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Record W2513084932 · doi:10.5539/mas.v10n11p239

Effect of Fertilizer Management in Combination with Soil Conditioner on Yield of Cassava Cultivated on Coarse-Textured Soil in Thailand

2016· article· en· W2513084932 on OpenAlexvenueno aff
Pongpet Pongsivapai, Chaisit Thongjoo, Jutamas Romkaew, Tawatchai Inboonchuay

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsnot available
FundersKasetsart University
KeywordsFertilizerAgronomyRandomized block designSoil conditionerOrganic matterOrganic fertilizerCation-exchange capacityEnvironmental scienceChemistrySoil waterSoil scienceBiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.142

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.243
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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