Association between calving interval and productive traits in dairy cattle over different inseminations using artificial neural network

Calving interval is sought to be substantially affected by management and environmental effects. Using intellectual algorithms of machine learning methods to investigate complex systems are growing and these algorithms could be assumed as right approach to analysis dairy cattle industry data. Artificial neural networks which are part of artificial intelligence, gain from these algorithms. The objective of this study was to find the association between productive and reproductive traits with calving interval over different course of inseminations. It was assumed that extracting this association may improve the management of this trait.
In this research, the productive and reproductive traits from FOKA, an agriculture and animal husbandry, associated to Isfahan Vahdat Cooperative was used. The records were calving interval (day), the length of dry off period (day), number of insemination, total milk production (kg), total fat production (kg), total protein production (kg), adjusted milk production (kg), adjusted fat production (kg) and adjusted protein production (kg). The data were due to 15465 cows in which their parturition date spanned between 1368 and 1393. The data dimensions were reduced using elimination of high level of correlation among variables and principal component analysis before undertaking artificial neural network modeling. For each insemination, we learned a neural network. We used coefficient of determination and root mean square error to investigate the efficiency of neural network. The linear and nonlinear relationship among input variables with calving interval was measured using maximal information criterion.
The similar values of coefficient of determination and root mean square error due to neural network in different insemination obtained which would indicated that there is almost identical associations between calving interval input variables. Higher values of root mean square error obtained in neural network which learned based upon principal component analysis of independent data than elimination one. Therefore, it was concluded that elimination approach is suitable choice in this context. The maximal information criterion showed almost an identical association between calving interval and input variables over different inseminations. In this way, days in milk and milk production showed high amount of correlation with calving interval.
The results indicated that coefficient of determination and root mean square error of different inseminations was similar. Therefore, it is hardly expected that the association between calving interval and independent variables to be different over different inseminations. Based upon these results, it was elucidated that independent variables used in this study could fairly adequately predict the calving interval. Also, we could say that management – environmental conditions over different inseminations had minuscule effect on the associations of calving interval with independent variables.