Using pH-dependent CEC to determine lime requirement
Bibliographic record
Abstract
Controlling soil pH is important to ensure good crop yield. This study was conducted to determine whether the accuracy of the existing Shoemaker-McLean-Pratt (SMP) pH-buffer method could be improved by using the pH-dependent cation exchange capacity curve (CECpd). Soil pH, SMP and CECpd measurements were performed on 18 acid surface horizon soil samples, with textures from sandy loam to clay loam. These soils were incubated with three levels of calcium carbonate for 12 wk, after which the soil pH and the effective cation exchange capacity (CECe) were measured. The correlation coefficient (R2) for the CECpd and CECe curves was 0.96. The main factor affecting the slope of the curves is the soil organic matter content. The increase of CECe in the soil was also found to be directly proportional to the amount of lime applied, regardless of the type of soil. By using the slope of the Qv versus pH curve for each soil and the relationship between CECe and lime application, we were able to determine the lime required to raise the soil pH in water to 6.5. As an alternative to the current practice of using the SMP buffer, we propose that it should be possible to estimate the pH-dependent CEC curve from measurable soil properties (e.g., organic matter) and to estimate the lime requirement as the difference in CECpd between the existing and desired pH values. Once the slope of the Qv/pH relationship has been determined or estimated for a soil, the only measurement necessary for calculating lime requirement in subsequent years would be the soil pH. The proposed method would provide lime requirement estimates while decreasing the annual cost of soil analysis. Key words: Lime requirement, cation exchange capacity, Non-Ideal Competitive Adsorption, soil properties, organic matter, Fe oxides
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".