Farm-level and district efforts to improve water management during drought

1992 ◽  
Vol 6 (3) ◽  
pp. 189-199 ◽  
Author(s):  
Dennis Wichelns ◽  
David Cone
Keyword(s):  
Water Management ◽  
Farm Level

A dynamic model for water management at the farm level integrating strategic, tactical and operational decisions

2018 ◽  
Vol 100 ◽  
pp. 123-135 ◽  
Author(s):  
Marion Robert ◽  
Alban Thomas ◽  
Muddu Sekhar ◽  
Hélène Raynal ◽  
Éric Casellas ◽  
...  
Keyword(s):  
Water Management ◽  
Dynamic Model ◽  
Operational Decisions ◽  
Farm Level

scholarly journals EVALUATION OF SOIL WATER MANAGEMENT PROPERTIES BASED ON LIDAR DATA AND SOIL ANALYSES, AT FARM LEVEL

2019 ◽  
Vol 9 (2) ◽  
pp. 160-173
Author(s):  
Florent Demelezi ◽  
◽  
Bernadett Galya ◽  
Janos Tamas ◽  
Imri Demelezi ◽  
...  
Keyword(s):  
Water Management ◽  
Soil Water ◽  
Lidar Data ◽  
Farm Level

Building Farm-Level Capacities in Irrigation Water Management to Adapt to Climate Change

2017 ◽  
Vol 67 (1) ◽  
pp. 43-54 ◽  
Author(s):  
Krishna Reddy Kakumanu ◽  
Yella Reddy Kaluvai ◽  
Udaya Sekhar Nagothu ◽  
Narayan Reddy Lati ◽  
Gurava Reddy Kotapati ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Management ◽  
Irrigation Water ◽  
Farm Level ◽  
Irrigation Water Management

scholarly journals Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2611
Author(s):  
Bahram Saeidian ◽  
Mohammad Saadi Mesgari ◽  
Biswajeet Pradhan ◽  
Abdullah M. Alamri
Keyword(s):  
Water Management ◽  
Objective Function ◽  
Water Allocation ◽  
Optimal Allocation ◽  
Heuristic Algorithms ◽  
Imperialist Competitive Algorithm ◽  
Complex Problem ◽  
Bees Algorithm ◽  
Farm Level ◽  
Related Data

The present water crisis necessitates a frugal water management strategy. Deficit irrigation can be regarded as an efficient strategy for agricultural water management. Optimal allocation of water to agricultural farms is a computationally complex problem because of many factors, including limitations and constraints related to irrigation, numerous allocation states, and non-linearity and complexity of the objective function. Meta-heuristic algorithms are typically used to solve complex problems. The main objective of this study is to represent water allocation at farm level using temporal cultivation data as an optimisation problem, solve this problem using various meta-heuristic algorithms, and compare the results. The objective of the optimisation is to maximise the total income of all considered lands. The criteria of objective function value, convergence trend, robustness, runtime, and complexity of use and modelling are used to compare the algorithms. Finally, the algorithms are ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). The income resulting from the allocation of water by the imperialist competitive algorithm (ICA) was 1.006, 1.084, and 1.098 times that of particle swarm optimisation (PSO), bees algorithm (BA), and genetic algorithm (GA), respectively. The ICA and PSO were superior to the other algorithms in most evaluations. According to the results of TOPSIS, the algorithms, by order of priority, are ICA PSO, BA, and GA. In addition, the experience showed that using meta-heuristic algorithms, such as ICA, results in higher income (4.747 times) and improved management of water deficit than the commonly used area-based water allocation method.

FARM LEVEL OPTIMAL WATER MANAGEMENT: ASSISTANT FOR IRRIGATION UNDER DEFICIT (FLOW-AID)

2009 ◽  
pp. 247-254 ◽  
Author(s):  
J. Balendonck ◽  
C. Stanghellini ◽  
J. Hemming ◽  
F.L.K. Kempkes ◽  
B.A.J. Van Tuijl
Keyword(s):  
Water Management ◽  
Farm Level
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