The effects of nitrogen fertilizer rates and planting date on greenhouse gas emissions and potato production in Delta, British Columbia
Bibliographic record
Abstract
Crop production is a known source of CO₂, N₂O, and CH₄ emissions, yet greenhouse gas (GHG) emissions data from crops in the Lower Fraser River Valley (LFRV) are limited. Potatoes are the most prominent cash crop in the LFRV and are often associated with excess nitrogen (N) fertilizer use, a primary source of N₂O emissions. Soils in this region have poor drainage, which is exacerbated by precipitation in the spring and fall. Consequentially, the variable soil workability impacts the timing of potato planting and harvest. The objective of this study was to assess how N fertilizer rates (0, 90, and 120 kg N ha⁻¹) and planting date (typical and late) influence GHG emissions, potato production, and soil properties. The field experiment was established in 2018 at two fields in Delta, British Columbia (BC); one was classified as productive and the other as unproductive. Yield increased with N fertilizer rate at the productive field, but a cost-benefit analysis showed that the increase in yield between the 90 and 120 kg N ha⁻¹ treatments did not significantly outweigh the additional fertilizer costs. The yield at the unproductive field did not respond to N fertilizer, likely due to pre-existing salinity and drainage issues. Planting date did not affect yield at either of the fields. Over the growing season (May to October), there were no differences in total GHG emissions with either N fertilizer rate or planting date. November sampling at the productive field, following a precipitation event, showed N₂O emissions increase with N fertilizer treatments. To supplement the field experiment, a soil incubation experiment was conducted to better understand the interactive effects of temperature (4ºC and 20ºC) and soil water content (20% and 40% volumetric water content) on GHG emissions and soil N dynamics in soils fertilized with inorganic N. Nitrous oxide emissions were near zero when temperature was 4ºC or volumetric water content was 20%. This study emphasizes the importance of reviewing N fertilizer management at fields with signs of soil degradation that will, most likely, respond differently to N fertilizers than productive fields.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".