Water Deficit and Excess and the Main Physiological Disorders in Agricultural Crops
Bibliographic record
Abstract
The comprehension of the precise water consumption of agricultural crops is a valuable tool for establishing management programs and irrigation schedules. Appropriately, the purpose of this study was to promote a bibliographic review on the main reflexes of the inappropriate use of water and what this process can promote in the establishment and development of agricultural crops. Moreover, theoretical questions were raised regarding physiological responses triggered by soil water deficit and its effect on crop growth, critical periods for water deficit, physiological responses, and their effects on the growth of main agricultural crops. Information on the misuse of water resources and its effects have presented a series of manifestations to plants and, consequently, to agricultural production, such as a production depletion, reduction of carbon fixation, nutritional deficiency, reduction of plant height, reduction of thousand-grain weight, yellowing of leaves, reduction in germination percentage, among other factors. Correspondingly, water stress can cause a drastic reduction in leaf area, productivity decrease, stomatal closure, leaf senescence, reduced roots, reduced flowering, hampering crop emergence and stability, spikelet sterility, etc. Finally, studies aimed at the consequences of poor irrigation and/or inadequate precipitation values are of high importance, mainly due to the investigative improvement on the use of water in an effective and sustainable way.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".