Managing Fertigation Frequency and Level to Mitigate N2O and CO2 Emissions and NH3 Volatilization from Subsurface Drip-Fertigated Field in a Greenhouse
Bibliographic record
Abstract
Agricultural practices such as water and N management can contribute to greenhouse gas (GHG) emissions. Fertigation frequency and level are the two most important factors of irrigation scheduling. Proper irrigation management can establish moderate moist conditions throughout the crop growth period in the root zone and reduce GHG emissions and NH3 volatilization. The main objective was to evaluate the possibility of reducing soil N2O and CO2 emissions and NH3 volatilization without crop yield reduction by manipulating the subsurface-drip fertigation (SDF) frequency and level. An experiment was carried out adopting three SDF frequencies, High-Frequency (7-day, HF), Medium-Frequency (8-day, MF), and Low-Frequency (10-day fertigation intervals, LF), and two irrigation levels, 80% (I80) and 70% (I70) of amount in farmer’s common practice (1500 m3 ha−1). Urea, N > 46.2% at the rate of 90% of traditional fertilization level (270 Kg N ha−1) was injected with irrigation water. Results indicated that soil N2O, CO2, NO3−-N, NO2−-N, and water-filled pore space increased with fertigation frequency and an opposite pattern for NH4+-N and NH3. HF significantly (p < 0.05) increased crop yield by 45.1% and 49.2% compared to LF, under I80 and I70 levels, respectively. At the same irrigation level, HF was the optimum management practice. Person correlation analysis showed significant correlations between NO2−-N and N2O, CO2 and soil temperature, and NH4+-N and NH3. The study suggests that HF of SDF with emitters buried at 0.15 m depth helps to keep high Chinese cabbage yield increases GHG emissions, but is not significant, and decreases NH3 volatilization.
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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".