Use of animal performance and gas flux for estimating dry matter intake in growing steers.

IF 1.8 Q3 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Translational Animal Science Pub Date : 2025-08-20 eCollection Date: 2025-01-01 DOI:10.1093/tas/txaf111
Juan de J Vargas, Maya Swenson, Macarena Gomez-Salmoral, Liza Garcia, Eduardo M Paula, Leo G Sitorski, Leticia M Campos, Pedro H V Carvalho, K R Stackhouse-Lawson, Nicolas DiLorenzo, Sara E Place
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引用次数: 0

Abstract

Dry matter intake (DMI) estimation in ruminants is important for providing a balanced diet, increasing animal performance, and reducing nutrient excretion. Gas flux (CO 2 and CH 4 production) is related to DMI; however, there is limited information regarding the use of gas flux production when estimating DMI in growing steers. This study aimed to 1) determine the relationship of animal growth performance and gas flux variables with DMI of growing steers fed a backgrounding diet, and 2) evaluate the DMI accuracy of eight equations to predict DMI from growing steers fed a forage-based diet. The relationship between DMI, animal growth performance, and gas flux variables was evaluated in 130 backgrounding steers, and two equations were generated to predict DMI. Then, six retrieved equations from the literature and the two new equations were used to determine the prediction accuracy using an independent dataset. Models were compared based on the mean square prediction error (MSPE), the decomposition of the root MSPE (RMSPE), and the concordance correlation coefficient (CCC). In backgrounding steers, DMI had a positive and significant relationship (P < 0.01) with shrunk body weight (SBW), average daily gain, and CO2 and CH4 production. The production of CO2 and CH4 independently explained 48.1% and 40.9% of the observed DMI in growing steers, respectively. One equation retrieved from the literature had an excellent agreement with the observed DMI, with a CCC value of 0.93 and an RMSPE of 0.19 kg/d, representing 2.5% of the average DMI. That equation used SBW and dietary energy concentration. The use of CO2 production had adequate agreement with the observed DMI, with a CCC value of 0.73 and an RMSPE of 0.45 kg/d, representing 6% of the average DMI. Other equations had null to moderate agreement with the observed DMI, with CCC values ranging from 0.00 to 0.47 and an RMSPE from 0.51 to 4.40 kg/d. In conclusion, there is a positive relationship between DMI, animal growth performance, and gas flux in growing steers fed a backgrounding diet. In addition, CO2 production has the potential to be used to predict DMI in growing steers fed a forage-based diet. Future research is required to evaluate the relationship between CO2 production and DMI, especially under grazing conditions.

Abstract Image

Abstract Image

用动物生产性能和气体通量估算生长阉牛干物质采食量。
反刍动物干物质采食量(DMI)的估算对于提供均衡的日粮、提高动物生产性能和减少营养物质排泄具有重要意义。气体通量(CO 2和ch4产量)与DMI有关;然而,在估计生长中的舵机的DMI时,关于使用气体通量生产的信息有限。本研究旨在1)确定饲喂背景饲粮的生长肉牛生长性能和气体通量变量与DMI的关系,2)评估8个预测生长肉牛饲喂草料型饲粮DMI的方程的DMI准确性。在130头背景转向牛中评估了DMI、动物生长性能和气体通量变量之间的关系,并生成了两个方程来预测DMI。然后,使用从文献中检索到的6个方程和两个新方程来确定独立数据集的预测精度。基于均方预测误差(MSPE)、根均方预测误差分解(RMSPE)和一致性相关系数(CCC)对模型进行比较。背景肉牛DMI与缩重、平均日增重、CO2和CH4产量呈极显著正相关(p0.01)。CO2和CH4的产生分别解释了生长过程中观察到的DMI的48.1%和40.9%。从文献中检索到的一个方程与观测到的DMI非常吻合,CCC值为0.93,RMSPE为0.19 kg/d,代表平均DMI的2.5%。该方程使用了体重和膳食能量浓度。CO2产量的使用与观测到的DMI具有足够的一致性,CCC值为0.73,RMSPE为0.45 kg/d,占平均DMI的6%。其他方程与观测到的DMI一致,CCC值为0.00至0.47,RMSPE为0.51至4.40 kg/d。由此可见,饲喂背景饲粮的生长肉牛DMI与动物生长性能和气体通量呈正相关。此外,二氧化碳的产生有可能被用来预测饲喂以饲料为基础的饲料的生长公牛的DMI。未来的研究需要评估二氧化碳产量与DMI之间的关系,特别是在放牧条件下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Translational Animal Science
Translational Animal Science Veterinary-Veterinary (all)
CiteScore
2.80
自引率
15.40%
发文量
149
审稿时长
8 weeks
期刊介绍: Translational Animal Science (TAS) is the first open access-open review animal science journal, encompassing a broad scope of research topics in animal science. TAS focuses on translating basic science to innovation, and validation of these innovations by various segments of the allied animal industry. Readers of TAS will typically represent education, industry, and government, including research, teaching, administration, extension, management, quality assurance, product development, and technical services. Those interested in TAS typically include animal breeders, economists, embryologists, engineers, food scientists, geneticists, microbiologists, nutritionists, veterinarians, physiologists, processors, public health professionals, and others with an interest in animal production and applied aspects of animal sciences.
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