School of Agriculture, Food and Ecosystem Sciences - Research Publications

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    Impacts of Heat Stress on the Physiological and Production Responses of Lactating Dairy Cows Grazing Pastures over Hot Summer Months
    Osei-Amponsah, R ; Dunshea, FR ; Leury, BJ ; Cheng, L ; Cullen, B ; Joy, A ; Payyanakkal, A ; Zhang, MH ; Chauhan, SS (MDPI, 2020-01-19)
    Heat stress (HS), a major challenge for sustainable livestock production justifies the need for productive thermotolerant cattle. We measured body temperature (non-invasively using a FLIR T1200 thermal imaging camera), respiratory rate and panting scores of 120 Holstein Friesian cows at the University of Melbourne Dookie Dairy Farm weekly during the summer period (December 2018-February 2019). The effect of Temperature-Humidity Index (THI) on milk production, protein content, fat content was also measured. We categorized THI as low (≤72), moderate (73–82) and high (≥83) and observed a highly significant (P ≤ 0.01) effect of THI on respiratory rate (66.7, 84.7 and 109.1/min), panting scores (1.4, 1.9 and 2.3) and average body temperature of cows (38.4, 39.4 and 41.5). Average milk production parameters were also significantly (P ≤ 0.01) affected by THI: daily milk production dropped by 14% from high to low THI, milk temperature and fat% increased by 3% whilst protein% increased by 2%. Highly significant (P ≤ 0.01) positive correlations were obtained between THI and milk temperature, fat% and protein% whilst the reverse was observed between THI and milk yield, feed intake and rumination minutes. Under moderate and high THI, most cows sought shade, spent more time around watering points and showed signs of distress (excessive drooling and open mouth panting). These findings clearly indicate that lactating dairy cows grazing summer pastures experience severe HS compromising their welfare. The quantum of production losses, though significant may however be lower than previously reported in studies using climatic chambers.
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    Association of Thermotolerance with Milk Production, Feed Saver, Fertility and Fat Percentage Breeding Values in Holstein Friesian Dairy Cattle
    Osei-Amponsah, R ; Dunshea, F ; Leury, B ; Cheng, L ; Cullen, B ; Joy, A ; Payyanakkal, A ; Zhang, MH ; Chauhan, SS (MDPI, 2020-01-19)
    In Australia, heat waves are becoming hotter and longer, and more frequent, compromising dairy cattle welfare and productivity. Selection for heat tolerance (HT) may help to ensure sustainability of production under hot summer conditions. In a study at the University of Melbourne’s Dookie Robotic Dairy Farm, we identified the 20 most heat-sensitive and 20 most heat-tolerant cows in a herd of 150 Holstein Friesian lactating cows based on phenotypic responses (increase in body temperature, panting score, and decline in milk production) of dairy cows grazing pasture and given concentrate at milking during hot summer conditions for 3 months. Hair samples were collected from the tip of the tail according to a standard protocol for genotyping (Zoetis). Results based on 36 successfully genotyped cows indicated a significant variation in feed saved (FS) genomic estimated breeding values (GEBVs) across age indicating a potential for its selection. The thermotolerant group had relatively higher GEBV for FS and fat% but lower milk production potential. Highly significant (P≤ 0.05) negative correlations (-0.39 to -0.69) were observed between heat tolerance and current dairy industry economic indices (Balanced Performance Index (BPI), Type Weighted Index (TWI), Australian Selection Index (ASI) and milk production), while positive correlations exist between HT and feed saved (0.44) and fertility (0.27). These findings indicate a positive association between HT and feed saved, fertility, and fat percent breeding values. However, a more extensive study including large number of lactating cows is required to confirm these genomic associations and incorporating in future breeding objectives.
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    Genetic Selection for Thermotolerance in Ruminants
    Osei-Amponsah, R ; Chauhan, SS ; Leury, BJ ; Cheng, L ; Cullen, B ; Clarke, IJ ; Dunshea, FR (MDPI, 2019-11)
    Variations in climatic variables (temperature, humidity and solar radiation) negatively impact livestock growth, reproduction, and production. Heat stress, for instance, is a source of huge financial loss to livestock production globally. There have been significant advances in physical modifications of animal environment and nutritional interventions as tools of heat stress mitigation. Unfortunately, these are short-term solutions and may be unsustainable, costly, and not applicable to all production systems. Accordingly, there is a need for innovative, practical, and sustainable approaches to overcome the challenges posed by global warming and climate change-induced heat stress. This review highlights attempts to genetically select and breed ruminants for thermotolerance and thereby sustain production in the face of changing climates. One effective way is to incorporate sustainable heat abatement strategies in ruminant production. Improved knowledge of the physiology of ruminant acclimation to harsh environments, the opportunities and tools available for selecting and breeding thermotolerant ruminants, and the matching of animals to appropriate environments should help to minimise the effect of heat stress on sustainable animal genetic resource growth, production, and reproduction to ensure protein food security.
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    Heat Stress Impacts on Lactating Cows Grazing Australian Summer Pastures on an Automatic Robotic Dairy
    Osei-Amponsah, R ; Dunshea, FR ; Leury, BJ ; Cheng, L ; Cullen, B ; Joy, A ; Abhijith, A ; Zhang, MH ; Chauhan, SS (MDPI, 2020-05)
    The objective of this study was to measure the impacts of summer heat events on physiological parameters (body temperature, respiratory rate and panting scores), grazing behaviour and production parameters of lactating Holstein Friesian cows managed on an Automated Robotic Dairy during Australian summer. The severity of heat stress was measured using Temperature-Humidity Index (THI) and impacts of different THIs-low (≤72), moderate (73-82) and high (≥83)-on physiological responses and production performance were measured. There was a highly significant (p ≤ 0.01) effect of THI on respiratory rate (66.7, 84.7 and 109.1/min), panting scores (1.4, 1.9 and 2.3) and average body temperature of cows (38.4, 39.4 and 41.5 °C), which increased as THI increased from low to moderate to high over the summer. Average milk production parameters were also significantly (p ≤ 0.01) affected by THI, such that daily milk production dropped by 14% from low to high THI, milk temperature and fat% increased by 3%, whilst protein% increased by 2%. The lactation stage of cow had no significant effect on physiological parameters but affected (p ≤ 0.05) average daily milk yield and milk solids. Highly significant (p ≤ 0.01) positive correlations were obtained between THI and milk temperature, fat% and protein% whilst the reverse was observed between THI and milk yield, feed intake and rumination time. Under moderate and high THI, most cows sought shade, spent more time around watering points and showed signs of distress (excessive salivation and open mouth panting). In view of the expected future increase in the frequency and severity of heat events, additional strategies including selection and breeding for thermotolerance and dietary interventions to improve resilience of cows need to be pursued.
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    Artificial Intelligence Applied to a Robotic Dairy Farm to Model Milk Productivity and Quality based on Cow Data and Daily Environmental Parameters
    Fuentes, S ; Viejo, CG ; Cullen, B ; Tongson, E ; Chauhan, SS ; Dunshea, FR (MDPI, 2020-05)
    Increased global temperatures and climatic anomalies, such as heatwaves, as a product of climate change, are impacting the heat stress levels of farm animals. These impacts could have detrimental effects on the milk quality and productivity of dairy cows. This research used four years of data from a robotic dairy farm from 36 cows with similar heat tolerance (Model 1), and all 312 cows from the farm (Model 2). These data consisted of programmed concentrate feed and weight combined with weather parameters to develop supervised machine learning fitting models to predict milk yield, fat and protein content, and actual cow concentrate feed intake. Results showed highly accurate models, which were developed for cows with a similar genetic heat tolerance (Model 1: n = 116, 456; R = 0.87; slope = 0.76) and for all cows (Model 2: n = 665, 836; R = 0.86; slope = 0.74). Furthermore, an artificial intelligence (AI) system was proposed to increase or maintain a targeted level of milk quality by reducing heat stress that could be applied to a conventional dairy farm with minimal technology addition.