TMR Sampling: Valuable Exercise or a Random Number Generator?

被引:0
|
作者
Weiss, Bill [1 ]
Zhang, Peihua [1 ]
Goeser, John [2 ,3 ]
St-Pierre, Normand [1 ]
机构
[1] Ohio State Univ, Dept Anim Sci, Columbus, OH 43210 USA
[2] Rock River Lab Inc, Watertown, WI USA
[3] Univ Wisconsin, Dept Dairy Sci, Madison, WI 53706 USA
来源
TRI-STATE DAIRY NUTRITION CONFERENCE, 2016 | 2016年
关键词
DAIRY; DIETS;
D O I
暂无
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
Sampling and analyzing the total mixed ration (TMR) has several potential uses. It can be used to identify nutritional deficiencies or surpluses in the diet that was actually fed to the cows. It can be used to estimate manure excretion of nutrients via mass balance calculations. The consistency of ration delivery can be evaluated by sampling the TMR, and it can be used to determine whether the ration that is delivered to the cows is the same as the diet that was formulated. However, for any of these uses to be valid, the TMR sample must accurately reflect the diet that was actually fed. Previously, we found that sampling variation was substantial for TMR samples. This was investigated further by sampling three different TMR (one had silages and concentrate; one had silages, concentrate and hay, and one had silages, hay, whole cottonseed, and concentrate) using two different sampling protocols. One protocol was simple and consisted of taking several handfuls of TMR across the feed bunk. The other protocol consisted of putting trays in the feedbunk prior to feed delivery and then removing the trays filled with TMR, mixing, and sampling from the trays. Sampling protocol had very little effect on sampling variation or on the accuracy of the sample. Samples of TMR did not accurately estimate the true mineral concentrations (sodium, phosphorus, and copper) of the TMR. A single sample of TMR (using either protocol), however, generally gave an accurate estimate of the true concentration for dry matter (DM) and crude protein (CP) in the TMR. For neutral detergent fiber (NDF), a single sample had a high risk of being wrong (i.e., inaccurate), but taking duplicate samples and averaging the analytical results were generally accurate. TMR sampling can be accurate for macronutrients but care must be taken when sampling and often duplicate samples will be required.
引用
收藏
页码:137 / 147
页数:11
相关论文
共 50 条
  • [1] Sampling Based Random Number Generator for Stochastic Computing
    Karadeniz, M. Burak
    Altun, Mustafa
    2017 24TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS (ICECS), 2017, : 227 - 230
  • [2] An Unbiased Quantum Random Number Generator Based on Boson Sampling
    Shi, Jinjing
    Zhao, Tongge
    Wang, Yizhi
    Yu, Chunlin
    Lu, Yuhu
    Wu, Jiajie
    Shi, Ronghua
    Zhang, Shichao
    Peng, Shaoliang
    Wu, Junjie
    ADVANCED QUANTUM TECHNOLOGIES, 2024, 7 (01)
  • [3] Quantum Random Number Generator vs. Random Number Generator
    Mogos, Gabriela
    2016 INTERNATIONAL CONFERENCE ON COMMUNICATIONS (COMM 2016), 2016, : 423 - 426
  • [4] Fast compact true random number generator based on multiple sampling
    Choi, P.
    Lee, M. -K.
    Kim, D. K.
    ELECTRONICS LETTERS, 2017, 53 (13) : 841 - 842
  • [5] Random Number Generator Based on Metastabilities of Ring Oscillators and Irregular Sampling
    Kaysici, Halil Ibrahim
    Ergun, Salih
    2020 27TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS (ICECS), 2020,
  • [6] A Digital Random Number Generator Based on Chaotic Sampling of Regular Waveform
    Ozturk, Hikmet Seha
    Ergun, Salih
    2020 IEEE 63RD INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS (MWSCAS), 2020, : 178 - 181
  • [7] A Digital Random Number Generator Based on Irregular Sampling of Regular Waveform
    Acar, Burak
    Ergun, Salih
    2019 IEEE 10TH LATIN AMERICAN SYMPOSIUM ON CIRCUITS & SYSTEMS (LASCAS), 2019, : 221 - 224
  • [8] RANDOM NUMBER GENERATOR
    ISIDA, M
    IKEDA, H
    ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 1956, 8 (02) : 119 - 126
  • [9] Random Number Generator Based on Skew-tent Map and Chaotic Sampling
    Ergun, Salih
    Tanriseven, Sercan
    APCCAS 2020: PROCEEDINGS OF THE 2020 IEEE ASIA PACIFIC CONFERENCE ON CIRCUITS AND SYSTEMS (APCCAS 2020), 2020, : 224 - 227
  • [10] A Digital Random Number Generator Based on Regular Sampling of Double Scroll Chaos
    Karatas, Onur
    Ergun, Salih
    2021 IEEE ASIA PACIFIC CONFERENCE ON CIRCUITS AND SYSTEMS (APCCAS 2021) & 2021 IEEE CONFERENCE ON POSTGRADUATE RESEARCH IN MICROELECTRONICS AND ELECTRONICS (PRIMEASIA 2021), 2021, : 101 - 104