Soil test correlation of Olsen‐P for corn and soybean in a subtropical humid region
Bibliographic record
Abstract
Abstract Accurate phosphorus (P) fertilizer recommendations for soybean [ Glycine max (L.) Merr.] and corn ( Zea mays L.) are crucial for maximizing productivity and economic return while minimizing the environmental impact of fertilizer use. Currently, there are no calibrations for diagnosing P deficiencies in croplands of Bolivia. This study aimed to define and compare the P critical soil test value (CSTV) for corn and soybean using the Olsen method. Twenty fertilization field trials were conducted for corn and 75 for soybean in the Santa Cruz de la Sierra region, including treatments with and without P fertilization. Soil pre‐planting (0–20 cm) organic matter, Olsen P, pH, as well as yield were determined. Relative yield (RY) was estimated as the ratio of grain yield between the control and P‐fertilized treatments. The CSTV was calculated using the arcsine‐logarithm method, with data resampling through bootstrapping. The average yield response to added P was 259 kg ha −1 (+11.0%) for soybean and 545 kg ha −1 (+13.7%) for corn. For soybean, CSTVs were determined as 6.1 and 11.0 mg kg −1 ( r = 0.34, p = 0.002) for 90% and 95% of RY, respectively. For corn, the CSTVs were 8.1 and 13.9 mg kg −1 ( r = 0.34, p = 0.14) for 90% and 95% of RY, respectively. The confidence intervals suggest no differences between crops. The Olsen‐P is a promising tool for soil fertility recommendations in Bolivia. Yet, the modest goodness of fit obtained suggests the need for further research refining the performance of soil test P diagnosis in the region.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".