Search Results (29)

Devices for assessing the quality of animal environments are important for maintaining production animals, thus improving animal well-being and mitigating pollutant emissions. Therefore, an IoT system was developed and preliminarily assessed across various livestock housing types, including those for pigs, dairy cows, and rabbits. This system measures and transmits key parameters, such as ambient temperature; relative humidity; light intensity; sound pressure; levels of carbon dioxide, ammonia, and hydrogen sulfide; and particulate matter and volatile organic compound concentrations. These data are sent from the sensors to a gateway and then displayed on a dashboard for monitoring. A preliminary evaluation of the system’s performance in controlled conditions revealed that the device’s accuracy and precision were within 2.7% and 3.3% of the measured values, respectively. The system was deployed in three case studies involving rabbit, pig, and dairy cow farms. The results demonstrated its effectiveness in assessing pollutant emissions and identifying critical situations where gas concentrations exceeded threshold levels, thus posing a risk to the animals. By systematically applying this technology on livestock farms to obtain a detailed understanding of the microclimatic and air quality conditions in which the animals live, animal welfare can be significantly improved. Full article

A pilot anaerobic bioreactor requires near-daily monitoring and frequent maintenance. This study aimed to upgrade a pilot bioreactor into a low-cost IoT device via ESP32 microcontrollers. The methodology was based on remote data acquisition and online monitoring of various parameters towards assessing the anaerobic digestion performance. A semi-continuous tank bioreactor with a 60 L total volume was initially inoculated mainly with livestock manure and fed daily with a mixture of glucose, gelatin, and oleic acid, supplemented with a basic anaerobic medium. Under steady-state conditions, the organic loading rate was 2 g VS L_R⁻¹ d⁻¹. Sensors for pH, temperature, REDOX potential, and ammonium concentration, along with devices measuring biogas volume and methane content, were integrated and validated against analytical methods. Biogas production was recorded accurately, enabling the early detection of production declines through ex-situ data analysis. Methane concentration variance was less than 6% compared to gas chromatography, while temperature and pH deviations were 0.15% and 1.67%, respectively. Ammonia ion measurements required frequent recalibration due to larger fluctuations. This IoT-enhanced system effectively demonstrated real-time monitoring of critical bioreactor parameters, with ESP32 enabling advanced control and monitoring capabilities. Full article

Climate change is a worldwide problem that is manifested in livestock farming with a decrease in animal health and welfare and economic losses due to heat stress. Therefore, a precise and continuous recording of the barn climate is essential to be able to implement actions at a certain threshold. The aim of this study was to evaluate a logger for temperature and humidity (Kestrel Drop D2) marketed for on-farm use in comparison to various other temperature/humidity data loggers under field conditions. Four different sensors were used and placed in different settings in cattle barns to correlate temperature and humidity measurements. Data were recorded for over a year in total. The data were very highly correlated. Furthermore, the area under the curve for the evaluated logger in comparison to the other ones was 0.99 to 1.0, using a temperature–humidity index cut-off of 72, often set to define heat stress. In conclusion, the evaluated logger performed equally well as the other used devices. For on-farm use, it is suitable. Full article

A novel microfluidic ractopamine (RAC) detection platform consisting of a microfluidic RAC chip and a smart analysis device is proposed for the determination of RAC concentration in meat samples. This technology utilizes gold nanoparticles (AuNPs) modified with glutamic acid (GLU) and polyethyleneimine (PEI) to measure RAC concentration in food products. When RAC is present, AuNPs aggregate through hydrogen bonding, causing noticeable changes in their optical properties, which are detected using a self-built UV–visible micro-spectrophotometer. Within the range of 5 to 80 ppb, a linear relationship exists between the absorbance ratio (A_693nm/A_518nm) (Y) and RAC concentration (X), expressed as Y = 0.0054X + 0.4690, with a high coefficient of determination (R² = 0.9943). This method exhibits a detection limit of 1.0 ppb and achieves results within 3 min. The practical utility of this microfluidic assay is exemplified through the evaluation of RAC concentrations in 50 commercially available meat samples. The variance between concentrations measured using this platform and those determined via liquid chromatography–tandem mass spectrometry (LC-MS/MS) is less than 8.33%. These results underscore the viability of the microfluidic detection platform as a rapid and cost-effective solution for ensuring food safety and regulatory compliance within the livestock industry. Full article

Monitoring air quality in livestock farming facilities is crucial for ensuring the health and well-being of both animals and workers. As livestock farming can contribute to the emission of various gaseous and particulate pollutants, there is a pressing need for advanced air quality monitoring systems to manage and mitigate these emissions effectively. This study introduces a multi-sensor air quality monitoring system designed specifically for livestock farming environments. Utilizing open-source tools and low-cost sensors, the system can measure multiple air quality parameters simultaneously. The system architecture is based on SOLID principles to ensure robustness, scalability, and ease of maintenance. Understanding a trend of evolution of air quality monitoring from single-parameter measurements to a more holistic approach through the integration of multiple sensors, a multi-sensor platform is proposed in this work. This shift towards multi-sensor systems is driven by the recognition that a comprehensive understanding of air quality requires consideration of diverse pollutants and environmental factors. The aim of this study is to construct a multi-sensor air quality monitoring system with the use of open-source tools and low-cost sensors as a tool for Precision Livestock Farming (PLF). Analysis of the data collected by the multi-sensor device reveals some insights into the environmental conditions in the monitored barn. Time-series and correlation analyses revealed significant interactions between key environmental parameters, such as strong positive correlations between ammonia and hydrogen sulfide, and between total volatile organic compounds and carbon dioxide. These relationships highlight the critical impact of these odorants on air quality, emphasizing the need for effective barn environmental controls to manage these factors. Full article

We introduce a high-throughput 3D scanning system designed to accurately measure cattle phenotypes. This scanner employs an array of depth sensors, i.e., time-of-flight (ToF) sensors, each controlled by dedicated embedded devices. The sensors generate high-fidelity 3D point clouds, which are automatically stitched using a point could segmentation approach through deep learning. The deep learner combines raw RGB and depth data to identify correspondences between the multiple 3D point clouds, thus creating a single and accurate mesh that reconstructs the cattle geometry on the fly. In order to evaluate the performance of our system, we implemented a two-fold validation process. Initially, we quantitatively tested the scanner for its ability to determine accurate volume and surface area measurements in a controlled environment featuring known objects. Next, we explored the impact and need for multi-device synchronization when scanning moving targets (cattle). Finally, we performed qualitative and quantitative measurements on cattle. The experimental results demonstrate that the proposed system is capable of producing high-quality meshes of untamed cattle with accurate volume and surface area measurements for livestock studies. Full article

The dairy industry is facing criticism for its role in exacerbating global GHG emissions, as climate change becomes an increasingly pressing issue. These emissions mostly originate from methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂). An optimal strategy involves the creation of an economical monitoring device to evaluate methane emissions from dairy animals. Livestock production systems encounter difficulties because of escalating food demand and environmental concerns. Enhancing animal productivity via nutrition, feeding management, reproduction, or genetics can result in a decrease in CH₄ emissions per unit of meat or milk. This CH₄ unit approach allows for a more accurate comparison of emissions across different animal production systems, considering variations in productivity. Expressing methane emissions per unit allows for easier comparison between different sources of emissions. Expressing emissions per unit (e.g., per cow) highlights the relative impact of these sources on the environment. By quantifying emissions on a per unit basis, it becomes easier to identify high-emission sources and target mitigation efforts accordingly. Many environmental policies and regulations focus on reducing emissions per unit of activity or output. By focusing on emissions per unit, policymakers and producers can work together to implement practices that lower emissions without sacrificing productivity. Expressing methane emissions in this way aligns with policy goals aimed at curbing overall greenhouse gas emissions. While it is true that total emissions affect the atmosphere globally, breaking down emissions per unit helps to understand the specific contributions of different activities and sectors to overall greenhouse gas emissions. Tackling cattle health issues can increase productivity, reduce GHG emissions, and improve animal welfare. Addressing livestock health issues can also provide favourable impacts on human health by reducing the prevalence of infectious illnesses in livestock, thereby mitigating the likelihood of zoonotic infections transmitting to humans. The progress in animal health offers the potential for a future in which the likelihood of animal diseases is reduced because of improved immunity, more effective preventative techniques, earlier identification, and innovative treatments. The primary objective of veterinary medicine is to eradicate clinical infectious diseases in small groups of animals. However, as the animal population grows, the emphasis shifts towards proactive treatment to tackle subclinical diseases and enhance production. Proactive treatment encompasses the consistent monitoring and implementation of preventive measures, such as vaccination and adherence to appropriate nutrition. Through the implementation of these measures, the livestock industry may enhance both animal well-being and mitigate the release of methane and nitrous oxide, thereby fostering environmental sustainability. In addition, advocating for sustainable farming methods and providing farmers with education on the significance of mitigating GHG emissions can bolster the industry’s endeavours to tackle climate change and infectious illnesses. This will result in a more robust and environmentally sustainable agriculture industry. This review seeks to conduct a thorough examination of the correlation between the health condition of cattle, the composition of milk produced, and the emissions of methane gas. It aims to identify areas where research is lacking and to provide guidance for future scientific investigations, policy making, and industry practices. The goal is to address the difficulties associated with methane emissions in the cattle industry. The primary global health challenge is to identify the causative relationship between climate change and infectious illnesses. Reducing CH₄ and N₂O emissions from digestive fermentation and animal manure can be achieved by improving animal well-being and limiting disease and mortality. Full article

From connected sensors in soils, on animals or crops, and on drones, to various software and services that are available, “smart” technologies are changing the way farming is carried out. These technologies allow producers to look beyond what the eye can see by collecting non-traditional data and then using analytics tools to improve both food sustainability and profitability. “Smart Agriculture/farming” (SA) or “Digital Agriculture” (DA), often used interchangeably, refer to precision agriculture that is thus connected in a network of sensing and acting. It is a concept that employs modern information technologies, precision climate information, and crop/livestock developmental information to connect production variables to increase the quantity and quality of agricultural and food products. This is achieved by measuring and analyzing variables accurately, feeding the information into the cloud from edge devices, extracting trends from the various data, and subsequently providing information back to the producer in a timely manner. Smart agriculture covers many disciplines, including biology, mechanical engineering, automation, machine learning, artificial intelligence, and information technology-digital platforms. Minimum standards have been proposed for stakeholders with the aim to move toward this highly anticipated and ever-changing revolution. These foundational standards encompass the following general categories, including precise articulation of objectives, and baseline standards for the Internet of Things (IoT), including network infrastructure (e.g., stable 4G or 5G networks or a wireless local area network (WLAN) are available to end users). To sum up, SA aims to improve production efficiency, enhance the quality and quantity of agricultural products, reduce costs, and improve the environmental footprint of the industry. SA’s ecosystem should be industry self-governed and collaboratively financed. SA stakeholders and end-users’ facilities should meet standard equipment requirements, such as sensor accuracy, end data collectors, relevant industry compliant software, and trusted data analytics. The SA user is willing to be part of the SA ecosystem. This short perspective aims to summarize digital/smart agriculture concept in plain language. Full article

Non-contact devices have been used in the measurement of body temperature in livestock production as a tool for testing disease in different species. However, there are few studies about the variation and correlations in body temperature between rectal temperature (RT) and non-contact devices such as non-contact infrared thermometers (NCIT) and thermal imaging/infrared thermography (IRT). The objective of this work was to evaluate the accuracy of non-contact devices to measure the body temperature in sheep, considering six body regions and the possibility of implementing these systems in herd management. The experiment was carried out at the experimental farm of the Catholic University of Valencia, located in the municipality of Massanassa in July of 2021, with 72 dry manchega ewes, and we compared the rectal temperature with two types of non-contact infrared devices for the assessment of body temperature in healthy sheep. Except for the temperature taken by NCIT at the muzzle, the correlation between RT vs. NCIT or IRT showed a low significance or was difficult to use for practical flock management purposes. In addition, the variability between devices was high, which implies that measurements should be interpreted with caution in warm climates and open pens, such as most sheep farms in the Spanish Mediterranean area. The use of infrared cameras devices to assess body temperature may have a promising future, but in order to be widely applied as a routine management method on farms, the system needs to become cheaper, simpler in terms of measurements and quicker in terms of analyzing results. Full article

Generally, measuring the weight of livestock is difficult; it is time consuming, inconvenient, and stressful for both livestock farms and livestock to be measured. Therefore, these problems must be resolved to boost convenience and reduce economic costs. In this study, we develop a portable prediction system that can automatically predict the weights of pigs, which are commonly used for consumption among livestock, using Raspberry Pi. The proposed system consists of three parts: pig image data capture, pig weight prediction, and the visualization of the predicted results. First, the pig image data are captured using a three-dimensional depth camera. Second, the pig weight is predicted by segmenting the livestock from the input image using the Raspberry Pi module and extracting features from the segmented image. Third, a 10.1-inch monitor is used to visually show the predicted results. To evaluate the performance of the constructed prediction device, the device is learned using the 3D sensor dataset collected from specific breeding farms, and the efficiency of the system is evaluated using separate verification data. The evaluation results show that the proposed device achieves approximately 10.702 for RMSE, 8.348 for MAPE, and 0.146 for MASE predictive power. Full article

Conventional methods for monitoring ammonia (NH₃) emissions from livestock farms have several challenges, such as a poor environment for measurement, difficulty in accessing livestock, and problems with long-term measurement. To address these issues, we applied various neural network models for the long-term prediction of NH₃ concentrations from sow farms in this study. Environmental parameters, including temperature, humidity, ventilation rate, and past records of NH₃ concentrations, were given as inputs to the models. These neural network models took the encoder or the feature extracting parts from the representative deep learning models, including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Transformer, to encode temporal patterns of time series. However, all of these models adopted dense layers for the decoder to format the task of long-term prediction as a regression problem. Due to their regression nature, all models showed a robust performance in predicting long-term NH₃ concentrations at a scale of weeks or even months despite there being a relatively short period of input signals (a few days to a week). Given one week of input, LSTM showed the minimum mean absolute errors (MAE) of 1.83, 1.78, and 1.87 ppm for the prediction of one, two, and three weeks, respectively, whereas Transformer performed best with a MAE of 1.73 ppm for a four-week prediction. In the long-term estimation of spanning months, LSTM showed the minimum MAEs of 1.95 and 1.90 ppm when trained on predicting two and three weeks of windows. At the same condition, Transformer gave the minimum MAEs of 1.87 and 1.83 when trained on predicting one and four weeks of windows. Overall, the neural network models can facilitate the prediction of national-level NH₃ emissions, the development of mitigation strategies for NH₃-derived air pollutants, odor management, and the monitoring of animal-rearing environments. Further, their integration of real-time measurement devices can significantly prolong device longevity and offer substantial cost savings. Full article

Among the techniques for capturing nearby groundwater, the covered horizontal well (draining gallery) stands out in its different types of water mine, qanat, and cimbre. The water collected by these means is used to supply people and livestock, in irrigation, in the movement of hydraulic devices, etc. Because they are carried to the surface by gravity (without the need for energy) and because only the recharging of the groundwater table that takes place after the rains are captured, they serve as models for sustainable water use. The measured flow is variable depending on the rainfall and infiltration, but the quality of the water makes it its own water resources of great interest at the local level. The study area is the territory of the Southeast of Spain (more than 22,000 km²), with a rich hydraulic heritage. The research is a regional analysis (diachronic and compared) of several socio-hydric systems, with extensive fieldwork. Full article

Cogeneration sources play a very important role in the power industry with dispersed renewable sources with forced generation (e.g., photovoltaics and wind generators). They also fit into the circular economy by increasing the efficiency of fuel use, including biogas from agricultural or livestock waste. The aim of our research was to develop an effective source of electricity powered by agricultural biogas. The most important features of such a source are: operation in on-grid and off-grid mode, as well as a low cost of the device and uncomplicated operation. In addition, in Europe, the source of electricity connected to the power grid must meet the technical requirements of the “Network Codes Requirements for Generators” (NC RfG) network code. The appropriate certificate is easier to obtain using a power converter system for the source. For this purpose, an induction generator with a converter system and a small battery was planned. A converter system was developed and built, and then tests were carried out in various operating modes. During the measurements, it was confirmed that the requirements for the quality of electricity for off-grid and on-grid operation modes were met. The assumed maximum time of voltage recovery after changing the operating mode, amounting to 40 ms, was not exceeded. Furthermore, the limit values of phase voltages with unsymmetrical load, amounting to ±10% of the rated voltage, were not exceeded. In the battery usage off-grid mode, the time after a step change in the load power was not longer than 2 s. Full article

Bovine maternal behavior is known to be influenced by a variety of factors including hormonal mediation, breed, age, parity, host genetics and general management practices. Following centuries of varying levels of domestication processes, the behavior of the bovine cow has altered from that of her original wild ungulate ancestors, although many maternal instincts have remained unchanged. The influence of maternal behavior on calf health and performance is of interest to cow-calf beef production operations, as in most instances, the cow is solely responsible for rearing the calf until weaning. However, investigating the magnitude of this influence is challenging, in part because objective measurement of behavioral traits is difficult, particularly in extensive settings. In recent years, while a number of remote monitoring devices have been developed that afford opportunities for objective measurement of behavioral traits in livestock, characterization of physiological mechanisms that underlie superior maternal behavior, including identification of potential biomarkers remains elusive in cattle. Hormonal profiles during the periparturient period have been shown to influence behavioral patterns in both current and future generations in other mammalian species and may provide insights into the physiology of bovine maternal behavior. Therefore, the aim of this review is to describe general characteristics of bovine maternal behavior and the factors known to influence it, including hormonal drivers, through which cross-reference to other species is made. Current methods of measuring and assessing behavior that may also be applicable to most production settings have also been reviewed. At present, there is no known hormonal assay that can be used to measure and/or reliably predict bovine maternal behavior post-calving or across generations. Being able to objectively assess superior maternal behavior, whether that be through remote monitoring, hormonal profiling or indirectly through measuring calf performance will be beneficial to livestock industries in the future. Full article

After corn straw and livestock manure are fermented and decomposed, grass seeds are added. The substrate grass seed blanket is made by screw extrusion, applied to park greening, square greening, protective greening, and residential area greening. With this device, the purpose of reducing the labor force and improving space utilization rate can be achieved. The working principle of the substrate grass seed blanket extrusion device is mainly described, and the extrusion process is analyzed including: compaction and filling stage, surface deformation and compaction stage, plastic deformation stage, and molding stage. The main experimental factors are determined through theoretical analysis of screw size and working parameters, including screw pitch, screw length, screw diameter, and screw speed. Using the EDEM simulation analysis method, taking the quality of extruded particles and the uniformity of grass seed mixing as test indexes, and under the condition of the same extrusion time of 30 s using Design-Expert software to carry out an orthogonal quadratic rotation combination test, a significant regression model was obtained. The effects of different parameters and extrusion conditions on grass seed blanket forming influence were studied by response surface analysis. The optimal working parameters were obtained: screw speed 250 r·min⁻¹, screw pitch 120~80 mm, screw diameter 240 mm, and screw length 400 mm. With the same extrusion time of 30 s, extruded pellet mass was 2620 g, calculated mass flow rate was 131 g/s, and the grass seed mixing uniformity was 92.35%. Under the optimal simulation conditions, the prototype was manufactured, and the actual verification test was carried out. The errors between the measured values of extruded substrate quality and grass seed mixing uniformity and the simulation test results were 3.4% and 2.5%, respectively, which met the requirements of the grass seed blanket extrusion molding device. Full article