摘要
Agent模型是研究农业土地系统复杂性与动态性的有效工具。在农作物空间格局变化模拟模型(CroPaDy,an agent-based model for simulating crop pattern dynamics)概念化设计的基础上,借助MATLAB平台开放性、矩阵运算能力强等特点,实现CroPaDy模型的数值模拟,并以黑龙江省宾县调查数据为依据,完成模型的区域实证研究。基于MATLAB的模型实现过程充分考虑了CroPaDy模型的多层次性(土地流转行为与作物选择行为)成功实现了3个子模块的动态嵌套模拟:1)Agent生成模块。基于已有的多源GIS数据、统计数据、典型调查数据、以及个体的通用规则,利用蒙特卡洛方法生成每一个个体Agent的属性信息;2)Agent分类模块。基于调查数据对受访农户进行态度聚类分析,然后借助人工神经网络方法确定所有生成的Agent所在的类型;3)Agent决策模块。利用概率方法,计算特定周期内每个Agent的决策行为。区域实证研究中,直接将空间耕地网格作为个体Agent,实现区域全覆盖(网格大小设置为114 m×114 m,约等于户均耕地面积),模拟结果表明,研究区2010年玉米、大豆、水稻、烤烟的模拟结果分别为2 6055.9、5 192.2、3 506.8、3 983.9 hm2,利用宾县统计年鉴(2010)进行验证,模型总体模拟精度达90%以上。CroPaDy模型的设计与实现科学合理,具有较强的理论性与可操作性,能够用以表达特定区域内的农作物空间格局及其动态变化过程。
Crop pattern is a key element in agricultural land systems other than land use and land cover. Crop pattern dynamic changes take place very frequently, but they are not always easily observable, making many difficulties for analysis. As an effective tool for understanding the driver, process and consequence of agricultural land system changes, the spatially-explicit agent-based land change models have successfully been applied in representing human and natural interactions on agricultural landscapes. With the assumption that the crop pattern at a regional level is the aggregation of crop choices at the filed level, we conceptualized an agent-based model to simulate crop pattern dynamics at a regional scale （CroPaDy）, which was supposed to represent the frequent but uneasily observed crop pattern changes in agricultural land systems. The conceptualization of CroPaDy model was designed strictly following the standard protocol for agent-based modeling. However, the computational model hinders its application because it needs a grid-based representation and the model itself is complicated with multi objectives, and nested by 3 interactive sub modules. As CroPaDy model can hardly been developed by the common agent-based modeling platforms, such as RePast, NetLogo, and Swarm, we are trying to use another alternative MATLAB to realize an empirical based application in an agricultural region of Northeast China, by taking the advantage of powerful and open-accessed matrix computing ability of MATLAB. We coded the model for the 3 interactive sub modules in steps： 1） Agents generating module. The Monte Carlo method was used to generate the internal factors （family attributes） for each individual agent in the full coverage study region by combining GIS data, statistical data, survey data and the individual based blanket rules. 2） Agent classifying module. The back propagation artificial neural network method was used to automatically classify the generated agents to groups based on the performance of surveyed a
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2014年第12期105-114,共10页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点基础研究发展计划项目(2010CB951504)
国家自然科学基金项目(40930101和41271112)
中央级公益性科研院所专项资金资助项目(IARRP-2014-16)
