Carbon, Water, and Energy Exchanges in a Sweet Potato Agricultural Field in the Great Lakes Region
Bibliographic record
Abstract
Carbon, water, and energy exchanges were measured in an agricultural field in the Great Lakes region near Turkey Point in southeastern Ontario, Canada. The site is part of Turkey Point Environmental Observatory and associated with the Ameriflux, global Fluxnet, Global Water Future Observatories and Global Centre for Transboundary Waters initiatives. It is known as CA-TPA in Ameriflux and global Fluxnet. In this study energy, water and carbon fluxes as well as meteorological and hydrological variables were measured in Sweet Potato (Ipomoea batatas) crop for two years (2022 and 2024). An open-path eddy covariance system and an automatic weather station were used for this purpose. The field was planted with a cover crop (rye grass and winter wheat) in both years during the winter months which was mowed into the soil in April. Sweet potato was planted in June and harvested in October with harvested crop yield of about 25 and 30 metric tons ha-1 in 2022 and 2024, respectively. The study results showed that on an annual basis the field was a net source of carbon in 2022 and a small sink of carbon in 2024 with net ecosystem productivity (NEP) of -86 ± 10 and 34 ± 11 g C m-2 y-1, respectively. Corresponding annual net ecosystem carbon balance (NECB) values were -630 and -619 g C m⁻² y⁻¹, in 2022 and 2024, respectively. Respective annual evapotranspiration (ET) values were 746 and 759 mm y-1. This sweet potato focused study is aimed to fill a current gap in the literature, as it is a crop that is rarely observed for its carbon sequestration potential as well as energy and water dynamics in North America. It also underscores the necessity of considering diverse crop types when exploring their potential for carbon sequestration and nature-based solutions to climate change.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 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 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".