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Record W4283363814 · doi:10.1002/ldr.4403

Agronomic technology to promote sustainable utilization of newly created farmland in the Chinese Loess Plateau

2022· article· en· W4283363814 on OpenAlex
Yurui Li, Xuanchang Zhang, Yansui Liu, Yongsheng Wang, Yunxin Huang, Zhi Lu, Weilun Feng, Zongfeng Chen, Hongan Wei

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

VenueLand Degradation and Development · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsUniversity of Victoria
FundersNational Key Research and Development Program of ChinaChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsAgronomyEnvironmental scienceSowingSoil qualityMonocroppingRapeseedSoil waterAgricultureSoil scienceGeographyBiology

Abstract

fetched live from OpenAlex

Abstract The new farmland created by land consolidation often faces the problems of poor soil structure and low productivity, which cause potential degradation risk. The Gully Land Consolidation Program (GLCP) significantly increased the quantity of farmland in the Chinese Loess Plateau (LP), but the research on a comprehensive method of simultaneously improving soil quality, agricultural profit, and utilization efficiency of newly created farmland (NCF) is relatively scant. This study explored an agronomic technology to improve soil quality, agricultural profit, and utilization efficiency NCF by the GLCP in the LP. Our field experiment was carried out in Yangjuangou catchment with seven soil treatments and planting Brassica napus (B. napus) on these soils: dry mixing Malan Loess and red clay at volumetric ratios of 1:0 (MR10), 5:1 (MR51), 2:1 (MR21), 1:1 (MR11), 1:2 (MR12), 1:5 (MR15), and 0:1 (MR01). The results showed that: the soil microstructure, physico‐chemical properties, and productivity of NCF were significantly improved after soil reconstruction by dry mixing Malan Loess and red clay. More specifically, the MR51 boosted the root thickness and fresh weight of B. napus by 78.69% and 45.01% compared to that of red clay (MR01). Crop optimization by the B. napus helped to increase the agricultural profits of NCF. The proposed three portfolios of B. napus ' silage, vegetable plus rapeseed, and vegetable plus silage enhanced the profits by 35.39%, 57.05%, and 66.93% in comparison with that of traditional crop planting, respectively. Therefore, industrial integration through effective, ecological and economic (3E) agriculture could advance sustainable utilization of NCF. Further, developing efficient agriculture, animal husbandry, agricultural products processing industry, and ecological tourism would enhance the multi‐functional value of farmland. Our study suggests that targeted agronomic technology based on agricultural geographical engineering oriented to human‐environment interaction can provide technical support for minimizing the degradation risk of NCF and generating more sustainable development in ecologically fragile areas.

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 score0.070
Threshold uncertainty score0.143

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 studies0.0000.000
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 head0.017
GPT teacher head0.235
Teacher spread0.218 · 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