OPTIMIZATION FOR CASH CROP PLANNING USING GENETIC ALGORITHM: A CASE STUDY OF UPPER MUN BASIN, NAKHON

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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. Kampanad Bhaktikul
• Assoc.Prof. Charlie Navanugraha

Faculty of Environment and Resource
Studied Mahidol University
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Outline

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

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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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Agriculture areas 113 million rais (18.06 million
hectare)
321 million rais (51.36 million hectare)
1 hectare 6.25 rais
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Agriculture 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
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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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Genetic Algorithm
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Genetic Algorithm
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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.

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Objective Function
Constrain
Decision variable was the cultivation area
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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)

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Results
Suitable crops from GA in dry season
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Results
Comparison of maximum profits and soil nutrient
loss with the application of FAO 1985 and
weight-score in dry season.
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Results
Comparison of maximum profits and soil nutrient
loss with the application of FAO 1985 and
weight-score in rainy season.
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Comparison of maximum profits with the
application of FAO 1985 and weight-score.
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Comparison of soil nutrients loss with the
application of FAO 1985 and weight-score.
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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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Thank You
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Soil physical and chemical properties
Temperature Soil drainage Effective soil
depth Organic matters Available
phosphorous Soluble potassium
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Soil physical and chemical properties
Cation exchange capacity Base saturation
percentage Electrical conductivity of
saturation Soil texture Slope Moisture
availability