Modeling and forecasting of agricultural crop production and economic stability based on Gross Regional Domestic Product
Bibliographic record
Abstract
The Philippine economy is largely dependent on agriculture with a total land area of 11.6 million hectares dedicated to crops. Rice and corn are the leading produce so far, occupying around 5.5 million of the total number of hectares. This figure is followed by 4.8 million hectares taken up by major crops consisting of fruits such as coconut, pineapple, banana, and mango, and other crops in demand like sugar and coffee. The other 1.3 million hectares are distributed to other minor crops. The Philippine economy is largely sustained by crop production because in spite of the occurrence of natural disasters and economic pitfalls (such as unemployment or displacement) that regularly hit the nation, people are able to continually feed themselves and support each other. In 2002, Eastern Visayas registered 330.8 thousand farms for agricultural use, covering 723 thousand hectares. Among the provinces in Eastern Visayas, Leyte shared the highest number of farms with 136.2 thousand, covering 258.6 thousand hectares of agricultural land. Samar ranked second with 57 thousand farms, covering 102 thousand hectares, while Northern Samar came in third with 49.9 thousand farms, covering 179.5 thousand hectares. Hence, the Philippines’ quests for global competitiveness and food security requires an effective and efficient crop forecasting system that can be used for monitoring as well as strategic and tactical decision-making on crop production. This study aimed to forecast the agricultural crop production and economic stability based on GRDP in Eastern Visayas from 2nd quarter of 2015 to 4th quarter of 2017. The findings of the study give significant effects and impacts on Philippine economy which threatens our agricultural production in the country. The study also showed no significant relationship between the agricultural production like production volume, area harvested and yields per hectare of corn and palay and the economic stability in terms of GRDP per capita, imports and exports of agricultural products. We therefore established that the economic stability based on gross domestic product is not a predictive factor on the agricultural crop production in Region VIII.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".