Managing inoculation failure of field pea and chickpea based on spectral responses
Bibliographic record
Abstract
Pulse crop production is expanding in semiarid regions of the Northern Plains, and depends on successful biological N2-fixation. Inoculation failure, resulting in plant N deficiency and economic crop loss, might be alleviated by remedial N fertilizer application. The experiment was conducted using no-till management at two dryland sites in Montana in 1999 and 2000, where field pea and chickpea were grown in cereal stubble. Shoot biomass, shoot biomass N concentration, seed yield and seed N concentration were measured for uninoculated and inoculated controls and compared with remedial fertilizer N applied 0, 4, 6, and 8 wk after seeding. Spectral reflectance was compared for the inoculated and uninoculated controls. For field pea and chickpea, the critical period for fertilizer N application to prevent yield loss occurred within 6 wk of seeding (P ≤ 0.05). Logistic regression models derived from spectral reflectance had overall accuracies of 84 and 60% for detecting uninoculated control treatments in field pea and chickpea, respectively. The field pea model had a high degree of accuracy 6 wk after seeding, indicating it was capable of assisting a decision to apply remedial N fertilizer. Spectral reflectance provided a window of opportunity of 1 wk to apply remedial N fertilizer to attain full yield potential. Key words: Chickpea, field pea, inoculant failure, nitrogen, spectral reflectance
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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 | 0.001 | 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".