日粮净能预测育肥猪生长性能模型的建立和验证

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2015. J. Anim.Sci. 93(6):2826-2839
日粮净能预测育肥猪生长性能模型的建立和验证
S. Nitikanchana, S. S. Dritz, M. D.Tokach, J. M. DeRouchey, R. D. Goodband和B. J. White
本论文收集41个试验报告（共285个能量水平处理），通过荟萃分析（Meta分析）建立运用日粮净能预测生长性能的模型。日粮能量和营养水平由NRC原料数据库计算得到。本论文研究了生长性能和影响因子（如净能、体重、粗蛋白、SID赖氨酸、粗纤维、中性洗涤纤维NDF、酸性洗涤纤维ADF、脂肪、灰分）的相关关系（线性相关和二次线性相关关系），以及影响因子之间的互作关系，并且以AIC（Akaike information criteria）为准则，确定合适的预测模型。最初的预测模型中包括了净能和粗蛋白的互作或净能和SID赖氨酸的互作。但是当SID赖氨酸低于需要量的试验数据移除后，这些互作对生长性能的影响就不再显著。在含净能和体重的模型中增加日粮脂肪这一影响因素后可以显著改善模型G:F（增重耗料比）的预测效果，表明日粮净能可能低估了脂肪对G:F的影响。Meta分析表明，只有在日粮中其它营养素（如赖氨酸）都能满足动物需要后，日粮净能才能准确预测不同日粮原料和环境条件下猪只的日增重。统计结果表明随着日粮净能和体重的增加，日增重随之提高，但是在体重高于87kg时日增重随之降低。G:F值随日粮净能和脂肪含量的提高而提高，但是随体重的增加而降低。本论文还通过2个试验、5个日粮处理、543头育肥猪评估模型预测的生长性能与实际生长性能的差异。5个日粮处理包括3个不同水平的净能（通过在玉米-豆粕型基础日粮中添加次粉、大豆皮、含油8-9%的DDGS和精炼动物油，调节净能水平）。具体处理信息如下：1）30%DDGS、20%次粉、4-5%大豆皮（低能处理组）；2）20%次粉、4-5%大豆皮（低能处理组二）；3）玉米豆粕型日粮（对照组，中等能量水平）；4）处理二日粮添加3.7%精炼动物油使其净能水平与处理三相同；5）玉米豆粕型日粮添加3.7%精炼动物油（高能组）。试验结果（日增重和G:F）与模型预测结果十分接近，但是纤维含量最高的低能量组（30%DDGS日粮）偏差较大，模型预测的日增重和G:F高估了3%和6%。因此，本模型可以准确预测不同净能水平下生长育肥猪的生长速度和饲料效率，在超高纤维低能量日粮的极端情况下效果相对不理想。
译者注：
荟萃分析为一种对不同研究结果进行收集、合并及统计分析的方法。荟萃分析的主要目的是将以往的研究结果更为客观的综合反映出来。研究者并不进行原始的研究，而是将研究已获得的结果进行综合分析。
AIC信息准则是衡量统计模型拟合优良性的一种标准，为日本统计学家赤池弘次创立和发展的，可以权衡所估计模型的复杂度和此模型拟合数据的优良性。
Regression analysis to predict growth performance from dietary net energy in growing-finishing pigs
S. Nitikanchana, S. S. Dritz, M. D.Tokach, J. M. DeRouchey, R. D. Goodband and B. J. White
   Data from 41 trials with multiple energy levels (285 observations) were used in a meta-analysis to predict growth performance based on dietary NE concentration. Nutrient and energy concentrationsin all diets were estimated using the NRC ingredient library. Predictor variables examined for best fit models using Akaike information criteria included linear and quadratic terms of NE, BW, CP, standardized ileal digestible (SID) Lys, crude fiber, NDF, ADF, fat, ash, and their interactions.The initial best fit models included interactions between NE and CP or SID Lys.After removal of the observations that fed SID Lys below the suggested requirement, these terms were no longer significant. Including dietary fat in the model with NE and BW significantly improved the G:F prediction model, indicating that NE may underestimate the influence of fat on G:F. The meta-analysis indicated that, as long as diets are adequate for other nutrients(i.e., Lys), dietary NE is adequate to predict changes in ADG across different dietary ingredients and conditions. The analysis indicates that ADG increases with increasing dietary NE and BW but decreases when BW is above 87 kg. The G:F ratio improves with increasing dietary NE and fat but decreases with increasing BW. The regression equations were then evaluated by comparing the actual and predicted performance of 543 finishing pigs in 2 trials fed 5 dietary treatments, included 3 different levels of NE by adding wheat middlings,soybean hulls, dried distillers grains with solubles (DDGS; 8 to 9% oil), or choice white grease (CWG) to a corn-soybean meal-based diet. Diets were 1) 30%DDGS, 20% wheat middlings, and 4 to 5% soybean hulls (low energy);2) 20% wheat middlings and 4 to 5% soybean hulls (low energy); 3) acorn-soybean meal diet (medium energy); 4) diet 2 supplemented with 3.7% CWG to equalize the NE level to diet 3 (medium energy); and 5) a corn-soybean meal diet with 3.7% CWG (high energy). Only small differences were observed between predicted and observed values of ADG and G:F except for the low-energy diet containing the greatest fiber content (30% DDGS diet), where ADG and G:F were overpredicted by 3 to 6%. Therefore, the prediction equations provided a good estimation of the growth rate and feed efficiency of growing-finishing pigs fed different levels of dietary NE except for the pigs fed the low-energy diet containing the greatest fiber content.