The Chinese Academy of Agricultural Sciences (CAAS) and the International Food Policy Research Institute (IFPRI) jointly hosted the International Conference on Climate Change and Food Security (ICCCFS) November 6-8, 2011 in Beijing, China. This conference provided a forum for leading international scientists and young researchers to present their latest research findings, exchange their research ideas, and share their experiences in the field of climate change and food security. The event included technical sessions, poster sessions, and social events. The conference results and recommendations were presented at the global climate talks in Durban, South Africa during an official side event on December 1.

1. International Conference on Climate Change and Food Security 2011
Conceptualizing an agent-
based model to simulate crop
基于Agent模拟的农业土地利用
pattern dynamics (CroPaDy)
格局动态机理研究
for food security assessment
研究方案讨论
Reporter: Yu Qiangyi
Contributed authors: Wu Wenbin, Yang Peng, Xia Tian, Huajun Tang
2011 - 11 - 8

2. 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)

3. 1. Background and incentives
‾ ABM/LUCC studies (Matthews et al., 2007; Parker et al., 2008; Parker et
• Agent-based modeling for land change analysis
‾ Agricultural ABM/LUCC (Happe et al., 2011; Le et al., 2010;
al., 2003; Rindfuss et al., 2008; Robinson et al., 2007).
Schreinemachers and Berger, 2011; Valbuena et al., 2010)
‾ Regional applications? (Valbuena et al., 2010)
• Shortages
‾ Co-evolving interconnections between environment and human agents? (Le
‾ No applications on crop pattern dynamics? (Yu et al., 2011)
et al., 2010)

4. 2. Model conceptualization
• What is agent?
(Yu et al., 2011)
(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.

5. 2. Model conceptualization

6. 2.1 Driving forces
• Internal and external factors instead of macro-statistic
variables (biophysical and socioeconomic factors).
• Internal factors are underlying causes while regulated
by external factors.
Modified from
Valbuena et al., (2010)
• Co-evolution of internal and external factors.
• External factors cause homogeneous impacts while
internal factors are totally heterogeneous.

7. 2 Model conceptualization

8. 2.2 Decision making processes
• Crop pattern on farmland Agent
are directly linked with Agent
human land use decisions.
• Multiple internal and
external factors have to
be simplified and
classified into several Agent
one-to-one combinations. Land use decisions
• 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.
• Mathematical method: factor analysis (simplifying and classifying)+
“bounded utility-maximizing” function (determining).

9. 2. Model conceptualization

10. 2.3 Consequences
• Possible options to actual actions
farming decisions: farming
• Three levels of options:
abandonment or farming
expansion
crop choices: select what crop for
farming
intensification and extensification
management decisions:
• Consequences as feedbacks to
driving forces (Yu et al., 2011)

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

12. 3. Model parameterization
Sub-modules of CroPaDy:
⨀ Agents generating module
⨀ Agent simplifying and classifying module
⨀ Agent decision-making module

13. 3.1 Agent generating module
‾
Generating agent attributes
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.

14. 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.

15. 3.1 Agent generating module
Spatially referencing households’ decisions with their land parcels
‾ Combining various GIS data including cadaster and dedicated production block
(farmer’s block, physical block) system to spatially reference households to their
land parcels.
‾The vector land parcels are the basic simulation units.
Land Parcel Identification System (Milenov and Kay, 2006; Sagris and Devos, 2008)

16. 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.

17. 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.

18. 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:

19. 3.3 Agent decision-making module

20. 3.3 Agent decision-making module

21. 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.
• Model implementation: Northeast China
(Grimm et al., 2006; Grimm et al., 2010) and (Smajgl et al., 2011)
• Possible limitations:
Not including management decisions
The environment has no spatial differences
Innovative try in in simplifying agent attributes to behavioral parameters

22. Thank you!