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Design and performance evaluation of the six-row side deep fertilizer applicator for paddy fields

2024· article· en· W4405714535 on OpenAlexaff
Kemoh Bangura, Shuanglong Wu, Zhenyu Tang, Xiao Feng, Renjun Hu, Yinghu Cai, Yuhao Zhou, Zhang Liang, Zhiwei Zeng, Ernest Owusu-Sekyere, Long Qi, Hao Gong

Bibliographic record

VenueInternational journal of agricultural and biological engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsUniversity of Manitoba
FundersEarmarked Fund for Modern Agro-industry Technology Research System
KeywordsPaddy fieldFertilizerAgricultural engineeringMathematicsAgronomyEnvironmental scienceEngineeringBiology

Abstract

fetched live from OpenAlex

Deep application of chemical fertilizer is an alternative method to improve the fertilizer utilization efficiency of directly seeded and transplanted rice and minimize the adverse effects of fertilizer on the environment. Different fertilization machines have been introduced for fertilizer deep placement. However, machines for this purpose have not been widely accepted due to the problem of inconsistent performance in applications. In response to this problem, this study developed a six-row deep fertilizer applicator with an improved discharge device. The structural design of the discharge device was optimized, and field performance experiments were conducted on the entire machine. First, a single row operation model of the fertilizer applicator was established based on the Discrete Element Method (DEM). Three spiral grooved wheel speeds were used to test the uniformity and accuracy of fertilization. The optimization test results showed that the spiral grooved wheel has good fertilizer discharge effect at a speed of 40 r/min, a groove radius of 6 mm, a grooved wheel working length of 50 mm, and a grooved wheel spiral angle of 45°. The coefficient of variation of fertilizer application uniformity under these parameter settings was 6.30%. Field experiments were conducted to test the machine’s performance under static and dynamic conditions. The static test results showed that the consistency and stability variations of fertilization in each row were less than 5%. When the expected fertilization rates were 150, 225, 300, and 375 kg/hm2, the fertilization accuracy of the six-row fertilization machine was 95.5%, and the overall deviation from the actual fertilization rates was less than 5%. The study provides a new tool for the advancement of rice fertilization technology and lays a research foundation for the development of efficient and precise rice fertilization machinery. Key words: deep fertilization device, structural design, DEM simulation, field experiment DOI: 10.25165/j.ijabe.20241706.8598 Citation: Bangura K, Wu S L, Tang Z Y, Feng X, Hu R J, Cai Y H, et al. Design and performance evaluation of the six-rowside deep fertilizer applicator for paddy fields. Int J Agric & Biol Eng, 2024; 17(6): 166–175.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.121

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.228
Teacher spread0.207 · 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 designSimulation or modeling
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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