Regionalization of outputs of two crop protection models using geostatistical tools and NOAA-AVHRR images
Bibliographic record
Abstract
Crop protection forecasting models currently use meteorological data observed at stations to produce pest infection and development indices. The indices are then extrapolated to the regional level by assuming that the weather conditions at the stations are similar to those in neighbouring fields in the region, which is not necessarily the case. Hence, this has a significant impact on the quality of the recommendations and diagnoses based on computerized plant protection models. The regionalization of model outputs between the stations comprising the weather network, using geostatistical techniques such as cokriging in conjunction with satellite data, is a worthwhile approach for addressing this need. The objective of this study is to develop and apply a methodology for regionalization of infection indices produced by two crop protection models contained in the CIPRA (Computer Centre for Agricultural Pest Forecasting) system, using geostatistical tools and NOAA-AVHRR images. This approach will help enhance our crop pest management and forecasting capabilities while optimizing the use of pest control products in vegetable crops in Quebec. To achieve our objective, a cokriging method was applied to regionalize the model outputs using air temperature and relative humidity estimated from NOAA-AVHRR images. The results were then validated against a regionalization approach using ordinary kriging and two conventional interpolation techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".