The use of data mining methods for dystocia detection in Polish Holstein-Friesian Black-and-White cattle

Classification of environmental factors potentially motivating for dairy cows to access shade

The aim of this Research Communication was to apply the data mining technique to classify which environmental factors have the potential to motivate dairy cows to access natural shade. We defined two different areas at the silvopastoral system: shaded and sunny. Environmental factors and the frequency that dairy cows used each area were measured during four days, for 8 h each day. The shaded areas were the most used by dairy cows and presented the lowest mean values of all environmental factors. Solar radiation was the environmental factor with most potential to classify the dairy cow's decision to access shaded areas. Data mining is a machine learning technique with great potential to characterize the influence of the thermal environment in the cows' decision at the pasture.

Classification and regression tree analysis to predict calving ease in Sussex heifers using pelvic area dimensions and morphological traits

Objectives

The objective of this study was to assess the relationship between pelvic measurements, namely pelvic height (PH), pelvic area (PA), and pelvic width, and linear body measurement traits, such as hindquarters width, rump length (RL), hip height (HH), body weight (BW), shoulder height, chest depth (CD), body length, and also calve birth weight (CBW) under different calving ease scores, namely no assistance, gently pull, and hard pull.

Material and Methods

A total of 51 first calf Sussex heifers at 24 months old, weighing approximately 440 ± 40.26 kg, were used for this study. Two-year-old bulls (n = 2), weighing approximately 800 kg, were used for mating the 51 heifers with a bull to cow ratio of 1:30 and 1:21. The fertility of bulls was assessed by a private veterinarian before the mating season. Calving ease was scored as follows: 1 = no assistance during parturition (normal), 2 = heifer assistance as gently pull, 3 = heifer assistance as hard pull, 4 = heifer cannot calf, 5 = heifer calved a dead calf, and 6 = heifer calf with the abnormal position.

Results

Descriptive statistics indicated that PH (p < 0.01) and RL (p < 0.05) were positively remarkable and different among calving ease scores. Correlation results of the no assistance Sussex heifers group revealed that CBW was positively correlated (p < 0.05) with PH. Classification and regression tree CART results demonstrated that PH, BW, CD, and HH play an important role in predicting calving ease in Sussex heifers.

Conclusion

The findings suggest that there is an association among PAs and biometric traits. The present study might help farmers select animals with higher PH, BW, CD, and HH during breeding to lower dystocia incidents during parturition. However, further studies need to be carried out in CART modeling to reduce dystocia in a larger sample size of Sussex heifers or other cattle breeds.

The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76-99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24-99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00-97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood's model parameters.