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Record W2161043277 · doi:10.17221/207/2012-pse

Soil water cycle and crop water use efficiency after long-term nitrogen fertilization in Loess Plateau

2013· article· en· W2161043277 on OpenAlexaff
Baiyan Wang, Wenzhao Liu, Qingwu Xue, Tinghui Dang, Chao Gao, Jiquan Chen, B. Zhang

Bibliographic record

VenuePlant Soil and Environment · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersShanxi Province Science Foundation for Youths
KeywordsWater-use efficiencyAgronomyHuman fertilizationSowingAnimal scienceNitrogenChemistryBiologyIrrigation

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the effect of nitrogen (N) management on soil water recharge, available soil water at sowing (ASWS), soil water depletion, and wheat (Triticum aestivum L.) yield and water use efficiency (WUE) after long-term fertilization. We collected data from 2 experiments in 2 growing seasons. Treatments varied from no fertilization (CK), single N or phosphorus (P), N and P (NP), to NP plus manure (NPM). Comparing to CK and single N or P treatments, NP and NPM reduced rainfall infiltration depth by 20-60 cm, increased water recharge by 16-21 mm, and decreased ASWS by 89-133 mm in 0-300 cm profile. However, crop yield and WUE continuously increased in NP and NPM treatments after 22 years of fertilization. Yield ranged from 3458 to 3782 kg/ha in NP or NPM but was 1246-1531 kg/ha in CK and single N or P. WUE in CK and single N or P treatments was < 6 kg/ha/mm but increased to 12.1 kg/ha/mm in a NP treatment. The NP and NPM fertilization provided benefits for increased yield and WUE but resulted in lower ASWS. Increasing ASWS may be important for sustainable yield after long-term fertilization.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.392

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 head0.009
GPT teacher head0.164
Teacher spread0.155 · 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 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

Citations8
Published2013
Admission routes1
Has abstractyes

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