Pre-weaning mortality represents a major economic loss and welfare concern for the global pork industry. Caffeine administration prior to, or after, parturition positively affects metabolic parameters associated with survival in newborn animals. However, its effects on piglet viability and survival when administered within the first 24 h of life have not been evaluated. This study determined the effect of caffeine treatment during the 24 h postpartum period on piglet viability, growth and survival. Piglets received 30 or 0 mg of caffeine orally at birth or 8 to 12 h following birth and again at 24 h. Vitality, meconium staining and blood lactate were assessed at birth. Time to first reach the udder and suckle was recorded. Body weight and rectal temperature were measured at 10 min, 24 and 72 h after birth. A blood sample for analysis of serum immunoglobulin G was collected at 24 h, and BW and survival were monitored to 20 days of age. Caffeine had no effect on body temperature, blood glucose, serum immunoglobulin G concentration or weight (P > 0.05). However, when caffeine was administered at birth, it significantly increased 24 h mortality in piglets (P < 0.05). Piglet mortality between birth and weaning also tended to be higher for piglets receiving caffeine at birth (P = 0.063). These data demonstrate that caffeine administration to piglets at birth, but not 8 to 12 h after birth, impaired piglet survival. Further research into caffeine dosage and timing postpartum is required to establish its efficacy.

Fear and environmental stressors may negatively affect the welfare of farm animals such as pigs. The present study investigated the effects of music and positive handling on reproductive performance of sows (n = 1014; parity 1 to 8) from a commercial pig farm practicing a batch farrowing system. Every 2 weeks, 56 sows were moved from the gestation unit to conventional-crated farrowing houses 1 week prior to expected farrowing. Treated (T; n = 299) and control (C; n = 715) sows were included in the study. In the farrowing houses, auditory enrichment (music from a radio) was provided to sows of T groups daily from 0600 to 1800 h until the end of lactation. Until the day of farrowing, T sows were additionally subjected, for 15 s per day per sow, to continuous back scratching by one member of farm staff. Litter performance and piglet mortality were recorded and analysed between T and C sows using linear mixed regression models. The number of liveborn piglets (C 13.85 v. T 13.26) and liveborn corrected for fostering (C 13.85 v. T 13.43) was significantly higher (P < 0.05) in C groups compared to the T groups. The number of stillborn piglets was 0.60 and 0.72 in T and C groups, respectively (P > 0.05). With regard to piglet mortality, a linear mixed regression model showed a significant overall effect of treatment in reducing piglet mortality (P < 0.01). Yet, the effect of treatment varied according to litter size (number of liveborn piglets) with a diminishing treatment effect in sows with a high litter size (P < 0.01). Pre-weaning survival was improved in the current study by the combined effect of daily back scratching of sows prior to farrowing and providing music to sows and piglets during lactation. Further research is needed to assess the separate effects of both interventions.

The assessment of the completeness of milk-out in dairy cows is one of the indicators used to evaluate and optimise the milking process. A number of different methods and thresholds are available for this purpose, but procedures and validation of the methods are not always described in detail, and may vary between studies. The objective of this study was to introduce and evaluate a new, precisely defined hand-milking method (DEFINED) and to compare its outcome with two commonly applied methods to assess the completeness of milking: visual scoring of the degree of quarter filling (VISUAL) and quantitative assessment of the number of easy strips (EASYSTRIPS). Each of the three methods was applied in 131 Holstein cows of six dairy herds in northern Germany. The assessment of milk-out was carried out by three experienced but non-regular milkers (evaluators). Each evaluator visited the six herds once during afternoon milking. To avoid any transitions, the interval between visits of two evaluators was at least 2 days. Maximum hand-milking time per cow was set to 60 s. The total strip yield collected in 60 s (SY60) by the application of a strip frequency of 1 Hz was used as a reference for the amount of milk left in the investigated quarter after machine-milking. The three methods were evaluated by analysing their statistical relationship with SY60, and by ranking their suitability for quantitative or qualitative assessment of milk-out. VISUAL and SY60 were not related, indicating that VISUAL was unsuitable for estimating the amount of milk left actually in the udder quarters. The strip yield in 15 s (DEFINED) and SY60 was significantly related, but results varied among evaluators. With regard to EASYSTRIPS, a significant relationship with SY60 was found, but the results were influenced by evaluator and herd. The findings of this study imply that DEFINED allows a rapid and farm-independent quantitative estimate of the post-milking strip yield. Likewise, EASYSTRIPS was meaningful in assessing milk-out of quarters in a given herd, whereas VISUAL allowed neither a quantitative nor a qualitative assessment of post-milking strip yield or milk-out. Thresholds for complete or incomplete milk-out by DEFINED must be lower than those commonly applied in 15 s of post-milking.

Europe’s roadmap to a low-carbon economy aims to cut greenhouse gas (GHG) emissions 80% below 1990 levels by 2050. Beef production is an important source of GHG emissions and is expected to increase as the world population grows. LIFE BEEF CARBON is a voluntary European initiative that aims to reduce GHG emissions per unit of beef (carbon footprint) by 15% over a 10-year period on 2172 farms in four large beef-producing countries. Changes in farms beef carbon footprint are normally estimated via simulation modelling, but the methods current models apply differ. Thus, our initial goal was to develop a common modelling framework to estimate beef farms carbon footprint. The framework was developed for a diverse set of Western Europe farms located in Ireland, Spain, Italy and France. Whole farm and life cycle assessment (LCA) models were selected to quantify emissions for the different production contexts and harmonized. Carbon Audit was chosen for Ireland, Bovid-CO2 for Spain and CAP’2ER for France and Italy. All models were tested using 20 case study farms, that is, 5 per country and quantified GHG emissions associated with on-farm live weight gain. The comparison showed the ranking of beef systems gross carbon footprint was consistent across the three models. Suckler to weaning or store systems generally had the highest carbon footprint followed by suckler to beef systems and fattening beef systems. When applied to the same farm, Carbon Audit’s footprint estimates were slightly lower than CAP’2ER, but marginally higher than Bovid-CO2. These differences occurred because the models were adapted to a specific region’s production circumstances, which meant their emission factors for key sources; that is, methane from enteric fermentation and GHG emissions from concentrates were less accurate when used outside their target region. Thus, for the common modelling framework, region-specific LCA models were chosen to estimate beef carbon footprints instead of a single generic model. Additionally, the Carbon Audit and Bovid-CO2 models were updated to include carbon removal by soil and other environmental metrics included in CAP’2ER, for example, acidification. This allows all models to assess the effect carbon mitigation strategies have on other potential pollutants. Several options were identified to reduce beef farms carbon footprint, for example, improving genetic merit. These options were assessed for beef systems, and a mitigation plan was created by each nation. The cumulative mitigation effect of the LIFE BEEF CARBON plan was estimated to exceed the projects reduction target (−15%).

In animal breeding, genetic parameters along with economic weights (EWs) of traits are applied. Profit functions currently used to calculate rabbit traits’ EWs do not consider nutrient requirements based on animal weight, growth rate and doe reproductive status. Therefore, the aim of this study was to develop a flexible bioeconomic model of rabbit-production systems and implement it in a computer programme in order to calculate economic values and relative EWs for rabbit traits. The bioeconomic model includes calculation of the doe age structure in the stationary state of a doe population; calculation of progeny structure; modelling growth, digestible energy, feed and water requirements for does in different reproductive statuses and for all progeny groups using a normative approach; calculation of the total feed and non-feed costs, revenues and profit per doe and per year; calculation of marginal economic values for up to 20 production and functional traits and estimation of the relative EWs of selected traits. The application of the programme is shown through an example calculation of trait economic values for a typical Czech commercial rabbit-production system. The trait economic value expresses the change in profit per doe and per year when the trait mean is increased by one unit. The programme developed is primarily useful for selection purposes in rabbit-breeding systems. Using this programme, some economic analyses of the impact of production, management and economic circumstances on the economic efficiency of various rabbit-production systems can also be performed.

With the increasing availability of large amounts of data in the livestock domain, we face the challenge to store, combine and analyse these data efficiently. With this study, we explored the use of a data lake for storing and analysing data to improve scalability and interoperability. Data originated from a 2-day animal experiment in which the gait score of approximately 200 turkeys was determined through visual inspection by an expert. Additionally, inertial measurement units (IMUs), a 3D-video camera and a force plate (FP) were installed to explore the effectiveness of these sensors in automating the visual gait scoring. We deployed a data lake using the IMU and FP data of a single day of that animal experiment. This encompasses data from 84 turkeys for which we preprocessed by performing an ‘extract, transform and load’ (ETL-) procedure. To test scalability of the ETL-procedure, we simulated increasing volumes of the available data from this animal experiment and computed the ‘wall time’ (elapsed real time) for converting FP data into comma-separated files and storing these files. With a simulated data set of 30 000 turkeys, the wall time reduced from 1 h to less than 15 min, when 12 cores were used compared to 1 core. This demonstrated the ETL-procedure to be scalable. Subsequently, a machine learning (ML) pipeline was developed to test the potential of a data lake to automatically distinguish between two classses, that is, very bad gait scores v. other scores. In conclusion, we have set up a dedicated customized data lake, loaded data and developed a prediction model via the creation of an ML pipeline. A data lake appears to be a useful tool to face the challenge of storing, combining and analysing increasing volumes of data of varying nature in an effective manner.

Heart rate variability (HRV) is a proxy measure of autonomic function and can be used as an indicator of swine stress. While traditional linear measures are used to distinguish between stressed and unstressed treatments, inclusion of nonlinear HRV measures that evaluate data structure and organization shows promise for improving HRV interpretation. The objective of this study was to evaluate the inclusion of nonlinear HRV measures in response to an acute heat episode. Twenty 12- to 14-week-old growing pigs were individually housed for 7 days and acclimated to thermoneutral conditions (20.35°C ± 0.01°C; 67.6% ± 0.2% RH) before undergoing one of the two treatments: (1) thermoneutral control (TN; n = 10 pigs) or (2) acute heat stress (HS; n = 10 pigs; 32.6°C ± 0.1°C; 26.2% ± 0.1% RH). In Phase 1 of the experimental procedure (P1; 60 min), pigs underwent a baseline HRV measurement period in thermoneutral conditions before treatment [Phase 2; P2; 60 min once gastrointestinal temperature (Tg) reached 40.6°C], where HS pigs were exposed to heated conditions and TN pigs remained in thermoneutral conditions. After P2, all pigs were moved back to thermoneutral conditions (Phase 3; P3; 60 min). During each phase, Tg data were collected every 5 min and behavioural data were collected to evaluate the amount of time each pig spent in an active posture. Additionally, linear (time and frequency domain) and nonlinear [sample entropy (SampEn), de-trended fluctuation analysis, percentage recurrence, percentage determinism (%DET), mean diagonal line length in a recurrence plot] HRV measures were quantified. Heat stressed pigs exhibited greater Tg (P = 0.002) and spent less time in an active posture compared to TN pigs during P2 (P = 0.0003). Additionally, low frequency to high frequency ratio was greater in HS pigs during P3 compared to TN pigs (P = 0.02). SampEn was reduced in HS pigs during P2 (P = 0.01) and P3 (P = 0.03) compared to TN pigs. Heat stressed pigs exhibited greater %DET during P3 (P = 0.03) and tended to have greater %DET (P = 0.09) during P2 than TN pigs. No differences between treatments were detected for the remaining HRV measures. In conclusion, linear HRV measures were largely unchanged during P2. However, changes to SampEn and %DET suggest increased heat stress as a result of the acute heat episode. Future work should continue to evaluate the benefits of including nonlinear HRV measures in HRV analysis of swine heat stress.

There is increasing public concern about poultry welfare; the quality of animal welfare is closely related to the quality of livestock products and the health of consumers. Good animal welfare promotes the healthy growth of poultry, which can reduce the disease rate and improve the production quality and capacity. As behaviour responses are an important expression of welfare, the study of behaviour is a simple and non-invasive method to assess animal welfare. The use of modern technology offers the possibility to monitor the behaviour of broilers and laying hens in a continuous and automated way. This paper reviews the latest technologies used for monitoring the behaviour of broilers and laying hens under both experimental conditions and commercial applications and discusses the potential of developing a precision livestock farming (PLF) system. The techniques that are presented and discussed include sound analysis, which can be an online tool to automatically monitor poultry behaviour non-invasively at the group level; wireless, wearable sensors with radio-frequency identification devices, which can automatically identify individual chickens, track the location and movement of individuals in real time and quantify some behavioural traits accordingly and image processing technology, which can be considered a direct tool for measuring behaviours, especially activity behaviours and disease early warning. All of these technologies can monitor and analyse poultry behaviour, at the group level or individual level, on commercial farms. However, the popularity and adoption of these technologies has been hampered by the logistics of applying them to thousands and tens of thousands of birds on commercial farms. This review discusses the advantages and disadvantages of these techniques in commercial applications and presents evidence that they provide potential tools to automatically monitor the behaviours of broilers and laying hens on commercial farms. However, there still has a long way to go to develop a PLF system to detect and predict abnormal situations.

In order to reduce antimicrobial use in pig production, the consequences of insufficient biosecurity and welfare problems need to be known. This study aimed to investigate associations between the number of antimicrobial treatments per fattening pig, and biosecurity, indicators for animal welfare as well as the prevalence of lesions at slaughter. The data used in this study were extracted from the pig health and welfare classification system (Sikava), which gathers data on medicine usage, meat inspection, animal welfare and the condition of farm buildings from over 95% of pig production in Finland. The data were registered during years from 2011 to 2013. Upon antimicrobial prescription, information on the number of fattening pigs treated and the main reason for treatment was recorded. In addition, at least 4 times per year, pig farms registered in Sikava were visited by the farm veterinarian who assessed, among other things, biosecurity and indicators for animal welfare (air quality, condition of facilities, cleanliness, enrichment and stocking density). Finally, data from slaughterhouse inspections were collected (number of carcasses with joint infection, abscesses, lung lesions, pleurisy and liver lesions). For analysis, these datasets were aggregated at the farm level to a quarter of a year. During the studied period, the mean number of antimicrobial treatments per fattening pig per 3 months was equal to 0.09. The main reasons for antimicrobial treatments were musculoskeletal diseases, tail biting and respiratory disorders (42, 33 and 12% of diagnoses, respectively). The meat inspection scoring indicated that as much as 14.7% of all pigs had pleurisy, 5.3% liver lesions and 4.1% abscesses. A standard zero-inflated negative binomial model was used to identify factors associated with the number of antimicrobial treatments per pig. The count of antimicrobial treatments per pig increased with the size of a farm. Regardless of prevalence of lesions, farms with poor drinking equipment, insufficient enrichment and a combination of poor condition of pens and high stocking density were associated with an increased number of antimicrobial treatments for musculoskeletal diseases per pig. Problems with stocking density and enrichment were associated with the number of antimicrobial treatments for tail biting, although these results depended on prevalence of joint infections. Problems with air quality and the combination of poor cleanliness and poor condition of facilities were associated with increased number of antimicrobial treatments due to respiratory diseases. This study suggests that by improving biosecurity and welfare at pig farms, antimicrobial use can be reduced.

Until recently, beef carcass payment grids were predominantly based on weight and fatness categories with some adjustment for age, defined as number of adult teeth, to determine the price received by Australian beef producers for slaughter cattle. With the introduction of the Meat Standards Australia (MSA) grading system, the beef industry has moved towards payments that account for intramuscular fat (IMF) content (marble score (MarbSc)) and MSA grades. The possibility of a payment system based on lean meat yield (LMY, %) has also been raised. The BeefSpecs suite of tools has been developed to assist producers to meet current market specifications, specifically P8-rump fat and hot standard carcass weight (HCW). A series of equations have now been developed to partition empty body fat and fat-free weight into carcass fat-free mass (FFM) and fat mass (FM) and then into flesh FFM (FleshFFM) and flesh FM (FleshFM) to predict carcass components from live cattle assessments. These components then predict denuded lean (kg) and finally LMY (%) that contribute to emerging market specifications. The equations, along with the MarbSc equation, are described and then evaluated using two independent datasets. The decomposition of evaluation datasets demonstrates that error in prediction of HCW (kg), bone weight (BoneWt, kg), FleshFFM (kg), FleshFM (kg), MarbSc and chemical IMF percentage (ChemIMF%) is shown to be largely random error (%) in evaluation dataset 1, though error for ChemIMF% was primarily slope bias (%) in evaluation dataset 1, and BoneWt had substantial mean bias (%) in evaluation dataset 2. High modelling efficiencies of 0.97 and 0.95 for predicting HCW for evaluation datasets 1 and 2, respectively, suggest a high level of accuracy and precision in the prediction of HCW. The new outputs of the model are then described as to their role in estimating MSA index scores. The modelling system to partition chemical components of the empty body into carcass components is not dependent on the base modelling system used to derive empty body FFM and FM. This can be considered a general process that could be used with any appropriate model of body composition.

Dietary chitosan (CS) supplementation could improve the growth rate, small intestinal morphology, nutrients apparent digestibility and digestive enzyme activities in pigs, broiler chickens, rats and fish, whereas no data has been reported about the effect of CS on the growing Huoyan geese. Therefore, this study was designed to investigate the effects of CS on growth rate, small intestinal morphology, nutrients apparent utilization and digestive enzyme activities of growing Huoyan geese. Three hundred and twenty (28 days of age, gender balance) Huoyan geese were randomly divided into control, CS100, CS200 and CS400 groups (based on BW) with 20 geese per pen and 4 replicates pen per group, and the feeding experiment lasted for 4 weeks. The 4 diets contained 0, 100, 200 and 400 mg CS per kg feed, respectively. The results showed that CS200 groups had higher average daily gain, final BW, apparent utilization of DM and CP, and lower feed/gain ratio compared with the control group (P < 0.05). Meanwhile, CS100 and CS200 groups had higher villus height, villus height/crypt depth ratio and lower crypt depth in duodenum and jejunum than those in the control group (P < 0.05). The geese in CS100 and CS200 groups had higher villus height, villus height/crypt depth ratio and lower crypt depth of ileum compared with those in control and CS400 groups (P < 0.05). In addition, compared with the control group, CS200 group has higher trypsin activities and lower lipase activities in duodenal, jejunal and ileal contents (P < 0.05). The results suggested that addition of 200 mg/kg CS had positive effects on growth rate, small intestinal morphology, nutrients apparent utilization and digestive enzyme activities of growing Huoyan geese.

Use of antimicrobials for food-producing animals is a major public concern due to the risk of antimicrobial resistance. Although dairy production has a relatively low usage of antimicrobials, the potential for further reduction should be explored. The objective of the study was to estimate the current differences in antimicrobial use in Danish organic and conventional dairy herds and to describe the differences between them. Based on data from three different sources, 2604 herds (306 organic and 2298 conventional) were identified for the study. These herds had been either organic or conventional for the entire period from 2015 to 2018. Antimicrobial use was calculated as the treatment incidence in Animal Daily Doses (ADDs)/100 animals/day for three age groups: adult cattle, young stock and calves. For adult cattle, the ratio of median treatment incidence between conventional and organic production ranged from 2.8 : 1 to 3.4 : 1, depending on the specific year. For cows, 25% of the organic herds had a higher treatment incidence than the 25% of conventional herds with the lowest treatment incidence. Antimicrobial use for young stock was low and at a similar level in both the organic and conventional production systems. For calves, the median treatment incidence was 1.2 times higher in conventional herds and 1.6 times higher for the 75th percentile. Analyses of treatment incidence in adult cattle showed an overall decrease from 2015 to 2018 in both organic and conventional herds. The decrease was greater for the conventional herds (0.12 ADD/100 animals/day) compared to the organic herds (0.04 ADD/100 animals/day) over the 4-year period. In addition, herd size was an important risk factor for treatment incidence in conventional herds, increasing by 0.07 ADD/100 animals/day per 100 cows, whereas herd size had a minor influence on the treatment incidence in organic herds. The results of this study demonstrate the large variation in antimicrobial use within both organic and conventional herds, suggesting that further reduction is possible. Furthermore, herd size appears to be a risk factor in conventional herds but not in organic herds – an aspect that should be studied in more detail.

Oestrus detection remains a problem in the dairy cattle industry. Therefore, automatic detection systems have been developed to detect specific behavioural changes at oestrus. Vocal behaviour has not been considered in such automatic oestrus detection systems in cattle, though the vocalisation rate is known to increase during oestrus. The main challenge in using vocalisation to detect oestrus is correctly identifying the calling individual when animals are moving freely in large groups, as oestrus needs to be detected at an individual level. Therefore, we aimed to automate vocalisation recording and caller identification in group-housed dairy cows. This paper first presents the details of such a system and then presents the results of a pilot study validating its functionality, in which the automatic detection of calls from individual heifers was compared to video-based assessment of these calls by a trained human observer, a technique that has, until now, been considered the ‘gold standard’. We developed a collar-based cattle call monitor (CCM) with structure-borne and airborne sound microphones and a recording unit and developed a postprocessing algorithm to identify the caller by matching the information from both microphones. Five group-housed heifers, each in the perioestrus or oestrus period, were equipped with a CCM prototype for 5 days. The recorded audio data were subsequently analysed and compared with audiovisual recordings. Overall, 1404 vocalisations from the focus heifers and 721 vocalisations from group mates were obtained. Vocalisations during collar changes or malfunctions of the CCM were omitted from the evaluation. The results showed that the CCM had a sensitivity of 87% and a specificity of 94%. The negative and positive predictive values were 80% and 96%, respectively. These results show that the detection of individual vocalisations and the correct identification of callers are possible, even in freely moving group-housed cattle. The results are promising for the future use of vocalisation in automatic oestrus detection systems.

The growing interest in rotational crossbreeding in Western countries is due to its potential to improve reproductive and health performances of cows. Although a large amount of research focuses on assessing crossbred cows’ performances, how to manage the transition from purebred to rotational crossbred herds is under-explored. Based on a retrospective analysis of French dairy herd case studies, we aimed to identify and characterise technical pathways to make such a transition. In 2018, we performed semi-directive interviews on 26 commercial dairy farms. Data were collected to describe changes in breeding, replacement and culling management practices from the first crossbred mating with purebred cows to the management of a mainly crossbred herd in 2018. Based on a multivariate analysis, we identified two main guidelines structuring technical pathways to move towards rotational crossbred herds: (i) the depth and scale of change (i.e. farm v. herd) associated with the introduction of rotational crossbreeding in the whole-farm dynamics and (ii) the changes in herd replacement and breeding practices to adapt to the evolution of herd demographics induced by the evolution of the dairy crossbred mating rate over time (high from the beginning v. distributed over time). Hierarchical clustering discriminated three groups of farmers differing in their technical pathway to move towards a rotational crossbred herd. In pathway 1, farmers customised one or several rotational crossbreeding schemes to support whole-farm transition towards an organic or grass-based system. Once the scheme stabilised, they quickly implemented it and had to readjust replacement and culling practices to regulate imbalance in herd demographics induced by the improvement in cow fertility. In pathway 2, farmers also customised one or several rotational crossbreeding schemes to support whole-farm redesign but they implemented it more gradually in the herd, which induced no major imbalance in herd demographics. In pathway 3, farmers predefined a relatively well-known rotational crossbreeding scheme to correct fertility issues of purebred cows without any changes at the farm level. They implemented it quickly from the beginning and had to adapt herd replacement and culling to regulate imbalance in herd demographics induced by the improvement in cow fertility. These first empirical evidences on how dairy farmers manage the transition from a purebred to rotational crossbred herd provide original scientific and operational contributions.

High-starch diets (HSDs) fed to high-producing ruminants are often responsible for rumen dysfunction and could impair animal health and production. Feeding HSDs are often characterized by transient rumen pH depression, accurate monitoring of which requires costly or invasive methods. Numerous clinical signs can be followed to monitor such diet changes but no specific indicator is able to make a statement at animal level on-farm. The aim of this pilot study was to assess a combination of non-invasive indicators in dairy cows able to monitor a HSD in experimental conditions. A longitudinal study was conducted in 11 primiparous dairy cows fed with two different diets during three successive periods: a 4-week control period (P1) with a low-starch diet (LSD; 13% starch), a 4-week period with an HSD (P2, 35% starch) and a 3-week recovery period (P3) again with the LSD. Animal behaviour was monitored throughout the experiment, and faeces, urine, saliva, milk and blood were sampled simultaneously in each animal at least once a week for analysis. A total of 136 variables were screened by successive statistical approaches including: partial least squares-discriminant analysis, multivariate analysis and mixed-effect models. Finally, 16 indicators were selected as the most representative of a HSD challenge. A generalized linear mixed model analysis was applied to highlight parsimonious combinations of indicators able to identify animals under our experimental conditions. Eighteen models were established and the combination of milk urea nitrogen, blood bicarbonate and feed intake was the best to detect the different periods of the challenge with both 100% of specificity and sensitivity. Other indicators such as the number of drinking acts, fat:protein ratio in milk, urine, and faecal pH, were the most frequently used in the proposed models. Finally, the established models highlight the necessity for animals to have more than 1 week of recovery diet to return to their initial control state after a HSD challenge. This pilot study demonstrates the interest of using combinations of non-invasive indicators to monitor feed changes from a LSD to a HSD to dairy cows in order to improve prevention of rumen dysfunction on-farm. However, the adjustment and robustness of the proposed combinations of indicators need to be challenged using a greater number of animals as well as different acidogenic conditions before being applied on-farm.

It is known that pigs can acquire flavour preferences by brief social interactions with conspecifics that previously consumed a flavoured solid feed. However, there is no information about whether a flavoured solution could support flavour preferences through social transmission. Ninety-six pigs (49 days old) were housed in 12 pens (8 pigs/pen). Four animals per pen were randomly selected to act as observers and four as demonstrators. Demonstrator animals were temporarily moved to an empty pen where a protein solution was offered (porcine digestive peptides (PDPs), 4% weight/volume) with the addition of 0.075% aniseed (six pens) or garlic (six pens) powdered artificial flavours for 30 min. Afterwards, demonstrators were returned to interact with observer animals for 30 min. A choice test (30 min) between aniseed and garlic PDP was performed for each observer group after the interaction. Observers showed a higher intake of solutions previously consumed by their demonstrator conspecifics (648 v. 468 ml; SEM 61.36, P < 0.05). As with flavoured solid feeds, protein solutions containing artificial flavours can create preferences in pigs for those flavours through social transmission from conspecifics.

The competitiveness and sustainability of low input cost dairy production systems are generally supported by efficient use of pasture in the diets. Therefore, pasture intake directly affects overall efficiency of these systems. We aimed to assess feeding and grazing management main factors that affect pasture dry matter intake (DMI) in commercial dairy farms during the different seasons of the year. Fortnightly visits to 28 commercial dairies were carried out between June 2016 and May 2017 to record production and price, supplement offered and price, pasture access time (PAT), herbage mass (HM) and allowance (HA). Only farms with the most contrasting estimated pasture DMI per cow (eDMI) were compared as systems with high (HPI; N = 8) or low (LPI; N = 8) pasture DMI. Despite a lower individual milk production in HPI than LPI (19.0 v. 23.3 ± 0.7 l/cow, P < 0.01), daily margin over feeding cost was not different between groups (3.07 v. 2.93 ± 0.15 U$S/cow for HPI and LPI, respectively). During autumn and winter, HPI cows ingested more pasture than LPI cows (8.3 v. 4.6 and 5.9 v. 2.9 ± 0.55 kg DM/cow per day, respectively, P < 0.01) although PAT, HM and HA were similar between groups. Both groups offered high supplementation levels during these seasons, even though greater in LPI than HPI (14.7 v. 9.7 ± 0.7 kg DM supplement/cow per day, respectively, P < 0.01). On the other hand, differences between groups for both pasture and supplement DMI were more contrasting during spring and summer (13.1 v. 7.3 ± 0.5 and 4.0 v. 11.4 ± 0.4 kg DM/cow per day for HPI and LPI, respectively, P < 0.01), with higher PAT in both seasons (P < 0.05) and higher HA during summer in HPI than LPI (P < 0.01). Unlike LPI, during these seasons HPI adjusted offered supplement according to HA, achieving a higher pasture eDMI and making more efficient use of available pastoral resource than LPI. As there was no grazing limiting condition for pasture harvesting in either group, the main factor affecting pasture DMI was a pasture by supplement substitution effect. These results reinforce the importance of an efficient grazing management, and using supplements to nutritionally complement pasture intake rather than as a direct way to increase milk production.

Heat stress is a global issue constraining pig productivity, and it is likely to intensify under future climate change. Technological advances in earth observation have made tools available that enable identification and mapping livestock species that are at risk of exposure to heat stress due to climate change. Here, we present a methodology to map the current and likely future heat stress risk in pigs using R software by combining the effects of temperature and relative humidity. We applied the method to growing-finishing pigs in Uganda. We mapped monthly heat stress risk and quantified the number of pigs exposed to heat stress using 18 global circulation models and projected impacts in the 2050s. Results show that more than 800 000 pigs in Uganda will be affected by heat stress in the future. The results can feed into evidence-based policy, planning and targeted resource allocation in the livestock sector.

Digitalisation is an integral part of modern agriculture. Several digital technologies are available for different animal species and form the basis for precision livestock farming. However, there is a lack of clarity as to which digital technologies are currently used in agricultural practice. Thus, this work aims to present for the first time the status quo in Swiss livestock farming as an example of a highly developed, small-scale and diverse structured agriculture. In this context, the article focuses on the adoption of electronic sensors and measuring devices, electronic controls and electronic data-processing options and the usage of robotics in ruminant farming, namely, for dairy cattle, dairy goats, suckler cows, beef cattle and meat-sheep. Furthermore, the use of electronic ear tags for pigs and the smartphone usage for barn monitoring on poultry farms was assessed. To better understand the adoption process, farm and farmer’s characteristics associated with the adoption of (1) implemented and (2) new digital technologies in ruminant farming were assessed using regression analyses, which is classified at a 10% adoption hurdle. The results showed clear differences in the adoption rates between different agricultural enterprises, with both types of digital technologies tending to be used the most in dairy farming. Easy-to-use sensors and measuring devices such as those integrated in the milking parlour were more widespread than data processing technologies such as those used for disease detection. The husbandry system further determined the use of digital technologies, with the result that farmers with tie stall barns were less likely to use digital technologies than farmers with loose housing systems. Additional studies of farmers’ determinants and prospects of implementation can help identify barriers in the adoption of digital technologies.