A Survey of Dairy Cattle Behavior in Different Barns in Northern Italy

Can a gradual weaning and separation process reduce weaning distress in dam-reared dairy calves? A comparison with the 2-step method

The weaning and separation phase remains one of the biggest challenges for cow-calf contact systems, but a gradual process that better mimics the naturally occurring reduction in milk intake has not yet been scientifically investigated. Therefore, the aim of our study was to compare behavioral and physiological indicators of distress in 3-mo-old dam-reared dairy calves (with previous full-time cow-calf contact) weaned and separated either via gradual reduction of contact time with the dam (GR; 1 wk of half-day contact, 1 wk of morning contact, and 1 wk of fence-line contact before complete separation, n = 18) or via 2-step weaning using a nose flap (NF, 2 wk of access to the dam with a nose flap, 1 wk of fence-line contact before complete separation, n = 18). Behavior was recorded 1 wk before (or for lying 3 wk before) weaning start and during the 3 wk weaning and separation period with direct observations on 4 d/wk or via accelerometers (locomotor play, lying behavior). Blood and fecal samples were taken twice per week from weaning start until 3 wk after weaning start. Calves were weighed weekly. Statistical analysis was conducted using (generalized) linear mixed models. Over the whole weaning and separation phase, NF calves showed a stronger decrease in the number of lying bouts, amount of locomotor play, and ADG, as well as a higher increase in TMR feeding time compared with GR calves, whereas GR calves vocalized more often and showed more searching behavior than NF calves. Also, the neutrophil:lymphocyte ratio of NF calves was elevated on d 3 after insertion of the nose flaps compared with baseline, but showed no change for GR calves on any sampling day. Overall, results point toward a favorable effect of a gradual weaning strategy on reduction of weaning and separation distress in dam-reared dairy calves, but the method requires further improvement from the protocol used in our study.

Behavior of Cows in the Lying Area When the Exit Gates in the Pens Are Opened: How Many Cows Are Forced to Get Up to Go to the Milking Parlor?

Equipping a farm with a milking parlor requires moving groups of cows from their pens to the part of the barn where milking takes place. The task of moving cows, carried out two or three times each day, shows links to the lying area of the barn. When the cows are taken from the pen to the milking parlor, some of them may be lying down, and this lying down must be interrupted. The forced standing up of cows can be considered in terms of their welfare. The aim of the study was to examine the number of cows lying in the stalls at the time of opening the exit gates in the pens in order to take the cows to the milking parlor. The study covered four pens, each with 12 cows. The behavior of the cows in the pens before morning and afternoon milking was recorded over 26 days. In the analysis, the dependent variable was the number of lying cows, and the independent variables were the time of milking and the level of sand in the lying stalls. The results of the study showed the significance of differences in the number of lying cows for stalls with a low and high level of sand, both in the case of morning and afternoon milking. Differences in the number of lying cows were also found when comparing the time before morning and afternoon milking. To compare the tendency of individual cows to lie down before going to milking, an index of forced standing up was proposed. The research conducted showed differences in the behavior of cows before leaving the pen to the milking parlor. The stage to reduce the forced standing up of cows is to equip the farm with an automatic milking system (AMS) instead of using a milking parlor. In barns with AMS, cows have full freedom to get up and approach the milking stall. The results of the observations are thus an additional argument confirming the benefits of using an automatic milking system, considered in terms of the welfare of dairy cows, regarding their lying down and getting up.

Effects of Heat Stress in Dairy Cows Raised in the Confined System: A Scientometric Review

Due to climate change, heat stress is a growing problem for the dairy industry. Based on this, annual economic losses in the dairy sector are verified mainly on a large scale. Despite several publications on thermal stress in lactating dairy cows in confinement systems, there need to be published reviews addressing this issue systematically. Our objective was to scientometrically analyze the effects of heat stress in dairy cows managed in a confinement system. Based on PRISMA guidelines, research articles were identified, screened, and summarized based on inclusion criteria for heat stress in a confinement system. Data was obtained from the Web of Science. A total of 604 scientific articles published between 2000 and April 2022 were considered. Data was then analyzed using Microsoft Excel and CiteSpace. The results pointed to a significant increase in studies on heat stress in lactating cows housed in confinement systems. The main research areas were Agriculture, Dairy Animal Science and Veterinary Sciences. The USA showed the highest concentration of studies (31.12%), followed by China (14.90%). Emerging themes included heat stress and behavior. The most influential journals were the Journal of Dairy Science and the Journal of Animal Science. The top authors were L. H. Baumgard and R. J. Collier. The leading institutions were the Chinese Academy of Agricultural Sciences, followed by the State University System of Florida and the University of Florida. The study maps the significant research domains on heat stress of lactating cows in confinement systems, discusses implications and explanations and highlights emerging trends.

Development of a New Wearable 3D Sensor Node and Innovative Open Classification System for Dairy Cows’ Behavior

Simple Summary

In order to keep dairy cows under satisfactory health and welfare conditions, it is very important to monitor the animals in their living environment. With the support of technology, and, in particular, with the installation of sensors on neck-collars, cow behavior can be adequately monitored, and different behavioral patterns can be classified. In this study, an open and customizable device has been developed to classify the behaviors of dairy cows. The device communicates with a mobile application via Bluetooth to acquire raw data from behavioral observations and via an ad hoc radio channel to send the data from the device to the gateway. After observing 32 cows on 3 farms for a total of 108 h, several machine learning algorithms were trained to classify their behaviors. The decision tree algorithm was found to be the best compromise between complexity and accuracy to classify standing, lying, eating, and ruminating. The open nature of the system enables the addition of other functions (e.g., localization) and the integration with other information sources, e.g., climatic sensors, to provide a more complete picture of cow health and welfare in the barn.

Abstract

Monitoring dairy cattle behavior can improve the detection of health and welfare issues for early interventions. Often commercial sensors do not provide researchers with sufficient raw and open data; therefore, the aim of this study was to develop an open and customizable system to classify cattle behaviors. A 3D accelerometer device and host-board (i.e., sensor node) were embedded in a case and fixed on a dairy cow collar. It was developed to work in two modes: (1) acquisition mode, where a mobile application supported the raw data collection during observations; and (2) operating mode, where data was processed and sent to a gateway and on the cloud. Accelerations were sampled at 25 Hz and behaviors were classified in 10-min windows. Several algorithms were trained with the 108 h of behavioral data acquired from 32 cows on 3 farms, and after evaluating their computational/memory complexity and accuracy, the Decision Tree algorithm was selected. This model detected standing, lying, eating, and ruminating with an average accuracy of 85.12%. The open nature of this system enables for the addition of other functions (e.g., real-time localization of cows) and the integration with other information sources, e.g., microenvironment and air quality sensors, thereby enhancing data processing potential.

Dairy Cow Behavior Is Affected by Period, Time of Day and Housing

Simple Summary

Many factors, such as the climate, period of the year, time of day and housing, are known to affect cow behavior. However, it is not yet clear what is the combined effect of these factors. For instance, it is unclear whether warmer weather only alters cow behavior during the day or also during the night. Therefore, a survey was performed on eight dairy cow farms in Northern Italy in three periods: summer, winter and a temperate season (spring or autumn). Sensors were installed to monitor the temperature and humidity. Cow behavior was monitored with cameras and with accelerometers that were placed on their legs. These methods allow us to determine how much time the cows spent lying, standing or feeding. We found that both daytime and nighttime behavior differed between the periods and that housing had an effect not only on the behavior itself but also on how it changed between the periods and from daytime to nighttime. These findings show the importance of measuring behavior during both daytime and nighttime and illustrate the influence of the barn structure and farm management on cow behavior and welfare.

Abstract

Dairy cow behavior is affected by external and endogenous factors, including time of year, barn microclimate, time of day and housing. However, little is known about the combined effects of these factors. Data were collected on eight farms in Northern Italy during summer, winter and a temperate season. The temperature-humidity index (THI) was recorded using environmental sensors, whereas cow behavior was monitored using leg accelerometers and cameras. Period, time of day and their interaction all significantly affected lying, standing and feeding behavior. However, although THI had a significant negative effect on lying and a positive effect on standing during daytime (all p < 0.001), during nighttime, it only had a significant negative effect on lying duration and mean lying bout duration (p < 0.001 for both). There was also significant variation between farms in all behavioral parameters, as well as interactions with period and time of day. For instance, farm differences in lying duration were more pronounced during daytime than during nighttime. These findings show how housing can interact with other factors, such as period of the year and time of day, and illustrate the influence of barn structure and farm management on cow behavior and, consequently, their welfare.

Carbon myopia: The urgent need for integrated social, economic and environmental action in the livestock sector

Livestock have long been integral to food production systems, often not by choice but by need. While our knowledge of livestock greenhouse gas (GHG) emissions mitigation has evolved, the prevailing focus has been-somewhat myopically-on technology applications associated with mitigation. Here, we (1) examine the global distribution of livestock GHG emissions, (2) explore social, economic and environmental co-benefits and trade-offs associated with mitigation interventions and (3) critique approaches for quantifying GHG emissions. This review uncovered many insights. First, while GHG emissions from ruminant livestock are greatest in low- and middle-income countries (LMIC; globally, 66% of emissions are produced by Latin America and the Caribbean, East and southeast Asia and south Asia), the majority of mitigation strategies are designed for developed countries. This serious concern is heightened by the fact that 80% of growth in global meat production over the next decade will occur in LMIC. Second, few studies concurrently assess social, economic and environmental aspects of mitigation. Of the 54 interventions reviewed, only 16 had triple-bottom line benefit with medium-high mitigation potential. Third, while efforts designed to stimulate the adoption of strategies allowing both emissions reduction (ER) and carbon sequestration (CS) would achieve the greatest net emissions mitigation, CS measures have greater potential mitigation and co-benefits. The scientific community must shift attention away from the prevailing myopic lens on carbon, towards more holistic, systems-based, multi-metric approaches that carefully consider the raison d'être for livestock systems. Consequential life cycle assessments and systems-aligned 'socio-economic planetary boundaries' offer useful starting points that may uncover leverage points and cross-scale emergent properties. The derivation of harmonized, globally reconciled sustainability metrics requires iterative dialogue between stakeholders at all levels. Greater emphasis on the simultaneous characterization of multiple sustainability dimensions would help avoid situations where progress made in one area causes maladaptive outcomes in other areas.

Combining epidemiological and ecological methods to quantify social effects on Escherichia coli transmission

Enteric microparasites like Escherichia coli use multiple transmission pathways to propagate within and between host populations. Characterizing the relative transmission risk attributable to host social relationships and direct physical contact between individuals is paramount for understanding how microparasites like E. coli spread within affected communities and estimating colonization rates. To measure these effects, we carried out commensal E. coli transmission experiments in two cattle (Bos taurus) herds, wherein all individuals were equipped with real-time location tracking devices. Following transmission experiments in this model system, we derived temporally dynamic social and contact networks from location data. Estimated social affiliations and dyadic contact frequencies during transmission experiments informed pairwise accelerated failure time models that we used to quantify effects of these sociobehavioural variables on weekly E. coli colonization risk in these populations. We found that sociobehavioural variables alone were ultimately poor predictors of E. coli colonization in feedlot cattle, but can have significant effects on colonization hazard rates (p ≤ 0.05). We show, however, that observed effects were not consistent between similar populations. This work demonstrates that transmission experiments can be combined with real-time location data collection and processing procedures to create an effective framework for quantifying sociobehavioural effects on microparasite transmission.

Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions

Precision Livestock Farming (PLF) relies on several technological approaches to acquire, in the most efficient way, precise and real-time data concerning production and welfare of individual animals. In this regard, in the dairy sector, PLF devices are being increasingly adopted, automatic milking systems (AMSs) are becoming increasingly widespread, and monitoring systems for animals and environmental conditions are becoming common tools in herd management. As a consequence, a great amount of daily recorded data concerning individual animals are available for the farmers and they could be used effectively for the calibration of numerical models to be used for the prediction of future animal production trends. On the other hand, the machine learning approaches in PLF are nowadays considered an extremely promising solution in the research field of livestock farms and the application of these techniques in the dairy cattle farming would increase sustainability and efficiency of the sector. The study aims to define, train, and test a model developed through machine learning techniques, adopting a Random Forest algorithm, having the main goal to assess the trend in daily milk yield of a single cow in relation to environmental conditions. The model has been calibrated and tested on the data collected on 91 lactating cows of a dairy farm, located in northern Italy, and equipped with an AMS and thermo-hygrometric sensors during the years 2016-2017. In the statistical model, having seven predictor features, the daily milk yield is evaluated as a function of the position of the day in the lactation curve and the indoor barn conditions expressed in terms of daily average of the temperature-humidity index (THI) in the same day and its value in each of the five previous days. In this way, extreme hot conditions inducing heat stress effects can be considered in the yield predictions by the model. The average relative prediction error of the milk yield of each cow is about 18% of daily production, and only 2% of the total milk production.

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.

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.