Title: OPTIMIZATION FOR CASH CROP PLANNING USING GENETIC ALGORITHM: A CASE STUDY OF UPPER MUN BASIN, NAKHON
1OPTIMIZATION 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
2Outline
- Background and Significance of the study
- Genetic Algorithm
- Research Objectives
- Method
- Results
- Conclusions
3Background 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.
4Agriculture areas 113 million rais (18.06 million
hectare)
321 million rais (51.36 million hectare)
1 hectare 6.25 rais
5Agriculture areas 113 million rais (18.06 million
hectare)
321 million rais (51.36 million hectare)
Qualified lands available for agriculture 34
million rais (5.44 million hectare)
1 hectare 6.25 rais
6Background 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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8Genetic Algorithm
9Genetic Algorithm
10Research 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.
11Objective Function
Constrain
Decision variable was the cultivation area
12Methods
- 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)
13Results
Suitable crops from GA in dry season
14Results
Comparison of maximum profits and soil nutrient
loss with the application of FAO 1985 and
weight-score in dry season.
15Results
Comparison of maximum profits and soil nutrient
loss with the application of FAO 1985 and
weight-score in rainy season.
16Comparison of maximum profits with the
application of FAO 1985 and weight-score.
17Comparison of soil nutrients loss with the
application of FAO 1985 and weight-score.
18Conclusions
- 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.
19Thank You
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21Soil physical and chemical properties
Temperature Soil drainage Effective soil
depth Organic matters Available
phosphorous Soluble potassium
22Soil physical and chemical properties
Cation exchange capacity Base saturation
percentage Electrical conductivity of
saturation Soil texture Slope Moisture
availability