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Record W2028303409 · doi:10.1002/joc.1677

Climate, agricultural production and hydrological balance in the North China Plain

2008· article· en· W2028303409 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Climatology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
FundersCommonwealth Scientific and Industrial Research OrganisationMcMaster University
KeywordsEvapotranspirationEnvironmental scienceWater balancePrecipitationIrrigationAgricultureWater useHydrology (agriculture)AgroforestryAgronomyGeographyEcologyGeology

Abstract

fetched live from OpenAlex

Abstract The North China Plain (NCP) is the largest agricultural production area in China. The extensive use of groundwater for irrigation agriculture under variable climatic conditions has resulted in the rapid decline of the groundwater table especially in areas north of the Yellow River, leading to hydrological imbalance and unsustainable agricultural production. This article analyses the sustainable level of vegetation/crop water use under the NCP climate by mimicking the evapotranspiration of a natural forest ecosystem. Such a system would have a mean annual evapotranspiration ranging from 470 mm/year in the northern to 910 mm/year in the southern part of the plain, leading to a mean annual water excess (rainfall minus evapotranspiration) ranging from 21 to 124 mm/year. The natural forest ecosystem would use less water than the current wheat/maize double cropping system. To mimic the water use of the natural system, dryland farming has to be practiced, and wheat and maize crops would have a water deficit of 90–435 and 0–257 mm/year, respectively. Under average conditions, this would mean that all the areas north of the 36°N line have to abandon winter wheat production. Stopping irrigation will lead to significantly lower wheat yields (average yield 0.8 t/ha in the north to 5.2 t/ha in the south) and increased variability in wheat and maize yield both interannually and spatially. Better management practices, such as opportunity cropping (what and when to crop depending on climate and soil conditions rather than a set annual cycle), better use of climate forecast information to direct decision making, are required in order to achieve maximum return in good years while minimising cost in bad years. Analysis on rainfall and potential evapotranspiration (PET) from 1961 to 2000 shows that there has been an increasing trend in crop water deficit in the northern part, but a decreasing trend in the southern part of the plain. It remains to be further studied whether this reflects long‐term climate change or only a part of the climate variability. Copyright © 2008 Royal Meteorological Society

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.

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 score0.014
Threshold uncertainty score0.151

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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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