In Quebec, the phase-out of the conventional cage (CC) system for egg production, is expected to be completed by 2036, with a transition to alternative systems such as enriched colonies (ECs) and cage-free (CF) housing. This study aimed to assess Greenhouse gas (GHG), and ammoniac (NH3) emissions associated with those systems. The investigation involved one visit per farm to 30 commercial laying hen facilities in Southern Québec, Canada. The findings revealed that the CF system exhibited the highest numerical average of CO2 emissions (3207 ± 2423 mg h-1 hen-1), followed by CCs (2835 ± 877 mg h-1 hen-1) and ECs (2597 ± 949 mg h-1 hen-1). Furthermore, the EC system had the lowest average CH4 emissions (0.93 ± 0.54 mg h-1 hen-1), while CC (1.07 ± 0.41 mg h-1 hen-1) and CF (1.27 ± 1.11 mg h-1 hen-1) facilities had higher values. Emissions of N2O were similar across all three systems (0.04 to 0.05 ± 0.05 mg h-1 hen-1). The study revealed significant differences in NH3 emissions among CC (2.0 ± 1.0 mg h-1 hen-1), EC (2.5 ± 2.0 mg h-1 hen-1), and CF egg production systems (11.2 ± 15.9 mg h-1 hen-1).

The Great Lakes, also known as the Great Lakes of North America, are a series of interconnected freshwater lakes located in the upper mid-east region of North America located at the border of Canada and the United States of America (USA). The Great Lakes are a source of drinking water for 10% of Americans and 25% of Canadians. Human activities have significantly degraded the Great Lakes in the past few decades. Against this backdrop, conducting a detailed study to assess the water quality and its quantification in the Canadian Great Lakes Watershed (CGLW) seems imperative. This study used the LTHIA model to analyze the surface runoff and two Non-Point Source pollution – total suspended solids (TSS) and total phosphorus (TP) of the Canadian Great Lakes watershed. The temporal analysis showed the highest runoff, TSS and TP in the Northern Lake Erie sub-watershed in 1954. In contrast, the lowest was observed in the Northwestern Lake Superior sub-watershed in 1952. The spatial analysis showed higher runoff, TSS and TP in the Eastern Lake Huron and Northern Lake Erie sub-watersheds. The decadal analysis revealed higher runoff, TSS and TP in 1980-90, 1990-99 and 2000-09. The climate change analysis revealed more variation in the runoff, TSS, and TP were projected in mid-century (2035-64) compared to end-century (2070-99). Finally, it has been shown that the LTHIA model can successfully simulate both water quantity and quality-related processes and climate change effects.

This research, which focuses on validating the simulated soil volume in two distinct wheel loader buckets, relies heavily on field tests to validate the simulation method. The study compared validation iterations to volume data from corresponding field tests performed on a standardized soil pile. The soil particle properties were determined by specific soil characterization tests, which were then meticulously virtually replicated to calibrate the simulation materials accurately. The study compared the simulated and actual soil volumes in the wheel loader buckets using Discrete-Element Method (DEM), Light Detection and Ranging (LiDAR), and real-time simulation. The weight-based method data extracted from the field tests were used as a benchmark for the methodology comparison. The study found that bucket B at speed one (low speed) had a significantly larger capacity than the other bucket and speed combinations, as demonstrated by the results of the weigh-based method. The LiDAR methodology presented excellent volume prediction capacity, with some sectionalization in the results due to the field methodology. The study validated the precision simulation capacity to simulate the volume of soil in the wheel loader buckets by constant simulation results in between the value limits of the benchmark results. The accuracy assessment of the real-time simulation method was agreeably surprising, with results constantly near the precision simulation. The study also describes the methodologies for wheel loader field tests, measurements of physical test material, virtual material calibration using DEM, real-time simulation, statistical comparison between estimation methodologies, and results explanation.

Information on soil water status and dynamics is needed for agricultural management, as well as engineering and environmental investigations. Water status and dynamics in the vadose zone are primarily influenced by two fundamental properties: soil water content (SWC) and soil hydraulic properties (SHP). The application of ground penetrating radar (GPR) for monitoring and estimating these properties has received wider attention and has significantly advanced in recent years. While SWC estimation using GPR has been well-reviewed over the years, SHP estimation has not received the same attention. Notably, there has been increasing research on SHP estimation using GPR in the last decade. This paper reviews the recent studies and advances in applying GPR to study soil water dynamics and SHP estimation. We compared the progress and advantages of the three techniques (Borehole, Surface, and Off-ground), identified key issues affecting their application, and noted future research opportunities. By synthesizing these studies, this review paper aims to draw attention to evolving methodologies in GPR applications for monitoring soil water dynamics and SHP estimation as good indicators of soil hydraulic resistance and how these opportunities can be harnessed to improve soil water management.

Soil water content (SWC) plays a critical role in crop yield, irrigation scheduling, and water resources management. In the Canadian Prairies, the SWC in the rootzone from rainfall is rarely sufficient to satisfy crop water requirements. Thus, an understanding of the soil water dynamics is important for effective water management. Hydrologic modelling helps us to understand the underlying processes controlling and affecting soil water movement and distribution. The reference evapotranspiration (ETref) is a key input in most hydrologic models; thus, the estimation method could affect simulation results and inferences. The FAO Penman-Monteith (FAO PM) is recommended as a standard model. However, it is limited by requiring too many weather variables that are not readily available. Thus, simple empirical ETref models have been developed as an alternative. Soil moisture sensors were installed at 0.2, 0.4, 0.6, 0.8, and 1 m depths to measure SWC. SWC was first modelled in a rainfed potato farm in Winkler, Manitoba, using the FAO PM equation as input in the HYDRUS-1D model. Statistical and graphical results showed that the HYDRUS model performed well in simulating SWC with R2 ranging from 0.6 to 0.9, RMSE from 0.003 to 0.03 m3/m3, MAE varying between 0.00932 and 0.0197 m3/m3 and MPE from -1.91 to 1.67%. The impacts of different ETref equations with varying weather inputs on soil water dynamics and seasonal potato crop evapotranspiration (ETc) were further investigated. The results showed that measured SWC and SWC predicted using Irmak, Priestly-Taylor, and the FAO PM equations were not statistically different. Similar results were also obtained for ETc. Hence, under limited data, the Irmak and Priestly – Taylor ETref equations are suitable alternatives that could provide accurate and reliable results for water management in southern Manitoba.

Starting in 2027, Canadian regulations will require regular exercise for tie-stall dairy cows. Producers commonly use pasture-like outdoor pens, but these might not meet environmental regulations as leachate can carry nutrient-loaded runoff. Alternative methods using improved filtering media are needed. This study evaluated the removal capacity of different depths of materials (gravel, woodchips, sphagnum peat moss, and biochar) as a strategy for manure treatment in outdoor exercise pens used to provide movement opportunities to dairy cows. A laboratory experiment was performed using 15 PVC columns (n = 3), with a diameter of 5 cm and a length of 50 cm, filled with different combinations of products for 3 weeks. The increasing depth (10 to 40 cm) of a mix of sphagnum peat moss, wood chips, and biochar in the columns linearly increased the removal efficiency of chemical oxygen demand (50 to 74%), total nitrogen (60 to 97%), phosphates (34 to 59%), and suspended solids (14 to 61%). However, this removal efficiency was time-dependent, as a greater removal rate was observed during the first week (+30% relative to weeks 2 and 3). The filter media with a 300 mm depth of a mix composed of sphagnum peat moss (70%), woodchips (20%) and biochar (10%) was more effective in removing nutrients. However, the treated effluent still surpassed the allowable post-filtration limit. This emphasizes the need for supplementary filtration measures to ensure the safe discharge of effluent into the environment.

Despite its beginning in the 1850’s and being first in Canada to purchase a tile drainage trencher, subsurface drainage of agricultural lands in Quebec is poorly documented, which the present paper will try to document from 1850 to 1970. In Quebec, Catholic priests and monks played an important role in educating rural communities by establishing French agricultural schools throughout the province. For the English rural communities, Macdonald College (Macdonald Campus of McGill University) played a major role especially in preparing plans, besides promoting the technology. The Quebec Ministry of Agriculture encouraged subsurface drainage early in 1912 but would prefer investing in land clearing and watercourse deepening to establish more farms, from the employment needs created by WWI, the great 1930 depression and WWII. This work mostly completed in the early 1960’s, the Quebec Government would then initiate a major subsurface drainage program, allowing private enterprises to take over shortly after 1967. Although the Ministry changed names several times even after 1967, the term ‘Ministry of Agriculture’ will be used throughout this article. To compare trencher performance, a 15 m average spacing is presumed. This paper is limited to the main events and persons involved, without being able to cover them all.

This paper emphasizes the essential role of a support person for faculty teaching and assessing the Canadian Engineering Accreditation Board (CEAB) graduate attributes as part of an ongoing accreditation cycle. It details the continuous program improvement process adopted by the Department of Biosystems Engineering at the University of Manitoba, and the role of engineering stakeholders. It recounts a study that details the supportive efforts of a Research Associate who helped to validate and implement rubrics with individual professors as outcomes-based tools for teaching and assessing the 12 CEAB graduate attributes, which resulted in the creation of 14 rubrics for 12 courses. Findings included new pedagogical understandings, the appreciation of individual support from the Research Associate, and the continued use of rubrics; the work led most professors to think deeply and in new ways about teaching and assessment. There was evidence that six professors engaged in ‘reverse design’, developing rubrics with targeted learning outcomes and course materials in mind. The work led to critical improvement in teaching practices and evidence of continual program improvement. Despite overall engagement and success, some professors continued to struggle with the concept and use of rubrics. In sum, this experience emphasizes the benefit of a dedicated person to support professors to implement rubrics, and in creating and sustaining an outcomes-based assessment culture in the department.

This study evaluated six segmentation methods (clustering, flood-fill, graph-cut, colour-thresholding, watershed, and Otsu’s-thresholding) for segmentation accuracy and classification accuracy in discriminating Fusarium infected corn grains using RGB colour images. The segmentation accuracy was calculated using Jaccard similarity index and Dice coefficient in comparison with the gold standard (manual segmentation method). Flood-fill and graph-cut methods showed the highest segmentation accuracy of 77% and 87% for Jaccard and Dice evaluation metrics, respectively. Pre-trained convolution neural network (CNN) and support vector machine (SVM) were used to evaluate the effect of segmentation methods on classification accuracy using segmented images and extracted features from the segmented images, respectively. The SVM based two-class model to discriminate healthy and Fusarium infected corn grains yielded the classification accuracy of 84%, 79%, 78%, 74%, 69% and 65% for graph-cut, watershed, clustering, flood-fill, colour-thresholding, and Otsu’s-thresholding, respectively. In pretrained CNN model, the classification accuracies were 93%, 88%, 87%, 84%, 61% and 59% for flood-fill, graph-cut, colour-thresholding, clustering, watershed, and Otsu’s-thresholding, respectively. Jaccard and Dice evaluation metrics showed the highest correlation with the pretrained CNN classification accuracies with R2 values of 0.9693 and 0.9727, respectively. The correlation with SVM classification accuracies were R2–0.505 for Jaccard and R2–0.5151 for Dice evaluation metrics.