Effect of Square Planting and Nitrogen Scheduling on Yield and Micrometeorological Parameters in Maize
Bibliographic record
Abstract
<p class="1">Altering plant spatial distribution induces changes in micro-meteorology of crop canopy and have cumulative effect on yield. Creation of suitable plant spatial distribution is indispensable to explore the beneficial effects of spatial pattern and it could be possible only through altering rectangular spatial distribution, which is commonly practiced in maize. Most of the nitrogen (N) scheduling approaches are growth stages based without considering crop demand and soil fertility status which may not yield better, warrants need based N management for better production in maize. The present study has investigated effects of spatial pattern and nitrogen scheduling on intercepted photosynthetically active radiation (IPAR), light extinction co-efficient (‘k’), leaf temperature (LT) and productivity of maize. The experiments were conducted during 2011 and 2012 at Tamil Nadu Agricultural University, Coimbatore. Treatments were laid out in split-plot design and replicated thrice. Treatments were: six levels of spatial pattern (60 × 25, 30 × 30, 35 × 35, 40 × 40, 45 × 45 and 50 × 50 cm). Growth stage based and need based approach of leaf color chart based (LCC) nitrogen scheduling were imposed. Field experimental results indicated that maize canopy under 30 × 30 and 35 × 35 cm spatial pattern intercepted 10 to 15% more light compared to rectangular pattern (60 × 25 cm). The ‘k’ value and leaf temperature were reduced under this pattern. Higher maize grain yield (GY) was recorded at 35 × 35 cm spatial distribution. The LCC based N scheduling recorded higher values of IPAR, LT and lower ‘k’ values. Square planting favored canopy micro-meterological parameters and which in turn enhanced grain yield of maize.</p>
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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 | 0.001 | 0.001 |
| 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".