Enhancing the systems productivity and water use efficiency through coordinated soil water sharing and compensation in strip-intercropping
Bibliographic record
Abstract
In arid areas, water shortage is threating agricultural sustainability, and strip-intercropping may serve as a strategy to alleviate the challenge. Here we show that strip-intercropping enhances the spatial distributions of soil water across the 0-110 cm rooting zones, improves the coordination of soil water sharing during the co-growth period, and provides compensatory effect for available soil water. In a three-year (2009-2011) experiment, shorter-season pea (Pisum sativum L.) was sown in alternate strips with longer-season maize (Zea mays L.) without or with an artificially-inserted root barrier (a solid plastic sheet) between the strips. The intercropped pea used soil water mostly in the top 20-cm layers, whereas maize plants were able to absorb water from deeper-layers of the neighboring pea strips. After pea harvest, the intercropped maize obtained compensatory soil water from the pea strips. The pea-maize intercropping without the root barrier increased grain yield by 25% and enhanced water use efficiency by 24% compared with the intercropping with the root barrier. The improvement in crop yield and water use efficiency was partly attributable to the coordinated soil water sharing between the inter-strips and the compensatory effect from the early-maturing pea to the late-maturing maize.
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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 | 0.001 | 0.001 |
| 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".