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Record W4403446628 · doi:10.1016/j.agwat.2024.109103

Integrated effects of polyethylene/biodegradable residual film on soil hydrothermal conditions and spring maize growth in rain-fed dryland

2024· article· en· W4403446628 on OpenAlexaff
Guixin Zhang, Shibo Zhang, Zhenqing Xia, Jingxuan Bai, Mengke Wu, Haidong Lu

Bibliographic record

VenueAgricultural Water Management · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsMinistry of Agriculture
FundersKey Research and Development Projects of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsHydrothermal circulationSpring (device)ResidualAgronomyEnvironmental sciencePolyethyleneGeologyMaterials scienceBiologyMathematicsComposite materialEngineering

Abstract

fetched live from OpenAlex

Mulch technology has significantly enhanced agricultural production in arid and semi-arid regions worldwide. However, the long-term use of traditional plastic mulch has caused environmental concerns due to persistent residues. Biodegradable mulch offers a potential solution to these issues. But little is known about the effects of different residual films on soil hydrothermal properties, and how this ultimately drives maize growth and yield formation. To address this gap, we conducted a two-year field experiment involving low-density polyethylene film (LDPE) and polylactic acid film (PLA), at three residual levels (75 kg ha −1 ,150 kg ha −1 , and 300 kg ha −1 ), with a control having no residual film. Our findings showed that increased amounts of residual film increased soil bulk density and decreased soil porosity, leading to decreased soil water storage and increased soil temperature. The structure equation model indicated that these deteriorated soil hydrothermal conditions hindered maize root growth, resulting in lower yield and hydrothermal use efficiency. In the second year of this experiment, the film mass density of PLA treatments declined significantly compared to LDPE, leading to fewer adverse effects on soil physical structure, moisture, and temperature. Betters soil hydrothermal environment favor maize biomass accumulation and yield formation. Compared to LDPE treatments, the grain yield, water use efficiency, and soil accumulated temperature use efficiency of PLA treatments increased by an average of 3.98 %, 3.86 %, and 4.42 %. Therefore, we recommend eco-safe PLA mulch as a sustainable alternative to LDPE mulch for maize production in arid and semi-arid areas. The rapid degradation of Polylactic Acid residues results in minimal disruption to the soil physical structure and hydrothermal environment. Consequently, the root growth and resource acquisition ability of spring maize are restored, ensuring a certain extent of grain yield and hydrothermal use efficiency. The plus/minus sign in parentheses indicates an increase/decrease in that indicator under Polylactic Acid residue treatments compared to Polyethylene residue treatments. WUE: water use efficiency, PUE: precipitation use efficiency, TUE: soil accumulated temperature use efficiency. The structural equation model (SEM) describes the main pathways of residual films influencing the spring maize growth. R 2 denotes the proportion of variance explained. Red, blue, and gray arrows indicate positive, negative, and not significant correlations, respectively. Numbers on arrows are standardized path coefficients. Significant path coefficients are marked with asterisks: *, P < 0.05; **, P < 0.01. • Mulch residues impact soil-crop system mainly in the 0–40 cm soil layer. • 150 kg ha −1 is the threshold for negative effects of mulch residues. • Rapid degradation of PLA residues leads to better soil hydrothermal properties. • Improved soil hydrothermal conditions increase maize root growth and grain yield.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.318

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 head0.003
GPT teacher head0.171
Teacher spread0.168 · 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 designBench or experimental
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

Citations5
Published2024
Admission routes1
Has abstractyes

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