The Determination of Optimal Crop Pattern with Aim of Reduction in Hazards of Environmental

Kakhki, Mahmoud Daneshvar; Sahnoushi, Nasser; Abadi, F. S. R.

doi:10.3844/ajabssp.2009.305.310

Cited by 13 publications

(4 citation statements)

References 14 publications

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“…Both the dominance of fuzzy GP over the crisp one and somewhat the in optimality of some high water consuming crops for sustainability of farming systems, have demonstrated in some other areas of Iran especially due to the relative similarity of determinant productive resources' situation of insufficiency. Notably, the results of Mohaddes and Mohayidin (2008), Daneshvar Kakhki et al (2009), Soltani et al (2011 and Mirkarimi et al (2013) are some instances. Sharma et al (2007) have also illustrated better recommendations on optimal land allocation for different crops in the planning process, based on the fuzzy GP compared to the ordinary GP and especially LP solutions in the rural region of Ghaziabad district of Uttar Pradesh, India.…”

Section: Resultsmentioning

confidence: 95%

Application of Fuzzy Multi-Objective Programming in Optimization of Crop Production Planning

Fasakhodi¹

2015

Asian J. of Agricultural Research

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Planning for the optimal use of productive resources in agricultural systems leads to the conservation in addition to the promotion of farmers' socio-economical conditions. Being certain or precise in any decision making in agricultural planning is impossible. Fuzzy mathematical programming techniques, developed in recent decades, are the most appropriate and applicable approaches to include the uncertainty in crop planning and productive resources management. Using the multi-objective Fuzzy Goal Programming (FGP) approach, the farming system of a rural region located in the central of Iran has investigated in this study in order to identify the optimal cropping pattern and land use planning under uncertainty. For this purpose, several objectives like maximizing the area under cultivation, net return and employment opportunities and simultaneously the land, capital, monthly water and labor force requirements and availabilities, crop rotation and a crop lower bound production constraint imprecisely considered as fuzzy goals. The needed data gathered through fieldwork operations. In multi-objective programming context, as the results revealed, the constraints of the productive resources are more determinant in land allocation than the objective functions. To illustrate the precedence of the cited FGP model, the results were quantitatively compared with the existing situation and a crisp goal programming model containing the same objectives and constraints. The precedence mainly pertained to the goals of objective functions. The crop-mix in FGP pattern change achieved considerable conservation of water and capital resources and improvement of income generation of the agricultural system, with almost no variation in the cultivation area.Key words: Rural farming system, optimal cropping, uncertainty, fuzzy multi-objective programming INTRODUCTIONMathematical Programming (MP) has been a widely used tool for studying and analyzing agricultural systems. Beginning with the well-known Linear Programming (LP) model, operations research has supplied us with a great variety of theoretically sound models (Lara and Stancu-Minasian, 1999). Optimization procedures have been receiving much attention in agricultural economic research over several decades. The LP is a single objective optimization technique and most of the farm planning problems are multi-objective in nature. Crop area planning or agricultural planning arenas involve multiple, conflicting and non-commensurable criteria and a compromise satisfying decision is looking for. Issues of risk, resources conservation and sustainability, environmental quality and social aspects of farming systems are as important as economic efficiency. It is clearly impossible to develop a single objective that satisfies all

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Section: Resultsmentioning

confidence: 95%

Application of Fuzzy Multi-Objective Programming in Optimization of Crop Production Planning

Fasakhodi¹

2015

Asian J. of Agricultural Research

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“…The FGP model has been found to uplift the benefit of farmers and employment opportunities better than the current practice meeting the environmental performance objectives (Wang et al, 2006;Kakhki et al, 2009;Garg and Singh, 2010). Pal and Moitra (2004) employing FGP as a multi-objective mathematical model decision making for long-range production in agricultural systems with five goals, meeting land utilization, productive resources use (which include workforce, water consumption, and fertilizer requirement sub-goals), production level, cash requirement, and profit goals, pronounced the significance of FGP for agricultural resource decision making to promote sustainable performance of agriculture.…”

Section: Fuzzy Goal Programming (Fgp)mentioning

confidence: 99%

Potential of mathematical model-based decision making to promote sustainable performance of agriculture in developing countries: A review article

Mellaku

Sebsibe²

2022

Heliyon

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“…Zaho et al (2009) have build a multi objective linear programming using genetic algorithm for the typical agriculture circular system with different objective such as production value maximization, total crop yields maximization and ecological benefit maximization by reusing the waste. Kakhki et al (2009) have developed the Fuzzy Multi Objective Linear Fractional Programming (FMOLFP) for the determination of optimal crop pattern with objective of reduction in hazards of environment. Regulwar and Anand Raj (2009) have formulated a monthly Multi Objective Multireservoir model and solved by Genetic algorithm under fuzzy environment.…”

Section: Introductionmentioning

confidence: 99%

Irrigation Planning Under Uncertainty—A Multi Objective Fuzzy Linear Programming Approach

Regulwar

Gurav

2010

Water Resour Manage

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The problem of irrigation planning becomes more complex by considering an uncertainty. The uncertainties can be tackled by formulating the problem of irrigation planning as Fuzzy Linear Programming (FLP). FLP models can incorporate the scenario of real world problem. In the present study, Multi Objective Fuzzy Linear Programming (MOFLP) irrigation planning model is formulated for deriving the optimal cropping pattern plan for the case study of Jayakwadi project in the Godavari river sub basin in Maharashtra State, India. Four conflicting objectives are considered such as Net Benefits (NB), Crop/Yield Production (CP), Employment Generation/Labour Requirement (EG) and Manure Utilization (MU). Four different cases are considered to incorporate the uncertainty in MOFLP model. To include the uncertainty in irrigation planning problem only objectives are taken as fuzzy and constraints are crisp in nature in Case-I. To consider the uncertainty involved in availability of resources, in Case-II the stipulations are fuzzy. The technological coefficients are fuzzy in Case-III. The Case-IV includes both technological coefficients and stipulations fuzzy. The level of satisfaction (λ) works out to be 0.58, 0.50, 0.50 and 0.28 respectively for Case-I to IV. The results obtained in Case-IV are more realistic and promising as it involves the uncertainty in technological coefficients and stipulations simultaneously.

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The Determination of Optimal Crop Pattern with Aim of Reduction in Hazards of Environmental

Cited by 13 publications

References 14 publications

Application of Fuzzy Multi-Objective Programming in Optimization of Crop Production Planning

Application of Fuzzy Multi-Objective Programming in Optimization of Crop Production Planning

Potential of mathematical model-based decision making to promote sustainable performance of agriculture in developing countries: A review article

Irrigation Planning Under Uncertainty—A Multi Objective Fuzzy Linear Programming Approach

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