First lactation 305 days milk yield (FL305DMY) of progenies is an important criteria for selectio... more First lactation 305 days milk yield (FL305DMY) of progenies is an important criteria for selection of sires. In field progeny testing programme, prediction of FL305DMY based on test day records will reduce the cost of recording. The present study was aimed at evaluating the efficiency of different methods of prediction for FL305DMY based on monthly test day milk yield records in Karan Fries cattle and evaluation of sires based on the performance of its progenies. Methods of prediction were Wood’s incomplete gamma function and Artificial neural network (ANN). Efficiency of prediction was compared using error of prediction. Root mean square error was least for ANN method (5.72%). The attempt was made to evaluate Karan Fries sires based on actual and predicted FL305DMY so obtained utilising BLUP- AM method of sire evaluation. The breeding values of 48 Karan Fries sires having 3 or more daughters were estimated. Comparison of ranking of sires based on different methods of prediction w...
Various studies have proved that linear traits have strong relationship with milk productivity b... more Various studies have proved that linear traits have strong relationship with milk productivity but no such models are available for selection of animals based on linear traits. The present study conducted during 2020-22, is an attempt to develop an intelligent model using machine learning algorithms to predict peak milk yield based on its linear traits for selection of best dairy animals. A dataset on 14 linear traits of 259 buffalos across 5 lactations with peak milk yield was created and used for developing models. Data was collected from the buffalos having 8 to 26 kg peak milk yield maintained at Animal Farm Section, Central Institute for Research on Buffaloes, Hisar and also from private farms maintained by farmers. Predictive models were developed using various machine learning algorithms (artificial neural network, support vector regression and random forest) along with multi-linear regression executed on WEKA machine learning platform. Performance of these models was evalua...
Gainful livestock farming requires selective breeding of animals with certain heritable desirable... more Gainful livestock farming requires selective breeding of animals with certain heritable desirable traits which gives profitability in terms of farm produce. Modern dairy animals are selected for traits which directly or indirectly contribute to high milk production. The concept of "feed conversion efficiency" in terms of milk production is now vigorously taken up by researchers and farm managers for recognizing and breeding efficient milk-producing animals. The whole concept of economic farming thus requires identification of "elite" animals, meeting above criteria as base population for the farm enterprise. Farmers and animal traders have been selecting best animals based on certain physical characters, which were also accepted by the breeding scientists as phenotypes. Data mining allows uncovering of hidden patterns in the data for better understanding of data relationship for developing suitable models for further improvements. Along with artificial intelligen...
The present study was performed to observe the significant effect of individuality of dairy buffa... more The present study was performed to observe the significant effect of individuality of dairy buffaloes on the acoustic features of their vocal signals. The mean call duration, pitch, 1st formant, periodicity and degree of voice breaks of adult lactating Murrah buffaloes were observed to be 2.15±0.05 s, 143.48 ± 2.51 Hz, 900.11 ± 4.21 Hz, 95.31% (183.95 periodic pulses out of 192.99 pulses) and 20.78 ± 0.89 % respectively. Analysis of bioacoustics features extracted from 300 voice samples of 10 adult lactating Murrah buffaloes revealed that differences for amplitudes (minimum, maximum and mean), total energy, mean power, pitch (median, minimum, range and mean), intensities (mean, minimum and maximum), formants (F1, F2, F3, F4 and F5), bandwidths (B1, B3 and B4), number of pulse, number of period, mean period, unvoiced frames, degree of voice breaks, jitter, shimmer, mean noise to harmonic ratio (%) and mean harmonic to noise ratio (dB) were highly significant. Out of these only few ac...
Selection of elite sires is the base of animal breeding therefore; if the sire can be selected mu... more Selection of elite sires is the base of animal breeding therefore; if the sire can be selected much earlier on the basis of its progeny’s performance then the genetic response will be higher. In the present study, 305 days lactation milk yield (LMY) of Sahiwal cows is predicted using back propagation networks based on partial lactation records of 10 test-days milk yield to rank the sires at younger age to improve the quality of milking animals and reducing generation intervals. Neural networks were trained using four back propagation algorithms viz. Bayesian regularization (BR), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM) and BFGS QuasiNewton (BFG). The data set (587 records) was divided in to two training and test data sets at 66.67%-33.33% and 75%25% respectively. Results showed that the prediction accuracy of the models increased with the addition of test-days milk yield as input variable. The neural network models were able to predict LMY with more than 80% accurac...
In the present study, first lactation 305-day lactation milk yield (FL305DMY) was predicted by ar... more In the present study, first lactation 305-day lactation milk yield (FL305DMY) was predicted by artificial neural network (ANN) using monthly test day milk yields records of 588 Sahiwal cows. A total of five monthly test day milk yields (2, 3, 5, 7, 8 monthly test day record) were used in neural networks to train data using Bayesian regularization (BR) algorithm. Results showed that the accuracy of prediction of all the models increased with the addition of test day milk yields as input variables. The best neural network model was able to predict FL305DMY with 93.18% accuracy. Further, comparison was made between multiple linear regression (MLR) and ANN for accuracy of prediction and there was no significant different found between ANN and MLR for prediction of FL305DMY in Sahiwal cows.
2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 2015
The present investigation was undertaken to find the efficiency of three data mining techniques t... more The present investigation was undertaken to find the efficiency of three data mining techniques to study the sex ratio distortion in crossbred dairy cattle. Information on the date of birth, sex of calf, sire and dam identification number, parity number of dam and date of conception of dam of 873 crossbred calves born to 89 bulls at Cattle Breeding Farm, Thumburmuzhi, Kerala for the period from 1991 to 2005 were utilized for the present study. The three classificatory techniques viz., logistic regression, decision tree and artificial neural networks were employed using the Weka (Witten and Frank, 2005) software. Confusion matrix was developed by each classifier and evaluation of the technique was done by using parameters viz. kappa statistic, mean absolute error, root mean squared error, relative absolute error and root relative squared error. The results revealed that classification by logistic regression was the best as it had maximum (58.72) percentage of correctly classified ins...
Peri-parturient period in high yielding cows always associated with a series of nutritional, bioc... more Peri-parturient period in high yielding cows always associated with a series of nutritional, biochemical, hormonal changes that leads to lameness in later lactation. Extra supplementation of vitamins and minerals responsible for hoof health is recommended in some dairy herds during this period. The present study was conducted by fortifying 10, 20 and 30 mg/d of biotin and 2 g/d of zinc sulphate during pre- and post-partum period (2 months before and 2 months after calving) of 40 high yielding lame crossbred KF cows, distributed into control and three treatment groups. The study revealed that, fortification of transition diet with biotin and Zn significantly reduced the lameness score and recovery time in supplemental groups compare to control group. In T-1, the lameness score was reduced by 1.3, T-2 reduction was maximum by 2, T-3 by 1.5, and in control 0.02 with their respective recovery period of hoof lesions were 38.9±1.1 (T-1) 19.12±1.8 (T-2), 25.3±1.4 (T-3) and 56.2±2.7 days. T...
A comparative study of connectionist network (also known as artificial neural network, ANN)) and ... more A comparative study of connectionist network (also known as artificial neural network, ANN)) and multiple regression is made to predict the first lactation 305 days or less milk yield (FL305DMY) from early body weights in Sahiwal cattle. A multilayer feed forward network with back propagation of error learning mechanism was used for prediction. Data collected from 221 Sahiwal heifers were partitioned into 2 data sets namely training data set comprising 75% data to build the neural network model and test data set comprising 25% to test the model. Early body weights, viz. birth weight, body weights at 6, 12, 18, 24, 30 months and weight at first calving were used as input variables and FL305DMY was considered as output variable. The same training and test data sets were used for multiple linear regression analysis (MLR). The prediction efficiency of both models was compared using the R2 value and root mean square error (RMSE). The accuracy of prediction from both the models was observ...
First lactation 305 days milk yield (FL305DMY) of progenies is an important criteria for selectio... more First lactation 305 days milk yield (FL305DMY) of progenies is an important criteria for selection of sires. In field progeny testing programme, prediction of FL305DMY based on test day records will reduce the cost of recording. The present study was aimed at evaluating the efficiency of different methods of prediction for FL305DMY based on monthly test day milk yield records in Karan Fries cattle and evaluation of sires based on the performance of its progenies. Methods of prediction were Wood’s incomplete gamma function and Artificial neural network (ANN). Efficiency of prediction was compared using error of prediction. Root mean square error was least for ANN method (5.72%). The attempt was made to evaluate Karan Fries sires based on actual and predicted FL305DMY so obtained utilising BLUP- AM method of sire evaluation. The breeding values of 48 Karan Fries sires having 3 or more daughters were estimated. Comparison of ranking of sires based on different methods of prediction w...
Various studies have proved that linear traits have strong relationship with milk productivity b... more Various studies have proved that linear traits have strong relationship with milk productivity but no such models are available for selection of animals based on linear traits. The present study conducted during 2020-22, is an attempt to develop an intelligent model using machine learning algorithms to predict peak milk yield based on its linear traits for selection of best dairy animals. A dataset on 14 linear traits of 259 buffalos across 5 lactations with peak milk yield was created and used for developing models. Data was collected from the buffalos having 8 to 26 kg peak milk yield maintained at Animal Farm Section, Central Institute for Research on Buffaloes, Hisar and also from private farms maintained by farmers. Predictive models were developed using various machine learning algorithms (artificial neural network, support vector regression and random forest) along with multi-linear regression executed on WEKA machine learning platform. Performance of these models was evalua...
Gainful livestock farming requires selective breeding of animals with certain heritable desirable... more Gainful livestock farming requires selective breeding of animals with certain heritable desirable traits which gives profitability in terms of farm produce. Modern dairy animals are selected for traits which directly or indirectly contribute to high milk production. The concept of "feed conversion efficiency" in terms of milk production is now vigorously taken up by researchers and farm managers for recognizing and breeding efficient milk-producing animals. The whole concept of economic farming thus requires identification of "elite" animals, meeting above criteria as base population for the farm enterprise. Farmers and animal traders have been selecting best animals based on certain physical characters, which were also accepted by the breeding scientists as phenotypes. Data mining allows uncovering of hidden patterns in the data for better understanding of data relationship for developing suitable models for further improvements. Along with artificial intelligen...
The present study was performed to observe the significant effect of individuality of dairy buffa... more The present study was performed to observe the significant effect of individuality of dairy buffaloes on the acoustic features of their vocal signals. The mean call duration, pitch, 1st formant, periodicity and degree of voice breaks of adult lactating Murrah buffaloes were observed to be 2.15±0.05 s, 143.48 ± 2.51 Hz, 900.11 ± 4.21 Hz, 95.31% (183.95 periodic pulses out of 192.99 pulses) and 20.78 ± 0.89 % respectively. Analysis of bioacoustics features extracted from 300 voice samples of 10 adult lactating Murrah buffaloes revealed that differences for amplitudes (minimum, maximum and mean), total energy, mean power, pitch (median, minimum, range and mean), intensities (mean, minimum and maximum), formants (F1, F2, F3, F4 and F5), bandwidths (B1, B3 and B4), number of pulse, number of period, mean period, unvoiced frames, degree of voice breaks, jitter, shimmer, mean noise to harmonic ratio (%) and mean harmonic to noise ratio (dB) were highly significant. Out of these only few ac...
Selection of elite sires is the base of animal breeding therefore; if the sire can be selected mu... more Selection of elite sires is the base of animal breeding therefore; if the sire can be selected much earlier on the basis of its progeny’s performance then the genetic response will be higher. In the present study, 305 days lactation milk yield (LMY) of Sahiwal cows is predicted using back propagation networks based on partial lactation records of 10 test-days milk yield to rank the sires at younger age to improve the quality of milking animals and reducing generation intervals. Neural networks were trained using four back propagation algorithms viz. Bayesian regularization (BR), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM) and BFGS QuasiNewton (BFG). The data set (587 records) was divided in to two training and test data sets at 66.67%-33.33% and 75%25% respectively. Results showed that the prediction accuracy of the models increased with the addition of test-days milk yield as input variable. The neural network models were able to predict LMY with more than 80% accurac...
In the present study, first lactation 305-day lactation milk yield (FL305DMY) was predicted by ar... more In the present study, first lactation 305-day lactation milk yield (FL305DMY) was predicted by artificial neural network (ANN) using monthly test day milk yields records of 588 Sahiwal cows. A total of five monthly test day milk yields (2, 3, 5, 7, 8 monthly test day record) were used in neural networks to train data using Bayesian regularization (BR) algorithm. Results showed that the accuracy of prediction of all the models increased with the addition of test day milk yields as input variables. The best neural network model was able to predict FL305DMY with 93.18% accuracy. Further, comparison was made between multiple linear regression (MLR) and ANN for accuracy of prediction and there was no significant different found between ANN and MLR for prediction of FL305DMY in Sahiwal cows.
2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 2015
The present investigation was undertaken to find the efficiency of three data mining techniques t... more The present investigation was undertaken to find the efficiency of three data mining techniques to study the sex ratio distortion in crossbred dairy cattle. Information on the date of birth, sex of calf, sire and dam identification number, parity number of dam and date of conception of dam of 873 crossbred calves born to 89 bulls at Cattle Breeding Farm, Thumburmuzhi, Kerala for the period from 1991 to 2005 were utilized for the present study. The three classificatory techniques viz., logistic regression, decision tree and artificial neural networks were employed using the Weka (Witten and Frank, 2005) software. Confusion matrix was developed by each classifier and evaluation of the technique was done by using parameters viz. kappa statistic, mean absolute error, root mean squared error, relative absolute error and root relative squared error. The results revealed that classification by logistic regression was the best as it had maximum (58.72) percentage of correctly classified ins...
Peri-parturient period in high yielding cows always associated with a series of nutritional, bioc... more Peri-parturient period in high yielding cows always associated with a series of nutritional, biochemical, hormonal changes that leads to lameness in later lactation. Extra supplementation of vitamins and minerals responsible for hoof health is recommended in some dairy herds during this period. The present study was conducted by fortifying 10, 20 and 30 mg/d of biotin and 2 g/d of zinc sulphate during pre- and post-partum period (2 months before and 2 months after calving) of 40 high yielding lame crossbred KF cows, distributed into control and three treatment groups. The study revealed that, fortification of transition diet with biotin and Zn significantly reduced the lameness score and recovery time in supplemental groups compare to control group. In T-1, the lameness score was reduced by 1.3, T-2 reduction was maximum by 2, T-3 by 1.5, and in control 0.02 with their respective recovery period of hoof lesions were 38.9±1.1 (T-1) 19.12±1.8 (T-2), 25.3±1.4 (T-3) and 56.2±2.7 days. T...
A comparative study of connectionist network (also known as artificial neural network, ANN)) and ... more A comparative study of connectionist network (also known as artificial neural network, ANN)) and multiple regression is made to predict the first lactation 305 days or less milk yield (FL305DMY) from early body weights in Sahiwal cattle. A multilayer feed forward network with back propagation of error learning mechanism was used for prediction. Data collected from 221 Sahiwal heifers were partitioned into 2 data sets namely training data set comprising 75% data to build the neural network model and test data set comprising 25% to test the model. Early body weights, viz. birth weight, body weights at 6, 12, 18, 24, 30 months and weight at first calving were used as input variables and FL305DMY was considered as output variable. The same training and test data sets were used for multiple linear regression analysis (MLR). The prediction efficiency of both models was compared using the R2 value and root mean square error (RMSE). The accuracy of prediction from both the models was observ...
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