Bermudagrass Seasonal Responses to Nitrogen Fertilization and Irrigation Detected Using Optical Sensing
Bibliographic record
Abstract
The objective of this study was to evaluate seasonal differences in bermudagrass response to N fertilization and irrigation by using optical sensing. A second objective was to determine if optical sensing could measure N status when the turf response to N was confounded by differences in moisture status. Bermudagrasses (Cynodon dactylon L.) ‘Rivera’ and ‘Yukon’ were managed under three irrigation treatments and six N treatments during the growing seasons in 2003 and 2004. Turf quality, normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red light reflectance in relation to near infrared reflectance (R/NIR), and green light reflectance in relation to near infrared reflectance (G/NIR) were measured. Bermudagrass demonstrated a noticeable third‐order polynomial seasonal trend in response to N and irrigation treatment, and this trend was characterized as early‐, peak‐, mid‐ and late‐season responses. Normalized difference vegetation index and GNDVI demonstrated a better relationship with turf quality and N status than R/NIR and G/NIR. A comparison among the four indices showed NDVI to be more closely correlated with irrigation, N fertilization, and turf quality. Minimum acceptable and target NDVI were developed by seasonal period based on visual turf quality assessment. It was also found that NDVI response to N fertilization was not strongly affected by irrigation treatment and could be used as an indicator of N status and need regardless of irrigation treatment.
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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) | 0.000 | 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 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".