Yield advantage and water productivity of maize-mungbean inter-cropping systems in the Dry Zone of Sri Lanka; a modelling approach
Bibliographic record
Abstract
Water is not efficiently used in most of the cropping systems in South Asia. Therefore, water-efficient agriculturalpractices are of immense importance when increasing the area to be cultivated, and/or conserving rain water tobe used in drier regions through irrigation. Inter-cropping of maize (Zea maize L.) with mungbean (Vigna radiateL.) R. Wilczek may offer benefits in utilising water efficiently and maintaining or improving land productivity.Simulation models are useful tools to assess the performance of agricultural systems. The present study wasconducted to evaluate the irrigation water requirement and yield of the maize-mungbean inter-cropping system incomparison with mono-cropping, using APSIM. Simulation results revealed that the maize-mungbean intercroprequired only 4 % more water than the maize mono-crop (P>0.05). Moreover, intercrop maize yield was only3 % less than that of the maize mono-crop (P>0.05) However, yield of mungbean was 21 % less in the intercroppingsystem than the mono-crop system (P<0.05). The land-equivalent ratio of the maize-mungbean intercroppingsystem was 1.8. Efficient use of water under the inter-cropping system, with a similar yield of maize tothat obtained under mono-crop of maize, combined with an additional mungbean harvest highlights the greaterefficiency of the maize-mungbean inter-cropping system in the Dry Zone of Sri Lanka.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".