Addressing challenges in controlled environment agriculture to grow food in northern Canada
Bibliographic record
Abstract
Food insecurity affects various regions of the world. With the increasing population and natural disasters linked to climate change, agricultural yields are at risk which may lead to an increase of food price, deepening the issue of worldwide food insecurity. Promoting local food production is one of the various ways to mitigate this worldwide issue. However, conventional agriculture is not suited to the conditions of the remote locations often faced with food insecurity, such as northern Canada, where the high cost of transportation is a critical factor that contributes to the inaccessibility of fresh produce.Growing food in a remote location is a complex problem that must be solved via inclusive and innovative solutions. Agricultural practices to allow food production in northern Canada exists and northern agriculture has seen a rise in the past few years. One of these solutions is the use of Control Environment Agriculture (CEA), via indoor agricultural systems using soilless growing methods, such as hydroponics, and the use of electrical lighting. Even with years of research and production experience, they still come with challenges. This thesis proposes three solution to three different issues facing CEA for remote food production, heat and energy efficiency, labor requirements and fertilizer demand.This thesis presents three studies. The first focuses on the Canadian Integrated Northern Greenhouse (CING), a hybrid in between a growth chamber and a northern greenhouse designed to use natural resources to reduce energy requirements for northern food production. To reduce electrical lighting and benefit from the Sun’s natural light and heat, the CING was designed and prototyped by McGill students. Lettuce was grown during the four-season test of this food production unit it. The greatest yield obtained in the CING was in March 2019, where the plants grown achieved 72% of the dry mass of the plants grown in the research greenhouse. The CING relied on supplemental heating to successfully grow plants but demonstrated the potential for northern applications.The second study focuses on the comparative test of innovative vertical hydroponic configurations for shipping-container plant factories. Specifically, three systems were designed based on aeroponics, nutrient film technique (NFT), stagnant and flowing shallow water culture. Performance of each system was assessed in terms of lettuce biomass yield, uniformity and ease of use. During the test, a metal ion contamination occurred, causing a bias on the results. However, the stagnant shallow water culture was the technique preferred by the industrial partner, for its larger yield resulting from the ability to be independent of the continuous nutrient solution distribution.The third study focuses on the optimization of an organic nutrient solution, brewed using fresh chicken manure extracts and vermicompost leachate. The goal was to produce an organic nutrient solution with a similar nutrient ratio to a conventional hydroponic nutrient solution. The preliminary experiment occurred during the four-season testing of the CING, where a nutrient solution prepared with vermicompost leachate was compared to an inorganic solution. By mixing the concentrated vermicompost leachate with chicken manure extracts within a bioreactor, Biojuice was brewed and compared to an inorganic nutrient solution by growing lettuce in hydroponic conditions. The N-P-K ratio of the Biojuice and the inorganic nutrient solution were comparable, respectively 4.6-1-7.9 and 7-1-7.5 . The Biojuice yielded lettuce with fresh mass 15% higher than the inorganic nutrient solution at an electrical conductivity of 1.1 mS/cm. At higher electrical conductivity of 1.5 and 1.6 mS/cm, the Biojuice lettuce yield were respectively 44% and 69% lower than the inorganic nutrient solution. This result is explained by a calcium deficiency in the plants caused by a nutrient ratio in-balanced mixed with a high sodium content
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".