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As a convenient method, the closed chamber method has been applied to determine gaseous emission fluxes from fully open animal feeding operations despite the measured fluxes being theoretically affected by deployment time, wind speed over the emitting surface and detected gas mass. This laboratory study evaluated the effects of deployment time (0 to 120 min) and external surface wind speed (ESWS) (0.00, 0.25, 0.50, 0.75, 1.00, 1.50 and 2.00 m sec-1) on the measurement accuracy of a 300 mm (diameter) × 400 mm (height) (D300×H400) closed chamber using methane (CH4), nitrous oxide (N2O) and sulfur hexafluoride (SF6) as reference gases. The results showed that the overall deviation ratio between the measured and reference CH4 fluxes ranged from 9.99 % to -37.32 % and the flux was overestimated in the first 20 min. The measured N2O and SF6 emissions were smaller than the reference fluxes using the chamber. N2O measurement accuracy decreased from -14.47 to -35.09% with deployment time extended to 120 min, while SF6 accuracy sharply increased in the first 40 min, with the deviation stabilizing at approximately -5.00%. CH4, N2O and SF6 measurements were significantly affected by deployment time and ESWS (P<0.05), and the interaction of those two factors greatly influenced CH4 and SF6 measurements (P<0.05). With the D300×H400 closed chamber, deployment times of 20 to 30 min and 10 to 20 min are recommended to measure CH4 and N2O, respectively, from the open operations of dairy farms under wind speeds lower than 2 m sec-1. Implications: This study recommended the suitable deployment times and wind speeds for using a D300 × H400 closed chamber to measure CH4, N2O, and SF6 in an open system, such as a dairy open lot and manure stockpile, to help researchers and other related industry workers get accurate data for gas emission rate. Deployment times of 20 to 30 min and 10 to 20 min were recommended to measure CH4 and N2O emissions using the D300 × H400 closed chamber, respectively, from the open operations of dairy farms under wind speeds lower than 2 m sec−1. For the measurement of SF6, a typical tracer gas, a deployment of 70 to 90 min was suggested. [ABSTRACT FROM AUTHOR]

The open lots and manure stockpiles of dairy farm are major sources of greenhouse gas (GHG) emissions in typical dairy cow housing and manure management system in China. GHG (CO2, CH4and N2O) emissions from the ground level of brick-paved open lots and uncovered manure stockpiles were estimated according to the field measurements of a typical dairy farm in Beijing by closed chambers in four consecutive seasons. Location variation and manure removal strategy impacts were assessed on GHG emissions from the open lots. Estimated CO2, CH4and N2O emissions from the ground level of the open lots were 137.5±64.7 kg hd-1yr-1, 0.45±0.21 kg hd-1yr-1and 0.13±0.08 kg hd-1yr-1, respectively. There were remarkable location variations of GHG emissions from different zones (cubicle zone vs. aisle zone) of the open lot. However, the emissions from the whole open lot were less affected by the locations. After manure removal, lower CH4but higher N2O emitted from the open lot. Estimated CO2, CH4and N2O emissions from stockpile with a stacking height of 55±12 cm were 858.9±375.8 kg hd-1yr-1, 8.5±5.4 kg hd-1yr-1and 2.3±1.1 kg hd-1yr-1, respectively. In situ storage duration, which estimated by manure volatile solid contents (VS), would affect GHG emissions from stockpiles. Much higher N2O was emitted from stockpiles in summer due to longer manure storage. Implications: This study deals with greenhouse gas (GHG) emissions from open lots and stockpiles. It’s an increasing area of concern in some livestock producing countries. The Intergovernmental Panel on Climate Change (IPCC) methodology is commonly used for estimation of national GHG emission inventories. There is a shortage of on-farm information to evaluate the accuracy of these equations and default emission factors. This work provides valuable information for improving accounting practices within China or for similar manure management practice in other countries. [ABSTRACT FROM PUBLISHER]

Open lots are commonly used to provide outdoor free ranging spaces for dairy cows in many countries such as China and the USA. For this type of operation, the manure characteristics, floor types and environmental conditions, which potentially affect gaseous emissions from manure, are quite different from those of indoor systems. The estimation of NH 3 emissions from manure depends greatly on the mass transfer coefficient, an empirical constant determined by environmental factors in the process-based emission model. Using a scale model, this study measured ammonia emissions to be expected from dairy open lots, based on different combinations of different surface air velocities (0.6–2.2 m s−1), air temperatures (15–35 °C) and relative humidity values (20–60%). Based on the two-film theory, the overall mass transfer coefficient (K OL) of NH 3 was calculated from directly measured NH 3 emission rates and the ammonium nitrogen content, temperature, and pH of manure. As an indicator of the driving potential of gaseous volatilisation, the vapour pressure deficit (VPD) was estimated using air temperature and relative humidity. As the VPD and surface air velocity increased, the measured NH 3 emission rates ranged from 1.35 to 19.18 mg kg−1 h−1, and the estimated K OL of NH 3 from dairy open lots was within the range of 1.48 × 10−6 – 7.86 × 10−6 m s−1. Using the air velocity and VPD as variables, a non-linear empirical model was developed to estimate the K OL for NH 3 emissions from manure on dairy open lots (R 2 = 0.69). The normalised mean error of the model was approximately 19.4%. • Overall mass transfer coefficient (K OL) was estimated based on two-film theory. • K OL of NH 3 emission was in the range of 1.48–7.86 10−6 m s−1 from dairy open lots. • Air vapour pressure deficit (VPD) was positively related to NH 3 emission and K OL. • A nonlinear model was obtained to estimate K OL using air velocity and VPD. [ABSTRACT FROM AUTHOR]

Robotic milking systems (RMS) are increasingly utilised by modern livestock farmers because they can reduce labour costs, and they have the potential to collect data that will improve animal welfare and animal productivity through better monitoring. Sensors and devices installed in RMS enable farmers to routinely collect data on environment conditions, individual animal's behaviours, health, productivity, and milk quality. This dataset can be used to train artificial intelligence algorithms to predict trends in these variables. This study developed a machine learning framework using 5 years' behaviour, heath and productivity data from 80 cows in a robotic dairy farm. Here we demonstrate the development of a framework to automatically train models with up-to-date farm data and predict daily milk yield, composition (fat and protein content) and frequency of individual cow milking during the subsequent 28 days. A time series cross-validation was applied to simulate the application of this framework under commercial conditions and to evaluate the performance. A high accuracy of prediction (R2 > 0.90 and overall accuracy > 80%) was achieved with the models created by this framework. The practical potential of using such frameworks to enhance the management efficiency and animal welfare in robotic dairy farms is discussed. • A machine learning framework was developed to predict cow production performance. • Models were trained using 5 years' data collected from robotic milking system. • Prediction of individual cow production performance got an accuracy >80% (R2 > 0.90). [ABSTRACT FROM AUTHOR]

Robotic milking systems (RMS) have been demonstrated to reduce on-farm labour requirements and collect significant individual-level data in relation to animal health, welfare and production, but they are still largely underutilised. Studies on the relationship between heat stress, animal behaviour and robotic milking performance in a RMS are still insufficient. To model such a relationship, this study focused on analysing the data collected from a RMS system. Animal response indicators of heat stress assessment were rumination time (RT), milk temperature (MT) and daily milk yield (DMY). In addition, RMS milking behaviour, i.e. time of milking (TM), milking frequency (MF), milking duration (MD), milking speed (MS) and milk yield per milking (MY) were also monitored. A new index of rumination efficiency (REI) was created to evaluate rumination efficiency under heat stress, defined as the ratio between DMY and RT. Using multiple broken-line regression, it was found that a 1 °C rise in daily mean temperature could reduce RT by 5.12 min, decrease REI by 0.07 kg·cow−1·h−1, and increase low efficiency milking by 1%. Moreover, the study also found cows prefer to milk between 7:00–9:00AM, and 86% of milking events happened during this period. No significant correlation was found between heat stress and milking behaviour. However, delaying the first milking event of the day and controlling milking intervals to <4 h, was beneficial for REI and robotic milking performance. • Measure rumination efficiency using robotic dairy farming facilities. • Define new index to evaluate heat stress in robotic milking system. • Find cows have no adopted milking behaviour against heat stress. • Determine the decline of robotic milking performance under heat stress. • Modified milking arrangement was recommended with estimated financial benefit. [ABSTRACT FROM AUTHOR]

Heat stress is a significant challenge in dairy farming systems. Dairy cows under heat stress will encounter impaired welfare leading to production losses. As the frequency and magnitude of heat stress events increase in the coming decades, a focus on heat stress reduction studies becomes important. Modelling and on-farm experiments have been used to assess the effects of heat stress on livestock over the last few decades. Mitigation solutions including optimal shed structure, ventilation, feeding regimes, farm management and genetic selection have all been explored. However, under different farm conditions, the heat tolerance and coping ability of dairy cows can vary significantly. Until now, the results from different mathematical models have provided a variety of heat stress thresholds for on-farm use. In practice, it is still costly to determine an accurate heat stress level in order to identify the mitigation requirements. This review summarises previous studies on the effects of heat stress on intensively reared dairy cows and different mitigation approaches. We have undertaken a comparative analysis of thermal indices, animal responses, and mitigation approaches. Recommendations are then given for developing a framework to enhance the measurement, assessment and mitigation of heat stress. Robust monitoring systems, big data analyses and artificial intelligence algorithms are needed for the future development of dynamic, self-calibrating model-based systems, which could provide real-time assessment and minimisation of heat stress. • Devices for monitoring heat stress on cattle are assessed including IOT functions. • Complexity and inconsistency limit the usage of published thermal comfort index. • Fans plus misters are still the most cost effective heat stress mitigation approach. • Conceptual model for developing a globalised framework is proposed. [ABSTRACT FROM AUTHOR]

Thresholds of heat stress are identified by determining the values of thermal comfort indices with significant change of animal responses. However, published thresholds may lead to inaccuracy when dealing with specific climate conditions, animal breeds and production factors. Thus, determining dynamic thresholds might provide better assessment of heat stress, with self-calibration capabilities. In this study, a large dataset of individual age, body mass (BM), days in milk (DIM), daily milk yield (DMY) and milk temperature (MT) of 126 lactating Holstein cows was collected from a robotic dairy farm over five years. The ambient temperature data was collected from a local weather station and processed as daily minimum and mean temperature (T min and T mean). For the whole herd, a new series of heat stress thresholds with stages were defined as comfort stage, milk heat stress, effective heat stress and critical heat stress. The definition was based on the cow's responses in DMY and MT, which provides a potential approach to accurately alert for heat stress in robotic farming systems by using the existing data source. For the specific individuals, dynamic thresholds of heat stress were identified and categorised using the decision tree machine learning model. The categorisation achieved 79–94% overall accuracy, and demonstrated the importance of cooling cows during their early lactation period. • Use broken-line regression to assess cow's heat stress link to production factors. • Decision tree model to choose dynamic threshold of heat stress for individual cow. • Identify multi-thresholds indicating none, mild, effective and critical heat stress. • Demonstrate the importance of cooling cows during early lactation stage. [ABSTRACT FROM AUTHOR]