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Record W4415979411 · doi:10.5539/jas.v17n12p63

Effects of Industrial Organic Fertilisers on the Growth and the Yield of Irrigated Rice on Sandy Loam Soil

2025· article· W4415979411 on OpenAlexvenueno aff
Yéboua F. Kouassi, Pierre-Marie Janvier Coffi, Odon Clément N’cho, Waogninlin Amed Ouattara, Tamia J. Ama-Abina

Bibliographic record

VenueJournal of Agricultural Science · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
FundersInternational Fund for Agricultural Development
KeywordsLoamPanicleFertilizerCrop yieldYield (engineering)Soil water

Abstract

fetched live from OpenAlex

Rice has become the main staple food of the Ivorian population, especially in urban areas. However, its production, by soils mineral fertilization in irrigated rice cultivation, remains a concern due to the leaching of fertilizers having harmful effects on the aquatic environment. This study aimed to evaluate the effects of industrial organic fertilisers on the growth and yield of irrigated rice on sandy loam soil. The experimental design used was in randomized Fisher blocks, with 3 replicates and 5 treatments, namely control (T), mineral fertilizer (MF) and two organic fertilizers (BioDeposit and Biofertil). MF was applied at dose of 50 kg N, 25 kg P2O5 and 103 kg K2O ha-1. As for BioDeposit, the Agro variant (BdW) was applied to the soil at 1,400 kg ha-1, and the Elixir variant, on the leaves, at 200 L ha-1, in addition to the soil application (BdA). Biofertil was applied at 600 kg ha-1. MF plots had the highest plant height (58.01 cm), number of panicles per plant (13) and paddy rice yield (3.76 t ha-1), with a 54.10% yield increase compared with control plots. The agronomic efficiency of MF was also the highest (3.6), followed by that of BdS (0.06). Those of BdA and Bf were negative. However, the effects of these fertilisers were not significantly different (p > 0.05) between treatments. Considering the performance of MF, which requires strict compliance with application measures, BioDeposit-Agro could be recommended for irrigated rice cultivation on predominantly sandy soils.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.203
Teacher spread0.188 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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