Modelling of daily rhythms of behavioural patterns in growing pigs on two commercial farms

Modelling of Animal Activity, Illuminance, and Noise on a Weaned Piglet Farm

Measuring animal activity and its evolution in real time is useful for animal welfare assessment. In addition, illuminance and noise level are two factors that can improve our understanding of animal activity. This study aims to establish relationships between animal activity as measured by passive infrared sensors, and both illuminance and noise level on a conventional weaned piglet farm. First, regression models were applied, and then cosine models with three harmonics were developed using least squares with a Generalized Reduced Gradient Nonlinear method. Finally, all the models were validated. Linear models showed positive correlations, with values between 0.40 and 0.56. Cosine models drew clear patterns of daily animal activity, illuminance and noise level with two peaks, one in the morning and one in the afternoon, coinciding with human activity inside the building, with a preference for inactivity at night-time and around midday. Cosine model fitting revealed strong correlations, both in the measurement and validation periods, for animal activity (R = 0.97 and 0.92), illuminance (R = 0.95 and 0.91) and noise level (R = 0.99 and 0.92). The developed models could be easily implemented in animal welfare monitoring systems and could provide useful information about animal activity through continuous monitoring of illuminance or noise levels.

Water use patterns within each day: Variation between batches of growing pigs in commercial production systems

Objective: To measure, describe, and compare the water use patterns within each day for multiple cohorts of weaner, grower, and finisher pigs in farm buildings. Materials and methods: Prospective, observational cohort studies of the water use patterns within each day were conducted in 5 pig buildings using either a turbine or ultrasonic water flow meter attached to the main water pipe entering each building. Water use data were collected from multiple batches of pigs (second-stage weaners over eleven 48-day periods and grower-finishers over 4 periods of 21-43 days). Semi-parametric models of pig water use patterns within each day were estimated using the brms software package in R. To estimate the interacting effects of time and pig body weight on water use by pigs, we used tensor product smooths for time and pig body weight. Results: The water use pattern within each day varied between the cohorts, and the pattern of many cohorts changed as the pigs gained weight. Some patterns were unimodal and others were bimodal, with the main peak in water use occurring early afternoon to late afternoon. Implications: Water use patterns of pigs within each day varied between and within cohorts. The water use pattern of one cohort cannot be used reliably to predict that of other cohorts, even if they are reared in the same building. Water use pattern data may be valuable for optimizing in-water antimicrobial dosing regimens.

Use of drinkers by finisher pigs depend on drinker location, pig age, time of day, stocking density and tail damage

Water is a vital nutrient for mammals, including the pig. Despite this, the use of drinkers and water have not yet been explicitly quantified across the finisher period. The current study aimed at gaining greater insight into finisher pigs' drinker use and its relation to drinker location, age, time of day, stocking density, enrichment provision and tail damage. The experiment included 110 pens of finisher pigs over a 9-week period, with two drinker cups per pen. Pens had a stocking density of either 0.73 m²/pig (n = 54 pens, 18 pigs per pen) or 1.21 m²/pig (n = 56 pens, 11 pigs per pen), were either provided with straw (n = 54, 150 g per pig and day) or not (n = 56), and had pigs with either undocked (n = 50) or docked tails (n = 60). Drinker use was recorded automatically by water-flow meters and summed to L and number of activations per hour and pig. Pens never experiencing a tail damage event (at least one pig in the pen with a bleeding tail) were used to investigate the normal drinker use of finisher pigs (n = 56). The water use of pigs increased from 3.7 to 8.2 L per pig and day during the 9 weeks, and this increase was mainly seen during the two large peaks of the diurnal pattern within the pigs' active period (06:00-18:00 h). No such increase was seen in the activation frequency at average 50 activations per pig and day. A decrease in stocking density increased both water use and activation frequency during the active period, suggesting that pigs at the standard space allowance and pig:drinker ratio could be restricted in their access to the drinking cups. The pigs also seemed to prefer to use the drinking cup closest to the feeder. Water use and activation frequency did not change the last 3 days prior to an event of tail damage, but general differences were seen between pens with and without a tail damage event. The current results may explain the success of previous studies in classifying tail damage pens from pens without tail damage using sensor data on drinker use.

Water consumption and wastage behaviour in pigs: implications for antimicrobial administration and stewardship

Daily water use and wastage patterns of pigs have major effects on the efficacy of in-water antimicrobial dosing events when conducted for metaphylaxis or to treat clinical disease. However, daily water use and wastage patterns of pigs are not routinely quantified on farms and are not well understood. We conducted a prospective, observational 27-day study of the daily water use and wastage patterns of a pen group of 15 finisher pigs reared in a farm building. We found that the group of pigs wasted a median of 36.5% of the water used per day. We developed models of the patterns of water used and wasted by pigs over each 24-h period using a Bayesian statistical method with the brm() function in the brms package. Both patterns were uni-modal, peaking at 1400-1700, and closely aligned. Wastage was slightly greater during hours of higher water use. We have shown that it is feasible to quantify the water use and wastage patterns of pigs in farm buildings using a system that records and aggregates data, and analyses them using hierarchical generalised additive models. This system could support more efficacious in-water antimicrobial dosing on farms, and better antimicrobial stewardship, by helping to reduce the quantities of antimicrobials used and disseminated into the environment.

Circadian rhythms of the mRNA abundances of clock genes and glucose transporters in the jejunum of weanling-growing pigs

Background

Whether abundance of glucose transporter mRNAs in the small intestine of pigs shows circadian rhythms and its regulation by clock genes was still unknown.

Objectives

We examined whether the abundance of glucose transporters and clock genes mRNAs in the small intestine of pigs shows circadian rhythms.

Methods

Twenty barrows (4 weeks old) were reared under 12 h bright and 12 h dark lighting conditions. During the 3‐week feeding trial, pigs were allowed free access to feed. The abundances of the mRNA of glucose transporters (SGLT1 and GLUT2) and clock genes (Bmal1, Per1, Per2, and Cry2) in the intestine were measured at four time points (ZT2, ZT8, ZT14, and ZT20).

Results

In the jejunum, the abundance of SGLT1 mRNA was higher at ZT20 and ZT2 and lower at ZT8 and ZT14 (p < 0.05). The abundances of GLUT2 mRNA in the jejunum at ZTs 20 and 2 were tended to be higher than those at ZTs 8 and 14 (p = 0.05). In the jejunum, the abundance of Bmal1 mRNA was higher at ZT8 and ZT14 than at ZT20 and ZT2 (p < 0.05). Further, the abundance of Per1 mRNA at ZT2 was higher than those at the other sampling times (p < 0.05). The abundance of Per1 mRNA at ZT8 was higher than that at ZT14 (p < 0.05), while that of Per2 mRNA was higher at ZT2 than those at ZTs 20 and 14 (p < 0.05).

Conclusion

We speculate that these circadian rhythms of abundances of glucose transporter mRNAs are regulated by the clock genes expressed in the jejunum.

Dairy Cows Activity under Heat Stress: A Case Study in Spain

Heat stress plays a role in livestock production in warm climates. Heat stress conditions impair animal welfare and compromise the productive and reproductive performance of dairy cattle. Under heat stress conditions, dairy cattle modify their behavior. Thus, the assessment of behavior alterations can be an indicator of environmental or physiological anomalies. Moreover, precision livestock farming allows for the individual and constant monitoring of animal behavior, arising as a tool to assess animal welfare. The purpose of this study was to evaluate the effect of heat stress on the behavior of dairy cows using activity sensors. The study was carried out in Tinajeros (Albacete, Spain) during the summer of 2020. Activity sensors were installed in 40 cows registering 6 different behaviors. Environmental conditions (temperature and humidity) were also monitored. Hourly data was calculated for both animal behavior and environmental conditions. Temperature and Heat Index (THI) was calculated for each hour. The accumulated THI during the previous 24 h period was determined for each hour, and the hours were statistically classified in quartiles according to the accumulated THI. Two groups were defined as Q4 for no stress and Q1 for heat stress. The results showed that animal behavior was altered under heat stress conditions. Increasing THI produces an increase in general activity, changes in feeding patterns and a decrease in rumination and resting behaviors, which is detrimental to animal welfare. Daily behavioral patterns were also affected. Under heat stress conditions, a reduction in resting behavior during the warmest hours and in rumination during the night was observed. In conclusion, heat stress affected all behaviors recorded as well as the daily patterns of the cows. Precision livestock farming sensors and the modelling of daily patterns were useful tools for monitoring animal behavior and detecting changes due to heat stress.

Long-Term Measurement of Piglet Activity Using Passive Infrared Detectors

Measuring animal activity is useful for monitoring animal welfare in real time. In this regard, passive infrared detectors have been used in recent years to quantify piglet activity because of their robustness and ease of use. This study was conducted on a commercial farm in Northwest Spain during six complete breeding cycles. The hourly average activity of weaned piglets with a body mass of 6-20 kg was recorded and further analyzed by using a multiplicative decomposition of the series followed by a wavelet analysis. Finally, the real series were compared to the theoretical models of activity. Results showed a high level of movement immediately after weaning and a sustained level of activity throughout the cycles. The daily behavior of the piglets followed a clear circadian pattern with several peaks of activity. No differences in behavior were observed between spring-summer cycles and autumn-winter cycles. Single-peak models achieved the best predictive results. In addition, the installed sensors were found to underestimate mild activity.

Prediction of Tail Biting Events in Finisher Pigs from Automatically Recorded Sensor Data

Simple Summary

Tail biting is a major animal welfare issue within modern pig production, and tail biting should be prevented whenever possible. If the farmer could get an alarm when a pen of pigs is at high risk of developing tail damage, the farmer would be able to take timely action to prevent tail damage in specific pens. In the current investigation, a method for prediction of tail biting events was developed and tested in a real-life setting. The method used changes in pigs’ drinking behaviour and in the temperature of the pen. The method was able to alarm the farmer about 12 of the 14 tail biting events prior to serious tail damage. However, the farmer did also get false alarms on 30% of the days without tail biting events, which is not optimal. Thus, the farmer could use the alarms as indications of which pens to pay greater attention to. The next step could be to expand the method to include behavioural changes that are more specific to tail biting such as changes in the pigs’ tail posture.

Abstract

Tail biting in pigs is an animal welfare problem, and tail biting should be prevented from developing into tail damage. One strategy could be to predict events of tail biting so that the farmer can make timely interventions in specific pens. In the current investigation, sensor data on water usage (water flow and activation frequency) and pen temperature (above solid and slatted floor) were included in the development of a prediction algorithm for tail biting. Steps in the development included modelling of data sources with dynamic linear models, optimisation and training of artificial neural networks and combining predictions of the single data sources with a Bayesian ensemble strategy. Lastly, the Bayesian ensemble combination was tested on a separate batch of finisher pigs in a real-life setting. The final prediction algorithm had an AUC > 0.80, and thus it does seem possible to predict events of tail biting from already available sensor data. However, around 30% of the no-event days were false alarms, and more event-specific predictors are needed. Thus, it was suggested that farmers could use the alarms to point out pens that need greater attention.

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

Frequent BW monitoring of growing pigs can be useful for identifying production (e.g. feeding), health and welfare problems. However, in order to construct a tool which will properly recognize abnormalities in pigs' growth a precise description of the growth process should be used. In this study we proposed a new model of pig growth accounting for daily fluctuations in BW. Body weight measurements of 1710 pigs (865 gilts and 843 barrows) originating from five consecutive batches from a Danish commercial farm were collected. Pigs were inserted into a large pen (maximum capacity=400) between November 2014 and September 2015. On average, each pig was observed for 42 days and weighed 3.6 times a day when passing from the resting to feeding area. Altogether, 243,160 BW measurements were recorded. A multilevel model of pig growth was constructed and fitted to available data. The BW of pigs was modeled as a quadratic function of time. A diurnal pattern was incorporated into the model by a cosine wave with known length (24 h). The model included pig effect which was defined as a random autoregressive process with exponential correlation. Variance of within-pigs error was assumed to increase with time. Because only five batches were observed, it was not possible to obtain the random effect for batch. However, in order to account for the batch effect the model included interactions between batch and fixed parameters: intercept, time, square value of time and cosine wave. The gender effect was not significant and was removed from the final model. For all batches, morning and afternoon peaks in the frequency of visits to the feeding area could be distinguished. According to results, pigs were lighter in the morning and heavier in the evening (minimum BW was reached around 1000 h and maximum around 2200 h). However, the exact time of obtaining maximum and minimum BW during the day differed between batches. Pigs had access to natural light and, therefore, existing differences could be explained by varying daylight level during observations periods. Because the diurnal amplitude for pig growth varied between batches from 0.9 to 1.4 kg, BW monitoring tools based on frequent measurements should account for diurnal variation in BW of pigs. This proposed description of growth will be built into a monitoring tool (a dynamic linear model) and applied to farm data in future studies.

Growing pigs' drinking behaviour: number of visits, duration, water intake and diurnal variation

Individual drinking patterns are a potential tool for disease monitoring in pigs. However, to date, individual pig drinking behaviour has not been described, and effects of external factors have not been examined. The aim of this study was to perform detailed quantification of drinking behaviour of growing pigs and to examine effects of period of day and effects of competition for access to the drinking nipple on the drinking behaviour, amount of water used and water wastage. In all, 52 cross-bred castrated male pigs (live weight 20.5±1.7 kg; mean±s.d.) maintained as either 3 (N3) or 10 (N10) pigs per pen and water nipple (four groups/treatment) were used. All pigs were fitted with a transponder ear tag. A radio frequency identification reader recorded and time stamped visits at the nipple. In each pen, water flow was logged every second. The drinking behaviour was recorded for 4 consecutive days and analysed using a linear mixed model. Overall, the pigs spent 594 s at the nipple during 24 h distributed among 44 visits. During this period, 5 l of water were used, of which >30% was wasted. Social competition did not affect the drinking behaviour over 24 h, except for the proportion of interrupted visits where pigs, kept with recommended nipple availability (N10), showed an increased proportion of interrupted drinking bouts compared with pigs kept at very low level of competition (N3) (0.18±0.01 v. 0.11±0.01; P<0.01). However, splitting data into 8-h periods (P1, P2, P3) starting from 0600 h revealed differences between treatments, showing that in N3, water use per visit was lower in P1 than P2 and P3 (110±10 v. 126±7 and 132±7 ml; P<0.05), whereas in N10, the water used per visit was higher during P3 than during the other periods (P1: 107±14 ml, P2: 112±10 ml v. P3: 151±10 ml; P<0.001). A similar pattern was found for visit duration. In N3, fewer nipple visits were observed in P2 than P1 (15.6±1.2 v. 22.0±1.2; P<0.001), whereas no difference was found between P1 and P2 in N10. The results demonstrate that growing pigs at the two levels of competition maintained a comparable level of 24 h water intake by changing behavioural variables involved in drinking. This dynamic characteristic of drinking behaviour means that if individual drinking patterns are to be used as disease monitoring tools, it is important to consider effects of external factors and include data on period level to allow rapid detection of behavioural changes.