Assessment of nitrogen fixing trees for soil amelioration in degraded agricultural lands
Bibliographic record
Abstract
According to Environment and Climate Change Canada, roughly 7.4 million hectares of former cropland in the Prairie provinces are classified as degraded, with organic carbon levels too low to support profitable grain production without heavy fertiliser input. Nitrogen fixing trees (NFTs) offer a biological path to rebuild these soils, yet species performance data under Saskatchewan’s cold semi-arid climate are scarce. This research compared three NFT species—red alder (Alnus rubra), black locust (Robinia pseudoacacia), and Russian olive (Elaeagnus angustifolia)—planted on degraded cropland at Prairie Agricultural University, Saskatoon, from May 2018 to October 2023. Soil organic carbon, total nitrogen, bulk density, pH, microbial biomass carbon, and earthworm density were measured under each species and compared with an unplanted degraded control and an adjacent undisturbed grassland reference. After five years, A. rubra raised soil OC from 1.12% to 2.31% and total N from 0.084% to 0.168%—recovering approximately 86% and 87% of the undisturbed reference values, respectively. R. pseudoacacia performed nearly as well. Microbial biomass carbon doubled under both species. These results confirm that NFT plantations can substantially restore fertility in degraded Prairie soils within a five-year timeframe.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".