn cultivars were randomly selected from the main growing areas of China (Table 3) to test the accuracy of ME prediction model established in Experiment 1. The ME contents of each of the 6 corn cultivars were determined in vivo using 4 replicates of three 18-wk-old White Pekin ducks (3.8 to 4.0 kg) each per sample.
Chemical Analysis After completion of excreta collection in each ME trial, all excreta samples were dried at 65°C for 48 h, then re-
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Ingredient Corn Soybean meal Soybean oil Sodium chloride Limestone Calcium phosphate DL-Methionine LysineⴢHCl Vitamin-mineral premix1 Analyzed nutrient content DM CP Crude fiber Ether extract Ash Calculated nutrient content ME,2 kcal/kg Lys Met Calcium Total phosphorus
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RESEARCH NOTE Table 2. Composition of calibration samples, Experiment 1 Analyzed nutrients on DM basis1
Ingredients, % Calibration sample
Corn gluten meal
Corn hull
Corn germ
Corn starch
42.24 45.08 54.08 66.19 68.35 71.08 74.42 74.61 75.65 76.72 76.91 77.94 78.21 79.24 81.00 82.25 83.02 83.03 83.51 83.52 84.90 85.15 85.15 85.36 85.53 86.63 86.74 88.50 91.62 100.00
8.01 6.86 6.00 0.26 3.33 3.52 0.28 3.15 — 0.09 2.20 — 4.92 2.02 0.87 4.38 — 0.92 3.28 1.27 0.18 — 1.11 2.01 0.50 — 1.58 — 2.09 —
9.78 20.15 20.80 15.44 — 8.15 24.41 11.87 10.87 22.67 15.13 20.77 16.17 12.82 17.76 11.40 9.30 15.17 12.54 13.68 14.86 6.68 9.00 9.58 12.77 9.20 11.60 7.41 4.19 —
— — — — 0.27 — 0.89 — 1.16 — — 0.21 0.70 — 0.37 — 2.64 0.88 0.67 1.53 0.06 1.49 — 3.05 1.20 0.35 0.08 2.85 2.10 —
39.97 27.91 19.12 18.11 28.05 17.25 — 10.37 12.32 0.52 5.76 1.08 — 5.92 — 1.97 5.04 — — — — 6.68 4.74 — — 3.82 — 1.24 — —
CF
CP
Ether extract
1.9 3.1 3.5 2.9 1.1 2.2 3.5 2.3 2.4 3.4 2.8 3.7 3.5 2.8 3.5 2.5 2.5 3.4 2.9 2.9 3.3 2.2 2.4 2.8 2.7 2.4 2.6 2.3 2.0 1.6 2.7 0.6
10.8 11.3 11.5 8.5 8.9 10 9.9 10.6 8.7 9.8 10.7 9.7 13 10.5 10.2 12.6 9.2 10.4 11.9 10.8 10 9 9.5 10.8 10.2 9.2 10.9 9.1 10.5 9.3 10.3 1.1
2.3 3.0 3.4 3.1 2.4 3.0 4.5 3.2 3.7 4.2 3.6 4.3 4.6 3.5 4.3 3.8 4.8 4.2 3.9 4.4 3.8 4.3 3.2 5.3 4.3 3.8 3.3 5.0 4.8 3.6 3.9 0.7
Ash
ADF
NDF
GE
AME
0.8 0.9 1.0 1.1 1.1 1.1 1.1 1.1 1.1 1.2 1.1 1.2 1.2 1.2 1.4 1.2 1.5 1.1 1.3 1.1 1.1 1.3 1.3 1.2 1.3 1.4 1.2 1.4 1.3 1.3 1.2 0.2
2.9 5.1 5.4 4.1 1.8 3.5 6.8 4.0 3.8 6.4 4.5 6.2 5.6 4.3 5.7 4.2 4.2 5.5 4.4 4.9 5.2 3.3 3.9 4.6 4.3 4.1 4.2 3.8 3.4 2.5 4.4 1.1
11.1 16.9 18.4 15.4 6.0 11.8 21.8 13.9 13.6 21.1 15.8 20.4 16.6 15.3 18.3 14.5 14.7 17.5 15.1 16.6 16.5 12.4 13.2 14.5 15.0 14.0 14.7 12.6 11.9 9.4 15.0 3.3
4,493 4,571 4,621 4,488 4,458 4,518 4,642 4,558 4,517 4,590 4,582 4,599 4,664 4,584 4,623 4,598 4,608 4,603 4,643 4,610 4,587 4,541 4,489 4,654 4,582 4,506 4,595 4,579 4,592 4,489 4,573 55
3,610 3,419 3,316 3,417 3,851 3,560 3,214 3,509 3,482 3,253 3,473 3,274 3,415 3,468 3,448 3,480 3,570 3,412 3,500 3,436 3,451 3,556 3,462 3,506 3,429 3,514 3,425 3,561 3,669 3,712 3,480 130
% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Mean SD
AMEn
TME
TMEn
3,873 3,679 3,575 3,683 4,111 3,820 3,477 3,773 3,743 3,515 3,739 3,534 3,679 3,729 3,708 3,739 3,828 3,681 3,761 3,702 3,718 3,818 3,723 3,767 3,689 3,776 3,686 3,820 3,932 3,977 3,742 130
3,711 3,515 3,414 3,543 3,952 3,665 3,338 3,623 3,605 3,368 3,572 3,396 3,504 3,569 3,559 3,578 3,680 3,525 3,598 3,545 3,562 3,678 3,579 3,635 3,538 3,630 3,550 3,680 3,774 3,816 3,590 128
kcal/kg 3,535 3,342 3,241 3,365 3,779 3,492 3,162 3,447 3,431 3,193 3,395 3,222 3,327 3,395 3,386 3,405 3,508 3,346 3,424 3,368 3,384 3,503 3,405 3,460 3,365 3,455 3,376 3,507 3,598 3,639 3,415 128
1
Mean of 3 determinations per sample. CF = crude fiber; ADF = acid detergent fiber; NDF = neutral detergent fiber; GE = gross energy.
equilibrated with air for 24 h and ground through a 0.5mm screen before analysis. The feedstuff and excreta were analyzed for DM (method 934.01), CP (method 954.01), crude fiber (CF; method 962.09), ether extract (method 920.39), ash (method 942.05), and nitrogen (method 955.04) using procedures of the AOAC (1990). Energy contents of feedstuff and excreta were determined by using a Parr 1281 automatic adiabatic calorimeter (Parr Instrument Co., Moline, IL). The ADF and NDF contents
of feedstuffs were determined according to the procedure described by Van Soest (1963) and Van Soest et al. (1991), respectively. The AME, AMEn, TME, and TMEn of the samples were calculated according to the procedure described by Adeola et al. (1997).
Statistical Analysis Possible relationships between chemical composition and ME content were analyzed with correlation and step-
Table 3. Growth location, variety, and nutrients of test corn samples, Experiment 2 Test corn samples Item Growth location Variety Color Nutrient,1 DM basis GE, kcal/kg CP, g/kg Ether extract, g/kg CF, g/kg Ash, g/kg NDF, g/kg ADF, g/kg
1
2
3
4
5
6
Heilongjiang Baidan 9 White
Shanxi Nongda 108 Yellow
Hebei Haideng 3 Yellow
Shandong Yedan 981 White
Shaanxi Zhengdan 958 Yellow
Xingjiang Dika 656 Yellow
4,491 85 39 16 22 94 33
4,546 85 45 15 12 85 27
4,541 94 41 13 14 65 25
4,524 102 37 15 16 79 25
4,508 86 39 13 13 63 23
4,520 81 36 11 12 61 22
1 Mean of 3 determinations per sample. CF = crude fiber; ADF = acid detergent fiber; NDF = neutral detergent fiber; GE = gross energy.
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Corn
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Table 4. Correlation coefficients between chemical composition2 and ME of calibration samples, Experiment 1
CP Ether extract Ash ADF NDF GE AME AMEn TME TMEn
CF
CP
Ether extract
Ash
ADF
NDF
GE
AME
AMEn
TME
0.35 0.38 −0.08 0.96 0.95 0.71 −0.90 −0.91 −0.90 −0.91
0.05 −0.26 0.31 0.26 0.62 −0.23 −0.29 −0.23 −0.29
0.60 0.43 0.38 0.69 −0.26 −0.24 −0.26 −0.24
−0.05 −0.07 0.16 0.15 0.18 0.15 0.17
0.98 0.72 −0.91 −0.92 −0.91 −0.92
0.67 −0.95 −0.95 −0.95 −0.95
−0.54 −0.56 −0.54 −0.55
0.99 0.99 0.99
0.99 0.99
0.99
Correlation whose absolute value is more than 0.38 is significantly different from zero at P < 0.05. CF = crude fiber; ADF = acid detergent fiber; NDF = neutral detergent fiber; GE = gross energy.
1 2
RESULTS AND DISCUSSION As planned, the chemical composition of calibration samples varied to a great extent in Experiment 1 (Table 1). The mean gross energy (GE) content was 4,573 kcal/ kg of DM, and ranged from 4,458 to 4,664 kcal/kg of DM, which was similar to that in a previous study by Lessire et al. (2003). The mean CP content was 103 g/kg of DM with a range from 85 to 130 g/kg of DM. The mean CF, ADF, and NDF contents were 27, 44, and 150 g/kg of DM with ranges from 11 to 37, 18 to 68, and 60 to 218 g/ kg of DM, respectively. The mean ether extract content was 39 g/kg of DM and ranged from 23 to 53 g/kg of DM. The mean ash was 12 g/kg of DM and ranged from 8 to 15 g/kg of DM. The values of these nutrients from corn published in Chinese Feed Database (CFD, 2005) were in the range obtained in our experiment. The mean AMEn was 3,415 kcal/kg of DM, which was similar to the value of 3,503 kcal/kg of DM in Pekin ducks reported by King et al. (1997). The CFD (2005) and NRC (1994) AMEn values for corn in cockerel were 3,740 and 3,764 kcal/kg of DM, respectively, which is in the range of 3,162 to 3,779 kcal/kg obtained for calibration samples in our experiment. However, the mean AMEn of 30 sam-
ples in our study was less than that of 37 corn samples in adult cockerels reported by Lessire et al. (2003), which might be due in part to the high-oil corn used. In our study, the variation of GE in calibration samples was less than that of the 4 ME measures, which suggested that the difference in chemical composition could affect the availability of energy of corn in ducks. This phenomenon was in accordance with a study with cockerels reported by Dale (1994). The range of chemical composition and ME contents in 30 calibration samples was greater than that of 37 corn samples observed by Lessire et al. (2003), Leeson et al. (1993), Dale (1994), and Dale and Jackson (1994). This result was advantageous for establishing a ME prediction model according to the results of Carre´ (1990). The correlation coefficients between AME and AMEn, TME, and TMEn of calibration samples were significantly high (0.99; P < 0.05; Table 4). These results were in accordance with the findings of Sibbald (1982), Lessire et al. (2003), and Francesch et al. (2002). Our study also indicated that nitrogen-corrected values were also proportional to the AME or TME value in the duck feedstuffs. Therefore, the 4 ME measures (AME, AMEn, TME, and TMEn) in the calibration samples had almost the same relationship with their chemical composition. Correlation analyses (Table 4) showed that the AME, AMEn, TME, and TMEn were highly negatively correlated with CF, ADF, and NDF contents, and moderately correlated with GE content (average of the 4 ME r values were −0.905, −0.915, −0.95, and −0.55, respectively) in the calibration samples. In contrast, no significant correlations were found with CP, ether extract, and ash content. The GE content was also correlated positively with CF, ADF,
Table 5. Equations of prediction of the ME (kcal/kg of DM) values of corn according to neutral detergent fiber (NDF) and gross energy (GE) contents (%, DM basis), Experiment 1 ME
Equation
AME AMEn TME TMEn
2,299.1 2,509.8 2,606.0 2,708.2
1
– – – –
41.6 40.4 41.4 40.3
× × × ×
Residual standard deviation.
NDF NDF NDF NDF
+ + + +
0.394 0.330 0.384 0.325
× × × ×
GE GE GE GE
R2
RSD,1 kcal/kg
P-value
0.9181 0.9200 0.9154 0.9188
38.6 37.6 39.2 37.8
<0.0001 <0.0001 <0.0001 <0.0001
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wise regression analysis using the CORR and REG procedures of SAS (SAS Institute, 1990). The correlation and regression coefficients were considered different from zero at P < 0.05. Residual standard deviation was used for measuring the goodness-of-fit of linear models. The smaller was RSD, the better fitting was the model. This was done by the procedure described by Kaps and Lamberson (2004).
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0.3724 0.5285 4,034 59 0.2504
4,026 77 3,701 57 0.7509
3,713 72 3,770 58 3,766 78
The observed ME values were determined with the in vivo method; the predicted ME values were calculated according to the neutral detergent fiber and gross energy contents of corn.
1
3,886 71
3,877 56
5 −32 19 11 33 17 3,789 3,843 3,922 3,860 3,919 3,931 3,794 3,811 3,941 3,871 3,952 3,948 −19 −61 5 14 20 −8 3,941 4,000 4,081 4,016 4,076 4,089 3,922 3,939 4,086 4,030 4,096 4,081 9 −30 22 14 35 21 3,612 3,667 3,746 3,684 3,743 3,755 3,621 3,637 3,768 3,698 3,778 3,776 −16 −58 9 18 22 −3 3,662 3,679 3,827 3,771 3,835 3,823
1 2 3 4 5 6 Statistics Mean SD P-value
3,678 3,738 3,817 3,752 3,812 3,825
Predicted Predicted Predicted Observed Test corn
Difference
Observed Predicted
Difference
Observed
TME, kcal/kg AMEn, kcal/kg AME, kcal/kg
NDF, CP, and ether extract contents (average of r values were 0.71, 0.72, 0.67, 0.62, and 0.69, respectively). However, the ME content was negatively correlated with GE content in our study, which did not agree with the results of Lessire et al. (2003). In those chemical compositions that were significantly correlated with the ME of calibration samples, any 2 of NDF, CF and ADF had highly significant correlations between themselves (r ≥ 0.95). Correlation between the ME and NDF contents was greatest among those 3 fiber contents, which indicated that the effect of CF and ADF contents on the ME content of calibration samples could be explained by NDF content. Therefore, the present results indicated that the ME content of corn calibration samples in adult ducks might be largely dependent on NDF and GE content. Similar observations have also been reported on the ME content of corn in cockerels (Lessire et al., 2003). Using correlations and stepwise regression analysis, the equations to predict ME content of corn in ducks were established according to the significant linear relationship between ME, NDF, and GE content (Table 5). The equations based on NDF and GE contents for predicting AME, AMEn, TME, and TMEn contents had high accuracy with R2 > 0.90 and RSD <40 kcal/kg, which indicated that only less than 10% of the observed variation in the ME content of corn calibration samples resulted from factors other than NDF and GE content. This suggested that the accuracy of the prediction model for ME was close to that observed in vivo during a classic metabolizable energy experiment, which was in accordance with the previous results of Lessire et al. (2003) and Dale (1994), who used cockerels as the test animal. To test the suitability of these models (Table 5) to predict the ME content of a normal corn sample, the ME content of 6 samples of corn was measured by both the in vivo method and prediction models. Our results showed that the chemical compositions (Table 3) and ME content determined by using the in vivo method of 6 samples were all in the range of the 30 corn calibration samples (Table 2). The maximum absolute difference between ME determined by the in vivo method and the prediction model was 61 kcal/kg (Table 6). The fluctuation of ME measurements among 4 replications with the in vivo method in one sample of corn ranged from 31 to 178 kcal/kg. This suggested that the accuracy of prediction models for ME was close to that obtained in vivo. Therefore, the prediction models established from 30 corn calibration samples as described in this article can be used to predict the ME content of corn for ducks with acceptable accuracy.
ACKNOWLEDGMENTS We gratefully acknowledge the financial support of Basic Science Research Program (ywf-td-4), State Science and Technique Support Project (2006BAD12B01–1), and the State Commonwealth Research Project (2005DIB4J033) in China. We also wish to thank W. Huang, L. Zhao, Y. W. Dong, Q. J. Wang, X. H. Jiang, M. Zhao,
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Table 6. Comparison of ME contents in corn determined by using the in vivo method and prediction model,1 Experiment 2
Difference
Observed
TMEn, kcal/kg
Difference
RESEARCH NOTE
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and R. P. Wang (Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing) for their help in force feeding and excreta samples collection.
REFERENCES
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Adeola, O., D. Ragland, and D. King. 1997. Feeding and excreta collection techniques in metabolizable energy assays for ducks. Poult. Sci. 76:728–732. Association of Official Analytical Chemists (AOAC). 1990. Official Methods of Analysis. Association of Official Analytical Chemists, Washington, DC. Carre´, B. 1990. Predicting the dietary energy value of poultry feeds. Pages 283–300 in Feedstuff Evaluation. J. Wiseman, and D. J. A. Cole, ed. Butterworths, London, UK. Chinese Feed Database (CFD). 2005. Tables of Feed Composition and Nutritive Values for Poultry in China. The Center of Chinese Feed Database Information, Beijing. http://www. Chinafeeddata.org.cn Accessed May 2007. (in Chinese) Connor, J. K., A. R. Neill, and K. M. Barram. 1976. The metabolizable energy content for the chicken of maize and sorghum grain hybrids grown at several geographical regions. Aust. J. Exp. Agric. Anim. Husb. 16:699–703. Dale, N. 1994. Relationship between bushel weight, metabolizable energy, and protein content of corn from an adverse growing season. J. Appl. Poult. Res. 3:83–86. Dale, N., and D. Jackson. 1994. True metabolizable energy of corn fractions. J. Appl. Poult. Res. 3:179–183. Fan, H. P. 2003. Comparative study of the digestion of feed nutrients between cockerel and drake. MS thesis. Chinese Academy of Agricultural Sciences, Beijing. Francesch, M., K. Bernard, and J. M. McNab. 2002. Comparison of two direct bioassays using 3-week-old broilers to measure
the metabolisable energy of diets containing cereals high in fibre: Differences between true and apparent metabolisable energy values. Br. Poult. Sci. 43:580–587. Kaps, M., and W. Lamberson. 2004. Biostatistics for Animal Science. CABI Publishing, Cambridge, UK. King, D., D. Raglang, and O. Adeola. 1997. Apparent and true metabolizable energy values of feedstuffs for ducks. Poult. Sci. 76:1418–1423. Leeson, S., J. D. Summers, and T. R. Daynard. 1977. The effect of kernel maturity at harvest as measured by moisture content, on the metabolizable energy value of corn. Poult. Sci. 56:154–156. Leeson, S., A. Yersin, and L. Volker. 1993. Nutritive values of 1992 corn crop. J. Appl. Poult. Res. 2:208–213. Lessire, M., J. M. Hallouis, B. Barrier-Guillot, M. Champion, and N. Femenias. 2003. Prediction of the metabolisable energy value of maize in adult cockerel. Br. Poult. Sci. 44:813–814. Mollah, Y., and E. F. Annison. 1981. Prediction of metabolisable energy of wheat, maize and sorghum in poultry diets from chemical compositon. Proc. Nutr. Soc. Aust. 6:137. National Research Council. 1994. Nutrient Requirements of Poultry. 9th rev. ed. Natl. Acad. Press, Washington, DC. SAS Institute. 1990. SAS/STAT User’s Guide: Statistics. SAS Institute Inc., Cary, NC. Sibbald, I. R. 1976. A bioassay for true metabolizable energy in feedingstuffs. Poult. Sci. 55:303–308. Sibbald, I. R. 1982. Measurement of bioavailable energy in poultry feeding stuffs: A review. Can. J. Anim. Sci. 62:983–1048. Van Soest, P. J. 1963. Use of detergents in the analysis of fibrous feeds. II. A rapid method for the determination of fiber and lignin. J. Am. Off. Anal. Chem. 46:829–835. Van Soest, P. J., J. B. Robertson, and B. A. Lewis. 1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74:3583–3597.