Laying Hen Houses Research Articles

Seasonal Variability of Particulate Matter and Ammonia Emissions in a Laying Hen House in Korea

Seasonal Variability of Particulate Matter and Ammonia Emissions in a Laying Hen House in Korea

Indoor Concentration Distributions of Ammonia and Sulfur-Based Odorous Substances According to Types of Laying Hen Houses in South Korea

Indoor Concentration Distributions of Ammonia and Sulfur-Based Odorous Substances According to Types of Laying Hen Houses in South Korea

Providing ramps during lay has larger impacts on laying hens than ramps at rearing

Commercial laying hen housing is shifting from traditional cages to non-cage housing systems, such as the aviary, which has gained popularity due to potential for more species-typical behavior. However, birds housed in aviaries may have difficulties moving through the vertical tiers of the system leading to health problems such as keel bone fracture (KBF). One possible way to improve movement is to add ramps into an aviary system, allowing hens to walk between tiers rather than jump or fly. The objective of this study was to evaluate the impact of adding ramps to rearing and laying aviaries on bird health, production, and movement across vertical tiers of the aviary. Lohmann Selected Leghorn pullets were raised in 2 treatments: 4 pens (600 birds/pen) were raised with wire mesh ramps to aid movement between aviary tiers (RR) and 4 pens (600 birds/pen) were raised without ramps (RO). At 17 wk of age (WOA), birds were moved to the laying facility, in which 16 aviary pens with 225 birds/pen were populated. Half the pens (n = 8) were supplemented with wire mesh ramps (LR) and the other half were not (LO). Within each laying treatment group, 4 pens were populated with RR hens and 4 pens were populated with RO hens, creating 4 treatment combinations (RRLR, RRLO, ROLR, ROLO). From each pen, 15 focal hens were selected for radiographic imaging of their keel bones taken at 21, 36, 45, and 60 WOA and the images were subsequently scored for KBF severity. Focal hens were also scored for feather condition and footpad quality at 36 and 60 WOA using a standardized welfare assessment protocol. The number of downward transitions among aviary areas and falls were recorded at 19 to 20 and 30 to 31 WOA. Data were analyzed using (generalized) linear mixed models in R software. When ramps were available, they were used in most of the observed downward transitions (79% in ROLR and 86% in RRLR). Hens who received ramps in lay (i.e., RRLR and ROLR) showed more transitions immediately after lights on compared to midday or dusk phases (p < 0.001), performed more transitions from the first aviary tier compared to nest or top tier (p = 0.013) and had lower KBF severity than those who did not receive ramps in the laying aviaries (ROLO, RRLO; p < 0.001). At 60 WOA, hens in the RRLR treatment had greater feather coverage than those in ROLR and RRLO treatments (p < 0.001). Birds in the RRLR treatment had better foot health overall than those in treatments without ramps in lay (p = 0.018). Providing ramps to hens in aviaries appeared to be the preferred means of transitioning between aviary tiers though had positive effects on welfare parameters such as food health, feather coverage, and KBF severity, without negative impacts on production. Benefits were seen primarily when ramps were provided in lay, though their installation in rearing provided evidence of easier adaptation to the laying barn. Our study supports providing ramps throughout the lifetime of the bird to accommodate hens' preferred means of moving vertically in aviaries and deliver consequent benefits to health and welfare.

8. Ammonia emission factors for broiler and laying hen housing

8. Ammonia emission factors for broiler and laying hen housing

Authentication of Laying Hen Housing Systems Based on Egg Yolk Using 1H NMR Spectroscopy and Machine Learning.

(1) Background: The authenticity of eggs in relation to the housing system of laying hens is susceptible to food fraud due to the potential for egg mislabeling. (2) Methods: A total of 4188 egg yolks, obtained from four different breeds of laying hens housed in colony cage, barn, free-range, and organic systems, were analyzed using 1H NMR spectroscopy. The data of the resulting 1H NMR spectra were used for different machine learning methods to build classification models for the four housing systems. (3) Results: The comparison of the seven computed models showed that the support vector machine (SVM) model gave the best results with a cross-validation accuracy of 98.5%. The test of classification models with eggs from supermarkets showed that only a maximum of 62.8% of samples were classified according to the housing system labeled on the eggs. (4) Conclusion: The classification models developed in this study included the largest sample size compared to the literature. The SVM model is most suitable for evaluating 1H NMR data in terms of the hen housing system. The test with supermarket samples showed that more authentic samples to analyze influencing factors such as breed, feeding, and housing changes are required.

Spatiotemporal variations in emission and particulate matter concentration outside a concentrated layer feeding operation

A year-round monitoring of Particulate Matter (PM) less than 2.5 μm in aerodynamic diameter (PM2.5) and Total Suspended Particulate (TSP) from a conventional cage laying hen house was conducted to reveal their spatiotemporal variations of concentrations downwind of tunnel fans. The PM Emission Rate (ER) and the correlations between PM with meteorological parameters and ventilation rate were studied. Ten self-developed Particle Concentration Monitoring Units were distributed to multiple horizontal (2, 25, and 50 m) and vertical (2 and 5 m) locations for PM examination. Four PM2.5 concentration categories were classified: good (0–35 μg m−3), moderate (35–75 μg m−3), unhealthy (75–150 μg m−3), and very unhealthy (150–350 μg m−3). Annual and monthly percentages of PM2.5 and TSP sampling hours for each category were calculated. Significant temporal variations in ER were observed, with monthly average ER ranging from 0.05 to 0.86 mg d−1 hen−1 for PM2.5 and 1.40–8.90 mg d−1 hen−1 for TSP. Approximately 10–40% of PM2.5 sampling hours exceeded 75 μg m−3, while no TSP concentration was higher than the recommended threshold. The monthly percentage for the “very unhealthy” PM2.5 category was higher in winter, whereas the highest monthly percentage for the 800–1,200 μg m−3 TSP category was found in summer. Higher PM2.5 concentrations were prone to be observed at further sampling points, while higher TSP concentrations were mostly detected near the fans or on both sides of the sampling area. PM2.5 and TSP were positively correlated with ambient temperature and ventilation rate, while negatively correlated with relative humidity.

Thermal characterization and ventilation assessment of a battery-caged laying hen housing in the humid tropic climate.

The indoor climate to which livestock are exposed is a critical factor influencing their performance and productivity. Elevated air temperature and relative humidity could result in heat stress for laying hens. This situation results in severe adverse effects such as weight loss and mortality. Egg fertility and hatchability are also impacted. Consequently, a study was carried out in a naturally ventilated battery-caged laying hen house to measure climatic variables (air temperature, relative humidity and air velocity). The degree of heat stress was assessed using the temperature-humidity index (THI), and the index of temperature and air velocity (ITV) was also evaluated. According to the results obtained, birds reared within the study building would spend most of their productive life under stressful thermal conditions, which could significantly hamper their performance. The air velocity was below 1.0 ms-1 for most of the internal part of the housing, meaning natural air movement at the location was insufficient to provide a suitable environment for the birds. A high THI was recorded for nearly the entire study period. This high THI could indicate high relative humidity about air temperature. The observed ITV values (ITV > 25) suggest that birds throughout the building could be perpetually uncomfortable. The thermal and velocity profile within the structure could further be assessed numerically using computational fluid dynamics. This would enable engineers to make modifications to improve living conditions within the building.

Variation of PM2.5 and PM10 in emissions and chemical compositions in different seasons from a manure-belt laying hen house

Particulate matter (PM) emissions from animal houses and the corresponding hazard have raised increasing attention during recent years. In this study, a large-scale manure-belt laying hen house located in Beijing, China was selected as the experimental site for the study of the emission rates (ER) and chemical compositions of PM2.5 and PM10 in 3 seasons, namely, summer, autumn, and winter, to investigate their possible influences on ambient air quality and human health. The results showed that the mean ER from the hen house in summer, autumn, and winter were 9.0 ± 1.7, 2.4 ± 0.7, and 1.9 ± 0.7 mg hen-1 d-1 for PM2.5 (P < 0.05), and 30.7 ± 1.1, 12.8 ± 1.5, and 10.9 ± 0.9 mg hen-1 d-1 for PM10 (P < 0.05), respectively. Moreover, large amounts of secondary inorganic aerosols (SIA) were observed inside the house in summer, accounting for 11.4 and 9.6% of indoor PM2.5 and PM10 mass, respectively, compared with the value of <1.4% in autumn and winter. Among the 31 detected elements in indoor PM, arsenic concentration exceeded the threshold set in legislation. Zn had a notably high concentration of 3,403 to 4,432 ng m-3 in indoor PM10, which was 28 to 71 times higher than that in ambient PM10. The findings suggest that the poultry-raising house emit PM2.5 and PM10 containing SIA and toxic heavy-metal elements such as As and Zn to the ambient with much more emissions in summer than in autumn and winter. Considering the increasing development of poultry-raising farming in China, the potential hazard derived from the exhaust of PM2.5 and PM10 should be focused on, especially during summer.

Successful long-term control of poultry red mite (Dermanyssus gallinae) infestations in floor-kept laying hens via integrated pest management—a case report

This case report describes the successful control of poultry red mite [PRM] (Dermanyssus gallinae) infestations in an experimental laying hen house via a combined use of cleaning and disinfection measure, the preventive application of a synthetic silica-based acaricide and frequent mite monitoring. The high number of PRM in the laying hen house was reduced by 99.8% by treatment with fluralaner (Exzolt®, MSD Animal Health Unterschleißheim, Germany; 0.5 mg/kg body weight via drinking water twice, 7 days apart). After the laying hens were removed, the hen house was dry-cleaned, wet-cleaned and disinfected. After drying, synthetic amorphous silica (Fossil Shield® instant white, Bein GmbH, Eiterfeld, Germany) was applied as a preventive measure before the hen house was restocked with pullets for two housing periods of 58 and 52 weeks. Over these periods (i.e. more than 2 years), no PRM was detected during mite monitoring at two-week intervals via tube traps and visual monitoring. This result therefore suggests that the combined use of appropriate chemical and physical prevention measures within an integrated pest management regime can be successfully used for the long-term control of PRM. This could reduce the use of acaricidal drugs, thereby helping maintain their efficacy.

Effects of different laying periods on airborne bacterial diversity and antibiotic resistance genes in layer hen houses.

Poultry farms are a complex environment for close contact between humans and animals. Accumulating evidence has indicated that pathogens and drug resistance genes in chicken houses may pose a serious threat to public health and economic concerns. However, insufficient knowledge of the indoor aerosol microbiome and resistome profiles of layer hen houses hampers the understanding of their health effects. Environmental surveillance of antibiotic resistance may contribute to a better understanding and management of the human exposure risk of bioaerosols under the environmental conditions of chicken houses. In addition, the chicken house has a long operation cycle, and the bacterial diversity and antibiotic resistance genes of aerosols in different periods may be different. In this study, air samples were collected from 18 chicken houses on three farms, including the early laying period (EL), peak laying period (PL), and late laying period (LL). 16S rRNA gene sequencing and metagenomics were used to study the composition of the bacteria and resistome in aerosols of layer hen houses and the results showed that they varied with laying period. The highest alpha diversity of bacteria was observed in PL bioaerosols. The dominant bacterial phyla included Firmicutes, Bacteroidetes and Proteobacteria. Three potential pathogenic bacterial genera (Bacteroides, Corynebacterium and Fusobacterium) were found. The most abundant ARG type was aminoglycosides in all laying periods. In total, 22 possible ARG host genera were detected. ARG subtypes and abundance were both higher in LL. Network analysis also showed higher co-occurrence patterns between the bacteria and resistome in bioaerosols. The laying period plays an important role in the bacterial community and resistome in layer house aerosols.

The relationship between different laying hen housing systems in Lithuania and egg production quality and chemical composition

The relationship between different laying hen housing systems in Lithuania and egg production quality and chemical composition

A Machine Learning Framework Based on Extreme Gradient Boosting to Predict the Occurrence and Development of Infectious Diseases in Laying Hen Farms, Taking H9N2 as an Example.

The H9N2 avian influenza virus has become one of the dominant subtypes of avian influenza virus in poultry and has been significantly harmful to chickens in China, with great economic losses in terms of reduced egg production or high mortality by co-infection with other pathogens. A prediction of H9N2 status based on easily available production data with high accuracy would be important and essential to prevent and control H9N2 outbreaks in advance. This study developed a machine learning framework based on the XGBoost classification algorithm using 3 months' laying rates and mortalities collected from three H9N2-infected laying hen houses with complete onset cycles. A framework was developed to automatically predict the H9N2 status of individual house for future 3 days (H9N2 status + 0, H9N2 status + 1, H9N2 status + 2) with five time frames (day + 0, day - 1, day - 2, day - 3, day - 4). It had been proven that a high accuracy rate > 90%, a recall rate > 90%, a precision rate of >80%, and an area under the curve of the receiver operator characteristic ≥ 0.85 could be achieved with the prediction models. Models with day + 0 and day - 1 were highly recommended to predict H9N2 status + 0 and H9N2 status + 1 for the direct or auxiliary monitoring of its occurrence and development. Such a framework could provide new insights into predicting H9N2 outbreaks, and other practical potential applications to assist in disease monitor were also considerable.

Assessing Indoor Climate Control in a Water-Pad System for Small-Scale Agriculture in Taiwan: A CFD Study on Fan Modes.

Heat stress poses a significant challenge to egg production in layer hens. High temperatures can disrupt the physiological functions of these birds, leading to reduced egg production and lower egg quality. This study evaluated the microclimate of laying hen houses using different management systems to determine the impact of heat stress on productivity and hen health. The results showed that the ALPS system, which manages the hen feeding environment, effectively improved productivity and decreased the daily death rate. In the traditional layer house, the daily death rate decreased by 0.045%, ranging from 0.086% to 0.041%, while the daily production rate increased by 3.51%, ranging from 69.73% to 73.24%. On the other hand, in a water-pad layer house, the daily death rate decreased by 0.033%, ranging from 0.082% to 0.049%, while the daily production rate increased by 21.3%, ranging from 70.8% to 92.1%. The simplified hen model helped design the indoor microclimate of commercial layer houses. The average difference in the model was about 4.4%. The study also demonstrated that fan models lowered the house's average temperature and reduced the impact of heat stress on hen health and egg production. Findings indicate the need to control the humidity of inlet air to regulate temperature and humidity, and suggest that Model 3 is an energy-saving and intelligent solution for small-scale agriculture. The humidity of the inlet air affects the temperature experienced by the hens. The THI drops to the alert zone (70-75) when humidity is below 70%. In subtropical regions, we consider it necessary to control the humidity of the inlet air.

Comparison of Antimicrobial Resistance among Commensal Escherichia coli Isolated from Retail Table Eggs Produced by Laying Hens from the Cage and Non-Cage Housing Systems in Western Australia.

Antimicrobial resistance (AMR) has become a global public health concern in recent decades. Although several investigations evaluated AMR in commensal and pathogenic bacteria from different foods of animal origin in Australia, there is a lack of studies that compared AMR in commensal E. coli isolated from retail table eggs obtained from different laying hen housing systems. This study aimed to determine AMR and differences in AMR patterns among E. coli isolates recovered from retail table eggs sourced from caged and non-caged housing systems in Western Australia. Commensal E. coli isolates were tested for susceptibility to 14 antimicrobials using a broth microdilution method. Clustering analyses and logistic regression models were applied to identify patterns and differences in AMR. Overall, there were moderate to high frequencies of resistance to the antimicrobials of lower importance used in Australian human medicine (tetracycline, ampicillin, trimethoprim, and sulfamethoxazole) in the isolates sourced from the eggs of two production systems. All E. coli isolates were susceptible to all critically important antimicrobials except the very low level of resistance to ciprofloxacin. E. coli isolates from eggs of non-caged systems had higher odds of resistance to tetracycline (OR = 5.76, p < 0.001) and ampicillin (OR = 3.42, p ≤ 0.01) compared to the isolates from eggs of caged systems. Moreover, the number of antimicrobials to which an E. coli isolate was resistant was significantly higher in table eggs from non-caged systems than isolates from caged systems' eggs. Considering the conservative approach in using antimicrobials in the Australian layer flocks, our findings highlight the potential role of the environment or human-related factors in the dissemination and emergence of AMR in commensal E. coli, particularly in retail table eggs of non-cage system origin. Further comprehensive epidemiological studies are required to better understand the role of different egg production systems in the emergence and dissemination of AMR in commensal E. coli.

A modified discrete grey model with improved prediction performance for indoor air temperatures in laying hen houses

The accurate prediction of indoor air temperatures is vital for the timely control of thermal environments in livestock buildings. However, ensuring the accuracy of air temperature predictions is challenging due to the uncertainty introduced by external factors and periodic fluctuations. New approaches are required to improve the prediction accuracy of models for indoor air temperatures. A discrete model is developed that incorporates time-period groups (TPGs), the group buffered rolling (GBR) mechanism, and TPG factors. The measured temperature data were divided into four TPGs in chronological order, and the GBR mechanism with TPG factors eliminated fluctuations and periodicity from the time-series data. The efficacy of the proposed model was verified by comparing its results with those of two existing models (the grey model and discrete grey model) and filed experiments in layer hen houses. The traditional model only reflected the increasing trend of original air temperature data, but did not reflect periodic fluctuations. The mean absolute percentage error (APE) was >10% in the traditional model. The GBR mechanism and TPG factors allowed the new model to reflect deviations in the original time-series at each time point in real-time, thus minimizing the APE and improving the prediction performance. The modified model was superior to the traditional models, exhibited higher accuracy and reliable performance (APE < 10%), and accurately captured the periodic fluctuations in air temperature. It is a helpful tool for predicting indoor air temperatures, and may help in the development of improved control strategies for the indoor thermal environment. • A new discrete grey model modified with the group buffered rolling mechanism. • The GBR-DGM shows improved prediction performance for indoor air temperature. • Accurately predicts temporal fluctuations in air temperatures in a layer hen house. • Applications include the development of improved environmental control systems.

Exposure to a Virtual Environment Induces Biological and Microbiota Changes in Onset-of-Lay Hens

Increasing demand for cage-free eggs arises from goals to provide hens with better welfare, particularly in terms of natural behavior. However, most laying hens are kept in conventional cages, and cage-free systems can present challenges, such as injuries, floor eggs, and bacterial infections. We proposed using virtual reality (VR) as a feasible means for combining the positive attributes of natural environments while mitigating health risks. To our knowledge, no animal study has provided evidence that VR can trigger biological changes to improve animal health and well-being nor whether VR can affect the gut microbiota. In this study, we used VR technology to simulate a natural environment in laying hen housing. Early-lay White Leghorn hens were placed in pens with (VR) or without (CON) video projections displaying free-range chickens interacting with indoor and outdoor environmental features over 5 days. Using in vitro blood bactericidal assays, VR hens exhibited higher resistance against avian pathogenic Escherichia coli versus CON (p &amp;lt; 0.05), which was positively associated with corticosterone levels (p &amp;lt; 0.01). Analyzing intestinal neurochemicals via ultra-high pressure liquid chromatography, salsolinol was the only neurochemical metabolite affected by VR, being greater in CON ileal content (p &amp;lt; 0.0001), in VR ileal mucus (p &amp;lt; 0.01), and in VR ceca tissue (p &amp;lt; 0.05). Using 16S rRNA sequencing and QIIME2 analyses, no differences in alpha nor beta diversity were determined between groups. Although several genera (Megamonas, Ruminococcus, Slackia) were reduced in VR hens versus CON, Mucispirillum schaedleri (member of Deferribacteres Phylum) was the only taxon increased in VR hens, being elevated in ileal mucus (p &amp;lt; 0.05). Lastly, using the QIIME2 plugin mmvec to map microbe-metabolite co-occurrences, we identified several positive relationships between bacterial phyla and neurochemical metabolites, notably finding dopamine and salsolinol levels were related to Deferribacteres and Tenericutes levels. In conclusion, we found that several biological parameters were influenced by VR treatment in hens, suggesting that VR can be used to improve host resistance to pathogens and gut health in poultry.

Characterizing manure drying practices in Iowa commercial egg production facilities

As laying hen housing styles change throughout Iowa to meet legislative and consumer demands, drying of manure can be economically expensive and energy intensive. With about 50% of total electricity consumption and about 60% of annual electricity costs attributed to manure drying, this represents an opportunity for producers to decrease production costs while reducing electrical grid strain. To understand current practices and potential opportunities, survey data were collected from 34 Iowa egg producers in fall 2020, representing egg-laying ( n = 50), breeding ( n = 30), and rearing ( n = 20) facilities. Average (± SD) reported number of weekly manure removal events was 2.2 ± 0.50 for all housing styles with 2.4 ± 0.09 for cage-free aviaries and 2.2 ± 0.58 for conventional cage. Reported daily manure blower operation was 14.2 ± 11.0 h/d for all facilities, with cage-free aviaries operating blowers for 10.0 ± 11.8 h/d while conventional cage houses operated blowers for 15.1 ±10.8 h/d. On a per thousand bird basis, reported overall energy usage was 10.1 kWh per day, 308 kWh per month, and 3.7 MWh per year. Energy costs per thousand birds were US$0.81 per day, US$25 per month, and US$296 per year. Respondents from 16 facilities reported their facility did not have smart meters (i.e., monitoring voltage, current, power factor, and energy consumption), while 10 reported smart meter usage and 5 reported they were unsure. Managers were no more likely to be aware of facility peak demand or track peak demand regardless of whether the facility had a smart meter (aware: P = 0.09; track: P = 0.26). Interestingly, all 21 respondents reported being aware of peak demand incentives; however, only 9 (43%) respondents indicated their facilities participate in these incentives. These results provide a characterization of manure drying practices and energy usage in Iowa egg production facilities. There are opportunities to impact the manure drying process through control strategies that optimize blower energy based on environmental conditions, target manure moisture content, and peak demand periods.

Assessing environmental control strategies in cage-free aviary housing systems: Egg production analysis and Random Forest modeling

• Three environmental control strategies were applied in experimental aviary systems. • Modelling egg production contributes insights on interactions in laying hen houses. • A Random Forest model was developed to estimate laid eggs from laying hens. • Error associated with the laid eggs’ estimation were below 0.45%. • Temperature was a key variable since gas emissions were under suggested thresholds. Since 1990, worldwide egg production has increased on average 2.8% per year. This increase has drawn the attention of animal welfare advocates. In Canada, new challenges have emerged, among them: increased awareness in animal welfare and environmental footprint and a shift to cage-free egg production systems (CFSs). Welfare assessment of environmental control strategies (ECSs), and deployment of early warning egg production systems based on on-line monitoring, have become an important field of research to understand interactions and mitigate environmental issues related to these CFSs. This study assessed the effect of selected ECSs on hen-day egg production (HDEP) and daily egg cleanliness (EGC) in experimental CFSs, using Principal Component Analysis and Linear Discriminant Analysis. The assumption that air quality conditions could play an important role in HDEP predictions was addressed by developing a machine learning method based on Random Forest models (RF) to predict HDEP daily fluctuations using measured hygrothermal and air quality conditions as input variables. A variable importance analysis further confirmed the governing variables of the egg production time series. Following that, an inquiry-driven scenario analysis was performed to identify potential changes in egg production. Results showed that the ECSs did not disrupt the egg production in the experimental CFSs. Meanwhile, A RF model with a window size of 14 days showed satisfactory performance predicting HDEP daily fluctuations with a RMSE of 0.176% and 0.368%, and a R 2 of 0.94 and 0.78, for training and testing, respectively. The temperature was the dominant governing variable among the predictors, followed by hen’s age and relative humidity. Finally, the scenario analysis revealed that a 5% temperature's increase could negatively affect the egg yield. The outcomes of this study aim to contribute to the on-line monitoring and control activities in laying hen facilities as an important aspect in Precision Livestock Farming.

Prediction of laying hen house odor concentrations using machine learning models based on small sample data

• XGBoost model served better to predict odor concentration in laying hen houses than SVR and BPNN. • XGBoost algorithm had good performance and strong learning ability to deal with the odor datasets with small-sample and non-linearity characteristics. • Gas components and environment variables directly related to gas production were the key drivers in developing odor prediction model. • The knowledge mining ability of XGBoost model on odor datasets collected from laying hen house was good. In laying hen farm, odor measurement and reduction are necessary for clean environment management and human/animal health care. However, limited quantitative data and expensive detection technologies preclude an accurate assessment of odor reduction practices. This study compared the odor prediction ability of three different machine learning models of extreme gradient boosting (XGBoost), support vector regression (SVR), and back-propagation neural networks (BPNN) based on small sample size odor datasets collected from laying hen houses to achieve timely odor monitoring and check the significant components affecting the odor concentration. The input variables were ammonia (NH 3 ) concentration, hydrogen sulfide (H 2 S) concentration, temperature, relative humidity (RH), and ventilation rate, and the output value was odor concentration. Results showed the XGBoost model had the highest prediction ability and the most accuracy with the R 2 of 0.88, followed by BPNN (R 2 = 0.75) and SVR (R 2 = 0.66). XGBoost model could be a useful tool to timely predict odor concentration with moderate accuracy. In addition, in the trained XGBoost model, NH 3 concentration was the most important factor, followed by the H 2 S concentration, temperature, RH, and ventilation rate, which indicated the gas components and environment variables related to gas production were the key drivers in training odor prediction model. This study also forecasted the influences of each gas and environmental factors on odor concentrations and verified the good knowledge mining ability of XGBoost model. The practical potentials are considerable of using XGBoost model to replace human assessors and to apply to laying hen house environmental control as the odor sensor.

Keel bone damage affects behavioral and physiological responses related to stress and fear in two strains of laying hens.

Keel bone damage (KBD) is more prevalent in alternative laying hen housing systems than in conventional cages, and its incidence differs from strain to strain. However, the information of KBD in Lindian chickens, a native Chinese strain, is limited. To investigate the effect of KBD on fearfulness and physiological indicators of stress in Lindian chickens and commercial laying hens, a total of two hundred 25-wk-old chickens (100 Hy-line Brown and 100 Lindian chickens) were studied for 7wk. The birds were housed in furnished cages with 10 birds per cage for each strain. At 32-wk of age, the birds in each strain were divided into normal (NK), deviated (DK), and fractured (FK) hens according to the keel bone status. Ten birds in each keel bone status per strain were subsequently selected to collect blood for the determination of stress and fear-related indicators, including corticosterone, serotonin, interleukin-1β, and interleukin-6, and measure fear responses, including novel object test (NOT), human approach test (HAT), and tonic immobility (TI) test. The results showed that egg production was lower and the incidence of keel bone fractures was higher in Lindian chickens than in Hy-line Brown hens (P < 0.05). Lindian chickens showed a significantly increased whole blood serotonin content, NOT-latency, HAT-score, and TI induction times (P < 0.05) and decreased serum interleukin-6 content and TI-duration (P < 0.05) compared with Hy-line Brown hens. Additionally, FK hens had significantly elevated whole blood corticosterone, serum interleukin-1β and interleukin-6 levels, TI-duration, and NOT-latency (P < 0.05), and a reduced whole blood serotonin content (P < 0.05) compared with NK and DK hens. Our results indicated that KBD affected stress and fear responses, and this impact was mainly reflected by FK hens compared with NK and DK hens. We suggest that keel bone fractures are the main factor impairing hen welfare. Besides, the incidence of keel bone fractures and stress and fear responses of Lindian chickens are more severe than Hy-line Brown laying hens, indicating that the strain type can affect the health and welfare of laying hens.