Effect of Soil Aggregate Size and Organic Matter on Tomato Early Growth, Yield and Root and Soil Physicochemical Properties
Bibliographic record
Abstract
This study investigates the independent and combined effects of soil aggregate size (A1: <2 mm, A2: 2-4 mm, A3: >4-8 mm) and organic matter (OM) on tomato growth and soil properties.A pot experiment with a completely randomized design evaluated six treatments (a1b0, a1b1, a2b0, a2b1, a3b0, a3b1), where B1 represents the addition of 10% cow manure compost by soil dry weight, while B0 indicates no compost addition.Results demonstrated that OM alone significantly enhanced early root growth, plant height (79 cm vs. 44.6 cm without OM), leaf count (161 vs. 47 leaves), and fruit yield, which increased by a factor of 39 compared to non-OM treatments.Larger aggregates (>4-8 mm) significantly reduced soil bulk density (0.84 vs. 1.22 g cm⁻³ in A1) and increased available phosphorus by 30-40%.Interactions between OM and aggregate size significantly influenced tomato yield, total soil nitrogen, and hydraulic conductivity.The combination of large aggregates and OM (a3b1) boosted total nitrogen by 200-300% and fruit yield by 39 times compared to a1b0.While OM primarily enhanced root vigor and nutrient availability, aggregate size modulated phosphorus accessibility and physical soil structure.These findings underscore OM's dominant role in improving productivity and soil fertility, while aggregate size plays a crucial role in optimizing soil structure.Strategic integration of OM and aggregate management can enhance sustainable agricultural practices by balancing soil health and crop performance.
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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) | 0.001 | 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".