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Shinde, S. D.
- Application of Path Analysis for Contribution of Reproductive Traits in Lactation Milk Yield of Half Breed Cattle
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Authors
V. A. Jadhav
1,
S. D. Shinde
2
Affiliations
1 Department of Computer Science and Statistics, N.E.S. Science College, Nanded (M.S.), IN
2 Department of Statistics, N.E.S. Science College, Nanded (M.S.), IN
1 Department of Computer Science and Statistics, N.E.S. Science College, Nanded (M.S.), IN
2 Department of Statistics, N.E.S. Science College, Nanded (M.S.), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 1 (2017), Pagination: 15-20Abstract
The use of the path co-efficient analysis methodology in half breed cattle revealed that, the reproductive trait- lactation length (LL) had appreciable effect in total lactation milk yield (LMY). Correlation of LL with total LMY was highly significant ranges from 0.267 to 0.522. Direct effect of LL and indirect effects of SP and CI through LL were positive and higher in all lactations. The direct per cent contribution of LL in LMYwas 13.49 to 29.25 per cent. However, SP and CI showed negligible direct contribution in LMY upto 3rd lactation and for 4th to 6th lactation ranged from 4.887 to 14.276 and 5.189 to 22.903 in LMY.AFC showed significant and negative correlation with LMY. Its direct effect also negative and indirect effects through other traits were negligible in LMY.That indicates early age of first calving increases the LMY in different lactations. Therefore, it is suggested that more emphasis may be given on these traits (AFC, SP, CL and LL) for future breeding programme.Keywords
Half Breed Cattle, Direct and Indirect Path Co-Efficients, Correlation Co-Efficient, Reproductive Traits, LMY.
References
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- Prediction of SAR for Groundwater along the Kham River in Aurangabad District, Maharashtra using ANN
Abstract Views :144 |
PDF Views:0
Authors
Affiliations
1 Govt. College of Engineering, Aurangabad, Maharashtra, IN
1 Govt. College of Engineering, Aurangabad, Maharashtra, IN
Source
International Journal of Engineering Research, Vol 5, No 2 (2016), Pagination: 149-152Abstract
Monitoring groundwater quality is important as the aquifers are vulnerable to contamination due to point sources and non-point sources. This study presents an artificial neural network model for predicting Sodium Adsorption Ratio (SAR) values for well samples. The data for 3 years from 27 wells to the left bank and 27 wells to the right bank of the Kham River in Aurangabad district, India is used for this investigation. A Correlation analysis is performed to select the input parameters that show a strong relation between Sodium and SAR (0.8412). Electrical Conductivity, Sodium, Magnesium and Calcium are used as input parameters in the prediction model. The Levenberg-Marquardt algorithm is selected as the best out of the 12 Back-propagation algorithms and optimal neuron number is determined as 10 for the model. The model tracked the experimental data closely giving a correlation coefficient of R=0.9380.The results obtained from the model shows that Artificial Neural Network could be used as an applied tool for the prediction of irrigation water parameters, especially, SAR.Keywords
Artificial Neural Network (ANN), Correlation, MAE, RMSE, Sodium Adsorption Ratio (SAR).Full Text