PMID- 33171026
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20211015
IS  - 2765-0189 (Print)
IS  - 2765-0235 (Electronic)
IS  - 2765-0189 (Linking)
VI  - 34
IP  - 10
DP  - 2021 Oct
TI  - Evaluation of the equation for predicting dry matter intake of lactating dairy 
      cows in the Korean feeding standards for dairy cattle.
PG  - 1623-1631
LID - 10.5713/ajas.20.0684 [doi]
AB  - OBJECTIVE: This study aimed to validate and evaluate the dry matter (DM) intake 
      prediction model of the Korean feeding standards for dairy cattle (KFSD). 
      METHODS: The KFSD DM intake (DMI) model was developed using a database containing 
      the data from the Journal of Dairy Science from 2006 to 2011 (1,065 observations 
      287 studies). The development (458 observations from 103 studies) and evaluation 
      databases (168 observations from 74 studies) were constructed from the database. 
      The body weight (kg; BW), metabolic BW (BW0.75, MBW), 4% fat-corrected milk 
      (FCM), forage as a percentage of dietary DM, and the dietary content of nutrients 
      (% DM) were chosen as possible explanatory variables. A random coefficient model 
      with the study as a random variable and a linear model without the random effect 
      was used to select model variables and estimate parameters, respectively, during 
      the model development. The best-fit equation was compared to published equations, 
      and sensitivity analysis of the prediction equation was conducted. The KFSD model 
      was also evaluated using in vivo feeding trial data. RESULTS: The KFSD DMI 
      equation is 4.103 (+/-2.994)+0.112 (+/-0.022)xMBW+0.284 (+/-0.020) xFCM-0.119 
      (+/-0.028)xneutral detergent fiber (NDF), explaining 47% of the variation in the 
      evaluation dataset with no mean nor slope bias (p>0.05). The root mean square 
      prediction error was 2.70 kg/d, best among the tested equations. The sensitivity 
      analysis showed that the model is the most sensitive to FCM, followed by MBW and 
      NDF. With the in vivo data, the KFSD equation showed slightly higher precision 
      (R2 = 0.39) than the NRC equation (R2 = 0.37), with a mean bias of 1.19 kg and no 
      slope bias (p>0.05). CONCLUSION: The KFSD DMI model is suitable for predicting 
      the DMI of lactating dairy cows in practical situations in Korea.
FAU - Lee, Mingyung
AU  - Lee M
AD  - Division of Animal and Dairy Sciences, Chungnam National University, Daejeon 
      34134, Korea.
FAU - Lee, Junsung
AU  - Lee J
AD  - Division of Animal and Dairy Sciences, Chungnam National University, Daejeon 
      34134, Korea.
FAU - Jeon, Seoyoung
AU  - Jeon S
AD  - Division of Animal and Dairy Sciences, Chungnam National University, Daejeon 
      34134, Korea.
FAU - Park, Seong-Min
AU  - Park SM
AD  - National Institute of Animal Science, Rural Development Administration, Cheonan 
      31000, Korea.
FAU - Ki, Kwang-Seok
AU  - Ki KS
AD  - National Institute of Animal Science, Rural Development Administration, Cheonan 
      31000, Korea.
FAU - Seo, Seongwon
AU  - Seo S
AD  - Division of Animal and Dairy Sciences, Chungnam National University, Daejeon 
      34134, Korea.
LA  - eng
GR  - PJ0150482021/Rural Development Administration/
PT  - Journal Article
DEP - 20201109
PL  - Korea (South)
TA  - Anim Biosci
JT  - Animal bioscience
JID - 101774366
PMC - PMC8495351
OTO - NOTNLM
OT  - Dry Matter Intake
OT  - Lactating Dairy Cow
OT  - Meta-analysis
OT  - Modeling
COIS- CONFLICT OF INTEREST We certify that there is no conflict of interest with any 
      financial organization regarding the material discussed in the manuscript.
EDAT- 2020/11/11 06:00
MHDA- 2020/11/11 06:01
PMCR- 2021/10/01
CRDT- 2020/11/10 18:20
PHST- 2020/09/28 00:00 [received]
PHST- 2020/11/09 00:00 [accepted]
PHST- 2020/11/11 06:00 [pubmed]
PHST- 2020/11/11 06:01 [medline]
PHST- 2020/11/10 18:20 [entrez]
PHST- 2021/10/01 00:00 [pmc-release]
AID - ajas.20.0684 [pii]
AID - ajas-20-0684 [pii]
AID - 10.5713/ajas.20.0684 [doi]
PST - ppublish
SO  - Anim Biosci. 2021 Oct;34(10):1623-1631. doi: 10.5713/ajas.20.0684. Epub 2020 Nov 
      9.