基于农户决策的农业土地系统变化模型研究
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摘要
模型是研究农业土地系统及其变化的主要方法，已有模型研究存在的不足表现在：第一，对“农业土地系统”的概念认识不够；第二，对农业土地系统变化过程中的人类决策行为认识不够。针对这两点不足，本研究根据农业土地系统概念（即土地权属、农作物格局、农业集约化），选取黑龙江省宾县的3个乡镇为研究区域开展农户调查，从人类决策行为的角度出发，研究农业土地系统变化过程及机理机制，并在此基础上开发设计一个具有自主知识产权的Agent农业土地系统变化动态模拟模型（CroPaDy）。主要研究结论及创新点如下：
(1)利用文献综述方法梳理Agent农业土地变化模型（农业ABM/LUCC）的最新研究进展，为模型构建提供依据。科学表达农户决策始终是构建农业ABM/LUCC的核心内容，但当前农业ABM/LUCC一般基于农户属性构建决策模型，很大程度上忽略了态度在决策过程中的作用。CroPaDy将创新性的利用农户态度建模。
(2)利用农户调查获取的土地利用决策信息分析农业土地系统的时空特征：研究区土地流转普遍发生，户均土地面积由1980年代初期的1.3公顷增加至2010年代初期的2.6公顷，未来土地流转将更为普遍；作物选择表现出多样性减少（小麦、高粱、谷子等不再被种植），以及主导性增加的趋势（玉米面积持续扩大）；灌溉条件格局与河流的分布极为相关，而农资投入（包括种子、农药、化肥）、土地流转、以及作物选择的空间的格局与道路通达性相关。
(3)利用“农户态度得分”分析农业土地系统变化的驱动机制：首先对各因子的得分进行累加并排序，直观判断各因子对土地流转与作物选择的影响程度；其次利用二元logit回归模型定量表达农户态度与其真实决策之间的统计关系。logit模型具有显著性统计关系的因子多数累加得分较高，表明大多数被农户认为重要的因子对其真实的行为产生了影响。其中，内部因素是影响土地流转的重要因素，而作物选择主要受外部因素的影响。
(4) CroPaDy模型严格依据“ODD程序”等标准化程序进行设计。概念化方面，将模型设计成一个闭合的环路（驱动因素分析—决策过程分析—行为结果分析），并将农户态度作为影响决策行为的关键驱动机制；计算化方面，将模型设计成Agent生成模块、分类模块、以及决策模块3大相互联动的子模块。建模过程采用蒙特卡洛方法、聚类分析方法、人工神经网络方法、概率方法等诸多方法，确保模拟细节的体现。
(5)将空间耕地网格作为个体Agent，利用MATLAB编程技术实现模型模拟与参数调试，并在ArcGIS环境下，完成模拟结果的空间展示与分析：研究区2010年与2015年的土地流转率分别为51.85%与58.90%，且空间上看，新甸镇略高，模拟结果与时空特征分析结果基本保持一致；研究区2010年玉米、水稻、大豆、烤烟的模拟结果分别为26055.9、3506.75、5192.2、3983.85（公顷），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。
Existing at the center of the coupled human and natural systems, an agricultural system is defnedas a complex, human-managed land use system intended to provide foods and services for humans.Promoted by the current global change and sustainability science, several achievements have beenmade in agricultural land system studies, such as the exploration on spatial-temporal characteristicsand their drivers of agricultural land change, and the integrated simulations. In spite of considerableprogress, grand challenges still remain in this emerging field. One critical problem is that currentpractices based on the characterization of different land use and land cover types are overlooking themulti-functions of land systems; and the other is that the effects of human decision-making related tostakeholders in agricultural land management were not addressed properly in the prior studies. Tosolve these problems, the study mainly used household survey data at a typical agricultural area ofNortheast China to explore the spatial-temporal changes and drivers of “agricultural land system” inrespect of land tenure, crop allocation and agricultural intensification. Then, the study tried toconceptualize an agent-based model for simulating crop pattern dynamics at the regional scale. Theconclusions and the innovative points of this study are summarized as follows:
(1) A literature review suggests that ABM/LUCCs (Agent-based agricultural land changemodeling) bring theoretical and methodological innovations in land change modeling. They will helpto facilitate the integrated analysis benefited from both natural sciences and social sciences to form abetter understanding on the dynamics and complexity of agricultural land systems. Although there aresubstantial differences between models, the fundamental role of ABM/LUCCs is to express farmers’land use decisions and allocate them on regional level landscapes.
(2) The survey shows that land transfer was fairly common across the study area: farmlandacreage per household is almost doubled from an average of1.3ha by early1980s to2.6ha by early2010s. It also indicates an increase in land transfers over time with a sharp decrease of the averageperiod of land transfer contracts. Crop choice displays a trend of decreasing diversity as several cerealcrops such as wheat, sorghum, and millet are no longer grown in the study region and the vastmajority of the beans area has been replaced by maize and tobacco since the early1980s. Landtransfers may be a cause for the increase of the dominance of a small number of crops at the samplelevel, but are not the main driver for changes in cropping structure at the region level. Irrigationintensity is related to the locations of rivers while agricultural inputs, along with land transfer andcrop allocation, show a spatial pattern which is related to the spatial variation in road accessibility andeconomic conditions.
(3) Farmer’s attitudes in specific land use decisions were analyzed using the point-scoringmethod, and binary-logit regression models were further used to explain driving forces for landsystem changes. Most factors with significant coefficients in logit results have the higher score in total, indicating that most of the factors perceived as important also play a role in actual land changedecisions at individual level. Although farmers have different attitudes towards agricultural decisions,two family characteristics (education level and the initially allocated land rights) and twosocioeconomic factors (infrastructure and crop prices) proved to be most important in makingdecisions on land transfer, while a number of external factors have substantially influenced theirdecisions on crop choice.
(4) The CroPaDy model is developed based on the above achievements. The conceptual model is aclosed-loop comprised by driving forces, decision making processes, and consequences. Farmers’attitudes are the determining mechanism to decision making. The computational model links threesub-models named agents generating module, agent classifying module, and agent decision-makingmodule respectively. Common methods including Monte Carlo, Clustering, Artificial Neural Network,and Probabilistic Approach are used in model parameterization. The CroPaDy model is conceptualizedstrictly according to the ODD Protocol proposed by Grimm et al.(2010) and the GeneralizedFramework for Parameterization of ABM proposed by Smajgl et al.(2011)
(5) The main program of CroPaDy model was coded in MATLAB, then the output of matrixcalculation was presented and spatially analyzed in ArcGIS environment. The model results suggestthat land transfer rate in the study area is51.85%and58.90%in2010s and2015s respectively. AndXinDian town gets a relatively high transfer rate than the rest two towns, which is accordant with theprevious characteristic analysis. The crop area of maize, rice, soybean, and tobacco are26055.9、3506.75、5192.2、3983.85ha respectively. When comparing them with the local statistic yearbook, theoverall accuracy of CroPaDy model can reach as high as90%.
(1)利用文献综述方法梳理Agent农业土地变化模型（农业ABM/LUCC）的最新研究进展，为模型构建提供依据。科学表达农户决策始终是构建农业ABM/LUCC的核心内容，但当前农业ABM/LUCC一般基于农户属性构建决策模型，很大程度上忽略了态度在决策过程中的作用。CroPaDy将创新性的利用农户态度建模。
(2)利用农户调查获取的土地利用决策信息分析农业土地系统的时空特征：研究区土地流转普遍发生，户均土地面积由1980年代初期的1.3公顷增加至2010年代初期的2.6公顷，未来土地流转将更为普遍；作物选择表现出多样性减少（小麦、高粱、谷子等不再被种植），以及主导性增加的趋势（玉米面积持续扩大）；灌溉条件格局与河流的分布极为相关，而农资投入（包括种子、农药、化肥）、土地流转、以及作物选择的空间的格局与道路通达性相关。
(3)利用“农户态度得分”分析农业土地系统变化的驱动机制：首先对各因子的得分进行累加并排序，直观判断各因子对土地流转与作物选择的影响程度；其次利用二元logit回归模型定量表达农户态度与其真实决策之间的统计关系。logit模型具有显著性统计关系的因子多数累加得分较高，表明大多数被农户认为重要的因子对其真实的行为产生了影响。其中，内部因素是影响土地流转的重要因素，而作物选择主要受外部因素的影响。
(4) CroPaDy模型严格依据“ODD程序”等标准化程序进行设计。概念化方面，将模型设计成一个闭合的环路（驱动因素分析—决策过程分析—行为结果分析），并将农户态度作为影响决策行为的关键驱动机制；计算化方面，将模型设计成Agent生成模块、分类模块、以及决策模块3大相互联动的子模块。建模过程采用蒙特卡洛方法、聚类分析方法、人工神经网络方法、概率方法等诸多方法，确保模拟细节的体现。
(5)将空间耕地网格作为个体Agent，利用MATLAB编程技术实现模型模拟与参数调试，并在ArcGIS环境下，完成模拟结果的空间展示与分析：研究区2010年与2015年的土地流转率分别为51.85%与58.90%，且空间上看，新甸镇略高，模拟结果与时空特征分析结果基本保持一致；研究区2010年玉米、水稻、大豆、烤烟的模拟结果分别为26055.9、3506.75、5192.2、3983.85（公顷），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。
Existing at the center of the coupled human and natural systems, an agricultural system is defnedas a complex, human-managed land use system intended to provide foods and services for humans.Promoted by the current global change and sustainability science, several achievements have beenmade in agricultural land system studies, such as the exploration on spatial-temporal characteristicsand their drivers of agricultural land change, and the integrated simulations. In spite of considerableprogress, grand challenges still remain in this emerging field. One critical problem is that currentpractices based on the characterization of different land use and land cover types are overlooking themulti-functions of land systems; and the other is that the effects of human decision-making related tostakeholders in agricultural land management were not addressed properly in the prior studies. Tosolve these problems, the study mainly used household survey data at a typical agricultural area ofNortheast China to explore the spatial-temporal changes and drivers of “agricultural land system” inrespect of land tenure, crop allocation and agricultural intensification. Then, the study tried toconceptualize an agent-based model for simulating crop pattern dynamics at the regional scale. Theconclusions and the innovative points of this study are summarized as follows:
(1) A literature review suggests that ABM/LUCCs (Agent-based agricultural land changemodeling) bring theoretical and methodological innovations in land change modeling. They will helpto facilitate the integrated analysis benefited from both natural sciences and social sciences to form abetter understanding on the dynamics and complexity of agricultural land systems. Although there aresubstantial differences between models, the fundamental role of ABM/LUCCs is to express farmers’land use decisions and allocate them on regional level landscapes.
(2) The survey shows that land transfer was fairly common across the study area: farmlandacreage per household is almost doubled from an average of1.3ha by early1980s to2.6ha by early2010s. It also indicates an increase in land transfers over time with a sharp decrease of the averageperiod of land transfer contracts. Crop choice displays a trend of decreasing diversity as several cerealcrops such as wheat, sorghum, and millet are no longer grown in the study region and the vastmajority of the beans area has been replaced by maize and tobacco since the early1980s. Landtransfers may be a cause for the increase of the dominance of a small number of crops at the samplelevel, but are not the main driver for changes in cropping structure at the region level. Irrigationintensity is related to the locations of rivers while agricultural inputs, along with land transfer andcrop allocation, show a spatial pattern which is related to the spatial variation in road accessibility andeconomic conditions.
(3) Farmer’s attitudes in specific land use decisions were analyzed using the point-scoringmethod, and binary-logit regression models were further used to explain driving forces for landsystem changes. Most factors with significant coefficients in logit results have the higher score in total, indicating that most of the factors perceived as important also play a role in actual land changedecisions at individual level. Although farmers have different attitudes towards agricultural decisions,two family characteristics (education level and the initially allocated land rights) and twosocioeconomic factors (infrastructure and crop prices) proved to be most important in makingdecisions on land transfer, while a number of external factors have substantially influenced theirdecisions on crop choice.
(4) The CroPaDy model is developed based on the above achievements. The conceptual model is aclosed-loop comprised by driving forces, decision making processes, and consequences. Farmers’attitudes are the determining mechanism to decision making. The computational model links threesub-models named agents generating module, agent classifying module, and agent decision-makingmodule respectively. Common methods including Monte Carlo, Clustering, Artificial Neural Network,and Probabilistic Approach are used in model parameterization. The CroPaDy model is conceptualizedstrictly according to the ODD Protocol proposed by Grimm et al.(2010) and the GeneralizedFramework for Parameterization of ABM proposed by Smajgl et al.(2011)
(5) The main program of CroPaDy model was coded in MATLAB, then the output of matrixcalculation was presented and spatially analyzed in ArcGIS environment. The model results suggestthat land transfer rate in the study area is51.85%and58.90%in2010s and2015s respectively. AndXinDian town gets a relatively high transfer rate than the rest two towns, which is accordant with theprevious characteristic analysis. The crop area of maize, rice, soybean, and tobacco are26055.9、3506.75、5192.2、3983.85ha respectively. When comparing them with the local statistic yearbook, theoverall accuracy of CroPaDy model can reach as high as90%.
引文
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