热带条件下NDFap和iNDF的预测模式

IF 2.5 2区农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE

Animal Feed Science and Technology Pub Date : 2025-02-17 DOI:10.1016/j.anifeedsci.2025.116271

Sebastião de Campos Valadares Filho, Antonio de Sousa Brito Neto, Felipe Henrique de Moura, Douglas Teixeira Saraiva, Nathália Veloso Trópia, Samira Silveira Moreira, Dhones Rodrigues de Andrade, Pauliane Pucetti

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引用次数: 0

摘要

我们的目的是建立和评估中性洗涤纤维经灰分和蛋白质残留校正（NDFap）和不可消化中性洗涤纤维（iNDF）的估算方程。所有饲料数据均来自巴西牛饲料组成表（CQBAL 4.0）中的信息。此外，本研究还使用了来自14项研究的瘤胃食糜、胃肠食糜和粪便的信息，包括本方法研究的化学特征。构建了8个数据子集来预测：粗饲料、精料、瘤胃消化物、胃肠消化物和粪便的NDFap；粗饲料、精料和粪便的不饱和脂肪酸。在每个数据子集中，80% %的信息用于模型开发，20% %用于模型评估。为了预测nfap，假设nfap与NDF含量之间存在线性关系，并拟合简单的线性回归方程。对潜在预测变量进行Pearson相关分析和逐步选择。利用模型评价系统软件对方程进行评价。亚组数据表明，粗饲料（237.9 ~ 777 g/kg）、精料（27.2 ~ 600 g/kg）、瘤胃食糜（341.7 ~ 673.9 g/kg）、胃肠食糜（19.1 ~ 662.6 g/kg）和粪便（243.1 ~ 721.8 g/kg）的NDFap含量存在较大差异；粗料（80 ~ 394.7 g/kg）、精料（2.4 ~ 370 g/kg）和粪便（72.7 ~ 624.6 g/kg）中iNDF含量差异显著。粗饲料、精料、瘤胃食糜、胃肠食糜和粪便的NDFap预测值与实测值呈线性关系，其截距≤0 (P ≥ 0.19)，斜率≤1 (P ≥ 0.13)，R2和CCC值均较高。所生成的粗饲料indef预测方程均适用，在观测值与预测值关系图中截距为零和/或斜率为1时P ≥ 0.08。对于浓缩物和粪便的iNDF，只有幂函数方程具有较好的预测效果（截距和斜率P ≥ 0.21）。本研究开发的模型将为估计饲料的能量值提供支持，特别是在直接评估有限的情况下。

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Models for predicting NDFap and iNDF in tropical conditions

Our objective was to develop and evaluate equations for estimating neutral detergent fiber corrected for ash and protein residue (NDF_ap) and indigestible neutral detergent fiber (_iNDF). All feeds data were obtained from information cataloged in the Brazilian Tables of Feed Composition for Cattle (CQBAL 4.0). In addition, information from ruminal digesta, omasal digesta and feces derived from 14 studies was used, which included the chemical characteristics studied in this approach. Eight subsets of data were constructed to predict: NDF_ap of roughages, concentrates, ruminal digesta, omasal digesta, and feces; _iNDF of roughages, concentrates and feces. Within each data subset, 80 % of the information was used for model development and 20 % for model evaluation. To predict the NDF_ap the existence of a linear relationship between the NDF_ap and NDF contents was assumed, and simple linear regression equations were fitted. A Pearson correlation analysis and Stepwise selection process was performed between _iNDF and the potential predictor variables. The evaluation of the equations was carried out using the Model Evaluation System Software. The subset data represented a wide variation in NDF_ap content of roughage (237.9–777 g/kg), concentrates (27.2–600 g/kg), rumen digesta (341.7–673.9 g/kg), omasal digesta (19.1–662.6 g/kg) and feces (243.1–721.8 g/kg); and in the _iNDF content of roughage (80–394.7 g/kg), concentrates (2.4–370 g/kg) and feces (72.7–624.6 g/kg). The linear relationships between the observed and predicted values of NDF_ap for roughages, concentrates, ruminal digesta, omasal digesta and feces presented an intercept not different from zero (P ≥ 0.19) and a slope not different from one (P ≥ 0.13), and high values of R² and CCC. All equations generated to predict roughage _iNDF were suitable, presenting P ≥ 0.08 for intercept equal to zero and/or slope equal to one in the plot of the relationship between observed and predicted values. For _iNDF of concentrates and feces, the power function was the only equation that showed good predictive performance (P ≥ 0.21 for intercept and slope). The models developed in this study will provide support in estimating the energy value of feeds, especially in conditions where direct assessments are limited.

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来源期刊

Animal Feed Science and Technology 农林科学-奶制品与动物科学

CiteScore

6.00

自引率

6.20%

发文量

266

审稿时长

3 months

期刊介绍： Animal Feed Science and Technology is a unique journal publishing scientific papers of international interest focusing on animal feeds and their feeding. Papers describing research on feed for ruminants and non-ruminants, including poultry, horses, companion animals and aquatic animals, are welcome. The journal covers the following areas: Nutritive value of feeds (e.g., assessment, improvement) Methods of conserving and processing feeds that affect their nutritional value Agronomic and climatic factors influencing the nutritive value of feeds Utilization of feeds and the improvement of such Metabolic, production, reproduction and health responses, as well as potential environmental impacts, of diet inputs and feed technologies (e.g., feeds, feed additives, feed components, mycotoxins) Mathematical models relating directly to animal-feed interactions Analytical and experimental methods for feed evaluation Environmental impacts of feed technologies in animal production.