Fecal amylase-treated neutral detergent fiber and ash contents as independent variables can predict metabolizable energy and coefficient of energy digestibility of diets for growing pigs without dietary information

IF 2.5 2区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Jeonghyeon Son , Jongkeon Kim , Hyunwoong Jo , Beob Gyun Kim
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引用次数: 0

Abstract

The objectives of the present study were to develop prediction equations for digestible energy (DE), metabolizable energy (ME), and coefficient of energy digestibility (ED) of swine diets using fecal nutrients as independent variables without dietary information and to validate the prediction equations. A total of 262 fecal samples obtained from growing pigs fed 27 diets in 4 experiments were used to develop prediction equations for DE, ME, and ED. These equations were validated using 95 fecal samples obtained from growing pigs fed 12 diets in an additional experiment. The diets were based mainly on corn, soybean meal, and other plant-originated feed ingredients. In the digestibility experiments, feces and urine were quantitatively collected using the marker-to-marker procedure. Diet and fecal samples were analyzed for dry matter (DM), crude protein, ash, and amylase-treated neutral detergent fiber (aNDF). The DE of the 27 diets ranged from 12,164 to 18,301 kJ/kg DM and ME of 24 diets ranged from 11,603 to 16,657 kJ/kg DM. Coefficients of ED for the 27 diets ranged from 0.667 to 0.919. Fecal ash was positively correlated with DE (r = 0.81; P < 0.001), ME (r = 0.66; P < 0.001), and coefficient of ED (r = 0.67; P < 0.001) whereas fecal aNDF was negatively correlated with DE (r = –0.66; P < 0.001), ME (r = –0.74; P < 0.001), and coefficient of ED (r = –0.67; P < 0.001). The prediction equations developed using fecal nutrients were: DE (kJ/kg DM) = 15,077 + (13.72 × ash) – (4.65 × aNDF) (R2 = 0.71; P < 0.001); ME (kJ/kg DM) = 17,067 + (6.54 × ash) – (7.56 × aNDF) (R2 = 0.63; P < 0.001); and coefficient of ED = 0.881 + (0.00037 × ash) – (0.00028 × aNDF) (R2 = 0.57; P < 0.001) where nutrients are expressed as g/kg DM. Based on the regression analysis for validation of the equations, the prediction equations for DE and coefficient of ED using only fecal aNDF did not have mean bias or linear bias. In conclusion, DE values, ME values, and coefficient of ED for swine diets are correlated with fecal ash and aNDF concentrations and the energy values can be predicted using fecal ash and aNDF concentrations without dietary information.

粪便淀粉酶处理的中性洗涤纤维和灰分含量作为自变量可以预测无饲粮信息条件下生长猪日粮代谢能和能量消化系数
本研究的目的是在没有饮食信息的情况下,利用粪便营养素作为自变量,建立猪日粮可消化能(DE)、代谢能(ME)和能量消化率(ED)的预测方程,并验证预测方程。在4个实验中,从喂食27个日粮的生长猪中总共获得262个粪便样本,用于建立DE、ME和ED的预测方程。在另一个实验中使用从喂食12个日粮生长猪中获得的95个粪便样本验证了这些方程。日粮主要以玉米、豆粕和其他植物源性饲料成分为基础。在消化率实验中,使用标记对标记程序定量收集粪便和尿液。分析饮食和粪便样品的干物质(DM)、粗蛋白、灰分和淀粉酶处理的中性洗涤剂纤维(aNDF)。27日粮的DE为12164~18301kJ/kg DM,24日粮的ME为11603~16657kJ/kg DM。27日粮ED系数为0.667~0.919。粪便灰分与DE(r=0.81;P<;0.001)、ME(r=0.66;P>;0.001)和ED系数(r=0.62;P&;0.001)呈正相关,而粪便aNDF与DE(r=-0.66;P&<;0.001),利用粪便养分建立的预测方程为:DE(kJ/kg DM)=15077+(13.72×ash)-(4.65×aNDF)(R2=0.71;P<;0.001);ME(kJ/kg DM)=17067+(6.54×灰分)-(7.56×aNDF)(R2=0.63;P<;0.001);ED系数=0.881+(0.00037×ash)-(0.00028×aNDF)(R2=0.57;P<;0.001),其中营养素以g/kg DM表示。基于对方程验证的回归分析,仅使用粪便aNDF的DE和ED系数的预测方程不存在平均偏差或线性偏差。总之,猪日粮的DE值、ME值和ED系数与粪灰和aNDF浓度相关,并且可以在没有日粮信息的情况下使用粪灰和aNDF浓度来预测能量值。
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来源期刊
Animal Feed Science and Technology
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.
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