Inoculation and Soil Texture Effects on Yield and Yield Components of Mungbean
Bibliographic record
Abstract
Mungbean [Vigna radiata (L.) Wilczek] is a short-duration and relatively drought-tolerant crop grown predominantly in the tropics. This grain legume can improve soil fertility through biological nitrogen (N) fixation. To assess the effects of Bradyrhizobium (group I) inoculation on yield and yield attributes of mungbean, a greenhouse study was conducted during Fall 2016 with two mungbean cultivars (‘Berken’ and ‘OK2000’), two inoculum treatments (inoculated and uninoculated), and two soil textures (loamy sand and silt loam). Pots were laid out in a completely randomized design and treatment combinations were replicated seven times. The main effects of cultivar and soil texture significantly (P ≤ 0.05) affected mungbean seed weight and plant residue mass. Seed yield (13%), plant residue (22%), and protein content (6%) of OK2000 were significantly higher than Berken cultivar. A 31% seed yield and 40% plant residue increase were recorded on silt loam soil compared to loamy sand soil. Significant increase in plant height (18%) and number of pods per plant (21%) were also recorded when mungbean plants were grown on silt loam compared to loamy sand soil. Bradyrhizobium inoculation significantly increased the number of pods per plant, the number of seeds per plant, and seed yield. [Cultivar × inoculation] and [cultivar × soil texture] interactions had significant (P ≤ 0.05) effects on number of seeds per pods and plant height, respectively. Understanding the agronomic practices and soil physical properties that may limit mungbean production could help in optimizing its establishment and growth in non-traditional growing areas.
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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) | 0.001 | 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".