. Air quality management is essential for sustainable livestock production. Manure generates odorous and greenhouse gas emissions during storage and cropland application. Ammonia (NH3) emissions during manure application to soil represent a loss of nitrogen and decrease the value of manure. Biochar (BC) can mitigate emissions during manure storage. In this research, we hypothesized that BC-treated manure generates less emissions after post-storage application to cropland soil. This study tested the effect of BC thickness (dose) and manure treatment timing on gaseous emissions with controlled lab-scale experiments. Both NH3 and hydrogen sulfide (H2S) emissions from cropland soil amended with BCtreated manure were reduced. Overall, average percent reductions in the first 6 h / 24 h was 89.9% / 89.7% and 59.6% / 60.8% for NH3 and H2S emissions, respectively, when manure was treated with BC immediately prior to application to the soil. When manure was treated with BC during storage, emissions of NH3 and H2S were reduced 20.7% / 21.8% and 12.2% / 13.5%, respectively. The percent reductions in NH3 and H2S for 13- and 6.5-mm BC application thicknesses were not significantly different, but the 2.5-mm application thickness treatment was less effective at reducing gas emissions. The effects of BC application on methane), carbon dioxide, and odor were inconclusive. Observational data indicated a possible mitigation trend in odor, especially for the BC treatment added immediately prior to application to soil, but further trials are needed. Scaling up the technoeconomic analysis showed that less than 6 m3 of BC would be needed to treat more than 900 m2 surface of stored manure in a typical 1,200-head swine barn with a uniform 6.5 mm thick BC layer. Subsequent cropland application of such BC-treated manure would result in 0.1 m3 ha-1 BC addition to soil in a typical corn-soybean crop rotation system. This research shows that BC has the potential to mitigate gaseous emissions while promoting nutrient cycling and the sustainability of livestock waste as a fertilizer.

Swine manure management systems (MMSs) are critical for nutrient cycling. Common swine MMS storage types in the United States are deep pits, anaerobic lagoons, and slurry storages. This study presents a synthesis of literature documenting nutrient and carbon (C) partitioning via a material mass balance approach for four manure treatment technologies: acidification (ACID; n = 4 papers), aeration (AER; n = 16), solid-liquid separation (SLS; n = 11), and anaerobic digestion (AD; n = 23) that can be used in-line with one or more of these common storage types. Mass balance data were primarily extracted for nitrogen (N), phosphorus (P), and C (AD only) from lab and pilot-scale studies, with nutrient flows normalized to influent characteristics. The ACID papers suggested significant reductions in gaseous losses with ACID; however, the effluent concentration and available mass/volume and gas data together did not document realistic N partitioning. Among SLS systems, urine-feces segregation and filtration retained the most and least amount of solids in the solid fraction, respectively. AD converts organic matter into biogas; for most mesophilic digesters, 56%-66% of the C remained in the effluent; the mass retained in the sludge is underreported. In AER studies, the partitioning of N into different N gases depended on the amount of AER. As pass-through technologies, AD and SLS are easier to incorporate in lagoon and slurry storage systems. ACID and AER can be physically incorporated in common MMSs; however, treatment could hinder anaerobic lagoon function. Understanding nutrient partitioning will benefit from concurrent monitoring of multiple nutrients in the liquid, solid, and/or gas streams.

Highlights Nine treatments of a ventilation and cooling strategy based on the HS2I index were developed. The treatments were tested with pigs (45-120 kg) exposed to artificial heatwaves in experimental rooms. Zootechnical performance showed no advantage in using larger sprinkling water volumes. The combination of an HS2I of 3.0 and reduced sprinkling water volumes may not avoid heat stress for heavier pigs. Abstract. During the growing-finishing phase of pig production, mitigating heat stress is essential to maximize growth performance, improve feed efficiency, and minimize risks of deteriorating animal welfare and farm profitability. Housed Swine Heat Stress Index (HS2I) assesses pigs’ thermal comfort into a dimensionless metric scored from 0 (no heat stress) to 10 (severe heat stress), where 3 marks the beginning of heat stress along the range. Focusing on thermal comfort, a ventilation and cooling strategy based on the HS2I was implemented through precise control of air velocity and cooling using water sprinklers. The objective of this study was to optimize this strategy by adjusting two main parameters: the HS2I setpoints (2.0, 2.5, or 3.0) and the water sprinkling volumes (partial, gradual, or complete). Experiments were conducted in laboratory rooms housing 14 pigs under simulated summer-like conditions to test nine functional treatments of the strategy. The results showed that reduced water use through partial sprinkling was generally adequate to maintain pig performance, regardless of the selected HS2I setpoint. However, the combination of an HS2I setpoint of 3.0 with a reduced sprinkling strategy may expose heavier pigs (>100 kg) to heat stress. These findings offer practical insights for optimizing cooling strategies in pig production systems that face heat stress challenges. Keywords: Animal index, Direct cooling, Heat stress mitigation, Recirculation fans, Sprinkling systems, Swine production, Zootechnical performance.

Highlights Draft EPA emission models for swine open-source manure storage were systematically evaluated. Draft EPA models performed poorly on estimating pollutant emissions when input variables were beyond NAEMS data. An unanticipated sensitivity of the draft EPA model outputs to climate was identified. Draft EPA models may need further revision and improvement before being adopted by the swine industry. ABSTRACT. In August 2021, the United States (U.S.) Environmental Protection Agency (EPA) released draft models to estimate daily NH 3 and H 2 S emissions from open-source swine manure storages (swine facilities are evaluated in Part I of this series), that is, breeding/gestation lagoons, and grow-finish lagoons and storage basins using inputs of daily mean ambient temperature and wind speed. These models were developed by the EPA using refined datasets generated by the National Air Emissions Monitoring Study fieldwork completed in 2009. This paper evaluated the robustness of the EPA draft models using a “one-factor-at-a-time” sensitivity analysis. Also, annual emission estimates were calculated using inputs reflective of typical U.S. swine housing practices and ambient conditions of representative swine producing regions in the U.S. Draft model outputs predicted NH 3 emissions from lagoons to be greater than values reported in the current literature, depending on ambient temperature and magnitude of wind speed. The draft models predicted significantly greater NH 3 emissions from storage basins than those reported in the current literature. These current draft emission models cannot be used to accurately characterize the wide range of swine open-source manure storages where management across regions varies considerably. Revisions are suggested to accommodate a greater range of climates and scale for practical emission estimations. While these models may provide some insight into estimating emissions, they may not fully encapsulate the complexity and variability of emissions from swine open-source manure storages. Keywords: Air quality, Ammonia, Basin, Hydrogen sulfide, Lagoon, Pigs, Slurry.

Turkey poult performance, feed intake, and water consumption are negatively altered with thermal stress (heat or cold stress). During a recent study, poults were unexpectedly exposed to differential thermal stress during the first week of life. To determine the lasting impacts, performance data from the trial were analyzed to determine the impacts of mild thermal stress during the first week on lifetime performance. In this study, thermal stress did not affect poult growth performance and feed intake.

Providing amino acids, energy, and minerals are costly dietary components in formulating diets for turkeys, with corn, soybean meal, and oils being of utmost interest due to their percent inclusion levels. In addition, the highest feed consumption occurs in the grower-finisher (GF) phase of production making diet formulation during this period of growth critically important. Reducing feed cost through reductions in expensive energy sources (e.g., fats) in a turkey diet while maintaining performance and carcass traits could greatly benefit turkey production profitability. The current study evaluated the effects of the addition of a commercial energy sparing feed additive (Enercore®, Biosen LLC) in a series of reducing energy diets. Dietary treatments included a commercial control diet (CON) and 3 experimental diets with different levels of reduced Kcal to equal a 50 Kcal/kg deficit (ESFA 1), 70 Kcal/kg deficit (ESFA 2), and 100 Kcal/kg deficit (ESFA 3) with the inclusion of 1 kg/tonne of Enercore. At placement, 1,800 male turkeys were evenly placed across dietary treatments (n = 8 per treatment) and provided their dietary treatment through 18 wk of age. Body weights and feed weights were taken every 5 wk and before loadout. At 17 wk of age, one tom per pen was weighed and euthanized to determine breast yield and deboned thigh yield. Body weights and mortality percent were similar across diets at the end of 18 wk of age (P > 0.140). Whereas, mortality-adjusted FCR was altered with dietary treatment with toms fed ESFA 2 having the lowest mortality-adjusted FCR (P < 0.001) compared to all other dietary treatments. For carcass measurements, live weights of birds sampled, breast yield, and deboned thigh yield were similar between treatments (P > 0.220). In conclusion, removal of 50 and 70 Kcal/kg of energy in turkey diets supplemented with an energy sparing feed additive did not significantly alter body weight or carcass yield but only the removal of 70 Kcal/kg with the ESFA, Enercore®, improved feed conversion.

The source of energy in turkey diets is one of the highest feed costs per unit to turkey producers. Reducing the cost by reducing energy concentration in a turkey diet while maintaining performance and health would economically benefit turkey producers. The reduction of energy may, however, impact immune competency, metabolism, and bone structure by diverting energy to sustain growth. This study evaluated the effects of the addition of a commercial energy sparing feed additive (ESFA) product. Dietary treatments included a control diet (CON) and 3 experimental diets with reduced calories to equal a 50 kcal/kg deficit (ESFA 1), 70 kcal/kg deficit (ESFA 2), and 100 kcal/kg deficit (ESFA 3) with the addition of (Enercore®, Biosen) at 1 kg/tonne. Toms were placed on experimental diets from placement through 18 wk of age. At 17 wks of age, one tom from each pen was weighed and euthanized to determine health indicator status which included bone ash from the right thigh bone, spleen weight to examine potential shifts in the immune system, plasma ketone body concentrations, liver weight and liver score, and small intestine morphology. Tom live weights (P = 0.937), liver score (P = 0.248), ketone body concentration (P = 0.997), small intestine morphology (P > 0.120) and bone ash (P = 0.156) were similar across dietary treatments. In contrast, relative spleen weight was altered by dietary treatment (P = 0.044) with ESFA 3 spleen yield significantly less than CON (P = 0.005). In conclusion, the removal of 50 and 70 kcal/kg of energy in turkey diets supplemented with an energy sparing feed additive did not significantly alter body weight or metabolic traits.

Highlights Draft EPA emission models for swine facilities were systematically evaluated. Draft EPA models performed poorly on estimating pollutant emissions when input variables were outside of NAEMS data. An unanticipated sensitivity of the draft EPA model outputs to pig inventory and climate was identified. Draft EPA models may need further revision and improvement before being adopted by the swine industry. ABSTRACT. In August 2021, the United States (U.S.) Environmental Protection Agency (EPA) released draft models to estimate daily NH 3 , H 2 S, PM 10 , PM 2.5 , and TSP emissions from breeding/gestation, farrowing, and grow-finish swine facilities (open-source swine manure storages are evaluated in Part II of this series) using inputs of daily mean ambient temperature, relative humidity (RH), cycle day (only farrowing), and live animal weight (animal inventory multiplied by animal body weight). For breeding/gestation and grow-finish facilities, unique models for NH 3 and H 2 S emissions were created based on manure storage classifications of deep-pit, shallow-pit, and no differentiation between deep- and shallow-pits. These models were developed from refined datasets generated by the National Air Emissions Monitoring Study fieldwork completed in 2009 and notably do not include nurseries, wean-finish facilities, gilt development units, and boar studs. This study evaluated the robustness of the EPA draft models by using a “one-factor-at-a-time” sensitivity analysis and an assessment of annual emission estimates using inputs reflective of current U.S. swine housing practices and ambient conditions of representative locations in the U.S. Results revealed that draft EPA model outputs exhibit erratic trends as input factors are changed with others held constant; namely, the influence of animal inventory and animal weight tends to cause drastic changes in draft model emission outputs. The calculation of annual emissions showed insensitivity to ambient conditions and varying levels of agreement with previously published studies. These current draft emission models cannot be used to accurately characterize the wide range of swine facilities where management and climate conditions across regions vary considerably. Revisions are suggested to accommodate a greater range of swine facility types, inventories for practical emission estimations, and climate zones. While these models may provide some insight into estimating emissions, they may not fully encapsulate the complexity and variability of emissions from swine facilities. Keywords: Air quality, Ammonia, Buildings, Hydrogen sulfide, Models, Particulate matter, Pigs.

Ultraviolet-C (UV-C) germicidal light can effectively inactivate airborne pathogens and mitigate the transmission of infectious diseases. As the application of UV-C for disinfection gains popularity, practical estimation of UV irradiance is essential in determining the UV fluence (dose) and designing tubular UV lamp configurations for indoor air treatment. It is generally understood that the inverse square (∼1/d2) law (i.e., irradiance is proportional to the inverse square of the distance) applies well to point light sources. However, there has been a recognition that the ∼1/d2 law does not work well for tubular light sources in the commonly defined near-field applications where the UV source is relatively close to the treated air. Therefore, practical near-field irradiation estimation is needed for designing portable air cleaners and heating, ventilation, and air conditioning (HVAC) ducts with built-in UV light bulbs. This research investigated UV-C light irradiance from tubular (L = 0.9 m) light bulbs at near distances inside an air cleaner prototype duct under three power output (1-, 4-, and 8-bulb) scenarios and conducted theoretical estimation based on a line-source irradiation model. Similarly sized visible fluorescent bulbs were used as a reference. The data were fitted on both ∼1/d2 and ∼1/d correlation of irradiance with distance. Both measured and line source estimated data fit better (i.e., evaluated by R-square, standard errors, root mean squared errors) with the ∼1/d than the ∼1/d2 relationship in the near distance. Although the differences between the measured and the modeled were observed, the pattern of light distribution generally follows an inverse relationship (∼1/d) with distances (d) shorter than two tubular bulb lengths (d < 2L). The pattern applies to both UV and visible light tested in this study. It is recommended that the inverse (∼1/d) correlation be used for near-distance estimation of light distribution, especially for disinfection purposes in air ducting for indoor air quality improvement and airborne disease mitigation.

This paper presents a visual deep learning approach to automatically determine hock and knee angles from sow images. Lameness is the second largest reason for culling of breeding herd females and relies on human observers to provide visual scoring for detection which can be slow, subjective, and inconsistent. A deep learning model classified and detected ten and two key body landmarks from the side and rear profile images, respectively (mean average precision = 0.94). Trigonometric-based formulae were derived to calculate hock and knee angles using the features extracted from the imagery. Automated angle measurements were compared with manual results from each image (average root mean square error (RMSE) = 4.13°), where all correlation slopes (average R 2 = 0.84) were statistically different from zero (p < 0.05); all automated measurements were in statistical agreement with manually collected measurements using the Bland-Altman procedure. This approach will be of interest to animal geneticists, scientists, and practitioners for obtaining objective angle measurements that can be factored into gilt replacement criteria to optimize sow breeding units.

Clean indoor air is crucial for human lives in residential and workplace settings, including food safety and supply chains. Since the COVID-19 outbreak, disinfecting air has become vital for the public as the SARS-CoV-2 virus and other infectious diseases transmit via inhalable aerosols. Ultraviolet (UV) light is known to be effective in disinfecting air. However, it is still challenging to properly design practical UV applications with common light design software. Visible light analysis software, AGi32, assists architectural applications. AGi32 utilizes digitized luminaire IES files to model light intensity on user-designed geometry in common far-field uses. IES files are based on far-field photometry to model light intensity at different distances and angles from the source. The common application of IES files is to model visible light intensities; however, IES files generated for UV light are still rarely available. Due to the increasing need for surface and air disinfection, modeling UV light intensity using IES files may be beneficial for designing and evaluating systems containing UV light bulbs in near-field applications. There is limited information regarding the accuracy of UV modeling in AGi32 software using IES files. Therefore, the research objectives were to (1) create IES files of a UV-C germicidal lamp (254 nm) and a visible fluorescent lamp with almost identical metrics; (2) compare the IES files output with physical UV measurements inside and outside of an air duct; (3) develop correlations between light intensities of visible and UV light bulbs. Linear correlations were observed when comparing UV irradiance and visible illuminance for both measured and modeled data. The results indicated high variability between the measured and modeled light data, signifying the importance of further investigation of potential error sources and improving the accuracy of modeling.

Precision Livestock Farming (PLF) involves the real-time monitoring of images, sounds, and other biological, physiological, and environmental parameters to assess and improve animal health and welfare within intensive and extensive production systems [...]

Indoor air, especially with suspended particulate matter (PM), can be a carrier of airborne infectious pathogens. Without sufficient ventilation, airborne infectious diseases can be transmitted from one person to another. Indoor air quality (IAQ) significantly impacts people's daily lives as people spend 90% of their time indoors. An industrial-grade air cleaner prototype (filtration + ultraviolet light) was previously upgraded to clean indoor air to improve IAQ on two metrics: particulate matter (PM) and viable airborne bacteria. Previous experiments were conducted to test its removal efficiency on PM and airborne bacteria between the inlet and treated air. However, the longer-term improvement on IAQ would be more informative. Therefore, this research focused on quantifying longer-term improvement in a testing environment (poultry facility) loaded with high and variable PM and airborne bacteria concentrations. A 25-day experiment was conducted to treat indoor air using an air cleaner prototype with intermittent ON and OFF days in which PM and viable airborne bacteria were measured to quantify the treatment effect. The results showed an average of 55% reduction of total suspended particulate (TSP) concentration between OFF days (110 μg/m3) and ON days (49 μg/m3). An average of 47% reduction of total airborne viable bacteria concentrations was achieved between OFF days (∼3200 CFU/m3) and ON days (∼2000 CFU/m3). A cross-validation (CV) model was established to predict PM concentrations with five input variables, including the status of the air cleaner, time (h), ambient temperature, indoor relative humidity, and day of the week to help simulate the air-cleaning effect of this prototype. The model can approximately predict the air quality trend, and future improvements may be made to improve its accuracy.

Highlights A mobile application embedded onto smart mobile devices was developed for on-site chicken health assessment based on fecal images. A trained deep learning image classification model was programmed into the application for classifying healthy birds or unhealthy birds infected with Coccidiosis, Salmonella, and Newcastle disease. Animal caretakers can capture fecal images on farms, upload them to the developed application on their mobile devices, and receive health assessment results during daily flock inspection. The study demonstrates a successful proof-of-concept system but requires further work for consolidating system performance. Abstract. Rapid and accurate chicken health assessment can assist producers in making timely decisions, reducing disease transmission, improving animal welfare, and decreasing economic loss. The objective of this research was to develop and evaluate a proof-of-concept mobile application system to assist caretakers in assessing chicken health during their daily flock inspections. A computer server was built to assign users with different usage credentials and receive uploaded fecal images. A dataset containing fecal images from healthy and unhealthy birds (infected with Coccidiosis, Salmonella, and Newcastle disease) was used for classification model development. The modified MobileNetV2 model with additional layers of artificial neural networks was selected after a comparative evaluation of six models. The developed model was embedded into a local server for image classification. An application was developed and deployed, allowing a user with the application on a mobile device to upload a fecal image to a website hosted on the server and receive results processed by the model. Health status is transferred back to the user and can be shared with production managers. The system achieved over 90% accuracy for identifying diseases, and the whole operational procedure took less than one second. This proof-of-concept demonstrates the feasibility of a potential framework for mobile poultry health assessment based on fecal images. However, further development is needed to expand applicability to different production systems through the collection of fecal images from various genetic lines, ages, feed components, housing backgrounds, and flooring types in the poultry industry and improve system performance. Keywords: Artificial intelligence, Coccidiosis, Newcastle disease, Salmonella, Software development.

Highlights Draft EPA emission models for laying hen facilities were systematically evaluated. The models performed poorly on predicting the air pollutants when input variables were out of the NAEMS data range. A key finding was the unanticipated sensitivity of the draft model outputs to bird inventory and climate zones. Further revision and improvement may be necessary for draft models before they can be adopted by the egg industry. Abstract. In August 2021, the U.S. Environmental Protection Agency (EPA) released draft models to estimate daily NH3, H2S, PM10, PM2.5, and TSP emissions from egg-layer houses (high-rise and manure-belt) and manure storage using inputs of daily mean ambient temperature, relative humidity (RH), and hen inventory. These models were developed from refined datasets generated by the National Air Emissions Monitoring Study fieldwork completed in 2009. Notably, they do not include data for cage-free housing. Currently, 66% of U.S. laying hens are housed in cages; thus, these models, if adopted, will have a substantial impact on the U.S. egg industry. This study evaluated the EPA draft models’ robustness and assessed model outputs for egg production systems under differing climate scenarios. The EPA draft models distort emission factors for bird inventories to be lower or higher than those used to develop the models. With inventory held constant, the marginal influence of ambient temperature and RH on daily emissions varied substantially, with some values falling below the measurement detection threshold while others exceeding literature findings. For twelve representative U.S. locations representing differing climates, substantial differences in emission factors were found for bird inventories outside the range in the database. Annual emissions estimated from inventories used to develop the EPA models also varied by location. We conclude that the current draft EPA emission models cannot be used to the degree of precision that is suitable to apply to a wide range of layer facilities, particularly cage-free systems. Revisions are suggested to accommodate a greater range of climates, laying hen facility types, and inventories for practical emission estimations. Keywords: Air quality, Ammonia, Egg production, Emission model, Hydrogen sulfide, Particulate matter, Poultry.

In August 2021, the U.S. Environmental Protection Agency (EPA) released draft models to estimate daily NH3, H2S, PM2.5, PM10, and total suspended particulates emissions from U.S. broiler operations using inputs of daily mean ambient temperature, relative humidity, and live animal weight. The EPA developed these models using datasets collected between 2005 and 2007. It is important to note that over 15 yr have passed since the original data were collected, and broiler genetics, nutrition, and management have improved considerably. These models, if adopted, could have substantial impact on the U.S. broiler industry regarding air emission management and regulation. The objectives of this work were to 1) evaluate the draft EPA emissions model robustness and practicality, and 2) assess the estimated annual emissions for broiler operations in representative U.S. climates using the draft EPA models. The draft EPA emissions models and coefficients were coded and checked against example calculations provided in the draft EPA report, with discrepancies noted. The draft EPA models with log transforms were found to be overly complicated for deployment by most laypersons intending to estimate daily annual emissions from a house or a site with multiple houses. Substantive, fundamental model challenges were uncovered due to narrow model input ranges. For example, lower bird inventory (<20,000 birds per house) generally resulted in much greater emission factors per bird, but should be relatively constant for different inventories at the same environmental conditions; all particulate matter models predicted negative emissions at lower bird inventories; and of the 5 air pollutant models presented, only ammonia was found to be potentially limiting for larger farms in terms of exceeding the threshold of 100 tons per yr. We conclude that the current draft EPA emissions models are not appropriate for accurately or reasonably estimating emissions for a realistic range of broiler operations in their current forms. Revisions are suggested to accommodate the range of climates encountered within the U.S. broiler industry and to better reflect relations found in the scientific literature.

In-barn heat processing of mass swine mortalities to inactivate pathogens could facilitate more carcass disposal options and reduce the risk of pathogen spread in the event of a foreign animal disease (FAD) outbreak. A 12.2 × 12.2 × 2.4 m (W × L × H) heat processing room was created using a temporary wall inside a de-commissioned commercial gestation barn in northwest Iowa. Eighteen swine carcasses (six per group) divided into three weight groups (mean ± SD initial carcass weights: 31.8 ± 3.3, 102.7 ± 8.1, and 226.3 ± 27.6 kg) were randomly assigned a location inside the room. Three carcasses per weight group were placed directly on concrete slats and on a raised platform. One carcass per weight group and placement (n=6) was instrumented with five temperature sensors, inserted into the brain, pleura, peritoneal, ham, and bone marrow of the femur, and a sensor was attached directly to the skin surface. Environmental conditions (ambient and room) and carcass temperatures were collected at 15-min intervals. Carcasses were subjected to an average room temperature of 57.3 ± 1.2°C for 14 days. The average (±SD) reduction from initial weight for the carcasses on slats was 45.0 ± 4.70% (feeder), 33.0 ± 8.30% (market), and 34.0 ± 15.80% (sow), and for the carcasses on a raised platform, it was 39.0 ± 6.80% (feeder), 49.0 ± 11.30% (market), and 45.0 ± 6.70% (sow). There was a significant interaction between carcass placement (slats and raised) and carcass weight loss for the market weight group. When average carcass surface temperature was at 40.6, 43.3, and 46.1°C (data grouped for analysis), the average internal carcass temperature for most measurement locations was significantly different across carcass weight groups and between the carcasses on a raised platform and those on slats. This preliminary analysis of carcass weight loss, leachate production, and temperature variation in carcasses of different sizes can be used for planning and evaluating mass swine mortality management strategies.

Highlights Carcass and room temperatures, as well as CO, CO 2 , O 2 , and NH 3 , were continuously monitored. NH 3 release was approximately half when carcass leachate was removed from the shallow pit. Gompertz and logistic models fit data well for daily carcass mass reduction and leachate production. Abstract. A catastrophic mortality event for swine would present numerous challenges with the management and disposal of infected carcasses. This study explored a new strategy for biosecure in-barn processing of swine carcasses as an alternative to traditional management and disposal approaches. A small-scale, mobile laboratory with two discovery rooms (DRs), replicating a swine finishing facility, was constructed to execute tests of in-barn disposal methods. Carcasses were desiccated by subjection to heat at a room air temperature of 43°C (110°F) for 16 days. Three carcasses (average = 82 kg, SE=1.27 kg) were elevated over individual leachate collection systems in DRA, thereby removing leachate from the room. Three carcasses in DRB were placed on concrete slats with cumulative leachate collection in the pit below. Environmental data were collected for DR, outdoor, and slat temperatures; and CO2, CO, O2, and NH3 gas concentrations. Carcasses were characterized by rectal and shoulder temperature monitoring and daily weighing of carcasses and leachate in DRA. The air exchange rate for this unventilated system was quantified based on wind and thermal-driven infiltration. Room environments were compared for thermal performance and gas levels. Carcass temperatures were compared, and data suggested no significant impact of flooring material on internal carcass temperature. Gompertz and logistic models were fit to leachate production data and carcass mass reduction data. Ammonia generation rates were found to have a peak production rate of 96.5 g AU-1 day-1 (15.8 g animal-1 day-1) in DRA and 120 g AU-1 day-1 (19.7 g animal-1 day-1) in DRB. Over the study, the generation of NH3 in DRB (360 g) was nearly twice that of DRA (182 g) due to leachate removal. Further quantification and qualification of in-barn management strategies will better define biosecure disposal approaches in the event of a catastrophic mortality event. Keywords: Ammonia, Catastrophic event, Disposal, Foreign animal disease, Mortality management, Pig.

UV-C lamps are a practical means to inactivate airborne pathogens and mitigate transmission. Practical estimation of UV irradiance is essential in designing tubular UV lamp configurations for indoor air treatment. It is generally understood that the inverse square distance law applies well to point light sources. One of the common estimation methods to estimate UV irradiance was the inverse square law (the irradiance is inversely proportional to the square of the distance from the light source). However, that law mostly applies to far-field conditions (assuming UV irradiation from a point source that is at least five times far as the length of the UV bulb from the measurement point), while in many applications, the effective irradiation is only in the near distance (shorter than five times of the length of the UV bulb), especially in portable air cleaners and small-scale Heating, Ventilation, and Air-Conditioning (HVAC) ducts. Therefore, a practical estimation method is needed for near-distance applications. This research investigated UV-C light irradiance from tubular (L = 0.9 m) light bulbs (and similarly sized visible fluorescent bulbs used as a reference) at near- and far-field in three power output (1-, 4-, and 8-bulb) scenarios and modeled them with a line- and a point-source models. The data were fitted on both the inverse square and inverse correlation of irradiance versus distance. Alternatively defined near-field distance (d<2L) irradiation has an acceptable inverse relationship over distance, while alternatively defined far-field distance (≥ 2L) irradiation approximately follows an inverse square relationship. The findings are beneficial for UV irradiance estimation for indoor air quality improvement and airborne disease mitigation.