A Simulated Weather-Driven Bio-Economic Optimization Model for Agricultural Planning
Bibliographic record
Abstract
This study develops a weather-driven bio-economic optimization framework for agricultural planning in Guyana by integrating weather simulation, crop modeling, and multi-objective optimization. Precipitation was modeled using a first-order Markov chain with fitted distribution, while temperature and relative humidity were simulated using stochastic differential equations. Reference evapotranspiration was estimated using an artificial neural network. These simulated weather variables were then used as inputs to AquaCrop to estimate rice, maize, and soybean yields across multiple planting intervals. A multi-objective optimization model was then applied to optimize gross profit, economic water productivity, and land use efficiency. Validation at the Rose Hall Estate showed strong accuracy for rice and maize (MAPE < 10%) and moderate accuracy for soybeans. Scenario analyses for the 2024–2025 season, assuming 25% and 50% export targets, revealed that rice–maize double cropping produced the highest profitability, while soybean–maize combinations were less favorable. The framework replaces static yield assumptions with dynamic, simulation-driven models that incorporate price forecasts and allow substitution of alternative forecasting or crop simulators to enhance precision. The scenario-based design provides a flexible decision-support platform for optimizing crop selection, planting intervals, and resource allocation under climate variability and market uncertainty. Moreover, the framework is scalable and well-suited for evidence-based agricultural planning.
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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 | 0.000 | 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".