Exploring land use options for agricultural
development in Bac Kan Province, Vietnam
This work will be presented by Dr. Pham Quang Ha ( Vietnam LUPAS Coordinator at
the AEZ-LADA Workshop, Bangkok, Thailand, 10-14 November 2003)
LUPAS project was carried out to exploring land use options for agricultural development at
provincial level in using Backan province as a case study in period 2000-2002. Different
scenarios were formulated under linkage between natural resources evaluation, socio-economic
study and multiple goal linear programming for food security and environmental protection.
This approach is now developed and adjusted for district levels with different agro-ecological
zones both in North and in South Vietnam by NISF team and further research is enforced with
the collaboration of the project entilled “Systems Research for Integrated Resource
Management and Land Use Analysis in East and Southeast Asia” (IRMLA) INCO-Dev,
CEC, Brussels coordinated by Alterra, Wageningen.
The follows names were key member of VIETNAM-IRRI LUPAS team:
BUI Tan Yen1, KAM Suan Pheng2, PHAM Quang Ha1, CHU Thai Hoanh2, BUI Huy Hien1,
CASTELLA Jean-Christophe3, HO Quang Duc1, VU Dinh Tuan1, VU Nguyen4, CAO Ky Son1
1
National Institute for Soils and Fertilizers (NISF), Chem, Hanoi, Vietnam
International Rice Research Institute (IRRI), Los Banos, Laguna, Philippines
3
Institut de Recherche pour le Development (IRD, France) – IRRI, Hanoi, Vietnam
4
Vietnam Agricultural Science Institute (VASI), Ha Noi, Vietnam
2
ABSTRACT
A study was carried out in the poor, mountainous Bac Kan Province in the
northern part of Vietnam to explore land use options and analyze tradeoffs between
different development objectives. The methodology is based on a GIS-linked multiple
goal linear programming (MGLP) modeling tool, the Land Use Planning and Analysis
Systems (LUPAS). The LUPAS methodology comprises three main components: (i)
evaluation of the resources required for agricultural development, (ii) estimation of
inputs and outputs for production activities, and (iii) optimization of land and resource
allocation subjected to policy views and development objectives.
This paper describes the use of regional-level soils information in developing the
knowledge base for the modeling. In the first step of qualitative land evaluation, timeinvariant terrain and soil properties were used for analyzing slope-soil-crop suitability.
Because of the complex topography of the province, we used the raster GIS data
structure for more efficient handling of the highly heterogeneous spatial variables. The
slope-soil-crop suitability assessed at grid cell level for 18 crop types was used to
delineate land units (LUs) within commune boundaries. The second step takes into
account dynamic climatic factors that influence crop-season suitability at each LU.
Thirdly, each LU was evaluated for its suitability for selected land use types (LUTs),
which are combinations of crop-seasons. Data on agricultural production collected at
commune level were used to quantify inputs and outputs associated with existing and
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promising LUTs for each LU. The input-output tables were used in optimization
modeling.
The model computes the optimal allocation of resources (land, water, labor,
capital) to achieve user-specified development objectives, under certain assumptions
about the levels of resource availability and technology use. The results indicate which
LUTs are selected, how many resources are needed, and the production levels and
incomes expected. The model results are mapped to show the spatial pattern of the
selected LUTs. The outputs provide a rational basis for geographical targeting of
different land use types within a particular region.
Key words:
Land use planning, resource evaluation, soil-crop suitability, optimization model, GIS
INTRODUCTION
Bac Kan, a province of 459,000 ha in the upper part of the Red River basin, is
one of the least developed and poorest provinces in Vietnam (Figure 1). The majority
of the population (83%) is rural and dependent on agriculture. Increasing population and
greater market integration places higher pressure on the use of the land and its
resources. Because of the mountainous terrain, only about 5% of the land area is
considered suitable for agricultural production. In reality, increasing areas of sloping
land are being used for food production, giving rise to concerns about long-term
sustainability of the natural resource base to support the demands for food production
and income generation.
The province faces the challenge of identifying opportunities and optimizing the
use of limited land and water resources for crop, livestock and forestry production. Land
use planning needs to take into account not only of the biophysical conditions but also
the socioeconomic circumstances and policy environment. The province needs to
consider different possible options under various development objectives, and
understand the trade-offs among different scenarios of resource allocation to meet these
objectives.
In this paper, we describe the use of a land use planning and analysis tool for
exploring land use options for Bac Kan province, and illustrate the potential of this tool
to assist policy makers in decision support for resource allocation to meet certain
development objectives.
MATERIALS AND METHODS
The Land Use Planning and Analysis system (LUPAS) is a methodology
developed for exploring options for land and resource use allocation, using a
combination of resource evaluation methods with interactive modeling using multi-goal
linear programming approach (Roetter et al. 1998). LUPAS is linked with geographical
information systems (GIS) to provide an explicit spatial dimension as well as facilitate
integration and analysis of biophysical and socioeconomic data. LUPAS is organized to
three main components (Figure 2): (i) evaluation of the resources required for
agricultural development, (ii) estimation of inputs and outputs for production activities,
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and (iii) optimization of land and resource allocation subjected to policy views and
development objectives.
1. Resource evaluation
Resource evaluation is fundamental to the LUPAS methodology. It is carried out
to assess if the existing or intended use is appropriate or can be supported by the
resource base, or conversely for what kinds of uses the land is suited or has potential
for. It is necessary to define or delineate spatial units of land for resource evaluation.
Qualitative resource evaluation is carried out in Component (i) based on biophysical
conditions. This is followed by a more quantitative analysis in Component (ii), taking
into consideration quantities of resources and outputs, and the socio-economics of
production.
1.1 Qualitative evaluation
The main task in qualitative evaluation is to match the qualities of the land with
the requirements of the intended land use type. The land qualities are influenced largely
by environmental factors such as climate, topography, soils, hydrology and constrained
to some extent by present land cover/land use. All these factors vary across space and
time. Particularly given the rugged terrain of Bac Kan province, the spatial variability
of soil and hydrology is very high, and the limited arable land is also highly fragmented.
These conditions lead to a high diversity of existing and promising land use types
(LUTs). In determining the requirements of the LUTs, we need to take into
consideration
 Different crop types, e.g. rice, maize, citrus trees.
 Crops grown in different seasons, i.e. in spring, summer and winter
 Combinations of crops, i.e. the number and kinds of crops grown in a year.
When matching the LUT requirements with the land qualities we need to take
into consideration not only spatial but also temporal variability of the resource base. The
temporal variation is influenced by climatic and hydrological factors. Because of the
complex topography of the province, we use the raster GIS data structure to capture this
spatial heterogeneity rather than attempt to delineate large homogeneous land units
(LUs) in the vector mode (Kam and Hoanh, 1998).
The qualitative resource evaluation was carried out in a number of steps, as
described briefly below. The methodological details are described in Kam, et al., (2000).
We first considered the time-invariant factors, i.e. match the terrain and soil conditions
against the requirements of particular crop types (e.g. maize, rice, potato, etc.). Thus the
slope-soil-crop suitability was determined for selected crop types at each cell in the
raster representation of the province. Grid cells having similar slope-soil-crop suitability
for the selected crop types were then clustered into land units (LUs). The LUs were
defined within land administrative units, in this case the commune, so that socioeconomic data collected at commune level would be incorporated at the quantitative
evaluation stage.
In the next steps of the qualitative evaluation, the dynamic factors are taken into
account by:
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a) matching, for each LU, the climatic (seasonal temperature and, for non-irrigated
conditions, rainfall) characteristics with the requirements of the crop-season to
determine crop-season suitability, e.g. a LU may be more suitable for summer rice
than for spring rice because of temperature constraints; and
b) determining, for each LU, the combined suitability of LUTs, which are specific
combinations of crop-seasons.
A LUT may consist of only one crop (e.g. summer upland rice only, or a
perennial crop), or have combinations of two or three crops grown in different seasons
of the year. For example a two-crop LUT may comprise summer rice followed by
winter vegetables; a three-crop LUT may be spring rice followed by summer maize and
winter potato.
The approach that we developed for this study constitutes a modification of the
FAO land evaluation procedure (FAO, 1976; 1993), whereby the LU is delineated first,
taking into consideration climatic, terrain and soil factors, and then suitability evaluation
is done for crop types of interest. Our approach overcomes a number of limitations of
the FAO approach, particularly in dealing with multiple crops in different seasons and
with high spatial heterogeneity of environmental factors.
1.2 Quantitative evaluation
At this stage of resource evaluation, we determined quantities and values of
main- and by-products that can be obtained, assuming certain levels of technology used,
as well as corresponding levels and costs of inputs (including agricultural chemicals,
labor, water and transportation). Data and information on inputs and outputs for each
crop type at low and high technology levels were collected from field surveys and
various research findings, and from consultation with local experts. Using these data,
input/output tables were constructed for all LUs for existing and promising LUTs.
These tables, which encapsulate quantified outcomes of the resource evaluation, are fed
into the LUPAS optimization model.
2. Land use Optimization
In the land use optimization process, the following steps are implemented: (a)
determine the land use objective functions, (b) determine resource constraints to land
use, and (c) find optimal land use options for user-defined objectives by using a
multiple goal linear programming model.
In step (a), we translated development objectives into objective functions for the
model. Some development objectives, as articulated by key stakeholders in Bac Kan
province (including provincial government authorities, line agencies concerned with
resource management such as provincial and district agriculture departments), are as
listed below
 Increase agricultural production through intensification and diversification;
 Increase regional income not only from agriculture, but also forestry sector;
 Minimize encroachment of agricultural activities into forest land; and
 Intensify agriculture in the lowlands, thereby relieving pressure and minimize
soil degradation in the fragile sloping lands.
In step (b), we determined the underlying assumptions about resource
availability, e.g. constraints/limits on the quanta or sharing of land, water and labor
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resources and of financial capital. The assumptions made were based on information
gathered about existing situations (e.g. fiscal policies, institutional arrangements) and
future development plans (e.g. for infrastructure development, investment schemes).
Each, or combinations, of these assumptions when applied to run the optimization
model in step (c), would generate different scenarios of resource allocation that
optimizes (i.e. maximizes or minimizes) the objective function of interest. Each scenario
result gives the selected LUT(s) assigned to each LU for the optimal solution. Since the
results are computed for each LU, it is possible to display the results in the form of
maps by linking the optimization model with GIS. Post-optimization analysis is carried
out to interpret the results of the scenarios modeled, and to determine the implications of
each scenario.
3. The use of terrain, soil and climatic data in resource evaluation
A recent land use map of Bac Kan province at 1:100.000 scale (GDLA, 1998)
was used to mask out areas that are physically and legally unavailable for agriculture
such as rocky mountains, water bodies, settlements, and forest areas designated for
special purposes (national parks, protection forests and special purpose forests). All
subsequent analysis was carried out only for areas that are not masked, which constitute
56% of the total for the province.
A digital elevation model of Bac Kan province (Figure 1) was used to generate a
slope map, which was used in conjunction with a rasterized soil map (NISF, 1973) to
determine, for each 250-m grid cell, the suitability for 18 crop types based on slope and
soil requirements. The weighted linear combination method (Sys, et al., 1993) was used
to match the slope and soil requirements for each crop type. The weight assigned to
each factor for each crop type reflects the relative importance of the factor in
influencing crop performance and yield. A total of 2,506 LUs were formed within 122
communes, based on slope-soil-crop suitability.
Gridded surfaces of average monthly temperature and rainfall were generated by
spatial interpolation of weather data obtained from 12 stations within and surrounding
Bac Kan province. Areas presently under irrigation were estimated from existing
lowland rice area. These data were used to determine the suitability, at each LU, for 27
crop-seasons, which were in turn used to determine the combined suitability for 42
LUTs. Of these 42 existing and promising LUTs, 10 are three-crop, 18 are two-crop
and 14 are one-crop LUTs (of which 6 involve annual crops and 8 involve non-annual
crops).
RESULTS & DISCUSSION
Resource evaluation
Much of Bac Kan province is composed of mountain ranges and plateaus
(Figure 1). The valleys are narrow and occur in pockets; the broadest valley runs in a
general northwest-to-south direction down the center of the province. These valleys are
associated with narrow strips of alluvial soils (Figure 1). The gentlest slopes are
associated with the valley bottoms and the plateaus (the most extensive one is located in
the southwestern corner of the province in Cho Don district). Under present land use
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conditions, cropping is most intensive in the narrow valleys where the soils are thicker
and more fertile and water availability is good.
Figures 3(a) to 3(e) map out the suitability for selected crop-seasons. Larger
areas are suited for summer rice than for spring rice because of the more favorable
temperature and rainfall conditions in the summer months for rice cultivation. The
limited LUs suited for spring rice are confined to the valley floors where temperatures
are warmer and irrigation water is available in the early part of the year. Comparatively,
more areas are suited for maize and sweet potato than for rice. The spatial distribution
of areas suited for spring maize differs partly from those suited for spring tobacco and
winter sweet potato; the difference is most marked at the eastern and western parts of
the province. The suitability rating for winter sweet potato is generally higher for most
of the suitable LUs than that for spring tobacco.
Figures 4(a) to 4(c) map out the results of the qualitative land evaluation, based
on LUs that are suited for one-, two- and three-crop LUTs respectively, the map legend
indicating the number of different LUTs that are suitable at each LU. There are only
limited LUs that are suitable for three-crop LUTs, and these tend to be confined to the
valleys where irrigation is possible (Figure 4a). The LUs in the northeastern corner
(shaded orange) are suited for a smaller number three-crop LUTs because of the colder
temperatures (dropping from 20 to 11oC) and lower rainfall (ranging from 20 - 50mm
per month) there during the winter months of October to January. Not surprisingly,
there is a much high number of LUs that are suited for one-crop LUTs, and the LUs that
are suited for a greater variety of one-crop LUTs (up to a maximum of 7) are those in
the valleys and the plateau areas (Figure 4c). Figure 4b shows that LUs suited for high
numbers of two-crop LUTs (shades of green) show a distinct Y-shaped spatial pattern
traversing the center of the province. The western arm and the stem of this Y-shape
correspond with the most contiguous and broadest valley described above. The eastern
arm corresponds with northeastern corner of Ngan Son district. There also appears to be
a distinct difference between the eastern and western mountainous parts of the province
separated by the Y-shape. The LUs in the eastern part are suited for higher number of
two-crop LUTs (as shown by the pinkish magenta and yellow colors) compared with the
western highlands and plateau (bluish magenta and red colors). The plateau area in the
southwestern corner in Cho Don district is suited for a low number of two-crop LUTs
despite the gentler slopes and more favorable winter temperatures compared with the
northeastern corner.
The results of the qualitative resource evaluation are intermediate outputs of the
LUPAS methodology; nevertheless they are informative for assessing the potential of
different areas in the province for particular land use types.
Interpretation of model results
The MGLP model generates the optimal allocation of resources (land, water,
labor and capital) to achieve user-specified land use objectives, under certain
assumptions about the resource availability and technology application. The results
indicate which LUTs are selected in each LU, how much resources are needed, and the
production and incomes expected. Since the population is irregularly distributed in this
mountainous province, two scenarios in labor availability were considered: (i) labor
force in each commune is only used for land use in its territory; and (ii) labor force can
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be shared among communes. The possibility of labor sharing among communes is
determined based on the accessibility with the help of GIS.
Figure 5 shows the trade-offs between two objectives of food production and
annual regional income. The curves lift upwards from (a) to (f) with the liberation of
more resource constraints. When land is the only constraint, the optimal curve (f) is
highest, while with all constraints in land, capital, water and labor without sharing, the
optimal curve (a) is lowest. The current situation (white star) represents the present
focus on food production by local people at present. The distances between the curves
(a), (b) and (d) clearly indicate that availability of capital and water are two major
constraints. The change in direction of curves from (a), (b) and (c) to (d), (e) and (f)
shows that when water and capital constraints are removed, food production increases
faster than regional income. A large gap between curves (d) and (e) compared with
minor gap between curves (b) and (c) indicates that labor sharing would have significant
effects only if water and capital constraints are overcome.
Figure 6 shows the main LUTs under two extreme scenarios when land resource
is the only constraint: maximize regional income without requirement of food
production, i.e. point I in curve (f), and maximize food production without paying
attention to regional income, i.e. point F in curve (f). In the first scenario, suitable fruit
trees and two- or three cash crops are selected, while in the second, obviously food
crops as rice and maize predominate.
CONCLUSIONS
Conventional soil-crop suitability analysis stops at determining if particular
crops can be grown on certain soil types. In this study we take suitability analysis
further by considering not only the season for growing the crop but also the combination
of crops to be grown in the three main planting seasons in the year (i.e. the land use
types). In addition, the quantification of inputs and outputs for different LUTs at
different technology levels takes into consideration socio-economic factors. Finally, the
use of the information gathered in an interactive land use planning tool extends the
utility of suitability analysis and land evaluation for exploring optimal allocation of
resources under different development objectives.
Applied to a geographical area where the driving factors for land use are highly
variable in space and time, we have had to develop methodologies for data processing
and analysis that would capture this heterogeneity. The results of the qualitative
resource evaluation show a distinct spatial association of LUs suited for more intensive
cultivation with areas of distinct terrain, soil and hydrological characteristics.
The model results also show that removal of the most serious constraint, i.e.
capital, would substantially improve the capacity to increase food production and
regional income. Under the present status of limited capital within the province,
improving water availability would enable higher levels of food production and regional
income to be attained. In the absence of capital constraint, labor would emerge as the
next most constraining factor; hence sharing of labor resources among communes would
further increase food production and regional income.
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REFERENCES
FAO, 1976. A framework for land evaluation. FAO Soils Bulletin No. 32, Rome, Italy,
72 pp.
FAO, 1993. Guidelines for land use planning. Interdepartmental Working Group on
Land Use Planning. FAO, Rome, Italy, 140 pp.
General Department of Land Administration (GDLA). 1998. Land use map of Bac Kan
Province, scale 1:100,000. Institute of Land Survey and Planning - General
Department of Land Administration, Ha Noi, 1998.
Kam, S.P. and C. T. Hoanh. 1998. Implementing GIS in a decision support system for
analyzing the balance between rice supply and demand. Paper presented at the
GISDECO conference in Pretoria, South Africa, October 1998. 12 pp.
Kam, S.P, B.T.Yen and C.T. Hoanh. 2000. Land evaluation for optimizing land and
resource use for agricultural production in a heterogeneous mountainous
environment - a case study of Bac Kan province, Vietnam. IRRI.GISDECO,2000.
National Institute for Soils and Fertilizers (NISF). 1973. Soil map of Bac Thai
Province, scale 1:100,000. Soil Research Group - National Institute for Soils and
Fertilizers in collaboration with Soil Research Group of Department of
Agriculture - Bac Thai Province (1963 - 1967). Agricultural Publishing House,
Published 1973.
Roetter, R. and C.T. Hoanh. 1999. Exploring land use options under multiple goals in
support of natural resource management at sub-national level. In: NN Kinh, PS
Teng, CT Hoanh and JC Castella (eds), Towards an Ecoregional Approach for
Natural Resource Management in the Red River Basin of Vietnam. Ministry of
Agriculture and Rural Development, Hanoi, Vietnam and International Rice
Research Institute, Los Baños, Philippines, 29-57.
Sys, C., E. Van Ranst, J. Debaveye and F. Beernaert. 1993. Land Evaluation Part III Crop requirement. Belgium General Administration for Development
Cooperation. Agricultural Publications No. 7.
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N
W
Bac Kan
province
Vietnam
E
S
Ba Be
Ngan Son
Bach Thong
Cho Don
Na Ri
Bac Kan
Soil types
Lo¹ i ®Êt Ký hiÖu
Ld
FH
FQa
FQj
FQk
FQp
FQq
FQs
FQv
Fa
Fj
Fk
Fp
Fq
Fs
Fv
Ao,Hå
J
Ld
Ldk
Lf
Luc
Luk
P
P'
Pb
Pi
Elevation, m
Cho Moi
Figure 1: Location, digital elevation map (superimposed with district boundaries) and
soil map of Bac Kan province.
2. Resource evaluation
Delineation of
land units,
resource
assessment,
land suitability
analysis &
yield estimation
Constraints concerning resources:
area, water, labour & capital
3. Land use optimization
Input/output
tables of
production
activities
Optimization
Model using
MGLP
Land use options &
Goal achievements
Data
Maps
Objective functions
Policy views
1. Scenario construction
Figure 2. The GIS-based Land Use Planning and Analysis System (LUPAS)
S1: Highly suitable
S2: Suitable
S3: Marginally suitable
N: Not suitable
a. Spring Rice
b. Summer Rice
c. Spring Maize d. Spring Tobacco e. Winter Sweet Potato
Figure 3. Suitability for selected crop-seasons
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a. Three-crop LUTs
b. Two-crop LUTs
c. One-crop LUTs
Figure 4. Suitability for one-, two- and three-crop land use types (legend shows the
number of 1-, 2- and 3-crop LUTs that are suitable at each land unit)
Tradeoff between
Food production & Regional income
800
I
Only land constraint
700
f
Regional income (bil VND)
Land & sharing labor
600
d
e
Land & labor w/o
sharing
500
Land, capital &
sharing labor
b
400
a
Land, capital & labor
w/o sharing
c
F
300
Land, capital, water,
labor w/o sharing
200
Current situation of
food production &
regional income
Current
100
0
50
100
150
200
250
300
350
Food production (thousand tons)
Figure 5: Optimal lines of food production and regional income in different scenarios
GOAL:
Maximize regional Income
CONSTRAINT:
Land resource
Lut_21
Legend
0_Apricot_0
0_Citrus_0
0_SoybM_MaizeW
MungbS_RiceM_0
SoybS_RiceM_PotatoW
VegS_VegM_VegW
GOAL:
Maximize food
CONSTRAINT:
Land resource
Legend
Lut_11
RiceS_RiceM_MaizeW
RiceS_RiceM_PotatoW
RiceS_RiceM_VegW
MaizeS_RiceM_MaizeW
MaizeS_RiceM_PotatoW
RiceS_RiceM_0
MaizeS_RiceM_0
0_RiceM_SPoW
Figure 6. Maps of model outputs for two scenarios
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