豬胴體背脂、腹脂、額骨脂肪碘值預測模型的建立

畜牧編輯

　　2015. J. Anim. Sci. 93:1666-1678

豬胴體背脂、腹脂、額骨脂肪碘值預測模型的建立　　

　　C.B. Paulk, J. R. Bergstrom, M. D. Tokach, S. S. Dritz, D. D. Burnett, E. W.Stephenson, M. A. Vaughn, J. M. DeRouchey, R. D. Goodband, J. L. Nelssen and J.M. Gonzalez

　　本研究收集已發表的資料來建立豬胴體背脂、腹脂和額骨脂肪碘值的預測模型，并通過動物試驗評估預測模型的有效性。最終的數據庫包括與背脂相關24篇、腹脂相關21篇、額骨脂肪相關29篇。試驗處理開始前的日糧為初始日糧（INT），試驗處理開始后的為處理日糧（FIN）。將預測模型的影響因子按類別分為五組：1）日糧脂肪酸組成，初始日糧和處理日糧的C16:1、C18:1、C18:2、C18:3、必需脂肪酸（EFA）、不飽和脂肪酸的含量、日糧碘值（IV）；2）飼喂初始日糧和處理日糧的天數；3）初始日糧和處理日糧的ME或NE；4）生長性能，初重、末重、ADG、ADFI、G:F；5）胴體指標：熱胴體重（HCW）和背脂厚。通過SAS的PROC MIXED程序建立回歸方程。根據貝葉斯準則評估方程的有效性。最佳的預測方程為：背脂IV= 84.83 + (6.87 × INT EFA) – (3.90 × FIN EFA) – (0.12 × INT天數) – (1.30 × FIN 天數) – (0.11 × INT EFA × FIN天數) + (0.048 × FIN EFA × INT天數) + (0.12 × FINEFA × FIN天數) – (0.0060 × FIN NE) + (0.0005 × FIN NE ×FIN天數) – (0.26 × 背脂厚)；背脂IV = 106.16 + (6.21 × INT EFA) – (1.50 × FIN天數) – (0.11 × INT EFA × FIN天數) – (0.012 × INTNE) + (0.00069 × INT NE × FIN天數) – (0.18 × HCW) – (0.25×背脂厚)；額骨脂肪IV = 85.50 + (1.08 ×INT EFA) + (0.87 × FIN EFA) – (0.014 × INT天數) – (0.050× FIN天數) + (0.038 × INT EFA × INT天數) + (0.054 × FIN EFA × FIN天數) – (0.0066 ×INT NE) + (0.071 × INT 體重) – (2.19 × 日均采食量) – (0.29 × 背膘厚). 動物試驗的處理包括一個玉米豆粕型（無油添加）對照組，和3（4%的牛油、4%豆油、2%牛油加2%豆油）×3（飼喂期0-42d、42-84d、0-84d）因子設計。在碘值低于65g/100g時，預測背脂、腹脂、額骨脂肪碘值較高。在實際碘值高于約74g/110g時，或混合油飼喂0-84d和42-84d時，低估了腹脂的碘值?？偟膩碚f，除了如上所提示的例外，建立的模型能運用日糧和動物生長性能等指標準確的預測胴體脂肪指標。

　　Equations generated to predict iodine value of pork carcass back, belly, and jowl fat

　　C. B. Paulk, J. R. Bergstrom, M. D. Tokach, S. S. Dritz, D. D. Burnett, E. W. Stephenson, M. A. Vaughn, J. M. DeRouchey, R. D. Goodband, J. L. Nelssen and J. M. Gonzalez

　　Data from existing literature were used to generate equations to predict finishing pig back, belly, and jowl fat iodine values (IV) and an experiment was conducted to evaluate these equations. The final database included 24, 21, and 29 papers for back, belly, and jowl fat IV, respectively. For experiments that changed dietary fatty acid composition, initial (INT) diets were defined as those fed before the change in diet composition and final (FIN) diets were those fed after. The predictor variables tested were divided into 5 groups: 1) diet fat composition (dietary percent C16:1, C18:1, C18:2, C18:3, EFA, unsaturated fatty acids, and IV product) for both INT and FIN diets, 2) day feeding the INT and FIN diets, 3) ME or NE of the INT and FIN diet, 4) live performance criteria (initial BW, final BW, ADG, ADFI, and G:F), and 5) carcass criteria (HCW and backfat thickness). The PROC MIXED procedure of SAS (SAS Inst., Inc., Cary, NC) was used to develop regression equations. Evaluation of models with significant terms was then conducted based on the Bayesian information criterion. The optimum equations to predict back, belly, and jowl fat IV were backfat IV = 84.83 + (6.87 × INT EFA) – (3.90 × FIN EFA) – (0.12 × INT days) – (1.30 × FIN days) – (0.11 × INT EFA × FIN days) + (0.048 × FIN EFA × INT days) + (0.12 × FIN EFA × FIN days) – (0.0060 × FIN NE) + (0.0005 × FIN NE × FIN days) – (0.26 × backfat depth); belly fat IV = 106.16 + (6.21 × INT EFA) – (1.50 × FIN days) – (0.11 × INT EFA × FIN days) – (0.012 × INT NE) + (0.00069 × INT NE × FIN days) – (0.18 × HCW) – (0.25 × backfat depth); and jowl fat IV = 85.50 + (1.08 × INT EFA) + (0.87 × FIN EFA) – (0.014 × INT days) – (0.050 × FIN days) + (0.038 × INT EFA × INT days) + (0.054 × FIN EFA × FIN days) – (0.0066 × INT NE) + (0.071 × INT BW) – (2.19 × ADFI) – (0.29 × backfat depth). Dietary treatments from the evaluation experiment consisted of a corn–soybean meal control diet with no added fat or a 3 × 3 factorial arrangement with main effects of fat source (4% tallow, 4% soybean oil, or a blend of 2% tallow and 2% soybean oil) and feeding duration (d 0 to 42, 42 to 84, or 0 to 84). The back, belly, and jowl fat IV equations tended to overestimate IV when observed IV were less than approximately 65 g/100 g and underestimate belly fat IV when actual IV are greater than approximately 74 g/100 g or when the fat blend was fed from d 0 to 84 or 42 to 84. Overall, with the exceptions noted, the regression equations were an accurate tool for predicting carcass fat quality based on dietary and pig performance factors。由企業提高的稿件