The Oldman Watershed Council (OWC) is a community-based nonprofit in southwestern Alberta that is improving watershed health through education, action, and stewardship. Since 2015, we have been training landowners and volunteers in bioengineering techniques, working side by side to restore riparian habitat and mitigate the impacts of flood and drought in our semi-arid climate. In 2022, our small team of staff and volunteers planted over 8400 willow stakes on public and private agricultural land. Outcomes include environmental, economic, and social benefits - such as improved ecosystem services, livestock health, and community capacity. In this way, our holistic, grassroots bioengineering activities contribute to sustainable solutions that restore ecosystems and communities.

The increasing concern over climate change and plastic waste has led to a shift towards sustainable alternatives in the plastics industry. This project aims to develop an agricultural-based carbon black masterbatch for plastic processing via rotational and injection molding. The masterbatch is made using renewable cellulosic materials, including agricultural hemp biomass and wood materials. Bioplastics and recycled plastics are also used, along with additives, to create the carbon masterbatch for molding. Advanced bioprocessing techniques, including grinding, extrusion, screening, blending, compounding, and physical and biological testing, are used in the development of the masterbatch. The manufacturing process employed by this project results in concentrated masterbatches that are carbon-negative, thereby reducing the carbon footprint of plastics. Furthermore, these masterbatches are compostable and biodegradable, providing a potential solution to the global plastic waste problem. The use of sustainable feedstock to develop the concentrated biocarbon for polymer and other industries enhances the environmental credentials of this project. The research conducted in this project has significant implications for the plastics industry. By utilizing renewable feedstocks and employing sustainable manufacturing processes, it is possible to reduce the carbon footprint of plastics and tackle the growing plastic waste problem. The findings of this project have the potential to transform the industry by providing sustainable alternatives that address both environmental and economic concerns.

The rising global food demand due to the increasing population has made sustainable food production obligatory. Animal-based food production necessitates more water and land resources than their plant-based alternative. Moreover, plant-based protein produces less greenhouse gas emissions. Current plant-based protein isolation through wet fractionation seems unsustainable due to its immense reliability on water, chemicals, and energy/heat. Dry fractionation techniques such as air classification and sieving have limitations due to physical properties such as particle density and size. Triboelectrostatic separation (TES) is a novel dry fractionation technique previously used in mineral and recycling industries. This technique can also be used for agro-particle segregation. In this investigation of TES, the impact of milling parameters and particle protein content on the separation characteristics (protein enrichment, mass yield, and protein separation efficiency) have been analyzed for yellow pea flour, faba bean flour, and yellow pea flour concentrates (protein and starch). A solid particle dispenser pneumatically charges the flour particles through a polyethylene(PE) tribocharger at an airflow rate of 2.1 m3/h. The electric field strength of the separator was configured at 100kV/m. Particle size distribution and scanning electron microscopy (SEM) have been used to analyze the pre- and post-separation of flour particles to validate the acquired results.

: Hydrological processes play an important role in nutrient dynamics in pasture

landscapes because nutrient export is facilitated by water movement. Hydrological

processes, in turn, are affected by climatic conditions. The objective of this study was to

use the physically-based Cold Regions Hydrological Model to assess the impact of

future climate projections on the hydrology of a pasture-dominated basin within the

Beaver Creek watershed in Manitoba. This ungauged basin covers an area of 46.6 km2

,and it is characterized by flat topography, loamy sand soils, and pasture land cover

(over 70% of the area). Historical simulations (1998-2020) were carried out and

assessed based on regional estimates of the annual water yields and

evapotranspiration (ET). The model reproduced annual water yields satisfactorily, with a

Nash-Sutcliffe efficiency (NSE) of 0.66, and a percent bias (pbias) of 8.2%. As for ET, 9

out of 14 years (64%) were well-captured within the range of long-term average ET in

the Prairies. The calibrated model was forced with an ensemble future climate dataset

(2021-2079) for two emissions scenarios (i.e., RCP 4.5 and 8.5). The results showed

that annual stream discharge will increase by 23.69% and 31.77%, while ET will also

increase by 18.39% and 22.22%, respectively. Moreover, the timing of the projected

peak discharges in both emissions scenarios took place one month earlier as compared

to that of the historical period, implying a generally warmer climate with earlier

snowmelt. Greater stream discharge in both emissions scenarios could potentially lead

to increased risk of nutrient export.

Momordica charantia, commonly known as bitter gourd and one of the economically important medicinal plants belonging to family Cucurbitaceae, is used as an antidiabetic and antihyperglycemic agent. Therefore, the present study was designed to compare the bioactive components (ascorbic acid, tocopherols, total phenols, flavonoids and o-dihydroxyphenols) in whole fruit and its parts viz. epicarp, mesocarp, endocarp and seeds of five genotypes (PAUBG-98, PAUBG-119, PAUBG-191, PAUBG-224 and PAUBG-259) of Momordica spp. The data revealed that all the genotypes possessed variable amounts of ascorbic acid, tocopherols and phenolics. Ascorbic acid and tocopherols ranged from 5.58-74.23 mg/100g fresh weight (FW) and 0.17-2.21 mg/100g FW, respectively, with maximum content in whole fruit followed by endocarp. Similarly, total phenols, flavonoids and o-dihydroxy phenols varied from 42.00-558.33, 28.77-382.52, 8.04-106.90 mg/100g FW, respectively, with maximum content in whole fruit. Variety PAUBG-98 was found to be the desirable cultivar with high amount of ascorbic acid, tocopherols, phenols, flavonoids and o-dihydroxy phenols with the values of 74.23, 2.21, 558.33, 382.52 and 106.90 mg/100g FW, respectively, in whole fruit followed by endocarp and mesocarp. Among all genotypes, whole fruit of PAUBG-98 was found to be dominating in bioactive components. From the present study, it can be concluded that antioxidants like vitamins and phenolics are present abundantly in bitter gourd, which can provide protection against many ailments.

Cattail biomass is an abundant, low-cost source of fiber in the Prairie region. We are investigating the feasibility of converting waste cattail biomass fibre into paper, with a biodegradable biopolymer coating, for the manufacture of fully compostable coffee cups. The current study involves the production of biomass paper by alkali-pulping of cattail plant leaves. Investigation of optimum pulping conditions has been going on by applying a cooking temperature of 90 °C for 4 hours and the addition of 2.5 - 15% sodium hydroxide, as well as beating the fibre pulp for 17 seconds in a conventional blender at the low-medium setting. The extracted fibres exhibited a 22 - 32% yield with an average length of 5.1 cm. The average weight and thickness of the paper sheets produced, were 300 g/m2 and 0.43 mm, respectively, which conformed to the specifications for commercial coffee cup paper. Initially, the biodegradable polymer for the cup lining will be polylactic acid (PLA), and experiments to optimize the application method, polymer concentration, and solvent used are currently on-going. Polymer coated paper sheets will be evaluated for their mechanical, thermal, and hydrodynamic characteristics, as well as how they compare to available commercial coffee cup-grade paper. The use of other biodegradable polymers, such as polyhydroxyalkanoate (PHA) will also be investigated. This study will enable the development of affordable, environmentally friendly paper cups for the food and beverage industry and provide an opportunity to maximise the utilisation of local biomass resources.

Application of microfluidic technology in the food sector is rapidly emerging as a promising tool. Microfluidic platform has gained significant attention because of its miniaturized system and provides several benefits over a conventional macroscale system. This study focuses on the bibliometric analysis of global research trends on the applications of microfluidic technology in the food sector. The publication data mining was carried out using the keywords “Microfluidics OR microfluidic” AND food (TITLE-ABS-KEY) which resulted in 745 documents using Scopus database. This data was used for further analysis of research trends. It was observed that 64.16 % of the total publication was article, 18.8% was review paper, conference paper contributed 12.75%, book chapter (3.49%), and other publication (0.8%). The analysis showed that China and the United States were the leading countries in food microfluidics research and development activities as reflected in the publication records. The record indicated that Purdue University was the most productive organization with 31 documents and 816 citations during the assessment period. Yanbin Li was observed to be the most prolific author in food microfluidics publication with 31 documents. The publication data revealed that Lab on a Chip and Biosensors and Bioelectronics were the leading journals in terms of total citations and number of publications, respectively. Currently, most of the research activities on food microfluidics are focused on food safety analysis. The application of microfluidic technology in the food sector is relatively new, yet it is progressing very rapidly over the last few years.

Potato virus Y (PVY) has been a long-standing problem for potato growers over the world, due to its ability to cause significant reductions in crop yields. The yield losses due to PVY may range from 10% to 80%, depending on the severity of the infection and the potato variety. The new necrotic strains of PVY cause mild symptoms in the foliage, making it challenging to detect infected plants. Consequently, identifying and disposing of infected plants (known as "roguing") has become more difficult. There is a growing demand to create solutions that aid growers in identifying potato plants that have been infected with PVY. In past studies, deep learning based convolutional neural networks (CNNs) have shown the ability to successfully make distinctions between various plants, weeds, and diseases. In this study, the use of these models for the detection of PVY-infected plants has been explored and extended. Different deep learning models are trained on the imagery dataset of healthy and PVY-infected potato plants grown under greenhouse conditions. The evaluation metrics used were accuracy, precision, recall. The trained models with 10-fold cross-validation achieved classification accuracy scores of over 80% while classifying the healthy and PVY-infected potato plants. The models were also able to accurately detect PVY-infected plants even when the symptoms were mild, which is essential for early detection and prevention of the spread of the virus. These models may assist roguers in the real-time identification of PVY-infected plants that may help in controlling the disease spread and improving the crop yield.

Fields tests for wheel loaders are costly, time-consuming, weather-dependent, and require highly-skilled operators to ensure providing constant volume of material delivered from each bucket. Some methods have been developed throughout the years to predict soil volume dug and transported by each bucket per cycle and its corresponding reaction forces.

This poster presentation focuses on the validation of the simulated soil volume contained in a bucket as function of time during scooping of material. The simulated and actual soil volumes contained in the wheel loader buckets are compared using Discrete-Element Method (DEM), LIDAR, and 3D scanning technologies.

The top surfaces of each soil surcharge pile in the bucket are determined from simulation and validated using data from LIDAR and 3D scanning. These surfaces are combined with the bucket geometry to create hollow shell model. These virtual hollow shell surfaces are then filled with DEM realistic particles to determine the volume created by the void between the top surface and the bucket cavity. Various properties of DEM soil particles were measured using physical material from testing site.

Methane gas (CH4) is one of the primary greenhouses gas (GHG), and major emissions from agriculture are from the livestock industry because of the enteric fermentation in large ruminants. Precise and consistent methane emission measurements are critical for formulating mitigation approaches to lessen the impact of livestock operations on climate change. Top-down and bottom-up methods are the two primary techniques that have emerged for calculating methane emissions from dairy and beef animals. This review highlights methane detection and characterization methodologies, including their scopes, benefits, and drawbacks. Bottom-up approaches, such as feed intake measurements, metabolic methods, and mechanistic simulation models can provide precise information on methane generation sources and mechanisms. Top-down approaches, including remote sensing and atmospheric modeling, can generate spatiotemporal integrated estimates of emissions at broader scales. While each method has advantages and disadvantages, combining the two may result in the most thorough and accurate evaluation of methane emissions from feedlots. This assessment will assist researchers and policymakers seeking to implement Canada’s 2030 Emissions Reduction Plan to reach an emissions reduction target of 40-45% below 2005 levels by 2030 and net-zero emissions by 2050.

Pulses are popular sources of starch, protein, and other essential minerals. However, attention is being shifted to how market opportunities could be created for their co-products after fractionation, especially starch. The development of novel materials (e.g., bioplastics) is one way of expanding their industrial applications. This study, therefore explored the potentials of utilizing air classified and isolated pea, lentil, and faba bean starches as a precursor for fabricating mesoporous bioaerogels via freeze-drying technique. The results evidenced ultra-low densities (<0.1 m2/g), mesopore, high porosities (~99%), low surface areas (SBET = ~4-18 m2/g) for all the aerogels. Adsorption isotherm showed a typical Type II and III profiles while the thermogravimetric analysis showed more weight loss (74.39-78.12%) in aerogels mostly developed from isolated starches. Microstructural studies showed a unique distribution of pores within the developed aerogels. FTIR and XPS studies confirmed the presence of an amide (I, II, III) at different absorption bands range (~1600 – 1200 cm-1) and functional groups (carboxylic group and the amide group), respectively. All the aerogels samples became stiffer with a corresponding increase in load, and a reversible deformation in the linear region was identified at less than 5% strain. Comparatively, saturated PSBs from air classified starch at a relative humidity of 95% showed a drastic reduction in their compressive moduli (CM), while PSBs from isolated starch experienced markedly high CM. Moisture saturation was achieved at 72 h for all the samples.

Lowbush blueberry (Vaccinium angustifolium Ait.) is a major commodity cultivated in Atlantic Canada. An operator of a wild blueberry harvester faces fatigue from manually adjusting the height of the harvester’s head to account for spatial variations in plant height, fruit zone, and field topography. For ease of operation, a deep learning-supported machine vision control system has been developed to detect the fruit height, taking into account the variability of a wild blueberry field's variability and precisely auto-adjust the header picking teeth' position rakes. The OpenCV AI Kit (OAK-D) was used with YOLOv4-Tiny deep learning model using code developed in Python to match the fruit zone to the harvester’s head position. The system accuracy was statistically evaluated with R2 (coefficient of determination) and σ (standard deviation) measured on the difference in distances between the picker teeth and average fruit heights, which were 72, 43% and 0.021, 0.023 m for the auto and manual head adjustment systems, respectively. This innovative system performed well in weed-free areas but required further work to operate in weedy sections of the fields efficiently. Benefits of using this system include automated control of the harvester’s head to match the header picking rake height to the appropriate level within the fruit zone while reducing the operator’s stress.

Mapping spatiotemporal variability of soil compaction is a significant concern in agriculture and is essential for managing soil health and maximizing crop yields. The objective of this study was to compare the performance of Ordinary Least Squares (OLS) regression and Geographically Weighted Regression (GWR) data analysis tools for soil compaction mapping using Soil Apparent Electro-Magnetic Parameters (AEMP). A field experiment was conducted in Pasadena, NL under two different land use types and soil AEMP data were collected using two different Electromagnetic induction sensors. Four different soil compaction levels were selected based on the number of passes given by a compaction roller and soil penetration resistance was measured with a cone penetrometer. The OLS and GWR models were developed using soil AEMP data and corresponding soil compaction measurements. The study found that the GWR model outperformed the OLS model for soil compaction mapping (R2 = 0.97). A better fit GWR model captured the spatial heterogeneity of soil compaction more accurately than the OLS model. The study also found that the performance of the models varied across four different compaction levels, with the GWR model providing better results for higher compaction levels The study highlights the importance of using advanced statistical models such as GWR for improving the prediction accuracy and precision in mapping important soil properties like compaction. Furthermore, this study opens up a new research avenue for the application of machine learning techniques in soil compaction mapping, which can potentially improve the accuracy of the maps to support precision agriculture.

The reduction of food waste is crucial for sustainable food production and resource conservation especially amid growing global concerns over food insecurity. Fresh fruits such as strawberries although highly sought after by consumers are often challenged with post-harvest ripening causing them to quickly lose appealing properties such as color, firmness, aroma, juiciness, and nutritional value. This results in an overall short shelf-life, unsellable products, and food waste generation. Biodegradable coatings specifically sodium alginate-calcium chloride, were investigated to prevent food spoilage and extend the shelf-life of fresh strawberries. This research optimized coating thickness and modified it with small molecules such as ascorbic acid and nanomaterials of metal and metal oxides (Silver and Iron oxide). Nanoparticles were synthesized in lab using various methods, including chemical co-precipitation and green synthesis, and were characterized based on their size distributions, shape uniformity, and chemical compositions with analysis by TEM, XRD, EDXRF, and FT-IR. Sensory properties of strawberries were evaluated over 7 days at temperatures ranging from 4 to 17°C using colorimetric analysis, shear/puncture tests, and visible inspection for mold or spoilage. The greatest statistical differences were observed in the Color (C), lightness (L), and red/green (a) color during days 5 and 6. The uncoated and ascorbic acid coated strawberries showing a sharp decrease of 55 to 70%, while sodium alginate alone and with nanomaterials only showed a 10 to 35% decrease. The resulting films also exhibited antimicrobial properties, preventing mold growth for all except uncoated and ascorbic acid coated strawberries by day 6.

Phosphorus (P) losses from agricultural soils in Canada and the United States, as dissolved in water and attached to suspended sediment, are the most significant source of P pollution in surface water bodies. Computer simulation models can help to develop better management practices to mitigate P losses from agricultural soils. This study compares the performance of two recently developed field-scale models, RZWQM2-P and DRAINMOD-P, with respect to prediction of dissolved reactive P (DRP) and total P (TP) losses in runoff and subsurface drainage. The two models were evaluated using a five-year observed data set from a subsurface-drained field in northwest Ohio. Model efficiency criteria of NSE, IOA, and PBIAS, were used to compare the performance of the RZWQM2-P and DRAINMOD-P in predicting the P losses. The results shed light on the strengths and limitations of each of the models with respect to P loss prediction, as well as their sensitivity to key governing factors. The findings will provide further insights into opportunities to improve algorithms and/or process representation in the respective models.

Non-availability of fast and non-destructive quality detection system continues to constitute major constraints in the agriculture and Food industries. The case of yam quality assessment is far left behind in the adoption of recent and emerging technologies for quality identification. Our earlier work has demonstrated the use of LDA learning algorithm in yam quality recognition. However, knowing that the modelling approach used to identify discriminating features in quality detection might not be the best to translate those features into Industrial application, this present study tested in addition to LDA, other machine learning algorithms including Neural Networks, Logistic Regression, Support Vector Machine (SVM), K-Nearest Neigbours (KNN), and Random Forest (RF). A total of 600 (300 white and 300 yellow) yam tubers, acquired from the National Root Crops Research Institute, Umudike, Nigeria, were used in this study. Each yam batch comprises 100 good, 100 diseased damaged and 100 insect damaged samples. Acoustic data were collected at 200Hz operating frequency and data analysed on WEKA platform. Comparative analysis of combination of classifiers using the area under ROC curve (AUC) evaluation metric showed that KNN and SVM perform optimally and thereby presented as suitable for building a robust yam quality recognition system.

Sugar beet (Beta vulgaris, L.) in southern Alberta is one of the major crops, being perishable and a seasonal crop it needs to be processed immediately to avoid spoilage. Thus, sugar beet growers are in search of finding a long-term solution for sugar beet mash to maintain sucrose integrity that will overcome the challenges presented by storing the sugar beet outdoor in large piles. Therefore, the present investigation was studied using specific rotation measurement to evaluate the impact of storage conditions (aerobic/anaerobic), temperature, pH stability (acid catalyzed), agitation speed and alkaline salt (potassium hydroxide) solution on the kinetics of sucrose inversion. The inversion rate constant (K) of sucrose and pseudo first order linear reaction validated using two different methods, namely Infinite Time (6.85*10-4 h-1) and Kezdy-Swinbourne (7.15 *10 -4 h -1), which tend to be very slow (almost negligible during the experiment). Further, microbial analysis of samples was also conducted before and during the experiment. It was found that after 14 days of storage, yeast and mould growth was 1.53 *104 CFU/gm and bacterial growth was 2.07*105 CFU/gm. Therefore, investigation concludes that microbial stability and sucrose inversion rate is very slow and can be considered that the sucrose integrity is still maintained. Thus, the outcomes of study will be further utilized to design the methodology for long term storage of sugar beets.

The ability to accurately characterize land use/land cover (LULC) is critical for understanding the effects of climate change on the landscape and ensuring sustainable management of natural assets. Classification of land use/land cover data needs to be precise; therefore, to choose the most effective land use/ land cover analysis strategy, it is vital to carefully evaluate the performance of various combinations of remotely sensed data and classification algorithms. This study aims to understand the land use/ land cover analysis for the City of Charlottetown, Canada, using different remotely sensed data (Landsat8, Sentinel2, and Planet Scope) using three different classifiers, including support vector machine (SVM), maximum likelihood (ML), and random forest (RF) in ArcGIS Pro. Overall accuracy and kappa coefficient were calculated for all combinations of data and classification algorithms. Then Change detection analysis was done using the combination of remotely sensed data and land use/land cover classifier with higher accuracy to quantify the change in land use/land cover classes throughout the study. Results show that the SVM classifier performs best with Planet Scope data with an overall accuracy of 94%, followed by Sentinel 2 data with 91% and Landsat 8 with 89%. Further change detection analysis showed that 13.80% of forested areas had been changed to bare land, and 14.10% of the forested regions had been converted to urban class. For land use/land cover analysis, finding the best classifier to use with remotely sensed data is a significant goal of this research. The outcomes will shed light on how to approach similar problems.

As the competing demand for water increases, effective water management is necessary to optimize water use in agricultural fields. Knowledge of soil water content and crop water uptake pattern will help farmers to optimize water use in agriculture and help to maximize production by protecting the crops from water stress. Capacitive soil moisture sensors were used to monitor the root zone water uptake pattern in Wheat to help manage the water within the root zone. The soil water content as a function of depth and time can be used to calibrate and validate a HYDRUS 2D/3D model of the field. The validated model could simulate different water table management scenarios in the field. Capacitive soil moisture sensors will be installed at three depths (30, 60, and 90 cm), and soil water content data will be collected. Weather data will also be collected simultaneously, and these data will be used as inputs to calibrate and validate the water uptake patterns in the HYDRUS 2D/3D model. Using the calibrated model, long-term weather data will be used to simulate water uptake patterns.

Foam fractionation is a cheap, environmentally friendly technique that can be used to concentrate and isolate proteins. Since research in this area has typically focussed on concentrating dilute protein solutions, little work has been done on the separation and recovery of different protein fractions from pea flour. The aim of this work was to investigate two-stage foam fractionation on the recovery of pea flour proteins in solution. In particular, the effect of concentration, pH, air flow rate and liquid loading volume on the protein recovery and enrichment were examined and the functional properties of the resultant protein fractions were characterized. The results showed after the first stage of foam fractionation, protein enrichment and recovery were 2.12 and 86.4%, respectively and after the second stage, enrichment was 5.49. Extracted proteins showed improved foaming, emulsification, oil and water holding capacity and solubility. As a result, recovery, enrichment, and enhancement of the functional properties of proteins extracted from pea flour can be effectively accomplished through foam fractionation.

Global climate is changing promptly and has become a significant environmental issue. The increased greenhouse gas (GHG) concentrations associated with the anthropogenic activity is the primary cause of global warming. Many sources contribute to emissions; however, agriculture is one of them and acts as a source and sink of GHGs emissions. This study aims to quantify and predict GHG emissions during the growing season of the potato crop. The LICOR environmental instruments will be used to quantify the emissions from soil and crop. These instruments provide high-quality data of high spatial and temporal resolution. The soil emissions and crop gas exchange data will be measured at regular intervals of time. To develop the denitrification and decomposition (DNDC) model, input parameters will be determined, including climatic data and soil’s physical, chemical, and hydraulic properties. The DNDC model, artificial intelligence (AI), and deep learning (DL) models will be calibrated and validated using the observed emissions data during the potato growing season. Different scenario analyses using historical and future climatic data will be simulated for developing relationships of climatic parameters with emissions using DNDC, AI, and DL models. These state-of-the-art AI and DL techniques will help to predict emissions with minimal input parameters. These techniques can be upscaled to develop high-resolution emission maps at a regional scale.

The proliferation of disposable paper cups in landfills and oceans is an alarming environmental issue. These cups take years to degrade because of the polyethylene (PE) lining and release harmful microplastics, posing a severe threat to the ecosystem. This study explores the feasibility of using mycelium-based coffee cups as an eco-friendly alternative to traditional cups. Mycelium, the vegetative part of a fungus, can overgrow and form robust and lightweight structures. The research explores the steps in fabricating mycelium materials, including substrate selection and preparation, inoculation, growth, and molding. The researchers cultured mycelium from three fungi, Ganoderma lucidum, Pleurotus ostreatus, and Polyporus squamosus. G. lucidum was selected among these fungi for its faster growth and denser mycelium network. Maximum yield of G. lucidum mycelia mass was achieved using Yeast Extract Peptone Dextrose (YEPD) medium. Canola (Brassica napus L.) straw and cattail (Typha sp.) biomass were used as substrates for fungal growth. Cattails formed firmer, more robust materials because of the high moisture and hemicellulose content. Adding gypsum (CaSO_4.2H_2 O) in the substrate mixture to maintain pH at 4.5-5.0, showed faster and denser mycelium growth. The study evaluates the feasible solvent selection for polylactic acid (PLA) polymer and their application techniques (solvent casting vs. spray), as well as limitations to coating the inner side of the cup. Mechanical, thermal, and barrier properties of the mycelial material will be analyzed and compared to commercial coffee cups. The study also highlights potential challenges and limitations that need to be addressed for large-scale adoption.

An emerging demand for high-yielding in agricultural productions calls for higher consumptions of energy and synthetic fertilizers, which lead to increased greenhouse gas emissions and water/soil pollutions. Smart farming provides promising solutions to alleviate these problems by enabling site-specific managing and monitoring of crops. Here, we design a smart-farming system composed of a novel multifunctional double-network hydrogel. This hydrogel can be developed into 1) a clean energy harvesting device (a triboelectric nanogenerator) for a self-powered LED lighting system, 2) a supercapacitor for energy storage, 3) a plant growth sensor, and 4) an ammonia sensor. The double-network hydrogel is composed of polyacrylic acid and conductive reduced graphene oxide, and coated with polyaniline (PAA-RGO-PANI). The hydrogel exhibits remarkable stretchability (650%) and mechanical strength (1050 kPa), satisfying the requirements of for plant growth sensors. Triboelectric nanogenerators made from the hydrogel shows clean energy harvesting potential, which provides high power density at 424 mW/m2 from sound wave, wind, and mechanic pressure. The supercapacitor can be stable at 2330 mF/cm2 after 5000 charge–discharge cycles. This self-powered smart farming system shows great potential in natural resource utilization and plant management in modern agriculture.

Carrots (Daucus carota L.) are recognized as one of the most important root vegetables due to their high nutritional value, as they are rich in bioactive compounds such as carotenoids and phenolics. After processing, large quantities of carrot residues such as disformed carrots, pomace, and peels are eventually discarded. However, these are still rich in antioxidants that can be recovered and used as functional ingredients. Conventional extraction of carotenoids and phenolics uses organic solvents that are toxic such as hexane and methanol. Therefore alternative approaches to add value to these residues are an attractive prospect. Natural Deep Eutectic Solvents (NADESs) consist of naturally derived compounds such as secondary plant metabolites. This study investigated pH-responsive switchable NADESs with a hydrophobicity-hydrophilicity conversion mechanism for the sequential extraction of carotenoids and polyphenols from carrots. Three different types of fatty acid (c8, c10, c12) and two types of terpene (DL-menthol, thymol) were used for the NADES synthesis, and ammonium hydroxide and citric acid were used for adjusting the pH to switch between phases. Ultrasound-assisted extraction was performed and extraction parameters were investigated for the best yield of polyphenols and carotenoids. This strategy for extraction is novel as all chemicals used in this process are considered GRAS “generally recognized safe” and the resultant extracts could be directly used in the formulation of various food and consumer products.

Research was done on hempcrete and like products such as flaxcrete and canolacrete to determine their thermal conductivity. Hempcrete is potential sustainable building material substitute for insulation as it uses leftover plant stalk after crop harvesting has been completed. Because the main ingredient is a waste material, it has great potential as a lower carbon emission alternative to current insulations. The research was done for a local builder that is currently using hempcrete (a mixture of hemp hurd, lime, cement and water) and wanted to verify the R-value. Batches of the different cretes were made using identical mixing ratios and were cured in box molds. Additionally, cylindrical molds were used to create samples for compressive and tensile tests and the low strength results confirmed that these materials are best suited as insulation products and not for load bearing applications. All blocks cured for a minimum of 28 days to let the cement obtain a near-full cure, then the thermal conductivity using measured using a Fox 314 and this was translated into R-value per inch. The R-value per inch for Flaxcrete was found to be 4.357, for hempcrete it was 5.186, and for canolacrete it was 4.416. For comparison these values are better than the 4.3 R-value per inch offered by the commonly used fiberglass batts and rolls. Research like this is an important part of developing standards and obtaining information so that these cretes can be easily permitted for commercial use to construct greener homes for the future.

Polymers are inevitable techno-functional materials used in food and bioprocessing. Polymers vary in crystallinity, chemical composition, functional properties, and their suitability for target industrial applications. A commonly synthesized polymer during the thermal processing of sugar is known as caramel. Caramel gives distinct brown color to food products, but its functional properties are not fully elucidated. The objectives of this research were to: 1. Develop a bioactive caramel using sucrose and quercetin as the antioxidant. 2. Determine the encapsulation efficiency of quercetin in caramel, and 3. Understand the effect of quercetin concentration and processing conditions (temperature, and time) on the physicochemical properties of caramel. Based on an experimental factorial design, sucrose and quercetin (1-5%) powder mixtures were heated in a tube furnace at temperatures between 160-250 °C, under nitrogen gas atmosphere at a flow rate of 0.5 mL/min for 15-45 min. Data was analyzed at a significance level of 0.05. Results showed a significant difference in solid mass with increase in temperature, from 99.9±0.4% at 160 °C to 66.8±0.9% at 250 °C, at 30 min, and 95:5 w/w %. Density of the solid caramel was 1.06±0.02 g/cm3 at 200°C being significantly different from 1.46±0.05 g/cm3 of samples treated at 180, 220, and 250 °C. At 200 °C, solid mass and density were not significantly different by processing time or by quercetin concentration, making temperature the important factor in the crystal transformation of sucrose to caramels. Complete characterizations of the caramels will affirm its potential as a techno-functional ingredient in bio-based applications.

Precision agriculture is the practice of utilizing technology and equipment to make more informed decisions with respect to best management practices such as the 4R’s nutrient management principle (right source, right rate, right time, right place), while increasing productivity and reducing unnecessary inputs, waste, and environmental impacts. Life cycle assessment is a standardized sustainability management tool for quantifying resource inputs, outputs, emissions, and associated environmental impacts across the life cycle of a product system or industry as a whole “from cradle to grave”. Life cycle assessment studies involving and/or evaluating precision agriculture technologies are particularly absent in the North American broad acre crop agriculture context. An extensive review of peer-reviewed literature published within the last 20 years was completed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method regarding the environmental concerns associated with crop production, precision agriculture technologies which address the most materialistic environmental impacts, and an economic assessment of market-ready/available precision agriculture technologies. The outcome of this study provided the support to answer questions such as “which precision agriculture technologies have merits in both environmental sustainability and economic viability? Is there a particular suite of precision agriculture technologies which target or attempt to address the most environmentally impactful aspects of agriculture?”. This research supports effective decision making for farmers and policymakers tasked with enabling a climate-friendly Canadian agri-food system with respect to the actual emission reduction potential associated with using precision agriculture and best management practices.

The use of thin and lightweight masonry eco-friendly veneers is becoming increasingly popular as a cost-effective and versatile alternative to traditional stone or full-depth masonry. Veneer manufacturers have long used lightweight clay-based aggregates (LWA) to create light veneers that offer functional advantages, such as improved thermal and acoustic properties. However, the production process for expanded clay aggregates used in LWA is energy-intensive and costly, prompting manufacturers to look for more sustainable alternatives. The partial replacement of LWA with eco-aggregates made from recycled plastic materials and agricultural cellulosic residues can significantly reduce the weight of the veneer, lower production costs, and enhance insulation and water resistance performance and mechanical properties. This approach diverts waste from landfills, reduces carbon dioxide emissions, and provides an economically viable use for non-recyclable plastic waste and agricultural residues.

By incorporating eco-aggregates into their production processes, veneer manufacturers can provide a more environmentally friendly alternative to traditional masonry while reducing costs and improving product performance. The eco-friendly veneers develop using wet-cast moulding in a latex mould created by spraying latex over pre-arranged natural stones. A cement mixture containing lightweight aggregates (LWA), eco-aggregates, colourants, and additives introduce into the surface-designed mould to create the desired external finish. The eco-aggregate is prepared using a thermo-blending process, further enhancing the sustainability of the manufacturing process. Using waste materials in the aggregate mixture results in over 20% savings in production costs, reducing the weight of the veneers and improving their water resistance and R-value.

This research investigated the application of combined ultrasound- steam and combined ultrasound -water in pasteurizing acidified carrots. A specially designed ultrasound steam chamber was used to process acidified carrots in brine pre-packaged in flexible retort pouches. The pouches were subjected under different processing conditions: steam alone and combined ultrasound -steam in presence and absence of added air. Ultrasound -steam process (F90°C = 10 min) targeting Clostridium botulinum type E spores was developed and compared with conventional hot water (HW) process and combined ultrasound hot water (UHW) processes resulting in equivalent microbial safety. Compared with an equivalent HW process or steam alone process, ultrasound assisted steam and HW process reduced the total processing time, reduced cook value time, and improved quality uniformity in the products. Quality evaluation showed the impacts of ultrasound steam processing on each quality attribute of carrot products depending on the specific quality parameter selected.

Climate change, extreme weather events, and irregular rainfall patterns have increased the need for supplemental irrigation in rainfed regions such as Prince Edward Island (PEI). This study aims to develop a variable rate irrigation (VRI) system to enhance crop productivity in PEI. This project will develop, implement, and evaluate the VRI technology in water-stressed areas to optimize water resources with site-specific irrigation. In the first phase of this project, Soil Water and Topographic (SWAT) maps will use to collect the electrical conductivity (Eca) and topographic data. Soil moisture probes and a weather station will be installed at the experimental field. An Unmanned Aerial Vehicle (UAV) equipped with a thermal sensor and Time Domain Reflectometer (TDR) will also be used to determine the moisture conditions and validate the moisture data. In the second phase, a central pivot irrigation system with variable rate controllers will be used to optimize water use efficiency based on moisture prescription maps created using the intelligent decision-making system. This project aimed to evaluate VRI technology's feasibility in ensuring sustainable irrigation in PEI. The outcome of this study ensures precision irrigation management by conserving water resources for future generations. The proposed methods and some initial investigation results will be presented at the conference.

Southern Manitoba has shallow water tables during the growing season. Therefore, subsurface drainage plays a significant role in increasing crop yield. A two-year (2020-2021) field study was conducted in Winkler, Manitoba, to evaluate soybean yield performance under different water management treatments. The three treatments were controlled drainage (CD), free drainage (FD), and no drainage (ND). For the entire growing season (May – September), the total precipitation was 67% of the long-term average in both 2020 and 2021. The 2020 average soybean yields were 285, 337, and 272 kg/ha for ND, FD, and CD treatments, respectively. Although they were not statistically significantly different, the FD plots had higher yields. Similarly in 2021, the soybean yields were not significantly different giving 752, 754, 757 kg/ha for ND, FD, and CD treatments, respectively. Among the soybean growth stages, flowering and pod filling stages are the most critical stages requiring adequate soil moisture that has an impact on final yield. Although, the growing season rainfall was 228.3 and 228.0 mm for 2020 and 2021, respectively, the rainfall distribution had a significant yield impact. In 2020, the higher rainfall was in July but in 2021 the higher rainfall was in August coinciding with the pod filling stage. Therefore, the higher yield in 2021 compared to 2020, indicates the need for adequate soil moisture during the pod filling stage.

With an increasing world population, maintenance of a steady food supply requires development and use of high yielding crop plants that can withstand the dynamic climatic conditions. Castor bean seeds have a complex agronomic trait that affect their longevity, germination rate and ability to tolerate stress. The recent climatic warming trends and increasing temperature variability negatively impact seed tolerance and reduced seed germination rate as castor seeds required lower temperatures for germination and seedling establishments. Castor bean seeds are of commercial importance due to their oil content which has high anti-inflammatory, anthelmintic, anti-bacterial, laxative, and abortifacient properties. Hence, it is important to identify methods that can help improve seed vigour by increasing seed germination rate. In this study, impact of microwave pre-treatment at different power levels (10, 50 and 100 %) and exposure time (5, 10 and 15 seconds) have been evaluated. Seed germination rate were estimated and impact of microwave pre-treatment on seed quality were further analyzed.

In this study, porous softwood-activated carbon from a thermo-catalytic reforming (TCR) unit was considered for supercapacitor application. The biochar was obtained through intermediate pyrolysis (400 – 500 °C) and catalytic reforming (600 – 700 °C) at a N2 flow rate of 12 LPM. In the current work, we investigate the potential of TCR biochar as an electrode material for supercapacitor application. The TCR biochar was synthesized through the pyrolysis of softwood biomass waste using a TCR process, and its electrochemical performance was evaluated using various techniques, including Cyclic Voltammetry (CV), Galvanostatic Charge-Discharge (GCD), and electrochemical impedance spectroscopy. Though this technique reveals superior properties for oil and gas, its exploration in the field of biochar and electrode application is limited which makes this work novel. Brunauer-Emmett-Teller (BET) was analyzed for all samples and the sample with the highest specific surface area was considered for further characterization studies. The energy and power density were evaluated from the GCD in addition to cycling stability and capacity retention. Further, scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray diffraction (XRD), Raman spectroscopy, Fourier transform infrared spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS) were also studied to support the performance of the supercapacitor.