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A process of validation assesses the appropriateness and usefulness of a tool for its intended purpose within a specific context. Ideally, the validation of a tool should describe the range of purposes and contexts in which it is appropriate. This generally cannot be done completely. Therefore, when such a wide validation cannot be done, the validation process needs to refer clearly to the purpose of the use of the tool and to which animals it is going to be applied. The technological development has led to an increase in the number of sensors and devices that are available for measuring cattle behaviour. The technical solutions behind the sensors can differ substantially, sensors use accelerometer technology, sound recording or image analysis to mention some, and even within each solution, there is a large amount of factors that can influence the quality of the sensor’s information output or battery capacity. A careful evaluation of the purpose and validation of the outputs from sensors for measuring cattle behaviour is required before use. [...] Many behavioural tests are conducted in different research units. Regardless of the nature and design of the experiment, the basic aspects of a standardised behavioural test must be followed to generate data, which is unambiguous and accurate., Chapter of the Living Handbook Methods in cattle physiology and behaviour – Recommendations from the SmartCow consortium

Chapter of the Living Handbook Methods in cattle physiology and behaviour – Recommendations from the SmartCow consortium

Calves are an important investment on a cattle farm as their loss can cause substantial financial damage. Young calves are susceptible to a range of different diseases caused by pathogens, which can lead to morbidity and mortality. Additionally, calf management, including cow-calf separation, colostrum feeding, housing, dehorning and vaccination, can directly impact the health of calves. This chapter is therefore dedicated to different methods for assessing specific traits in calves. The current focus in calf rearing globally is optimal growth performance; therefore, WM or MR with a high nutritional value is offered to calves. Manual feeding is considered the conventional method of milk feeding. Automated milk feeders provide an alternative to the manual approach by exploiting computer-based systems that enable group housing of calves, yet at the same time monitor individual milk intake. Despite the positives associated with automated feeders in group stalls, appropriate management programmes must be implemented to prevent calves being exposed to disease due to intensified group housing. Previously, the most common approach to body composition analysis was to slaughter the animal and assess the whole tissue. Despite providing accurate results, this method requires the animal to be sacrificed. Additionally, this method does not necessarily present an unambiguous outcome if the aim of the study is to monitor continuous growth in the animal. Therefore, it is essential to develop non-invasive methods capable of producing precise data on live animals. Scholz stated that the key characteristic of such non-invasive methods for body or carcass composition measurement is to exploit electromagnetic or mechanical energies, which are able to pass completely or partially through body tissues e.g., muscle, adipose tissue and bone. An important advantage of such novel imaging techniques is the ability to introduce new traits which can be directly incorporated in future phenotyping programs. However, most of these techniques are relatively new in cattle research. Therefore, future studies should focus on assessing whether the sedation process has any impact on the measurements acquired from the animal using these techniques., Chapter of the Living Handbook Methods in cattle physiology and behaviour – Recommendations from the SmartCow consortium

Measuring bodyweight (BW) in cattle is essential for many research and management activities such as calculating dietary energy requirements for maintenance, calculating average daily gain and evaluating breeding values which include BW as a trait. Frequent monitoring of BW in dairy cows is necessary as changes in BW can help assess the energy balance of an animal. Precise understanding of BW gain of growing and fattening cattle or body mass changes during lactation is crucial in developing nutritional management programs and assessing feed efficiency, as well as the overall animal health status. Weigh scales are the most widely used method for determining BW in cattle, despite being timeconsuming and potentially costly to implement. Current advancements in technology have led to the development of automated weighing scales. Such technologies provide a more sensitive and less labourintensive approach for measuring BW changes in cattle. However, few investigations have been conducted to validate and compare automated walk-over weighing scales, coupled with radio frequency identification (RFID) ear tags, with conventional static weighing scales.

Twenty-four individually housed Holstein bulls (456 ± 6.9 kg of body weight and 292 ± 1.4 d of age) were enrolled in a complete randomized experiment involving four dietary treatments to evaluate the potential effect of mash particle size of diets in finishing beef diets on behavior, digestibility, and macro- and microscopic changes of the digestive tract. The four treatments were all ingredients sieved at 2 mm (HM2), all ingredients sieved at 3 mm (HM3), all ingredients, but corn, sieved at 2 mm and corn at 10 mm (HM210), and all ingredients, but corn, sieved a 3 mm and corn at 10 mm (HM310). For the HM210 and HM310 mashes, corn ground at 10 mm was mixed with the remaining concentrate ingredients ground at 2 or 3 mm, respectively. Concentrate (36% corn, 19% barley, 15% corn gluten feed, 8.4% wheat; 14% crude protein, 3.28 Mcal of ME/kg) consumption was recorded daily and straw consumption weekly. To register behavior, animals were filmed for 24 h on a weekly basis. At day 49 of study nutrient digestibility was estimated. Bulls were slaughtered after 56 d of exposure to treatments. Digestive tract and hepatic lesions were recorded, and tissue samples from the digestive tract collected. Geometric mean particle size was 0.61 ± 0.041, 0.76 ± 0.041, 0.62 ± 0.041, 0.73 ± 0.041 mm, and percentage of particles between 0.5 and 1 mm were 68 ± 2.9, 46 ± 1.7, 46 ± 5.0, and 39 ± 3.3 g/100 g for HM2, HM210, HM3, and HM310, respectively. Performance, total tract digestibility, or digestive tract integrity did not differ when ingredients were ground at 2 or 3 mm. Grinding corn with a hammer mill sieve size of 10 mm reduced feed efficiency and decreased total tract apparent dry matter, and organic matter digestibility compared with treatments from which all ingredients were ground at 2 or 3 mm. Straw intake was greatest and starch digestibility was least in the HM210 treatment. Last, only minor differences among treatments in rumen wall color, rumen papillae fusion, and histological conformation were observed. In summary, to improve feed efficiency, grinding corn at 10 mm is not recommended. In the present study, grinding procedure did not have a great effect on behavior and/or digestive tract health; however, under commercial conditions (group housing), grinding procedures that cause small mean particle sizes or particle size heterogeneity may increase the risk to suffer digestive tract lesions. info:eu-repo/semantics/publishedVersion

The aim of this study was to compare ordination methods for microbiota distance matrices, in order to estimate variance components for complex traits prediction including the microbiome in its estimation. Seven ordination methods for building distance (or dissimilarity) matrices were tested; real (n=70) and simulated (n=1000) data were analysed to estimate variance components including phenotypes, genotypes and microbiome information. The seven methods were: the one reported in Ross et al. (2013), multidimensional scaling (MDS), principal coordinates analysis (PCoA), detrended correspondence analysis (DCA), non-metric multidimensional scaling (NMDS), redundancy analysis (RDA) and constrained correspondence analysis (CCA). MDS and PCoA methods yielded exactly the same matrix. From simulation analysis it can be inferred that ordination methods of MDS/PCoA, RDA and CCA are as suitable as or even better than previously reported by Ross et al. (2013) for prediction of microbiability, contrasting with DCA and NMDS which performed poorly as predictive methods for microbiability. From real data analysis, low heritability for feed efficiency and microbiability were obtained and mid to high correlations between the genetic background of the hosts and the phenotypes or microbiota and phenotypes were obtained with methods that performed better in the simulation, indicating that it might be a relationship linking genotype-microbiome-phenotype which could be used in prediction of complex traits. UCR::Vicerrectoría de Docencia::Ciencias Agroalimentarias::Facultad de Ciencias Agroalimentarias::Escuela de Zootecnia UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Agroalimentarias::Centro de Investigación en Nutrición Animal (CINA)