Spatial Optimization of Agricultural Land Use Based on Cross-Entropy Method

An integrated optimization model was developed for the spatial distribution of agricultural crops in order to efficiently utilize agricultural water and land resources simultaneously. The model is based on the spatial distribution of crop suitability, spatial distribution of population density, and agricultural land use data. Multi-source remote sensing data are combined with constraints of optimal crop area, which are obtained from agricultural cropping pattern optimization model. Using the middle reaches of the Heihe River basin as an example, the spatial distribution of maize and wheat were optimized by minimizing cross-entropy between crop distribution probabilities and desired but unknown distribution probabilities. Results showed that the area of maize should increase and the area of wheat should decrease in the study area compared with the situation in 2013. The comprehensive suitable area distribution of maize is approximately in accordance with the distribution in the present situation; however, the comprehensive suitable area distribution of wheat is not consistent with the distribution in the present situation. Through optimization, the high proportion of maize and wheat area was more concentrated than before. The maize area with more than 80% allocation concentrates on the south of the study area, and the wheat area with more than 30% allocation concentrates on the central part of the study area. The outcome of this study provides a scientific basis for farmers to select crops that are suitable in a particular area.

[1]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[2]  Liangzhi You,et al.  An entropy approach to spatial disaggregation of agricultural production , 2006 .

[3]  Vijay P. Singh,et al.  Optimal Allocation of Agricultural Water Resources Based on Virtual Water Subdivision in Shiyang River Basin , 2014, Water Resources Management.

[4]  Xinyue Ye,et al.  Coarse-grained parallel genetic algorithm applied to a vector based land use allocation optimization problem: the case study of Tongzhou Newtown, Beijing, China , 2013, Stochastic Environmental Research and Risk Assessment.

[5]  Vijay P. Singh,et al.  Entropy Theory and its Application in Environmental and Water Engineering: Singh/Entropy Theory and its Application in Environmental and Water Engineering , 2013 .

[6]  Shaozhong Kang,et al.  Spatio-temporal distribution of irrigation water productivity and its driving factors for cereal crops in Hexi Corridor, Northwest China , 2017 .

[7]  Shaowen Wang,et al.  Sustainable land use optimization using Boundary-based Fast Genetic Algorithm , 2012, Comput. Environ. Urban Syst..

[8]  Liangzhi You,et al.  Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach , 2009 .

[9]  Yaolin Liu,et al.  Regional land-use allocation with a spatially explicit genetic algorithm , 2014, Landscape and Ecological Engineering.

[10]  A. K. Lohani,et al.  Fuzzy Multiobjective and Linear Programming Based Management Models for Optimal Land-Water-Crop System Planning , 2006 .

[11]  Wei Tang,et al.  A land-use spatial optimization model based on genetic optimization and game theory , 2015, Comput. Environ. Urban Syst..

[12]  Yu Yan,et al.  Model of urban land-use spatial optimization based on particle swarm optimization algorithm. , 2010 .

[13]  Li Zhang,et al.  Spatio-temporal dynamics of maize cropping system in Northeast China between 1980 and 2010 by using spatial production allocation model , 2014, Journal of Geographical Sciences.

[14]  S. Baja,et al.  Developing a User Friendly Decision Tool for Agricultural Land Use Allocation at a Regional Scale , 2017 .

[15]  M. Taleai,et al.  A GIS-based agricultural land-use allocation model coupling increase and decrease in land demand , 2014 .

[16]  H. Kazemi,et al.  Development of a land use suitability model for saffron (Crocus sativus L.) cultivation by multi-criteria evaluation and spatial analysis , 2017 .

[17]  R. Jana,et al.  A GIS-integrated fuzzy rule-based inference system for land suitability evaluation in agricultural watersheds , 2009 .

[18]  Anthony Gar-On Yeh,et al.  Neural-network-based cellular automata for simulating multiple land use changes using GIS , 2002, Int. J. Geogr. Inf. Sci..

[19]  Liangzhi You,et al.  Generating Global Crop Distribution Maps: From Census to Grid , 2014 .

[20]  T. Ray,et al.  An improved evolutionary algorithm for solving multi-objective crop planning models , 2009 .

[21]  Y. P. Li,et al.  A multistage irrigation water allocation model for agricultural land-use planning under uncertainty , 2013 .

[22]  Mohammad Karimi,et al.  Modeling land use interaction using linguistic variables , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[23]  Xia Li,et al.  Combining system dynamics and hybrid particle swarm optimization for land use allocation , 2013 .

[24]  Guohe Huang,et al.  Crop planning and water resource allocation for sustainable development of an irrigation region in China under multiple uncertainties , 2016 .

[25]  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 .

[26]  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 .

[27]  Stephan Dabbert,et al.  Modeling the impact of global change on regional agricultural land use through an activity-based non-linear programming approach , 2009 .

[28]  M. Zarghami,et al.  A new bankruptcy method for conflict resolution in water resources allocation. , 2014, Journal of environmental management.

[29]  R. Mason,et al.  Land suitability evaluation for changing spatial organization in Urmia County towards conservation of Urmia Lake , 2017 .

[30]  Yaolin Liu,et al.  Land Use Zoning at the County Level Based on a Multi-Objective Particle Swarm Optimization Algorithm: A Case Study from Yicheng, China , 2012, International journal of environmental research and public health.

[31]  Patrick Durand,et al.  Modeling spatio-temporal crop allocation patterns by a stochastic decision tree method, considering agronomic driving factors , 2010 .

[32]  Huajun Tang,et al.  Change analysis of rice area and production in China during the past three decades , 2013, Journal of Geographical Sciences.

[33]  H. Akinci,et al.  Agricultural land use suitability analysis using GIS and AHP technique , 2013 .

[34]  Aixia Yang,et al.  Finer Resolution Land-Cover Mapping Using Multiple Classifiers and Multisource Remotely Sensed Data in the Heihe River Basin , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[35]  L. You,et al.  Assessing the spatial distribution of crop areas using a cross-entropy method , 2005 .

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

[37]  Xin Li,et al.  An improved Genetic Algorithm for spatial optimization of multi-objective and multi-site land use allocation , 2016, Comput. Environ. Urban Syst..

[38]  Animesh Biswas,et al.  Application of fuzzy goal programming technique to land use planning in agricultural system , 2005 .

[39]  Bo Zhong,et al.  Land cover mapping using time series HJ-1/CCD data , 2014, Science China Earth Sciences.

[40]  Yun Chen,et al.  Modeling and implementation of classification rule discovery by ant colony optimisation for spatial land-use suitability assessment , 2011, Comput. Environ. Urban Syst..

[41]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[42]  S. Kullback,et al.  Information Theory and Statistics , 1959 .