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Record W4281492466 · doi:10.3390/agriculture12060746

Effects of Climate Warming on the Potential Northern Planting Boundaries of Three Main Grain Crops in China

2022· article· en· W4281492466 on OpenAlexaff
Wen Zhuo, Shibo Fang, Yuping Ma, Rui Zhang, Lei Wang, Mengqian Li, Jiansu Zhang, Xinran Gao

Bibliographic record

VenueAgriculture · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsMcMaster University
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsSowingGlobal warmingEnvironmental scienceAgricultureAgronomyClimate changeCroppingChinaPlateau (mathematics)GeographyAgroforestryBiologyEcology

Abstract

fetched live from OpenAlex

The production of wheat, maize and rice accounts for more than 90% of the total grain production of China. Assessing the impact of climate warming on suitable planting regions, especially the potential northern planting boundaries of these crops, is therefore critical to help guide agricultural policymaking and further maintain food security. In this study, we analyzed the effect of climate warming on the potential northern planting boundaries of three specific crops (winter wheat, spring maize, double and triple rice cropping systems) during two time periods (1961–1990 and 1991–2020) using meteorological data from 2437 national weather stations. Results show that the potential planting boundaries of these crops present a northward movement and a westward expansion during the time period of 1991–2020 under the background of temperature increase compared with the time period of 1961–1990. Moreover, the boundaries of winter wheat and spring maize also show a trend of expansion to high-altitude areas (e.g., the Qinghai–Tibet Plateau). The average moving distance of these crops ranged from 20 km to 300 km. In general, the potential planting boundaries of winter wheat, spring maize, double and triple rice cropping systems changed significantly due to climate warming, and the suitable planting area was increased. Our study aims to provide a more recent and accurate result than those of previous studies, which is expected to strengthen our understanding of the effect of climate change on the potential northern planting boundaries of the three main grain crops in China.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.195
Teacher spread0.186 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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