The Effect of Irrigation Intervals on the Growth and Yield of Quinoa Crop and Its Components
Bibliographic record
Abstract
This experiment was conducted to study the effect of irrigation intervals on growth, yield and its components and some of the chemical characteristics of the soil after the harvest of quinoa (Chenopodium quinoa willd) plant. Three treatments were used as follow: T1 (twice irrigation every week, which is the common in the region), T2 (once irrigation every week) and T3 (twice irrigation every two weeks) using in a randomized complete block design with four replicates. The crop coefficient (Kc) value differed according to the stage of growth where the results showed that the T2 treatment gave the highest mean in all the studied traits followed by the T3 treatment in all traits except the number of seed/m2. The results also confirmed that the increase in water reduced the agronomic traits such as harvest index, number of seeds and yield of seeds and straw/m2. Also it showed that the pH values in soils were not significantly affected by irrigation, while Ec significantly affected. Correlation coefficient was negative with the most traits and low with the number of grain (0.34) under overall studied treatments which confirms that quinoa is a plant that needs limited amounts of irrigation water. On the other hand there was positive strong correlation between the harvest index and grain yield (0.92). The results showed that moisture stress treatments increased the concentration of the ionic, NH4-N and NO3-N significantly compared to soils which do not have moisture stress (T1, T2). We assume that the development based on Kc during growth-stages helps in irrigation management and provides precise water applications for quinoa plant. These results indicate that the water requirements of quinoa plant are limited and that quinoa plant growth is not affected by the lack of irrigation water on the crop and its qualities.
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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 | 0.001 |
| 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".