Topography‐driven variability in soil greenhouse gas emissions during potato growth season
Bibliographic record
Abstract
Abstract Topographical variations strongly influence the spatial variability of soil physicochemical properties by affecting water retention, nutrient distribution and biochemical activity. These topography‐driven differences in soil dynamics can significantly impact greenhouse gas (GHG) emissions. Understanding the variation in GHG emissions over the growing season across topographic changes can facilitate the development of targeted precision agriculture strategies to mitigate GHG emissions. The objectives of this study were to evaluate the influence of topographical variations on soil properties and to assess the spatiotemporal variations of CO 2 and N 2 O emissions throughout the various crop‐growing stages (CGS) of the potato growing season. Moreover, the impact of topography on potato yield was also examined. The experiment was conducted at Victoria Potato Farm, Prince Edward Island, Canada. A substantial N 2 O flux (80 g ha −1 day −1 ) was emitted after fertilizer application over the early CGS, and the upper positions had the highest cumulative N 2 O emissions (993 g ha −1 ), which aligned with the higher observed soil moisture in this zone. This finding highlights the critical importance of managing fertilizer application, as well as implementing mitigation strategies based on the spatial variability of soil properties to reduce emissions following fertilization. During the mid and late CGS, the depressional positions showed the highest cumulative N 2 O emissions (90 and 70 g ha −1 , respectively). The highest cumulative CO 2 emission was observed from the upper positions during the early CGS (1580 kg ha −1 ); however, the highest emissions were observed in the depressional areas during the mid and late CGS (1415 and 605 kg ha −1 , respectively). Overall, the total N 2 O emission from the three zones accounting for both the differences in each zone's GHG fluxes and the length of each CGS indicated 43% emission in the upper areas, 32% and 25% for the depressional and mid‐slope positions, respectively. These values were 32%, 36% and 32% for CO 2 in the upper, depressional and mid‐slope positions. This emission pattern aligns with the elevated soil‐activated carbon (AC), biological nitrogen availability (BNA) values and soil respiration rates in upper and depressional areas. In this study, significantly higher yields were also observed in depressional areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".