Agriculture in the Boreal Forest: Exploring Soil Carbon, Fertility, and Land Use Change to Support Sustainable Northern Food Systems
Bibliographic record
Abstract
The traditional food system of many northern communities is currently being challenged by the increasing prevalence of natural disturbances brought on by disproportionate climatic warming. To alleviate resulting issues of food insecurity, northern communities are incorporating agriculture as a supplemental piece to their food system due to the increased temperatures, longer growing seasons, and changes to nutrient availability associated with climate change. However, the boreal forest represents the largest pool of soil organic carbon (SOC) in the terrestrial environment and land use change associated with agricultural cultivation has the potential to release large amounts of carbon to the atmosphere. With the unique challenges associated with cultivating northern soils, farmers have an interest in soil data that facilitates an understanding of how agriculture impacts soil fertility and SOC, and how effective their management practices are for the conditions they cultivate. To alleviate these knowledge gaps, we partnered with the communities of Kakisa and Enterprise and seven farmers in the Northwest Territories (NWT), Canada during this thesis to address the following broad objectives, 1) Understand global trends in boreal forest soil organic carbon stock dynamics in cultivated landscapes, 2) Assess variability, drivers, and relationships between soil fertility and soil organic carbon stocks in pre-cultivation sited across the partnered communities, 3) Use southern NWT farms as a case study to understand the impact of agricultural land use change and management on soil carbon and fertility in the boreal forest. Results of this interdisciplinary work indicate that SOC stocks decline 69 1% globally and 66 1% in our partnered NWT farm sites in the first 30 years after cultivation. Additionally, we determined there was an inverse relationship between SOC and soil fertility in pre-cultivation soils, but a positive relationship between SOC and macronutrient fertility in soils that are currently cultivated in the NWT. Further, our results indicate that compost addition in conjunction with a ‘no till’ approach promotes higher soil fertility and SOC stocks. Together, this thesis provides an understanding of soil fertility and SOC dynamics in boreal agricultural soils and can be utilized by communities to make subsequent land management decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".