Linear modeling analysis using for determining the factors affecting 305-day milk yield

Animal Science and Technology and Inspection of Animal Products • Arq. Bras. Med. Vet. Zootec. 73 (04) • Jul-Aug 2021 • https://doi.org/10.1590/1678-4162-12346 copy

Linear modeling analysis using for determining the factors affecting 305-day milk yield

[Análise de modelagem linear usado para determinar os fatores que afetam o rendimento leiteiro de 305 dias]

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The purpose of this study was to model the factors affecting the 305-day milk yield of dairy cows by using Automatic Linear Modeling Technique (ALM). The data set of this study consisted of eight different cow breeds grown in eight province of Turkey. Results of ALM showed that the accuracy of the model was 64.2 % means that 64.2% of the variation in the 305-day milk yield could be explained by the constructed model. Created model was consisted of four factors namely the Breed, Lactation Length, Parity, and Province. Therefore, those selected factors were more efficient than the others in predicting the 305-day milk yield

Keywords:
305-day milk yield; automatic linear modeling; prediction; dairy cows

Authorship

S. Genç

Kirsehir Ahi Evran University ˗ Faculty of Agriculture ˗ Kirsehir, TurkeyKirsehir Ahi Evran UniversityTurkeyKirsehir, TurkeyKirsehir Ahi Evran University ˗ Faculty of Agriculture ˗ Kirsehir, Turkey

http://orcid.org/0000-0002-1512-9072

M. Mendeş

Canakkale Onsekiz Mart University ˗ Faculty of Agriculture ˗ Canakkale, TurkeyCanakkale Onsekiz Mart UniversityTurkeyCanakkale, TurkeyCanakkale Onsekiz Mart University ˗ Faculty of Agriculture ˗ Canakkale, Turkey

http://orcid.org/0000-0003-0109-9914

E-mail: serdargenc1983@gmail.com

SCIMAGO INSTITUTIONS RANKINGS

Figures

Figures (4)

Thumbnail

Figure 1
Accuracy Level of the Model. Importance levels of the predictors have been presented in Figure 2.

Thumbnail

Figure 2
Importance Levels of the Variables or Predictors. After the most important factors or variables are determined (Figure 2), the Automatic Linear Modeling is re-run using just importance variables namely Breed, Lactation Length, Parity, and Province. The final results of Automatic Linear Modeling are presented in Figure 3 and 4.

Thumbnail

Figure 3
Diagram for showing statistical significance of variables or factors. This diagram is corresponding to the ANOVA table after multiple linear regression analysis. The predictors are ordered from top to bottom in order of importance levels, and the thickness of each line shows the statistical significance (P-values) of the relevant effect. As it is seen from the diagram in Figure 3, the Breed is the most important predictor or the factor affect the 305-day milk yield estimates, and it is followed by lactation length, parity, and province based on importance levels of them. The effect of all four predictors were statistically significant (P=0.000).

Thumbnail

Figure 4
Diagram for showing the effect of significant factors. Especially in recent years, regression-based methods which can present the results graphically have become very popular (Berry et al., 2007BERRY, D.P.; BUCKLEY, F.; DILLON, P. Body condition score and live-weight effects on milk production in Irish Holstein-Friesian dairy cows. Animal, v.1, p.1351-1359, 2007.; Mendes and Akkartal, 2009MENDES, M.; AKKARTAL, E. Regression tree analysis for predicting slaughter weight in broilers. Ital. J. Anim. Sci., v.8, p.615-624, 2009.). Although the Automatic Linear Modeling is not commonly used regression-based method, we think that this method will become increasingly use for investigating relations between a dependent and many independent variables and determining the factors affecting interested variable. As a result of the ALM, the four most important factors were determined as breed, lactation length, parity, and province. Therefore, these factors should be included to the model in predicting the 305-day milk yield. With this model, 64.2% of the variation in the 305-day milk yield could be explained (Mendes and Akkartal, 2009; Topal et al., 2010TOPAL, M.; AKSAKAL, V.; BAYRAM, B. et al. An analysis of the factors affecting birth weight and actual milk yield in swedish red cattle using regression tree analysis. J. Anim. Plant Sci., v.20, p.63-69, 2010.).

Figure 1 Accuracy Level of the Model. Importance levels of the predictors have been presented in Figure 2.

Figure 2 Importance Levels of the Variables or Predictors. After the most important factors or variables are determined (Figure 2), the Automatic Linear Modeling is re-run using just importance variables namely Breed, Lactation Length, Parity, and Province. The final results of Automatic Linear Modeling are presented in Figure 3 and 4.

Figure 3 Diagram for showing statistical significance of variables or factors. This diagram is corresponding to the ANOVA table after multiple linear regression analysis. The predictors are ordered from top to bottom in order of importance levels, and the thickness of each line shows the statistical significance (P-values) of the relevant effect. As it is seen from the diagram in Figure 3, the Breed is the most important predictor or the factor affect the 305-day milk yield estimates, and it is followed by lactation length, parity, and province based on importance levels of them. The effect of all four predictors were statistically significant (P=0.000).

Figure 4 Diagram for showing the effect of significant factors. Especially in recent years, regression-based methods which can present the results graphically have become very popular (**Berry et al., 2007BERRY, D.P.; BUCKLEY, F.; DILLON, P. Body condition score and live-weight effects on milk production in Irish Holstein-Friesian dairy cows. Animal, v.1, p.1351-1359, 2007.; Mendes and Akkartal, 2009MENDES, M.; AKKARTAL, E. Regression tree analysis for predicting slaughter weight in broilers. Ital. J. Anim. Sci., v.8, p.615-624, 2009.). Although the Automatic Linear Modeling is not commonly used regression-based method, we think that this method will become increasingly use for investigating relations between a dependent and many independent variables and determining the factors affecting interested variable. As a result of the ALM, the four most important factors were determined as breed, lactation length, parity, and province. Therefore, these factors should be included to the model in predicting the 305-day milk yield. With this model, 64.2% of the variation in the 305-day milk yield could be explained (Mendes and Akkartal, 2009; Topal et al., 2010**TOPAL, M.; AKSAKAL, V.; BAYRAM, B. et al. An analysis of the factors affecting birth weight and actual milk yield in swedish red cattle using regression tree analysis. J. Anim. Plant Sci., v.20, p.63-69, 2010.).

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Linear modeling analysis using for determining the factors affecting 305-day milk yield

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[1] E-mail: serdargenc1983@gmail.com