Biodegradable mulch films support root proliferation and yield in water-saving rice production
Bibliographic record
Abstract
• Biodegradable mulch films enhanced the soil conditions in water-saving rice production. • Enhanced spatial distribution of the root system improved rice resistance to stunting. • Optimized root development contributed to rice productivity and sustainability with biodegradable mulch films. Water-saving rice systems must maintain yield targets while reducing water consumption. Applying biodegradable film to cover the soil surface reduces water loss through evapotranspiration, establishing a warmer, more humid microenvironment for rice growth compared to traditional paddy rice systems. This study examined soil water regimes for rice production in northeast China, comparing rice growth with and without biodegradable mulch film under continuous flooding, drip irrigation, and controlled irrigation conditions. The implementation of biodegradable mulch film elevated soil temperature and sustained soil moisture during early rice development. Continuous flooding with biodegradable mulch film yielded the highest rice production (9.4 Mg ha -1 ) and net profit of approximately 11,800 CNY ha -1 . Drip irrigation with biodegradable mulch film achieved maximum water efficiency, demonstrating the highest water productivity (1.25 kg m -3 ) and minimum water consumption (235 mm). Root length, weight, and surface area in the 0-40 cm soil layer exhibited positive correlations with water productivity, shoot dry matter, and yield, indicating that root morphological characteristics, particularly during the panicle initiation stage, enhanced rice production and water conservation. The findings demonstrate that biodegradable mulch film created favorable soil conditions for root proliferation, enabling higher yields in water-saving rice systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".