Integration optimal irrigation schedule with biochar applications can economize water and maintain yield in cotton&sugarbeet monoculture and intercropping
Bibliographic record
Abstract
<p>Arid areas in the world are pressed to grow more crop per drop of water to meet food security. Soil salinity, an unavoidable challenges in arid areas is exacerbating the situations. Management practices such as use of salt tolerant crops including cotton and sugarbeet, plastic mulched drip irrigation, intercropping and application of biochar are often recommended and adopted in many arid areas including Xinjiang, one of the northwestern provinces in China with arid climate. Studying the effectiveness of these management practices, however, are often difficult, costly and time-consuming. For example. difficulty in obtaining information on the dynamics of soil water and salt restricts comprehensive understanding on the effectiveness of soil amendments such as biochar’s impact in increasing yield and optimizing irrigation schedules, while numerical simulations show promise. The objectives of this study were to simulate the dynamics of soil water, salt and root water update (RWU) of cotton and sugarbeet in monoculture and intercropping systems in an arid climatic condition to minimize soil water loss through optimal irrigation under plastic mulched drip irrigation systems. A 3-year field experimental results from a cotton and sugarbeet monoculture and intercropping systems from Xinjiang, China was used to calibrate and validate HYDRUS-2D model. Soil water and salt dynamics were measured at fields with biochar applied at 0 t ha-1 (CK), 10 t ha<sup>-1</sup> (B10) and 25 t ha<sup>-1</sup> (B25) and the soil hydraulic and solute transport parameters were optimized in HYDRUS-2D, that was calibrated and validated to satisfy the requirements of minimum simulation accuracy (R<sup>2</sup>>0.75, RRMSE<14.2% and NSE>0.73). Simulation showed that the application of biochar increased storage of soil water and salt. The RWU ranked as B10> B25> CK, which was consistent with soil water storage (SWS) and yield. Soil water balance components indicated that the application of biochar at 10 t ha<sup>-1</sup> increased RWU, reduced Ea and water drainage. to the results were helpful to understand the mechanisms of biochar and intercropping in increasing crop yield. The adjusted irrigation schedule can save up to 50 mm of irrigation water and 50 Yuan costs per hectare for farmers. The research provides a reference for agricultural production in arid and semi-arid areas.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".