Synergistic Effects of Biochar and Irrigation on Sugar Beet Growth, Yield, Quality, and Economic Benefit in Arid Regions
Bibliographic record
Abstract
Water scarcity hinders sustainable agriculture in arid and semi-arid regions. This study investigated the combined effects of trickle irrigation and biochar application on sugar beet cultivation in northwest China’s arid and semi-arid regions, addressing challenges of water scarcity. Three-year field experiments were conducted using plastic film mulch, four irrigation levels based on crop evapotranspiration (0.6–1.2 ETc), and four biochar application rates (0–30 t ha−1). Results showed that biochar application increased sugar beet germination rates by 7.2–24.5% and enhanced relative chlorophyll content by 3.1–22.1%. Optimal combinations of irrigation and biochar significantly improved growth indices and yield, with maximum values observed under the highest irrigation (1.2 ETc) and 10 t ha−1 biochar treatment. However, the 1.0 ETc irrigation treatment with 10 t ha−1 biochar demonstrated superior water use efficiency (14.8% higher), sustainable yield index (1.1% higher), and economic benefits (1.4% higher) compared to the highest irrigation treatment. Considering growth, yield, quality, water use efficiency, sustainability, and economic factors, an irrigation level of 1.0 ETc combined with a biochar application rate of 10 t ha−1 is recommended for sugar beet cultivation in Xinjiang. This study provides valuable insights and practical strategies for water conservation, high yield, and quality improvement in sugar beet cultivation under arid and semi-arid conditions, contributing to sustainable agricultural practices in water-scarce regions.
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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) | 0.000 | 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".