Predicting agronomic performance of barley using canopy reflectance data
Bibliographic record
Abstract
The ability to accurately and rapidly predetermine agronomic performance would be desirable in most plant breeding programs. Remote sensing of canopy reflectance is a quick and nondestructive method that may be useful in the estimation of agronomic performance. Studies were conducted at Fargo and Langdon, North Dakota, to determine the effectiveness of a multispectral radiometer in estimating yield, kernel plumpness (KP), and 1000-kernel weight (TKW) in barley. Canopy reflectance was measured in eight (500–850 nm) discrete narrow-wavelength bands. Three types of reflectance models were evaluated: simple models using one to four wavelengths, simple ratio and normalized difference vegetation indices (NDVI) using green, red, and near-infrared wavelengths, and soil-adjusted vegetation indices (SAVI). The relationship between canopy reflectance and agronomic performance was significantly influenced by environment, growth stage, and plant genotype. Grain yield was best estimated near GS73 (0.84 < R2 < 0.92) at Fargo and at GS83 (0.55 < R2 < 0.81) at Langdon. In contrast, KP and TKW could be estimated at both late (GS83; 0.68 < R2 < 0.93) and early (GS24–GS47; 0.72 < R2 < 0.91) growth stages. The 550-nm and 800-nm wavelengths are critical for development of predictive models. A simple model using 550-nm, 600-nm, and 800-nm from GS47-GS73 gave significant (0.45 < R2 < 0.64) estimation of agronomic performance across all environments. In contrast, simple ratio, NDVI, and SAVI were less effective (0.05 < R2 < 0.77) in predicting agronomic performance. Remote sensing using canopy reflectance is a potential tool to estimate agronomic performance of barley, but genotypic and crop stage factors affect this method. Further studies are needed to improve the usefulness of multispectral radiometry in predicting agronomic performance. Key words: Crop yield, Hordeum vulgare, kernel plumpness, remote sensing
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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 | 0.000 | 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".