Prediction equations of the metabolizable energy in corn developed by chemical composition and enzymatic hydrolysate gross energy for roosters
ABSTRACT: Two experiments were conducted to establish the prediction equations for AME and TME of corn based on chemical composition and enzymatic hydrolysate gross energy (EHGE) in roosters. In experiment 1, eighty 32-wk-old Hy-line Brown roosters with an average body weight of 2.55 ± 0.21 kg were...
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| Format: | Article |
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Elsevier
2024-02-01
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| Series: | Poultry Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S003257912300768X |
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| author | Kai Li Guosong Bai Chunran Teng Zhengqun Liu Lei Liu Honglin Yan Jianchuan Zhou Ruqing Zhong Liang Chen Hongfu Zhang |
| author_facet | Kai Li Guosong Bai Chunran Teng Zhengqun Liu Lei Liu Honglin Yan Jianchuan Zhou Ruqing Zhong Liang Chen Hongfu Zhang |
| author_sort | Kai Li |
| collection | DOAJ |
| description | ABSTRACT: Two experiments were conducted to establish the prediction equations for AME and TME of corn based on chemical composition and enzymatic hydrolysate gross energy (EHGE) in roosters. In experiment 1, eighty 32-wk-old Hy-line Brown roosters with an average body weight of 2.55 ± 0.21 kg were randomly assigned to 10 diet treatments in a completely randomized design to determine AME and TME by the force-feeding method. Each treatment had 8 replicates with 1 bird per replicate. The 10 test diets used in the experiment were formulated with corn (including 96.10%) as the sole source of energy. In experiment 2, the EHGE of 14 corn samples was measured by the computer-controlled simulated digestion system (CCSDS) with 5 replicates of each sample. The average AME and TME values of corn were 14.58 and 16.46 MJ/kg DM, respectively. The EHGE of 14 corn samples ranged from 14.66 to 15.89 (the mean was 15.24) MJ/kg DM. The best-fit equations for corn based on chemical composition were AME (MJ/kg DM) = 14.5504 + 0.1166 × ether extract (EE) + 0.5058 × Ash − 0.0957 × neutral detergent fiber (NDF) (R2 = 0.8194, residual standard deviation (RSD) = 0.0860, P < 0.01) and TME (MJ/kg DM) = 16.0625 + 0.1314 × EE + 0.4725 × Ash − 0.0872 × NDF (R2 = 0.7867, RSD = 0.0860, P < 0.01). The best-fit equations for corn based on EHGE were AME (MJ/kg DM) = 7.8883 + 0.4568 × EHGE (R2 = 0.8587, RSD = 0.0693, P < 0.01) and TME (MJ/kg DM) = 10.0099 + 0.4228 × EHGE (R2 = 0.8720, RSD = 0.0608, P < 0.01). The differences between determined and predicted values from equations established based on EHGE were lower than those observed from chemical composition equations. These results indicated that EHGE measured with CCSDS could predict the AME and TME of corn for roosters with high accuracy. |
| first_indexed | 2024-03-08T03:36:45Z |
| format | Article |
| id | doaj.art-f96c18a5d97b474aac67b4a676dedcfb |
| institution | Directory Open Access Journal |
| issn | 0032-5791 |
| language | English |
| last_indexed | 2024-03-08T03:36:45Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
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| series | Poultry Science |
| spelling | doaj.art-f96c18a5d97b474aac67b4a676dedcfb2024-02-10T04:43:13ZengElsevierPoultry Science0032-57912024-02-011032103249Prediction equations of the metabolizable energy in corn developed by chemical composition and enzymatic hydrolysate gross energy for roostersKai Li0Guosong Bai1Chunran Teng2Zhengqun Liu3Lei Liu4Honglin Yan5Jianchuan Zhou6Ruqing Zhong7Liang Chen8Hongfu Zhang9State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaInstitute of Animal Science and Veterinary, Tianjin Academy of Agricultural Sciences, Tianjin 300381, ChinaState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaSchool of Life Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, ChinaSichuan Tie Qi Li Shi Food Co. Ltd., Mianyang 621010, ChinaState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; Corresponding author:State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, ChinaABSTRACT: Two experiments were conducted to establish the prediction equations for AME and TME of corn based on chemical composition and enzymatic hydrolysate gross energy (EHGE) in roosters. In experiment 1, eighty 32-wk-old Hy-line Brown roosters with an average body weight of 2.55 ± 0.21 kg were randomly assigned to 10 diet treatments in a completely randomized design to determine AME and TME by the force-feeding method. Each treatment had 8 replicates with 1 bird per replicate. The 10 test diets used in the experiment were formulated with corn (including 96.10%) as the sole source of energy. In experiment 2, the EHGE of 14 corn samples was measured by the computer-controlled simulated digestion system (CCSDS) with 5 replicates of each sample. The average AME and TME values of corn were 14.58 and 16.46 MJ/kg DM, respectively. The EHGE of 14 corn samples ranged from 14.66 to 15.89 (the mean was 15.24) MJ/kg DM. The best-fit equations for corn based on chemical composition were AME (MJ/kg DM) = 14.5504 + 0.1166 × ether extract (EE) + 0.5058 × Ash − 0.0957 × neutral detergent fiber (NDF) (R2 = 0.8194, residual standard deviation (RSD) = 0.0860, P < 0.01) and TME (MJ/kg DM) = 16.0625 + 0.1314 × EE + 0.4725 × Ash − 0.0872 × NDF (R2 = 0.7867, RSD = 0.0860, P < 0.01). The best-fit equations for corn based on EHGE were AME (MJ/kg DM) = 7.8883 + 0.4568 × EHGE (R2 = 0.8587, RSD = 0.0693, P < 0.01) and TME (MJ/kg DM) = 10.0099 + 0.4228 × EHGE (R2 = 0.8720, RSD = 0.0608, P < 0.01). The differences between determined and predicted values from equations established based on EHGE were lower than those observed from chemical composition equations. These results indicated that EHGE measured with CCSDS could predict the AME and TME of corn for roosters with high accuracy.http://www.sciencedirect.com/science/article/pii/S003257912300768Xmetabolizable energyenzymatic hydrolysate gross energyprediction equationcornrooster |
| spellingShingle | Kai Li Guosong Bai Chunran Teng Zhengqun Liu Lei Liu Honglin Yan Jianchuan Zhou Ruqing Zhong Liang Chen Hongfu Zhang Prediction equations of the metabolizable energy in corn developed by chemical composition and enzymatic hydrolysate gross energy for roosters Poultry Science metabolizable energy enzymatic hydrolysate gross energy prediction equation corn rooster |
| title | Prediction equations of the metabolizable energy in corn developed by chemical composition and enzymatic hydrolysate gross energy for roosters |
| title_full | Prediction equations of the metabolizable energy in corn developed by chemical composition and enzymatic hydrolysate gross energy for roosters |
| title_fullStr | Prediction equations of the metabolizable energy in corn developed by chemical composition and enzymatic hydrolysate gross energy for roosters |
| title_full_unstemmed | Prediction equations of the metabolizable energy in corn developed by chemical composition and enzymatic hydrolysate gross energy for roosters |
| title_short | Prediction equations of the metabolizable energy in corn developed by chemical composition and enzymatic hydrolysate gross energy for roosters |
| title_sort | prediction equations of the metabolizable energy in corn developed by chemical composition and enzymatic hydrolysate gross energy for roosters |
| topic | metabolizable energy enzymatic hydrolysate gross energy prediction equation corn rooster |
| url | http://www.sciencedirect.com/science/article/pii/S003257912300768X |
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