Compositional nutrient analysis of greenhouse grown ryegrass ( <i>Lolium multiforum</i> ) fertilized with raw and hydrothermal-treated igneous rock as diagnostic tool for balanced fertilization in tropical environments
Bibliographic record
Abstract
Due to the escalating demand for conventional potassium sources, and their unavailability in many African countries, there is a growing interest in hydrothermally treated igneous rocks as viable alternatives. These materials exhibit promising potential in enhancing potassium release, comparable to traditional sources like muriate of potash (M.O.P.). However, it remains uncertain whether they can adequately fulfill the nutrient requirements of plants. In this study, we evaluated nutrient balance in ryegrass (Lolium multiflorum) using raw and hydrothermally treated K-bearing silicate rocks as fertilizers. The Compositional Nutrient Diagnostic (CND) approach was used to assess the plants’ nutrient status, involving identification of high-yield subpopulations, and setting a yield cutoff. We derived a theoretical threshold for nutrient imbalance (CNDr2) using statistical methods like the chi-square distribution function and low-yield subpopulation proportion. CNDr2 was validated via Cate–Nelson partition and sum of squared individual nutrient indexes. Results showed a yield cutoff of 37.759 kg ha−1, distinguishing low-yield and high-yield subpopulations based on cumulative variance ratio functions from survey data. The theoretical threshold derived using the chi-square function was 10.1, subsequently validated by the Cate–Nelson method. Critical CND nutrient indices were symmetrical around zero, but their sum indicated foliar nutrient imbalance, notably zinc, potassium, and copper excess, attributed to 20 interactions identified (4 synergetic and 16 antagonistic). This study underscores hydrothermal treatment’s efficacy in improving nutrient availability and achieving a balanced nutrient profile compared to raw materials. It offers a promising solution for enhancing agricultural productivity, especially in tropical regions like Africa, where traditional sources are scarce.
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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 | 0.000 | 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".