DIGITAL SOIL MAPPING FOR

GROUNDNUT CULTIVATION.ACASE

STUDY OF KANO STATE,NIGERIA

In recent years, geo-spatial 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.

By

Egwim Michael.U.

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A REASEARCH ARTICLE

ON

DIGITAL SOIL MAPPING FOR GROUNDNUT CULTIVATION

A CASE STUDY OF KANO STATE.

BY EGWIM UCHECHUKWU MICHAEL

FEB.22ND, 2014

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PREFACE I have great satisfaction in introducing this study on 'Digital Soil Mapping. A case study of Kano state' to policy makers,academicians,government funtionaries,NGOs,International agencies and every institution interested in improving and maintaining soil data in Nigeria. This work was initiated by a weekly GIS meeting organized by the GIS department of eHealth Africa, Usually called 'Arc Meeting'. The study, which has been conducted and completed within a record time of three months, is an excellent example of cooperative endeavor among group members of ehealth Africa. It was quite an ambitious task to undertake such a massive study amid several office activities within same period. The tenacity, motivation and enthusiasm among the research team helped in the completion of this work without insurmountable obstacles. The ultimate goal for all that participated in the research was to add valuable contribution to a serious gap in this particular field of study and equally motivate others to do more. Profound gratitude and appreciation goes to Frank Salet our adorable GIS Manager for his inspiration

and Motivation during the course of this research.

Others include Nazeef Ahmad, Oladipo Akinpelumi, Adanna Obioha, Tosin Ilesanmi, Oroghene Adia and

Festus Olusola for their support to this work.

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ABSTRACT

Soil, a valuable natural resource can be said to play a part across the range of human existence and its

knowledge is fundamental to its utilization and management. Soil maps provide a means of gaining

understanding about the soil, but limitations in accuracy, revision and mode of presentation– relating to

graphics or digits–of such maps seem to stimulate lack of soil data in many places.

However, much of the human activity has been treated with significant uncertainties, and many

environmental processes relating to soil are poorly understood. Digital soil mapping seems to hold a

strong prospect for addressing these challenges, with an interesting flexibility for utilizing many recent

technologies.

In recent years, geo-spatial 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 suitable for groundnut cultivation based on

surface soil texture for Kano state. The resulting geodatabase is expected to be linked to an already

existing central database.

This research espouses the concepts behind building a functional Database management system that is

flexible, and user-friendly.

An ISO data which represents just one property of soil (Texture) serves as the primary Non-spatial

attribute table that will be linked with other spatial attribute table to develop the soil geodatabase.

Choropleth maps were used to provide visual view of potential areas within Kano state that will support

the cultivation of groundnut.

The developed geodatabase has the ability to upload a soil map of the study area, perform spatial

queries by attribute or using a query builder, and performs suitability analysis on three different soil

textures for groundnut cultivation.

ESRI ArcGIS, Microsoft excel and AutoCAD are essential software applications that will be used for this

research.

Keywords: ArcGIS, AutoCAD, Digital Soil Map, Database, Groundnut, Correlation and regression analysis,

thematic maps.

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INTRODUCTION

Groundnut is the 13th most important food crop of the world. It is the world’s 4th most important source

of edible oil and the 3rd most important source of vegetable protein. Groundnut seeds contain high

quality edible oil (50%), easily digestible protein (25%) and carbohydrate (20%). Groundnut attracts

revenue, no wonder it is dubbed a “cash-crop”.

The groundnut pyramids used to be landmarks around Kofar Mazugal, Brigade, Bebeji and Dawakin

Kudu in Kano State where sacks of groundnuts were packed and piled one upon another with systematic

expertise that forms a triangular shape much like the pyramids in Egypt, thus making it the second

pyramid in Africa and the world in general. Unfortunately this pride is lost as a result of neglect on

Agriculture and fundamentally lack of understanding about soil.

Due to soils complexity and the vast amount of data associated with it, an information system capable of

handling its storage, retrieval, manipulation, processing and display is highly crucial to its management.

In the last 30 years or so, new technologies have been developed to quantitatively map the soil in the

form of soil classes or soil properties. This approach is now widely known among soil scientist as digital

soil mapping (DSM).Lagacherie & Mcbratney (2007) defined DSM as the processes involved with “the

creation and population of spatial soil information systems by the use of field observational methods

coupled with spatial and non-spatial soil inference systems”. An efficient and effective tool for managing

and mapping soil alongside its vast and robust database is GIS.

A Geographic Information System or GIS is a computer system that allows you to map, model, query,

and analyze large quantities of data within a single database according to their location. GIS gives you

the power to create maps, integrate information, visualize scenarios, present powerful ideas, and

develop effective solutions.

Digital soil mapping is a task towards optimizing the usefulness of soils in terms of suitability for crop

production. Several studies (Hengl et al., 2003; Kempen et al., 2012) have shown that such operations

are effective ways of ensuring steady availability of soil data. They tend to guarantee regular update of

soil data and remove limitations in their uses. New sets of data often result from further analysis of data

obtained by such operations (Zhu et al., 1997 and Zhu et al., 2001), and they tend to help overcome the

limitations placed by traditional soil maps. With these benefits associated with such soil mapping

technology, its implementation remains unaccomplished in many places in Nigeria and much

environmental issues are still unresolved.

The distribution of soil components in a particular soil is influenced by the five factors of soil formation:

parent material, time, climate, organisms, and topography (Jenny 1941). Each one of these factors plays

a direct and overlapping role in influencing the suitability of a soil for agriculture.

For the purpose of this study, Topography in relation to spot heights is considered a primary factor for

the distribution of soil components.

This research will equally espouse the database model and design adopted.

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STATEMENT OF THE PROBLEM

Nigeria is among countries with poor National geo-database infrastructure, yet rich in soil surveys. The

inventory of soil data dated back in the late colonial to early post independent period. The first

provisional soil map of Nigeria was published by survey department, Lagos in 1952.This was followed by

the publication of the CCTA soil map of Africa(FAO,1964) and the FAO soil map of the world(FAO-

UNESCO,1974). Since then there is hardly any serious efforts towards a national/local scale soil

inventory. Most of these data are old and obsolete. Many of them are stored in analogue formats

thereby making them less accessible and suitable for this research. Global interpolation methods were

used to produce most of the soil maps hence cannot be reliable considering the accuracy and details

required for the study area (Kano State) .This condition makes it almost impossible to work with the

available data within agencies responsible for soil data in the country.

Another major setback is lack of a National Geospatial Database Infrastructure (NGDI). A NGDI provides a base or structure of practices and relationships among data producers and users that facilitate data sharing and use. Nigeria has started developing her own (NGDI), and standards have been set on how NGDI should be managed. Yet a major challenge has been getting all the major players in the GIS community both public and private sectors commit to this development. A National Geospatial Data Infrastructure is therefore necessary to facilitate the harmonious production, usage and dissemination of geographic data in a country. A functional NGDI will facilitate: . A successful Development of Cadastral Database for the Country

.Then Creation of a large pool of current digital topographic and thematic Databases for the Country .Increased funding for Geospatial Infrastructural activities in the country

. Increased efforts at capacity building, training and research AIM AND OBJECTIVES

The aim of this project is to produce a digital soil map suitable for groundnut cultivation based on

surface soil texture.

Research question: Is there a relationship between Slope and the distribution of Soil-particle size of a

place?

This endeavor will further facilitate the following;

Determining the relationship between Heights(slope) and soil particle distribution

Production of qualitative soil thematic maps for the state

Estimation of percentage area coverage of the different types of soil available within the state.

Determining the most suitable location for the cultivation of ground-nut

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

Kano state were groundnut can be can be grown.

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METHODOLOGY

Requirements for this study include; Satellite Imageries soil standard, Topographic Map, ArcGIS 10.1,

AutoCAD

Kano consist of soils having horizons that are similar in soil color, soil texture, soil structure, soil size, coarseness, mineral and chemical composition, and arrangement in the soil profile. A few findings revealed that Groundnuts grow best in well-drained, red-coloured, yellow-red and red, fertile, sandy to sandy loam soils with a pH range of 5, 5 to 7, 0. Saline soils are not suitable because groundnuts have a very low salt tolerance. Soils with more than 20% clay and stones will result in poor yield and make harvesting difficult. Shallow and compacted soils are not preferred as the taproot of groundnuts can penetrate to the soil to a depth of about 2 m. The most suitable soil forms are Avalon, Bainsvlei, Clovelly,Hutton, Pinedene and Glencoe. Attributes of soils are depth, texture, drainage, pH, course, fragments, slope, base saturation and climatic conditions such as rainfall, temperature and length of dry season etc.

However like earlier said, this study is based on only texture. The international standard organization

(ISO) data on soil texture will serve as the secondary soil data for this research.

Kano state boundary was broken into parcels by a gridding system of an area of (10,000 Square

meters) representing hectares using AutoCAD.

Figure 01.

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CARTOGRAPHIC MODELING AND METHODOLOGICAL WORKFLOW

Figure 02.

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SOIL DATABASE DESIGN

Here is a list of things we needed to look-out for during our design;

-Identifying Entities; Identifying Relationships; Identifying Attributes; Assigning Keys, Defining the Attribute's Data, Normalization, subtypes and domain.

Entity Relationship Model

An entity-relationship diagram creates a conceptual model of the data. An entity is an object of interest that we do not wish to further subdivide. Four entities are identified in the data: LGA, Ward, Parcel and Soil.

Entity Relationship Model

Figure 03

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Relationship Table

Figure 04

The key field in the origin class of a relationship is called the primary key and is often abbreviated as PK. Unlike a true primary key, the values in the primary key field in a relationship are not required to be unique for every object.

The key field in the destination class is called the foreign key and is often abbreviated as FK. It contains

values that match those of the primary key field in the origin class. Again, the key field values do not

need to be unique for each row.

Primary and Foreign Keys

Figure 05

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DATA NORMALIZATION

Data Normalization creates a logical view of the data as it will be laid out in tables and map layers.

As a first principle, there will be a table or map layer for each entity.

In this case the soil map unit and the LGA ward is a map layer, while parcel entities are tables.

The attribute table for the soil map has identifier columns for area, perimeter, and columns for description, parcel, ward and LGA.

The land parcel table has columns for the land parcel identifier and for the ward identifier(s) that indicate which ward the land parcels belongs.

A Simplified database system environment

Figure 06

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SUBTYPES AND DOMAINS

A Domain was created and grain size represented the 13 classes of soil particles sizes. The grainCode

served as a unique identifier\primary key that will be used to obtain other necessary information from

the non-spatial attribute table (ISO Data).The domain was created on the Geodatabase for our soils map

unit.

Subtypes were also created to accommodate the different types of soils based on coarseness. This was

quite necessary to reduce redundancies in multiple feature data sets of same information or details.

Figure 07

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RESULTS

Correlation (scatter plot) and Regression analysis were used in this research to find if there exists a

relationship between spot heights and soil particle sizes within the study area.

A scatter plot is used when a variable exists that is below the control of the experimenter. It can be used

to suggest various kinds of correlations between variables with a certain confidence interval

Correlations may be positive (rising), negative (falling), or null (uncorrelated). If the pattern of dots

slopes from lower left to upper right, it suggests a positive correlation between the variables being

studied. If the pattern of dots slopes from upper left to lower right, it suggests a negative correlation.

From the result of our Scatter plot matrix it is evident or significant that there is a positive correlation

between spot-heights and grain-size.

SCATTER PLOT MATRIX RESULT USING ArcGIS 10

Figure 08

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Exploratory Regression evaluates all possible combinations of the input candidate explanatory variables,

looking for OLS (Ordinary Least Squares) models that best explain the dependent variable within the

context of user-specified criteria.

Illustration of Explanatory Regression Analysis

Figure 09

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The result of our exploratory regression analysis shows that there is a very high level of significance

100%. It further explains the direct relationship between the dependent variable-Grain-Size and

independent variable-Spot-Heights

EXPLORATORY REGRESSION ANALYSIS RESULT USING ArcGIS 10.1

Figure 10

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Figure 11

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Figure 12

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Figure 13

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Figure 14

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Figure 15

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Figure 16

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AREA AND PERCENTAGE COVERAGE OF SUITABILE LAND

Figure 17

Figure 18

About 305916 hectares of land is considered suitable for ground –nut cultivation in Kano state.

That represents 14.4% of the total land mass of the state.

Most suitable location for ground-nut cultivation will be determined by the highest area coverage of fine

sandy soil.

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Figure 19

Most Suitable Land for Ground-nut Cultivation in Kano State

About 41,210 hectares of land is considered most suitable for ground-nut cultivation in Kano state. That

represents about 13.5% of the area suitable for ground-nut cultivation.

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Figure 20

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Conclusion

This research and other studies demonstrate new mapping incentives and stimulate new ideas to make

digital soil mapping practicable from local to national and ultimately global scales.

Building on these results, future research should focus on building a functional soil information system

(SIS) in order to provide a comprehensive approach to deal with cultivation of multiple crops for within

state level.

We conclude that the resulting prediction maps were not only accomplished for the sake of comparison.

The results of this study will be given to the responsible authorities in Kano State for further assessment,

especially in those LGAs regarded most suitable for groundnut cultivation. The information provided by

these maps shall also help planners, farmers, Local and foreign investors, national and state government

agencies in making vital decisions

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References

http://www.moanr.kano.gov.ng/

http://www.freedomradionig.com/index.php/39-icetheme/editorials/159-groundnut-pyramids-lost-

pride-of-the-north

http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_052933.pdf

Jenny 1941

Hengl et al., 2003; Kempen et al., 2012

Zhu et al., 1997 and Zhu et al., 2001

Egwim Uchechukwu Michael. ADVANCEMENTS IN GIS: PROSPECTS AND CHALLENGES. PAPER

PRESENTATION ON WORLD GIS DAY CONFERENCE 2013

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