In Canada, 35.6 million tonnes of municipal solid waste (MSW) was generated in 2018, of which 30-35% was comprised of food waste. A major proportion of this food waste (up to 77%) has been diverted to landfills which causes significant greenhouse gas emissions (GHG) and incurs over $3 billion in landfill management. Thus, as a sustainable approach to waste management and resource recovery - the acidogenic fermentation process is gaining attention which transforms food waste into valuable products such as short-chain fatty acids (SCFAs). However, acidogenic fermentation processes are typically studied at higher temperatures (35-55⁰C), which may require significant energy input for heating in cooler climates like Canada. This study aimed to optimize the acidogenic fermentation process for food waste at room temperature (22-24⁰C) by investigating the impact of inoculum type, inoculum pre-treatment, and pH on hydrolysis and SCFA production. Results showed that non-heated inoculums achieved higher hydrolysis. Heat pre-treatment significantly impacted SCFA production, with the maximum SCFA yield of 238 g CODSCFA/kg VSadded achieved with heat pre-treated anaerobic digested sludge inoculum. Butyrate was the dominant SCFA component for all inoculum types, and the highest hydrogen yield was achieved with non-heated inoculums. Neutral to alkaline pH favored the hydrolysis, while the maximum SCFA production was achieved at neutral pH i.e. 238 g CODSCFA/kg VSadded. Acetate comprised the major fraction of SCFA at alkaline pH, while butyrate was the dominant SCFA at neutral. The alkaline pH favored hydrogen production, resulting in the highest hydrogen yield at pH 9.

The availability and low cost of canola fiber make it a viable renewable resource for manufacturing value-added textile and biocomposite materials. Despite a number of studies conducted on canola fiber extraction, lack of fiber flexibility and softness necessitates further research in this area to effectively utilize canola for textiles and biocomposites. Canola fibers were extracted from stems after harvesting the seeds using water retting at 40°C and subjected to alkaline, peroxide, and enzymatic treatments. For enzymatic treatments, cellulase, amylase, pectinase, and xylanase enzymes were paired up in various combinations to identify the optimal chemical treatment, where the samples were first degummed in hot water at 90°C and then softened with an oil-in-water emulsion before being treated with enzymatic solution at pH 4.0 and 45°C and dried at room temperature. The treated samples were evaluated for softness, flexibility, and fineness. It was found that only fibers that underwent a treatment using a combination of pectinase and xylanase exhibited improved fiber softness, flexibility, and luster, while fineness data revealed a reduction in the fiber diameter as well as the fiber length. The removal of gummy substances from the fiber is believed to have refined it, which has caused the change in fiber fineness. Further, fiber individualization is maximized progressively with the advancement of processing steps. The soft and flexible canola fibers obtained after pectinase and xylanase treatment could be used for apparel and industrial applications. However, the shortening of the canola fiber’s length following chemical processing could limit its potential for spinning.

Wild blueberries (Vaccinium angustifolium Ait.) are a perennial crop in northeastern North America. Conventional management relies on costly broadcast applications of agrochemicals such as herbicides. There is an opportunity to reduce agrochemical usage through the implementation of targeted spray applications using machine vision systems. Machine vision techniques such as deep learning convolutional neural networks depend on high-quality images for decision-making. Cameras with rolling shutters produce blurring and warping when capturing images in motion, necessitating the use of more expensive cameras with global shutters. This study examines four cameras for potential use on a machine vision smart sprayer in wild blueberry. Two cameras from FLIR (Blackfly S, Firefly DL) capture images using a global shutter, while cameras from Logitech (c920) and Luxonis (OAK-D) use rolling shutters. Field tests were conducted in commercial wild blueberry fields located in central and northern Nova Scotia, Canada, from late April to early June 2021, 2022, and 2023. The cameras were affixed to the boom of a prototype agricultural sprayer, up to 0.91 m above the ground with their lenses pointed downward. Images were captured while the sprayer moved at speeds up to 2.7 m/s. The images were assessed for blur using a Laplacian kernel. The results of this analysis will be presented at conference. Future work will involve the development of a machine vision smart sprayer to target weeds in wild blueberry fields. By using a machine vision smart sprayer, growers can achieve substantial cost savings by selectively applying agrochemicals based on real-time visual data.

The popularity of the circular economy concept has grown recently due to increased awareness of wasteful business practices and a growing recognition that the current resource use model is inefficient and unsustainable. While there is a general consensus that a transition to a circular economy involves recycling waste streams and using them to produce new high-value products, no strong agreement has been reached with regard to accomplishing this task. Additionally, most frameworks approach the circular economy as an environmental and economic problem only, not considering how society is impacted by decisions in the economic and environmental sectors. In order to reach its full potential as a holistic paradigm for sustainability, the circular economy must incorporate the impact of circularity on social welfare. The agricultural sector, and specifically the poultry industry, is a good starting point from which to examine the potential methods for incorporating social welfare considerations into circular economy frameworks.

One proposed method for circular economy implementation is the ReSOLVE framework, developed by the Ellen MacArthur Foundation. The ReSOLVE framework is unique in that it provides strategic goals (Regenerate, Share, Optimize, Loop, Virtualize and Exchange) to help businesses and governments make plans to increase circularity. Using the poultry industry as a case study, this research investigates the theoretical potential social implications of increasing circularity via the methods outlined in the ReSOLVE framework, adding a social perspective to the discussion of innovative waste management strategies.