The Effects of Polyphenols Provided in ovo on the Hatchability and Growth of Broiler Chicks
Jacob Foster, Jing Lu & Stephanie Collins
Animal Nutrition Group, Department of Animal Science and Aquaculture, Dalhousie University
In ovo technology can be used to prevent lower body weights in chicks caused by finite nutrient availability during embryonic development and after hatching. At day 17 of incubation, 240 eggs (60/treatment) from Cobb broilers were provided one of four in ovo injection treatments: non-injection (control), diluent (2.5 mL), tea polyphenol (6.25mg/2.5mL diluent), or seaweed polyphenol (6.25mg/2.5mL diluent). Chi-square analysis showed no significant differences between treatments during incubation (P=0.911), or at hatching (P=0.555). Chicks were raised (9 birds/pen; 4 pens/per treatment) until 36 days of age. Body weight and feed consumption were measured weekly, and blood glucose levels were measured after hatch, following placement, and on day 7. There were no significant differences among treatments for average weight, despite significantly higher feed intake during week one for chicks provided tea polyphenols in ovo (P=0.012). Chicks provided seaweed polyphenols had significantly higher weight gains than all other treatments at week one (P=0.033). Chicks provided diluent gained the most weight during week three (P<0.001). At week four, daily weight gain for the same group was significantly lower than other treatments (P<0.001). Chicks provided diluent injection had a higher feed conversion ratio (FCR) (P<0.001) and protein efficiency ratio (PER) (P<0.001) during week three. During week four, FCR and PER was significantly lower in the diluent treatment (P=0.002, P=0.009) compared to the other treatments. There were no significant differences between treatments for blood glucose measurements. Future research should consider the effect of in ovo polyphenols on gastrointestinal development and glycogen storage.
Leveraging Satellite Data for Greenhouse Gas Mitigation in Canadian Poultry Farming
Bubacarr Jobarteh & Suresh Neethirajan
Mooanalytica Research Group, Department of Animal Science and Aquaculture, Dalhousie University
Accurate monitoring of greenhouse gas (GHG) emissions from poultry farms is essential for effective climate change mitigation. This study integrates satellite imagery with advanced machine learning techniques to analyze methane (CH₄) and carbon dioxide (CO₂) emissions from over 1,300 poultry farms and processors across Canada from 2019 to 2023. Utilizing high-resolution atmospheric data from Sentinel-5P and NASA’s OCO-2 satellites, emissions were systematically mapped both temporally and spatially. We employed Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting (XGBoost) models to forecast emission trends and identify primary emission drivers. The LSTM model demonstrated superior predictive accuracy, achieving the lowest Root Mean Square Error (RMSE) values of 10 for CH₄ and 362 for CO₂. Additionally, benchmarking of emissions data was conducted to establish performance standards and monitor progress towards reduction targets. These findings provide valuable insights for policymakers and industry stakeholders, facilitating the development of targeted emission reduction strategies that align with regulatory standards and promote environmental sustainability. By combining state-of-the-art data analytics with satellite-based monitoring, this research enhances the precision and efficiency of GHG tracking in the Canadian poultry sector. Furthermore, it establishes a robust framework for formulating effective climate change mitigation strategies, thereby supporting Canada’s broader environmental objectives and advancing sustainable agricultural practices.
Growth Performance and Gut Development of Broilers Fed with Potato Peel Meal as a Partial Carbohydrate Source Replacement for Corn in the Grower and Finisher Phases
Xujie Li, Jing Lu, Jacob Foster & Stephanie Collins
Animal Nutrition Group, Department of Animal Science and Aquaculture, Dalhousie University
The rising cost and fluctuating availability of traditional feed ingredients such as corn and soybean pose challenges to sustainable poultry production. Potato peel, a carbohydrate-rich byproduct from the potato industry, presents a potential alternative; however, its use is limited due to the presence of antinutritional factors and high levels of non-starch polysaccharides (NSPs), which could negatively affect digestibility and growth performance. This study evaluated the potential of using potato peel as an alternative carbohydrate source to replace corn in broiler diets. A total of 576 broilers (14 days of age) were allocated into four dietary treatments: a basal diet without enzyme supplementation (control), a basal diet with enzyme supplementation, a diet with 10% potato peel inclusion, and a diet with 10% potato inclusion and with enzyme supplementation. On day 33 of age, one male bird per pen was euthanized and their digestive organ weight was expressed on a relative body weight basis. Results demonstrated that broilers fed 10% potato peel meal had a lower (P<0.05) body weight gain, feed intake and poor feed conversion ratio. Diets with 10% potato peel meal resulted in heavier relative weights of the gizzard, duodenum and pancreas, ileum, and large intestine compared to those fed with control diet. These initial findings suggest that to incorporate potato peel meal broiler diets, further dietary optimization will be needed. This may include reducing the dietary inclusion level and refining enzyme formulations to enhance the feasibility of potato peel as a sustainable feed ingredient in broiler production. The findings provide insights for poultry producers seeking alternative carbohydrate sources to reduce feed costs while maintaining production standards.
Unlocking Poultry Vocalizations – Artificial Intelligence for Welfare Monitoring and Sustainable Farming
Venkatraman Manikandan & Suresh Neethirajan
Mooanalytica Research Group, Department of Animal Science and Aquaculture, Dalhousie University
Employing Artificial Intelligence (AI) for decryption of poultry vocalizations is a revolutionary development in addressing issues of animal welfare and sustainable agriculture. Our study employs advanced models of Natural Language Processing (NLP), like Wav2Vec 2.0 and BERT, to classify poultry calls corresponding to distress, feeding, mating, etc. Results indicate that the chickens expressing stress produce vocalizations with higher pitch and reduced tonal complexity, correlating with physiological constraints like muscle tension and altered respiration. Increased negative sentiment during stress shows greater utility for real-time emotion and health monitoring with vocal pattern sentiment analysis. Our study also explores the fusion of acoustic data with precision livestock farming systems so that farmers can adjust proactively to environmental conditions restraints, maximize resource allocation and minimize productivity losses due to undue stress. Moreover, this study contributes to understanding poultry vocalizations in terms of their ecological role from a biodiversity and environmental stressors perspective. By fostering environmental consciousness and ethical farming as well as offering noninvasive and scalable monitoring, this AI powered strategy goes beyond conventional welfare approaches and supports intelligent systems. Our approach lays the groundwork for an AI oriented future for sustainable agriculture by linking poultry health and behavior with ecosystem interactions.
Black Soldier Fly and Laying Hens – Impacts of Black Soldier Fly Larvae Meal on Performance and Egg Quality of Brown Lohmann-Lite Laying Hens
Jing Lu, Hannah Facey, Janice MacIsaac & Stephanie Collins
Animal Nutrition Group, Department of Animal Science and Aquaculture, Dalhousie University
Black soldier flies thrive at high densities, need little water and consume organic waste. Black soldier fly larvae meal (BSFM) is rich in protein (40-56%) and minerals like calcium, phosphorus, and potassium, making it an excellent ingredient for laying hens. Black soldier fly larvae are high in fatty acids such as lauric acid, which is known to possess antimicrobial properties. In a completely randomized design, 180 Lohmann Brown-Lite hens (52 weeks of age) were housed in 36 units in a conventional housing system (3 treatments x 12 units per treatment x 5 birds per unit) for 20 weeks (5 periods x 4 weeks). Each unit received one of three diets containing 0%, 6.5%, or 13% BSFM. Up to 13% BSFM inclusion in the diet had minimal effects on laying hen performance and the physical traits of the eggs produced. However, laying hens fed 13% BSFM laid smaller eggs during the later production periods. Both 6.5% and 13% BSFM led to darker and a more orange yolk color, which could have potential implications for consumer preferences. Egg yolks from birds fed 6.5 and 13% BSFM had higher levels of the antimicrobial lauric acid and increased saturation due to assimilated fatty acids from BSFM. In addition to the minimal impact on laying hen performance, the potential physiological benefits from this feed ingredient should be considered.
Accelerating Climate Action – High-Resolution Methane Mapping in Canadian Poultry Farms
Prajesh Padmanabhan & Suresh Neethirajan
Mooanalytica Research Group, Department of Animal Science and Aquaculture, Dalhousie University
Reducing methane emissions is critical to achieving Canada’s 2030 Emissions Reduction Plan. However, the scarcity of publicly available datasets on livestock management presents significant challenges for non-intrusive monitoring of greenhouse gas (GHG) emissions. Satellite-based methane monitoring provides unparalleled advantages in scalability, cost-efficiency, and integration into policy frameworks. Leveraging the spatial and temporal capabilities of high-resolution satellite imagery, the MooAnalytica research group has developed a framework for mapping methane emissions from poultry farms based on Sentinel-2 imagery and a Multi-BandSingle-Pass (MBSP) approach, enabling cost-effective, scalable, and real-time monitoring. The methodology incorporates shortwave infrared (SWIR) bands for methane detection, combined with feature engineering techniques and Kalman filter to enhance data reliability. Applied to 129 Canadian poultry farms, the model estimated annual methane emissions at 5456.30 tonnes CH₄, with an average of 51.47 tonnes CH₄ per farm during 2023. Validation of the results demonstrated strong model performance, yielding a 95% confidence interval (56.47–74.04 tonnes/year) and a relative uncertainty of ±6.79%. This framework represents a scientifically robust tool for emissions profiling and identification of methane emission hotspots. Future developments will focus on creating region and farm specific emissions benchmarks, supporting sustainable agricultural practices and enabling targeted interventions to mitigate methane emissions effectively.
Adapting the Cool Farm Tool for Net Zero Poultry Farming in Atlantic Canada
Kashfia Sailunaz, Mackenzie Tapp, Shuqiang Zhang & Suresh Neethirajan
Mooanalytica Research Group, Department of Animal Science and Aquaculture, Dalhousie University
Agriculture accounts for roughly 25% of global greenhouse gas (GHG) emissions, and poultry alone contributes an estimated 10–12% of agriculture’s overall footprint. Achieving net-zero targets in this sector requires tools that can accurately gauge emissions, guide interventions, and inspire behavior change. The Cool Farm Tool (CFT)—widely recognized in Europe— could offer a carbon footprint calculator for livestock and crops, encompassing GHG emissions, biodiversity, water use, and food loss. Despite its proven effectiveness, its adoption remains limited in Atlantic Canada, especially in poultry operations. Our research aims to adapt the CFT to Nova Scotia’s unique conditions, including local climates, feed formulations, housing structures, and manure management practices. We will integrate Artificial Intelligence-driven decision support to refine how farms capture real-time data, thereby improving the tool’s accuracy and usability. By collaborating with poultry producers, policymakers, and industry stakeholders, we seek to pinpoint barriers to implementation and create tailored solutions for Atlantic Canada’s diverse farm profiles. This initiative will not only demonstrate best practices for reducing emissions but also foster transparency and accountability in the transition to regenerative agriculture. Ultimately, our adaptation of the CFT aspires to accelerate Canada’s journey toward net-zero poultry farming.
Advancing Poultry Welfare with Artificial Intelligence – Integrative Multimodal Analysis for Laying Hens
Suresh Neethirajan
Mooanalytica Research Group, Department of Animal Science and Aquaculture, Dalhousie University
Traditional monitoring of laying hens often focuses on isolated indicators, potentially missing critical welfare signals. Our project tackles this gap by merging multiple data streams—audio, video, thermal imaging, and environmental metrics—into an all-encompassing, real-time monitoring system. By fusing these diverse inputs, we aim to capture nuanced changes in hen behavior, comfort, and health within free-run housing systems. Three key objectives anchor our research:
- Refine Welfare Indicators – We will identify and validate the most salient markers of hen well-being, ensuring robust, evidence-based monitoring.
- Develop an Integrated Framework – Using advanced AI and IoT technologies, we will design a platform that continuously tracks these indicators, empowering farmers with immediate insights.
- Decode Hen Vocalizations – By applying machine learning to vocal patterns, we strive to identify early warning signs of stress and unmet needs.
While promising, this approach also grapples with issues of data overload, sensor calibration, and privacy. To address these concerns, the system will be rigorously tested at the Atlantic Poultry Research Institute, refined through stakeholder feedback, and aligned with ethical guidelines. Ultimately, our integrated analytics aim to improve welfare practices, boost productivity, and set new standards for a sustainable and humane poultry industry