Long−Term Fertilization Increased Nitrous Oxide Emissions from Croplands Reclaimed from Desert
Bibliographic record
Abstract
Few studies have investigated how the reclamation of the desert to cropland affects soil nitrous oxide (N2O) emissions. A study site was initiated in 2005 at the southern Taklimakan Desert by establishing four fields along the desert−oasis ecotone. Three fields were reclaimed as croplands for continuous cotton (Gossypium hirsutum L.) production and received (1) high, (2) low, and (3) no fertilizer inputs and the fourth field remained as a native desert. Static-chamber monitoring of N2O flux was conducted from April 2019 to March 2021. N2O emissions occurred in cropland mainly after drip fertigation during the growing season. The two−year N2O emissions in the desert, no fertilizer, low fertilizer, and high fertilizer were 248, 670, 2232, and 3615 g N ha−1, respectively. The native desert was a weak source of N2O emissions, with the non−growing season emissions accounting for 66% of the annual emissions. N2O emissions from farmland mainly occurred over the growing season, accounting for 73−98% of the total annual emissions. The N2O flux was positively related to soil NO3−−N concentration and soil water−filled pore space (WFPS), highlighting the importance of soil N availability and moisture in affecting N2O emissions in extremely arid areas. Our results demonstrate that land reclamation from the desert to cropland significantly increased N2O emissions due to high nutrient inputs. Thus, appropriate fertilizer management in the reclaimed land is essential for maintaining yield, improving soil quality, and reducing N2O emissions for continuous cotton production.
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 | 0.000 |
| 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) | 0.001 | 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".