Agronomic evaluation of polymer-coated urea and urease and nitrification inhibitors for cotton production under drip-fertigation in a dry climate
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Interest in the use of enhanced-efficiency nitrogen (N) fertilizers (EENFs) has increased in recent years due to their potential to increase crop yield and reduce environmental N loss. Drip-fertigation is widely used for crop production in arid regions to improve water and nutrient use efficiency whereas the effectiveness of EENFs with drip irrigation remains unclear. A field experiment was conducted in 2015 and 2016 to examine the effects of EENFs on yield, N use and quality of cotton ( Gossypium hirsutum ) grown under drip-fertigation in arid NW China. Treatments included an unfertilized control and application of 240 kg N ha −1 by polymer-coated urea (ESN), urea alone, or urea plus urease (NBPT) and nitrification (DCD) inhibitors. ESN was all banded in the plant row at planting, whereas urea was applied with 20% N banded at planting and 80% N by six fertigation events over the growing season. Results showed there was generally no treatment effect on seed and lint yield, N concentration or allocations, N recovery efficiency and fiber quality index of cotton. A lack of treatment effect could be due to N supplied with drip-fertigation better synthesized with crop N needs and the relatively high soil native NO 3 − availability, which hindered the effect of polymer-coated urea and double inhibitors. These results highlight the challenge of the employment of EENFs products for drip-fertigation system in arid area. Further research is required to define the field conditions under which the agronomic efficiency of EENFs products may be achieved in accordance with weather conditions.
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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 | 0.002 | 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 it