A multi-risk assessment framework for agricultural land use optimization

Uncertainties and risks in agricultural land use allocation may affect agricultural sustainable development, which leads to the necessity to effectively assess risks when optimally allocating agricultural land use. In this study, a framework of multi-risk assessment for agricultural land use allocation was developed. In the decision-making of agricultural land use optimization, the developed framework is capable of fully describing multiple risks using uncertainty methodology, including fuzzy, stochastic and interval methods. Moreover, the framework can help managers fully understand risks associated with various water supply scenarios, targets and optimization schemes of agricultural land use optimization. A real-world case study in Minqin, northwest of China was conducted to demonstrate the feasibility and practicability of the developed framework. The runoff volume of Caiqi was divided into five intervals by interior-outer-set model which represents risk levels in risk assessment of water supply. Then, results of agricultural land use allocation expressed by Shannon information entropy of water use structure and relevant optimal targets were obtained by Monte Carlo method. Sensitivity analysis on water use efficiency, available cultivated area and the ratio of grain crops to commercial crops will help decision makers better understand the potential risk conditions and then provide guidance in choosing suitable plans of agricultural land use.

[1]  Guohe Huang,et al.  Grey linear programming, its solving approach, and its application , 1993 .

[2]  Ping Guo,et al.  Multiobjective Stochastic Fractional Goal Programming Model for Water Resources Optimal Allocation among Industries , 2016 .

[3]  Niels C. Lind,et al.  Accountability, Risk, and the ALARP (As Low As Reasonably Practicable) limit of benefit , 2014 .

[4]  Guohe Huang,et al.  Assessment of uncertainty effects on crop planning and irrigation water supply using a Monte Carlo simulation based dual-interval stochastic programming method , 2017 .

[5]  Mo Li,et al.  A coupled random fuzzy two-stage programming model for crop area optimization—A case study of the middle Heihe River basin, China , 2015 .

[6]  L. Garrote,et al.  Challenges to Manage the Risk of Water Scarcity and Climate Change in the Mediterranean , 2007 .

[7]  Yurui Fan,et al.  Inexact Fuzzy Stochastic Chance Constraint Programming for Emergency Evacuation in Qinshan Nuclear Power Plant under Uncertainty , 2017 .

[8]  Jun Guo,et al.  Fuzzy risk analysis of flood disasters based on diffused-interior-outer-set model , 2012, Expert Syst. Appl..

[9]  Hironori Yagi,et al.  An Empirical Application of the Linear Programming Model for Agricultural Land Use Planning through the Valuation of Negative Externalities Caused by Abandoning Farmland in Marginal Areas , 2008 .

[10]  Louis R. Nemzer,et al.  Shannon information entropy in the canonical genetic code. , 2017, Journal of theoretical biology.

[11]  Li-Hua Feng,et al.  Analysis on fuzzy risk of landfall typhoon in Zhejiang province of China , 2009, Math. Comput. Simul..

[12]  Mo Li,et al.  A multi-objective optimal allocation model for irrigation water resources under multiple uncertainties , 2014 .

[13]  Hiroshi Inoue,et al.  Soft risk maps of natural disasters and their applications to decision-making , 2007, Inf. Sci..

[14]  Ping Guo,et al.  Two-stage fuzzy chance-constrained programming: application to water resources management under dual uncertainties , 2009 .

[15]  Yi Liu,et al.  The practical research on flood risk analysis based on IIOSM and fuzzy α-cut technique , 2012 .

[16]  Yozo Nakahara,et al.  On the linear programming problems with interval coefficients , 1992 .

[17]  Vijay P. Singh,et al.  An efficient irrigation water allocation model under uncertainty , 2016 .

[18]  Prof. D. P. Patil,et al.  Management of Uncertainty In Supply Chain , 2012 .

[19]  Edward A. McBean,et al.  An Integrated Risk Analysis Method for Planning Water Resource Systems to Support Sustainable Development of An Arid Region , 2017 .

[20]  Huang Chong-fu,et al.  Principle of information diffusion , 1997 .

[21]  Ajay K. Singh,et al.  Land and water management planning for increasing farm income in irrigated dry areas , 2015 .

[22]  Dongguo Shao,et al.  Multi-Objective Risk Assessment on Water Resources Optimal Allocation , 2009 .

[23]  Chenglong Zhang,et al.  Uncertain and multi-objective programming models for crop planting structure optimization , 2016 .

[24]  R. Hornbeck,et al.  The Historically Evolving Impact of the Ogallala Aquifer: Agricultural Adaptation to Groundwater and Drought , 2014 .

[25]  Terje Aven.  Risk Analysis Methods , 2008 .

[26]  Chongfu Huang,et al.  Principle of information diffusion , 1997, Fuzzy Sets Syst..

[27]  S. Kaplan,et al.  On The Quantitative Definition of Risk , 1981 .

[28]  G. Huang,et al.  Management of Uncertain Information for Environmental Systems Using a Multistage Fuzzy-Stochastic Programming Model with Soft Constraints , 2011 .

[29]  Mo Li,et al.  Simulation-Based Inexact Fuzzy Semi-Infinite Programming Method for Agricultural Cultivated Area Planning in the Shiyang River Basin , 2017 .

[30]  Liudong Zhang,et al.  An Improved Solving Approach for Interval-Parameter Programming and Application to an Optimal Allocation of Irrigation Water Problem , 2015, Water Resources Management.

[31]  G H Huang,et al.  Interval-parameter semi-infinite fuzzy-stochastic mixed-integer programming approach for environmental management under multiple uncertainties. , 2010, Waste management.

[32]  James J. Buckley,et al.  On using a-cuts to evaluate fuzzy equations , 1990 .

[33]  Huang Chong-fu.  An application of calculated fuzzy risk , 2002 .

[34]  Usman T. Khan,et al.  Comparing A Bayesian and Fuzzy Number Approach to Uncertainty Quantification in Short-Term Dissolved Oxygen Prediction , 2017 .

[35]  Guowei Ma,et al.  Multi-level explosion risk analysis (MLERA) for accidental gas explosion events in super-large FLNG facilities , 2017 .

[36]  Ping Guo,et al.  Fuzzy multi-objective linear programming applying to crop area planning , 2010 .

[37]  Q. Li.  Fuzzy approach to analysis of flood risk based on variable fuzzy sets and improved information diffusion methods , 2013 .

[38]  Chenglong Zhang,et al.  Risk analysis methods of the water resources system under uncertainty , 2015 .

[39]  Chenglong Zhang,et al.  Trend detection and stochastic simulation prediction of streamflow at Yingluoxia hydrological station, Heihe River Basin, China , 2017 .

[40]  Abhishek Chaudhary,et al.  Bayesian Monte Carlo and maximum likelihood approach for uncertainty estimation and risk management: Application to lake oxygen recovery model. , 2017, Water research.

[41]  Shinsook Yoon,et al.  Weighting Method Based on Entropy Analysis for Multi-sensor Data Fusion in Wireless Sensor Networks , 2011, FGIT-DTA/BSBT.

[42]  Vijay P. Singh,et al.  An uncertainty-based framework for agricultural water-land resources allocation and risk evaluation. , 2016 .

[43]  Chongfu Huang,et al.  Cartographic Representation of the Uncertainty Related to Natural Disaster Risk: Overview and State of the Art , 2004, CASDMKM.

[44]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[45]  P. J. Parks.  Explaining "Irrational" Land Use: Risk Aversion and Marginal Agricultural Land , 1995 .