optimization for cash crop planning using genetic algorithm: a case study of upper mun basin, nakhon...
TRANSCRIPT
OPTIMIZATION FOR CASH CROP PLANNING USING GENETIC ALGORITHM: A CASE
STUDY OF UPPER MUN BASIN, NAKHON RATCHASIMA PROVINCE
Patpida PatcharanuntawatAssoc.Prof. Kampanad BhaktikulAssoc.Prof. Charlie Navanugraha
Faculty of Environment and Resource StudiedMahidol University
Outline
• Background and Significance of the study
• Genetic Algorithm
• Research Objectives
• Method
• Results
• Conclusions
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.
Agriculture areas
113 million rais
(18.06 million hectare)
321 million rais (51.36 million hectare)
1 hectare = 6.25 rais
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
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.
Genetic Algorithm
chromosome
Gene(Decision Variable)
Genetic Algorithm
Chromosomes
Original Species(Parents)
New Species(Offspring)
1. Selection
2. crossover and mutation
3. Replacement
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
2m
0j
2
0kijkikiij
n
0 iiii PRAFProdNACPProd MaxZ
Objective Function
Constrain
n
1ijij1 MaxSAR
m
1j
n
1ijij1 MaxSAPIf then
Decision variable was the cultivation area
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)
Results
Irrigation Project
Suitable Crops
FAO1985 Weight-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
ResultsComparison 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)
FAO1985 weight-score
FAO1985 weight-score
ltk17
2 30,3 7
4 7 7 6
mb 67 908
1 9 3 9
lc12
4
834 4 1 22
lpp13
6 1,147 4 6 4 4
Results
Irrigation Project
maximum profits
(millionbaht)
nutrient loss
(millionbaht)
FAO1985 weight-score
FAO1985 weight-score
ltk 562
29
4
9 2 108
mb 8 3 13
8
3 2 54
lc31,6 4
8 4 103 31
lpp29,8 7
157 9 2 54
Comparison of maximum profits and soil nutrient loss with the application of FAO 1985 and weight-score in rainy season.
0
500
1000
1500
2000
2500
3000
3500
ltk mb lc lpp
irrigation project
profit (million baht)
FAO1985 (dry season)
weight-score (dry season)
FAO1985 (rainy season)
weight-score (rainy season)
Comparison of maximum profits with the application of FAO 1985 and weight-score.
0
20
40
60
80
100
120
ltk mb lc lpp
irrigation project
nutrient loss (million baht)
FAO1985 (dry season)
weight-score (dry season)
FAO1985 (rainy season)
weight-score (rainy season)
Comparison of soil nutrients loss with the application of FAO 1985 and weight-score.
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.
Temperature
Soil drainage
Effective soil depth
Organic matters
Available phosphorous
Soluble potassium
Soil physical and chemical properties
Cation exchange capacity
Base saturation percentage
Electrical conductivity of saturation
Soil texture
Slope
Moisture availability
Soil physical and chemical properties