Early Prediction of Soybean Yield from Canopy Reflectance Measurements
Bibliographic record
Abstract
Correlations between plant canopy reflectance and aboveground biomass can possibly be used for early prediction of crop yield. Field experiments were conducted in 1998 and 1999 on two soil types to assess whether measurements of canopy reflectance at given stages of development could be used to discriminate high from low potential yields among genotypes with known differences in potential grain yield and whether a consistent relationship between yield and canopy reflectance could be used for screening and predicting soybean [ Glycine max (L.) Merr.] yield in a variety trial. A 3‐by‐42 factorial experiment, arranged in a randomized complete block design with three replications, was used on each soil type for both years. Three population densities (25, 50, and 75 seeds m −2 ) represented low, optimum, and high levels. Forty‐two historical varieties represented nearly six decades (1934–1992) of soybean yield improvement in Canada. Canopy reflectance was measured with a hand‐held multispectral radiometer on three sampling dates (approximately R2, R4, and R5 stages) for each site. Grain yield at harvest was measured. Soybean grain yield was highly positively correlated with canopy reflectance, expressed as normalized difference vegetation index (NDVI), at all sampling dates. Regression analyses showed a positive relationship between NDVI and grain yield, with R 2 up to 0.80 ( P < 0.01) and progressive improvement from R2 to R5 stages. Population density did not affect the yield–NDVI relationship at the development stages studied. Our data suggest that canopy reflectance measured nondestructively between R4 and R5 stages adequately discriminates high‐ from low‐yielding genotypes and provides a reliable, fast, repeatable indicator for screening and ranking soybean genotypes based on the relationship between NDVI and grain yield ( R 2 ranged from 0.44–0.80).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".