Comprehensive Assessment of Local and Exotic Sorghum Genotypes for Forage Production and Quality Under Drought Conditions
Bibliographic record
Abstract
Sorghum ( Sorghum bicolor L.), locally known as jowar, is a vital summer fodder crop in Pakistan, significantly contributing to livestock sustenance. However, drought stress poses a critical challenge by reducing plant biomass and elevating hydrogen cyanide (HCN) content, a toxic antiquality component that endangers livestock health. This study aimed to identify sorghum genotypes with improved fodder yield and reduced HCN content under drought stress. Seventy diverse genotypes were evaluated in a hydroponic system under three polyethylene glycol (PEG) levels (0%, 5%, and 10%) in a two‐factor factorial experiment arranged in a completely randomized design (CRD). Analysis of variance (ANOVA) revealed highly significant ( p < 0.05) genotype, treatment, and genotype × treatment interaction effects across all measured traits, indicating considerable genetic variability in drought responses. Drought stress significantly increased root length (RL) (3.2–13.2 cm) and decreased several morphological traits including shoot length (SL), shoot fresh and dry weights (SFW and SDW), and chlorophyll (23.4–42.8 μg cm −2 ) and fodder quality traits including crude protein (CP) (15.4%–24.1%) and crude fiber (CF). Principal component analysis (PCA) explained 72.4% of the total variance in the first three components, identifying SDW, SFW, RL, and SL as key contributors to drought tolerance. Correlation analysis revealed significant positive and negative correlations among the traits under all normal and drought conditions. Despite these reductions, genotypes such as Sorg‐60, Sorg‐66, and Sorg‐7 showed superior performance in both biomass and quality traits, while Sorg‐53 and Sorg‐56 exhibited high sensitivity to drought. Based on PCA biplot positioning and trait performance, 20 genotypes (10 highly tolerant and 10 highly sensitive) were selected for field evaluation under normal and drought conditions using a randomized complete block design (RCBD). Morphological, physiological, and fodder quality traits showed comparatively low reduction under drought conditions in tolerant genotype compared to drought‐sensitive genotypes. Statistical analyses supported the findings and highlighted promising genotypes for use in future sorghum breeding programs aimed at enhancing forage yield and nutritional safety under water‐limited environments.
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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".