Comparison of ammonia air concentration before and during the spread of COVID-19 in Lombardy (Italy) using ground-based and satellite data.

Several anthropogenic activities have undergone major changes following the spread of the COVID-19 pandemic, which in turn has had consequences on the environment. The effect on air pollution has been studied in detail in the literature, although some pollutants, such as ammonia (NH3), have received comparatively less attention to date. Focusing on the case of Lombardy in Northern Italy, this study aimed to evaluate changes in NH3 atmospheric concentration on a temporal scale (the years from 2013 to 2019 compared to 2020) and on a spatial scale (countryside, city, and mountain areas). For this purpose, ground-based (from public air quality control units scattered throughout the region) and satellite observations (from IASI sensors on board MetOp-A and MetOp-B) were collected and analyzed. For ground-based measurements, a marked spatial variability is observed between the different areas while, as regards the comparison between periods, statistically significant differences were observed only for the countryside areas (+31% in 2020 compared to previous years). The satellite data show similar patterns but do not present statistically significant differences neither between different areas, nor between the two periods. In general, there have been no reduction effects of atmospheric NH3 as a consequence of COVID-19. This calls into question the role of the agricultural sector, which is known to be the largest responsible for NH3 emissions. Even if the direct comparison between the two datasets shows little correlation, their contextual consideration allows making more robust considerations regarding air pollutants.

Describing the trend of ammonia, particulate matter and nitrogen oxides: The role of livestock activities in northern Italy during Covid-19 quarantine.

Nitrogen oxides (NOx), sulphur oxides (SOx) and ammonia (NH3) are among the main contributors to the formation of secondary particulate matter (PM2.5), which represent a severe risk to human health. Even if important improvements have been achieved worldwide, traffic, industrial activities, and the energy sector are mostly responsible for NOx and SOx release; instead, the agricultural sector is mainly responsible for NH3 emissions. Due to the emergency of coronavirus disease, in Italy schools and universities have been locked down from late February 2020, followed in March by almost all production and industrial activities as well as road transport, except for the agricultural ones. This study aims to analyze NH3, PM2.5 and NOx emissions in principal livestock provinces in the Lombardy region (Brescia, Cremona, Lodi, and Mantua) to evaluate if and how air emissions have changed during this quarantine period respect to 2016-2019. For each province, meteorological and air quality data were collected from the database of the Regional Agency for the Protection of the Environment, considering both data stations located in the city and the countryside. In the 2020 selected period, PM2.5 reduction was higher compared to the previous years, especially in February and March. Respect to February, PM2.5 released in March in the city stations reduced by 19%-32% in 2016-2019 and by 21%-41% in 2020. Similarly, NOx data of 2020 were lower than in the 2016-2019 period (reduction in March respect to February of 22-42% for 2016-2019 and of 43-62% for 2020); in particular, this can be observed in city stations, because of the current reduction in anthropogenic emissions related to traffic and industrial activities. A different trend with no reductions was observed for NH3 emissions, as agricultural activities have not stopped during the lockdown. Air quality is affected by many variables, for which making conclusions requires a holistic perspective. Therefore, all sectors must play a role to contribute to the reduction of harmful pollutants.

Technical note: Validation and comparison of 2 commercially available activity loggers.

To validate the accuracy of 2 commercially available activity loggers in determining lying, standing, walking, and number of steps in dairy cows, 30 cows were fitted with the CowScout Leg (GEA Farm Technologies, Bönen, Germany) system and the IceTag (IceRobotics Ltd., Edinburgh, Scotland) system. The CowScout Leg logger reports standing and lying in 15-min periods, whereas the IceTag logger reports standing and lying every second. To make data comparable, the IceTag data were therefore also summarized over 15-min periods corresponding to the paired CowScout Leg sensor. These data from the 2 systems were then analyzed (more than 1,000 cow days in total). Video recordings of a total of 29.5 h were used for labeling the behaviors of the selected cows (n = 10) and these labels were used as a gold standard to determine the accuracy with which these 2 loggers can record behavioral states lying, standing, walking, and the behavioral event number of steps. A concordance correlation coefficient analysis showed that both the standing and lying durations obtained with the 2 systems were almost perfectly correlated with the video labeling (IceTag: ρc = 0.999 and 0.999, respectively; CowScout Leg: ρc = 0.995 and 0.996, respectively). However, both loggers performed poorly regarding number of steps (classified as an event; IceTag: ρc = 0.629; CowScout Leg: ρc = 0.678) and CowScout Leg did not detect walking (classified as a state) very accurately (ρc = 0.860). The IceTag system does not measure walking behavior. When comparing the 2 loggers, the correlation between them for standing and lying was substantial (ρc = 0.953 and ρc = 0.953, respectively). The number of steps poorly correlated between the 2 loggers (ρc = 0.593), which might be due to the CowScout Leg logger being attached to the front leg and the IceTag logger being attached to the hind leg. We conclude that both the IceTag and the CowScout Leg logger are able to record standing and lying almost perfectly, but the step counting by both loggers and the walking recording by the CowScout Leg logger are not very accurate.

A Feasibility Study on the Use of a Structured Light Depth-Camera for Three-Dimensional Body Measurements of Dairy Cows in Free-Stall Barns.

Frequent checks on livestock's body growth can help reducing problems related to cow infertility or other welfare implications, and recognizing health's anomalies. In the last ten years, optical methods have been proposed to extract information on various parameters while avoiding direct contact with animals' body, generally causes stress. This research aims to evaluate a new monitoring system, which is suitable to frequently check calves and cow's growth through a three-dimensional analysis of their bodies' portions. The innovative system is based on multiple acquisitions from a low cost Structured Light Depth-Camera (Microsoft Kinect™ v1). The metrological performance of the instrument is proved through an uncertainty analysis and a proper calibration procedure. The paper reports application of the depth camera for extraction of different body parameters. Expanded uncertainty ranging between 3 and 15 mm is reported in the case of ten repeated measurements. Coefficients of determination R² > 0.84 and deviations lower than 6% from manual measurements where in general detected in the case of head size, hips distance, withers to tail length, chest girth, hips, and withers height. Conversely, lower performances where recognized in the case of animal depth (R² = 0.74) and back slope (R² = 0.12).

Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds.

Bovine respiratory disease (BRD) complex in calves impairs health and welfare and causes severe economic losses for the Stockperson. Early recognition of BRD should lead to earlier veterinary (antibiotic/anti-inflammatory) treatment interventions thereby reducing the severity of the disease and associated costs. Coughing is one of the clinical manifestations of BRD. It is believed that by automatically and continuously monitoring the sounds within calf houses, and analysing the coughing frequency, early recognition of BRD in calves is possible. Therefore, the objective of the present study was to develop an automated calf cough monitor and examine its potential as an early warning system for BRD in artificially reared dairy calves. The coughing sounds of 62 calves were continuously recorded by a microphone over a three-month period. A sound analysis algorithm was developed to distinguish calf coughs from other sounds (e.g. mechanical sounds). During the sound recording period the health of the calves was assessed and scored periodically per week by a trained human observer. Calves presenting with BRD received antibiotic and/or anti-inflammatory treatment and the dates of treatment were recorded. This treatment date reference served as a comparison for the investigation of whether an increase in coughing frequency could be related to calves developing BRD. The calf cough detection algorithm achieved 50.3% sensitivity, 99.2% specificity and 87.5% precision. Four out of five periods, where coughing frequency was observed to be increased, coincided with the development of BRD in more than one calf. This period of increased coughing frequency was always observed before the calves were treated. Therefore, the calf cough monitor has the potential to identify early onset of BRD in calves.

Effect of extended heat stress in dairy cows on productive and behavioral traits.

This study evaluates the response of dairy cows to short and extended heat stressing conditions (from 1 to 28 days), as expressed in changes in their behavior. Due to climate change, heat stress and strong heat waves are frequently affecting the productivity and behavior of dairy cows. In the five years under study from 2018 to 2022, two were characterized by extremely strong heat waves occurring in the region analyzed in this study (Northern Italy). The dairy cattle farm involved in this study is located in Northern Italy and includes about 1 600 Holstein Friesian lactating dairy cows. Phenotypic data were provided by the Afimilk system and compromised behavioral and productive traits. Behavioral traits analyzed were activity, rest time, rest bouts, rest ratio, rest per bout and restlessness. Production traits were daily milk yield, average milking time, somatic cell count, fat percentage, protein percentage and lactose percentage. Climate data came from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources database. Heat stress was analyzed considering Temperature-Humidity Index (THI) averaged over 28 different time windows of continuous heat stress. Results showed that rest time and milk yield were the two traits most affected by the increased THI. Rest time was immediately affected by high THI, showing a marked decrease already from 1d window and maintaining this all over the other windows. Furthermore, results show that rest time and rest ratio were only slightly negatively correlated with milk yield (-0.14 and -0.15). In addition, heat stress has a different effect depending on parity and lactation stages on the studied traits. In conclusion, the results indicate that heat stress increases activity and compromises milk production, rest time and milk quality traits. Results further suggest that rest time can be a better parameter than activity to describe the effects of heat stress on dairy cattle. The novel approach used in this study is based on the use of different time windows (up to 28 days) before the emergence of undesired THI and allows to identify the traits that are immediately influenced by the undesirable THI values and those that are influenced only after a prolonged heat stress period.

Air treatment technologies in pig farms. Life cycle assessment of dry and wet scrubbers in Northern Italy and Northeastern Spain.

Over the years, different solutions were developed and tested to reduce the emissions of ammonia and particulate matter from the livestock facilities. The environmental performances of these solutions were not always evaluated in detail. This study examines the environmental footprint of pig production at farm gate, with a focus on emissions from housing. Using Life Cycle Assessment, the environmental impact of pig production in a transition farm in Spain and in two finishing farms in Italy was evaluated considering three scenarios (one baseline and two of them involving an air treatment technology: wet scrubber or dry scrubber). The study goal was to quantify the environmental footprint of pig production in different scenarios, identify key environmental hotspots, and to assess impact reduction efficiency due to the two assessed technologies, analyze the environmental trade-offs that come with the use of these technologies, and identify potential for improvements. Both wet and dry scrubbers showed potential for reducing emissions in pig housing, affecting environmental impact categories related to air pollutants such as particulate matter, acidification and eutrophication. However, there were trade-offs between emissions reduction and categories related to energy and resource use. The infrastructure and consumables required to operate the scrubber added to the impacts compared to the baseline. The dry scrubber showed a more favorable balance between emission reduction and trade-offs. In this regard, results were similar for the Spanish and Italian farms, although there were slight variations. Scrubbers had a greater effect in the Italian farms due to their use along longer periods of the pig fattening (closed cycle farms) compared to the Spanish farm (transition farm). Scrubbers are environmentally promising, especially where acidification, eutrophication and particulate matter are local problems. However, they alone cannot fully address the complex environmental impacts of pig production, which require comprehensive interventions across the supply chain.

[Human exposure to livestock pollutants]. / Determinanti abientali per la valutazione dell'esposizione degli addetti in zootecnia.

The paper describes the sources and characteristics of the particulate matter (PM) generated in pig and poultry facilities. PM origins from the food, the peeling of the skin, loss of fur or feathers, from feces and litter, if present. The PM concentrations measured during the day change due to the management of the litter, the distribution of the food and the cleanliness of the shelters. There are several methods to protect the welfare of animals and stockmen, among which the ventilated ceilings are today the most innovative choice.

[Assessment and prevention of zoonoses: "one health approach"]. / Valutazione e prevenzione delle zoonosi: lo "one health approach".

Zoonotic pathologies represent diseases that can be transmittable from animals to humans and vice versa. In most cases zoonotic agents are bacteria or viruses and represent a huge problem for health. Zoonosis could represent easily solvable diseases such as simple infections or even deathly such as prion infections. They could be directly transmittable as tuberculosis or brucellosis or indirectly transmittable through vectors as biological fluids or foods from animal production. The increasing production and the globalization of animal food production have caused the spread of zoonosis worldwide turning this topic into a global problem. It is necessary to enforce the actual scientific collaboration between all countries in order to counteract the spread of these pathologies. About this topic WHO, FAO and OIE took part to the world project "one health" highlighting as most important topics the research on Rabies virus, influenza virus and on antibiotic resistance. In particular antibiotic resistance represents one of the most important topics of the last decade due to the inappropriate use of antibiotics, from animal production to human health. This last topic represents a serious problem for health system worldwide. This paper is mainly based on zoonoses such as avian flu, BSE and brucellosis and will describe the strategies used to limit their expansion.

Environmental effect of improved forage fertilization practices in the beef production chain.

Feeding is one of the most important factors influencing production efficiency and the environmental impact of livestock production. This study evaluates the possibility of reducing the impact of beef cattle production by optimizing the fertilization management of home-grown forage on the same farms. To this end, two scenarios were compared on two beef cattle farms in northern Italy, a baseline scenario (BS) and a scenario with optimized management (OMS) in terms of nitrogen fertilizer use. The cradle-to-gate LCA (Life Cycle Assessment) approach was used to compare the environmental performances in the different scenarios. Two different functional units (1 t dry matter of forage self-produced and 1 kg live weight of beef cattle produced) were used to express the results in relation to different stages of the supply chain. Inventory data were translated into indicators to reflect environmental pressures as well as resource scarcity by means of the ReCiPe 2016 Midpoint (H) method. The reduction of synthetic nitrogen fertilization, particularly during top fertilization, maintain yields at satisfactory levels while substantially reducing most of the evaluated impacts (e.g., Climate change from 17 % to 23 %). On the other hand, trade-offs among the different impact categories can be identified (e.g., terrestrial acidification grows up to 52 % for wheat silage). The optimization of the fertilization also involves a reduction in the impact of the feed as a whole and then of the beef cattle produced, even though the increasing number of external inputs, not affected by best fertilization practices, for each of these two phases leads to increasingly smaller reductions in impact. Ultimately, the optimization of internal crop production practices is important from an environmental point of view for farms but represents only one of the possible mitigation interventions necessary to mitigate the entire agricultural supply chain.

Monitoring Volatile Organic Compounds and Aroma Profile of Robinia pseudoacacia L. Honey at Different Storage Temperatures during Shelf Life.

Bee honey has different volatile organic compound profiles that depend on the botanical origin and the state of conservation and which are mainly responsible for its specific aroma. During honey storage, the profile of these molecules and other indicators, such as 5-hydroxymethylfurfural and the diastatic index, can change depending on temperature and time. This study analyzed the variations that these parameters in acacia honey stored at three different temperatures for a total period of 550 days, using gas chromatography coupled with mass spectrometry and an electronic nose equipped with 10 different sensors. The results confirm that the composition of acacia honey varies over time due to both the reduction in the concentration of volatile molecules (e.g., formic acid, a natural acaricide) and the increase in compounds resulting from heat-dependent degradations (e.g., 5-hydroxymethylfurfural). This study supports the usefulness of the electronic nose for the early detection of aromatic alterations in honey subjected to high-temperature storage.

Climate change and socio-economic assessment of PLF in dairy farms: Three case studies.

Precision Livestock Farming (PLF) techniques include sensors and tools to install on livestock farms and/or animals to monitor them and support the decision making process of farmers, finally early detecting alerting conditions and improving the livestock efficiency. Direct consequences of this monitoring include enhanced animal welfare, health and productivity, improved farmer lifestyle, knowledge, and traceability of livestock products. The indirect consequences, instead, include improved Carbon Footprint and socio-economic indicators of livestock products. In this context, the aim of this paper is to develop an indicator applicable to dairy cattle farming that takes into account concurrently these indirect consequences. The indicator was developed combining the three sustainability pillars (with specific criteria): environmental (carbon footprint), social (5 freedoms of animal welfare and antimicrobial use) and economic (cost of technology and manpower use). The indicator was then tested on 3 dairy cattle farms located in Italy, where a baseline traditional scenario (BS) was compared with an alternative scenario (AS) where PLF techniques and improved management solutions were adopted. The results highlighted that the carbon footprint reduced in all AS by 6-9 %, and the socio-economic indicators entailed improvements in animals and workers welfare with some differences based on the tested technique. Investing in PLF techniques determines positive effects on all/almost all the criteria adopted for the sustainability indicator, with case-specific aspects to consider. Being a user-friendly tool that supports the testing of different scenarios, this indicator could be used by stakeholders (policy makers and farmers in particular) to identify the best direction towards investments and incentive policies.

Effects of TiO2 based photocatalytic paint on concentrations and emissions of pollutants and on animal performance in a swine weaning unit.

The beneficial effect of a TiO2-based photocatalytic treatment on the indoor air purification of a swine farm has been evaluated in a trial performed in two identical mechanically ventilated traditional weaning units, with 391 animals lodged in each of them. One unit was used as reference, whereas the walls of the second unit (260 m2) were coated with ca. 70 g m(-2) of TiO2 and irradiated with ten UV-A lamps. The environmental parameters (i.e. the ventilation rate, the internal and external temperature and relative humidity), together with NH3, CH4, CO2 and N2O concentrations in the exhaust ducts and PM10 emissions, were monitored in the two units throughout all of the production cycle (75 days). Significant decreases in CH4 concentration (ca. 27%, P < 0.05) and PM10 emission (ca. 17%, P < 0.01) were observed, together with an increase of the piglets' productive performance in the treated unit with respect to the reference one. Indeed, the ADG (Average Daily Gain of piglets) was 424 g vs. 414 g for the piglets lodged in the two units, respectively, with a significantly better feed conversion ratio (FCR, ratio between the food ingested by the animals and their weight gain) of 2.18 vs. 2.44 (P < 0.001). Therefore, the photocatalytic treatment with TiO2 coating had positive effects not only on methane concentration and particulate matter concentration and emission, but also significantly improved the feed conversion ratio of growing piglets, very likely due to the increased quality of indoor air, with positive economic repercussions for the farmer. Internal photocatalytic treatment in swine husbandry could thus be considered as a potential Best Available Technology (BAT).

The Potentialities of Machine Learning for Cow-Specific Milking: Automatically Setting Variables in Milking Machines.

In dairy farming, milking-related operations are time-consuming and expensive, but are also directly linked to the farm's economic profit. Therefore, reducing the duration of milking operations without harming the cows is paramount. This study aimed to test the variation in different parameters of milking operations on non-automatic milking machines to evaluate their effect on a herd and finally reduce the milking time. Two trials were set up on a dairy farm in Northern Italy to explore the influence of the pulsation ratio (60:40 vs. 65:35 pulsation ratio) and that of the detachment flow rate (600 g/min vs. 800 g/min) on milking performance, somatic cell counts, clinical mastitis, and teats score. Moreover, the innovative aspect of this study relates to the development of an optimized least-squares support vector machine (LSSVM) classification model based on the sparrow search algorithm (SSA) to predict the proper pulsation ratio and detachment flow rate for individual cows within the first two minutes of milking. The accuracy and precision of this model were 92% and 97% for shortening milking time at different pulsation ratios, and 78% and 79% for different detachment rates. The implementation of this algorithm in non-automatic milking machines could make milking operations cow-specific.

Measurements techniques and models to assess odor annoyance: A review.

Odors have received increasing attention among atmospheric pollutants. Indeed, odor emissions are a common source of complaints, affecting the quality of life of humans and animals. The odor is a property of a mixture of different volatile chemical species (sulfur, nitrogen, and volatile organic compounds) capable of stimulating the olfaction sense sufficiently to trigger a sensation of odor. The impact of odors on the surrounding areas depends on different factors, such as the amount of odors emitted from the site, the distance from the site, weather conditions, topography, other than odors sensitivity and tolerance of the neighborhood. Due to the complexity of the odor issue, the aim of this review was to give an overview of: (i) techniques (sensorial and analytical) that can be used to determine a quantitative and qualitative characterization; (ii) air dispersion models applied for the evaluation of the spatial and temporal distribution of atmospheric pollutants in terms of concentration in air and/or deposition in the studied domain; (iii) major sources of odor nuisance (waste and livestock); (iv) mitigation actions against odor impact. Among sensorial techniques dynamic olfactometry, field inspection, and recording from residents were considered; whereas, for analytical methodologies: gas chromatography-mass spectrometry, identification of specific compounds, and electronic nose. Both kinds of techniques evaluate the odor concentration. Instead, to account for the effective impact of odors on the population, air dispersion models are used. They can provide estimates of odor levels in both current and future emission scenarios. Moreover, they can be useful to estimate the efficiency of mitigation strategies. Most of the odor control strategies involve measures oriented to prevent, control dispersion, minimize the nuisance or remove the odorants from emissions, such as adequate process design, buffer zones, odor covers, and treatment technologies.

Relating Lying Behavior With Climate, Body Condition Score, and Milk Production in Dairy Cows.

Attention on animal behavior and welfare has been increasing. Scientific knowledge about the effect of behavior and welfare on animals' production augmented and made clear the need of improving their living conditions. Among the variables to monitor in dairy cattle farming, lying time represents a signal for health and welfare status as well as for milk production. The aim of this study is to identify the relationship among the lying behavior of dairy cows and milk production, body condition score (BCS), weather variables, and the temperature-humidity index (THI) in the barn from a dairy farm located in Northern Italy. One-year data were collected on this farm with sensors that allowed monitoring of the environmental conditions in the barn and the activity of primiparous lactating cows. Principal components analysis (PCA), factor analysis (FA), generalized linear model select (GLMSelect), and logistic analysis (LA) were carried out to get the relationships among variables. Among the main results, it emerges that the effect of weather parameters is quite restrained, except for THI > 70, which negatively affects the lying time. In addition, the most productive cows are found to lie down more than the less productive ones, and the parameters of milk production, lying time, and BCS are found to be linked by a similar trend.

A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary Study.

Coccidiosis is still one of the major parasitic infections in poultry. It is caused by protozoa of the genus Eimeria, which cause concrete economic losses due to malabsorption, bad feed conversion rate, reduced weight gain, and increased mortality. The greatest damage is registered in commercial poultry farms because birds are reared together in large numbers and high densities. Unfortunately, these enteric pathologies are not preventable, and their diagnosis is only available when the disease is full-blown. For these reasons, the preventive use of anticoccidials-some of these with antimicrobial action-is a common practice in intensive farming, and this type of management leads to the release of drugs in the environment which contributes to the phenomenon of antibiotic resistance. Due to the high relevance of this issue, the early detection of any health problem is of great importance to improve animal welfare in intensive farming. Three prototypes, previously calibrated and adjusted, were developed and tested in three different experimental poultry farms in order to evaluate whether the system was able to identify the coccidia infection in intensive poultry farms early. For this purpose, a data-driven machine learning algorithm was built, and specific critical values of volatile organic compounds (VOCs) were found to be associated with abnormal levels of oocystis count at an early stage of the disease. This result supports the feasibility of building an automatic data-driven machine learning algorithm for an early warning of coccidiosis.

Review: Environmental impact of livestock farming and Precision Livestock Farming as a mitigation strategy.

This paper reviews the environmental impact of current livestock practices and discusses the advantages offered by Precision Livestock Farming (PLF), as a potential strategy to mitigate environmental risks. PLF is defined as: "the application of process engineering principles and techniques to livestock farming to automatically monitor, model and manage animal production". The primary goal of PLF is to make livestock farming more economically, socially and environmentally sustainable and this can be obtained through the observation, interpretation of behaviours and, if possible, individual control of animals. Furthermore, adopting PLF to support management strategies, may lead to the reduction of the environmental impact of farms. Currently, few studies reported PLF efficacy in reducing the environmental impact, however further studies are necessary to better analyze the actual potential of PLF as a mitigation strategy. Literature shows the potentiality of the application of PLF, as the introduction of PLF in farms can lead to a reduction of Greenhouse gases (GHG) and ammonia (NH3) emission in air, nitrates and antibiotics pollution in water bodies, phosphorus, antibiotics and heavy metals in the soil.

Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production.

Consumption of animal products such as meat, milk, and eggs in first-world countries has leveled off, but it is rising precipitously in developing countries. Agriculture will have to increase its output to meet demand, opening the door to increased automation and technological innovation; intensified, sustainable farming; and precision livestock farming (PLF) applications. Early indicators of medical problems, which use sensors to alert cattle farmers early concerning individual animals that need special care, are proliferating. Wearable technologies dominate the market. In less-value-per-animal systems like sheep, goat, pig, poultry, and fish, one sensor, like a camera or robot per herd/flock/school, rather than one sensor per animal, will become common. PLF sensors generate huge amounts of data, and many actors benefit from PLF data. No standards currently exist for sharing sensor-generated data, limiting the use of commercial sensors. Technologies providing accurate data can enhance a well-managed farm. Development of methods to turn the data into actionable solutions is critical.

Effects of Climatic Conditions on the Lying Behavior of a Group of Primiparous Dairy Cows.

Currently, lying behavior can be assessed using continuous observations from sensors (e.g., accelerometers). The analysis of digital data deriving from accelerometers is an effective tool for studying livestock behaviors. Despite the large interest in the lying behavior of dairy cows, no reference was found in literature regarding the prediction of lying behavior as a function of the interaction of environmental parameters. The present study aimed to evaluate the influence of climatic conditions (temperature-humidity index, solar radiation, air velocity and rainfalls) on the lying behavior of a group of primiparous dairy cows, using data from accelerometers, and develop a prediction model to identify and predict the lying behavior of dairy cows as a function of the effects of environmental conditions. Results from the. GLM Procedure (SAS) showed that the model was highly significant (p < 0.001) and the r2 was 0.84. All of the effects in the model resulted in being highly significant (p < 0.001). This model, if validated properly, could be a valid early warning system to identify any deviation from the expected behavior, and to assess the effectiveness of thermal stress mitigation strategies.