基于Agent模拟的农业土地利用格局动态机理研究

研究方案讨论

Conceptualizing an agent-based model to simulate crop pattern dynamics (CroPaDy) for food security assessment

International Conference on Climate Change and Food Security 2011

Reporter: Yu Qiangyi Contributed authors: Wu Wenbin, Yang Peng, Xia Tian, Huajun Tang

2011 - 11 - 8

1. Background and incentives

• Land use, climate change and food security. ‾ Climate change affects crop yields. Land use change affects crop area. ‾ Food production: a sythetical issue of agricultural land, crop yields, crop use and

allocation (Foley et al., 2011)

• Land system: complexity and dynamics ‾ Coupled human and natural system (GLP, 2005; Liu et al., 2007)

• Land change analysis: tradition vs. innovation ‾ LUCC vs. land function (Verburg et al., 2009) and crop pattern (Tang et al., 2010) ‾ Biophysical processes vs. human dimensions (Rounsevell and Arneth, 2011) ‾ “Top-down” vs. “bottom-up” (Wu et al., 2008) ‾ “Decisions” vs. “conversions” (Yu et al., 2011) ‾ Static vs. dynamic (Veldkamp, 2009)

1. Background and incentives

• Agent-based modeling for land change analysis ‾ ABM/LUCC studies (Matthews et al., 2007; Parker et al., 2008; Parker et

al., 2003; Rindfuss et al., 2008; Robinson et al., 2007). ‾ Agricultural ABM/LUCC (Happe et al., 2011; Le et al., 2010;

Schreinemachers and Berger, 2011; Valbuena et al., 2010)

• Shortages ‾ Regional applications? (Valbuena et al., 2010) ‾ Co-evolving interconnections between environment and human agents? (Le

et al., 2010) ‾ No applications on crop pattern dynamics? (Yu et al., 2011)

2. Model conceptualization

• What is agent?

(a) bottom-level actors in agricultural land system (b) linkages to spatial landscape (through land tenure), (c) direct decision-makers for crop choice and farming strategy, (d) adapters to environmental changes, and (e) communicators to other agents.

(Yu et al., 2011)

2. Model conceptualization

• Internal and external factors instead of macro-statistic variables (biophysical and socioeconomic factors).

2.1 Driving forces

Modified from Valbuena et al., (2010)

• Internal factors are underlying causes while regulated by external factors.

• Co-evolution of internal and external factors. • External factors cause homogeneous impacts while

internal factors are totally heterogeneous.

2 Model conceptualization

• Crop pattern on farmland are directly linked with human land use decisions.

2.2 Decision making processes

Agent

Land use decisions

Agent

Agent

• External factors as conditions: crop yield, crop price, policy intervention, and social preference. While internal factors as correspondence: high yield pursuing, high price pursuing, policy interrupting, and individual preference.

• Multiple internal and external factors have to be simplified and classified into several one-to-one combinations.

• Mathematical method: factor analysis (simplifying and classifying)+ “bounded utility-maximizing” function (determining).

2. Model conceptualization

• Possible options to actual actions

2.3 Consequences

(Yu et al., 2011)

• Three levels of options: farming decisions: farming

abandonment or farming expansion

crop choices: select what crop for farming

management decisions: intensification and extensification

• Consequences as feedbacks to driving forces

• Generalized framework for parameterization of ABM/LUCC (Smajgl et al., 2011)

3. Model parameterization

3. Model parameterization

Sub-modules of CroPaDy:

⨀ Agents generating module

⨀ Agent simplifying and classifying module

⨀ Agent decision-making module

3.1 Agent generating module

‾ There are two approaches have been widely used in retrieving individual attributes from sample survey data. One is to use Monte Carlo techniques and the other is to use proportional methods (Robinson et al., 2007).

‾ For a given attribute i, the occurrence frequency (Fi) of each option value is counted based on sample survey data, by which to get the cumulative probability (Pi) distribution of this given attributes. Therefore the given attribute variable (Vi) and its occurrence frequency, cumulative probability are expressed as follows:

Where: i means the ID of attributes; k means the ID of option values; bik means the specific value of the given attribute; xik means the specific occurrence frequency of option value k.

Generating agent attributes

3.1 Agent generating module

This cumulative distribution function is used to randomly distribute the option values of given attribute i for the whole population. For this, a random integer between 0 and 1 is drawn for each agent and the option value is then read based on the one-to-one transformation from Pi to Vi. Using this method for the whole population of agent recreates the depicted empirical probabilistic distribution function for attribute i. Then the Monte Carlo procedure is repeated for all other attributes. Assuming that there are n agents with m attributes to be generated, the agent information can be expressed as:

Where: randO means sort the value set in random order; IDAttribute means the identity number of attribute variables; IDAgent means the identity number of individual agent; AM*N={ai,j} is a two dimensional matrix, where ai,j means the generated value of attribute i for agent j; A’M*N={a’i,j} is a two dimensional matrix, where a’i,j means the sample value of attribute i for agent j; ai, is a vector, means the value set of attribute i; bi is a vector, means the value set of option values of attribute i; Ki means the total number of option values of attribute i; Xi,k means the occurrence frequency of option value k for attribute i N*fi(bik) means the total number agent who have attribute value bik.

3.1 Agent generating module

‾ Combining various GIS data including cadaster and dedicated production block (farmer’s block, physical block) system to spatially reference households to their land parcels.

Land Parcel Identification System (Milenov and Kay, 2006; Sagris and Devos, 2008)

Spatially referencing households’ decisions with their land parcels

‾The vector land parcels are the basic simulation units.

3.1 Agent generating module

‾ The final result of generated agent population

‾ The results have to be checked for inconsistencies. (Berger and Schreinemachers, 2006) ‾ The generated information has to be updated every year.

3.2 Agent simplifying and classifying module

‾ Typology? (McKinney, 1950; Valbuena et al., 2008) ‾ Confirmatory Factor Analysis? ‾ In case we have a set of M (M > 4) observable random internal variables at each agent: yi = (y1, y2, y3, … ,

yM), taking those advantages of factor analysis, we are trying to classify the original variables into four principle common internal factors named high yield pursuing, high price pursuing, policy interrupting, and individual preference: F = (F1, F2, F3, F4). The original variables may be expressed as linear functions of the common factors in the Common Factor Model (Thurstone, 1947). Subsequently the factor scores were calculated as:

The combination of factor scores is transformed into a vector of weights that suggest the comprehensive ability/willingness combination of each specific agent.

3.3 Agent decision-making module

‾ Optimizing agent V.S. heuristic agent (Schreinemachers and Berger, 2006); ‾ Perfect rationality V.S. bounded rationality (Manson and Evans, 2007) ‾ Multinomial logistic model for representing bounded-rational decision making

mechanism, assuming that the utility function was following Gumbel distribution (Le et al., 2008; Wu et al., 2011)

‾ Bounded utility-maximizing function:

3.3 Agent decision-making module

3.3 Agent decision-making module

Summaries

• Integrating crop pattern dynamics with crop yield change, market fluctuation, and policy intervention

• Both the model conceptualization and parameterization are followed generalized modeling framework.

(Grimm et al., 2006; Grimm et al., 2010) and (Smajgl et al., 2011)

• Model implementation: Northeast China • Possible limitations: Not including management decisions The environment has no spatial differences Innovative try in in simplifying agent attributes to behavioral parameters

Thank you!