Using precision livestock farming (PLF) technologies to assess the impact of environmental stressors on animal welfare and production efficiency on modern dairy farms

In modern dairy farming systems, heat stress is still a significant challenge. Dairy cows will encounter sub-optimal welfare which can result in production decline, diseases and even mortality, especially for high-producing cows with lower heat tolerance. The frequency and magnitude of heat stress events or heat waves are predicted to keep increasing in coming decades associated with global warming. Therefore, greater attention is being paid to alleviating the effects of heat stress on dairy cows and livestock generally. Modelling and on-farm experiments have been undertaken in many countries to assess the influence of heat stress on livestock using modern computer technologies and other hi-tech tools. At the same time, mitigation approaches such as optimal shed structure, new cooling facilities, targeted feeding regimes, improved farm management and genetic selection have all been studied extensively. However, due to differences between farm conditions and varying heat tolerance of different breeds and coping ability, the results from different heat stress models provided a variety of thresholds for on-farm decision support. Therefore, determination of accurate heat stress thresholds to facilitate practical mitigation options are still difficult. This study was initiated by summarizing the progresses achieved by previous studies on intensively kept dairy cows in relation to measuring, assessing and mitigating their heat stress. By taking comparative analysis of the published studies about thermal indices, animal responses and mitigation solutions, a range of recommendations were given for developing more accurate assessment and designing of more effective mitigation options. The review suggested that for achieving accurate and applicable thresholds of heat stress, it is necessary to establish monitoring systems embedded into routine farm management systems, which can be an add-on unit of current robotic milking system (RMS). The robust monitoring system would measure real-time data from the ambient environment, animal responses, as well as the operation pattern of mitigations. Furthermore, by facilitating big-data analysis techniques to be used on individual farms, (or for individual animal) it might be possible to implement self-calibration procedure for the assessment, thresholds and control algorithms responding to varied cow’s production status, farm management factors and local climate changes. The follow-up research presented in this thesis demonstrated the possibility of establishing more accurate heat stress threshold by taking advantage of the routinely collected datasets on robotic dairy farms and local weather stations. The dairy farm observed in this study situated in a subtropical climate region, held around 150 lactating cows and applied RMS with semi-free traffic. The farm management system recorded specific production, health and behaviour information of each individual animal over 5-year period (2013-2017), which was utilized for the analysis in this study. The historical climate conditions were measured by local weather station with dataset accessible on a government website, which provided the data of daily thermal parameters for this research. Furthermore, data-loggers were also positioned on farm from April 2016 to November 2017 to measure thermal parameters hourly. By using the collected information, this study compared the performance of published thermal comfort indices (TCIs) as the indicators of cows’ responses to heat stress. These TCIs included temperature humidity index (THI), black globe humidity index (BGHI), environmental stress index (ESI), equivalent temperature index (ETI), heat load index (HLI), respiration rate index (RR) and comprehensive climate index (CCI). The comparison also included the basic thermal parameters: dry bulb temperature (Tdb), relative humidity (RH), wet bulb temperature (Twb) and dew point temperature (Tdp). The strength of their correlation with daily milk yield (DMY) and milk temperature (MT) was tested statistically. The regression analysis using climate dataset from local weather station and on-farm data-loggers were also compared to validate the accuracy of online data source. The statistical analysis found similar performance between TCIs and Tdb. It was also found that the inaccuracy of online data source, due to spatial variability between on-farm measurement and local weather station, could be neglected when modelling the association between TCIs and MT. A general threshold with significant decline of DMY was identified as THI>64 for cows with DMY around 31 kg/cow/day. As Tdb can provide sufficient accuracy in the prediction of heat stress, the dynamic thresholds of daily minimum and mean temperature (Tmin and Tmean) were then established using individual information of 126 cows. The dataset was grouped according to the age, body weight (BW) and days in milk (DIM) of cows. Specific thresholds for different groups were identified using single broke-line regression between temperature and DMY or MT. Machine learning model was applied to transform these thresholds of different group into a decision tree of dynamic thresholds, which achieved overall 94% accuracy with the thresholds of Tmin, and 79% accuracy with the thresholds of Tmean. Moreover, for the whole herd, multiple broken-line regression was applied, which established four stages of heat stress including as thermal comfort stage (Tmin 14 oC, Tmean > 16 oC) based on the change of DMY and MT. To gain more understanding of the heat stress influence on animal behaviours in RMS, extra dependent variables were imported into new models involving rumination time (RT), time of milking (TM), miking frequency (MF), milking duration (MD), milking speed (MS), and milk yield per milking (MY). A new index – rumination efficiency index (REI) was created to evaluate the efficiency of rumination. According to the multiple broken-line regression, 5 minutes reduction of RT, 0.08 kg/cow/hour reduction of REI and 1% increase of low efficiency miking (LEM) were found to be associated with raising 1 oC of Tmean. It was also demonstrated that cows could not adjust their pattern of milking behaviour (e.g. visiting time pattern) coping with heat stress. Statistically, 86% of their milking event happened between 07:00 AM and 09:00 AM. However, REI and RMS performance can be improved by adjusting the pattern of milking behaviour such as milking interval (MI). The financial comparison between current pattern and adjusted pattern estimated that nearly $400 daily benefit could be gained. In addition, this study also analysed the cumulative and lag effect of heat stress which were time-related. For the short-term effect, an intensity duration index (IDI) was defined by multiplying the mean temperature of the heat stress period with the duration of the period. Multiple levels of heat stress were then identified by IDI with different decline rate of DMY from -0.01 to -0.13 kg/cow/IDI. For long-term heat stress, the lag and cumulative effect was demonstrated by the negative correlation between the duration of heat stress during dry-off period and the production performance of the subsequent lactation period. The lag effect was found to be 3-4 days, while the cumulative effect could last for about 2 months. The regression between DMY and the average temperature of the period with heat stress during the 2 months before test day (HSmean) was found to perform stronger correlation (R2 equals 0.73-0.77) than the regression between DMY and same day’s temperature (R2 equals 0.65-0.68)

An innovative portable monitoring unit for air quality in animal housing

The Portable Monitor Unit (PMU) is a system developed to measure ammonia (NH3) and carbon-dioxide (CO2) concentrations in CAFOs. However, the NH3 electrochemical (EC) sensor used in the existing PMU design has become obsolete; moreover, the original PMU design required a substantial amount of manual work for system setup and data post-processing. Therefore, the objective of this project was to upgrade the PMU with a new NH3 EC sensor and data acquisition and control system. In this project, four different models of NH3 EC sensors were evaluated for suitability in this application. One, the HONEYWELL EC FX sensor, was selected as the replacement. It demonstrated sufficiently fast response time to a step change in NH3 (60 s to reach 95% equilibrium) and reasonable listed accuracy (±5 ppm at 100 ppm full scale). Other evaluation criteria were nonlinearity (maximum 3.8 ppm with 54 ppm NH3 reference gas), uncertainty (about ±3 ppm) and drift error (maximum 4.8 ppm within 48 h). The sensor was deemed to be acceptable based on these evaluations, and a multi-point calibration and 48 h laboratory evaluation with 24.3, 54 and 99.3 ppm NH¬3 reference gas. The new sensor was utilized in the upgraded PMU system with 5.5 min sampling (3 min line purge + 2.5 min measurement) and 54.5 min sensor purge. An Arduino microprocessor (Mega 2560) with extended function modules (Wireless SD shield, Real Time Clock shield, Relay and LCD screen) provided functions including sampling control, system auto-reset, data centralization, real-time data processing and wireless data transfer. The upgraded PMU (PMU III) was evaluated in two field tests at a commercial laying hen facility, and the system successfully implement the upgraded functions. The system was modified between the first and second field test mainly to improve the virtual timer and real-time data processing algorithm in its program. With the modified PMU III system, the data acquisition system uses a real time clock, so that during the measurement, real-time processing can provide reasonable results compared to the post-processing with consistency of 94%. A 12 h laboratory evaluation was performed to the NH3 sensor after the field tests for comparing the consistency with the prior 48 h laboratory evaluation, and thus demonstrated the reliability (maximum difference 2.6 ppm with 24.3 ppm NH3 reference gas) of the sensor during the field test

Climate change impact, adaptation, and mitigation in temperate grazing systems: a review

Managed temperate grasslands occupy 25% of the world, which is 70% of global agricultural land. These lands are an important source of food for the global population. This review paper examines the impacts of climate change on managed temperate grasslands and grassland-based livestock and effectiveness of adaptation and mitigation options and their interactions. The paper clarifies that moderately elevated atmospheric CO2 (eCO2) enhances photosynthesis, however it may be restiricted by variations in rainfall and temperature, shifts in plant’s growing seasons, and nutrient availability. Different responses of plant functional types and their photosynthetic pathways to the combined effects of climatic change may result in compositional changes in plant communities, while more research is required to clarify the specific responses. We have also considered how other interacting factors, such as a progressive nitrogen limitation (PNL) of soils under eCO2, may affect interactions of the animal and the environment and the associated production. In addition to observed and modelled declines in grasslands productivity, changes in forage quality are expected. The health and productivity of grassland-based livestock are expected to decline through direct and indirect effects from climate change. Livestock enterprises are also significant cause of increased global greenhouse gas (GHG) emissions (about 14.5%), so climate risk-management is partly to develop and apply effective mitigation measures. Overall, our finding indicates complex impact that will vary by region, with more negative than positive impacts. This means that both wins and losses for grassland managers can be expected in different circumstances, thus the analysis of climate change impact required with potential adaptations and mitigation strategies to be developed at local and regional levels

Hybrid plasmonic modes for enhanced refractive index sensing

Compared to single nanoparticles, strongly coupled plasmonic nanoparticles provide attractive advantages owing to their ability to exhibit multiple resonances with unique spectral features and higher local field intensity. These enhanced plasmonic properties of coupled metal nanoparticles have been used for various applications including realization of strong light-matter interaction, photocatalysis, and sensing applications. In this article, we review the basic physics of hybrid plasmonic modes in coupled metallic nanodimers and assess their potentials for refractive index sensing. In particular, we overview various modes of hybrid plasmons including bonding and antibonding modes in symmetric nanodimers, Fano resonances in asymmetric nanodimers, charge transfer plasmons in linked nanoparticle dimers, hybrid plasmon modes in nanoshells, and gap modes in particle-on-mirror configurations. Beyond the dimeric nanosystems, we also showcase the potentials of hybrid plasmonic modes in periodic nanoparticle arrays for sensing applications. Finally, based on the critical assessment of the recent researches on coupled plasmonic modes, the outlook on the future prospects of hybrid plasmon based refractometric sensing are discussed We believe that, given their tunable resonances and ultranarrow spectral signatures, coupled metal nanoparticles are expected to play key roles in developing precise plasmonic nanodevices with extreme sensitivity

Fast pyrolysis kinetics of waste tires and its products studied by a wireless-powered thermo-balance

Funding Information: Authors appreciate the financial support from the Liao Ning Revitalization Talents Program (grant number: XLYC2007179 ). Publisher Copyright: © 2023 The AuthorsPeer reviewedPublisher PD

SyzTrust: State-aware Fuzzing on Trusted OS Designed for IoT Devices

Trusted Execution Environments (TEEs) embedded in IoT devices provide a deployable solution to secure IoT applications at the hardware level. By design, in TEEs, the Trusted Operating System (Trusted OS) is the primary component. It enables the TEE to use security-based design techniques, such as data encryption and identity authentication. Once a Trusted OS has been exploited, the TEE can no longer ensure security. However, Trusted OSes for IoT devices have received little security analysis, which is challenging from several perspectives: (1) Trusted OSes are closed-source and have an unfavorable environment for sending test cases and collecting feedback. (2) Trusted OSes have complex data structures and require a stateful workflow, which limits existing vulnerability detection tools. To address the challenges, we present SyzTrust, the first state-aware fuzzing framework for vetting the security of resource-limited Trusted OSes. SyzTrust adopts a hardware-assisted framework to enable fuzzing Trusted OSes directly on IoT devices as well as tracking state and code coverage non-invasively. SyzTrust utilizes composite feedback to guide the fuzzer to effectively explore more states as well as to increase the code coverage. We evaluate SyzTrust on Trusted OSes from three major vendors: Samsung, Tsinglink Cloud, and Ali Cloud. These systems run on Cortex M23/33 MCUs, which provide the necessary abstraction for embedded TEEs. We discovered 70 previously unknown vulnerabilities in their Trusted OSes, receiving 10 new CVEs so far. Furthermore, compared to the baseline, SyzTrust has demonstrated significant improvements, including 66% higher code coverage, 651% higher state coverage, and 31% improved vulnerability-finding capability. We report all discovered new vulnerabilities to vendors and open source SyzTrust.Comment: To appear in the IEEE Symposium on Security and Privacy (IEEE S&P) 2024, San Francisco, CA, US