Agriculture System Modeling to Increase Productivity and Production Through Sustainable Resource Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it