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INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 2, No 1, 2011

© Copyright 2010 All rights reserved Integrated Publishing services

Research article ISSN 0976 – 4380

Submitted on September 2011 published on November 2011 133

Digital Soil Mapping in GIS Environment for Crop-Land Suitability

Analysis Eric K. Forkuo

1, Abrefa K. Nketia

2

1- Department of Geomatic Engineering, Kwame Nkrumah University of Science &

Technology, Private Mail Bag, Kumasi, Ghana

2- Soil Research Institute, Council for Scientific and Industrial Research, Academy Post

Office, Kwadaso-Kumasi, Ghana

[email protected]

ABSTRACT

In recent years, geo-computational technologies have emerged as useful tools in diverse

range of application traditionally catered for by soil surveys. These are used to record, store,

manipulate and retrieve data from many soil observations including their location and

elevation on the ground. The purpose of this study is to produce a digital soil map and to

develop an interactive geodatabase with crop-land suitability analysis on the growth and

production requirement of oil palm, cassava, and citrus in the study area. Also, to develop a

choropleth map that is able to provide a visual view of all potential areas within the district

study area where the selected crops can be grown. A geodatabase for crop land suitability

analysis (Crop Mapper) was developed with the combination of ESRI ArcGIS and Microsoft

SQL Server. The attribute data of the soils are effective depth, texture, drainage, pH, course

fragments, slope, base saturation and climatic conditions such as rainfall, temperature and

length of dry season. These attributes which were linked with spatial data to develop the soil

geodatabase model, were considered as important criteria to determine the crop land

suitability. The developed Crop Mapper has the ability to upload a soil map of the study area,

perform spatial queries by attribute or using a query builder, and perfumes suitability analysis

on three different soil series for three selected crops. Also, it is user friendly, fast and gives

more accurate results.

Keywords: Geodatabase, Digital soil map, Soil survey, GIS, Crop-land suitability

1. Introduction

Digital soil mapping becomes essential due to the decreasing, time-consuming, and expensive

field surveys which are no longer affordable by the soil surveys of the individual districts

(Behrens and Scholten, 2006). In recent years, geo-computational technologies have emerged

as useful tools in diverse range of application traditionally catered for by traditional soil

surveys. For example, advances in these technologies (geographic information science,

digital terrain modelling, remote sensing, and fuzzy logic) have created a tremendous

potential for improvement in the way that soil maps are produced (Scull et al, 2003). Also,

soil physicists and scientists in Ghana have tools to develop digital soil maps, to analyze

spatial soil structures, and to simulate spatial variation of soil properties (Burrough, 1991).

Over the past few decades, few observations of soils are made in reconnaissance surveys and

maps showing the distribution of different kinds of soil are made almost exclusively by the

informed interpretation of external features as seen on aerial photographs (Burrough, 1991).

This kind of mapping is a complex, subjective art which resists quantification and it is

therefore difficult to assess the latent value of the documents produced (Burrough, 1991).

Digital Soil Mapping in GIS Environment for Crop-Land Suitability Analysis

Eric K. Forkuo, Abrefa K. Nketia

International Journal of Geomatics and Geosciences

Volume 2 Issue 1, 2011 134

Hence, the ability of GIS (Geographic Information System) to record, store and retrieve data

from many soil observations including their location and elevation on the ground (i.e., the

exact location of a soil profile observation was rarely recorded in many early surveys.) has

paved the way for studies of the spatial variation of soil properties within the subjectively

delineated landscape units (Burrough, 1991).

According to Burrough (1991) soil survey applications were among the first uses of GIS.

Also, GIS is now firmly established in modern soil survey practice and are used for map

production, deriving suitability maps to meet users’ requests for special-purpose information

and for modelling environmental processes. Based on the literature review, it has been

realized that, the problems associated with soil data could be properly dealt with if the

potentials of GIS are tapped into.

Therefore, the purpose of this study is to produce a digital soil map and to develop an

interactive geodatabase with crop land suitability analysis (called Crop Mapper) on the

growth and production requirement of oil palm, cassava, and citrus in the study area. Also, to

develop a choropleth map that is able to provide a visual view of all potential areas within the district where the selected crops can be grown. That is, choropleth map which gives a visual

impression and spatial variation of soil properties of selected crops (oil palm, cassava and

citrus) within the study. The Crop Mapper is developed with the combination of ESRI

ArcGIS and Microsoft SQL server. That is the capture of spatial data (digitizing), processing

and spatial analyzes were performed within the GIS environment. Since soil data is spatial in

nature, it can be easily handled and analyzed using ERSI ArcGIS. Also, spatially related

information in ArcGIS can be managed, analyzed and presented

Despite the large amount of work published on GIS applications in literature, little has been

published in terms of digital soil mapping using GIS for crop land suitability analysis.

Kalogirou (2002) modeled a geographic information technology in conjunction with expert

systems called LEIGIS software to implement a land suitability evaluation model. The model

used is based on the Food and Agriculture Organisation (FAO) land classification for crops

and data which describes an agriculture area in terms of soil mechanics and environment. The

software developed allows the evaluation and presentation of any equivalent spatial dataset

and does not require special computer skills.

In a study carried out by Ramakrishnan et al (2009), it was observed that if soil information

is available in GIS then many professionals could assess the information for developmental

purposes, effective agricultural research and in advisory programs. Behrens and Scholten

(2006) also developed a digital soil map as a tool to generate spatial soil information which

provided solutions for the growing demand for high-resolution soil maps worldwide. The

paper summarized the present state of soil survey in terms of digitally available soil data,

applied digital soil mapping. Similarly, Behrens et al (2005) developed digital soil mapping

using artificial neural networks. This study focused on the development of a methodology

based on artificial neural networks that is able to spatially predict soil units.

Jayasinghe and Machida (2008) developed an interactive web-based GIS consulting portal

with crop-land suitability analysis, which provides information on Tomato and Cabbage

cultivation in Sri Lanka. Soil pH, topography, average annual temperature, and average

annual rain fall were considered as important criteria to determine crop-land suitability. Their

application utilized most of the open source components and is considered as a low cost

application which will be suited for developing country. In a series of paper (Smith et al,





2006, Bishop et al, 2001, Shi et al, 2001, McBratney et al, 2003) researchers developed

digital soil maps and also investigated the quality of the developed soil maps.

In Effland et al (2009), the authors discussed the theoretical developments in the

classification of tropical soil and also examined three of the eight national maps of Ghana.

The authors concluded that research in soil morphology and genesis is important for soil

mapping and classification with applications for land use and management in the tropics.

Also, Boateng (2005) used FAO methodology in a computer assisted programme to establish

areas which have potentials to support rice cultivation in Ghana. His methodology utilizes a

GIS approach for establishing a spatially inventory of land resource database to assess

potential areas suitable for sustainable rice production.

Other researchers like Debella-Gilo et al (2007) and Moore et al (1993) presented a new

digital terrain analysis and statistical analysis framework for digital soil mapping in a GIS

environment. Their research exemplified how digital terrain analysis can be used for digital

soil mapping. The rest of report outlines the background of the study area and methodology

(geodatabase design and implementation) that is organized to fulfill the research objectives. Also, results and application examples are provided and discussed. Conclusions and

discussions of future research works are outlined.

2. Digital Soil Mapping and Geodatabase Modelling

As mentioned before, the purpose of this study is to produces a digital soil map and to

develop an interactive geodatabase with crop land suitability analysis. The following sections

outline the method used to capture both spatial and attribute data, geo-reference, and design

and implement geodatabase. The background information on soil suitability classification,

choropleth mapping and the study area is also reviewed.

2.1 Soil Suitability Classification and Evaluation

Soil suitability classification involves the execution and interpretation of basic surveys of

climate, soils, vegetation and other aspects of land in terms of the requirements of alternative

forms of land use (FAO, 1976). Soil classifications are based on knowledge of crop

requirements, of prevailing soil conditions and of applied soil management (Ande, 2011).

Soil suitability evaluation, on the other hand, involves characterizing the soils in a given area

for specific land use type (Ande, 2011). Certain groups of activities are common to all types

of soil suitability evaluation and details of these activities which are carried out vary with

circumstances.

The suitability of a given piece of land is its natural ability to support a specific purpose and

this may be major kind of land use, such as rain fed agriculture, livestock production, forestry

(Ande, 2011). According to FAO (1976), two stage approaches is often used in resource

inventories for broad planning purposes and in studies for assessment of biological

productive potential. Also, there are four categories of decreasing generalization which are

recognized namely; orders, classes, subclasses and units. These classes are numbered

consequently by Arabic numbers, in sequence of decreasing degrees of suitability within the

order. More detailed information can be found in (Boateng, 2005; FAO, 1976). The design

and implantation of soil suitability are discussed in Section 2.5.

2.2 Soil Choropleth Map





Recent developments have also demonstrated the critical role that visualization plays in the

domain of exploratory spatial data analysis (Anselin, 1998). Among various visualization

techniques employed in this analysis, choropleth mapping is often used to display or discover

(1) patterns in spatial observations of enumeration areas such as counties or census tracts

(Monmonier, 1993) and (2) resulting spatial statistical outcomes, such as measures of local

spatial autocorrelation (Getis and Ord, 1992; Anselin, 1995). Choropleth maps, in general,

are created by shading or colouring aerial units with intensity proportional to the data values

associated with units (Kumar, 2003). Soil choropleth maps, on the other hand, shows the

region of interest divided by the boundaries into parcels of a particular soil type (Burgess and

Webster, 1984). They provide an easy way to visualize how a measurement varies across a

geographic area or it shows the level of variability within a region.

According to Burgess and Webster (1984) soil choropleth maps are the conventional product

of traditional soil surveys or classification. That is, classification was traditionally used as

instrument for producing choropleth maps. The tools include direct manipulation controls for specifying arbitrary class boundaries, graphs representing statistical distribution of attribute

values, means for automatic classification, calculation of statistical quality of classification,

and various color schemes that can be applied to represent classes on a choropleth map

(Andrienko et al, 2001).These maps are extensively used for data presentation and significant

efforts have been invested into development of various methods of data classification for

choropleth mapping (Andrienko et al, 2001). To design soil choropleth maps in GIS

environment, the user needs error-free polygon coverage with proper topology, a mapping

variable in the polygon attribute table, and a variable with a unique identification number for

each soil unit (Kumar, 2003). A more complete discussion appears in Kumar (2003). Soil

choropleth map of the study area for three selected crops are presented and discussed in

Section 3.

2.3 The Study Area

The study area (as can be seen in Figure 1a) is a proposed farmland in the Adansi West

District (now referred to as Obuasi Municipal District). The Municipality shares

boundaries with Amansie Central District to the west, Adansi North District to the north,

Adansi South District to the east and the Upper Denkyira District (in the Central

Region) to the south. The Municipality experiences semi-equatorial climatic conditions with

a double maximum rainfall regime. Temperatures are uniformly high all year and relative

humidity is highest in the wet season (Yeboah, 2008). The vegetation is predominantly a

degraded semi-deciduous forest.

The Municipality has generally an undulating terrain with more of the hills rising above 500

meters above sea level and the vegetation is predominantly a degraded semi-deciduous forest

(Yeboah, 2008).

The farming communities of Kwame Duoku, Odumase, Gyimisokakraba, Diewieso and

Akrofuom are the settlements (Figure 1b) found around the study area. The study area

shares boundary in the west with the Kumasi-Takoradi railway line while it is bordered

in the east by a north to south line passing through the Diewieso and Gyimisokakraba

settlements. The northern fringe, on the other hand occurs on the immediate north of

Kwame Duokrom and the southern fringes by the Aserebo stream which is just north of

Akrofuom. The study area lies within 6˚ 07΄ and 6˚ 11΄ North and 1˚ 35΄ and 1˚ 40΄





West. The land area is approximately 1670 hectares and the climate is suitable for

growing crops with wide variety.

Figure 1: Study Area

2.3.1 Characteristics of the Soils within the study Area

Soils in the study area are predominantly forest ochrosols developed under forest vegetation.

They are rich in humus and suitable for both cash and food crops production (Yeboah, 2008).

These soils consist of soils having soil horizons that are similar in soil color, soil texture, soil

structure, soil pH, consistence, mineral and chemical composition, and arrangement in the

soil profile. A soil profile which is the sequence of natural layers, or horizons, in a soil,

extends from the surface downward to unconsolidated material. Crops grown include citrus,

oil palm, cocoa, plantain, maize, cassava, vegetables (Yeboah, 2008). Soils of the study area

have been developed over the weathered products of Upper Birimian (Pre-Cambrian) and

Tarkwain rock formations which are noted for their rich mineral bearing potentials (Yeboah,

2008). The area had been surveyed at scales of 1: 250000 and 1: 20000 (Adu, 1992).

2.4 Data Source and Processing

Senayah et al (2003) is the main source of data for the project. The report consists of the soil

map of the project site and a technical report on how the data was acquired and the outcome

of the research. The anague soil map (as in Figure 1c) of the study area was digitized, edited

and geo-referenced. Digitizing, and geo-referencing maps are investigated in Forkuo (2008).

Also, distribution of control points and mathematical computation of root mean square errors

are discussed in Forkuo and Dorda (2011). As can be seen in Figure 2, 14 feature classes

were digitized in GIS environment. This consisted of soil series, profiles pits, contours, rivers,

control points, site boundary, villages, railway lines, roads, dam, and poultry farms. Also, the

following soil attributes were added: mean annual rainfall, mean annual temperature, soil

drainage, effective depth, soil reaction (pH), and base saturations.





Figure 2: Digitized Map of the Study Area in GIS Environment

2.5 Geodatabase

This Section describes the spatial data input and its resulting output and the relationships

existing between the various tables together with the processes required in producing these

output. Detailed information on geodatabase approach, storing and displaying maps, and

textual documents which includes the attributes of map features can be found in Forkuo and

Asiedu (2009). The database designed in this methodology is crop requirements database of

the three selected crops namely citrus, cassava and oil palm. As previously pointed out, in

this study, soil effective depth, texture, drainage, pH, course fragments, slope, base

saturation and climatic conditions such as rainfall, temperature and length of dry season

were considered as specific crop requirement factors to identify land suitability

according to FAO (1976). In developing the database, these crop requirements were

grouped based on their limitations called land suitability classes (as discussed in Section 2.1).

In all, there were five suitability classes namely: highly suitable (SI); moderately suitable

(S2); marginally suitable (S3); currently not suitable (N1); and permanently not suitable (N2).

These suitability classes form tables that hold the diagnostic factors of the various crops.

The crop requirements were used as entities of the various dataset and these were mapped

into Entity Relational Diagram (ERD) with Microsoft SQL Server to obtain the conceptual

model (Forkuo and Asiedu, 2009). Information on the design and implementation of the

models (i.e., conceptual, logical, physical) can be found in Forkuo and Asiedu (2009). Figure

3 shows the database schema of the three selected crops.





Figure 3: Database Schema

This is the first interface (called Land Mapping Units and Land Characteristics) that

allows only authorized users to have access to the program. It is a password-protected

interface that helps to protect the integrity of the inbuilt structures such as database. The

main form consists of an ArcReader Control embedded interface and inbuilt controls

and toolbars to manipulate maps. It serves as both input and output interface by

allowing the user to load (input) and display (output) a published map format (.pmf). As

can be seen in Figure 4, this form is designed to load the factor ratings from the SQL

database management system for any of the three selected crops.

Figure 4: Main Interface of Geodatabase





In addition to this, it is designed to be flexible so that the soil scientist can also edit,

update and, create and save factor ratings for existing and new crops respectively. The

main mapper (as in Figure 5) is a query builder which is used to apply a definition

query to a soil layer to display features with certain attributes. It is an easy to use

dialogue box for selecting or querying feature based on the attribute table in the

geodatabase. With this Main Mapper, the user can select features that meet refining

criteria. As can be seen in Figure 5, to perform a query, the user can write an expression

or use the dropdown menus in the designed query builder.

Figure 5: Query Builder Sample

3. Results and Discussion

Figure 4 shows the main interface of the developed geodatabase programme. This

programme has the ability to upload a soil map of the study area, perform spatial

queries by attribute or using a query builder (as in Figure 5), and perfume suitability

analysis with one of the selected crops on a soil series. The analysis involved the

matching of soil characteristics (soil depth, pH, slope, rainfall, temperature, etc) of the

land mapping units with crop growth requirement. The developed geodatabase

programme was tested on three different farm lands (Asikuma series, Mawso series and

Asuboa series) for three selected crops with suitability rating.

Also, soil choropleth maps which give a visual impression and spatial variation of soil

properties of selected crops (Oil palm, cassava and citrus) within the farm land in which some

selected crops can be grown are demonstrated in Figures 6 (a), (b), and (c). For example, in

Figure 6a (i.e., choropleth map of suitability ratings of Asikuma series for oil palm), the

lemon green color indicates the portion of land that is suitable for oil palm plantation within

the Asikuma soil series. Also Figure 6b shows a choropleth map of suitability ratings of

Mawso series for cassava.





(a) (b)(c)

Figure 6: Choropleth Maps of Suitability Ratings for (a) Oil Palm, (b) Cassava and (c) Citrus

It can been seen in the Figure, the same color is used to show land suitability for cassava

plantation for Mowso soil series and in Figure 6c (i.e., choropleth map of suitability ratings of

Asuboa series for Citrus) the lemon green color is used to show the extend of land suitable

for citrus cultivation within the Asuboa soil series. Apart from the soil choropleth maps

which give visual impression (as shown in Figure 6), detailed information in a tabular form

were also demonstrated in Figures 7 and Table 1. Figure 7 is an example of the screen capture

of the three selected soil series for three selected crops. Table 1 also summaries the results of

the soil suitability analysis of the three selected crops.

Figure 7: Example of Screen Capture of Suitability Ratings Analysis





Suitability assessment performed on Asikuma series for oil palm yielded the result in

Table 1a. It could be seen from this Table that the Asikuma series is potentially

moderately suitable for the growth of oil palm. Their limitations are currently moisture

and nutrient availability to the crop, which is represented by S2mn. Moisture

availability (m) and nutrient availability (n) are a combination of amount of such factors

as rainfall and low level of pH.

Also, suitability assessment was performed on Mawso series for cassava and the results

are shown in Table 1b. It is on the other hand, rated as S2r as can be seen in this Table.

This implies that Mawso series is potentially moderately suitable for the growth of

cassava and the limitation to its use is reflected in the high gravel content of subsoil.

The gravelly nature of the subsoil affects proper root development. Since cassava is a

root crop, there is the tendency that cassava will not grow to its maximum capacity in

such a soil.

Furthermore, a suitability assessment performed on Asuboa series and from the report in Table 1c, it could be observed that Asuboa series is currently graded N1n. This implies

that Asuboa Series is currently not suitable for the growth of citrus. The major

limitation to its use is nutrient availability (n) which is reflected in low pH levels

obtained. Potentially, it is an S2 which is moderately suitable. It can be seen that the

limitation to its use is rooting condition (r) and it is reflected in the high amount of

gravel in the subsoil. The gravelly nature of the subsoil inhibits proper root penetration.

Since citrus crops have long tap roots, it has fewer chances of doing well on such soil.

Table 1: Summary of Soil Suitability Analysis of Selected Crops

Land Characteristics Soil Suitability Rating for

(a)

Oil Palm

(b)

Cassava

(c)

Citrus

CLIMATE

Mean Annual Temperature(C) S1 S1 S1

Annual Rainfall(mm) S1 S2 S1

Length of Dry Season S2 S2 S1

TOPOGRAPHY

Slope (%) S1 S2 S2

SOIL

Effective Depth (mm) S2 S2 S1

Coarse Fragments(vol %) subsurface S1 S2 S2

Soil Reaction(pH) N1 S2 S1

Base Saturation(%) S1 S1 S1

Current Suitability N1n S2mm S2r

Potential Suitability S2r S2mm S2r

It is worth noting that the suitability ratings in Table 1 using the developed crop mapper

do not imply that assessment for other crops could yield the same results. This is

because every crop has got its own suitability ratings and depending on the crop

assessed for suitability, the result could be varying. Moreover, in designing the

geodatabase for the suitability ratings, not all requirements were included. This is

because different crop has different requirements for their suitability evaluation. That





notwithstanding, if a crop forms part of any of the classes of crops that have been

assessed, the crop mapper could be used to perform the assessment on how suitable a

land mapping unit could support the growth of such crops.

Other than that, the crop mapper allows the user to input in new suitability ratings for

new crop. This makes the program versatile to be used to analyze various crops based

on the diagnostic factors designed for this program. There is the need to also emphasize

that this kind of land evaluation is based solely on the physical and chemical

characteristics of soils neglecting basic economic factors and major agricultural

practices. The developed crop mapper for land suitability analysis is faster and gives

more accurate results provided the geodatabase is accurate. Also, as discussed in

Boateng (2005) the accuracy of this depends on reliable and accurate climate and soil

data, as well as expert knowledge in interpreting the data.

4. Conclusions and Recommendations

A digital soil map has been produced to store and develop an interactive geodatabase

(called crop mapper model). The fundamental conceptual elements of creating this model has been solved designed and developed using Microsoft SQL and GIS. This

crop mapper model has been used to analyze land suitability for the growth and

production of some of the selected crops in the study area. Also, Arc Reader control has

been customized and this will assist soil scientists to identify all current and future land

mapping units (or soil series) which are suitable for the growth. The video in the help

menu with sound inset will assist in empowering the end-user to use the program for

future analyses.

Again, a chloropleth thematic maps which have the potential of visual impression and

special variation of soil properties have been created. These maps delineated one

suitability rating from the other using color coding system. That is, a particular color (a

lemon green) was used to indicate the portion of land in the study area that is suitable

for the cultivation of cassava, citrus and oil palm. The developed model for land

suitability analysis was tested on three different soil series for three selected crops. It is

user friendly, fast and precise. However, the precision of the model depends on

reliability and accuracy of crop growth requirements (soil depth, pH, slope, rainfall,

temperature) used in this study.

The land crop suitability analysis could even be improved if crop mapping model is

updated for the production of many crops and the model is extended to cover the whole

country. Also, other factors such as humidity, digital terrain models, land use,

vegetation cover, geology and the influence of the topography should be considered.

The methodology and deliverables of this project will allow the Adansi West District to

utilize new planning guidelines to preserve and manage the soil fertility while managing

development in the region.

Acknowledgement

The success of this project will not have been possible if not with the help of the Director, Dr.

J. O. Fening and management of CSIR-SRI Ghana. The authors acknowledge the CSIR-

Soil Research Institute, Kumasi for their help and cooperation in acquiring the soil data

of the study area. CSIR-SRI provided data used for the analysis and their scientific technical

know-how is much appreciated. Mr. J. K. Senayah (Head of Department, Soil Genesis and





Classification) assisted with the interpretation of soil suitability data in determining factor

ratings and classification.

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