Agriculture System Modeling to Increase Productivity and Production Through Sustainable Resource Management
Bibliographic record
Abstract
Mismanagement of soil nutrients, poor site selection of loose soil, steep slopes for agriculture, parallel contour plowing, ground cover removal, and slash-and-burn contribute to soil degradation and erosion. Therefore, developing strategies and policies related to improving productivity, production, and better resource management is important to achieve a sustainable agriculture system. This paper aims to provide an analytical model of the agriculture system to increase productivity and production through sustainable resource management. System dynamics (SD) modeling was used to model the relationships between significant variables in improving land productivity, production, and sustainable resource management. SD can accommodate complexity and nonlinearity in real systems. Increasing resource management is required to achieve a sustainable agriculture system. Better resource management can be done using superior seeds according to location, balanced fertilization, and the application of plant-based pesticides. Productivity depends on water availability, rainfall, temperature, seed quality, the effect of the Jajar Legowo planting system, pest and disease control, soil nutrients, and soil fertility. Rice production is affected by milled rice production, rendement, and lost seeds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".