Improving Maize Production and Farmers’ Income Using System Dynamics Model
Bibliographic record
Abstract
Maize demand for feed, industry, and consumption is increasing in line with the increase in population and industry, while the supply of maize does not meet the demand. Therefore, it is necessary to identify the significant variables that affect maize cultivation and scenarios to increase maize production and farmers’ income using simulation model. As a method to develop the models, a system dynamics simulation model is used to accommodate internal and external variables that affect the production and farmers’ income which can be done using organic fertilizer, the integration between land expansion and organic fertilizer, and the implementation of precision agriculture. The simulation results show that land area, use of fertilizers, and technology adoption affect the production and income of maize farmers. The scenarios developed include organic fertilizer scenario, expansion and organic fertilizer scenario, and precision agriculture scenario. The resulting scenario can be used as a recommendation for the government and stakeholders in developing strategies and policies related to a sustainable maize farming system that can help increase the production and income of maize farmers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".