Predicting physiological responses of dairy cows using comprehensive variables

Shu, Hang; Li, Yongfeng; Bindelle, Jérôme; Jin, Zhongming; Fang, Tingting; Xing, Mingjie; Guo, Lei; Wang, Wensheng

doi:10.1016/j.compag.2023.107752

Cited by 6 publications

(6 citation statements)

References 62 publications

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“…Herbut et al (42) reported how thermal stress conditions in uenced nutrition, water intake, and standing, showing behavioural trends similar to those in the present work. As emerged in our study, the positive correlation between animal adaptation via standing behaviour and an increase in THI was also con rmed by Shu et al (30) which noting the association between standing animal and lower respiration rate, indicating greater heat dispersion through irradiation and not through respiration.…”

Section: Discussionsupporting

confidence: 90%

“…The increase in temperatures above speci c thresholds entails the need to increase dissipation through evaporative phenomena, which require the expenditure of energy by the animal (evaporative cooling both from cutaneous and respiratory surfaces) (15). The reported data con rmed the animals increased both RR and panting scores in HS, attempting to adapt to the increase in THI, demonstrating thus the importance of those parameters as evaluators of the state of welfare of the animal being phenomena highly correlated to the physiological responses at heat stress (30). Berman et al (31) reported that from 25°C, the RR showed signi cant increases and that an RR higher than 60 breaths/min indicates a heat stress condition (8), a threshold exceeded by cows in our NS period.…”

Section: Discussionmentioning

confidence: 68%

“…There are many responses to heat stress in dairy cows, starting from modifying the respiratory rate or the panting score, which are intended both to increase heat dissipation and maintain thermal homeostasis (29,30). As long as the ambient temperatures remain within an optimal range, heat dissipation occurs via irradiation, which involves a non-expenditure of energy.…”

Section: Discussionmentioning

confidence: 99%

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Effect of Heat Stress on physiological, behavioural, and production responses in lactating dairy cows

Sirtori,

Parrini,

Fabbri

et al. 2023

Preprint

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Heat stress is one of the most significant issues occurred in the dairy systems during the last decades with adverse consequences on welfare and production. This study aims to evaluate the effect of heat stress on the main major physiological, behavioural and productive parameters considering two dairy herds located in hill/mountain area. Twenty Friesian cows were checked in two climatic condition periods: no stress, with average diurnal THI < 72 and heat stress with THI > 72. Physiological indices (respiration rate, panting score, skin udder temperature, heat evaporation loss, and heat stress index) as well as milk yield, were during 5 trial consecutive days for each period. Animal behaviours were recorded from 9 a.m. to 4 p.m., including feeding, dynamic and grooming activities. In the heat stress period, cows presented higher values relative to physiological parameters. In addition, the cows decreased the feeding frequency (from 25.18 to 14.74%) increasing their standing (from 9.73 to 22%) and drinking (from 1.77 to 3.60%) behaviour. Milk yield decreased (-3kg/day/cow), as the climatic conditions worsened. The results of this study confirmed the need of more effective strategies development to mitigate the heat load in dairy cattle also in hill/mountain area.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 68%

Section: Discussionmentioning

confidence: 99%

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Effect of Heat Stress on physiological, behavioural, and production responses in lactating dairy cows

Sirtori,

Parrini,

Fabbri

et al. 2023

Preprint

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“…In particular, in [7], several models to predict the dairy cow's thermal state using several approaches such as bioclimatic indexes, machine learning or mechanistic modelling are reviewed [7]. In a recent work, Shu et al [8] tested several algorithms through machine learning, and stated that ANN gives the lower error in predicting the physiological responses of dairy cows, allowing a better cooldown decision.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the models analyzed in [7] assess the thermal behaviour of animals, but they do not address the estimation of the fur's drying time, apart from Chen et al [11]. Shu et al [8], on the other hand, assess the response of cows to the cooling treatment only afterwards, through different predictors of physiological states. Furthermore, most modelling approaches use ambient parameters (Ta, RH) which remain almost unchanged, even after sprinkler activation (apart from a possible increase in RH), which acts on the animal and does not appreciably change the Ta and RH of the ambient air.…”

Section: Introductionmentioning

confidence: 99%

An Active Drying Sensor to Drive Dairy Cow Sprinkling Cooling Systems

Liberati

2023

Sustainability

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The use of sprinkling with ventilation to cool dairy cows is considered an appropriate practice to reduce the negative effects of heat stress. However, due to climate change, water will increasingly become a limited resource, so we need to make water use more and more efficient. For this purpose, an active drying sensor has been developed in order to time the sprinkling cooling system. The sensor reproduces the thermal response of a cow, considering both sensible and latent heat exchange, and is located in the feeding alley, about two meters above the floor. This allows the fabric of the sensor (simulating the fur) to be wetted by the sprinkler, and blown by the fan. The water content of the sensor fabric during the drying time is monitored by measuring its electrical conductivity, allowing the estimation of the time the fur becomes dry. Another two specifically designed instruments are presented, the first to estimate the fur’s water content after spraying, and the second to detect the time the fur became dry. Sensor output, interpreted through a simplified model, gave a predicted drying time with an error ranging between −11.4% and +14.8% (R2 = 0.789). In the commercial barn where the experiments were conducted, the use of the sensor allowed an estimated reduction in water consumption of about 57%, with respect to the fixed timing normally used. As a perspective, the sensor could be used to assess cows’ heat stress level.

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