Free-choice pasture access for dry cows: Effects on health, behavior, and milk production.

Allowing dairy cattle to access pasture or outdoor areas is known to be beneficial for cows' welfare and is considered important by the general public. However, in confinement-based operations with high-yielding cows, pasture access may be difficult to implement, especially for lactating animals. Providing pasture access to heifers and dry cows seems a more feasible option for most farms. The objective of this study was to investigate the effects of providing high-yielding dairy cows with free-choice pasture access during the dry period on their health, behavior, and milk production. Over the study period, a total of 78 Holstein cows were assigned to 1 of 2 treatments during the dry period (51 ± 8 d): housing with free-choice access to the pasture (PAST) or housing continuously without any access to the outdoors (CTRL). After calving, all cows from both treatments were mixed and housed continuously. To assess the effects of the treatment on cows' performance, all animals enrolled were monitored both before calving and during the first 100 d in milk of the following lactation. The behavior of all cows involved was monitored continuously during the whole observation period using collar-based sensors. All cows were inspected monthly to assess lameness, hock lesions, cleanliness, and body condition score. During the period after calving (0-100 d in milk), milk production and composition were also monitored. Results showed that free-choice pasture access affected cows' feeding behavior. Before calving, the animals in PAST spent more time feeding than in CTRL and, interestingly, this difference tended to persist for several weeks after calving. During the dry period, cows in PAST were cleaner than in CTRL but no differences in locomotion and body condition score were found between the 2 groups. Free-choice pasture access during the dry period also affected milk production during the following lactation. The cows that spent the dry period in PAST produced more milk than CTRL counterparts, particularly for the animals that calved during summer. In the current study we have found that providing free-choice pasture access during the dry period can positively affect the performance of dairy cattle and represents a desirable practice in confinement-based dairy production systems.

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.

Animal Health in Compost-Bedded Pack and Cubicle Dairy Barns in Six European Countries.

The purpose of this study was to compare animal health in compost-bedded pack (CBP) and cubicle housing (CH) systems using data from dairy herd improvement associations. Thirty-two commercial dairy farms located in Austria, Germany, Italy, The Netherlands, Slovenia, and Sweden were included in the study. A matching design (pairing CBP and CH within country) according to herd selection criteria was used. We explored the following health indicators: somatic cell counts (SCC), high SCC, new high SCC, ketosis risk, prolonged calving intervals, dystocia, and stillbirth. Traits for culling and culling-related issues, such as length of life and length of productive life, were also included. We used multivariable (mixed) linear and logistic regression models to evaluate differences between the systems. Udder health, as measured by SCC, was inferior in CBP, although the geometric means were low in both systems. The incidence of stillbirths was higher in CBP, while prolonged calving intervals were fewer, indicating that there were fewer reproductive disorders. There were no differences in longevity between the systems, although CBP had lower proportions of first calvers. Overall, we conclude that there were few and minor differences in health and longevity between the CBP and CH systems in the European context.

Validation of a Commercial Collar-Based Sensor for Monitoring Eating and Ruminating Behaviour of Dairy Cows.

The use of sensor technologies to monitor cows' behavior is becoming commonplace in the context of dairy production. This study aimed at validating a commercial collar-based sensor system, the AFICollar® (Afimilk, Kibbutz Afikim, Israel), designed to monitor dairy cattle feeding and ruminating behavior. Additionally, the performances of two versions of the software for behavior classification, the current software AFIfarm® 5.4 and the updated version AFIfarm® 5.5, were compared. The study involved twenty Holstein-Friesian cows fitted with the collars. To evaluate the sensor performance under different feeding scenarios, the animals were divided into four groups and fed three different types of feed (total mixed ration, long hay, animals allowed to graze). Recordings of hourly rumination and feeding time produced by the sensor were compared with visual observation by scan sampling at 1 minute intervals using Spearman correlation, concordance correlation coefficient (CCC), Bland-Altman plots and linear mixed models for assessing the precision and accuracy of the system. The analyses confirmed that the updated software version V5.5 produced better detection performance than the current V5.4. The updated software version produced high correlations between visual observations and data recorded by the sensor for both feeding (r = 0.85, CCC = 0.86) and rumination (r = 0.83, CCC = 0.86). However, the limits of agreement for both behaviors remained quite wide (feeding: -19.60 min/h, 17.46 min/h; rumination: -15.80 min/h, 15.00 min/h). Type of feed did not produce significant effects on the agreement between visual observations and sensor recordings. Overall, the results indicate that the system can provide farmers with adequately accurate data on feeding and rumination time, and can be used to support herd management decisions. Despite all this, the precision of the system remained relatively limited, and should be improved with further developments in the classification algorithm.

Properties of conventional and alternative bedding materials for dairy cattle.

The bedding material used in barns for dairy cows has a significant effect on animal welfare and performance. Bedding influences the duration in which animals remain lying down and, consequently, the processes of rumination and milk production. It is crucial to have a complete understanding of the properties of bedding materials and the effects of alternative bedding materials on dairy cattle. This paper aims to evaluate the physical, chemical, and biological properties of various alternative and conventional bedding materials for dairy cattle for use in compost bedded pack or freestall barn systems. We analyzed 50 samples of 17 bedding materials produced in 3 European countries. We analyzed physical properties including the water holding capacity, porosity, moisture content, bulk density, dry bulk density, and particle size. Chemical analyses were performed to determine the total N, total organic C, and C:N ratio. In the biological analyses, the Escherichia coli count, total bacteria count, coliform count, and Klebsiella spp. count were assessed. The results demonstrated how the physical properties of the bedding materials may influence the chemical and biological properties. All of the materials presented adequate chemical properties to be used as bedding material. The physical properties of the bedding materials differed widely among the materials except for the dry bulk density, which presented no difference. Moreover, the contamination of each studied microorganism was observed for each bedding material to determine which material had the lowest level of contamination. Posidonia oceanica, Miscanthus grass, and spelt husks could be considered as a potential alternative material for use as bedding material for dairy cows in both systems (i.e., composted bedded pack and freestall). This experiment illustrated the importance of performing thorough physical, chemical, and biological analyses before implementing a material as bedding for dairy cattle.

Evaluation of the Physical Properties of Bedding Materials for Dairy Cattle Using Fuzzy Clustering Analysis.

The bedding materials used in dairy cow housing systems are extremely important for animal welfare and performance. A wide range of materials can be used as bedding for dairy cattle, but their physical properties must be analysed to evaluate their potential. In the present study, the physical properties of various bedding materials for dairy cattle were investigated, and different fuzzy clustering algorithms were employed to cluster these materials based on their physical properties. A total of 51 different bedding materials from various places in Europe were collected and tested. Physical analyses were carried out for the following parameters: bulk density (BD), water holding capacity (WHC), air-filled porosity (AFP), global density (GD), container capacity (CC), total effective porosity (TEP), saturated humidity (SH), humidity (H), and average particle size (APS). These data were analysed by principal components analysis (PCA) to reduce the amount of data and, subsequently, by fuzzy clustering analysis. Three clustering algorithms were tested: k-means (KM), fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. Furthermore, different numbers of clusters (2-8) were evaluated and subsequently compared using five validation indexes. The GK clustering algorithm with eight clusters fit better regarding the division of materials according to their properties. From this clustering analysis, it was possible to understand how the physical properties of the bedding materials may influence their behaviour. Among the materials that fit better as bedding materials for dairy cows, Posidonia oceanica (Cluster 6) can be considered an alternative material.

Machine Learning Based Prediction of Insufficient Herbage Allowance with Automated Feeding Behaviour and Activity Data.

Sensor technologies that measure grazing and ruminating behaviour as well as physical activities of individual cows are intended to be included in precision pasture management. One of the advantages of sensor data is they can be analysed to support farmers in many decision-making processes. This article thus considers the performance of a set of RumiWatchSystem recorded variables in the prediction of insufficient herbage allowance for spring calving dairy cows. Several commonly used models in machine learning (ML) were applied to the binary classification problem, i.e., sufficient or insufficient herbage allowance, and the predictive performance was compared based on the classification evaluation metrics. Most of the ML models and generalised linear model (GLM) performed similarly in leave-out-one-animal (LOOA) approach to validation studies. However, cross validation (CV) studies, where a portion of features in the test and training data resulted from the same cows, revealed that support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) performed relatively better than other candidate models. In general, these ML models attained 88% AUC (area under receiver operating characteristic curve) and around 80% sensitivity, specificity, accuracy, precision and F-score. This study further identified that number of rumination chews per day and grazing bites per minute were the most important predictors and examined the marginal effects of the variables on model prediction towards a decision support system.