Foliar Application of Biostimulants Alleviates Water Stress in Canola ( <i>Brassica napus</i> L.)
Bibliographic record
Abstract
ABSTRACT Global climate change is intensifying extreme precipitation events, leading to more frequent droughts and waterlogging and posing a serious threat to crop production. Although biostimulants show potential to improve crop resilience to water stress, their field efficacy and mechanisms remain debated and poorly understood. We conducted a three‐year field experiment with varying irrigation regimes and foliar applications of plant growth regulator and micronutrients to (1) develop a method for categorising field water conditions and (2) evaluate the effects of biostimulants on canola growth, nutrient uptake, and seed composition. The results demonstrated that clustering plant traits provided an effective means of classifying field data into distinct water stress levels. According to this classification, water stress was shown to cause a 10%–22% reduction in canola yield, primarily due to physiological disruptions, such as the impaired nutrient transport to the seeds. Foliar application of abscisic acid (ABA), silicon (Si), and zinc (Zn) at early flowering reduced drought‐induced yield loss by 11%–15% because of the partial restoration of plant physiological processes. ABA also significantly enhanced crop tolerance to soil water saturation, whereas micronutrients showed limited effectiveness. These effects were timing‐specific due to distinct physiological responses and source–sink nutrient balance, including the assimilation and allocation of carbon, nitrogen, and sulfur to reproductive organs. Based on the findings, we recommend applying ABA at early flowering to enhance yield stability under unpredictable weather conditions of drought and waterlogging.
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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".