Irrigation Management Strategies to Enhance Forage Yield, Feed Value, and Water-Use Efficiency of Sorghum Cultivars
Bibliographic record
Abstract
Water scarcity is a major obstacle to forage crop production in arid and semi-arid regions. In order to improve food security in these areas, it is imperative to employ suitable irrigation management techniques and identify drought-tolerant cultivars. A 2-year field experiment (2019–2020) was conducted in a semi-arid region of Iran to assess the impact of different irrigation methods and water deficit stress on forage sorghum cultivars’ yield, quality, and irrigation water-use efficiency (IWUE). The experiment involved two irrigation methods, i.e., drip (DRIP) and furrow (FURW), and three irrigation regimes supplied 100% (I100), 75% (I75), and 50% (I50) of the soil moisture deficit. In addition, two forage sorghum cultivars (hybrid Speedfeed and open-pollinated cultivar Pegah) were evaluated. This study revealed that the highest dry matter yield (27.24 Mg ha−1) was obtained under I100 × DRIP, whereas the maximum relative feed value (98.63%) was achieved under I50 × FURW. Using DRIP resulted in higher forage yield and IWUE compared to FURW, and the superiority of DRIP over FURW increased with the severity of the water deficit. The principal component analysis indicated that, as drought stress severity increased across all irrigation methods and cultivars, forage yield decreased, while quality increased. Plant height and leaf-to-stem ratio were found to be suitable indicators for comparing forage yield and quality, respectively, and they showed a negative correlation between the quality and quantity of forage. DRIP improved forage quality under I100 and I75, while FURW exhibited a better feed value under the I50 regime. Altogether, in order to achieve the best possible forage yield and quality while minimizing water usage, it is recommended to grow the Pegah cultivar and compensate for 75% of soil moisture deficiency using drip irrigation.
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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".