Effect of Fertilizer Management in Combination with Soil Conditioner on Yield of Cassava Cultivated on Coarse-Textured Soil in Thailand
Bibliographic record
Abstract
This study was conducted to determine the effect of fertilizer management in combination with soil conditioner (zeolite, pumice and organic fertilizer) on yield of cassava var. Huay Bong 60 cultivated on coarse-textured soil in Thailand. The experiment was arranged in a randomized complete block design. The results revealed that addition of chemical fertilizer combined with organic fertilizer rate 1,018.75 kg ha-1 gave the highest plant height, above ground biomass, fresh tuber yield, starch content, starch yield and concentration of primary nutrient in fresh tuber yield and were not difference from addition of 100% chemical fertilizer combined with zeolite rate 312.5 kg ha-1 (P≤0.01). After experiment, it was found that all treatments comprising sole chemical fertilizer at various rates or combined with soil conditioners were found to influence soil properties; soil pH was moderately to slightly acid, electrical conductivity (ECe) was non-saline soil, low to moderately low organic matter in soil, moderately low to moderate cation exchange capacity (CEC), low to moderate available P in soil; very low exchangeable K in soil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".