Land Suitability Evaluation for arable crops using GIS and Remote Sensing

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Land Suitability Evaluation for arable crops
using GIS and Remote Sensing

• Case study Makueni District, Kenya
• By
• Felix Nzive Mutua
• Department of Geomatic Engineering and
  Geoinformation Systems
• Jkuat

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CONTENTS

• Introduction
• Definitions
• Problem Statement
• Objectives
• Limitations
• Study Area
• Methodology
• Analysis

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INTRODUCTION

• Agricultural crop distribution is rarely limited
  to a crops native range. Increased crop range is
  largely the result of introduction of crops into
  new areas, which may in some cases be haphazard.
• The aspiration To develop a GIS model to
  evaluate the suitability of maize, sorghum and
  beans.

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DEFINITIONS

• Land suitability evaluation is the process of
  assessing the suitability of land for specified
  kinds of use.
• Land suitability classification the process of
  appraisal and grouping, of specific types of land
  in terms of their absolute or relative
  suitability for a specified kind of use.

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PROBLEM STATEMENT

• Makueni district is one the arid and semi arid
  districts of Kenya.
• Famine and drought are prevalent in this region,
  and this has had adverse effects on the local
  population.
• Climate change worsens the problems.
• Scattered datasets for decision-making make
  optimization difficult.

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OBJECTIVES

• Main objective
• To perform crop suitability and land evaluation
  for Makueni District using the integration of GIS
  and remote sensing technologies, within the
  context climate adaptability.

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Specific objectives

• To develop GIS layer maps representing soil
  attributes and ecological zones.
• To perform crop suitability analysis
• To delineate probable areas where irrigation can
  take place.
• To predict the consequences of change e.g.
  increased crop production or even severe hazards
  of soil erosion.
• To develop a digital elevation model (DEM) for
  visualization.

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PROJECT SIGNIFICANCE / JUSTIFICATION

• It will be easier to develop poverty alleviation
  strategies
• The project will provide information agricultural
  productivity optimization

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BENEFITS IN USE OF GIS

• Optimized crop production planning
• Easier crop site selection
• An accurate and elaborate database
• Ease in selecting development sites
• Ease in irrigation area selection and planning

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LIMITATIONS / WEAKNESSES

• Data availability
• Data capture / Data lineage / history
  inconclusiveness.
• Datasets incompatibility.
• Model Limitations (e.g. approximation of
  rainfall)

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AREA20 950 square kilometres.
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PROJECT METHODOLOGY

• Phase I Data Capture
• This phase included
• Identification (e.g. topomaps)
• Collection
• Preparation
• Data automation

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• Phase II a) DATA MANIPULATION
• Creation of soil resources database (type, pH,
  texture est.)
• Creation of temperature regimes (zones) database
• Creation of moisture regimes database (rainfall)
• Soil Texture Suitability
• Soil pH Suitability
• irrigation regimes delineation
• Development of Spatial selection Model.

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Soil texture
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Illustration soil pH
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Soil ph classification
Class value 5 Best for Maize
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Rainfall distribution
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Existing agricultural activity
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• Phase II b) Digital Elevation Model generation
• Two options
• 1. A DEM for Makueni is developed using local
  toposheets at a scale of 1 50,000
• Process includes digitization Spot heights
  and contours
• 2 .Use of a shuttle radar model ,being explored
  (resolution 90 meters)

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DEM ( from maps)
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DEM (under development)
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An example of a shuttle radar model for Morocco
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DATA ANALYSIS

• Several GIS analytic tools that are being used
• Spatial analyst (spatial modeler)
• 3D analyst for DEM generation
• Spatial query for Selecting suitable areas for
  crop establishment
• Buffering to select probable areas for
  irrigation
• Determine the respective areas and output

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PROXIMITY ANALYSIS

• Illustration of river buffering to
  determine to what extent irrigation along a
  river is possible

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• Phase III
• preparation of Hard Copy Map printouts
• Preparation of final report.

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EXPECTED RESULTS

• Maps showing
• Rainfall distribution
• Hydrology and irrigable lands
• Infrastructure and utility networks
• Regional and administrative boundaries
• Soil types and Agro-ecological zones
• Maize, beans and sorghum suitable growing regions

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RESOURCES BEING USED

• Software
• ArcGIS 9.0
• Erdas Imagine 8.6
• IDRISI Kilimanjaro
• PCI Geomatica
• Data sources
• FAO-Africover project
• ILRI
• Kenya soil survey
• Kenya meteorological department
• Central bureau of statistics
• Data types
• Topographic maps
• Soil type maps
• Meteorological data
• Administrative boundaries

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THE END

• Questions