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Record W2972188626 · doi:10.2134/agronj2018.10.0691

Integrated Water and Nitrogen Management Practices to Enhance Yield and Environmental Goals in Rice–Ratoon Rice Systems

2019· article· en· W2972188626 on OpenAlexaff
Lin Zhang, Peng Jiang, Xiaoyi Gou, Xingbing Zhou, Yongchuan Zhu, Mao Lıu, Xiong Hong, Fuxian Xu

Bibliographic record

VenueAgronomy Journal · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsMinistry of Agriculture
FundersNational Key Research and Development Program of China
KeywordsAgronomyOryza sativaFertilizerTILLINGCropMathematicsYield (engineering)Paddy fieldGrain yieldBiologyPhysics

Abstract

fetched live from OpenAlex

Water and N management play a vital role in rice ( Oryza sativa L.) production; however, limited information is available on the options to increase rice yield in rice–ratoon rice systems, including an appropriate combination of water regime (W), N application rate (N AR ) and N application method (N AM ). To address this question, field experiments were conducted with two Ws [simplified alternate wetting and drying (SAWD) and continuous flooding (CF)], four N ARs (control, N 0 ; 180 kg N ha −1 , N 180 ; 255 kg N ha −1 , N 255 ; and 330 kg N ha −1 , N 330 ) and two N AMs [45% of fertilizer pre‐plant, 15% of fertilizer at tilling, 40% of fertilizer at bud (BTB) and 45% of fertilizer pre‐plant, 15% of fertilizer at boot, 40% of fertilizer during grain filling (BPG)]. On average, the grain yields of the main crop, the ratoon crop, and their total for SAWD were 3.7%, 6.8%, and 4.4% higher than for CF, respectively. The relationships between N AR and the main crop, ratoon crop, and total yields were well fitted by quadratic equations. The rice yields of the main crop, ratoon rice, and their total under BPG were equal to or slightly higher than those under BTB. The interactive effect of W×N AR was significant on the main rice crop yield and total rice yield, but W×N AM , N AR ×N AM and W×N AR ×N AM were all related to the soil‐based yield. The use of integrated water and N management practices could achieve high yields and reduce water and N inputs in rice‐ratoon rice systems. Core Ideas The relationships between N AR and the main crop, ratoon crop, and total yields were well fitted by quadratic equations. The interactive effect of W×N AR was significant on the main rice crop yield and total rice yield, but W×N AM , N AR ×N AM and W×N AR ×N AM were all related to the soil‐based yield. The use of the SAWD‐N 180 –BPG treatment could achieve high yields and reduce water and N inputs in rice‐ratoon rice systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

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 science0.0000.001
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 head0.015
GPT teacher head0.222
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations31
Published2019
Admission routes1
Has abstractyes

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