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Patpida Patcharanuntawat Assoc.Prof. Kampanad Bhaktikul Assoc.Prof. Charlie Navanugraha

OPTIMIZATION FOR CASH CROP PLANNING USING GENETIC ALGORITHM: A CASE STUDY OF UPPER MUN BASIN, NAKHON RATCHASIMA PROVINCE. Patpida Patcharanuntawat Assoc.Prof. Kampanad Bhaktikul Assoc.Prof. Charlie Navanugraha. Faculty of Environment and Resource Studied Mahidol University. Outline.

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Patpida Patcharanuntawat Assoc.Prof. Kampanad Bhaktikul Assoc.Prof. Charlie Navanugraha

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  1. OPTIMIZATION FOR CASH CROP PLANNING USING GENETIC ALGORITHM: A CASE STUDY OF UPPER MUN BASIN, NAKHON RATCHASIMA PROVINCE Patpida Patcharanuntawat Assoc.Prof. Kampanad Bhaktikul Assoc.Prof. Charlie Navanugraha Faculty of Environment and Resource Studied Mahidol University

  2. Outline • Background and Significance of the study • Genetic Algorithm • Research Objectives • Method • Results • Conclusions

  3. Background and Significance of the study • Most people are agriculturist. • Qualified lands available for agriculture are less. • Thailand’s agricultural products per rai had tendency to decline.

  4. Agriculture areas 113 million rais (18.06million hectare) 321 millionrais (51.36 million hectare) 1 hectare = 6.25 rais

  5. Agriculture areas 113 million rais (18.06million hectare) 321 millionrais (51.36 million hectare) Qualified lands available for agriculture 34 million rais (5.44million hectare) 1 hectare = 6.25 rais

  6. Background and Significance of the study • Most people are agriculturist. • Qualified lands available for agriculture are less. • Thailand’s agricultural products per rai had tendency to decline.

  7. chromosome Gene (Decision Variable) Genetic Algorithm

  8. Chromosomes • 1. Selection Original Species (Parents) 3. Replacement 2. crossover and mutation New Species (Offspring) Genetic Algorithm

  9. Research Objectives • To develop the decision-making process in order to finding appropriate cash crops for cultivation - crop type - cultivation area - economic return rate - major soil nutrients loss as fertilizer value • To compare the finding results with the weight-score method.

  10. If then Objective Function Constrain Decision variable was the cultivation area

  11. Methods • Data Collection • GIS- Soil layer that suitable for cash crops • Land suitability for each cash crops (FAO & Weight-score) • Comparison of the results (FAO 1985 method and Weight-score method using Genetic Algorithm)

  12. Results Suitable crops from GA in dry season

  13. Results Comparison of maximum profits and soil nutrient loss with the application of FAO 1985 and weight-score in dry season.

  14. Results Comparison of maximum profits and soil nutrient loss with the application of FAO 1985 and weight-score in rainy season.

  15. Comparison of maximum profits with the application of FAO 1985 and weight-score.

  16. Comparison of soil nutrients loss with the application of FAO 1985 and weight-score.

  17. Conclusions • FAO1985, dry season was suitable for growing rice and sugar cane, rainy season rice and groundnut should be grown. • Weight-score, dry season was suitable for growing tomatoes and corns, rainy season rice and corns should be grown.

  18. Thank You

  19. Soil physical and chemical properties Temperature Soil drainage Effective soil depth Organic matters Available phosphorous Soluble potassium

  20. Soil physical and chemical properties Cation exchange capacity Base saturation percentage Electrical conductivity of saturation Soil texture Slope Moisture availability

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