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Record W4377861692 · doi:10.1016/j.jia.2023.05.034

The first factor affecting dryland winter wheat grain yield under various mulching measures: Spike number

2023· article· en· W4377861692 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Integrative Agriculture · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsMulchStrawAgronomySowingYield (engineering)Water contentMoistureSoil waterEnvironmental scienceField experimentCrop yieldAnimal scienceChemistrySoil scienceBiology

Abstract

fetched live from OpenAlex

Water is the key factor limiting dryland wheat grain yield. Mulching affects crop yield and yield components through affecting soil moisture. Further research is needed to determine the factors relationships between yield components and soil moisture with yield and identify the most important factors affecting grain yield under mulching measures. Long term 9-year field experiment in the Loess Plateau of Northwest China was carried out with a total of three treatments: no mulch (CK), plastic mulch (MP) and straw mulch (MS). Yield factors and soil moisture was measured, and relationships between them was explored by correlation analysis, structural equation model and significance analysis. The results showed that compared with CK, the average grain yield of MP and MS increased by 13.0 and 10.6%, respectively. The average annual grain yield of MP treatment was 134 kg ha-1 higher than the MS treatment. There was no significant difference in yield components among the three treatments (P<0.05). Soil water storage of MS treatment was greater than the MP treatment, although the differences were not statistically significant. Soil water storage during the summer fallow period (SWSSF) and soil water storage before sowing (SWSS) of MS were significantly higher than CK, which increased by 38.5 and 13.6%, respectively. The relationship between MP and CK was not statistically significant for SWSSF, but SWSS in MP was significantly higher than CK. In terms of soil water storage after harvest (SWSH) and water consumption in the growth period (ET), there were no significant differences among the three treatments. Based on the three analysis methods, we found that spike number and ET were positively correlated with grain yield. However, the relative importance of spike number to yield was the greatest in the MP and MS treatments, while that of ET was the greatest in CK. Sufficient SWSSF could indirectly increase spike number and ET in the three treatments. Mulch can improve yield and soil water storage. The most important factor affecting grain yield of dryland wheat was spike number under mulching, and ET in no mulch. The findings may help to understand the main factor to influence dryland wheat grain yield under mulching measures compared to no mulch.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.253
Teacher spread0.226 · 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