Prediction of lameness based on the percent body weight distribution to individual limbs of Karan Fries cows

Singh, Man; Lathwal, S. S.; Singh, Yajuvendra; Mohanty, T.K.; Ruhil, A. P.; Singh, Navav

doi:10.5958/0976-0555.2015.00144.2

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…One other reason may be the body weight of the animals according to the age of first calving. Even though body weights were not evaluated in the study, Singh et al (2012) reported that the percentage changes in body weight in animals were associated with the lameness incidence and that the incidence increased as the weight increased. Incidence of lameness is high in dairy cattle herds and arrangements are unfortunately limited for yield and in feeding program that supports the high milk yield that for decreasing this incidence.…”

Section: O N L I N E F I R S T a R T I C L Ementioning

confidence: 99%

Enamel Hypoplasia Analysis of Middle Miocene Mammals from Chabbar Syedan, Punjab, Pakistan

Nawaz¹,

Khan²

2022

PJZ

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The aim of this research was to determine the relationship between age at first calving (AFC) and first lactation milk yield [adjusted 305-day (MY 305 )], reproductive performance [days open (DO), calving interval (CI)] and diseases (lameness, mastitis, metritis and dystocia) in Simmental dairy cows bred under Turkey conditions. A total of 1110 Simmental cows (first calving to second calving) were assessed according to study criteria. While the relation between AFC and disease incidence and MY 305 was not found significant, the relation between AFC and CI and DO was found significant and the optimum interval of AFC was determined 684≤ İBY≤730 d for minimum CI and DO. Even though relation between AFC and diseases was not significant, it can be said that the same interval is advantageous for low disease incidence in Simmental cows. Furthermore, it can be accepted that the same interval is optimum also for the best lactation milk yield since it was not found a significant relationship among the average milk yields according to AFC. In conclusion, it can be said that planning the reproductive management practices according to these results will be beneficial for the optimal balance between economic benefit, animal health and milk yield.

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Section: O N L I N E F I R S T a R T I C L Ementioning

confidence: 99%

Enamel Hypoplasia Analysis of Middle Miocene Mammals from Chabbar Syedan, Punjab, Pakistan

Nawaz¹,

Khan²

2022

PJZ

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“…These learning algorithms are made to learn from data and improve prediction accuracy of targeted process (Skansi et al 2018). In dairy farms, machine learning has been used effectively in prediction of lameness (Singh et al 2015, Taneja et al 2020, mastitis (Kamphuis et al 2010, Dhoble et al 2019, calving time (Keceli et al 2020), estrus (Devi et al 2019), feed conversion efficiency-blood vitals correlation (Sikka et al 2020), and milk yield (Sharma et al 2007, Gandhi et al 2009, Dongre et al 2012, Manoj et al 2014. Most widely applied machine learning algorithms in animal production systems are artificial neural network (Kumar et al 2019), random forest (Shahinfar et al 2013), fuzzy logic/ Neurofuzzy (Shahinfar et al 2012) and support vector machine (Nguyena et al 2020).…”

mentioning

confidence: 99%

Machine learning algorithms for predicting peak yield in buffaloes using linear traits

SUNESH¹,

Balhara

Dahiya

et al. 2022

Indian J of Anim Sci

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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 evaluated using evaluation metrics root mean squared error (RMSE). Results revealed that the Artificial Neural Network (ANN) model performed best with minimum RMSE 2.0308. Rear udder height and Lactation number emerged as the two most important attributes affecting the peak milk yield. Such model will be useful and handy for the stakeholders in selection of best dairy animals based on linear traits in absence of authentic record of peak milk yield.

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Distribution analysis of single nucleotide polymorphisms related to feet and legs and their association with lameness in Vrindavani cattle

Prakash

Gaur

Pruthviraj

et al. 2019

Trop Anim Health Prod

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Prediction of lameness based on the percent body weight distribution to individual limbs of Karan Fries cows

Cited by 4 publications

References 21 publications

Enamel Hypoplasia Analysis of Middle Miocene Mammals from Chabbar Syedan, Punjab, Pakistan

Enamel Hypoplasia Analysis of Middle Miocene Mammals from Chabbar Syedan, Punjab, Pakistan

Machine learning algorithms for predicting peak yield in buffaloes using linear traits

Distribution analysis of single nucleotide polymorphisms related to feet and legs and their association with lameness in Vrindavani cattle

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