Macronutrient Solubility in Response to the pH of Soilless Container Substrates
Bibliographic record
Abstract
Abstract This study investigated the complex interactions between pH and nutrient availability in soilless substrates, focusing on how different nutrient solution formulations, substrate components, and liming materials influence macronutrient solubility. The objective was to develop a more sophisticated representation of pH-nutrient solubility relationships compared with charts currently used by horticulture professionals. Macronutrient concentration was measured in response to substrate-pH using Ca(OH)2 and Mg(OH)2 in four peat-based substrates (70% peat mixed with 30% perlite, pine bark, coconut coir, or vermiculite by volume). A range of lime rates and fertilizer formulations were applied, and resulting pH and macronutrient concentration in a water extract solution was analyzed. Nitrate-N and ammonium-N concentrations decreased at higher pH levels. Phosphorus concentration decreased, especially when limed with Ca(OH)2. Potassium availability was affected by substrate type and cation exchange capacity. Calcium solubility increased with Ca(OH)₂ but decreased with Mg(OH)₂ at elevated pH levels, whereas magnesium solubility showed a reverse pattern. Sulfate-sulfur concentration remained stable across treatments. Chemical equilibrium simulations with Visual MINTEQ highlighted the formation of insoluble Ca-P compounds with Ca(OH)2 as the lime source, and low solubility Mg-P compounds with Mg(OH)2 as lime, hindering available P. This study illustrates several key trends in pH effects on macronutrient solubility and plant availability that have practical implications for horticulture fertilizer management and emphasize the real-world complexity beyond widely-used pH solubility charts. The review of published plant studies highlights that tissue nutrient concentrations do not always correlate with nutrient solubility. This discrepancy is influenced by factors such as root-secreted compounds and pH modifications caused by nutrient uptake by plant roots.
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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 | 0.000 |
| 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 | 0.001 | 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".