Effectiveness of using vegetation index to delineate zones of different soil and crop grain production characteristics
Bibliographic record
Abstract
Cost-effective methods to map differences in productivity across fields have potential application for site-specific management of fertilizer and pesticides. In this study, zones were delineated for a field with hummocky topography in southwestern Saskatchewan by clustering the normalized difference vegetation index (NDVI) derived from Landsat TM information. Zones uniqueness were confirmed if zones differed in grain yield. Two different zones were delineated in the field. These zones had significant (P < 0.05) differences in soil factors related to productivity: average solum depth, spring soil nitrogen (N), phosphorus (P), and spring soil moisture. Over 4 yr, the two zones also had different spring wheat (Triticum aestivum L.) grain yield and protein content. The yield response to N-P fertilizer blend within each zone was different with no significant response to N-P fertilizer in the zone with higher NDVI values versus a significant response to N-P in the zone with lower NDVI. The results indicate a potential economic advantage to reducing fertilizer application to the zone without fertilizer response. Further, the residual soil NO3-N in the zone without fertilizer response was positively correlated with N application in the previous year. Therefore, there is a potential environmental benefit to reducing fertilizer application to that zone to decrease residual NO3-N, which can leach and contaminate ground water or can be denitrified to the greenhouse gas, N2O. Hence, this relatively simple and low-cost method of zone delineation has potential practical application to realize economic and environmental benefits from site-specific management of fertilizer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".