Impacts of Different Fertigation Indices of Center Pivot Sprinkling Machine on the Yield of Maize
Bibliographic record
Abstract
This paper mainly explores the impacts of different fertigation indices of center pivot sprinkling machine (CPSM) on the yield of maize in Northeast China.A total of three fertigation modes were designed: the fertigation based on the CPSM (F1), fertigation based on the micro-sprinkling system (MSS) (F2), and the fertigation mode in which the MSS sprays fertilizers while the CPSM sprays water for drip washing (F3).The three fertigation modes were combined with three water levels (W1-W3) and three fertilizer levels (N1-N3) were.First, the fertilizer uniformities of the three fertigation modes were tested.Then, orthogonal field tests were conducted to observe the growth traits and yield indices of maize in each phase of growth period, under different combinations of fertigation mode, water volume and fertilizer volume.Based on the test results, the authors identified the most significant factors and levels affecting the traits and yield in each phase.The results show that the three fertigation modes can be ranked as F2>F3> F1 in fertilizer uniformity; the highest maize yield (12,807.22kg/hm 2 ) and lowest maize yield (10,324.8kg/hm 2 ) were observed in Plot 5 (W2N2F3) and Plot 1 (W1N1F1), respectively.In general, it is recommended to adopt the combination W2N2F3 to boost the yield of maize.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".