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Model test on the relationship crude fiber consumption to the production
of milk fat on dairy cattle in Kudus Regency
To cite this article: Rudy Hartanto et al 2019 IOP Conf. Ser.: Earth Environ. Sci. 247 012033
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1st International Conference of Animal Science and Technology (ICAST) 2018
IOP Conf. Series: Earth and Environmental Science 247 (2019) 012033
IOP Publishing
doi:10.1088/1755-1315/247/1/012033
1
Model test on the relationship crude fiber consumption to the
production of milk fat on dairy cattle in Kudus Regency
Rudy Hartanto, Andhika Ragil Saputra, Eko Pangestu, Suranto Moch Sayuthi
Faculty of Animal and Agricultural Sciences, Diponegoro University, Semarang
50275, Indonesia
E-mail: rudyharta@gmail.com
Abstract. This research was completed at PT. Moeria, Kudus Regency, Central Java Province,
Indonesia using 35 samples (Holstein Friesian cattle, lactation period II-III and lactation month
5-9). The observati onal method was used in this study. Data obtained were analyzed using
linear and quadratic regressions, to predict the production and quality of milk fat from crude
fiber (CF) consumption that describe the diet. Analysis of variance was used to test the
significance of model, then used root mean square prediction error (RMSPE), residual variance
(RV) and coefficient of determination (R
2
) to developed equations as an indicator of the
goodness of model fit. The result showed that no relationship between milk fat concentration
(%) with CF consumption as predictor. There were significant relationship on between milk
production (kg); milk fat production (kg) with CF consumption as predictor, on linear and
quadratic model. In addition, a quadratic change in milk production (kg) (P = 0.050) and milk
fat production (kg) (P = 0.044) were observed with changed CF consumption; whereas the
quadratic equation has smal ler RV, smaller RMSPE and bigger R
2
than linear equation. It was
concluded that the model fit for this research was quadratic equati on.
1. Introduction
Kudus Regency is one of the big regency in Central Java Province, Indonesia. Production of milk in
Central Java Province is around 98,860 ton/year or 11.67% of milk production in Indonesia, the third
position after West Java Province and East Java Province [1]. The same with other place, milk
production and milk quality including milk fat production was affected by the quality of feed given.
Feed nutrient especially crude fiber has effect on production and quality of milk [2, 3]. In rumen,
crude fiber as a part of carbohydrate will be degradation by rumen microbes to volatile fatty acids
(acetate, propionate and butyrate) that is substrate for milk biosynthesis. Acetate is use for milk fat
biosynthesis especiall y short chai n fatt y acids that around 50% of milk fat [4, 5, 6]. Forage is
important for dairy cattle. Crude fiber from high quality grass have good digestibility, then feed with
more crude fiber content will producing more proportion of acetate and butyrate. The grass intake will
lead to produce more acetic acid and make the value of Acetate/Propionate ratio higher [7, 8, 9]. The
ration with the low fiber will decrease fat milk. The increasing in milk production was in line with
decreasing of milk fat concentration [10]. It is reasonable to suspect relationships between the crude
fiber consumption with the production and quality of milk fat. Therefore this research will be
conducted to find the model fit for those relationships.
1st International Conference of Animal Science and Technology (ICAST) 2018
IOP Conf. Series: Earth and Environmental Science 247 (2019) 012033
IOP Publishing
doi:10.1088/1755-1315/247/1/012033
2
2. Materials and methods
2.1. Database
This research was completed at PT Moeria, Kudus Regency, Central Java Province, Indonesia using
35 samples (Holstein Friesian cattle, lactation period II-III and lactation month 5-9). The observational
method was used in this study. The data were collected for 7 days for each cattle, including the amount
of feeding, feed consumption, milk production. Feed samples were analyzed proximate to determine
the content of feed ingredients especially crude fiber to calculate crude fiber consumption [11]. On the
7th day, 200 ml of milk samples from each cattle wer e obtained for the analysis of density and milk fat
concentration using Lactoscan. The production of milk fat was calculated from (concentration of milk
fat) x (milk production) [12].
2.2. Statistical analyses
Data were analyzed with regression analysis including linear and quadratic models using SPSS
software. T he crude fiber consumption was independent variable (X); milk production and milk fat
were dependent variables (Y). Analysis of variance was used to test the significance of model, then
used root mean square prediction error (RMSPE), residual variance (RV) and coefficient of
determination (R2) to developed equations as an indicator of the goodness of model fit [3]. RMSPE
calculated as:
= (
−
)
/
Where Oi is the observed value, and Pi is the predicted value. Square root of the MSPE (RMSPE)
is an estimate of the overall prediction error predicti on, then expressed as a proportion of the observed
mean [13].
3. Results
The equations of linear and quadratic models are shown in table 1. P-value showed that the significant
relationship (P d 0.05) were found on between milk productions (kg); milk fat production (kg) with
CF consumption (kg) as pr edictor, both on linear and quadratic model. In addition, the relationship
between CF consumption and milk fat concentration (%) was not significant, both on linear and
quadratic (P t 0.298). Changed CF consumption (kg) was affected (P d0.05) in a quadratic increase on
milk production (kg) and in a quadratic increase on milk fat production (kg).
4. Discussion
In this research, a quadratic model was the best model for prediction milk production and milk protei n
content by CF consumption, because has bigger R2, smaller RV and RMSPE than linear equation [3,
13], shown in table 1. In quadratic model for milk production, the turning point of the curve was on X
= 2.38 kg and Y = 6.68 kg. It’s mean that after CF consumption more than 2.38 kg will have positive
effect on milk production. The feed consumption including CF consumption has positive effect on
milk production, and the nutrient requirement of dairy cattle increases with milk production [6, 14].
Production and composition of milk varies with nutrients intake that is influenced by blood flow and
utilization of nutrients by mammary gland [15].The ration with produce more high in
acetate/propionate ratio will produce more high in milk production [12].
The CF consumption have not significant relationship with milk fat concentration, but significant
with milk fat production on linier and quadratic. In this quadratic model, the turning point of the curve
was on X = -1.05 kg and Y = -0.212 kg. It’s mean that if dairy cattle cons ume CF will have positive
effect on milk fat production. This happens because acetate and butyrate from degradation CF in
rumen by microbes is precursor for milk fat biosynthesis [4, 6].The milk fat production and milk fatty
1st International Conference of Animal Science and Technology (ICAST) 2018
IOP Conf. Series: Earth and Environmental Science 247 (2019) 012033
IOP Publishing
doi:10.1088/1755-1315/247/1/012033
3
acid profile responds rapidly and is very sensitive to changes in diet [14].The increasing fiber
digestibility is in line with increasing of acetate/propionate ratio or acetate production [16].The ration
with produce more high in acetate/propionate ratio will produce more high in production and quality
of milk fat [12].So that’s mean CF from high quality forage is very important in dairy cattle diet.
5. Conclusions
There are relationships between milk production and milk fat production with crude fiber consumption
as predictor. The model fit was quadratic equation because has bigger R2, smaller RV and RMSPE
than linear equation.
References
[1] Pusat Data dan Sistem Informasi Pertanian 2016 Outlook Milk (Susu) (Jakarta: Indonesian
Ministry of Agriculture)
[2] Bath D L, Dickinson F N, Tucker H A and Appleman R D 1985 Dairy Cattle : Principles,
Practices, Problem, Profit s 3th Ed (Philadelphia: Lea Febiger)
[3] Hartanto R, Jantra MAC, Santosa SAB and Purnomoadi A 2018 IOP Conf. Ser.: Earth Environ.
Sci.102:012053
[4] Larson and Smith 1978 Lactation (New York: Academic Press)
[5] McDonald P, Edwards R A, Greenhalg J F D and Morgan C A 2011 Animal Nutrition7th Ed
(London: Prentice Hall Inc.)
[6] Schmidt GH, van Vleck LD and Hutjuens MF 1988 Principles of Dairy Sciences 2nd Ed (New
Jersey: Prentice Hall Inc.)
[7] Moran and Chamberlain 2017 Increasing Domestic Milk Production in Developing Countries
(Clayton South: CSIRO Publishing)
[8] Suharlina, Astuti DA, Nahrowi, Jayanegara A and Abdullah L 2016 J. Indonesian Trop. Anim.
Agric.41(4):196-203
[9] Umar M, Arifin M and Purnomoadi A 2011 J. Indonesian Trop. Anim. Agric.36(3):213-218
[10] Widyobroto BP, Rochijan, Ismaya, Adiarto and Suranindyah Y Y 2016 J. Indonesian Trop.
Anim. Agric.41(2):83-90
[11] AOAC 2006 Methods of Analyses16th Ed (Rockville: Publ, AOAC)
[12] Poorkasegaran S and Yansari AT 2014 J. Anim. Sci. Biotechnol. 5:6.
[13] Ellis J L, et al.2009 J. Anim. Sci.87:1334-1345
[14] Schwendel BH, Wester TJ, Morel PCH, Tavendale MH et al. 2016 J. Dairy Sci.98:721–746
[15] Kume S and Tanabe S 1993 J. Dairy Sci.76:1654-1660.
[16] Weimer PJ, Stevenson DM, Mertens DR and Hall MB 2011 Anim. Feed Sci. Technol. 169:68–
78
1st International Conference of Animal Science and Technology (ICAST) 2018
IOP Conf. Series: Earth and Environmental Science 247 (2019) 012033
IOP Publishing
doi:10.1088/1755-1315/247/1/012033
4
Table 1. Summary and evaluation of linear and quadratic equations developed on relationship between CF consumption with milk production,
milk fat concentration and milk fat production
Variable Equation P-Value R R
2
RV RMSPE
(%)
Milk Production (Kg) (Y) and CF
Consumption (X)
- Linear
- Quadratic
Milk Fat Concentration (%) (Y) and
CF Consumption (X)
- Linear
- Quadratic
Milk Fat Production (Kg) (Y) and CF
Consumption (X)
- Linear
-
Quadratic
Y = 8.224 X – 16.404
Y = -22.126 X +4.643 X
2
+ 33.040
Y = – 0.616 X + 6.719
Y = 15.570 X – 2.476 X
2
– 19.650
Y = 0.318 X – 0.562
Y = 0.078 X + 0.037 X
2
– 0.171
0.014
0.050
0.298
0.399
0.012
0.044
0.411
0.414
0.181
0.236
0.420
0.421
0.169
0.171
0.033
0.056
0.177
0.178
11.389
11.358
0.382
0.373
0.016
0.016
31.454
31.410
12.971
12.816
25.903
25.903
* CF = Crude Fiber, R = Coefficient of Correlation, R
2
= Coefficient of Determination, RV = Residual Variance, RMSPE = Root Mean Square
Prediction Error
