{'fraction_non_alphanumeric': 0.013943112102621304, 'fraction_numerical': 0.002230897936419409, 'mean_word_length': 5.547445255474453, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A generalised addictive model to characterise dairy cows' responses to heat stress.

Heat stress is one of the most critical issues jeopardising animal welfare and productivity during the warm season in dairy cattle farms. The global trend of increase in average and peak temperatures is making the problem more and more serious. Many devices have been introduced in livestock farms to monitor and control temperature-humidity index, as well as animal behaviour and production parameters. The consequent availability of collected databases has increasingly enhanced the research aimed to understand the consequences of heat stress in cattle, in relation to genetic, reproductive, productive and behavioural features. Moreover, these investigations laid the foundations for the development, calibration, validation and test of numerical models quantifying the individual responses to heat stress conditions. In this work, a generalised addictive model with mixed effects has been developed to analyse the relationship between milk production, animal behaviour and environmental parameters based on data surveyed in 2016 in an Italian dairy farm. Each cow has been characterised in terms of her response to heat conditions, and the results led to define three classes of susceptibility to heat stress within the herd. These attributes have then been related to the various phenotypic parameters collected by the precision livestock farming devices used in the farm. The study provides a model to understand the effects of heat stress conditions on individual animals in relation to the main parameters describing their rearing conditions; moreover, the results contribute to improve the herd management by lending indications to define targeted treatments according to the cow's characteristics.