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OPTIMIZATION FOR CASH CROP PLANNING USING GENETIC ALGORITHM: A CASE STUDY OF UPPER MUN BASIN, NAKHON RATCHASIMA PROVINCE Patpida Patcharanuntawat Assoc.Prof.

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Presentation on theme: "OPTIMIZATION FOR CASH CROP PLANNING USING GENETIC ALGORITHM: A CASE STUDY OF UPPER MUN BASIN, NAKHON RATCHASIMA PROVINCE Patpida Patcharanuntawat Assoc.Prof."— Presentation transcript:

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. Thailands agricultural products per rai had tendency to decline.

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

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

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

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8 Genetic Algorithm chromosome Gene (Decision Variable)

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

10 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.

11 Objective Function Constrain If then Decision variable was the cultivation area

12 Methods 1.Data Collection 2.GIS- Soil layer that suitable for cash crops 3.Land suitability for each cash crops (FAO & Weight-score) 4.Comparison of the results (FAO 1985 method and Weight-score method using Genetic Algorithm)

13 Results Irrigation Project Suitable Crops FAO1985Weight-Score Lam Takhong (ltk) rice, sugar cane corn, soybean, groundnut,sugar cane mungbean, tomato Mun Bon (mb) rice, sugar cane corn, soybean, groundnut,sugar cane mungbean, tomato Lam Sae (lc) rice, sugar cane corn, groundnut, mungbean, tomato Lam Phraphlong (lpp) rice, soybean, groundnut, mungbean, sugar cane corn, soybean, groundnut,sugar cane mungbean, tomato Suitable crops from GA in dry season

14 Results Comparison of maximum profits and soil nutrient loss with the application of FAO 1985 and weight-score in dry season. Irrigation Project maximum profits (millionbaht) nutrient loss (millionbaht) FAO1985weight- score FAO1985weight- score ltk 1723, mb lc lpp 1361,

15 Results Irrigation Project maximum profits (millionbaht) nutrient loss (millionbaht) FAO1985weight- score FAO1985weight- score ltk mb lc 3, lpp 2, Comparison of maximum profits and soil nutrient loss with the application of FAO 1985 and weight-score in rainy season.

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

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

18 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.

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

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


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