Use of test-day records to predict first lactation 305-day milk yield using artificial neural network in Kenyan Holstein–Friesian dairy cows

Njubi, D.; Wakhungu, JW; Badamana, M. S.

doi:10.1007/s11250-009-9468-7

Cited by 26 publications

(13 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The maximum RMSE value belonged to MLR. Njubi et al (2010) R -pearson correlation coefficient; R 2 -coefficient of determination; σ -standard deviation; δ -the average difference between observed yields (OY) and predicted yields (PY); ρ -ratio; RMSE -root mean square error; RoM -ratio of mean.…”

Section: Resultsmentioning

confidence: 99%

“…Hosseinia et al (2007) estimated second parity milk yield and fat percentage of dairy cows based on first parity information using the neural network system. Njubi et al (2010) applied ANNs to predict first lactation 305-d milk yield using test-day records in Kenyan Holstein Friesian dairy cows. These studies have shown that total lactation yield and short-term milk yield are positively correlated (Rayalu et al, 1984;Shrivastava et al, 1988;Brutta et al, 1989;Jain et al, 1991;Jadhav et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks

Görgülü¹

2012

SA J. An. Sci.

View full text Add to dashboard Cite

Artificial neural networks (ANNs) have been shown to be a powerful tool for system modelling in a wide range of applications. In this paper, we focus on the capability of ANNs to predict 305-d milk yield in early lactation of Brown Swiss cattle, based on a few test-day records, and some environmental factors such as age, number of lactation and season of calving. The ANNs that were developed were compared with multiple linear regressions (MLR). The various ANNs were modelled and the best performing number of hidden layers, neurons and training algorithms retained. The best ANN model had input, hidden and output layers of tansig transfer function. The layers had 4, 8, and 1 neurons, respectively. It was determined that the mean predicted values calculated by the ANNs were closer to the real mean values without showing any statistical difference. On the other hand, the predicted mean values calculated by MLR and the real mean values were significantly different from each other. The best prediction in ANN method was seen in 1st, 2nd, 3rd, and 4th test-day records when these were recorded to the system as X 1 -X 8 in the ANN system. In this study, the prediction of 305-d milk yield by ANN gave better results that those of MLR, suggesting that ANN can be used as an alternative prediction tool. ________________________________________________________________________________

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks

Görgülü¹

2012

SA J. An. Sci.

View full text Add to dashboard Cite

show abstract

“…Compared to regression analysis, ANNs do not require prior knowledge of the problem, and the adjustment and matching between the input and output variables are performed without any assumption, thus ANNs are more suitable tools for estimation, and are more accurate than multiple linear regression, especially when models are combined in a system (Njubi, Wakhungu, & Badamana, 2010).…”

Section: Introductionmentioning

confidence: 99%

Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed

Ghotbaldini

Mohammadabadi

Nezamabadi‐pour

et al. 2019

Acta Sci. Anim. Sci.

View full text Add to dashboard Cite

The present study aimed to apply artificial neural networks to predict the breeding values of body weight in 6-month age of Kermani sheep. For this purpose, records of 867 lambs including lamb sex, dam age, birth weight, weaning weight, age at 3-month (3 months old), age at 6-month (6 months old) and body weight at 3 months of age were used. Firstly, genetic parameters of the animals were estimated using ASReml software. The data was then pre-processed for using in MATLAB software. After initial experiments on the appropriate neural network architecture for body weight at 6-month age, two networks were examined. A feed-forward back propagation multilayer perceptron (MLP) algorithm was used and 70% of all data used as training data, 15% as testing data and 15% as validating data, to prevent over-fitting of the artificial neural network. Results showed that the both networks capable to predict breeding values for body weight at 6 month-age in Kermani sheep. It can be concluded that artificial neural network has a good ability to predict growth traits in Kermani sheep with an acceptable speed and accuracy. Therefore, this network, instead of commonly-used procedures can be used to estimate the breeding values for productive and reproductive traits in domestic animals.

show abstract

“…In developing countries, there is limited level of milk recording, and the use of test-day models would result in reduced cost of recording as we could have longer intervals between milk recording and less-frequent collection of milk samples. In this way, the amount of information that can accrue from incorporating the majority of smallholders who have small herd sizes would be large (Njubi et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Genetic parameters for first lactation test-day milk flow in Holstein cows

Laureano

Bignardi

Faro

et al. 2012

Animal

View full text Add to dashboard Cite

Genetic parameters for test-day milk flow (TDMF) of 2175 first lactations of Holstein cows were estimated using multiple-trait and repeatability models. The models included the direct additive genetic effect as a random effect and contemporary group (defined as the year and month of test) and age of cow at calving (linear and quadratic effect) as fixed effects. For the repeatability model, in addition to the effects cited, the permanent environmental effect of the animal was also included as a random effect. Variance components were estimated using the restricted maximum likelihood method in single-and multiple-trait and repeatability analyses. The heritability estimates for TDMF ranged from 0.23 (TDMF 6) to 0.32 (TDMF 2 and TDMF 4) in single-trait analysis and from 0.28 (TDMF 7 and TDMF 10) to 0.37 (TDMF 4) in multiple-trait analysis. In general, higher heritabilities were observed at the beginning of lactation until the fourth month. Heritability estimated with the repeatability model was 0.27 and the coefficient of repeatability for first lactation TDMF was 0.66. The genetic correlations were positive and ranged from 0.72 (TDMF 1 and 10) to 0.97 (TDMF 4 and 5). The results indicate that milk flow should respond satisfactorily to selection, promoting rapid genetic gains because the estimated heritabilities were moderate to high. Higher genetic gains might be obtained if selection was performed in the TDMF 4. Both the repeatability model and the multiple-trait model are adequate for the genetic evaluation of animals in terms of milk flow, but the latter provides more accurate estimates of breeding values.Keywords: functional trait, genetic correlation, heritability, milkability ImplicationsThe milk flow trait can be considered as functional, of great importance in dairy cattle, both related to udder health on the efficiency of labor. A increased milk flow is associated with a decrease in occupancy time of labor and thus a decrease in spending on it. In addition, a faster milking implies a reduction in electricity costs and maintenance of machines. Studies investigating milk flow are still limited and the best approach for analyzing this trait has not been well defined.

show abstract

Use of test-day records to predict first lactation 305-day milk yield using artificial neural network in Kenyan Holstein–Friesian dairy cows

Cited by 26 publications

References 12 publications

Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks

Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks

Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed

Genetic parameters for first lactation test-day milk flow in Holstein cows

Contact Info

Product

Resources

About