Tolleson D R, Schafer D W
The University of Arizona, Agricultural Experiment Station, V Bar V Ranch, Rimrock, 86335.
J Anim Sci. 2014 Jan;92(1):349-58. doi: 10.2527/jas.2013-6631. Epub 2013 Dec 4.
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.
监测草原奶牛的营养状况具有挑战性。粪便的近红外光谱（NIRS）已被用于预测牛的日粮质量。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，就可以监测营养状况和动物性能。在一项研究中，使用了在同一牛群中放牧的约120头赫里福德牛和90头CGC杂交牛（50%红安格斯牛、25%塔兰泰拉牛和25%夏洛来牛），以确定在亚利桑那州中部商业规模的牧场条件下，粪便NIRS和NutbalPro预测体况评分（1=瘦，9=胖）的能力。牛群每隔10至20天在31000公顷的放牧区轮换一次。在每个放牧期的中点左右，大约每月在牧场采集牛的体况评分和粪便样本（约500克），这些粪便样本由5至10头牛的粪便混合而成。样本被冷冻，随后通过NIRS分析以预测日粮粗蛋白（CP）和可消化有机物（DOM）。除了粪便NIRS预测的日粮质量外，还将动物品种类型、繁殖状态和环境条件输入到每次粪便采样和体况评分日期的软件中。进行了三种不同的评估。首先，将粪便NIRS和NutbalPro得出的体况评分从每次采样向前推算，就好像这是“一次性”测量一样。其次，体况评分来自给定时期内两个采样日期之间预测体重变化的平均值。第三，调整模型输入以更好地反映当地动物和条件。粪便NIRS预测的日粮质量从冬季最低的约5%粗蛋白和57%可消化有机物到夏季最高的约11%粗蛋白和60%可消化有机物不等。日粮质量与观察到的季节变化和降水事件相关。在评估1中，不同品种类型之间观察到的和预测的体况评分差异不显著（P>0.1），但这些值在赫里福德牛中为0.1至1.1个体况评分单位，在CGC杂交牛中为0.0至0.9个单位。在评估2中，不同品种类型之间观察到的和预测的体况评分差异不显著（P>0.1），但这些值在赫里福德牛中为0.00至0.46个单位，在CGC杂交牛中为0.00至0.67个单位。在评估3中，观察到的和预测的体况评分之间的差异范围为0.04至0.28。预测的和观察到的体况评分之间的最大差异发生在日粮质量最低的时期。在本研究条件下，体况能够被准确预测，足以用于监测草原肉牛的营养状况。
