Optimising Water and Nitrogen Management for Drip‐Irrigated Maize in Oasis Farmland With Shallow Groundwater
Bibliographic record
Abstract
ABSTRACT Nitrogen (N), typically supplied through fertilisers, is essential for enhancing agricultural productivity, but over‐fertilisation—particularly in soils with high initial soil mineral nitrogen (N ISM )—can lead to nutrient pollution of both soil and water. In regions with shallow groundwater, optimising N application is essential yet understudied, particularly in balancing environmental protection and yield maximisation. To address this gap, field experiments were conducted in China's Mu Us Sandy Land in 2019 and 2021, with a pause in 2020. The 2019 study evaluated the effects of a 200 kg N ha −1 fertilisation rate on soil water dynamics, N behavior, and spring maize growth, whereas the 2021 drip‐irrigated trial tested a base rate of 73 kg N ha −1 supplemented with six additional N rates (0, 100, 150, 200, 250, and 300 kg N ha −1 ). Using the collected field data, a WHCNS soil‐crop model was developed, calibrated with 2019 data—including soil moisture, soil N concentration, and leaf area index—from the 200 kg N ha −1 treatment, and validated across all 2021 treatments. The model, highly sensitive to crop parameters, was further optimised using PEST to improve accuracy and then used to simulate the impacts of various water and N management strategies on water use, N fate, and crop growth under shallow groundwater conditions. Simulations revealed that high N ISM levels reduced the benefits of additional N for maize yield and resource use efficiency, whereas low N ISM conditions responded positively to increased N applications. An optimal N application rate of 200–250 kg N ha −1 , paired with a total water input of 473–516 mm, was identified as the most effective for maximising yield while minimising water and N losses.
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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".