Quantification of water uptake by winter wheat roots under different dripline burial depths using hydrogen and oxygen stable isotopes
Bibliographic record
Abstract
Water scarcity and inefficient irrigation are major challenges in arid and semi-arid regions. To address this, a two-year field experiment was conducted in the Guanzhong Plain to investigate the effects of dripline burial depth on winter wheat under surface drip irrigation (DI) and four subsurface drip irrigation (SDI) treatments (S10, S20, S30, S40; with dripline depths of 10, 20, 30, and 40 cm, respectively). Using stable hydrogen and oxygen isotope tracers (δ²H, δ¹⁸O) and the MixSIAR Bayesian mixing model, we systematically examined the impact of dripline burial depth on soil moisture distribution, root development, water uptake dynamics, and water use efficiency (WUE). Compared with DI, SDI significantly enhanced soil water storage after the jointing stage, with increases ranging from 0.63 % to 19.75 %. Winter wheat roots were concentrated in the top 0–20 cm of soil (accounting for 78.33–95.21 % of total root mass), and root dry weight density followed a bell-shaped curve across the growing season. Root water uptake patterns varied significantly across treatments. In the DI treatment, water was mainly absorbed from the 0–20 cm layer during early stages (regreening to flowering), shifting to the 20–60 cm layer during grain filling. In the S20 and S30 treatments, water uptake initially occurred in the shallow layer (0–20 cm), then moved deeper (20–60 cm) during jointing to flowering, and subsequently returned to the shallow layer during grain filling. Among all treatments, S20 produced the highest yield and WUE, with increases of 4.59–7.68 % and 8.02–13.63 % respectively, compared to DI. In conclusion, the 20 cm dripline burial depth for SDI optimized root-zone water availability and root distribution, driving a dynamic shift in winter wheat root water uptake (RWU) from the shallow 0–20 cm layer to the deeper 20–60 cm layer during the critical jointing–flowering stage, achieving precise spatiotemporal matching between irrigation supply and crop water demand. This configuration represents the most effective SDI scheme for winter wheat in the Guanzhong Plain and provides a theoretical and practical reference for optimizing irrigation strategies in arid agricultural regions. • Combines δD/δ¹ ⁸O and MixSIAR to quantify winter wheat root water uptake under subsurface drip irrigation (SDI). • SDI increases post-jointing soil water storage (0–100 cm) by 0.63–19.75 %, enhancing root-zone moisture. • Winter wheat root dry weight density peaks at flowering, showing a typical unimodal seasonal pattern. • Reveals burial depth–moisture–root–yield link: 20 cm SDI stabilizes 20–60 cm soil moisture, improving balance. • Optimal 20 cm SDI depth boosts yield 4.59–7.68 % and water use efficiency 8.02–13.63 % versus surface irrigation.
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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".