Environment and Planning B: Planning and Design 2007, volume 34, pages 261 ^ 278
DOI:10.1068/b31180
Modelling the spatial distribution of shifting cultivation
in Luangprabang, Lao PDR
Yumiko Wadaô
Center for Spatial Information Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo,
153-8505, Japan; e-mail: [email protected]
Krishnan S Rajan
International Institute of Information Technology, Gachibowli, Hyderabad 500032, Andhra
Pradesh, India; e-mail: [email protected]
Ryosuke Shibasaki
Center for Spatial Information Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo,
153-8505, Japan; e-mail: [email protected]
Received 21 January 2005; in revised form 27 September 2006; published online 6 February 2007
Abstract. This research develops an agent-based land-use model for shifting cultivation, where the
spatial distribution of crop cultivation is dynamically determined by the relationship between demand
and supply of crops. We apply and evaluate the model using statistical and geographic data from
Luangprabang Province, Laos, where rice and other crops are cultivated in various forms, including
shifting cultivation. Our model explicitly incorporates socioeconomic dimensions of shifting cultivation in which villages are assigned the role of decision makers. We evaluate the model by comparing
the simulation results with the existing statistical data and remote sensing images from the 1990s. Our
model provides reasonably satisfactory estimates of aggregate area and volume of each crop type at
the provincial level. We also evaluate the model across differing spatial resolutions for shifting
cultivation areas. We find that the model has limited explanatory power at higher spatial resolutions
of 0.5 km to 2.5 km grid cells, but can account for the spatial patterns fairly well at more aggregate
levels with the resolutions of 5 km to 10 km.
Introduction
Tropical deforestation is one of the primary global environmental concerns because its
implications range widely from soil erosion and the loss of biodiversity to global
climate change. Among many other reasons, land-use changes associated with agricultural expansion, from state-sponsored irrigation projects to the intensification of
shifting cultivation, are considered as a key force of deforestation (Geist and Lambin,
2002). Our understanding of land-use change is nevertheless still limited because of the
lack of detailed land-use data in often isolated tropical regions, and of the complexity
of decision-making processes concerning land use, which result from intricate socioeconomic and biophysical factors. Agent-based modelling has emerged as an attractive
alternative for understanding land-use dynamics because it appears to overcome some
of the limitations in the existing modelling approaches (Parker et al, 2003).
We explore the dynamics of land-use decision making by developing a microscale
agent-based model to simulate the spatial and temporal patterns of shifting cultivation,
using a mountainous region of Laos as an example. The study aims to deepen our
understanding of how shifting cultivation expands in space by replicating its processes
using an agent-based model, and by evaluating the validity of our model outcomes,
both in terms of aggregate change in cultivation area and volume, and in terms of the
spatial distribution of shifting cultivation.
ô Current address: Yumiko Yamamoto, Dow Science Building 296, Central Michigan University,
Mount Pleasant, MI 48859, USA.
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Y Wada, K S Rajan, R Shibasaki
Shifting cultivation is an important form of agriculture in tropical Asia, Africa,
and Central and South America, and opinions vary about its impact on deforestation
(eg Geist and Lambin, 2002; Ranjan and Upadhyay, 1999). Typically, farmers clear
(generally secondary) forests and plant such crops as rice, corn, and root vegetables.
After one or a few years of cultivation the cultivated land is temporarily abandoned for
ten to thirty years. Shifting cultivation provides stable output while the ecological
balance is intact, but insufficient fallow periods and overly intensive cultivation can
lower soil fertility, resulting in irrecoverable forestland. Two contemporary factors have
influenced the recent practice of shifting cultivation. First, the introduction of a
monetary economy has been leading farmers to grow crops for profit in local markets.
Second, an increasing population requires more food production. Both factors can
result in more intensified agricultural land use, including shorter fallow periods and
extended cultivation periods, and in the exploitation of primary forests. In this sense,
this traditionally sustainable agricultural method may become increasingly unsustainable in some areas, and this situation demands a better understanding of the impact of
changing shifting cultivation practices on forests.
Research on shifting cultivation has traditionally relied on descriptive methods,
combined with intensive fieldwork. Inoue (1990), for example, conducted interviews
with the elders and household heads of the Kenyah Dayak people of Borneo to understand how their practices of shifting cultivation changed as they migrated from the
upstream to the downstream region of the Mahakam River. Yin (1997) studied, based
on fieldwork, how tribes in Yunnan, China, managed to maintain shifting cultivation
under various external pressures against the farming practice. Tani (1998) examined the
evolving relationship between the National Forest Department and the Karen people
in the Bago mountain area, Myanmar, based on secondary data and interviews. These
studies reveal socioeconomic and cultural conditions of specific regions where shifting
cultivation is practiced. They offer qualitative insights on shifting cultivation and its
supporting mechanisms, but offer few quantitative insights on the practices and their
spatial distribution.
Some recent studies use remote sensing and geographic information systems to
collect and analyse data from large areas. Nagasawa et al (1998) examined geomorphological characteristics of shifting cultivation areas near Luangprabang, Laos, by
using satellite data. Similarly, Inthavong et al (2001) used geographic data to classify
agroecological regions in Laos. These monitoring studies are descriptive in nature
because they do not explain the driving mechanisms of spatial changes, but they
nevertheless elucidate quantitative changes in cultivated areas and locations, which
can be used to evaluate simulated results based on modelling.
In contrast to these monitoring studies, modelling research attempts more explicitly
to explain the interrelations between shifting cultivation and the tropical ecological
system [see Agarwal et al (2002) for a comprehensive review of land-use modelling
research, and Kaimowitz and Angelsen (1998) for deforestation modelling in particular]. Prasad et al (2001), for example, used the Century agroecological model (version
4.0), and simulated seventy-year change in the hilly areas of India between 1960 and
2030. Their model incorporates input to solid (eg fallen leaves), speed of soil organic
decomposition for different climatic conditions, plant growth, and soil moisture, to
examine how the twelve-year cycle of shifting cultivation affects soil condition. This
dynamic simulation model, however, devotes little attention to spatial patterns of landuse changes, and does not explicitly incorporate the complex behaviour of farmers. The
`conservation of land use and its effect' (CLUE) model includes both biophysical
aspects and human factors, and incorporates a cellular modelling method to examine
the spatial dynamics of land-use change (eg Veldkamp and Fresco, 1996). In this model
Modelling shifting cultivation in Luangprabang
263
the land use of each grid is determined in relation to the conditions of the surrounding
grid cells, and the model shows that changes in land-use patterns often occur in spatial
aggregation and along infrastuctures. Nevertheless, the model still underplays the
behaviours of individual and institutional agents, which may change over time, pacing,
for example, the penetration of a monetary economy and population growth.
Agent-based models complement existing modelling approaches to land-use dynamics
by focusing on human activities and their interactions with their environment. This
increasingly popular modelling approach recognises that system components may not
have fixed and equilibrating relationships, and that human decisionmaking is autonomous,
heterogeneous, and decentralised (Parker et al, 2003). Agent-based models are not pure
predictive tools, but it is hoped that their ability to represent socioeconomic and biophysical complexity will help explain the dynamic human ^ environment interrelations
(Lambin, 1997). The premise of the agent-based model appears especially attractive in
understanding shifting cultivation because the behaviours of agents (ie farmers) are likely
to be affected by multiple locally specific factors. Rajan and Shibasaki (2000), for example,
developed an agent-based model in their study of agricultural land-use change in
Thailand. In this model, farmer agents in each grid cell determine types of crops, the
expansion or contraction of farming areas, and the relocation of settlements by referring
to their collective household income from agricultural and nonagricultural revenues.
Their model is similar to the cellular model in their use of grid cells as the land-use unit,
but it treats farmers as more active agents by including various socioeconomic factors,
such as demographic compositions, and by incorporating how those factors may be
altered by the relocation of settlements.
There has been an emerging body of literature that applies agent-based models to
areas under shifting cultivation. Deadman et al (2004) explore land-use change in
Altamira, Brazil, using an agent-based model that simulates farming household behaviours, which are affected by factors such as household characteristics, subsistence
requirements, and soil ^ land-cover quality. With a similar intention, Walker (1999)
develops an agent-based model that simulates the dynamic relationship between shifting cultivation and forest structures by linking the market and farmer groups, crop
cycle and secondary forest transition, and leisure and food production. Walker et al
(2004) apply their agent-based model based on a Markovian probability transition
framework to explore the magnitude and spatial patterns of deforestation in Brazil.
Our study adds original insights to these existing agent-based modelling studies on
shifting cultivation in three main ways. First, most of these studies use households as
agents (eg Deadman et al, 2004; Evans and Kelley, 2004). We instead model clusters of
villages as agents. Using an analytical unit much larger than a household is partly the
consequence of data limitation, but we also choose this unit because our fieldwork has
revealed that decisions over the expansion and relocation of shifting cultivation are
often made at the level of villages, rather than that of individual households. Second,
our study responds to the mounting interest in the issue of validation in the agentbased model literature (Pontius et al, 2004; Veldkamp and Lambin, 2001) by evaluating
model outcomes with available statistical and remote sensing data. The use of multiple
data sources for validation is crucial for areas such as Laos, where reliable observed data
are extremely limited. We believe that the use of remote sensing images to evaluate the
model at different spatial resolutions makes a particularly important methodological
contribution to the emerging concern over the scale effects in agent-based models
(Agarwal et al, 2002; Evans and Kelley, 2004). Third, this study develops one of
the first carefully validated agent-based models that simulate the land-use dynamics
in the mountainous areas of the Indochina peninsula, offering additional empirical
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Y Wada, K S Rajan, R Shibasaki
insights into the existing studies on shifting cultivation, which have been conducted in
other regions (Deadman et al, 2004; Walker et al, 2004).
Study area
In Southeast Asia, shifting cultivation is practiced widely in Yunnan (China), northern
Thailand, and Kalimantan Island (Indonesia). In particular, Laos, which borders
with Thailand, China, and Vietnam, draws a large amount of its staple food from
upland rice (primarily glutinous rice), which is grown by shifting cultivation. The country
has large areas covered with forests, but rapid changes in forestland are expected for
three major reasons. First, open-economy policies, based on the `Chintanakan Mai' (new
thinking) ideology, since 1986 have been penetrating into the previously isolated regions in
the form of increasing cash-crop planting and the sales of surplus products. Second, the
land tenure and distribution policies, which classify forest areas by their use, are increasingly restricting the spatial extent of shifting cultivation. Third, the growing Laotian
population, which has increased from 2.9 million into 5.1 million people between 1976
and 1999 (State Planning Committee, 2000), has been driving the need for a larger food
supply.
Laos has approximately seventy ethnic groups, which make up three larger groups
depending on the elevation of their habitation: Lao Lum (400 meters or lower), Lao
Theung (400 to 1000 meters), and Lao Sung (1000 meters or higher). Traditionally, the
Lao Sung and Lao Theung practice shifting cultivation, while the Lao Lum cultivate
paddy fields (Chazee, 1999). The study area, Luangprabang (also referred to as
Louangphabang) Province, is located in the northern part of Laos, and has 90% of its
land (16 875 km2 ) covered by mountains and hills, where many Lao Theung and Lao
Sung people practice shifting cultivation (figure 1). The population of Luangprabang
0 50 100 km
0
50
100 miles
Figure 1. Map of study area, Luangpraban, Laos.
Modelling shifting cultivation in Luangprabang
265
grew from 238 000 to 406 000 people between 1976 and 1999 (State Planning Committee,
2000), at an average annual growth rate (3.0%) similar to the national average (3.2%).
Most of its people live in small villages scattered around the province. These villages are
characterized by residential settlements surrounded by mountains. The villagers typically cultivate irrigated and rain-fed rice, vegetables, and occasionally some cash crops
near their settlements, and travel to shifting cultivation fields at more distant locations.
At the time of our field study, the national government was only beginning to define
territorial boundaries of these villages (ie settlements plus hinterlands) through its land
redistribution project. Evidently, the lack of clearly defined boundaries poses a major
constraint on spatial analysis.
Shifting cultivation remains the dominant form of rice production in Luangprabang,
but irrigated and rain-fed rice fields have been increasing in recent years. In 1980 85.5%
of all rice fields (51700 hectares) were shifting cultivation fields, 1.4% were rain-fed rice,
and 0.6% were irrigated rice. By 1999 shifting cultivation occupied 72.4%, rain-fed
rice, and irrigated rice 5.2% of the total area of rice production (44 200 hectares).
Rain-fed and 0.6% were irrigated rice fields are both located in relatively flat plains,
typically near rivers, but differ substantially in terms of their capital costs and productivity. In contrast to rain-fed rice fields, irrigated rice fields are more capital intensive,
often requiring water pumping facilities, and more productive because cultivation is
possible during the dry season. Rain-fed rice fields clearly require less environmental
alternation than irrigated rice fields, but there is little documented evidence that the
latter has more harmful environmental consequences (eg on soils and forests).
Because the communities that practice shifting cultivation are in isolated mountainous areas of the province, reliable statistical data on socioeconomic conditions are
extremely limited. These conditions require our modelling research to combine detailed
firsthand insights about the farming communities with limited statistical and spatial
data. Our fieldwork was conducted with technical assistance from a larger research
project, PELUSSA (People, Environment and Land Use Systems in Mainland Southeast Asia). The project team conducted a field survey in twelve villages in the province
in 2000, and interviewed at least one village leader in each village. The survey included
questions about demography, land use, productivity, and production costs. We use the
information from the field survey to determine decision-making rules in our model.
Model
In our model a group of neighboring villages, or a village `cluster', plays the role of an
agent, which decides the areas of shifting cultivation and fallow every year. We consider our model `agent-based' despite the unorthodox use of village clusters as agents
[but see Agarwal et al (2002) who conceive a wide range of social organizations as
possible agents]. In our agent-based model, the agents (village clusters) interact with
different environmental conditions, and with other agents (eg through migration),
resulting in the formation of macroscale outcomes (ie expansion and relocation of
crop fields). All of these features are characteristic of agent-based models.
We resort to the village cluster as the main unit of decision making for the following reasons. First, our fieldwork informed us that the location selection and practice of
shifting cultivation is a predominantly collective enterprise at the village scale. This
situation contrasts, for example, to the Brazilian forest land-use change, in which
agricultural colonist households are the main agents of land-use change (Deadman
et al, 2004). In the Brazilian context these households are relatively autonomous (ie,
weak long-term ties between each other), are heterogeneous in terms of their local
knowledge (eg about soil quality) owing to different arrival times to the region, and
produce different cash crops depending on their changing household compositions
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Y Wada, K S Rajan, R Shibasaki
(eg households with working-age children can focus on labor-intensive crops). Under
this circumstance households are appropriate agent units in land-use change models.
In the Laotian context, however, individual households are strongly embedded in their
local village community, and interact intensively on a daily basis, leading to more
ready dissemination of local knowledge. They are also primarily concerned with the
production of rice for subsistence, regardless of their household compositions and
stages. Indeed, village-level behavior may not be assumed simply as an aggregation
of household-level actions, if the decision-making process involves negotiations of
conflicting household-level interests. Hence, village-based models may actually offer
more relevant insights into land-use dynamics than household-based models in this
particular societal context.
Second, modelling at the household or individual level would involve a far greater
amount of data than the modelling at the village cluster level, but the payoff for the
expected data collection and manipulation effort is far from clear. We feel that a
village-based model can sufficiently describe the land-use mechanisms for this particular type of agriculture in this region, and can be evaluated using currently available
observation data. Third, we would have ideally liked to use individual villages as
agents, but compromised to use village clusters whose boundaries are defined by river
basins, because of the lack of information on clear village boundaries at the time of
our field research. If such spatial data become available, we will certainly incorporate
them into our model.
The model consists of five main modules: irrigated and rain-fed rice, shifting
cultivation, agricultural income, population/migration, and land-use decision modules.
Crop types represented in the model are irrigated rice, rain-fed rice, upland rice, and
nonrice crops, which are represented by corn. Our primary focus is rice because
it remains the most important crop in Luangprabang Province, both in terms of
harvested area and in terms of staple diet (State Planning Committee, 2000). On the
basis of the simulation results of the first four modules, the land-use decision module
produces the land-use map for the end of each year (figure 2). Each agent determines a
Figure 2. Model framework.
Modelling shifting cultivation in Luangprabang
267
given year's land-use patterns through the modules, on the basis of the information for
the previous years. In our model, village clusters interact through migration that is
triggered by food shortage and a fall in income. We simulate the changes in land-use
patterns between 1990 and 1999, using the modified base map of Luangprabang
Province, which represents the land-use conditions in 1989.
Base map generation
The base map, a raster representation of the land-use patterns of Luangprabang
Province, includes spatial information of land use, elevation, slope, village, rivers,
and roads on 500 m 500 m grid cells. We developed the base map for our model by
modifying the provincial land-use map created by the National Office for Forest
Inventory and Planning, a government agency of Laos, in 1989. The original map
included land-use categories such as paddy fields, transition forest areas (referred to
as `unstocked'), and temporary crop fields (referred to as `ray'). Because these categories
were insufficiently detailed for the purpose of our study, we incorporated information
from State Planning Committee (2000), Nagasawa et al (1998), and elevation and slope
data onto the 1989 map.
The new base map is generated in three steps and distinguishes irrigated rice
paddies, rain-fed rice paddies, and shifting cultivation areas, which are further divided
into upland rice, other crops, and fallow areas. First, we determined the areas of
shifting cultivation, which met three criteria:
(1) the area is classified as temporary crop fields (ray) or savannah/bush areas in the
1989 published map, which explains that shifting cultivation areas are included in these
two categories;
(2) the area is located at an elevation of 400 m or higher, where most Lao Theung and
Lao Sung live and practice shifting cultivation (Chazee, 1999; Yamada, 2003);
(3) the area is located on a slope between 128 and 358, following the classification
scheme by Foppes et al (1993).
Second, we classified shifting cultivation areas into upland rice and other nonrice
crop fields by overlaying the 1989 map and the elevation and slope data. Some of these
areas were reclassified as irrigated rice and rain-fed rice fields if, upon further examination, the plots were unusually large for shifting cultivation fields. Other nonrice
crops include corn, potatoes, vegetables, beans, nuts, tobacco, sugar cane, and coffee
beans. Among these crops, corn is the most extensively produced crop after rice, and
our model uses corn as a surrogate of all other nonrice crops. Third, we used the transition
forest areas in the 1989 map as the fallow areas of shifting cultivation. These areas
are forests with a canopy density of 20% or less, and we consider that the characteristics of these areas approximate the conditions of fallow areas, where secondary
forests emerge after a period of cultivation.
Irrigated and rain-fed rice module
This module estimates production volumes of irrigated and rain-fed rice from the
population size and the estimated productivity of rice in each grid cell. We estimated
the grid-by-grid productivity by weighting the province-wide averages for irrigated and
rain-fed rice by elevation, slope, and existing land use. We first estimate the demand for
rice, using 600 grams per capita per day, which is the average rice consumption volume
in Luangprabang Province (State Planning Committee, 1999). The demand for rice
(tons) in grid cell i in year t is:
Dit ˆ
600 365 Pi
,
106
(1)
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Y Wada, K S Rajan, R Shibasaki
where Pi is the estimated population in grid cell i. We then use this demand data to
estimate the supply volume, which will be converted into cultivated area, using the
productivity estimates. The module first calculates the area and volume of the irrigated
rice fields, followed by those of rain-fed rice fields.
Irrigated rice field expansion is typically the decision of a higher political authority,
not only because of its enormous irrigation infrastructure costs, but also because it is
part of the state resettlement policies to move the upland tribes (Lao Theung and Lao
Sung) to lowland areas. For this reason we estimate the size and location of irrigated
rice fields, not based on agent's decision-making processes, but based on the conditions
of each grid cell alone.
In our model the total supply volume of rice, S, in the previous year (t ÿ 1) in each
village cluster is given by:
I
R
U
Stÿ1 ˆ Stÿ1
‡ Stÿ1
‡ Stÿ1
,
(2)
where the superscripts I, R, and U denote `irrigated', `rain-fed', and `upland', respectively. We assume that, if the volume of rice supply in year t ÿ 1 cannot meet the
demand volume in year t, then the supply will increase to fill the supply ^ demand
gap by DSi (ie St ˆ Stÿ1 ‡ DSt ). We decompose DSt into the three types of rice
(DStI , DStR , DStU ), using the following procedures. First, we have data on the annual
changes in the total area of irrigated rice fields (State Planning Committee, 2000), and
consider the increase in irrigated rice supply (DStI ) as exogenously given. If the
increase in the irrigated rice production satisfies the rice demand (Dt 4 Stÿ1 ‡ DStI ),
the module assumes no expansion of rain-fed and upland rice fields in that year.
If, however, the increase in the irrigated rice production does not meet the demand
(Dt > Stÿ1 ‡ DStI ), the module assumes some expansion of rain-fed rice production,
upland rice production, or both.
Second, in the case of an insufficient increase in irrigated rice supply, the module
allocates the expanded area, and production, to rain-fed rice, up to the level of 10%
area growth from the previous year. We set the 10% cap because the establishment of
rain-fed rice fields is a financially and technologically intensive project; therefore
unlimited expansion of this type of field is unlikely. If the increase in irrigated and
rain-fed rice supply is still insufficient to meet the demand (Di > Stÿ1 ‡ DStI ‡ DStR ),
the module assumes the remaining difference is compensated by the increase in upland
rice production through shifting cultivation. It is possible that the total rice supply
still does not meet the necessary demand (Dt > St ). In that case, there will be food
shortage in the village cluster, which may result in out-migration of households based
on the population ^ migration module.
Shifting cultivation module
A separate module is necessary for upland rice and other nonrice crops, which are
primarily grown using shifting cultivation, because agents' roles are more critical in
determining the area and location of this cultivation method. In this module we use the
village cluster, a set of neighboring villages, as the basic unit of agent, within which
shifting cultivation takes place. We divide Luangprabang Province into seven village
clusters according to the river basin characteristics (figure 3).
The volume and the area of upland rice production are estimated essentially in the
same way, using the productivity estimates for upland rice, except that the demand ^
supply balance is calculated at the level of village cluster. For nonrice crops (simply
referred to as `other crops') we also estimate the grid-by-grid productivity using the
province-wide productivity of corn in the same manner as for rice. The total demand
volume of nonrice crops in each village cluster is estimated, assuming the daily
Modelling shifting cultivation in Luangprabang
269
Village settlement
Village cluster boundary
0
0
25
50 km
25
50 miles
Figure 3. Locations of village settlements and village clusters, Luangprabang Province, 1997.
consumption of 100 grams per capita based on government statistics. The nonrice crop
supply capacity is estimated from the previous year's harvesting areas and the distribution map of crops. If the demand in year t exceeds the supply capacity, then we assume
that nonrice crop harvesting areas must expand to fill the demand ^ supply gap.
In shifting cultivation, unlike irrigated and rain-fed rice production, the location
of a cultivation area changes, even in the absence of an increase in crop production.
Its location shifts on the basis of the shifting cultivation cycle in a given year, which is
calculated as:
total available area
cultivation cycle ˆ
current cultivated area
ˆ
areas of fallow, upland rice, and other crops
.
areas of upland rice, and other crops
(3)
Two possible scenarios illustrate the relationship between location shifts of cultivation
areas and cultivation cycles (figure 4). In the first scenario [figure 4(a)] the total
available area includes nine grid cells, and three grid cells are cultivated in any given
year. In this case cultivation in the second year takes place in three of the six
uncultivated grid cells, and the third year's cultivation in the remaining three uncultivated grid cells. In the fourth year the grid cells cultivated in the first year pass
a sufficient fallow period of three years, and become available again for cultivation.
In this scenario the cultivation cycle of three years (ie 9=3 ˆ 3 years) is maintained
throughout the period.
In contrast, the second scenario shows an instance of an intensified cultivation
cycle [figure 4(b)]. The total cultivation area increases from three to four grid cells in
the second year and after (resulting in the cultivation cycle of 9=4 ˆ 2:25 years). In this
case three cells (shaded) are cultivated after insufficient fallow periods by the third year,
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Y Wada, K S Rajan, R Shibasaki
First year
Second year
Third year
Fourth year
(a) Relocation and no expansion
(b) Relocation and expansion
Insufficient fallow period (two years or less)
Figure 4. Two schematic examples showing how shifting cultivation affects cultivation cycles.
In both examples, the initial cultivation cycle is three years. (a) The total cultivated area remains
constant; and (b) the total cultivated area increases from the second year, resulting in a shorter
cultivation cycle.
and the necessary fallow period of three years cannot be maintained, resulting in
unsustainable land use.
Agricultural income and population ^ migration module
In the agricultural income module, the village-cluster-wide income is calculated on the
basis of the volume of crop production and the inflation-adjusted average market price
of each crop (International Monetary Fund, 1998; 2001). Because the field survey
revealed that few fertilizers and farm machines were used in the study area, these costs
are not included in the module. Annual income is the average value over three years.
The population ^ migration module specifies migration decisions and consequential
population change at the village cluster level. Criteria for out-migration decisions are
food shortage and a fall in income. The number of out-migrating households from a
given village cluster is determined so that food supply remains at least 95% of the
necessary demand, and that the drop in income remains 15% or less from the previous
year. This module also specifies the conditions for accepting in-migrants. It first
determines village clusters that can accept migrants based on two conditions: rice
supply exceeds its demand by 10% or more, and the average population density is
300 people or fewer per grid cell. It then determines grid cells within the candidate
village clusters that meet two conditions to accept migrants: the slope is 10% or less,
and the elevation is 1000 meters or less (because of the general tendency for migration
to lower land).
Land-use decision module
The main role of the land-use decision module is to decide on how much land is
needed for each crop type, and the location of these respective land uses based on
Modelling shifting cultivation in Luangprabang
271
the information from the first four modules. First, the location of newly added
irrigated and rain-fed rice field is chosen, based on the conditions of grid cells, which
are ranked on the basis of proximity to existing irrigated or rain-fed rice fields,
proximity to water, and slope characteristics. If there are more cells of the same rank
than are necessary to accommodate increased irrigated and rain-fed rice supply, then
the module chooses cells for increased rice production randomly amongst the candidate cells. The location of newly added upland rice fields, on the other hand, is
determined in the shifting cultivation module.
Second, for upland rice and other crops, the following year's cultivation area is
chosen randomly from cultivatable candidated areas, which are determined on the
basis of existing land use, elevation, slope, and fallow period. We resort to the random
selection of new cultivation areas because our fieldwork did not identify any particular
rules or customs among the Lao farmers in locating new shifting cultivation fields.
Obviously, this random selection rule limits the model's ability to produce high spatial
accuracy in replicating shifting cultivation areas. Third, if migration is to occur
between village clusters, the nearby village clusters are chosen first as the candidate
destinations, and then the more distant village clusters are chosen. If we have a similar
set of grid cells within a village cluster, the choice within them is made randomly.
Results and model evaluation
We evaluate the accuracy of simulation results using observation data in two ways.
First, the simulation results are compared with the aggregated statistics at the provincial level in terms of the cultivated area and production volume. Because these
observation data are unavailable at any subprovincial scales, we add up simulation
results from all village clusters, and compare the aggregated values with the observed
data at the provincial level. Second, we run and evaluate the model at a section of
Luangprabang Province, where we have more detailed observation data from remote
sensing image analysis (Nagasawa et al, 1998; figure 1). By focusing on this section we
are able to evaluate the model in terms of spatial distribution of shifting cultivation,
rather than only in terms of aggregated cultivation areas and volumes.
Cultivated area and production volume
We compare the simulated values of cultivated areas for each crop type (irrigated rice,
rain-fed rice, upland rice, and other crops) with the provincial statistical data, which
are available for 1990 and for the 1995 ^ 99 period (State Planning Committee, 2000).
For the cultivated areas of irrigated rice, the simulated values fit the statistical data well
[figure 5(a)]. We expected a good fit because the model uses the growth rate of irrigated
rice cultivation area as an exogenous variable. For rain-fed rice the error margins are
within 10% for the simulated and actual values in 1990 and 1998 [figure 5(b)]. Between
1995 and 1999 the model first overestimates the cultivated area, but, as the simulated
value follows a downward trend while the actual value follows an upward trend, the gap
between the two values narrows until 1998. By 1999 the model underestimates the
cultivation area. We speculate that the difference in the trends derives from the problem
in the productivity estimates, which convert production volume to cultivated area. The
model currently uses estimated productivity values from the available statistical data,
but more accurate and variable productivity, over time and in different areas, may need
to be taken into account. For upland rice, both the simulated and actual values show
downward trends in total cultivated areas since 1995 [figure 5(c)]. Between 1995 and
1999, the gap between the two values narrows from 16 800 to 9 000 hectares. The
estimated value consistently overestimates the actual value except in 1990. The 1990
actual value, in fact, seems suspiciously high, but we were unable to confirm the validity
272
Y Wada, K S Rajan, R Shibasaki
2
8
1
4
0
0
(a)
(b)
70
12
50
8
30
1999
1998
1997
1996
1995
1994
1993
(d)
Year
1992
1999
1998
1997
1996
1995
1994
1993
1992
1991
(c)
0
1991
4
10
1990
Area (1000 ha)
Observed
12
1990
Area (1000 ha)
Model
3
Year
Figure 5. A comparison of cultivation areas of four crop types from the model estimates and the
observed data, 1990 ^ 99: (a) irrigated rice, (b) rain-fed rice, (c) upland rice, (d) other crops.
Model
30
20
10
0
(b)
12
8
Year
(d)
1999
1998
1997
1996
1995
1994
1993
1992
0
1991
1999
1998
1997
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1991
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1990
Volume (1000 tonne)
(a)
(c)
Observed
40
10
8
6
4
2
0
1990
Volume (1000 tonne)
of the data. For other crops the patterns are similar to those of upland rice, where both
simulated and actual values show downwards trends, the simulated values overestimate,
and the 1990 actual value seems unexpectedly high [figure 5(d)]. For all the four crop
types, the simulated values resemble the actual values more accurately over time.
We conduct similar comparisons in terms of total production volume for each crop
type. Overall, the model somewhat underestimates the production volumes of irrigated
rice, and rain-fed rice, and overestimates the volume of other crops (figure 6). The
estimated volume of upland rice shows the closest fit to the observed value and its
trajectory.
We can gain further insight into the model by comparing the evaluation results of
production volume with those of cultivated area. For irrigated rice, we attribute the
good fit of area estimation and underestimated production volume to the problem with
the productivity estimates. The results for rain-fed rice also indicate a fairly good fit for
area and the underestimation of production volume. We cannot attribute this gap to
Year
Figure 6. A comparison of cultivation volumes of four crop types from the model estimates and
the observed data, 1990 ^ 99: (a) irrigated rice, (b) rain-fed rice, (c) upland rice, (d) other crops.
Modelling shifting cultivation in Luangprabang
273
8
8
(a)
Year
(b)
ÿ10%
Model (0%)
1999
1998
1997
1996
1995
1994
2
0
1999
1998
1997
1996
1995
1994
1993
1992
1991
0
1990
2
ÿ5%
4
1993
Observed
‡10%
1992
‡10% ‡20%
4
‡20%
6
1991
Model (0%)
1990
Area (1000 ha)
6
Observed
Volume (1000 tonne)
ÿ10%
ÿ5%
Year
Figure 7. Sensitivity analysis for (a) cultivation areas, and (b) cultivation volumes, of upland rice,
using different productivity estimates.
the productivity estimates in this case because a higher productivity estimate will lead
to a lower estimated value of upland rice, which seems already appropriately estimated.
We thus suspect that the daily rice consumption volume estimate may be too low. For
upland rice and other crops the cultivated area is overestimated, but the production
volume is slightly underestimated. Here again we suspect that inaccurate productivity
estimates play the main role.
To check for the effects of productivity estimates on the model outcomes, we
conduct a sensitivity analysis by varying productivity estimates. Figure 7 shows the
results for upland rice. It is apparent that, when the productivity estimate is adjusted
by some fractions (ÿ10%, ÿ5%, ‡10%, and ‡20%), the estimated crop area does not
change nearly as much as the estimated crop volume. Similar patterns were observed
for the other three crop types, thereby indicating that the crop volume is generally
more sensitive than the area to changes in productivity estimates in our model.
Spatial distribution of shifting cultivation areas
To verify and validate our model using another source of observed data, we compare
the spatial distribution of shifting cultivation areas from our model with the time-series
maps (1987 ^ 97) of Nagasawa et al (1998), who used remote sensing data to identify the
spatial distribution of shifting cultivation in the area of approximately 50 km square
around Luangprabang City. We apply our model to the remote sensing image boundaries, and observe how our simulated maps evolve in comparison with the observed data
in terms both of total cultivated areas and of grid-by-grid matching.
The total areas of shifting cultivation (ie upland rice and other crops) are first
compared (table 1). Our model consistently provides lower estimated values of cultivation areas; on average the simulated value is 83.8% of the observed value. Next we
compare the total areas of shifting cultivation plus rain-fed rice fields (table 1). We add
rain-fed rice fields because the original remote sensing data, used by Nagasawa et al
(1998), were primarily obtained during dry seasons, and we speculate that the rainfed rice fields after harvesting may have been erroneously interpreted as shifting
cultivation fields. This comparison yields a much better fit between the simulated
values and the observed values between 1990 and 1997 (table 1).
To evaluate the accuracyof our model in terms of the simulated spatial patterns,
we compare the distribution maps from our model with the remote sensing map on a
274
Y Wada, K S Rajan, R Shibasaki
Table 1. A comparison of the shifting cultivation areas from observed data and the model
estimates, 1990 ^ 97.
Year
1990
1993
1994
1995
1997
Observed
data (ha)
14 350
15 875
17 775
17 300
17 275
Shifting cultivation ˆ
upland rice ‡ other crops
Shifting cultivation ˆ
upland rice ‡ other crops ‡ rain-fed rice
area (ha)
fit (%)
area (ha)
fit (%)
11 975
14 275
14 275
14 300
14 300
83.4
89.9
80.3
82.7
82.8
14 200
17 175
17 450
17 775
18 475
99.0
108.2
98.2
102.7
107.0
Note: The area of shifting cultivation from the observed data is calculated using the 500 m grid.
The fit (%) is the ratio of the model estimate to the observed data.
grid-by-grid basis. We first compare the two maps at the highest resolution, the
500 m 500 m grid cell. The accuracy rate, where the location of shifting cultivation
grid was identical in both maps, is around 5%. This rate is equivalent to the rate of
completely random matching, suggesting that our model has little predictive power at
the 500 m grid level. The result is not surprising, however, because our model specifies
that the agent selects new fields randomly only to avoid repeated cultivation. By
looking at the remote sensing map, we find no particular spatial regularities in the
distribution of shifting agriculture, and consider that the grid-by-grid match at this
resolution is not acceptable (figure 8).
Shifting cultivation
River
Other
0
0
Remote sensing data
10
20 km
10
20 miles
Model simulation
Figure 8. A comparison of spatial patterns of shifting cultivation fields from the model and the
remote sensing (observed) data in a section of Luangprabang Province, 1997.
We then compare the two maps at lower resolutions: 2.5 km, 5 km, 10 km, and
20 km grid levels. Here we simply fit the model at the smallest resolution, 500 m,
and aggregate the cultivation areas up to these lower resolutions. We consider both
the areas of shifting cultivation (upland rice and other crops), and the areas of shifting
cultivation plus rain-fed rice. The scatter plots show the total shifting cultivation
area for each grid cell, based on the observed data on the horizontal axis, and the area
based on our model on the vertical axis (figure 9). Linear regression results indicate
that R 2 increases as the resolution becomes larger (eg R 2 ˆ 0:21 for the 2.5 km grid,
and R 2 ˆ 0:91 for the 20 km grid), which indicates that the model's accuracy
increases with the size of grid cells. This relation between the accuracy and grid size
was expected, however, because it means only that, as the resolution becomes lower
Modelling shifting cultivation in Luangprabang
275
y ˆ 0:8043x
R 2 ˆ 0:2163
y ˆ 0:8671x
R 2 ˆ 0:537
20
50
Model
15
40
30
10
20
5
0
10
0
5
10
15
0
20
(a)
0
10
20
y ˆ 0:9584x
R 2 ˆ 0:6393
40
50
y ˆ 1:0008x
R 2 ˆ 0:9107
150
Model
30
(b)
450
100
300
50
150
0
0
50
100
0
150
Observed
(c)
(d)
0
150
300
Observed
450
Figure 9. A comparison of shifting cultivation areas based on the model estimates and the
observed data at four spatial resolutions. Each value represents the number of 500 m grid cells
that are predicted by the model ( y-axis) and which are observed in the remote sensing image
(x-axis) as areas of shifting cultivation within each of the larger grid cells: (a) 2.5 km, (b) 5 km,
(c) 10 km, (d) 20 km.
(eg coarser raster landscape), the model is required to provide increasingly more
aggregate information about the locations of shifting cultivation fields.
Nevertheless, the plot of correlation coefficients for the model estimates and the
observed data at different resolutions shows that the coefficient rises sharply around
the 5 km grid level (figure 10), which can be considered as the optimal resolution at
Correlation coefficient
1.0
0.8
0.6
0.4
0.2
0.0
0
5
10
Grid size (km square)
15
20
Figure 10. Correlation coefficients between the model estimates and the observed data at five
spatial resolutions: 0.5 km, 2.5 km, 5 km, 10 km, and 20 km.
276
Y Wada, K S Rajan, R Shibasaki
which to run the model. Our field observations showed that the sizes of villages range
typically from 5 km to 10 km square. Land-use plans are generally designed at the
scale of the village, and the village is generally the unit of decision making concerning
shifting cultivation in this region. Therefore the evaluation results suggest that our
model based on the village-scale demand and supply mechanism can usefully provide
some degree of prediction at the 5 km to 10 km grid level.
Discussion
The work presented here adds to the literature that applies an agent-based model to
areas under shifting cultivation (eg Deadman et al, 2004). Our approach differs from
similar previous studies in that we use village clusters, rather than households, as
agents in the model, where land-use change is dynamically determined by the relationship between the demand and supply of crops. One of the central implications of our
study is that the agent unit must be chosen carefully on the basis of particular local
circumstances. We believe that villages are the most appropriate units of decision
making regarding shifting cultivation in Laos (although we had to resort to village
clusters due to a lack of boundary data), but this may not be the case in other sociocultural contexts. Our study demonstrates that village-based modelling is indeed
capable of producing encouraging results for pursuing this research strategy further.
Unlike in most other studies, we evaluate our model results using multiple sources of
observation data, both at the aggregate level using provincial statistics, and at a more
disaggregate level using location information of shifting cultivation based on remote
sensing image analysis. The results seem to validate the basic underlying mechanism
of the model that determines land-use patterns based on the balance between crop
demand and supply. Furthermore, by comparing model results and observed data at
different resolutions, we find that the model can account for the spatial patterns of
shifting cultivation approximately at the scale of villages in the area.
For further enhancement of the model, nevertheless, several limitations and possibilities must be addressed. We defined the seven village clusters by river basins and
other physical geographic information because we had little knowledge of precise
village boundaries. The central government's ongoing land redistribution project would
nevertheless have at least two consequences. First, it may provide us with clearly
defined boundary information and more disaggregated socioeconomic data, such as
numbers of residents and households, age and gender structures for each village,
migration flows, and more detailed financial data for households, on which we can
conduct more detailed analysis. Second, it may possibly alter the behaviors of local
people in terms of their land-use practice as local farmers are told to cultivate only
within their village boundaries.
The evaluation of the results suggests that one of the key estimated variables in our
model is the productivity estimate. More accurate estimates of productivity at different
locations may be obtained by crop growth models, such as the EPIC (erosion productivity-impact calculator) model (Williams and Meinardus, 2004), although necessary
soil and climate data are not readily available at spatial scales smaller than the
province at this moment. Similarly, our model assumes that each village cultivates a
mix of three different types of rice and the `other' crop, represented by corn. In reality,
each village grows a more diverse range of crops based, for example, on the distance
to rivers and on the degree to which the market economy has penetrated. If we can
differentiate patterns of crop mix using high-resolution remote sensing data and fieldwork,
we will be able to model more accurate land-use options.
Beyond incorporating more detailed spatial information and accurate productivity
estimates, we conceive several avenues of refining and extending our model. First, our
Modelling shifting cultivation in Luangprabang
277
model currently selects new locations of shifting cultivation randomly among variable
grids, because our field observation was unable to determine any consistent rules of
location selection. However, if we can obtain more detailed spatial information about
land conditions and about past cultivation histories, we will be able to refine the
locational logic of shifting cultivation. Second, it will become more critical to incorporate the effects of commercial crop cultivation on land-use change into our model.
We decided to focus on rice production, and to assume a closed, subsistence economy
within the province, because we identified only limited signs of market agriculture,
such as the cultivation of posaa (paper material), whose income was used to buy rice
during periods of food shortage, and to buy clothes, oil, and drugs. We would certainly
expect, however, the growth of a monetary economy along with changes in lifestyle,
such as an increased demand for luxury goods and for education, which will probably
result in the increased importance of commercial crops. Our future agent-based
models can certainly incorporate these new commercial crops by devising appropriate
modules. Third, the central government has a strong influence over the expansion of
irrigated rice fields in Laos, resembling the role of government in tropical forests in
Brazil (Walker et al, 2004). Consequently, our current model determines the degree of
irrigated rice expansion exogenously, but a more complete model should internalise the
mechanism of irrigation rice field dynamics as an outcome of agents' behaviors.
Finally, more refined models must accompany more sophisticated validation techniques. Although the primary challenge for validation may well be the lack of necessary
observation data, we think that our future modelling work can incorporate more
rigorous ways of evaluating the distribution maps of shifting cultivation fields based
on the model results and observed data. As Pontius (2002) suggests, one of the
challenges in validating land-use models is that the agreements between model results
and observed data in terms of area and location may vary depending on the level of
resolution. Some of the emerging statistical tools of validation (Pontius, 2002; Pontius
et al, 2004) may help us to improve the locational predictability of the model, and
more generally to address the issues of scale in agent-based modelling more effectively
(Evans and Kelley, 2004).
Acknowledgements. The authors are grateful to Dr Tom P Evans, Dr Steven Manson, and Helen
Hazen for their helpful comments and suggestions on an earlier draft of this manuscript. We would
also like to thank Professor Ryota Nagasawa, Professor Yasuyuki Kono, and Professor Takaaki
Niren for data provision and for facilitating the fieldwork.
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