Sugar kelp application for sustainable potato production in Prince Edward Island: Impacts on soil, greenhouse gas emissions, and yield
Bibliographic record
Abstract
Context Sugar kelp (SK) is a promising organic fertilizer with the potential to enhance crop yield, improve soil health, and reduce environmental impacts. However, its specific effects on soil quality, crop productivity, and particularly its role in climate change mitigation are still not well understood. Objectives This study evaluated the effects of SK, as seaweed-based organic fertilizer, and its combinations with IF on soil health, emissions of CO 2 and N 2 O, as well as CH 4 uptake, potato growth and yield during the 2023 and 2024 growing seasons in Prince Edward Island, Canada’s largest potato-producing province. Methods Field experiments were conducted over a two-year period (2023 and 2024). In 2023, treatments included: SK alone (2 tons ha - ¹), IF alone (meeting the full nitrogen (N) requirement), SK + IF (50 %-50 % N), and control (no fertilizer). In 2024, treatments were: IF alone, SK + IF (full N), SK + IF (80 % N), and control. The study measured soil organic matter, pH, P 2 O 5 , K 2 O, Ca, Mg, Cu, Zn, S, Mn, Fe, Na, Al, and NO 3 - , along with the Normalized Difference Vegetation Index (NDVI), and potato yield. Soil emission of CO 2 and N 2 O emissions, and soil CH 4 uptake were also measured using the Li-COR trace gas analyzer. Results Soil pH, organic matter, calcium, magnesium, and cation exchange capacity remained stable across treatments. Trace elements such as copper, iron, and zinc also showed minimal variation. However, the SK application significantly increased soil sodium concentrations in both years (p < 0.05). In 2024, nitrate (NO₃⁻-N) levels were significantly higher in the IF treatments than in the control. Cumulative CO₂ emissions and CH₄ uptake did not differ significantly among treatments in either year. IF-only treatments showed the highest cumulative N₂O emissions, whereas treatments combining SK with reduced IF significantly lowered cumulative N₂O emissions to levels similar to the control. These reduced-emission treatments maintained NDVI values and potato yields comparable to those of the full IF treatments, both of which outperformed the control. Conclusions These results suggest that combining SK with reduced IF can sustain potato yields while significantly lowering N₂O emissions. These findings highlight the potential of SK in sustainable fertilizer strategies; however, further long-term research and economic analysis are necessary to evaluate its broader viability in agriculture.
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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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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".