Surfactant and Limited Irrigation Effects on Forage and Seed Production and Water Use Efficiency in Alfalfa (Medicago Sativa L.)
Bibliographic record
Abstract
Alfalfa is one the most important forage resources in arid and semiarid regions of the world. To evaluate the response of alfalfa to limited irrigation and surfactant application, an experiment was conducted at Research Farm of College of Agriculture, University of Tehran, Iran, during 2013 and 2014 growing seasons. The experimental treatments were arranged as split plots based on a complete randomized block design with three replications. The limited irrigation treatments comprised of replenishment of 100%, 75% and 50% of weekly evaporation and plant water requirements assigned to the main plots. Water treatments of control (water alone) and water + surfactant, assigned to the subplots. The quantitative and physiological characteristics of alfalfa forage were recorded at 10% flowering stage. The seed yield of alfalfa was measured after the plants reached full physiological maturity stage. The result of the experiment showed that as the severity of limited irrigation increased, plant height, tiller number per plant, RWC, total forge yield and seed yield followed a decreasing trend. Across all the limited irrigation systems, surfactant application increased plant height, RWC, seed yield and total forge yield. As the severity of limited irrigation increased, water use efficiency (WUE) in forage yield followed a significant increasing trend. The highest forage (7500 kg/ha) and seed yield (820 kg/ha) under limited irrigation treatments were achieved at 75% weekly evaporation and plant water requirements + surfactant, while the highest irrigation water use efficiency for forage (1.5 kg/m3) and seed (0.16 kg/m3) production was observed in limited irrigation treatment of 50% weekly evaporation and plant water requirements + surfactant.
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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 | 0.000 |
| 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 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".