Open Journal of Soil Science, 2013, 3, 244-251
http://dx.doi.org/10.4236/ojss.2013.35029 Published Online September 2013 (http://www.scirp.org/journal/ojss)
Spatial Based Assessment of Land Suitability and
Availability for Maize (Zea mays L.) Development in
Maros Region, South Sulawesi, Indonesia
Nurmiaty1,2, Sumbangan Baja2,3*
1Department of Estate Crops Cultivation, Pangkep State Polytechnic of Agriculture, Pangkep, Indonesia; 2Center for Regional De-
velopment and Spatial Information (WITARIS), Hasanuddin University, Makassar, Indonesia; 3Department of Soil Science, Hasa-
nuddin University, Makassar, Indonesia.
Received July 9th, 2013; revised August 9th, 2013; accepted August 16th, 2013
Copyright © 2013 Nurmiaty, Sumbangan Baja. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
Recently, South Sulawesi Provincial government has launched the “gong” program with the main objective to optimize
all the resources (land, infrastructures, and farmers) in agriculture areas for maize production in the province. This study
is aimed at identifying the suitability and availability of land areas for maize development in Maros Region- the regency
having the most extensive agriculture production in South Sulawesi province. This study employed land evaluation
method in geographic information system (GIS) based on the FAO Framework for Land Evaluation. Land availability
was assessed from overlaying information on land use (obtained from available land use map and SPOT XS image in-
terpretation) and suitability classes based on the FAO Framework, as well as administration boundary map. The results
indicated that the S1 (highly suitable) class comprises a total area of approximately 34,468 ha, or about 24% from the
total area. The limiting factors for S2 (moderately suitable) and S3 (marginally suitable) classes are slope and nutrient
availability, but with the advanced management efforts (moderately input) such sub-classes can actually promote S3
class to S2 level. It was also found that from a total of 144,085 ha of the study area, potential maize development area
(for extensification) covers approximately 24,716 ha (or 35.6%). Tanralili, Bantimurung, and Simbang sub-districts
cover the largest suitable area, where no significant limiting factors exist. Surprisingly, potential development area for
maize in Camba, Mallawa, and Tompobulu sub-districts denotes minus values. This implies the facts that maize cultiva-
tion is still practiced on the land that is ecologically not suitable, where steep slope is the dominant limiting factor.
Keywords: GIS; Land Suitability; Land Availability; Spatial Analysis; Maize; Maros Region
1. Introduction
Maize (Zea mays L.) is the staple food for many people
in Indonesia. Since early 2000, Indonesia needs around
25 billion tons of maize every year, both for food and for
industry [1]. Local maize can be grown continuously
throughout the year, easier to plant and more economical
especially if grown organically. In South Sulawesi Prov-
ince, maize is grown everywhere in the southern parts of
the province. It is grown in a wide variety of environ-
ments. Yields can vary sharply from year to year and
from one farm land to another. In some areas growers
must lease a large fraction of the land that they use. This
causes them a number of problems. Now the cost to lease
good quality land has been very high—due to competi-
tion from other land uses. This can force growers to use
land of marginal quality, and it forces more intensive use
of the available land with tighter crop rotations. Unfor-
tunately, in such developing region, the climate and soil
data required to estimate land suitability and productivity
for selected crops are not always available. This makes it
difficult for local planners to map potential areas for de-
velopment of maize in the region.
In 2005, the provincial government of South Sulawesi
has set up a program called gong, a program for optimi-
zation of maize production. The main aim is to optimize
all resources (land, infrastructure, and farmer) in agri-
culture for the production of corn. The government’s
target of corn production was to reach a surplus of 1.5
million ton per annum [2]. However, such target has
*Corresponding author.

Spatial Based Assessment of Land Suitability and Availability for Maize (Zea mays L.)
Development in Maros Region, South Sulawesi, Indonesia
245
never been reached since the launch of that program. In
some areas where marginal land occurs in a large area
some efforts have been attempted to grow maize, using
traditional methods. As not all soils are suitable for
maize production because of inherent soil properties and
site conditions, including shallowness, steepness, erosion
susceptibility, poor internal drainage, wetness, flooding,
root zone limitations or poor water holding capacity, land
use practice requires properly land suitability and avail-
ability evaluation for development. Frequently, these
marginal soils are fallow or used as unmanaged range-
lands or woodlands. Production potential is often so low
that minimum tillage is not a viable alternative.
The primary aim of this paper is to assess spatial
variation of land suitability and availability for develop-
ment of maize at a regional scale, using information
made available from reconnaissance soil survey. This is
exemplified by the integration of a GIS and soils survey,
as used by [3-7], which is used for mapping of maize
suitability (using land systems as mapping unit under
varying climatic and soil conditions. Administration
boundary map was utilized as the basis for assessing land
availability.
2. Materials and Methods
2.1. Study Area
The study area (Maros Region) is located about 30 km
north of Makassar City, the capital of South Sulawesi
Province (Figure 1). It lies between latitudes 4˚711' and
5˚2'S, and stretches between longitudes 119˚453' to
119˚977'W. The area selected for this study includes
some parts of Maros District covering a total area of
144,085 ha. There area 14 sub-districts included in the
study region: Mandai, Moncongloe, Maros Baru, Marusu,
Turikale, Lau, Bontoa, Bantimurung, Simbang, Tanralili,
Tompobulu, Camba, Cenrana, Mallawa. According to
local statistical data [8], total population living in this
area is 303,211.
2.1.1. Cli m a te
According to Oldeman climate’s classification, the cli-
mate types in Maros Region are B2 and C, meanwhile,
based on Schmidt-Ferguson classification they are A and
B (extremely wet) with average amount of the rainfall is
3,346 mm/year. The rainfall distribution pattern indicates
that the dry season usually occurs in June until October
each year (generally very dry period takes place in 3 to 4
months) (Figure 2).
2.1.2. Topography
The study area consists of varying topography: flat, un-
dulating, rolling, hilly, and mountainous. The flat—un-
dulating areas (the slope gradient less than 15%) cover a
Figure 1. Sulawesi Island and the location of study area.
Figure 2. Distribution of monthly rainfall in the study are a .
total area of approximately 50,369 ha or 34.9% of Maros
Region. Such areas are usually used for paddy field, dry-
land agriculture, and also as mixed crops. One of the
unique characteristics of topography in this region is the
existence of karst landform which becomes conservation
zone, according to the spatial planning regulation.
2.1.3. Geology
Main geological formations found in this region include:
the Camba Formation consisting of ocean sediment rocks
mixed with a volcano rocks; alluvium deposits from the
lake and beach with assorted gravel, sands, clay, and
limestone rocks; the Tonasa Formation which consists of
limestone rocks, and the tefrit leusit with lava and breksi

Spatial Based Assessment of Land Suitability and Availability for Maize (Zea mays L.)
Development in Maros Region, South Sulawesi, Indonesia
246
[9].
2.1.4. Soils
Based on the land system map [9], the dominant soil type
in study area are dystropepts, with an average distribu-
tion coverage is 76.236 ha (52.9%), followed by tro-
paquepts and tropudults soil, with an average coverage is
38.640 ha (26.8%) and 7.980 ha (5.5%), respectively.
The other soil types found in the area are rendolls, eu-
tropepts, haplustults, and paleudults.
2.2. Analysis Procedure
The main procedure used in this study is depicted in Fig-
ure 3. Land suitability analysis was performed using the
method of land evaluation based on FAO (1976). Then,
the availability of land was analyzed using the method of
land use classification utilizing SPOT XS image with
resolution of 15 m. Results of the analysis, were then
overlaid through a spatial matching method to obtain the
potential development of maize based on administrative
boundary at a district level. In GIS, the database was
used as the basis for spatial developing strategy of maize.
2.3. Soil and Climate Data Bases and
Preliminary Data Processing
The main sources of data bases used in this study include:
1) digital topographic map; 2) soil map and soil charac-
teristics; 3) climate data; and 4) satellite data (SPOT XS
imagery). Some additional supporting data include ad-
ministration boundary at a sub-district level (Indonesian:
kecamatan), number of households and population in
each sub-district, and planting area and production of
maize.
Digital topographic maps of study area with a scale of
1:50,000 from the National Agency for Survey and Map-
ping were used as a reference for mapping. The digital
topographic maps were available in a vector GIS format,
makes it easier to build data bases in a standard vector
GIS. All the data layers were stored using UTM (Uni-
Figure 3. Schematic diagram of analysis procedure.
versal Transverse Mercator) coordinate system. As the
area of interest covers three sheets of topographic maps,
then a process of joining all the elements of map layer
is undertaken, before defining the boundary of study
area. The main topographic data layers used include
contour lines (25 meter interval), rivers and streams,
roads (main, secondary, and tertiary), and residential
sites.
The primary reference for soil data layers is the results
of soil survey undertaken by the local government of
Maros Regency. Land mapping units (based on topogra-
phy, land use, and geology) was derived from a land sys-
tem map to provide a basis for field survey. As many as
25 homogeneous mapping units were identified in the
area of interest, and soil sampling was done in 120 loca-
tions. Soil and climate characteristics surveyed and ana-
lysed include the followings (see Table 1). The data on
climate such as average temperature, rainfall, number of
dry months were obtained from local meteorological sta-
tion of Maros.
2.4. Land Use Mapping
The main aim of land use mapping in this study is to
provide thematic information on different land use cate-
gories, for use as the basis for assessing land availability.
The available updated local map of land use was used in
this study, in conjunction with visual interpretation of
SPOT XS images (20 m resolution).
In the study area, land use types are mostly dominated
by scrubs (50,062 ha), followed by dryland agriculture
(28,633 ha), paddy field (25,714 ha), forest (19,780 ha),
fish pond (10,115 ha), and the rest is consisted of resi-
dential, plantation, industrial area, international airport,
grass land, quarries, river, and unidentified ones with
relatively small area (Table 2).
Forest area is located in the northeast region, and ap-
proximately half of it could be found in the south east
region. Fish pond is concentrated on the coastal area
(west region), while residential basically spreads among
Table 1. Land quality and land characteristics used in the
analysis (base on [10]).
Land Qualities Land Characteristics
Temperature (t) Average temperature (˚C)
Water availability (w) Rainfall (mm), Number of dry months
Rooting condition (r) Texture, drainage, Soil depth (cm)
Nutrient retention (f) Clay CEC (cmol/kg), Base saturation (%),
pH (H2O), Organic C (%)
Toxicity (x) Salinity (dS/m)
Nutrient availability (n)N, P2O5, K2O
Terrain (s) Slope (%), Surface stoniness (%), Surface
outcrops (%)

Spatial Based Assessment of Land Suitability and Availability for Maize (Zea mays L.)
Development in Maros Region, South Sulawesi, Indonesia
247
Table 2. Land use types in the study area.
No. Land Use Area (ha)
1 International airport 195.8
2 Forest 19,780.1
3 Industrial area 39.4
4 Unidentified 9.3
5 Residential 3,907.1
6 Plantation 3,853.5
7 Grass land 435.0
8 Paddy field 25,713.5
9 Scrubs 50,062.2
10 River 1,079.7
11 Fish pond (tambak) 10,114.8
12 Quarries 261.3
13 Dryland agriculture 28,633.2
Total area 144,084.8
the downtown area (west region, located in north section
of the fish pond areas). Cropping and scrubs are found
scattered from the east to middle region of Maros Re-
gency (Figure 4). Previous study [11] also suggests those
findings.
2.5. Land Suitability Classification Approach
Land suitability classification in this study was under-
taken based on the framework for land evaluation guide-
lines [12], as used by [13-15]. The FAO’s land suitability
scheme is divided into Order, Class, Sub Class, and Unit.
Order is the global land suitability group, and is divided
into S (Suitable) and N (Not Suitable). Class is the land
suitability group within the Order level. Land suitability
classification is undertaken based on the level of detail of
the data available. For example, at a semi detailed map-
ping activity the S order is divided into Highly Suitable
(S1), Moderately Suitable (S2), and Marginally Suitable
(S3). In the “Not Suitable” order no further division is
made. Sub-Class is indicated by the type and level or
degree of limitations in each division. For example, land
unit having a limiting factor of rooting condition at a
marginal level is indicated by a Subclass S3rc. Further,
detailed divisions of Sub-Classes into Units can be made
according to differentiation in soil effective depths. The
effective depths of 50 - 70 cm and <50 cm, are respec-
tively classified as S3rc1 and S3rc2 [16].
In the present study, suitability classification is under-
taken at a class and sub-class levels according to the
FAO Framework for Land Evaluation [12]. The classifi-
cation at class level adopted is as follows [16,17]:
● Class S1 (highly suitable): land having no significant
limitation or only have minor limitations to sustain a
given land utilization type without significant reduc-
tion in productivity or benefits and will not require
major inputs above acceptable level.
● Class S2 (moderately suitable): land having limita-
tions which in aggregate are moderately severe for
sustained application of the given land utilization type;
the limitations will reduce productivity or benefits
and increase required inputs to the extent that the
overall advantage to be gained from the use, although
still attractive, will be appreciable compared to that
expected from Class S1 land.
● Class S3 (marginally suitable): land having limita-
tions which in aggregate are severe for sustained ap-
plication of the given land utilization type and will so
reduce productivity or benefits, or increase required
inputs, that any expenditure will only be marginally
justified.
● Class N (not suitable): land having very severe limi-
tations, as the range of inputs required is unjustifiable.
Matching processes between soil attributes and crop
requirements (as suggested in [12]) were performed in
simple overlay and spatial query methods in GIS (see
[18-20]). This is to identify spatially suitability classes
and subclasses, and the types and degrees of limiting
factors in each soil mapping unit. Final suitability maps
were then generated, where mapping units were used as a
basis for identifying different suitability subclass for
maize cultivation. Suitability level is given in actual and
potential terms. Actual suitability is a class or subclass
that is based on current soil conditions, i.e. without im-
provement efforts of applying any input, and the infor-
mation is based on physical environment data generated
from soil or land resources surveys. Potential suitability
is the level that the land unit could reach after a particular
improvement.
2.6. Spatial Analysis of Possible Maize
Development Areas
In this study, a spatial analysis was undertaken to iden-
tify the spatial matching between land suitability classes
and existing land use types in the study area using a sim-
ple overlay technique in GIS. An overlay technique was
also applied to identify a spatial match between the result
of overlay performed above (between land use and land
suitability) and administration boundary (sub-district or
kecamatan) layers, taking into account of existing maize
cultivation area, using the following function:




MDAa fPSS, EGL,= EMA (1)
where: MDA(a) = Maize development area for sub-
district (a), PSS = potential suitability sub-class area,
EGL = existing general land use type, and EMA =
existing maize area; all are expressed in ha.

Spatial Based Assessment of Land Suitability and Availability for Maize (Zea mays L.)
Development in Maros Region, South Sulawesi, Indonesia
248
Figure 4. Land use/land cover ty pe s in the study area.
The results reveal a possible development of area for
maize in every sub-district, taking into account of land
suitability as well as availability, after considering exist-
ing land use types. As the classification procedure was
done on a sub-class category basis, further analyses were
then performed to identify the type and degree of im-
provement of land characteristics at a certain sub-class
level (see [5]). Therefore, this procedure will bring about
the following results: 1) land availability, taking into
account of land suitability at a higher class; and 2) spatial
distribution of land sub-class category together with the
types and degree of improvement needed to develop
maize. As shown in their procedure above, both results
have considered administration boundary, so that appro-
priate management can be prepared in each sub-district,
through their agricultural development planning scheme.
for maize cultivation is in Tompobulu and Camba sub-
districts, followed by Moncongloe, Mandai, and Mallawa
subdistricts. The areas with less maize cultivation are
found in Maros Baru, Turikale, Lau, Bontoa, Bantimu-
rung, Simbang sub-disricts. The first two actually belong
to urban and suburban, while Lau and Bontoa are located
in the coastal area. However, the Bantimurung and Sim-
bang sub-districts are region in which most of the land
areas are functioned as paddy fields with intensive rice
cultivation. The data (Table 3) also show that all sub-
districts in the study area have productivity above or
equal to 5.0 ton/ha, except for Turikale and Bontoa
where no cultivation due to the fact that those sub-dis-
tricts are an urban area and coastal area.
3.2. Land Suitability Classification
3. Results and Discussion Distribution of the actual and potential suitability area
sub class for maize cultivation in the study area is shown
in Table 4. The S1 class comprises a total area of ap-
proximately 34,468 ha, or about 24% from total area un-
der studied. Most of the S1 classes were found in the
areas are now under cropping. Classes S2 and S3 cover
about 12% of the study area. The limiting factors for S2
and S3 sub-classes are slope and nutrients availability,
3.1. Maize Cultivation and Productivity
Table 3 presents the whole coverage of administration
area of Maros Regency, the extent of those areas that are
currently cultivated with maize, and also the productiv-
ities of maize at each sub-district. The total area used for
maize cultivation is 4.392 ha. The most extensive area

Spatial Based Assessment of Land Suitability and Availability for Maize (Zea mays L.)
Development in Maros Region, South Sulawesi, Indonesia
249
Table 3. Existing maize cultivation and productivity.
Sub-district (Kecamatan) Sub-district area (ha) Existing maize cultivation area (ha) in 2012* Productivity (ton/ha)*
Mandai 3,768.9 2.0 5.5
Moncongloe 4,571.1 583.0 5.7
Maros Baru 4,251.6 32.0 5.0
Marusu 4,344.0 5.0 5.0
Turikale 2,725.0 - -
Lau 4,548.6 15.0 5.3
Bontoa 5,875.6 - -
Bantimurung 15,389.8 5.0 5.3
Simbang 10,229.4 42.0 5.3
Tanralili 8,345.3 270.0 5.6
Tompobulu 25,280.2 2,336.0 5.7
Camba 11,893.6 560.0 5.6
Cenrana 20,356.6 155.0 5.6
Mallawa 22,505.1 387.0 5.8
Total 144,084.8 4,392.0 5.5
Note: *based on local statistical data in Maros Regency. Turikale and Bontoa are urban area along the coastal lines.
Table 4. Suitability sub-classes for maize in the study area.
Actual Sub-Class Potential
Sub-Class Area (ha) Percent from total area (%)
S1 34467.5 23.92
S2s 749.5 0.52
S2sf 654.3 0.45
S3s S2s 16158.8 11.21
Total Areal 52030.0 36.11
Note: The symbols following the suitability classes represent limiting fac-
tors (using commonly used codes in Indonesia): f = nutrient retention, s =
terrain. The remaining area for class N (not suitable) is accounted from
144084.8 ha substacted by 52030.0 ha = 92054.8 ha.
but with the advanced management efforts (moderately
input) such sub-classes can actually promote S3 into S2
class [10].
3.3. Distribution of Suitable Land According to
Sub-District
Spatial distribution of land suitability at sub class S1 and
S2 levels for each sub district of the study area is pre-
sented in Figure 5, while the statistics for development
potential at each sub class can be seen in Table 5.
The highest potential area for maize cultivation in
Maros regency can be found in Tanralili, Bantimurung,
and Simbang sub-districs. Maize is the prominent com-
modity for food in South Sulawesi, so that from the eco-
logical point of views, very extensive unsuitable areas
are now under cultivation. These could be seen in Camba,
Cenrana, and Mallawa sub-districts, where topography is
the dominant limiting factor, but maize is still developed
at some extents. For the local communities, maize is usu-
ally consumed at the unripe stage, known as “biralle
lolo” in Sulawesi. Only small amounts of maize that are
kept matured, and some proportions are used for fodder.
3.4. Potential Development Area for
Maize Cultivation
Although the availability of land for maize cultivation is
sufficient ecologically, but from the existing land use
point of view, the sum of area that is not available for
maize development is quite large. For example, the Ma-
ros Baru and Turikale sub-districts actually have good
potential suitability, but due to the existence of devel-
oped urban area (residential and industrial) and fishpond
along the coastal region, then the sum of actual availabil-
ity of land for maize cultivation is relatively low.
Table 5 indicates the real development potential for
maize in the study area (see column 6). The development
potential (for extensification) comprises about 24,716 ha
or 35.6% of the total study area. The highest develop-
ment potential can be found in Tanralili, Bantimurung,
and Simbang sub-districts, and the lowest is in Camba,
Mallawa, and Tompobulu sub-districts. Camba sub-dis-
trict has a minus potential value, due to the fact that local
communities are continuing to use some areas where

Spatial Based Assessment of Land Suitability and Availability for Maize (Zea mays L.)
Development in Maros Region, South Sulawesi, Indonesia
250
Figure 5. Land suitability map for maize development.
Table 5. Suitable area and real potential development area for maize.
Sub-district
(Kecamatan)
Total sub-district area
(ha)
Area for S1 and S2
classes (ha)
Existing maize cultivation
area (ha)*
Potential development
area (ha)
Real potential
development (ha)**
Mandai 3768.9 3303.1 2.0 2975.1 2252.6
Moncongloe 4571.1 2904.8 583.0 2135.8 2036.8
Maros Baru 4251.6 2289.4 32.0 2259.4 1347.3
Marusu 4344.0 2757.7 5.0 2665.7 2004.6
Turikale 2725.0 2497.6 - 2497.6 1752.3
Lau 4548.6 2756.5 15.0 2754.5 2175.2
Bontoa 5875.6 1947.7 - 1939.7 1119.5
Bantimurung 15389.8 4788.8 5.0 4759.8 4107.6
Simbang 10229.4 3688.0 42.0 3665.0 3446.0
Tanralili 8345.3 5669.5 270.0 5444.5 5059.4
Tompobulu 25280.2 1540.3 2336.0 (−235.8) −286.1
Camba 11893.6 280.0 560.0 (−1162.0) −1221.0
Cenrana 20356.6 1447.9 155.0 1372.9 1247.0
Mallawa 22505.1 - 387.0 (−325.0) −325.0
Total 144084.8 35871.2 4392.0 30747.2 24716.1
Note: *based on 2012 local statistical data in Maros District; **After deducting potential development area with land use types that cannot be converted to other
uses, such as urban/residential area, water body, conservation forest, and fish pond); Sub districts Turikale, Maros Baru, and Lau are included in urban area of
Maros, while Bontoa is coastal area.
ecologically is actually not suitable for maize cultivations (in this case slope gradient is the dominant limiting factor).

Spatial Based Assessment of Land Suitability and Availability for Maize (Zea mays L.)
Development in Maros Region, South Sulawesi, Indonesia
251
4. Conclusions
Land suitability and availability analyses have been per-
formed using a GIS to produce recommendations for
local government in devising agricultural land develop-
ment in Maros Regency. There are about 36.1% of the
area which is suitable for maize development, with the
main limiting factors include nutrient retention (f) and
terrain or slope (s).
With reference to land suitability classes for maize (at
level S1 and S2), existing land use type (derived from
SPOT XS images), and the area currently cultivated with
maize, there can still be found suitable areas for Maros
Region for maize development (for extensification pur-
poses). With the assumption that the regional govern-
ment will give the highest priority for area with potential
suitability class S1 (suitable) and S2 (moderately suitable)
for maize development, then there will be a total of
24,716 ha (or 35.6% from total suitable area S1, S2, and
S3) of land which is available for maize development.
5. Acknowledgements
The authors would like to thank the Maros Regency go-
vernment (through The Bappeda Maros) for providing
data, fund, and other supporting facilities for this study.
Advice and support given by the Center for Regional
Development and Spatial Information (WITARIS), Ha-
sanuddin University, Indonesia, are highly appreciated.
REFERENCES
[1] Government of the Republic of Indonesia, “Rencana
Pembangunan Jangka Panjang Nasional (RPJPN),” Bap-
penas, Jakarta, 2005.
[2] Government of South Sulawesi, “Gerakan Optimalisasi
Jagung (GONG),” Bappeda Sulsel, Makassar, 2005.
[3] M. H. Al-Mashreki, J. B. M. Akhir, S. A. Rahim, K. Md.
Desa, T. Lihan and A. R. Haider, ”Land Suitability Evalu-
ation For Sorghum Crop in the Ibb Governorate, Republic
of Yemen Using Remote Sensing And GIS Techniques,”
Austral ian Jou rnal of Basi c and Appl ied Sci ences, Vol. 5,
No. 3, 2011, pp. 359-368.
[4] A. A. Jafarzadeh, P. Alamdari, M. R. Neyshabouri and S.
Saedi, “Land Suitability Evaluation of Bilverdy Research
Station for Wheat, Barley, Alfalfa, Maize and Safflower,”
Soil and Water Research, Vol. 3, 2008, pp. 581-588.
[5] A. Keshavarzi, F. Sarmadian, A. Heidari and M. Omid,
“Land Suitability Evaluation Using Fuzzy Continuous
Classification (A Case Study: Ziaran Region),” Modern
Applied Science, Vol. 4, No. 7, 2010. pp. 72-81
[6] M. Albaji, A. A. Naseri, P. Papan and S. B. Nasab,
“Qualitative Evaluation of Land Suitability for Principal
Crops in The West Shoush Plain, Southwest Iran,” Bul-
garian Journal of Agricultural Science, Vol. 15, No. 2,
2009, pp. 135-145.
[7] M. Elaalem, “Land Suitability Evaluation for Sorghum
Based on Boolean and Fuzzy-Multi-Criteria Decision
Analysis Methods,” International Journal of Environ-
mental Science and Development, Vol. 3, No. 4, 2012, pp.
357-361.
[8] Badan Pusat Statistik Kabupaten Maros, “Kabupaten
Maros Dalam Angka,” Pemerintah Kabupaten Maros,
Maros, 2009.
[9] RePPProT, “The Land Resources of Indonesia: A Na-
tional Overview Main Report,” United Kingdom-ODA
NRI, Ditjen Pankim, Department of Transmigration, Ja-
karta, 1988.
[10] D. Djaenuddin, Basuni, S. Hardjowigeno, H. Subagyo, M.
Sukardi, I. M. Ds, N. Suharta, L. Hakim, Widagdo, J. Dai,
V. Suwandi, S. Bachri and E. R. Jordens, “Kesesuaian
Lahan untuk Tanaman Pertanian dan Tanaman Kehutanan
(Land Suitability for Agricultural and Silvicultural
Plants),” Second Land Resource Evaluation and Planning
Project, ADB Loan No. 1099 INO. Centre for Soil and
Agroclimate Research, Bogor, 1994.
[11] S. Baja dan A. M. Sallatu, “Analisis Penutup Lahan
Daerah Maros Sulawesi Selatan,” Berita Inderaja LAPAN,
Vol. 4, No. 7, 2006, pp. 16-18.
[12] FAO, “A Framework of Land Evaluation,” FAO Soil
Bulletin, No.6, Rome, 1976.
[13] Sh. Ashraf, R. Manoukyan and Sh. Ayoubi, “Land Suit-
ability Evaluation in Damghan Plain for Barley, Using
Compare and Conformity Methods (Northeast-Iran),”
Pakistan Journal of Biological Sciences, Vol. 14, No. 2,
2011, pp.123-127. doi:10.3923/pjbs.2011.123.127
[14] T.S. Babalola, T. Oso, A.S. Fasina and K. Godonu, “Land
Evaluation Studies of Two Wetland Soils in Nigeria,” In-
ternational Research Journal of Agricultural Science and
Soil Science, Vol. 1, No. 6, 2011, pp. 193-204.
[15] V. R. N. Chinene, “Land Evaluation Using The FAO
Framework: An Example from Zambia,” Soil Use and
Management, Vol. 8, No. 3, 2007. pp. 130-138.
[16] D. Djaenudin, H. Marwan and dan A. Hidayat, “Petunjuk
Teknis Evaluasi Lahan untuk Komoditas Pertanian,” Versi
3, Balai Penelitian Tanah, Puslitbang Tanah dan Agrok-
limat, Bogor, 2003.
[17] S. Baja, “Perencanaan Tata Guna Lahan dalam Pengem-
bangan Wilayah: Pendekatan Spasial dan Aplikasinya”
Penerbit Andi, Yogyakarta, 2012.
[18] S. Baja, D. M. Chapman and D. Dragovich, “A Concep-
tual Model for Assessing Agricultural Land Suitability at
a Catchment Level Using a Continuous Approach in
GIS,” Proceedings of the Geospatial Information and Ag-
riculture Conference, Sydney NSW Agriculture, Sydney,
16-19 July 2001, pp. 828-841.
[19] S. Baja, D. M. Chapman and D. Dragovich, “A Con-
ceptual Model for Defining and Assessing Land Man-
agement Units Using a Fuzzy Modelling Approach in
GIS Environment,” Environmental Management, Vol. 29,
No. 5, 2002, pp. 647-661.
doi:10.1007/s00267-001-0053-8
[20] S. Baja, “Metode Analitik Evaluasi Sumberdaya Lahan,”
Makassar, 2012.