Optimum Allocation of Land and Water Resources Based on Results of Benchmarking
Dr Avinash S Garudkar ME (IIT Roorkee), PhD (IIT Bombay), DIH (Israel)
Associate Professor Faculty of Engineering Water and Land Management Institute Aurangabad, Maharashtra, INDIA 1
Goal of Benchmarking The goal of BM is to improve the performance of an irrigation project as measured against its mission and objectives ; leading to Optimum Utilization of Land and Water Resources
2
System Performance Indicator Annual irrigation supply per unit irrigated area (m3/ha) Ratio of total quantity of water supplied for irrigation in all the seasons in year to area irrigated in that year. Varies with water availability, cropping pattern, soil type, system condition and management Objective is to improve water use efficiency
Agricultural Performance Indicator Output per unit irrigated water supply (Rs /m3) It is the ratio of output in terms of money of agricultural production from irrigated area to total quantity of water supplied for irrigation. It can be improved with optimal water use and improved varieties of the crops. The objective is to improve water productivity
Results of Benchmarking Results of various indicators of benchmarking viz. annual irrigation supply per unit irrigated area (m3/ha) ; output per unit irrigated water supply (Rs/m3 ), output per unit irrigated area (Rs /ha) indicate need to improve water use efficiency 5
Measures to Improve Irrigation Water Use Efficiency Structural measures : Repairs of structures, Restoration of canal , Selective lining etc. Non structural measures: Farmer as a target; Creating awareness , PIM etc. Optimum allocation of land and water resources
Optimum Water Release Policy The basic requirement is to know – When to release the water ? How much to release ? If multi-reservoir then from which reservoir ? And for how much area ? Considering climatic conditions, soil, crops (mainly crop growth stages)
Optimal Water Release Policy Water release policy should incorporate heterogeneity of the command area; so as to obtain maximum yield The rotation schedule should match the growth stages of majority of the crops Small improvement in operating policy may lead to large benefits
Optimal Water and Land Allocation Model Based on Genetic Algorithm Various studies have indicated that, when variables are more (like WR System) , heuristic approaches like Genetic Algorithm (GA) are more suitable Genetic Algorithm is a search method that mimics natural biological evolution process to find out near optimal solution
Genetic Algorithm is latest Optimization Technique based on “Darwin’s Evolution Theory” with wide applications in engineering
Cell, Chromosomes and Genes Cell Nucleus Chromosomes
Gene
DNA molecule (Chromosome) Cells: 75-100 trillions ??? Each Cell: 23 Chromosomes Genes: 30,000 to 40,000 ??? Total DNA Bases: 3 Billion?
Analogy Chromosome
Genes
DNA Sequence G T C A G G C C T
String (Variables) Single Variable
Coding: Binary/ Real
Basis of Genetic Algorithm
Parent chromosomes Cross over of genes Mutation of genes New chromosomes No of generations Adaptability- Solution
The Location and Index Plan of the Study Area
14
Chromosome / String of the Model Karanjwan
Waghad R1 C1 Rotation 1 T1
1
Punegaon
Ozerkhed
Palkhed
R1 C2 T1
R1 R2 River C1 T1 T1
R2 R3 River C1 T1 T1
R3 R4 River C1 T1 T1
R4 R5 River C1 T1 T1
R5 C2 T1
2
3
5
7
9
11
4
6
8
10
11 releases from different reservoirs * 19 rotations = 209 genes
Rotation 2
R1 C1 T2
R1 C2 T2
R1 River T2
R2 C1 T2
R2 R3 River C1 T2 T2
R3 River T2
R4 C1 T2
R4 River T2
R5 C1 T2
R5 C2 T2
12
13
14
15
16
18
19
20
21
22
R1 C1 T19
R1 C2 T19
R1 R2 River C1 T19 T19
R2 R3 River C1 T19 T19
Rotation 19
199 200 201
202
17
203
204
R3 R4 River C1 T19 T19
R4 R5 River C1 T19 T19
R5 C2 T19
205
207
209
206
208
Objective function: Maximization of total net benefits
Set of water release Next set of release Compute total benefits with simulation model considering crop growth stages
Termination Criteria?
Genetic Algorithm Model
No
Yes Stop
Flow Chart for Optimum Water Allocation Using GA
UNIT NO 24
No of crops = 9
Wheat, Maize, Gram, Cauliflower, Grapes Sugarcane, Tomato, Onion, Ground nut
Tomato : Surface Irrigation Field application effi. = 0.75 Grapes: Drip Irrigation Field application efficiency = 0.90
For Each Crop: Soil water balance Root growth model Crop growth stages Yield response
Area Optimization Model N=Percentage Irrigated area = N * ICA GA Optimization Model
N=N+∆N
Is N < 100 percent
Select Irrigated area with max benefits
Stop
Different Cases for Study 1) Case I :
Evaluation of existing water release policy for actual irrigated area
2) Case II : GA based optimal water release policy for existing irrigated area 3) Case III : GA based area optimization with area proportionate water distribution uniform proportion for all the projects.
Results of the Study Total net benefits for various cases were: 1) Case I : Evaluation of existing policy 314.28 M Rs 2) Case II : GA based optimal policy
350.04 M Rs
3) Case III : Area optimization
638.97 M Rs
In Case I and Case II irrigated area was same however it was more in Case III (deficit irrigation)
Benchmarking is not a magicians stick which will change everything unless There is a strong and active commitment from senior management to lead and implement the BM process and There is a willingness to change and adapt new practices based on findings
Thank You U R Welcome to Contact: Dr Avinash S Garudkar E mail: as_garudkar @ rediffmail . com Mobile: 098224 40820 Office : 0240 - 2379160 - 62 Ext 231