Predicting Soil Phosphorus‐Related Properties Using Near‐Infrared Reflectance Spectroscopy
Bibliographic record
Abstract
Near‐infrared reflectance spectroscopy (NIRS) is a rapid, inexpensive, and accurate analysis technique for a wide variety of materials, and it is increasingly used in soil science. The objectives of our study were to examine the potential of NIRS to predict (i) soil P extracted by two methods [Mehlich 3 (M3P) and water (Cp)], soil total P (TP), annual crop P‐uptake, and annual P‐budget, and (ii) other soil chemical properties [total C (TC), total N (TN), pH, and K, Al, Fe, Ca, Mg, Mn, Cu, and Zn extracted by Mehlich 3]. Soil samples ( n = 448) were taken over a 7‐yr period from an experimental site in Lévis (Québec, Canada) where timothy ( Phleum pratense L.) was grown under four combinations of P and N fertilizer. The NIRS equations were developed using 80% of the samples for calibration and 20% for validation. The predictive ability of NIRS was evaluated using the coefficient of determination of validation ( R v 2 ) and the ratio of standard error of prediction to standard deviation (RPD). Results show that M3P, Cp, crop annual P‐uptake, and annual P‐budget were not accurately predicted by NIRS ( R v 2 < 0.70 and RPD < 1.75). Similar results were found for K and Cu. However, NIRS predictions were moderately useful for TP, TN, Fe, and Zn (0.70 ≤ R v 2 < 0.80 and 1.75 ≤ RPD < 2.25), moderately successful for TC and Al (0.80 ≤ R v 2 < 0.90 and 2.25 ≤ RPD < 3.00), successful for pH and Mg (0.90 ≤ R v 2 ≤ 0.95 and 3.00 ≤ RPD ≤ 4.00), and excellent for Ca and Mn ( R v 2 > 0.95 and RPD > 4.00). The NIRS predictive ability of several soil properties appears to be related to their relationship with soil organic C. Although NIRS can predict several soil properties, prediction of total P was the only soil P‐related property, correlated to soil C, that was moderately useful.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".