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首页> 外文期刊>Journal of Animal Science >Application of fecal near-infrared spectroscopy and nutritional balance software to monitor diet quality and body condition in beef cows grazing Arizona rangeland.
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Application of fecal near-infrared spectroscopy and nutritional balance software to monitor diet quality and body condition in beef cows grazing Arizona rangeland.

机译:粪便近红外光谱和营养平衡软件在监控亚利桑那牧场的肉牛的饮食质量和身体状况中的应用。

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摘要

Monitoring the nutritional status of range cows is difficult. Near-infrared spectroscopy (NIRS) of feces has been used to predict diet quality in cattle. When fecal NIRS is coupled with decision support software such as the Nutritional Balance Analyzer (NUTBAL PRO), nutritional status and animal performance can be monitored. Approximately 120 Hereford and 90 CGC composite (50% Red Angus, 25% Tarentaise, and 25% Charolais) cows grazing in a single herd were used in a study to determine the ability of fecal NIRS and NutbalPro to project BCS (1=thin and 9=fat) under commercial scale rangeland conditions in central Arizona. Cattle were rotated across the 31,000 ha allotment at 10 to 20 d intervals. Cattle BCS and fecal samples (approximately 500 g) composited from 5 to 10 cows were collected in the pasture approximately monthly at the midpoint of each grazing period. Samples were frozen and later analyzed by NIRS for prediction of diet crude protein (CP) and digestible organic matter (DOM). Along with fecal NIRS predicted diet quality, animal breed type, reproductive status, and environmental conditions were input to the software for each fecal sampling and BCS date. Three different evaluations were performed. First, fecal NIRS and NutbalPro derived BCS was projected forward from each sampling as if it were a "one-time only" measurement. Second, BCS was derived from the average predicted weight change between 2 sampling dates for a given period. Third, inputs to the model were adjusted to better represent local animals and conditions. Fecal NIRS predicted diet quality varied from a minimum of approximately 5% CP and 57% DOM in winter to a maximum of approximately 11% CP and 60% DOM in summer. Diet quality correlated with observed seasonal changes and precipitation events. In evaluation 1, differences in observed versus projected BCS were not different (P>0.1) between breed types but these values ranged from 0.1 to 1.1 BCS in Herefords and 0.0 to 0.9 in CGC. In evaluation 2, differences in observed versus projected BCS were not different (P>0.1) between breed types but these values ranged from 0.00 to 0.46 in Hereford and 0.00 to 0.67 in CGC. In evaluation 3, the range of differences between observed and projected BCS was 0.04 to 0.28. The greatest difference in projected versus observed BCS occurred during periods of lowest diet quality. Body condition was predicted accurately enough to be useful in monitoring the nutrition of range beef cows under the conditions of this study.
机译:监测奶牛的营养状况非常困难。粪便的近红外光谱(NIRS)已用于预测牛的饮食质量。当粪便NIRS与诸如营养平衡分析仪(NUTBAL PRO)之类的决策支持软件结合使用时,可以监控营养状况和动物生长状况。在一项研究中,使用大约120头赫里福德和90头CGC复合材料(50%的红安格斯,25%的Tarentaise和25%的夏洛来牛)奶牛来确定粪便NIRS和NutbalPro投射BCS的能力(1 =瘦和9 =脂肪)在亚利桑那州中部的商业规模牧场条件下。每隔10到20天将牛转入31,000公顷的配给区。在每个放牧期的中点,大约每月在牧场上收集5至10头牛的牛BCS和粪便样本(约500克)。将样品冷冻并随后通过NIRS分析,以预测饮食中的粗蛋白(CP)和可消化的有机物(DOM)。与粪便NIRS一起,将每个粪便采样和BCS日期的预测饮食质量,动物品种类型,繁殖状况和环境条件输入到软件中。进行了三种不同的评估。首先,将粪便NIRS和NutbalPro衍生的BCS从每个采样中向前投影,就好像它是“一次性”测量一样。其次,BCS是根据给定时间段内两个采样日期之间的平均预测重量变化得出的。第三,对模型的输入进行了调整,以更好地代表当地的动物和条件。粪便NIRS预测的饮食质量从冬季的最低约5%CP和DOM的57%到夏季的最高约11%CP和DOM的60%不等。饮食质量与观察到的季节性变化和降水事件相关。在评估1中,不同品种之间观察到的BCS与预计的BCS的差异没有差异(P> 0.1),但在赫里福德,这些值的范围从0.1到1.1 BCS,在CGC的范围是0.0到0.9。在评估2中,不同品种之间观察到的BCS与预计的BCS的差异没有差异(P> 0.1),但在赫里福德,这些值的范围为0.00至0.46,在CGC中,这些值的范围为0.00至0.67。在评估3中,观察到的BCS与预计的BCS之间的差异范围为0.04至0.28。预计的BCS与观察到的BCS的最大差异发生在最低饮食质量时期。在本研究条件下,对身体状况的预测足够准确,可用于监测范围肉牛的营养。

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