Site‐specific effects of variable water supply and nitrogen fertilisation on winter wheat
Bibliographic record
Abstract
Abstract The plant‐available soil water, amount and distribution of rainfall or irrigation are primary factors that may affect yield and quality of winter wheat in heterogeneous fields. The objective of this 2‐y study was to vary N application and water supply in order to achieve a more mechanistic insight into the effects of underlying differences in the site‐specific productivity on heterogeneous fields. Two N fertilizer rates (120 and 180 kg N ha –1 ) and three different water supply treatments (rain sheltering, irrigation, rain‐fed) were compared on field sites with lower or higher plant available soil water capacities. On the whole, the site, rather than rainfall or N fertilisation, was the primary factor that accounted for variability in grain yield. Rainfall distribution during the growing season affected the overall yield level in a given year. The sites characterised by lower plant available water capacity did not show higher grain yield and improved quality with the increased N rate. This suggests that the reduced N rate should be recommended on these sites to take into account the environmental sustainability of N fertilisation. With respect to the higher N application at sites of high plant available soil water capacity, although the already high yield levels were not increased further, the protein quality was significantly improved in the first season within all treatments and in the second season in the irrigated treatments. Therefore, a higher N‐rate proved to be advantageous, especially considering that the residual nitrate levels after harvest were low. The study demonstrated that the response of winter wheat to water shortage or abundance and N fertilisation is site‐specific and dependent on the availability of soil water.
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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 | 0.001 | 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".