Higher precision level at individual laying performance tests in noncage housing systems

Genetic parameter estimates for the use of an aviary with winter garden by laying hens

The behavioral activity of laying hens in an aviary is indicative of their welfare and health. Furthermore, hens' usage of the different locations within an aviary has been shown to influence laying performance and egg quality. For example, hens that spent a longer duration of time in the nest during laying were observed to have lower laying performance. Therefore, understanding genetics of laying hens' usage of the aviary could be important for predicting egg quality, production traits and health and welfare. The objectives of this study were to estimate genetic parameters for duration of time spent at different locations within the aviary and an adjacent winter garden using a multivariate repeatability model and to compare correlations between time spent in these locations. For this study, a total of 1,106 Dekalb white laying hens (Hendrix Genetics) were genotyped using a proprietary 60K SNP array. These hens had access to 5 different zones within the aviary, which included the top level tier, nest box tier, lower level tier, floor littered area and a winter garden. Hens were in the aviary for a total of 290 d and daily records of duration were collected for each hen visit to any location in the aviary, culminating in a total of 937,740 records. Heritability estimates ranged from 0.05 (0.01) to 0.28 (0.03) for the duration of time spent in the different zones. The lowest heritability was estimated for time spent at the lower level tier, while a higher heritability was estimated for time spent in the floor littered area. A moderately high negative genetic correlation of -0.59 (0.08) was observed between time spent in the top level tier and time spent in the floor littered area, while a favorable correlation of 0.37 (0.14) was found between time spent in the lower level tier and time spent in the winter garden. The findings of this study show that the duration of time spent at different zones within an aviary has genetic basis and could be used for selecting animals for better performance and higher welfare.

Egg production in nests and nesting behaviour: genetic correlations with egg quality and BW for laying hens on the floor

In laying hen production, cage-free housing is growing rapidly to provide living conditions that meet hens' needs. Unlike cages, this housing requires nests for automatic collection of eggs, as eggs laid outside nests must be collected by hand. Selecting hens for nest-related traits, such as egg production in nests and nesting behaviour, could help meet the requirements of cage-free housing. However, genetic correlations between these traits and major traits of breeding programmes, such as egg quality or BW, are poorly known. In addition, the genetic determinism of major traits has rarely been studied under cage-free conditions. The objective of the present study was to estimate the heritability of egg quality and BW measured on the floor and their genetic correlations with nest-related traits. Egg production in nests was based on the laying rate in nests, laying rhythm (clutch number and mean oviposition time), and nest acceptance. Nesting behaviour was based on nest preference (mean distance between nests used for laying) and mean laying duration (time spent in the nest for laying). Nest-related traits were recorded from 24 to 64 weeks of age. BW and egg quality were measured at 50 and 55 weeks of age, respectively. Nest-related traits and identification of the eggs laid by each hen (for individual measurements of egg quality) were obtained using individual electronic nests used by hens raised in groups and on the floor. The phenotypes of 1 455 Rhode Island Red and 1 538 White Leghorn hens were analysed. Heritability coefficients and genetic correlations were estimated using a multi-trait animal model for each line. Heritability estimates for egg quality and BW were moderate to high for both lines (0.17-0.74). Overall, weak genetic correlations were estimated between nest-related traits and egg quality or BW for both lines. However, strong and antagonistic genetic correlations were estimated between eggshell strength and laying rate in the nests (-0.46 to -0.42) or laying rhythm (+0.46 to +0.68) for both lines. Several moderate-to-strong genetic correlations were found for White Leghorn between nest-related traits and egg weight, eggshell shape, albumen height, and BW. This study shows that nest-related traits can be used to select hens better adapted to cage-free housing without degrading overall egg quality and BW. It also shows that some traits, like the eggshell strength, must be carefully monitored if these new traits are included in breeding goals. These results must now be confirmed for other populations and larger datasets.

Nest preference and laying duration traits to select against floor eggs in laying hens

BACKGROUND

Floor eggs, which are defined as eggs that hens lay off-nest, are a major issue in cage-free layer poultry systems. They create additional work for farmers because they must be collected by hand. They are also usually soiled or broken, which results in economic losses. Nonetheless, knowledge about the genetics of nesting behavior is limited. The aim of this study was to estimate genetic parameters for traits related to nest preference for laying and to time spent in the nests used for laying (laying duration).

METHODS

Two pure lines of laying hens were studied: 927 Rhode Island Red and 980 White Leghorn. Electronic nests were used to record the nesting behavior of these hens in floor pens from 24 to 64 weeks of age. Nest preference was studied based on the mean distance between nests used for laying and the percentage of nests used for laying. Laying duration was studied based on mean laying duration, mean duration in the nest before laying, and mean duration in the nest after laying. Genetic parameters were estimated for each line using a restricted maximum-likelihood method applied to a pedigree-based multi-trait animal model.

RESULTS

Estimates of genetic parameters were similar for the two lines. Estimates of heritability ranged from 0.18 to 0.37 for nest preference traits and from 0.54 to 0.70 for laying duration traits. Estimates of genetic correlations of these traits with clutch number or mean oviposition time were favorable. Positive genetic correlations were estimated between nest preference and laying rate in the nests or nest acceptance for laying (+ 0.06 to + 0.37).

CONCLUSIONS

These results show that genetics influences traits related to nest preference and laying duration. Selecting hens that have no preference for particular nests and spend little time laying in the nests could help optimize nest use, reduce their occupation rate, and thus decrease the incidence of floor eggs in cage-free systems. Genetic correlations of these traits with other traits of interest related to hen welfare and egg quality have yet to be estimated.

An IoT-Based Breeding Egg Identification and Coding System for Selection of High-Quality Breeding Geese

Simple Summary

In the process of breeding geese, manually recording data causes the problems of missing and confusing characterization data; furthermore, manual intervention can lead to the stress response of breeding geese and affect the laying efficiency of breeding geese. In this study, we tried to combine the Internet of things and computer image technology to improve the accuracy of data recording, so as to achieve an accurate correspondence between breeding goose individual data and egg-laying data. Therefore, we developed and tested a breeding egg identification and coding system to realize the selection of high-quality breeding geese. The test results showed that the system realized a correspondence of 97.8% between breeding goose individual data and egg-laying data. The system realized the information recording of breeding geese under nonmanual intervention, realized the correspondence between individual data and egg-laying data, and improved the health and welfare of breeding geese.

Abstract

The selection of breeding geese requires the recording of egg production information to correspond to the identity of the breeding geese. However, due to the special physiological characteristics of breeding geese, manual recording in practice can affect the egg-laying performance of breeding geese and can also lead to problems of missing and confusing individual breeding goose data with the number of eggs laid by the geese. For contactless recording of breeding goose identity and egg production information for high-quality breeding, this paper proposes an Internet of things (IoT)-based breeding egg identification and coding method for the selection of high-quality breeding geese. At the sensing level, we deployed a radiofrequency identification (RFID)-based sensor. Each breeding goose wore a foot ring RFID tag on its leg, and the individual information was read by foot ring RFID readers placed at the bottom of the devices. Individual information was uploaded to the cloud server for database management through structured query language (MySQL). The target detection modules were mounted on top of the devices, and the breeding geese and eggs were detected in the delivery rooms by an improved single-shot multi-box detector (SSD) target detection algorithm. The egg body limit transmission device and contactless coding device were activated only in the case of breeding eggs, and the breeding goose information was printed on the egg bodies in the form of quick response codes (QR codes), which enabled the breeding egg information to correspond with the breeding goose information. An evaluative experiment was performed using a system for the selection of high-quality breeding geese, with web cameras and a cloud monitoring platform. The breeding geese were allowed 14 days to become accustomed to the experimental environment before monitoring began. The evaluative experiment results showed that the pass rate of egg body coding reached 98.25%, the improved SSD algorithm was 8.65% more accurate and 62.6 ms faster than traditional SSD, and the accuracy rate corresponding to the individual information of the breeding geese and the surface information of the goose eggs was 97.8%. The experimental results met the requirements of accurate marking of individual information of breeding geese, which can provide technical support for the selection of high-quality breeding geese.

Heritability of perching behavior and its genetic relationship with incidence of floor eggs in Rhode Island Red chickens

Background

As cage-free production systems become increasingly popular, behavioral traits such as nesting behavior and temperament have become more important. The objective of this study was to estimate heritabilities for frequency of perching and proportion of floor eggs and their genetic correlation in two Rhode Island Red lines.

Results

The percent of hens observed perching tended to increase and the proportion of eggs laid on the floor tended to decrease as the test progressed. This suggests the ability of hens to learn to use nests and perches. Under the bivariate repeatability model, estimates of heritability in the two lines were 0.22 ± 0.04 and 0.07 ± 0.05 for the percent of hens perching, and 0.52 ± 0.05 and 0.45 ± 0.05 for the percent of floor eggs. Estimates of the genetic correlation between perching and floor eggs were − 0.26 ± 0.14 and − 0.19 ± 0.27 for the two lines, suggesting that, genetically, there was some tendency for hens that better use perches to also use nests; but the phenotypic correlation was close to zero. Random regression models indicated the presence of a genetic component for learning ability.

Conclusions

In conclusion, perching and tendency to lay floor eggs were shown to be a learned behavior, which stresses the importance of proper management and training of pullets and young hens. A significant genetic component was found, confirming the possibility to improve nesting behavior for cage-free systems through genetic selection.

Free-range laying hens: using technology to show the dynamics and impact of hen movement

Free-range laying hens are provided with the opportunity to access various structural areas, including open floor space, feed areas, water lines, next boxes, perches, aviary tiers, winter gardens and ranges. Different individual location preferences can lead to the development of hen subpopulations that are characterised by various health, welfare and performance parameters. Understanding the complexity of hen movement and hen interactions within their environment provides an opportunity to limit the disadvantages that are associated with housing in loose husbandry systems and aids in decision-making. Monitoring hen movement using modern technologies such as radio-frequency identification (RFID), optical flow patterns, image analysis and three-dimensional (3D) cameras allows the accumulation of big data for data mining, clustering and machine learning. Integrating individual-based management systems into modern flock management will not only help improve the care of under-performing hens, but also ensure that elite hens are able to use their full genetic potential, allowing an ethical, sustainable and welfare friendly egg production. This review highlights the dynamics and impact of hen movement in free-range systems, reviews existing knowledge relevant for feeding hens in non-cage systems, and outlines recent technological advances and strategies to improve the management of free-range laying hens.

An RFID-Based Smart Nest Box: An Experimental Study of Laying Performance and Behavior of Individual Hens

This study designed a radio-frequency identification (RFID)-based Internet of Things (IoT) platform to create the core of a smart nest box. At the sensing level, we have deployed RFID-based sensors and egg detection sensors. A low-frequency RFID reader is installed in the bottom of the nest box and a foot ring RFID tag is worn on the leg of individual hens. The RFID-based sensors detect when a hen enters or exits the nest box. The egg-detection sensors are implemented with a resistance strain gauge pressure sensor, which weights the egg in the egg-collection tube. Thus, the smart nest box makes it possible to analyze the laying performance and behavior of individual hens. An evaluative experiment was performed using an enriched cage, a smart nest box, web camera, and monitoring console. The hens were allowed 14 days to become accustomed to the experimental environment before monitoring began. The proposed IoT platform makes it possible to analyze the egg yield of individual hens in real time, thereby enabling the replacement of hens with egg yield below a pre-defined level in order to meet the overall target egg yield rate. The results of this experiment demonstrate the efficacy of the proposed RFID-based smart nest box in monitoring the egg yield and laying behavior of individual hens.

Development of a Machine Vision Method for the Monitoring of Laying Hens and Detection of Multiple Nest Occupations

Free range systems can improve the welfare of laying hens. However, the access to environmental resources can be partially limited by social interactions, feeding of hens, and productivity, can be not stable and damaging behaviors, or negative events, can be observed more frequently than in conventional housing systems. In order to reach a real improvement of the hens' welfare the study of their laying performances and behaviors is necessary. With this purpose, many systems have been developed. However, most of them do not detect a multiple occupation of the nest negatively affecting the accuracy of data collected. To overcome this issue, a new "nest-usage-sensor" was developed and tested. It was based on the evaluation of thermografic images, as acquired by a thermo-camera, and the performing of patter recognitions on images acquired from the nest interior. The sensor was setup with a "Multiple Nest Occupation Threshold" of 796 colored pixels and a template of triangular shape and sizes of 43 × 33 pixels (high per base). It was tested through an experimental nesting system where 10 hens were reared for a month. Results showed that the evaluation of thermografic images could increase the detection performance of a multiple occupation of the nest and to apply an image pattern recognition technique could allow for counting the number of hens in the nest in case of a multiple occupation. As a consequence, the accuracy of data collected in studies on laying performances and behaviors of hens, reared in a free-range housing system, could result to be improved.

A Monitoring System for Laying Hens That Uses a Detection Sensor Based on Infrared Technology and Image Pattern Recognition

In Italy, organic egg production farms use free-range housing systems with a big outdoor area and a flock of no more than 500 hens. With additional devices and/or farming procedures, the whole flock could be forced to stay in the outdoor area for a limited time of the day. As a consequence, ozone treatments of housing areas could be performed in order to reduce the levels of atmospheric ammonia and bacterial load without risks, due by its toxicity, both for hens and workers. However, an automatic monitoring system, and a sensor able to detect the presence of animals, would be necessary. For this purpose, a first sensor was developed but some limits, related to the time necessary to detect a hen, were observed. In this study, significant improvements, for this sensor, are proposed. They were reached by an image pattern recognition technique that was applied to thermografic images acquired from the housing system. An experimental group of seven laying hens was selected for the tests, carried out for three weeks. The first week was used to set-up the sensor. Different templates, to use for the pattern recognition, were studied and different floor temperature shifts were investigated. At the end of these evaluations, a template of elliptical shape, and sizes of 135 × 63 pixels, was chosen. Furthermore, a temperature shift of one degree was selected to calculate, for each image, a color background threshold to apply in the following field tests. Obtained results showed an improvement of the sensor detection accuracy that reached values of sensitivity and specificity of 95.1% and 98.7%. In addition, the range of time necessary to detect a hen, or classify a case, was reduced at two seconds. This result could allow the sensor to control a bigger area of the housing system. Thus, the resulting monitoring system could allow to perform the sanitary treatments without risks both for animals and humans.

First Results of a Detection Sensor for the Monitoring of Laying Hens Reared in a Commercial Organic Egg Production Farm Based on the Use of Infrared Technology

The development of a monitoring system to identify the presence of laying hens, in a closed room of a free-range commercial organic egg production farm, was the aim of this study. This monitoring system was based on the infrared (IR) technology and had, as final target, a possible reduction of atmospheric ammonia levels and bacterial load. Tests were carried out for three weeks and involved 7 ISA (Institut de Sélection Animale) brown laying hens. The first 5 days was used to set up the detection sensor, while the other 15 days were used to evaluate the accuracy of the resulting monitoring system, in terms of sensitivity and specificity. The setup procedure included the evaluation of different color background (CB) thresholds, used to discriminate the information contents of the thermographic images. At the end of this procedure, a CB threshold equal to an increase of 3 °C from the floor temperature was chosen, and a cutoff level of 196 colored pixels was identified as the threshold to use to classify a positive case. The results of field tests showed that the developed monitoring system reached a fine detection accuracy (sensitivity = 97.9% and specificity = 94.9%) and the IR technology proved to be a possible solution for the development of a detection sensor necessary to reach the scope of this study.

Assessing Activity and Location of Individual Laying Hens in Large Groups Using Modern Technology

Tracking individual animals within large groups is increasingly possible, offering an exciting opportunity to researchers. Whereas previously only relatively indistinguishable groups of individual animals could be observed and combined into pen level data, we can now focus on individual actors within these large groups and track their activities across time and space with minimal intervention and disturbance. The development is particularly relevant to the poultry industry as, due to a shift away from battery cages, flock sizes are increasingly becoming larger and environments more complex. Many efforts have been made to track individual bird behavior and activity in large groups using a variety of methodologies with variable success. Of the technologies in use, each has associated benefits and detriments, which can make the approach more or less suitable for certain environments and experiments. Within this article, we have divided several tracking systems that are currently available into two major categories (radio frequency identification and radio signal strength) and review the strengths and weaknesses of each, as well as environments or conditions for which they may be most suitable. We also describe related topics including types of analysis for the data and concerns with selecting focal birds.

Early Onset of Laying and Bumblefoot Favor Keel Bone Fractures

Numerous studies have demonstrated influences of hybrid, feed, and housing on prevalence of keel bone fractures, but influences of behavior and production on an individual level are less known. In this longitudinal study, 80 white and brown laying hens were regularly checked for keel bone deviations and fractures while egg production was individually monitored using Radio Frequency Identification (RFID) from production until depopulation at 65 weeks of age. These focal birds were kept in eight pens with 20 hens per pen in total. About 62% of the hens had broken keel bones at depopulation. The occurrence of new fractures was temporally linked to egg laying: more new fractures occurred during the time when laying rates were highest. Hens with fractured keel bones at depopulation had laid their first egg earlier than hens with intact keel bones. However, the total number of eggs was neither correlated with the onset of egg laying nor with keel bone fractures. All birds with bumblefoot on both feet had a fracture at depopulation. Hens stayed in the nest for a longer time during egg laying during the ten days after the fracture than during the ten days before the fracture. In conclusion, a relationship between laying rates and keel bone fractures seems likely.

Preliminary evaluation of a nest usage sensor to detect double nest occupations of laying hens

Conventional cage systems will be replaced by housing systems that allow hens to move freely. These systems may improve hens' welfare, but they lead to some disadvantages: disease, bone fractures, cannibalism, piling and lower egg production. New selection criteria for existing commercial strains should be identified considering individual data about laying performance and the behavior of hens. Many recording systems have been developed to collect these data. However, the management of double nest occupations remains critical for the correct egg-to-hen assignment. To limit such events, most systems adopt specific trap devices and additional mechanical components. Others, instead, only prevent these occurrences by narrowing the nest, without any detection and management. The aim of this study was to develop and test a nest usage "sensor", based on imaging analysis, that is able to automatically detect a double nest occupation. Results showed that the developed sensor correctly identified the double nest occupation occurrences. Therefore, the imaging analysis resulted in being a useful solution that could simplify the nest construction for this type of recording system, allowing the collection of more precise and accurate data, since double nest occupations would be managed and the normal laying behavior of hens would not be discouraged by the presence of the trap devices.