Optimization of groundwater resource for balanced cropping pattern

Water Policy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 643-657
Author(s):  
Smita Varade ◽  
Jayantilal N. Patel
Keyword(s):  
Water Resources ◽  
Optimization Model ◽  
Pso Algorithm ◽  
Cropping Pattern ◽  
Groundwater Levels ◽  
Net Returns ◽  
Tlbo Algorithm ◽  
Net Benefits ◽  
Optimal Cropping ◽  
Teaching Learning

Abstract In this paper, an optimization model is formulated and optimal cropping pattern is suggested. Conjunctive use of water is not feasible for the study area as groundwater is the only source to fulfil irrigation demand. Water resources for the study area are limited. Best utilization of available water resources for increased net benefits is always advisable. Sinnar Taluka of Nasik district of Maharashtra state in India is considered as a study area. An existing cropping pattern is studied extensively and a new cropping pattern is suggested. Teaching–learning-based algorithm (TLBO) and particle swarm optimization (PSO) algorithm are applied to solve the optimization model. TLBO algorithm provides higher net returns as compared to PSO algorithm. TLBO and PSO saves up to 16.52% of water and benefits are increased by 35.81%. The study area is overexploited, this is fact. The new cropping pattern is suggested by considering minimum rainfall, i.e., 400 mm, so that in years when rainfall is above minimum rainfall the groundwater levels will be raised. In the proposed cropping pattern a few crop areas are majorly increased and a few crop areas are majorly reduced. The remaining crop areas are minorly increased or reduced and net benefits are increased and water demand decreased.

scholarly journals Self-adaptive multi-population-based Jaya algorithm to optimize the cropping pattern under a constraint environment

2019 ◽  
Vol 22 (2) ◽  
pp. 368-384
Author(s):  
Vijendra Kumar ◽  
S. M. Yadav
Keyword(s):  
Population Based ◽  
Cropping Pattern ◽  
Jaya Algorithm ◽  
Net Benefit ◽  
Invasive Weed ◽  
Cropping Patterns ◽  
Net Benefits ◽  
Optimal Cropping ◽  
Teaching Learning ◽  
Self Adaptive

Abstract Increasing population around the world, especially in India and China, has resulted in a drastic increase in water intake in both domestic and agricultural sectors. This, therefore, requires that water resources be planned and controlled wisely and effectively. With this consideration, the aim of the study is to achieve an optimal cropping pattern under a constrained environment. The objective is to maximize the net benefits with an optimum use of water. For optimization, a self-adaptive multi-population Jaya algorithm (SAMP-JA) has been used. For the Karjan reservoir in Gujarat State, India, two different models, i.e. maximum and average cropping patterns, were formulated based on the 75 per cent dependable inflow criteria. These two model scenarios are developed in such a way that either model can be selected by the farmer based on the crop area and its respective net benefits. Invasive weed optimization (IWO), particle swarm optimization (PSO), differential evolution (DE) and the firefly algorithm (FA) were compared to the results. The results show that the SAMP-JA obtained the maximum net benefit for both the models. The findings of the research are also compared with the actual cropping pattern. A significant increase has been noted in the cultivation of sugarcane, groundnut, wheat, millet, banana and castor. SAMP-JA has been noted to converge faster and outperforms PSO, DE, IWO, FA, teaching–learning-based optimization (TLBO), the Jaya algorithm (JA), elitist-JA and elitist-TLBO.

scholarly journals Hybrid Optimization Model for Conjunctive Use of Surface and Groundwater Resources in Water Deficit Irrigation System

2021 ◽  
Author(s):  
Karthikeyan MoothampalayamSampathkumar ◽  
Saravanan Ramasamy ◽  
Balamurugan Ramasubbu ◽  
Hamid Reza Pourghasemi ◽  
Saravanan Karuppanan ◽  
...  
Keyword(s):  
Water Resources ◽  
Water Deficit ◽  
Optimization Model ◽  
Conjunctive Use ◽  
Hybrid Optimization ◽  
Cropping Pattern ◽  
Net Benefit ◽  
Hybrid Optimization Model ◽  
Optimal Cropping ◽  
Surface And Groundwater

Increasing demand for food production with limited available water resources pose the threat to agricultural activities. The conjunctive allocation of water resources maximizes the net benefit of farmers efficiently. In this study, a novel hybrid optimization model was developed based on a genetic algorithm (GA), bacterial foraging optimization (BFO) and ant colony optimization (ACO) to maximize the net benefit of water deficit Sathanur reservoir command. The GA-based opti-mization model considered crop-related physical and economical parameters to derive optimal cropping patterns for three different conjunctive use policies and further allocation of surface and groundwater for different crops are enhanced with the BFO. The allocation of surface and groundwater for the head, middle and tail reach obtained from BFO is considered as input to ACO as a guiding mechanism to attain an optimal cropping pattern. Comparing the average produc-tivity values Policy 3 (3.665 Rs/m3) has better values relating to Policy 1 (3.662 Rs/m3) and Policy 2 (3.440 Rs/m3). Thus, the developed novel hybrid optimization model (GA-BFO-ACO) is very promising to enhance the farmer's net income as well as for the command area water conservation and can be replicated in other irrigated regions of the globe to overcome chronic land and water problems.

scholarly journals Hybrid optimization model for conjunctive use of surface and groundwater resources in water deficit irrigation system

2021 ◽  
Author(s):  
Karthikeyan Moothampalayam Sampathkumar ◽  
Saravanan Ramasamy ◽  
Balamurugan Ramasubbu ◽  
Saravanan Karuppanan ◽  
Balaji Lakshminarayanan
Keyword(s):  
Water Resources ◽  
Water Deficit ◽  
Optimization Model ◽  
Conjunctive Use ◽  
Hybrid Optimization ◽  
Cropping Pattern ◽  
Net Benefit ◽  
Hybrid Optimization Model ◽  
Optimal Cropping ◽  
Surface And Groundwater

Abstract The Increasing demand for food production with limited available water resources poses a threat to agricultural activities. Conventional optimization algorithm increases the processing stage and it performed with in the space, which is allocated from user. Therefore, the proposed work is utilized to design with better performance results. The conjunctive allocation of water resources maximizes the net benefit of farmers. In this study, a novel hybrid optimization model developed is first of its kind to resolve the sharing of water resources conflict among different reaches based on a genetic algorithm (GA), bacterial foraging optimization (BFO) and ant colony optimization (ACO) to maximize the net benefit of the water deficit Sathanur reservoir command. The GA-based optimization model considered crop-related physical and economic parameters to derive optimal cropping patterns for three different conjunctive use policies and further allocation of surface and groundwater for different crops are enhanced with the BFO. The allocation of surface and groundwater for the head, middle and tail reaches obtained from BFO is considered as an input to the ACO as a guiding mechanism to attain an optimal cropping pattern. Comparing the average productivity values, Policy 3 (3.665 Rs/m3) has better values relating to Policy 1 (3.662 Rs/m3) and Policy 2 (3.440 Rs/m3). Thus, developed novel hybrid optimization model (GA-BFO-ACO) is very promising for enhancing farmer's net income and can be replicated in other irrigated regions to overcome chronic water problems. The productivity value of policy 3 was 6.54% greater than that of policy 2, whereas that of policy 1 was 6.45% greater. Overall, the comparison shows the better performance analysis of various optimization is done successfully.

scholarly journals Optimal Cropping Pattern for Water Resources Management in Sistan Region of Iran using Goal Programming Method

Ecopersia ◽  
2016 ◽  
Vol 4 (4) ◽  
pp. 1555-1567 ◽  
Author(s):  
Mohammad Reza Dahmardeh Ghaleno ◽  
◽  
Vahedberdi Sheikh ◽  
Amir Sadoddin ◽  
Mahmood Sabouhi Sabouni ◽  
...  
Keyword(s):  
Water Resources ◽  
Water Resources Management ◽  
Goal Programming ◽  
Programming Method ◽  
Cropping Pattern ◽  
Resources Management ◽  
Optimal Cropping Pattern ◽  
Optimal Cropping

scholarly journals Optimal Land And Water Allocation Model For The Maximization of Net Agricultural Return In Drought Prone Regions

2021 ◽  
Author(s):  
Mulu Sewinet Kerebih ◽  
Ashok Kumar Keshari
Keyword(s):  
Water Resources ◽  
Rainfall Variability ◽  
Resources Allocation ◽  
Command Area ◽  
Cropping Pattern ◽  
Water Resources Allocation ◽  
Allocation Model ◽  
Cropping Patterns ◽  
The Government ◽  
Optimal Cropping

Abstract In this study, the land and water resources allocation model was developed to determine optimal cropping patterns and water resources allocations at different rainfall probability exceedance levels (PEs) to ensure maximum agricultural return in the Hormat-Golina valley irrigation command area, Ethiopia. To account the uncertainty of rainfall variability, the monthly dependable rainfall was estimated at three levels of reliability (20, 50 and 80% PEs) which are representing wet, normal and dry seasons based on regional experience. The irrigation water demand which was used as an input to the optimization model was estimated at each level of reliability by using CROPWAT model. The net annual returns of optimal cropping patterns were estimated as 181, 179 and 175 million Ethiopia Birr at 20 %, 50 % and 80 % PEs, respectively. The result of the optimal cropping pattern indicates that, the net annual return of the command area was increased to 45.75%, 45.84% and 47.01% than the Government targeted at 20%, 50% and 80% PEs, respectively. The findings reveal that the optimal land and water resources allocation model is very useful to the planners and decision makers to maximize the agricultural return particularly in areas where land and water resources are limited.

scholarly journals Multi Crop Optimization Using Linear Programming Model for Maximum Net Benefit

2018 ◽  
Vol 7 (3.12) ◽  
pp. 797
Author(s):  
Shreedhar R
Keyword(s):  
Linear Programming ◽  
Water Resources ◽  
Water Availability ◽  
Programming Model ◽  
Optimization Techniques ◽  
Cropping Pattern ◽  
Economic Returns ◽  
Net Benefit ◽  
Use Of Resources ◽  
Optimal Cropping

The water used for agriculture is 70% globally. This has resulted in new methods of saving water. Hence water saving techniques has to be practiced.  In water resources planning and management, optimization techniques is used for limited use of resources such as such as water, land, production cost, manpower, fertilizers, seeds, and pesticides. For cultivating each crop, the land area needs to be planned properly. Hence the crop pattern has to be decided optimally depending on available water resources and on economic basis. Therefore farmer needs to be educated to adopt optimum cropping pattern which maximises the economic returns. Hence the study is taken up to optimize the allocation of land areas to crops. The objective function for multi crop model were formulated using linear programming for maximizing the net benefits. The study resulted in optimal cropping pattern for different water availabilities ranging from 2000 Ha-m to 5500 Ha-m. The maximum net benefit for the study area varied from Rs. 53.2 Crores for 2000 Ha-m water availability to Rs.78 Crores at 5000 Ha-m water availability.  

scholarly journals Simulation-Optimization for Conjunctive Water Resources Management and Optimal Crop Planning in Kushabhadra-Bhargavi River Delta of Eastern India

2020 ◽  
Vol 17 (10) ◽  
pp. 3521
Author(s):  
Madan K. Jha ◽  
Richard C. Peralta ◽  
Sasmita Sahoo
Keyword(s):  
Water Resources ◽  
Simulation Model ◽  
Seawater Intrusion ◽  
Optimization Model ◽  
Crop Yields ◽  
Simulation Optimization ◽  
Optimization Models ◽  
Water Model ◽  
Cropping Pattern ◽  
Groundwater Exploitation

Water resources sustainability is a worldwide concern because of climate variability, growing population, and excessive groundwater exploitation in order to meet freshwater demand. Addressing these conflicting challenges sometimes can be aided by using both simulation and mathematical optimization tools. This study combines a groundwater-flow simulation model and two optimization models to develop optimal reconnaissance-level water management strategies. For a given set of hydrologic and management constraints, both of the optimization models are applied to part of the Mahanadi River basin groundwater system, which is an important source of water supply in Odisha State, India. The first optimization model employs a calibrated groundwater simulation model (MODFLOW-2005, the U.S. Geological Survey modular ground-water model) within the Simulation-Optimization MOdeling System (SOMOS) module number 1 (SOMO1) to estimate maximum permissible groundwater extraction, subject to suitable constraints that protect the aquifer from seawater intrusion. The second optimization model uses linear programming optimization to: (a) optimize conjunctive allocation of surface water and groundwater and (b) to determine a cropping pattern that maximizes net annual returns from crop yields, without causing seawater intrusion. Together, the optimization models consider the weather seasons, and the suitability and variability of existing cultivable land, crops, and the hydrogeologic system better than the models that do not employ the distributed maximum groundwater pumping rates that will not induce seawater intrusion. The optimization outcomes suggest that minimizing agricultural rice cultivation (especially during the non-monsoon season) and increasing crop diversification would improve farmers’ livelihoods and aid sustainable use of water resources.

scholarly journals Population based optimization algorithms improvement using the predictive particles

2020 ◽  
Vol 10 (3) ◽  
pp. 3261
Author(s):  
M. M. H. Elroby ◽  
S. F. Mekhamer ◽  
H. E. A. Talaat ◽  
M. A. Moustafa Hassan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Pso Algorithm ◽  
Population Based ◽  
The Other ◽  
Swarm Optimization ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Basic Philosophy

A new efficient improvement, called Predictive Particle Modification (PPM), is proposed in this paper. This modification makes the particle look to the near area before moving toward the best solution of the group. This modification can be applied to any population algorithm. The basic philosophy of PPM is explained in detail. To evaluate the performance of PPM, it is applied to Particle Swarm Optimization (PSO) algorithm and Teaching Learning Based Optimization (TLBO) algorithm then tested using 23 standard benchmark functions. The effectiveness of these modifications are compared with the other unmodified population optimization algorithms based on the best solution, average solution, and convergence rate.

Impact of Optimal Cropping Patterns on Incomes in a Punjab District

1976 ◽  
Vol 15 (2) ◽  
pp. 218-221
Author(s):  
M. Arshad Chaudhry
Keyword(s):  
Developing Countries ◽  
Linear Programming ◽  
Short Note ◽  
Cropping Pattern ◽  
Small Farms ◽  
Cropping Patterns ◽  
Punjab Province ◽  
Optimal Cropping

To improve farm incomes in developing countries, the foremost question that the farmer must address himself to is: what cropping pattern best uses the fixed resources in order to get the highest returns? During the last decade, the agricultural economists have shown great interest in applying the tools of linear programming to individual farms. Most of the studies conducted elsewhere have shown that, under existing cropping pattern, farm resources were not being utilized optimally on the small farms.[l, 4]. We conducted a survey in the canal-irrigated areas of the Punjab province of Pakistan1 to investigate into the same problem. This short note aims at identifying the opti¬mal cropping pattern and to estimate the increase in farm incomes as a result of a switch towards it on the sampled farms.

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