Effect of intercropping mungbean on maize and sunflower under weed-free and weedy conditions
Bibliographic record
Abstract
Intercropping is a common yet often discouraged agricultural practice in underdeveloped countries. This method is particularly beneficial for cereal crops. To combat this issue, incorporating leguminous crops with cereals is an effective technique for enhancing soil organic matter and macronutrient levels. The research trial was conducted in the Department of Agronomy at Bahauddin Zakariya University, Multan, in 2018. The crop varieties used in the experiment included maize (P-1439), sunflower (NKSINGI), and mung beans (HAZARI-2006). The results of the study established that intercropping significantly increased the cob diameter, the number of cobs per plant, biological yield, and grain yield for both crops. Data were collected on various observations from 35 days after sowing (DAS) until harvest. The allometric traits measured included leaf area index (LAI), leaf area duration (days), crop growth rate (g m-2 day-1), and chlorophyll content. Additionally, data on root traits, agronomic parameters, yield parameters, and weed-related traits were also collected. In conclusion, yields in weed-free conditions were significantly higher than in weedy environments. Furthermore, maize and sunflower grown alongside mung beans in weed-free regions produced maximum yields compared to weedy checks. The results indicate that intercropping maize and sunflowers with mung beans is more advantageous than planting these crops as sole varieties. Maize, whether grown alone or intercropped with mungbean under weed-free conditions, produced higher grain yields of 6.75 g/plot and 5.8 g/plot compared to weed-infested conditions. Notably, the lower yield of mung beans provided a higher economic value compared to the higher yields of maize and sunflower.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".