geo-spatial approach in soil & climatic data analysis for agro ...

GEO-SPATIAL APPROACH IN SOIL & CLIMATIC DATA ANALYSIS FOR AGRO-CLIMATIC SUITABILITY ASSESSMENT

OF MAJOR CROPS IN RAINFED AGRO-ECOSYSTEM

(A CASE STUDY OF PARTS OF MADHYA PRADESH)

ADITI SARKAR 2008

AGRICULTURE AND SOILS DIVISION INDIAN INSTITUTE OF REMOTE SENSING (NRSA)

DEPARTMENT OF SPACE. GOVT. OF INDIA 4, KALIDAS ROAD, DEHRADUN

GEO-SPATIAL APPROACH IN SOIL & CLIMATIC DATA ANALYSIS FOR AGRO-CLIMATIC SUITABILITY ASSESSMENT

OF MAJOR CROPS IN RAINFED AGRO-ECOSYSTEM

(A CASE STUDY OF PARTS OF MADHYA PRADESH)

Thesis submitted to the Andhra University in partial fulfilment of the requirements for the award of

Master of Technology in Remote Sensing and Geographic Information System.

ANDHRA UNIVERSITY

by ADITI SARKAR

Supervised by

DR.SURESH KUMAR DR.N.R. PATEL

AGRICULTURE AND SOILS DIVISION

INDIAN INSTITUTE OF REMOTE SENSING (NRSA) 4, KALIDAS ROAD, DEHRADUN

CERTIFICATE

This is to certify that Ms.Aditi Sarkar has carried out the pilot project titled “Geo-Spatial approach in Soil and Climatic Data Analysis for Agro-climatic Suitability Assessment of major crops in Rainfed Agro-ecosystem (A Case Study of parts of Madhya Pradesh)” in partial fulfilment of the requirements for the award of Master of Technology in Remote Sensing and GIS by the Andhra University. The project presented here in this report is an original work of the candidate and has been carried out from Agriculture and Soils Division under the able guidance of Dr. Suresh Kumar and Dr. N.R. Patel, at Indian Institute of Remote Sensing, Dehra Dun, India. It is recommended that the thesis be accepted for evaluation and award of M.Tech Degree. Supervisors: Dr. S.K. Saha Dr. Suresh Kumar Head Agriculture and Soils Division Dr. N.R. Patel

Dr.V.K.Dadhwal Dean, IIRS

Abstract India’s economic backbone is constituted by agriculture, which is mostly rain-fed in nature. These regions have limited access to irrigation, because of which the agricultural developmental planning in rainfed agro-ecosystem is often complicated by extremely diverse agro-climatic conditions. Continuous augmentation in demand has put severe strains on the limited natural resources thereby threatening the ecological balance. This foresees adequate resource management. Soil being an integral component of agro-ecosystem varies in type, quality and capability with varying climatic constraints. Climatic constraints when used in conjunction with soil resource information provide a sound basis for assessing agro-climatic suitability. Agro-climatic suitability assessment is gaining weightage as an important basis for sustainable agricultural developmental planning for rainfed agriculture. Soil and climate based agro-climatic suitability analysis enables to identify areas with permutation of homogenous climatic and soil conditions for which proper landuse planning strategies can be implemented. In recent years GIS has provided much-required spatial dimension to natural resource management and planning. GIS technology proved useful for integration of bio-climate, terrain and soil-resource-inventory information. In the present study, the agro-climatic suitability of three important rainfed landuse types, viz. LUT-1 (Pigeon pea), LUT-2 (Sorghum) and LUT-3 (Soyabean) with a geo-spatial approach was evaluated by analysing and integrating soil and climatic constraints. The suitability of soil in terms of its productivity along with climatic suitability in terms of LGP (length of growing period) and water limited yield potential was assessed. The impact of climate on soil suitability (FAO framework based) improvement was determined. In applying the GIS linkages for crop suitability analysis, the target was to achieve optimum utilisation of available land and climatic resources for sustainable rainfed production. Keywords: Landuse/ land cover; Soil Suitability; Potential Evapotranspiration; Water Balance; Spatial and Crop Specific LGP; Crop Suitability; Agro-Climatic Suitability Assessment.

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Acknowledgements At the onset, I would like to convey my sincere thanks and gratitude to the entire Agriculture and Soils Division of the Indian Institute of Remote Sensing, Dehradun, for providing me with the platform to undergo Masters of Technology in Remote Sensing and GIS in this reputed institute. I hereby extend my heartiest gratefulness to Dr.V.K.Dadhwal, Dean, IIRS, for his unrelenting and appreciative encouragement and effort towards providing all the necessities and support in terms of facilities and encouragement. I would specially consider the constant support and guidance from my supervisors, Dr.Suresh Kumar, Dr.N.R.Patel, Agriculture and Soils Division, IIRS. Hereby I thank them for their inexorable patience and supervision during the project work. They have been supportive throughout the project tenure giving me constant views related to project work. It was a complete learning process with both my extremely proficient guides. Here I make a special endeavour to thank the proficient faculty of the Division of Agriculture and Soils, Dr.Jitendra Prasad and nonetheless the divisional head Dr.S.K.Saha for their competence and support through the training period as well as the project duration. Dr.A.Velumurugan needs to be specially thanked for his technical guidance and support all through the tenure of the project without which, the completion of the project would have been tremendously complicated. A special gratitude is due towards Director, NRSA, for providing a 7-month Student Research Assistantship during the project tenure. I would extend my thankfulness to Mr. Rajesh Sharma and Mr. G.D. Bairagi, scientists of Madhya Pradesh Council of Science and Technology, for their relentless cooperation in providing data and information during my field visits. Lastly but not the least, I must express my thankfulness to dear friends Vidya A., Smita Majumdar, Saurabh Varma, for their constant support and spirit livening presence. Gautam Ghosh indeed deserves a special mention concerning his immense help during the last phase of the project. My friend Swati Saini must be thanked for the help she rendered me in completion of my project. Tushar Zanje, Rahul Patil and Hrushikesh Chavan do deserve gratitude for their patience, which was counted at all stages of my project. Special thanks are due to Subroto Nandy, JRF, Forestry and Ecology Division for his time-to-time help. Last of all I would sincerely concede the support, love and inspiration dispensed over me by my family during the training programme. It would never have been possible for me to complete this project without their presence overall. Place: DEHRADUN ADITI SARKAR Date: M.TECH (ASD DIVISION)

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DEDICATED TO MY

BABA, MA, SISTER

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Table of contents

1. INTRODUCTION…………………………………………….………………………..…1-7

1.1 Remote Sensing and GIS Applications in Generating Soil and Landuse Information…...2

1.2 Role of GIS……………………………………………………………………………….5

1.3 Objectives………………………………………………………………………………...7

2. LITERATURE REVIEW…………………………………………………………………8-34

2.1 Characterisation of Rainfed Areas………………………………………………………..8

2.2 Soil Water Balance Studies……………………………………………………………….9

2.2.1 Soil Water Relations…………………………………………………………….9

2.2.2 Available Soil Water…………………………………………………………...12

2.2.3 Evapotranspiration……………………………………………………………..13

2.2.4 Potential Evapotranspiration…………………………………………………...13

2.2.5 Crop Coefficient and Length of Growing Period………………………………14

2.3 Remote Sensing Applications in Landuse/ Land cover Analysis……………………….15

2.4 Remote Sensing and GIS Applications in Soil Resource Management…………………19

2.4.1 Soil Resource Inventory and Land Evaluation…………………………………21

2.4.2 Agro-ecological Zoning………………………………………………………..27

2.4.3 Agro-climatic Suitability Analysis…………………………………………….29

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3. STUDY AREA……………………………………………………………………………35-43

3.1 Location……………………………………………………………………………….35

3.2 Geology………………………………………………………………………………..38

3.3 Physiography………………………………………………………………………….38

3.4 Agro-climate…………………………………………………………………………..39

3.5 Drainage………………………………………………………………………………40

3.6 Natural Vegetation…………………………………………………………………….41

3.7 Soils……………………………………………………………………………………41

3.8 Agriculture and Landuse………………………………………………………………42

4. DATA USED AND METHODOLOGY…………………………………………….…44-88

4.1 Data Used……………………………………………………………………………....44

4.2 Methodology……………………………………………………………………………46

4.2.1 Preparation of Landuse/ Land cover………………………………………….48

4.2.2 Creation of Soil Database…………………………………………………….55

4.2.3 Climatic Database…………………………………………………………….58

4.2.4 Data Analysis…………………………………………………………………63

4.2.5 Climatic Potential Yield Estimation………………………………………….81

4.2.6 Soil Suitability Evaluation……………………………………………………82

4.2.7 Analysis of Agro-climatic Crop Suitability Assessment……………………..87

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5. RESULTS AND DISCUSSIONS………………………………………………………89-144

5.1 Landuse/ Land cover Mapping…………………………………………………………89

5.2 Preparation of Digital Soil Database…………………………………………………...96

5.2.1 Preparation of Digital Soil Map………………………………………………96

5.2.2 Available Water Holding Capacity…………………………………………...98

5.3 Climate Data Analysis…………………………………………………………………103

5.3.1 Length of Growing Period……………………………………………………106

5.4 Soil Suitability Analysis……………………………………………………………….117

5.4.1 Soil Productivity Assessment………………………………………………..117

5.4.2 FAO framework of Land Evaluation for Crop Suitability Analysis…………123

5.4.3 Soil Productivity Index based on crop suitability assessment……………….130

5.4.4 Climatic Yield Potential (Water Limited Yield Potential) Suitability Analysis…133

6 CONCLUSION…………………..………………………………………….………145-148

7 REFERENCES……………………………………………………………………..149-161

8 APPENDIX…………………………………………………………………………….i – xxxiv

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List of Tables 2.1 Conventional classification of Rainfed Agro-ecosystem based on moisture index………….....8 3.1 Agro-ecological sub regions with their salient features………………………………………..35 3.2 District wise agro-climatic regions and crop zones…………………………………………....42 3.3 District wise cropping pattern………………………………………………………………….42 3.4 District wise total area and area classification in Madhya Pradesh (2003-2004)……………...43 4.1 Satellite data specification……………………………………………………………………..44 4.2 Characteristics of AWiFS data…………………………………………………………………44 4.3 Ancillary data used……………………………………………………………………………..44 4.4 Signature separability matrix…………………………………………………………………..54 4.5 Adjustment co-efficients at 5° interval ………………………………………………………...60 4.6 Adjustment co-efficients at 1° interval…………………………………………………………60 4.7 Rating for soil moisture factor (H)……………………………………………………………..83 4.8 Rating for drainage factor (D)………………………………………………………………….84 4.9 Rating for soil depth factor (P)…………………………………………………………………84 4.10 Rating for soil texture factor (T)……………………………………………………………….84 4.11 Rating for base saturation factor (N)…………………………………………………………...85 4.12 Rating for CEC factor (A)……………………………………………………………………...85 4.13 Soil productivity classes………………………………………………………………………..86 4.14 Soil-site characteristics with corresponding land qualities…………………………………….86 5.1 Features of agro-ecological zones covering the study area ………………………………........89 5.2 Landuse/ land cover (kharif season) distribution in the study area …………………………...90 5.3 District wise distribution of area (%) under the dominant landuse types .……………………..93 5.4 Areal distribution of landuse (Kharif ) in AESR zones 10.1 and AESR 5.2…………………...95 5.5 Distribution of physiographic units in various districts………………………………………..96 5.6 District wise distribution of area (%) under AWHC classes…………………………………..101

5.7 Mean (−X ) and SD (δ ) of the estimated AWHC over the Physiographic Units

5.8 AESR zone-wise spatial LGP (days) with district coverage…………………………………..109 5.9 Areal extent of spatial LGP over Madhya Pradesh……………………………………………110 5.10 Areal extent of spatial LGP over study area……………………………………………………111 5.11 Distribution of area (%) in various LGP classes for various LUTs…………………………....115 5.12 Percent areal extent of soil productivity with respect to various physiographic units….……..119 5.13 District wise areal distribution in percentage of soil productivity……………………………..119 5.14 Distribution of kharif landuse/ land cover with their soil productivity…………………..……120 5.15 AESR zone-wise distribution of soil productivity (Area in %)…………………………..……121 5.16 Distribution of suitability class for LUT-1 (Pigeon pea)…………………………………..…..124 5.17 Areal distribution of LUT-1 (Pigeon pea) suitability based on physiographic units…………..125 5.18 Distribution of suitability class for LUT-2 (Sorghum)………………………………………...126 5.19 Areal distribution of LUT-2 (Sorghum) suitability based on physiographic units…………….126 5.20 Distribution of suitability class for LUT-3 (Soyabean)………………………………………..127 5.21 Areal distribution of LUT-3 (Soyabean) suitability based on physiographic units……………128

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List of Tables 5.22 Distribution of suitability for LUT-1 (Pigeon pea) under various SPI classes………………..130 5.23 Distribution of suitability for LUT-2 (Sorghum) under various SPI classes………………….131 5.24 Distribution of suitability for LUT-3 (Soyabean) under various SPI classes…………………132 5.25 Distribution of water limited yield potential classes for LUT-1 (Pigeon pea)………………..133 5.26 Distribution of water limited yield potential classes for LUT-2 (Sorghum)………………….134 5.27 Distribution of water limited yield potential classes for LUT-3 (Soyabean)…………………134 5.28 Distribution of soil suitability for LUT-1 under water limited yield potential classes……….139 5.29 Distribution of LUT-1 suitability modified with climatic yield potential suitability…………139 5.30 Distribution of soil suitability for LUT-2 under water limited yield potential classes……….140 5.31 Distribution of LUT-2 suitability modified with climatic yield potential suitability…………141 5.32 Distribution of soil suitability for LUT-3 under water limited yield potential classes……….141 5.33 Distribution of LUT-3 suitability modified with climatic yield potential suitability…………142

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List of Figures Fig 3.1: Study area showing parts of Madhya Pradesh…………………………………………36 Fig 3.2: Study area clipped from FCC…………………………………………………………..37 Fig 4.1a: Methodology to prepare Landuse/ land cover map and Soil database…………………46 Fig 4.1b: Methodology to assess Agro-climatic suitability of rainfed crops…………………….47 Fig 4.2: Forest map of study area……………………………………………………………….52 Fig 4.3: FAO New LocClim 1.03……………………………………………………………….61 Fig 4.4: Input parameter specification in FAO New LocClim 1.03…………………………….62 Fig 4.5: Length of Growing Period……………………………………………………………..64 Fig 4.6: Methodology of spatial LGP estimation……………………………………………….65 Fig 4.7: Model for determining half PET……………………………………………………….66 Fig 4.8: Model for determining LGP…………………………………………………………....67 Fig 4.9: BUDGET program……………………………………………………………………..68 Fig 4.10: Root zone as a reservoir………………………………………………………………..72 Fig 4.11: A time (t) – depth (z) grid……………………………………………………………...73 Fig 4.12 : Calculation scheme of BUDGET program…………………………………………….74 Fig 4.13: Partitioning of rainfall in effective rainfall, surface runoff and deep percolation……..75 Fig 4.14: Water stress factor (Ks)………………………………………………………………..78 Fig 4.15: Methodology of estimating crop specific LGP………………………………………..81 Fig 5.1: Landuse distribution (Area %)………………………………………………………...90 Fig 5.2: Landuse/ land cover map of kharif season…………………………………………….92 Fig 5.3: Soil texture and depth map…………………………………………………………….97 Fig 5.4: Relationship of soil texture to water retention in 1 m deep soil………………………99 Fig 5.5: Available Water Holding Capacity map……………………………………………...100 Fig 5.6: Areal distribution of AWHC in the study area……………………………………….101 Fig 5.7: 24 years average PET distribution over five selected stations in the study area……..103 Fig 5.8: 24 years average Rainfall distribution over five selected stations in the study area…104 Fig 5.9: 24 years average Rainfall and PET distribution in January for Madhya Pradesh……105 Fig 5.10: 24 years average Rainfall and PET distribution in June for Madhya Pradesh……….105 Fig 5.11: LGP (90 - 100 days) for a 10 km grid………………………………………………..107 Fig 5.12: LGP (100-120 days) for a 10 km grid………………………………………………..107 Fig 5.13: LGP (120-140 days) for a 10 km grid………………………………………………..108 Fig 5.14: LGP (140-150 days) for a 10 km grid………………………………………………..108 Fig 5.15: LGP distribution over Madhya Pradesh……………………………………………...109 Fig 5.16: LGP distribution over the study area………………………………………………....110 Fig 5.17: LUT-1 (Pigeon pea) growth stage specific ET-actual and ET-potential in clay soil…112 Fig 5.18: LUT-2 (Sorghum) growth stage specific ET-actual and ET-potential in clay ………113 Fig 5.19: LUT-3 (Soyabean) growth stage specific ET-actual and ET-potential in clay soil…..114

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List of Figures Fig 5.20: LGP (days) map for LUT-1 (Pigeon pea)……………………………………………...116 Fig 5.21: LGP (days) map for LUT-2 (Sorghum)………………………………………………..116 Fig 5.22: LGP (days) map for LUT-3 (Soyabean)……………………………………………….116 Fig 5.23: Soil Productivity Map……………………………………………………………..…..118 Fig 5.24: Area under various soil productivity classes……………………………………..……118 Fig 5.25: Areal extent of suitability class for LUT-1 (Pigeon pea)………………………..…….124 Fig 5.26: Areal extent of suitability class for LUT-2 (Sorghum)………………………….…….125 Fig 5.27: Areal extent of suitability class for LUT-3 (soyabean)………………………..………127 Fig 5.28: LUT-1 (Pigeon pea) suitability map…………………………………………..……….129 Fig 5.29: LUT-2 (Sorghum) suitability map…………………………………………..…………129 Fig 5.30: LUT-3 (Soyabean) suitability map………………………………………….…………129 Fig 5.31: LUT-1 (Pigeon pea) suitability distribution under various SPI classes………………..130 Fig 5.32: LUT-2 (Sorghum) suitability distribution under various SPI classes……………….…131 Fig 5.33: LUT-3 (Soyabean) suitability distribution under various SPI classes…………………132 Fig 5.34: PET and Rainfall distribution over Hoshangabad with high rainfall and deep soil conditions………………………………………………………………………………135 Fig 5.35: PET and Rainfall distribution over Bhopal with high rainfall and shallow soil conditions…………………………………………………………………………..….136 Fig 5.36: PET and Rainfall distribution over Raisen with low rainfall and deep soil conditions…………………………………………………………………………..….136 Fig 5.37: PET and Rainfall distribution over Ujjain with low rainfall and shallow soil conditions…………………………………………………………………………...…137 Fig 5.38: LUT-1 (Pigeon pea) suitability map by integrating climatic yield potential with soil suitability……………………………………………………………………………....143 Fig 5.39: LUT-2 (Sorghum) suitability map by integrating climatic yield potential with soil suitability……………………………………………………………………………....143 Fig 5.40: LUT-3 (Soyabean) suitability map by integrating climatic yield potential with soil suitability……………………………………………………………………………...143

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List of Plates Plate 1 Soil profile dug out Plate 2 Soyabean field awaiting harvest Plate 3 Intercropped soyabean Plate 4 i) Field under soyabean cultivation ii) Adjoining field ready for cultivation Plate 5 Soyabean harvested field Plate 6 Ripened jowar Plate 7 Field being prepared for rabi crop Plate 8 Rock outcrop

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Geo-Spatial Approach in Soil and Climatic Data Analysis for Agro-climatic Suitability Assessment of major crops in Rainfed Agro-Ecosystem

Chapter 1 INTRODUCTION

Land and water resources management has been identified as one of the priority areas for

achieving sustainable food security by raising land productivity, reversing land degradation and

water loss, and increasing biodiversity and the quality of the environment. An integrated

approach is needed to improve the ability of countries to plan and monitor the better use and

management of their land resources to increase agricultural productivity while maintaining land

and environmental quality.

Agriculture is the backbone of Indian economy, contributing about 40 % towards Gross

National Product (GNP) and providing livelihood to about seventy percent of the population.

Rainfed agro-ecosystem occupies a prominent position in the Indian agriculture. Nearly two

thirds of the nation’s cropped area is under rainfed agriculture. It contributes nearly 67 % of the

net cultivated area. The potential of rain-fed crop production is dependant on many factors, like

climate, soil type, slope etc. Agricultural developmental planning in rainfed agro-ecosystem is

often complicated by extremely diverse agro-ecological conditions underlying the agricultural

practices. With growing demand for agricultural lands, pressure on the rainfed areas has begun

to increase.

Crop yield is the function of many factors like weather, soil type and its nutrient status,

management practices and other inputs available. Of these, weather plays an important role,

probably more so in India where aberrant weather such as drought, flood, etc., is a rule rather

than an exception. Efficient crop planning, therefore, requires proper understanding of agro-

climatic conditions. This calls for collection, collation, analysis and interpretation of long-term

weather parameters available for each region to identify the length of the possible cropping

period taking into consideration the availability of water. Planning Commission has delineated

15 agro-climatic regions, which were proposed to form basis for agricultural planning for the

Eighth Plan. Although this system is not a permanent classification, it may provide to be a

highly applicable tool for strategic research, developmental along with policy planning.

A Case Study of Parts of Madhya Pradesh 1


1.1 RS and GIS Applications in Generating Soil and Land Use Information

Remote sensing satellites in recent years have emerged as a vital tool for generating the

biophysical information, which helps to evolve the optimal land use plan for sustainable

development of area. On the other hand, use of geographic information system (GIS) for

handling a large database on soils and land is becoming increasingly common.

Remote Sensing Applications:

Remote sensing is a powerful tool for the regional mapping of natural resources. With

the use of imageries during the early stages of development of remote sensing in the mid-

seventies, adequate progress has been achieved in the data interpretation. Digital processing of

remotely sensed data has gained momentum in the last ten to fifteen years, especially with the

availability of digital data. Advances in the quality and availability of remote sensing datasets

during the late 1990s and early 21st century have opened up new opportunities for

understanding land cover dynamics. As the pace of land cover change and development

increases, the scientific community needs to make maximum use of this valuable new resource.

Recently available improved spatial, spectral, radiometric and temporal resolution satellite data

can be used to characterize and map landscapes with greater thematic, spatial, and temporal

details. Studies have demonstrated the applicability of remotely sensed data at global (Friedl et

al., 2002; Hansen et al., 2000; European Commission, 2003), regional (Giri et al., 2003; Eva et

al., 2004) and national–local (Homer et al., 2002; Wessels et al., 2004) scales. These studies

used either fine- or coarse-resolution satellite data.

Landuse/ land cover inventories are assuming increasing importance in agricultural

planning based on agro climatic zones. Landuse/ land cover information permits a better

understanding of land utilization which is vital for developmental planning. This requires the

availability of timely and reliable information on type, extent and spatial distribution of landuse.

This can be achieved from various satellite based high-resolution remote sensing data.

Landgrebe (1979) suggested the use of space borne multispectral data to generate landuse/ land

cover map at operational level. Satellite images provide a powerful tool for the identification of



crops and when used with GIS have proved to be effective for landuse. In the eighties, AVHRR

data (1 km resolution) were in vogue to monitor crops. At regional level a number of data

sources have been used to derive landuse maps including LANDSAT TM (28m resolution) and

MSS (58m resolution) for high resolution studies. AWiFS is ideal for crop monitoring due to its

improved spatial, spectral and radiometric resolution. Landuse mapping for Haryana has been

done under NNRMS project. It helps in deriving better spatial landuse/ land cover units for

regional applications. Various categories were delineated through digital interpretation of Kharif

and Rabi season AWiFS FCC images with a view to identify the cropped areas apart from other

land uses (Ramesh et al. 2004). The details available in AWiFS data are best suited for crop-

estimation at district / zonal level. Taking advantage of the advanced capabilities of Resourcesat-

1 AWiFS sensor, “Crop Acreage and Production Estimation” for cotton crop in 15 major cotton

growing districts and Rabi-sorghum in 6 districts in Maharashtra have been attempted using

AWiFS data since it covers a large area (Swath 740 km combined). The 10-bit radiometric

resolution significantly improved the discrimination of Rabi sorghum from other crops. The

pixel size of 56 m suited the district level scale of mapping (Rajankar et al. 2004).

Soil Data:

Soil survey provides information on land characteristics and land qualities used to

evaluate soil resources. The knowledge of soils in respect of its characteristics, extent, spatial

distribution and its potentials for various land uses attain much importance for their optimal

utilization. Indian Remote Sensing Satellite (IRS) data are being extensively used for soil

resource-mapping purposes. It is found to be superior over topographical map for reconnaissance

level soil mapping in conventional method. These satellite data are used to generate soil maps of

various scales. Standard soil surveys are normally carried out in different mapping levels e.g.

Reconnaissance (1: 2,50,000 scale); Semi-detailed (1: 50,000 scale) and Detailed (1: 20,000 or

larger), depending on the requirement of the area. Use of soils according to their

suitability/capability is, therefore, important for sustainable agricultural production. The soil

mapping unit is the basic unit taken from the soil map. On small-scale maps, soil mapping units

rarely comprise single soils, but usually consist of a combination of a dominant soil with minor

associated soils. Each soil type occurring in each soil mapping unit is characterized in terms of its

land characteristics and qualities, which relate to the edaphic requirements of plants or to land-

use requirements for management or conservation.



Land evaluation is the process of assessing the potential of land for one or more kinds of

use. The land or land resources, taken in the broader sense– includes climatic, soil, water and

biological resources that are used to support human activities. It takes the biophysical inventory

of these resources a step beyond in interpreting their suitability for specific targeted uses. Land

evaluation is the process of relating, or matching, the characteristics and qualities of land with

the requirements of existing or intended uses. In its simplest form, land evaluation involves the

assessment of selected biophysical characteristics that are relevant for particular purposes. Land

evaluation for different uses, irrigability classification and capability classification are the

systematic appraisal of land and their distribution by classes on the basis of similar physical and

chemical properties with respect to their suitability for their sustainable production. FAO

Framework of Land Evaluation is most widely used for assessing the suitability of soils for

various kinds of Land Utilization Types (LUTs). Land Suitability may be defined as “the fitness

of a given type of land for a specified kind of land use” (FAO, 1983). It is a measure of how

well the qualities of a land unit match the requirements of a particular form of land use.

Suitability is assessed for each relevant use and each land unit identified in the study.

Climate Data:

IMD records climatic data by fixed observation networks of climatic stations.

Observations of climatic variables like rainfall, maximum and minimum temperature, wind

speed and solar radiation are obtainable on a daily, average monthly basis. In the last decade

significant efforts have been made in involving climatic station data (point) in developing

continuous two dimensional climatic surfaces using a range of spatial interpolation approaches.

These spatial datasets have been in wide use in regional scale agricultural and ecosystem studies.

Length of growing period (LGP) forms an analysis of monthly rainfall and evaporation

data for each grid cell. It is the period when both the thermal and moisture conditions are

suitable for crop growth. Under the FAO proposal, the spatial correspondence of distinct LGP

ranges is termed as “Agro-climatic Zone”. AEZ variables are more agriculturally specific and

linked to an expert knowledge rule base for the purpose of assessing potential crop suitability

(FAO/ IIASA, 2000). Assessment of LGP for individual years, based on the use of historical

rainfall data, enables quantification of the level of risk as well as the potential production under



average climatic conditions. Delineation of the agro-ecological units in the area is important for

crop planning as soil characteristics and length of growing period co-determine the choice of

crops for a particular area.

1.2 Role of GIS

Geographic Information System (GIS) have emerged as a powerful tool in the

management and analysis of the large amount of basic data and information, statistical, spatial

and temporal, needed to generate information products in the form of maps as well as tabular

and textual reports for land use decisions. In recent years FAO has been developing GIS in

linkage with its agro-ecological zoning and similar models, applying these to tackle issues of

land at regional levels. There has been remarkable progress in developing GIS- based tools for

land resources planning at regional scale.

GIS technology is very useful for automated logical integration of bio-climate, terrain

and soil resource information, which are required for landuse planning in a region. Generation of

spatial database from point database using various geo-statistical techniques is an important part

of GIS applications which aids in the integrated analysis. The system is capable of containing all

data required to solve resource management problems. Topographic maps, land resource map

and contour maps having physiographic, geographic and bio-climatic information forms primary

input of GIS for landuse planning. GIS is a vital tool to analyze a multi-layered database. Its

capabilities to process various data in spatial domain make the planning process easier. A GIS-

based decision support system creates opportunities as an invaluable tool for all aspects of the

land use planning process.

Land Use Planning:

For the last 20 years or so, the concept of Agro-ecological zones methodology has

become a widely discussed topic. The concept provides a standardized framework for the

characterizing climatic, soil and terrain conditions relevant to agricultural production. The

potential for sustainable food production is determined by physical factors like climatic

conditions, soil and terrain conditions etc.



In any scheme of planned development of the nation, agricultural landuse planning is of

basic significance (Noor Mohammad, 1978). The land resource inventory is essentially an

overlay of climatic and soil information. The resulting units are the agro-ecological zones, which

have a unique combination, or a specified range, of soil mapping units, growing period regimes,

and thermal regimes. An Agro-ecological Cell (AEC) is defined by a unique combination of

landform, soil and climatic characteristics. The AEC is the basic processing unit for physical

analysis in an AEZ study.

Agro-ecological zoning is an important basis for agricultural developmental planning as the

success and failure of any farming system is dependent on careful assessment of agro-climatic

resources. As a result sustainable agricultural developmental planning for rainfed agriculture is

increasingly being based on Agro-ecological Zones. Remote sensing technology is being

affectively utilized in India in various areas for sustainable agricultural development and

management. Most advanced AEZ investigations incorporate a series of databases, linked to GIS

and dedicated computer models, which have multiple potential applications to natural resource

management and land-use planning. Land may be considered either in its present condition or

after specified improvements. Landuse is essentially based on the climate, soil, and topography

and water availability. Integration of these information using GIS is finding increasing

application in crop specific modeling of agricultural products. It has been well conceived that

GIS has great role to play in landuse planning for sustainable development.

Landuse Planning concept involves the representation of land in layers of spatial

information and combination of layers of spatial information using geographic information

system (GIS). The undertaken project would help in developing a framework for the

characterization of soil, climate and terrain conditions relevant to agricultural production. A

systematic appraisal of the agro-ecological regions in terms of soils, climate, physiography and

conducive moisture availability will help in planning of appropriate landuse. The development

of GIS has enabled the available geo-referenced database to be harnessed with ease into multiple

layer digital form. The FAO-AEZ approach assessed the potential cultivation suitability of a

particular land area, depending on its soil, terrain and climate conditions - without consideration

of its current land cover or actual land use. The objective of the method was to assess whether a

particular land area could be used for cultivation, not to explain why the land is currently used in

a particular way.



Previous attempts made in order to prepare agricultural landuse plan at a regional level

based on the land potentialities considering the crop-specific limitations and potentials of the

existing climate, soil and terrain resources, have been very restricted. The FAO-AEZ approach

embarked upon would ensure an adequate landuse plan based on the crop suitability in terms of

all the previously mentioned characteristics through the fulfillment of the following objectives.

1.3 OBJECTIVES

• To prepare land use / land cover inventory using single date AWiFS (IRS-P6) satellite

data

• Agro climatic-soil suitability analysis for rainfed land utilization types using FAO-AEZ

approach

o To develop long term database of climate to assess spatial LGP using GIS;

o To generate soil data base to estimate AWHC and to assess crop specific LGP of

soil-mapping units;

o To assess soil productivity and agro-climatic suitability for rain-fed land

utilization types.



Chapter 2 LITERATURE REVIEW

India in the present day is posed with tremendous pressure of population on the land

resources. The increasing population with the limited land resources demands immediate

attention to the conservation of land resources, in particular the agricultural lands. Landuse

planning based on the landuse surveys is the first and foremost step to putting the land into

optimal usage and in this aspect the local and regional surveys are best suitable. Land is the most

important natural resource, which embodies soil, water and associated flora and fauna involving

the total ecosystem. Comprehensive information on the spatial distribution of land use/land

cover categories and the pattern of their change is a prerequisite for planning, utilization and

management of the land resources of the country. A brief review of literature related to the

objectives of the study are described below:

2.1 Characterization of Rain-fed Areas

Rain-fed areas are traditionally characterized and classified by moisture index, which are

based on water availability for crop growth and integrate data on quantum and distribution of

rainfall as detailed in Table 2.1. The rain-fed agro-ecosystem can be classified into arid, semi-

arid (dry), semi-arid (wet) and (dry) sub-humid.

Table 2.1: Conventional classification of rain-fed agro-ecosystem based on Moisture Index

Sl.No. Agro-ecosystem Mean Annual Rainfall

(mm)

Moisture Index

(%)

1. Arid < 500 66.7 to 80.0

2. Semi-arid (dry) 501-700 50.6 to 66.6

3. Semi-arid (moist) 701-1000 33.3 to 50.5

4. Sub-humid (dry) 1001-1600 0.0 to 33.3

Source: National Academy of Agricultural Sciences (CRIDA, 2007)



However, this traditional classification is now found inadequate in view of the complex

nature of climatic and edaphic factors influencing the productive potential of different sub-

regions within the rain-fed agro-ecosystem. Therefore, the concept of Agro- Ecological Regions

(AER) has come into prominence since 1992.

2.2 Soil Water Balance Studies

2.2.1. Soil Water Relations

Water is held in the soil because of the attraction between soil solids and water. This

force can be measured by moisture tension. A knowledge of the amount of water held by the soil

at the various tensions is required if we are to calculate the amount of water that is available to

plants, the water that can be accommodated before percolation starts, the amount of water that

needs to be used for irrigation, etc. For reasons of practical use and for the determination and

tabulation of soil moisture data, it is necessary to select definite tension levels as reference

points. All plants need water to grow and produce good yields. When plants are water stressed,

they close their stomata (the small holes in the leaf surface) and cannot photosynthesis

effectively. Best growth can be achieved only if plants have a suitable balance of water and air

in their root zones. Some stages in the growth of a crop are particularly sensitive to moisture

stress. This requires an understanding of the movement and storage of water in the root zone of

the crop and the rate of water use by the crop. Soil water dynamics can be thought of as

comparable to a sponge. When a sponge is saturated by soaking it in water when it is lifted out

of the water any excess water will drip off it. This is equivalent to drainage from the macro-

pores in the soil. Once the sponge has stopped dripping it is at field capacity. When the sponge

is squeezed it is easy to get the first half of the water out. This first squeeze is equivalent to

draining the sponge to the stress point and the water is removed like the RAWC (Readily

Available Water Capacity). Squeezing the second half of the sponge out is much harder. This is

like draining the sponge to permanent wilting point. The total water squeezed out of the sponge

from when it stopped dripping is the TAWC (Total Available Water Capacity). However, no

matter how hard the sponge is squeezed there is no way to get all the water out of it. The water

left is the equivalent to the hygroscopic water found in soil. From field capacity to the stress



point, it is easy to get the water. From the stress point to the permanent wilting point, plants find

it much harder to draw water from the soil and their growth is stunted. Below the permanent

wilting point, no further water can be removed and the plant dies.

Aggarwal, (1995) verified water balance of agro-ecological systems as a key parameter

for most physical and physiological processes within the soil-crop-climate system. Therefore it

is of high importance to calculate the water budget to reduce potential uncertainties in simulated

outputs.

Soil water retention term used in soil science literature, which can be defined as

predictive functions of certain soil properties from other more available, easily, routinely, or

cheaply measured properties. This concept arises in soil science as information on soil survey is

now highly demanded.

Johan Bouma coined the term ‘Pedotransfer’, as translating data we have into what we

need. The most readily available data come from soil survey, such as field morphology, soil

texture, structure and pH. Pedotransfer functions add value to this basic information by

translating them into estimates of other more laborious and expensively determined soil

properties. These functions fill the gap between the available soil data and the properties which

are more useful or required for a particular model or quality assessment. Pedotransfer functions

utilize various regression analysis and data mining techniques to extract rules associating basic

soil properties with more difficult to measure properties.

Varadan and Jayakumar (1991) studied the hydrophysical characteristics of typical

soil series in a rolling topography in a humid tropical region. The relationship of these properties

to water management problems like excess or low available water, water logging and draingae

were worked out in the process.

Srivastava et. al. (1990) studied the water retention characteristics of five sweel-shrink

soils of Chandrapur district, Maharashtra. The study showed that water retentivity at 33kPa and

1500 kPa tension was significantly and positively correlated with clay, silt+clay, CEC and

exchangeable Ca+Mg and negatively correlated to sand. The multiple regression equations were

developed to predict water retentivity using clay content and CEC values.



Jurgen Lamp and Kneib (1981) from Germany introduced the term pedofunction,

while Bouma and van Lanen (1986) used the term transfer function. To avoid confusion with

the term transfer function used in soil physics and in many other disciplines, Johan Bouma

(1989) later called it pedotransfer function.

Several useful correlations between important soil moisture constants and the more

easily measured properties of soil have been worked out. Water held at low suctions, viz. 10 &

33 KPa and the available water were highly correlated to clay and to silt and silt + clay fractions

(Abrol et. al. 1966).

In the 1970s more comprehensive research using large databases was developed. In the

USA, Gupta and Larson (1979) developed 12 functions relating particle-size distribution and

organic matter content to water content at potentials ranging from -4 kPa to -1500 kPa.

Clapp and Hornberger (1978) derived average values for the parameters of a power-

function water retention curve and saturated hydraulic conductivity for different texture classes.

A particularly good example is the study by Hall et al. (1977) from soil in England and

Wales; they established field capacity, permanent wilting point, available water content, and air

capacity as a function of textural class, and as well as deriving continuous functions estimating

these soil-water properties.

Several studies on profile water retention and release characteristics were reported and

soils were grouped according to their available water capacity by Khosla and Abrol, (1966).

Water retention characteristics were shown to be influenced mainly by the textural constituents

& nature of clay minerals.

Salter and Williams, (1965) explored relationships between texture classes and

available water capacity, which are now known as class PTFs. They also developed functions

relating the particle-size distribution to AWC, now known as continuous PTFs. They asserted

that their functions could predict AWC to a mean accuracy of 16 %.



With the introduction of the field capacity (FC) and permanent wilting point (PWP)

concepts by Frank Veihmeyer and Arthur Hendricksen, (1927), research during the period

1950-1980 attempted to correlate particle-size distribution, bulk density and organic matter

content with water content at field capacity (FC), permanent wilting point (PWP), and available

water capacity (AWC).

The need for soil water determination at Field Capacity and Permanent Wilting Point

arises frequently. Therefore, it is of a considerable interest to find out the relationship between

water retention and influencing physico-chemical properties of soil to compute prediction

equations. Gajbhiye (1990) worked upon surface samples of Maharashtra to estimate water

retention at field capacity and wilting point and developed correlation coefficients between

water retention indices.

2.2.2 Available Soil Water:

One of the main functions of soil is to store moisture and supply it to plants between

rainfalls or irrigations. Evaporation from the soil surface, transpiration by plants and deep

percolation combine to reduce soil moisture status between water applications. If the water

content becomes too low, plants become stressed. The plant available moisture storage capacity

of a soil provides a buffer, which determines a plant’s capacity to withstand dry spells.

When soil is saturated, all the pores are full of water, but after a day, all gravitational

water drains out, leaving the soil at field capacity. Plants then draw water out of the capillary

pores, readily at first and then with greater difficulty, until no more can be withdrawn and the

only water left are in the micro-pores. The soil is then at wilting point and without water

additions, plants die.

The amount of water available to plants is therefore determined by the capillary porosity

and is calculated by the difference in moisture content between field capacity and wilting point

(FC – PWP). This is the total available water storage of the soil. The amount of water in a soil

can be expressed as a percentage, or in millimeters per meter of soil.



Hillel, (1971) opined that the water content in soil affects the way the energy flux affects

the state and movement of water. To evaluate the field water cycle as a whole and the relative

magnitudes of the various processes comprising it over a period, it is necessary to consider the

field water balance.

2.2.3. Evapotranspiration

The impact of climate on crops is always transformed into evaporation, which in turn

depends upon the available water and energy. In case of cropped surfaces, the continuous loss in

form of vapour is called “evapotranspiration” since water loss is due to the combined

evaporation from soil and transpiration through plant surfaces. For crops, it is essential to

estimate maximum water loss under certain climatic conditions and under unlimited water

availability at root system level, i.e. the maximum evapotranspiration (ETM). The major factors

controlling evapotranspiration are water availability, available radiant energy and transport

mechanism to remove water vapor from the surface. They depend on soil moisture, land surface

temperature, air temperature etc. Nishida et al. (2003) used a combination of remote sensing

data, ancillary data and atmospheric data to account for all these factors for a final estimation of

evapotranspiration.

Olioso, (2002) derived evapotranspiration maps using remote sensing, which is not an

easy work. This calculation used different inputs like albedo, emissivity and roughness that are

derived from surface temperature. Maps of net radiation are generated to derive

evapotranspiration maps. The combination of two separate processes where water is lost on one

hand from soil surface by evaporation and on the other hand from crop by transpiration is

referred to as Evapotranspiration by Allen, (1998).

2.2.4. Potential Evapo-transpiration

Potential evapotranspiration (PET) indicates the evaporative demand (water demand of

the atmosphere) and depends upon the thermal and radiation regime. PET is an adjustment to

climatic conditions of the average ETM values of cropped surfaces in an optimum development

state, without any physiological constraints. It is the basic input parameter in water balance



computation for irrigation assessment and scheduling. The annual PET ranges between 1400-

1800 mm over most parts of the country. The concept of potential evapotranspiration was first

popularized by Thornthwaite (1948) who defined it as the water loss from surface completely

covered with vegetation and supplied with abundant water at all times. He developed one of the

best methods of estimating potential evapotranspiration. He related observations of consumption

use of water in irrigated areas in western US to air temperature, with adjustments for day length.

Earlier studies by Singh and Xu, (1997); Xu and Singh, (2000) have evaluated and

compared various popular empirical potential evapotranspiration equations that belonged to

three categories, viz. (1) mass-transfer-based methods, (2) radiation-based methods, and (3)

temperature-based methods, and the representative methods from each category were evaluated

by using the Penman–Monteith method as a standard.

Venkateshwarulu, (1987) illustrated how the choice of crops grown under rain-fed

conditions depends on the length of humid period available for crop growing. During SW

monsoon season, PET values range between 400-600 mm in most parts of the country, except

for the extreme south-eastern parts of Southern Peninsula where it is slightly higher.

Mather, (1978) evaluated potential evapotranspiration primarily as a function of climatic

conditions and not merely functions of vegetation types, soils, soil moisture or land management

practices.

Penman, (1956) defined it as the amount of water transpired in unit time by a short

green crop, completely shading the ground, of uniform height and never short of water. The

concept of potential evapotranspiration is an attempt to characterize the micro-meteorological

environment of a field in terms of an evaporative power.

2.2.5. Crop Coefficient (Kc) and Length of Growing Period

PET is a climatic variable, which is often referred to as the water requirement of a

conventional crop. However, the actual crops have individual requirements, which are related to

the stages of crop-development. In order to estimate the actual crop water requirement, the PET

values must be corrected. This correction brings us closer to the ETM values for the different



crop development phases. The PET values are transformed through crop coefficient (Kc). Kc

values higher than 1.0 (ETM>PET) refers to well developed crops, while Kc values lower than

1.0 (ETM<PET) correspond to bare soils or sparse crops. Crop coefficient is the ratio of

evapotranspiration for a given crop to evapotranspiration of a reference crop. It represents an

integration of effects of four primary characteristics that adjusts the crop from reference grasses

are crop height, albedo, canopy resistance, and evaporation from soil.

Allen, (1998) identified the factors determining Kc as crop type, climate, soil

evaporation, crop growth stage.

FAO, (1996) clearly explained the concept of growing period as an essential aspect to

AEZ. The growing period defines the period of the year when both moisture and temperature

conditions are suitable for crop production. The growing period provides a framework for

summarizing temporally variable elements of climate, which can then be compared with the

requirements and estimated responses of the plant. Temperature and soil moisture are key factors

in determining the distribution of rainfed crops in both space and time. The concept "length of

growth period" (LGP), that quantify the moisture attributes is defined as "the continuous period

of the year when precipitation exceeds half of Penman evapotranspiration plus a period required

to evapotranspire an assumed soil moisture reserve and when mean daily temperature exceeds

6.5 °C". LGP analysis is based either on average climatic data or on historic data for individual

years. Most early AEZ studies calculated LGP based on average monthly rainfall and PET.

2.3 Remote Sensing Applications in Landuse / Land cover Analysis

Today, remote sensing image data of the Earth’s surface acquired by spacecraft

platforms is readily available in a digital format. Digital remote sensing systems convert

electromagnetic energy (color, light, heat, etc.) to a digital form. Spatially, the data is composed

of discrete picture elements, or pixels, and radiometrically it is quantized into discrete brightness

levels. The great advantage of having data available digitally is that it can be processed by

computer either for machine assisted information extraction or for the enhancement by an image

interpreter.



In contemporary land-use and landcover mapping studies, land cover classes are often

derived from satellite imagery by utilizing computer-assisted image classification techniques.

Within the scope of the study, image classification is defined as the extraction of distinct classes

or themes, land use and land cover classification categories, from satellite imagery. There are

two primary methods of image classification utilized by image analysts, unsupervised and

supervised classification.

Several remote sensing application studies in the country on mapping on soils, land use

and land cover have been demonstrated and published. A large number of studies emanate from

regional programmes of resources mapping using remote sensing in an operational mode.

Raju, K. et al, (2006) assessed the landuse changes of Udumbanchola taluk by using

remote sensing techniques. Udumbanchola Taluk, located in the fragile zone of Western Ghats

in the Idukki District of Kerala has undergone severe land use changes in the past century. The

historic land use map was derived from topographic maps of Survey of India, surveyed in 1910

and published during 1912-14.The land use map of 1997 was generated through the visual

interpretation of IRS-1C LISS III images supported by ground truths and was observed that the

original land use systems was highly modified. It is also observed that the prevailing agro-

climatic conditions favorable for the sustenance of sensitive crops as cardamom has undergone

changes mainly due to the impact of large-scale land use modifications.

The remote sensing technology has been widely used by Joshi, P. K. et al, (2005) for

mapping the vegetation types in the tropical landscapes. However, in the temperate and alpine

arid regions of India very few studies have been conducted using this technique. In the

mountainous temperate arid conditions, the vegetation is largely confined to marsh meadows,

streams courses, river valleys and moist pockets close to snowfields. The ground truth collection

in these zones is physically challenging due to tough terrain and restricted mobility. The detailed

mapping of vegetation and other land use classes in these area is therefore, extremely difficult.

This paper describes the use of IRS-1D LISS III sensor for deciphering land cover details Nubra

Valley, northern portion of Ladakh Autonomous Hill Council, Jammu & Kashmir (India). This

analysis essentially emphasizes in bringing out various vegetation classes (especially Hippophae

rhamnoides and other medical plant communities) along the narrow river valleys.



Shetty et.al, (2005) suggested that in order to use the land optimally, information on the

existing landuse/ land cover is required along with the capacity to monitor the dynamics of

landuse that are a natural outcome of the changing demands. Landuse/ land cover have often

been used to describe maps that aim to provide information of both the natural features over

earth as well as human induced changes over earth that are closely related to the natural features.

Panigrahy, S. et al, (2004) conducted a study in the Bhatinda district of Punjab state for

mapping the cropping pattern and crop rotation, monitoring long-term changes in cropping

pattern by using the satellite based remote sensing data along other spatial and non-spatial

collateral data. Multi-date IRS LISS I and IRS WiFS sensor data have been used for the study.

Cropping pattern maps and crop rotation maps were generated for the years 1988 – 89 and 1998

– 99. The present study has shown the increase of cropping intensity significantly, mainly due to

increase in rice area. However, crop diversity has decreased mainly due to decline in the area

under the minor crops like pearl millet, gram, rapeseed / mustard. There is increase in area

coverage of cotton-wheat and rice-wheat rotation, at the expense of the minor crops.

Dutta, S. et.al, (2004) made an attempt to select suitable single date and combination of

multidate for wheat crop classification in Nalanda district of Bihar state where pulses and other

crops are also grown in rabi season. Multi temporal data acquired at different growth stages

increases the dimensionality information content and have advantage over single date data for

crop classification. Amongst the single date data, February data was found to be better for wheat

classification in comparison to November, January, March and April data. Combination of first

two principal components each derived from IRS LISS I for band data acquired in January and

February was found to be the best set. Wheat classification accuracy achieved was 94.54

percent.

Jayakumar, S. et.al. (2003) attempted to map land use / land cover and change detection

analysis in Kolli hill, part of Eastern Ghats of Tamil Nadu, using remote sensing and GIS. About

467 ha increase has been observed in single crop category and about 434ha, decrease has been

observed in land with or without scrub category. Majority of the area (13639 ha) is under

scrubland. Lesser changes could be noticed in double crop, plantation and barren /rocky



categories. Necessary measures should be taken to utilize the scrubland and to prevent the

conversion of cropland into scrubland. The identified wastelands, which are suitable for

agriculture , have to be utilized optimally to improve the economy of the people.

Suresh Kumar, et.al, (2002) visually interpreted standard false color composites on

1:50,000 scale in conjunction with soil survey to prepare physiographic-soil map. Thirteen

mapping units were delineated indicating soil association at family level. Soil and land resource

was evaluated for their land capability and irrigation suitability for its sustained use under

irrigation. Land capability and land irrigability maps generated as attribute map. These maps

were integrated to suggest potential land use maps. Current land use / land cover map prepared

by visual analysis was spatially analyzed in relation to potential land use to study potential

changes in land use / land covering using GIS. The study reveals that 14.66% area has no

limitation and can be brought to intensive agriculture by double cropping.

Sehgal, V. et.al, (1997) generated two band simulated WiFS data for five dates

corresponding to rabi sorghum growing season of 1993-94 for Aurangabad district of

Maharashtra. Ground truth data has been used for supervised classification of one date raw

image and five dates NDVI of simulated WiFS data and the results were compared with those

derived from single date IRS LISS I data. Analysis of classification accuracies indicate that

single date WiFS data gives slightly lower accuracy of 79 % against 81% obtained from single

date LISS I data. Overall accuracy for 5-date WiFS data is 96%, which shows that classification

performance of five date WiFS NDVI data is far superior to the single date data of IRS-1C

WiFS as well as the IRS LISS I. The study thus shows the importance of temporal domain od

data acquisition in sorghum crop discrimination. Growth profile for sorghum and other crop

classes were generated from multidate WiFS derived NDVI data. Differences in growth profiles

of sorghum vigour classes as well as amongst different crop types and forests corroborate the

premise of better discrimination of crop types and their vigour on multidate remotely sensed

data.



According to Deekshatulu & Joseph (1991), the term is used more commonly to denote

satellite remote sensing for identification of earth features by detecting the characteristic

electromagnetic radiation that is reflected/ emitted by the earth’s surface. It is a technology of

acquisition of data about the terrestrial and atmospheric objects and processes in and beyond the

range of human vision to derive information that can be quantified.

Remote Sensing is the science and art of obtaining information about an object, area, or

phenomenon through the analysis of data acquired by a device that is not in contact with object,

area, or phenomenon under investigation (Lillesand and Kiefer, 1987).

Iyengar, R.S et.al, (1980) studied Airborne multispectral data obtained over mono and

multiple cropping systems of small agriculture for two cropping seasons for a possible

development of crop spectral signature and to utilize such signatures for interpretation of

multispectral data and for assessing agricultural potentials of a region. In multiple cropping

systems, the unique crop spectral response exhibited by crop species at specific growth stages

facilitated interpretation and analysis of multispectral data with the knowledge of crop

phenology. For resolving spectral confusion between crop species due to growth stages of

different crop species, temporal data were observed to be useful. Development and use of crop

spectral signature for interpretation and analysis multispectral data related to mono cropping

system were found to be less complex and offer great promise because of minimum confusion.

2.4. Remote Sensing and GIS Applications in Soil Resource Management

Soil variability according to the soil surveyors are best depicted based on topographic

variations. Thus comes the role of remote sensing data. Multi-spectral satellite data provides

better information about the various landforms, geomorphic processes. The dynamic relationship

between the physiography and soils is utilized while deriving information on soils from multi-

temporal data. This is because the soils are resultant of same natural processes and conditions

that sculpture the land, which they inhabit.



Another recent development in the use of satellite data is to take advantage of increasing

amounts of geographical data available in conjunction with geographic information systems to

assist in interpretation. Geographical data describe objects from the real world in terms of

(a) Their position with respect to a known coordinate system,

(b) Their attributes that are unrelated to position (such as color, type, cost, pH, incidence

of disease, etc.) and

(c) Their spatial interrelations with each other (topological relations), which describe

how they are linked together or how one can travel between them as per Burrough, (1986).

The concept of the geographic information system emerged during the 1960’s and

1970’s as new trends arose in the means in which maps were being produced and used for

resource assessment, land evaluation, and planning. Essentially, this concept focuses on the

ability to develop a powerful set of tools for collecting, storing, retrieving at will, transforming,

and displaying spatial geographic data from the real world for specific analysis and inquiry. This

set of tools constitutes a geographical information system or geographic information system.

Burrough, (1986) opined Geographic information systems to be comprising of three main

components: computer hardware, sets of application software modules, and a proper

organization context.

With the increasingly widespread, combined implementation of remote sensing and GIS

technology, more natural resource professionals have been provided with efficient and accurate

tools for mapping and maintaining management information on forests and other natural

resources in regional areas. GIS technology is expanding, allowing for greater integration of

remote sensing with digital cartography, thus providing the means to produce more accurate

land use and land cover maps.



2.4.1. Soil Resource Inventory and Land Evaluation

Ziadat (2007) prepared large-scale soil maps, with adequate accuracy, is costly and time

consuming. The low homogeneity of soil mapping units in small-scale maps limits their use for

detailed planning. These limitations call for alternative approaches to support the land use

planning processes. Recently, models that utilize terrain attributes derived from digital elevation

models (DEM) are used to predict soil attributes. The objective of this research is to explore the

accuracy of land suitability classifications derived from predicted soil attributes versus those

derived from traditional soil maps. Three suitability maps were derived, one from predicted soil

attributes and two from traditional soil maps (scales 1:10,000 and 1:50,000). The accuracy of the

three suitability maps was verified using two methods: a set of 1469 field observations or an

interpolation from these observations (Thiessen polygons). The results indicated that the

accuracy of the suitability classification derived from predicted soil attributes compares

favorably with the accuracy of classifications derived from traditional soil maps. The use of an

interpolation between field observations provides better chance of estimating the accuracy of

suitability map.

Verdoodt and Ranst (2006) adapted a qualitative land suitability classification

procedure to translate the large-scale biophysical data supplied by a reconnaissance soil survey,

into five suitability classes. At local scale, the productivity of the soil units identified during the

semi-detailed soil survey is estimated using a three-level hierarchical crop productivity

estimator, simulating the potential, water-limited and land production potential. At the smallest

spatial and temporal resolution, a daily water balance approach is linked to a crop growth model,

using daily climatic data recorded at different meteorological stations and the description,

physical and chemical analyses of the soil profiles. The decision support system was applied and

validated using the land resources information system of Rwanda.

Nidumolu et.al, (2004) analysed landuse class maps and soil series to identify areas

having specific priorities with respect to agricultural landuse analysis under the project

Integrated Mission for Sustainable Development (IMSD) in India MSD used remote sensing

data supported by field investigations to generate landuse and soil maps. The approach identified

specific agricultural landuse analysis objectives, which match the farmers’ needs and objectives.



Suresh Kumar and N.R. Patel (2004) evaluated physical land evaluation methods and

highlighted their importance. Various land evaluation methods, viz. Land Productivity Index,

Soil Productivity Index, crop suitability based on FAO framework of land evaluation with

Boolean and fuzzy set method and Sys method were used to complete the study.

Ochola and Kerkidesb (2004) developed a prototype interactive spatial decision

support system (SDSS) to assist land use scientists, agricultural extension support personnel and

farmers to classify and characterize land quality, assess sustainable land management and

identify potential land use solutions at the farm recommendation unit and resource management

domain levels in Kenya. The system implements a generic land quality assessment framework

that integrates farmer-led participatory sustainability assessment with specialist input into a

multi-disciplinary perspective. The system was conceived in support of methodological studies

to identify, evaluate and interpret indicators of land quality. It provides a platform for

formulating indicators to describe biophysical, socio-cultural and economic processes in agro-

ecosystems. It integrates modular assessment, database management sub-systems and

geographical information system (GIS). It has been structured to include a project environment,

land quality data acquisition control, assessment and output engine and land quality spatial

analysis. The output includes visual land quality diagrams and interactive land quality and land

attribute quality maps. In its present structure, it is robust and specific enough to encapsulate.

Mouinou Igué (2004) developed soil information system. The data structure for the

description of the land resources was established according to the Soil and Terrain Digital

Database (SOTER) manual (Global and National Soils and Terrain Digital Databases (SOTER).

Procedures Manual International Soil Reference and Information Centre, Wageningen, The

Netherlands, 1993) with slight modifications. Based on field observations and data analysis, land

areas have been delineated showing similar response to management practices. On the

uppermost level, seven so called ‘terrain units’ could be distinguished. The main differentiating

criteria were landscape morphology, geology and hydro-morphology. The terrain units were

subdivided at a second level into 25 terrain components according to the soil parent material and

landform. The FAO/ITC land suitability procedure was used to identify crop specific constraints

to the production of sorghum, cowpea, maize, cotton, groundnuts and cassava.



With regard to the soil-induced limitations to crop production, unfavorable textural properties

prevail, except for terrain components on gabbro or basaltic parent material. Unfavorable

climatic conditions determine the suitability of this region for cotton, maize and cassava

production. The ranking of the physical suitability of the six crops for Central Benin was in the

order sorghum>groundnut>cowpea, cassava>maize>cotton. The problem of inter-annual

variability of precipitation and its effects on the climatic suitability was discussed.

Foody (2002) reviewed the production of thematic maps, such as those depicting land

cover, using an image classification is one of the most common applications of remote sensing.

Considerable research has been directed at the various components of the mapping process,

including the assessment of accuracy. This paper briefly reviews the background and methods of

classification accuracy assessment that are commonly used and recommended in the research

literature. It is, however, evident that the research community does not universally adopt the

approaches that are often recommended to it, perhaps a reflection of the problems associated

with accuracy assessment, and typically fails to achieve the accuracy targets commonly

specified. Eight broad problem areas that currently limit the ability to appropriately assess,

document, and use the accuracy of thematic maps derived from remote sensing are explored.

Suresh Kumar et al., (2002) carried out a study with the objectives of preparing

physiographic soil map on 1:50,000 scale using satellite data and assessing the potential landuse

in context to their land capability and irrigation suitability.

Ghaffari et al. (2000) had used GIS to match the suitability for main crop potato based

on the biological requirement of the crop and the quality and characteristics of land. The

methodology combined climate, land quality attributes that most influence crop suitability (long-

term) average annual rainfall, accumulated temperature, field capacity duration, topographical

data (slope and altitude) and soil water deficit). Social and economic factors are excluded. It

presented a GIS based land suitability model based on Simple Limitation Approach (SLA).

Suitability of potato was determined, based on matching the biological requirements of crop to

the quality and characteristics of land.



Nisar Ahmed et al. (2000) studied the use of fuzzy (partial) membership classification is

used to accommodate the above uncertainty in assigning the suitability classes to the pixel. The

evaluation of the spatial variability of relevant terrain parameters was carried out in a geographic

information system environment while assigning the land suitability for crops in the study area

of Kalyanakere sub-watershed in Karnataka. Nine parameters (eight of soil and one of

topography) were considered and suitability analysis is carried out by fuzzy membership

classification with due weightage factors included to accommodate the relative importance of

the soil parameters governing the crop productivity. According to the field information, the crop

being grown in maximum area is finger millet. However, the cropland evaluation results of the

present study reveal that maximum area is potentially suitable for growing groundnut.

Tamgadge et.al., (1999) determined the crop suitability of various crops of the Madhya

Pradesh soils taking into account crop requirement in terms of soil depth, Available Water

Holding Capacity, reaction and agro-climate. This proved the importance of soil information as

a significant input in determining crop suitability.

Sudha and Ravindranath (1999) assessed the availability of land and the potential for

biomass production in India to meet various demands for biomass, including modern bio-energy.

This was estimated by considering the various demands on land and its suitability. The biomass

production potential of energy plantations was assessed for different agro- ecological zones. The

total woody biomass production was estimated to be 321 Mt, based on biomass productivity in

the range 2 to 17 t/ha/yr for the different agro-ecological zones and considering the conservative

estimate of 43 Mha land availability for biomass production. A surplus of 231 Mt of biomass

(after meeting the increased demand for fuel-wood and timber by the year 2010) was estimated

to be available for energy, which has an electricity generation potential of 231 TWh. As a first

step, only the feasible physical potential of biomass production was assessed, along with an

analysis of barriers.



Bydekerke (1998) attempted to assess the suitability of land to grow cherimoya in the

province of Loja. Attributes of the physical environment, based on expert knowledge from

various regions growing cherimoya, were collected and used for the suitability assessment. Each

attribute (rainfall, KoÈppens' climatic classification, altitude, soil type and ecotype) was mapped

with the help of a GIS and classified on the basis of predefined growth requirements. Climatic

and soil requirements for cherimoya growth were identified and a land suitability map was

produced by overlaying the climatic and soil suitability maps. The climatic suitability map

reveals suitable regions within the 1500±2200 m altitude range, where favourable temperature

conditions prevail. About 24% of the study area is found to be suitable for cherimoya growth,

but only 2% is highly suitable. The most suitable areas are found in the south-east of the

province. In the rest of the province climatic or chemical soil fertility conditions are expected to

restrict cherimoya growth.

Bullock, P. (1997) described the FAO Agro-Ecological Zones Project is a step towards

agro-ecological harmony. It provides a basis for informing planners about land potential, the

suitability and yield potential for some 11 major crops important in the developing world and the

potential sustainability of the various cropping systems. It was one of the first schemes also to

quantify the amount of potentially cultivable land in developing countries. New developments,

which now allow the agro-ecological zones approach to be strengthened and made more

available, include the availability of well organized national and international land-related

databases, the development of crop suitability and production models of varying sophistication,

models for environmental risk assessment including risks from the adoption of particular

agricultural practices, and geographical information systems that now allow information to be

put to policy makers and others in a simple and useable format.

Hari Eswaran (1997) developed a Soil Taxonomy map, based on the FAO Soil Map of

the World, which together with other data, is used to make continent-level assessments of land

productivity and sustainability. Prime land occupies about 9.6% of Africa and the lands with

high potential occupy an area of about 6.7%. The medium- and low-potential lands, which

together occupy 28.3% of the area have major constraints for low-input agriculture. Resource-

poor farmers who live on these lands have high risks and, generally, the probability of



agriculture failure is high to very high. The remaining about 55% of the land consists of deserts

or other lands with major constraints even for low-input agriculture. The desert margins have

nomadic grazing which with increasing animal population is stressing the environment. A soil

quality analysis and an evaluation of sustainable production, based only on biophysical

considerations, suggest the need for major investments to enhance the productivity of the soil

resources of this continent.

Rao and Chandrashekhar, (1996) explained how the satellite data has proven its vast

potential in the mapping of soil resources of India at 1:250,000 scale by the National Bureau of

Soil Survey and Land Use Planning (Nagpur). Further one-third soil mapping has been taken up

under the Integrated Mission for Sustainable Development on 1:50,000 scale using IRS-LISS II

data.

Rossiter, (1996) defined land evaluation as a device for the strategic landuse planning. It

predicts land performance, including expected benefits from and constraints to productive

landuse, as well as the expected environmental degradation due to these uses.

Ngowi and Stocking (1989) demonstrated FAO procedure for farming systems which

include coconuts in the Coastal Belt of Tanzania. An extract of the land suitability map and

legend are given. Yield potential for coconuts varies from five nuts per palm per year on

unsuitable land with poor management practices to 80 nuts on the most suitable land with good

management. To assess optional land uses, cashew nuts, maize and rangeland were also

considered alongside coconuts. Planners and decision-makers can now use the results to target

scarce resources to optimal areas and to design viable farm units and farming systems for local

physical, social and economic conditions.

Busoni et al. (1986) illustrated that soil moisture affects the soil management more than

crop yield. Soil erosion is greatest when superficial run off can create rills.



FAO, (1978) discussed how adequate agricultural exploitation of the climatic potential

and maintenance of land productivity largely depends on soil fertility on an ecologically

sustainable basis. The requirements of soil must be understood in the context of limitations

imposed by landforms and other factors not inherent to soil but may have significant influence

on use that soil can be put to.

Young, (1976) opined that land evaluation aims to assess the land performance and its

production potential for specific purposes. It may be concerned with the present land

performance. It involves both the changes as well as their effects too. Thus land evaluation

should consider how the land is currently managed, and the future consequences given the

present practices still unchanged. It also considers whether a change in the management

practices along with present use is possible or not. Soil survey provides the user with

information about the soil and landform conditions at any location of interest.

Westin and Frazee, (1976) proved that satellite data has vast potential in soil mapping

and studies. Landsat MSS data enabled observation and delineation of soil patterns, landuse,

slopes drainage pattern and preparation of soil association maps for the entire South Dakota.

According to Klingebiel, (1966) soil maps were produced to suit needs with variety of

problems since they contain a wide range of details to show the basic soil differences.

2.4.2. Agro-ecological Zoning

The ability of the world’s natural resources to satisfy the needs of its growing population

is a fundamental issue, where the basic problem is mounting pressure on natural resources.

Limits to the productive capacity of land resources are set by climate, soil and landform

conditions, and by the use and management practices.

Patel et al., (2002) claimed that sustainable development in mountainous terrains is a

challenging task because of diverse and fragile ecosystems and spatial variability. This makes

planning of use of natural resources in hilly terrains more complex. The study was conducted in

part of Kumaon Himalayas to illustrate use of Remote Sensing and GIS as tools for agro –

ecological Zoning with terrain perspective. Remote Sensing and GIS based approach to



delineate Agro – ecological homogenous geographic areas were developed using soil resources

as input layers. Based on delineated Agro – ecological Zones, suitable land use were suggested.

Patel et al. (2000) used a Remote Sensing and GIS based approach to delineate Agro-

ecologically homogenous geographic areas by using soil resources, temperature, rainfall,

moisture and biomass as inputs. It was an attempt to incorporate new tools to extend

applicability of Agro-ecological Zones in the mountainous terrains. The system also facilitates

the enlargement of a particular geographic pocket to render more details on retrieval.

Mandal et al. (1999) used a set of criteria ranging from soil, physiography, bio-climate

and length of growing period to delineate agro-ecological zones at levels starting from national

level to watershed level for resource planning. Depending upon the soil, bioclimatic type and

physiographic situations, the country has been grouped into 20 agro-eco regions (AER) and 60

agro-eco subregions (AESR).

Saha and Pande, (1996), delineated 11 AEZ by using GIS to integrate Agro-climatic

and agro-edaphic zones. Remote Sensing and GIS based methodology is an important segment

for developing Agro-ecological Zones using satellite derived soil, landuse and irrigation regional

maps, DEM derived slope and ancillary data of land characteristics and agro-meteorological

inputs. The purpose of zoning, as carried out for rural landuse planning, is to separate areas with

similar sets of potentials and constraints for development. The AEZ, envisaged in FAO studies,

defines zones on the basis of combinations of bio-physical patterns (soil, landform, climate etc).

AEZ can be regarded as a set of core applications, leading to an assessment of land suitability

and potential productivity. Several studies were carried out using AEZ approach world over by

FAO (1994); Sehgal et al. (1990); Krishnan (1988); Higgins and Kassam (1981).

Venkateshwarulu et al., (1996); Mavi, (1984) suggested several approaches of AEZ in

past involving manual integration of agro-climatic and other natural resource data. As a result,

large amount of agro-ecological data could not be handled easily and aggregation was required

at an early stage in the analysis. This led to loss of information on spatial variability.

Pratap et al., (1998) well conceived that remote sensing and Geographic Information

System (GIS) have great role to play in agro-ecological zoning for sustainable development due



to multi-stage character of the comprehensive approach to agro-ecological zoning. Modern tools

such as satellite remote sensing and GIS have been providing newer dimensions to effectively

monitor and manage natural resources.

FAO, (1983) defined Agro-climatic zone as a land unit in terms of major climate,

superimposed on length of growing period (moisture availability period), whereas an agro-

ecological zone as the land unit carved out of agro- climatic zone superimposed on landform

which acts as modifier to climate and length of growing period.

Noor Mohammad, (1978) reflected how in any scheme of planned development of the

nation, agricultural landuse planning is of basic significance. Any plan aiming to utilize the

natural available resources depends solely on landuse surveys. Such surveys hold detailed

information regarding the existing landuse and misuse. This information when analyzed, results

in better understanding of the current landuse and thereby exploring the possibilities of further

developments.

2.4.3. Agro-Climatic Suitability Analysis

Climate is a predominant controlling factor in rain-fed agriculture due to spatial and

temporal variability in rainfall and temperature. In rain-fed agriculture, the moisture supply to

plants depends on the precipitation and the water-holding capacity of the soil. Under the tropical

conditions, moisture regime is the most important factor in influencing agricultural production,

as thermal regime is optimum to better crop growing environment. In the rain-fed regions, over

80per cent of the annual rainfall is received during the SW monsoon, although variability in

rainfall increases with decrease in its volume. The climate of a region is the sum of its

atmospheric conditions like temperature, precipitation, wind, humidity etc. It is a composite

picture of the variety of day-to-day weather conditions. It is therefore obvious that the weather

conditions vary from day-to-day and climate differs from place to place.

Tsubo et.al, (2007) described a semi-empirical model for estimating net lateral water

flow along a topo-sequence of rice fields. An accurate estimate of paddy water availability is

crucial for modelling rice productivity in such environments. However, modelling of water

balance in the sloping lowlands can be difficult due to problems in estimating lateral water



movement from high to low positions in the topo-sequence. Net lateral water flow is separated

into three sub-components: (i) lateral seepage through the bunds from field to field; (ii) surface

runoff over the bunds from field to field; (iii) water run-on from the catchment above the topo-

sequence. The lateral seepage is estimated using the Dupuit equation for steady unconfined flow.

Surface runoff over the bund is calculated as excess water depth above the bund height, while

run-on from the catchment is calculated using a rainfall–runoff relationship. The other water

balance components used in the model are rainfall (measured), paddy evapotranspiration (ET),

and downward water flow (within the field and under the bund). Paddy ET is estimated using the

FAO crop ET model.

Carrera-Hernandez and Gaskin (2007) analyzed the temporal variation of both

minimum and maximum temperature and rainfall, its correlation with elevation and whether or

not this relationship should be used when daily data are interpolated. In order to achieve this, the

monthly distribution of these variables is derived from daily interpolations, which is compared

to their monthly accumulated value for each climatological station. The interpolation methods

used to undertake the analysis were Ordinary Kriging (OK), Kriging with External Drift (KED),

Block Kriging with External Drift (BKED), Ordinary Kriging in a local neighborhood (OKl) and

Kriging with External Drift in a local neighborhood (KEDl).

Geerts et al. (2006) prepared an agro-climatic suitability library for crop production was

generated by using climatic data sets from 20 to 33 years for 41 meteorological stations in the

Bolivian Altiplano. Four agro-climatic indicators for the region were obtained by validated

calculation procedures. The reference evapotranspiration, the length of the rainy season, the

severity of intra-seasonal dry spells and the monthly frost risks were determined for each of the

stations. To get a geographical coverage, the point data were subsequently entered in a GIS

environment and interpolated using ordinary kriging, with or without incorporating anisotropy.

The presented case study focuses on quinoa, an important crop in the region that is cultivated

during the short and irregular rainfall season and that is well adapted to the frequent occurrence

of drought and frost. The GIS library was used to mark zones where deficit irrigation could

improve quinoa production.



Sepaskhah, et al. (2006) used soil water budget and simple relationships for

evapotranspiration partitioning, leaf area index (LAI) determination, and the transpiration

functions for dry matter and for harvest index to develop a model for growth and yield

production of cowpea under soil water stress conditions. The Food and Agriculture Organization

(FAO)–Penman and FAO–Penman–Monteith methods were used to estimate the reference crop

potential evapotranspiration ETo and the results of the soil water balance accordingly were

compared. The model was calibrated by a set of data from which the above relationships were

derived and then validated very well with another set of data obtained from an experiment in the

same area but in a different year. It was concluded that the FAO–Penman method for estimation

of reference crop potential evapotranspiration is superior to FAO–Penman–Monteith method in

the study area. The model is also capable of estimating dry matter production during the

growing season. Furthermore, it was shown that the model can be successfully applied for farm

irrigation management and scheduling. It was indicated that the optimum irrigation interval was

7 days with the amount of applied water of 5 cm for each irrigation event.

Kar and Verma (2005) analyzed spatial variation of climatic water balance, (water

surplus, actual evapotranspiration), probabilistic monthly monsoon rainfall and mapping of cold

periods in agro-ecological region (AER) 12.0 of India using GIS and models. Since, rice is the

dominant crop of the region, crop water requirements of rice was also spatially analyzed in

different agro-ecological subregions (AESRs) of the AER 12.0 using CROPWAT 4.0 model and

GIS. Study found that as per climatic water balance, large to moderate water surplus (520–70

mm) was available in AESR 12.1. The rainfall surplus of 220–370 mm was computed in AESR

12.2 and 370–520 mm in AESR 12.3 mm.

Mandal et al. (2005). attempted to find out the most sustainable soil for cotton under

varying rainfall through crop yield correlation with agro-environment factors, like soil

physiographic conditions, growing period rainfall, crop ET and phasic rainfall, by conducting

(farmers’) field experiment in a representative catena with four different soil types in central

India.



The international land evaluation criteria suggested by FAO show that Vertisols qualify

as a suitable category for cotton production under rainfed conditions. However, the present study

indicates that this categorization may need revision in view of the adverse climatic conditions of

central India. In order to improve the effectiveness of the FAO’s land evaluation criteria for sub-

tropical Vertisols, the study suggests that more emphasis be given to rainfall in the critical

growth phases related to crop yield and to soil hydraulic conductivity related to the Ca2+/Mg2+

ratio in computing land indices, rather than total quantum rainfall during the growing period.

Garcia et al. (2004). described low levels of rainfall, high evapotranspiration rate and

soils with low water retention capacity, water stress as a major constraint to crop production.

Under these conditions, irrigation would be an asset to reduce the increased risk for agriculture.

For that purpose, reliable reference evapotranspiration (E0) estimates for the design and

management of irrigation systems are necessary but not available. In this study, E0 calculated by

means of the Thornthwaite, Hargreaves–Samani and FAO Penman–Monteith equations is

compared with measured (lysimeters) grass crop evapotranspiration (Egrass) during the growing

period (October–April) at the Bolivian Highlands. The E0 estimated by means of the FAO

Penman–Monteith method agrees well with Egrass at the four locations. The temperature-based

Hargreaves–Samani formula is able to estimate the reference evapotranspiration at the northern

locations of the Altiplano but not at south due to the exclusion of aerodynamic factors affecting

evapotranspiration. The temperature (mean temperature)-based Thornthwaite formula strongly

underestimates E0 at all locations.

Aggarwal et al. (2002), using the recent climate change scenarios and WTGROWS,

estimated the impacts of climate change on wheat and other cereal crops.

Caldiz et.al, (2002) studied performance of an agro-ecological region to estimate the

potential yield of the crop with the LINTUL-POTATO simulation model, and to identify yield

determining, yield limiting and yield reducing factors for the Patagonia area of the Rio Negro

valley (Argentina), where the potato crop acreage is rapidly expanding. Suitable soils for potato

cultivation were identified at the great group soil level: torri-fluvents, torri-orthents and

haplargids. Potential yield of the crop proved similar to that obtained in other regions of the

world (>25 tonne ha−1 dry matter) and because of high irradiance and ample thermal amplitude,



dry matter content of the tubers can be higher than that obtained in other potato growing areas of

Argentina. In late plantings, high temperature is the most important yield-defining factor

because of its negative effect on dry matter partitioning. Inadequate soil tillage after land

reclamation, low soil fertility and persistently strong winds were identified as the most important

yield limiting factors, whereas yield reducing factors are not yet in evidence in these new areas.

Lal et al., (1999) demonstrated how the adverse impacts of likely water shortage on

wheat productivity could be minimized to a certain extent under elevated CO2 levels; these

impacts, however, would be largely maintained for rice crops, resulting in a net decline in rice

yields.

Lal et al., (1999) made significant studies to analyse the soyabean yield reduction. In the

state of Madhya Pradesh, where soybean is grown on 77 % of all agricultural land, some studies

suggest that soybean yields could go up by as much as 50 per cent if the concentration of carbon

dioxide in the atmosphere doubles. If maximum and minimum temperatures rise by 3°C and

3.5°C respectively, then soybean yields will decrease by five per cent compared to 1998.

Oumarou Badini (1997) outlined the procedure used to investigate the water-limited

growth environment of an improved millet cultivar in Burkina Faso. A daily time-step cropping

system simulation model (CropSyst) was used to simulate the soil water budget components and

millet production potential, both spatially and temporally, by coupling the model with databases

of soil type, long-term weather, and crop management using a geographic information system

(GIS). From the cropping model outputs, two agro-climatic indices (Aridity Index and Crop

Water Stress Index) that show the water-limited growth environment of the millet crop

throughout the country were quantified and mapped with the help of the GIS.

This allowed the identification of agro-climatic zones, as determined by the crop water needs.

Millet productivity decreased from the south to the north of the country in relation with rainfall

isolines and soil types. Locations with less than 500 mm of annual rainfall are marginal for

millet, particularly on planosols and arenosols. In regions with rainfall above 700 mm, moisture

availability is not a major limiting factor for the 90-day millet production, especially on

regosols, cambisols, acrisols and nitosols.



Overall, the approach followed in this study appeared promising for quantifying the growth

environment of millet as affected by soil type and weather. It could also help to provide

guidelines for crop water management and analysis of the suitability of improved crop cultivars.

Stewart et al. (1984) described a method for estimating long-term crop yield and

production potentials for spring wheat. The assessment was made at a map scale of 1:5 000 000,

and is based on map units from the Soils of Canada. Potential net biomass and dry matter yield

values were computed using procedures adapted from those described by the FAO. Potential

values were determined using a form of photosynthesis model, which calculates crop

photosynthesis response to temperature and radiation averaged over a growing season.

Anticipated yields were derived from these values by employing yield-reducing factors related

to moisture stress, autumn workday probability and soil constraints. The anticipated (corrected)

yield for each map unit was compared to the maximum potential yield obtainable in the country.

The results of the procedure were expressed as a quantitative land suitability assessment. Each

map unit was classified into one of six suitability classes. Areas with anticipated yields of less

than 20% of maximum potential were considered as not suitable for production. From this data

base, total crop production potentials were computed for the prairie provinces for various land-

use allocations.



Chapter 3 STUDY AREA

Madhya Pradesh, a prominent state of the Indian republic is renowned for its vastness,

population density (196/ km2 according to the 2001 census) and varied nature of terrain. Situated

far away from the Bay of Bengal, the region enjoys a high degree of continentality and

consequently decreasing precipitation during the south-west monsoon from east to west. It thus

quite a befitting area selected to carry out the undertaken project.

For an agricultural state like Madhya Pradesh, where the mainstay of people in general is

farming, crop water requirements in different seasons become quite important. The erratic south-

west monsoon can never be relied upon for concerning the total amount of precipitation, its

regional distribution. For any sound agricultural planning at regional scale, the seasonal

deficiencies must be known to the planners.

3.1 Location

Madhya Pradesh is the central province of India and its geographic limits range from

21°04’30”to 26°49’30”N latitude and 74°1’10”to 82°48’20”E longitudes. The state sprawls over

almost 308000 sq.km. with about 28% land covered with forest. For the current study, selective

districts (9) out of the 48 districts were chosen (Fig: 3.1). These districts cover the two climatic

sub-regions namely AESR 5.2 and 10.1. Their salient features are outlined in Table 3.1. This

covers an area of about 5004618.85 ha and has a geographical extent of 21° 54’ 19.60 N to 23°

53’ 48.12 N and 75° 08’ 00.64 E to 79° 38’ 17.48 E.

Table 3.1: Agro-ecological sub-regions with their salient features

Sl.No. Agro-Ecological Sub Region Districts Covered Description

1 5.2 Ujjain, Indore, Dewas Hot moist semi-arid ESR. Medium and deep clayey black soil. Medium to high AWC and LGP 120-150 days.

2 10.1 Bhopal, Sehore, Raisen, Harda, Hoshangabad,

Narsimhapur

Hot dry sub-humid ESR. Medium and deep clayey black soil. High AWC and LGP 150-180 days.

Source: Agro-ecological Regions of India NBSS & LUP Bulletin No. 24 (1992).



The figure shows a schematic view of the study area.

Figure 3.1: The Study Area Showing Parts of Madhya Pradesh



Figure 3.2: Study Area Clipped from FCC



3.2 Geology:

Madhya Pradesh represents all the pre-Cambrian rock system, namely Archaean,

Dharwar, Cuddapah and Vindhyan. Cambrian to middle carboniferous state is absent in this

state. The oldest group of rocks comprising of Archaeans and Proterozoic formation constitute

nearly 45% area of the state. The younger formation of Carboniferous to lower Cretaceous

comprising Gondwana Super Group covers 10% area while the formation of Cretaceous to

Pleistocene comprising mostly of Deccan Trap basalt constitutes 38% area of the state.

Geological formations of Pre-Cambrian period are marginally spread over the area. Formations

of recent or Pleistocene origin are found over large parts of India.

The study area that was covered in the project comes under this category. The vast

plateau mountains to the north of Narmada River and the adjoining areas of Malwa Plateau and

Gangetic Plains form the Vindhyan cover. Here the Deccan Trap and the alluvium conceal the

rocks. The lower Vindhyans are dominantly limestones, whereas the upper parts of the

succession are mostly sandstones. The Deccan Trap covers a part of Madhya Pradesh.

According to the geologists the Deccan Trap was formed as a result of the sub – aerial volcanic

activity associated with continental divergence in this part of the earth during the Mesozoic era.

The rocks found in this region are generally igneous in nature. The Gondwana and Vindhyan

include within its fold, parts of Madhya Pradesh.

3.3 Physiography:

Madhya Pradesh or as can be called as Central Province, is located in the geographic

heart of India. Depending upon elevation, slope and terrain ruggedness, three major

physiographic regions have been identified: North Deccan Plateau, Central Highlands and

Eastern Plateau. The state bestrides the Narmada River, running east and west between the

Vindhyan and Satpura ranges. These ranges and the Narmada are the traditional boundary

between the north and the south of India. Madhya Pradesh is bordered on the west by Gujrat, on

the northwest by Rajasthan, on the northeast by Uttar Pradesh, on the east by Chhattisgarh and

on the south by Maharashtra. The physical feature of an area affects its climate, vegetation and

agriculture.



The study area comprises of the following distinctive regions:

3.3.1 Malwa –

A plateau region of volcanic origin in the north-west of the state, north of the Vindhyan

range. The average elevation of the region is 500m and the landscape slopes towards the north.

Dewas, Indore are the major cities of the region, while Bhopal lies on the edge of the

Bundelkhand region. Ujjain is of historical importance in this region. The Malwa plateau

generally refers to the volcanic upland south of the Vindhyas.

3.3.2 Nimar –

The western portion of the Narmada River valley, lying south of the Vindhyas in the

south west part of the state.

3.3.3 Mahakoshal –

It is the south-eastern portion of the state, which includes the eastern end of the Narmada

river valley and the eastern Satpuras.

3.4 Agro-Climate:

India has a diverse agro-climate, topography and soil types on the basis of which it has

been categorized into various regions. Major part of the country is rainfed. Rainfall, therefore,

constitutes an important parameter in the classification of the country into various regions for

the purpose of planning. India has been divided into 15 agro-climatic zones based on climate, in

combination with soil and other factors that affect the agriculture in the region. The country has

been categorized into 20 Agro-Ecological Regions based on physiography, soils, climate,

growing period and also taking into account available water capacity of the soil. The agro-

ecological regions were subsequently refined to prepare a 60 Agro-Ecological Sub-Regions

(AESR) map for regional level planning using the detailed soil information at subgroup level,

physiography at land form level, and bioclimate (refined limits of arid, semi-arid and sub-humid

bioclimate) types and length of growing period (LGP). The AESR information is useful for



regional level planning and resource allocation. The information of length of growing period as

well as agro-ecological zoning supported by moisture availability index can act as an excellent

base for crop modelling and crop suitability evaluation.

The agro-climate of the sub-region is characterized by hot dry sub-humid with dry

summers and mild winters. Madhya Pradesh has a typical tropical climatic condition. Summers

are hot and in some places humid while winters are comfortable. The state has three main

seasons: winter (November through February), summer (March through May), and the monsoon

season (June through September). The temperature during summers ranges from maximum 33°C

to 44°C and minimum 30°C to 19°C. The temperature during the winter season ranges with the

maximum of 27°C to 10°C. The average daily temperature in the summers is nearly 35°C and

ranges between 15°C to 20°C in the winters. Madhya Pradesh enjoys widespread Indian

monsoon climate with maximum rain between June end and September. The average annual

rainfall varies from 700 – 2000 mm. A narrow strip starting from western district of Vidisha and

Bhopal and broadening south – east covering Narsimhapur, Jabalpur experiences higher rainfall

ranging between 1200 – 2000 mm. Indore has the minimum rainfall. Hailstorms and fog though

occur are rare. The best time to visit this place would be during the months of November to

February. The growing period lasts from 90-150 days, during which the average daily

temperature is below 30°C, but seldom falls below 20°C.

3.5 Drainage:

Madhya Pradesh represents great river basins and the watershed of a number of rivers.

Catchments of many rivers are lying in Madhya Pradesh. The Narmada originating from

Amarkantak and Tapti originating from Mulati Teh of Betul district divide the state into two,

with the northern part draining into the Ganga basin and the southern part into the Godavari and

Mahanadi systems. The Tawa river flowing above the Hoshangabad area is an important source

of irrigation. The Vindhyas form the southern boundary of the Ganga basin, with the western

part of the Ganga basin draining into the Yamuna and the eastern part directly into the Ganga

itself. All the rivers draining into the Ganga flow from south to north, with the Betwa, Ken

rivers being the main tributaries of the Yamuna.



3.6 Natural Vegetation:

The natural vegetation is tropical dry forest being endowed with rich and diverse forest

resources. This forest heartland of the country famous for its teak, tanks and temples is a

naturalist’s paradise with almost one third of its area covered with tropical forests of teak, sal,

butea, bombax, acacia and miscellaneous kinds. Forests in Madhya Pradesh cover about 27.9%

approximately of total area of the state. These forests, as usual have been subjected to general

misuse and over – exploitation by shifting cultivation, fires, grazing and lopping. The present

forest cover of Madhya Pradesh can be considered to be under various stages of degradation.

Almost the whole of south, central eastern and eastern Madhya Pradesh, receiving higher rainfall

is moister and greener than the west, north western and central western regions. Except for hill

tops like Pachmarhi, Bailadila, entire Madhya Pradesh falls under tropical Forests covering Semi

– evergreen forests, Moist Deciduous Forests, Dry Deciduous Forests and Thorn Forests.

3.7 Soils:

Vertisols of the peninsular India are the primary soil orders in the study area. They have

a very high water retaining capacity and low infiltration rates due to swelling of clay. Among the

rainfed soils, vertisols have high fertility. The post-rain crop production varies significantly with

soil depth, texture as they mainly determine the available water holding capacity. The dominant

soilscapes of the area are represented by gently to very gently sloping, shallow and moderately

deep, Ustorthents and Ustochrepts. They vary from dark brown to deep black in color and are

heavy textured. They are plastic and sticky when wet and show strong swelling and severe

shrinkage with varying moisture. The dominant soil groups are typified by the soils of Kheri

series and Suanther series, which are very fine and have shrink-swell properties. The surface of

the soil cracks during the dry spells and the subsurface shows shining pressure faces of the peds

indicating moderate to high shrink-swell potential. The clay content ranges between 40–75 %.

These are highly base saturated soils and the montmorrilonite constitutes the dominant clay

mineral in the exchange complex. Deep Black Soils cover the major parts Hoshangabad and

Narsimhapur districts. The clay content varies between 20% and 60%. They have high moisture

retention capacity.



3.8 Agriculture and Land use:

Madhya Pradesh is predominantly a land of forests and agriculture. Approximately

27.9% of the state is under forest cover. Agriculture occupies about 49 % of land area. Millets

and wheat in the central and northern regions, paddy in eastern and southeastern regions and

cotton in the southwestern parts are the major crops. The study area can be divided into the agro-

climatic regions and cropping zones (Table 3.2).

Table 3.2: District wise Agro-climatic regions and crop zones along with soil type and rainfall

Sl.No. Crop/Zones Agro-Climatic Regions Soil Type Rainfall (mm)

Districts Covered

1 Wheat zone Central Narmada Valley Deep black (deep) 1200 to 1600 Narsinghpur, Hoshangabad Sehore(Partly),Raisen(Partly)

2 Wheat zone Vindhya Plateau Medium black & deep black (Medium/Heavy)

1200 to 1400 Bhopal Raisen (partly) Sehore (partly)

3 Cotton/ Jowar Malwa Plateau (Medium) 1200 Ujjain, Dewas, Indore. 4 Cotton/ Jowar Nimar Plains (Medium) 1000 Harda

Source: National Informatics Centre, 2006, Directorate of Agriculture, Govt. of Madhya Pradesh, Bhopal.

The region has a semi-arid agro-climatic condition and most of the kharif crops are

grown under rain-fed conditions. The agriculture and allied services contribute about 31% share

in state economy and 71% of its working force directly engaged in agriculture. The food grains

production increased from 8.9 million tonnes in 1964-65 to 14.1 million tonnes in 2004-2005.

Table 3.3: District wise cropping pattern

Sl.No. Districts Crop_Pattern_1 Crop_Pattern_2 Crop_Pattern_3 1 Bhopal Soybean-wheat Soybean-gram Fallow-wheat 2 Dewas Soybean-wheat Soybean-gram Soybean-fallow 3 Harda Soybean-wheat Soybean-gram Soybean-fallow 4 Hoshangabad Soybean-wheat Soybean-gram Soybean-fallow 5 Indore Soybean-wheat Soybean-gram Soybean-fallow 6 Narsimhapur Soybean-gram Soybean-wheat Fallow-gram 7 Raisen Fallow-wheat Soybean-wheat Soybean-gram 8 Sehore Soybean-wheat Soybean-fallow Soybean-gram 9 Ujjain Soybean-wheat Soybean-gram Soybean-fallow

Source: Compendium of Agricultural Statistics, 1995, Directorate of Agriculture, Govt. of Madhya Pradesh, Bhopal.



Table 3.3 illustrates the district wise cropping pattern in the study area. This shows a

dominance of the soyabean as the first crop whereas wheat as the second crop.

Table 3.4: District wise total area and classification of area in Madhya Pradesh (2003-2004) in Sq.Km.

District Total Area NSA TCA Forest NA For Cultivation

Current Fallow

Fallow other than Current

Cropping Intensity

Bhopal 2778.80 1524.46 2147.18 441.06 350.44 47.11 35.46 146 Dewas 7013.07 3844.15 5752.73 2060.37 453.69 7.73 14.24 160 Harda 3305.79 1712.09 2986.00 1045.97 233.23 13.79 34.71 171 Hoshangabad 6686.89 2956.22 4928.71 2556.75 452.66 65.35 100.72 165 Indore 3830.97 2586.11 4258.14 522.08 308.90 21.09 29.73 178 Narsimhapur 5136.51 3017.36 4006.00 1362.07 251.27 40.36 57.72 140 Raisen 8487.46 4305.04 5000.37 3336.72 430.96 12.02 30.85 128 Sehore 6563.68 3820.07 5869.02 1725.97 470.94 9.57 45.11 155 Ujjain 6098.74 4882.72 7624.81 31.49 635.97 12.48 19.91 173 NSA: Net Sown Area TCA: Total Cropped Area

District wise area and classification of the study area has been given in Table 3.4. The

cropping intensity of the state is 135% and varies from district to district. Major crops grown in

the area are paddy, wheat (Rabi crop), maize and jowar among cereals, gram, tur, urad and

moong among pulses, while soyabean (kharif crop), groundnut and mustard among oilseeds. In

addition, cotton and sugarcane are grown. Horticulture crops like potato, onion, and garlic are

grown too. 75 % of the national soyabean production is contributed by the state, whereas 36 %

of gram is the state’s contribution to the national share of total production.

3.8.1 Landuse:

Dryland farming is the common practice in the region. The Kharif crops usually

cultivated in the area are sorghum, pearl-millet, pigeon-pea, groundnut, soybean, maize and

pulses. The common Rabi crops are sorghum, sunflower and gram. Wheat is grown under

irrigated conditions. Gram, wheat and vegetables are common Rabi season crops. Kharif

cropping is totally rainfed, whereas Rabi cropping is partly irrigated at critical stages growth.



Chapter 4 DATA USED & METHODOLOGY

4.1 Data Used

To achieve the objectives of the present study the following satellite data products,

ancillary data and softwares were used.

4.1.1 Remote Sensing Data

AWiFS Satellite data of 1st October 2005 pertaining to the study area was procured from

NDC, NRSA, Hyderabad. The specifications of the satellite and its products are described in

Table 4.1 and Table 4.2.

Table 4.1: Satellite data specifications

Path Row Date of Pass Sensor IRS – P6 Satellite

98 55 1- Oct-2005 AWiFS

Table 4.2: Characteristics of AWiFS data

Sensor AWiFS

Spatial Resolution 56m

Radiometric Resolution 10 bits

Swath 740 km (combined)

Temporal Resolution 5 days

(Band 2) G: 0.52-0.59 (Band 3) R: 0.62-0.68 (Band 4) NIR: 0.77-0.86

Spectral Bands

(Band 5) SWIR: 1.55-1.70

4.1.2 Ancillary Data

Table 4.3: Ancillary data used

Climatic Data Daily temperature and precipitation data (IMD) from 1980 to 2003 over 28 climatic stations (MP)

Soil Data • Soil Map (1:500,000 scale), NBSS & LUP • Soil Series of Madhya Pradesh (NBSS & LUP)

Topographical Maps SOI Toposheets of the Madhya Pradesh region – at scale 1:250,000. (Toposheets 46 M, 46 N, 55 (A, B, C, E, F, G, I, J, M, N)

Ancillary Data

Landsat – TM data (Path 144 to 147 and Row 43 to 45) covering the study area



The Soil Map published by the NBSS & LUP at 1:500, 000 scale was used for the

creation of the digital soil database along with the soil characteristics published by them. Survey

of India topographic sheets of the Madhya Pradesh region of numbers 46 M, 46 N, 55 (A, B, C,

E, F, G, I, J, M, N) at 1:250, 000 scale pertaining to the study area were used in the study.

4.1.3 Instruments Used

(i) Garmin GPS 12: GPS was used to obtain the geographical coordinates of the

observed field locations during the ground truth study for collecting soil and landuse/ land cover

information.

(ii) Soil Auger: Eldeman soil auger (1.5 m) was used to collect soil samples of the

surface and sub-surface layers.

(iii) Camera: camera was used to take field photographs.

4.1.4 Softwares Used

a) Image Processing: ERDAS IMAGINE 9.0

b) Geographical Information System: ARC GIS 9.1 – developed by ESRI, Inc. it

uses a geographic data model that represents spatial information as objects, features,

rasters, and other data types.

c) Spatial Interpolation of Climatic Data: FAO New LocClim 1.03 - New LocClim

is especially designed for the spatial interpolation of climatic data, offering the

possibility of producing climate maps from user provided station data. It allows

optimising the interpolation with respect to the data analysed.

d) Soil Water Balance Program: BUDGET 6.2 - The soil-water balance model

BUDGET (Raes, 2005), calculates the water storage in a cropped soil profile as

affected by the input and withdrawal of water for a given period. The program had

been developed in DELPHI platform, which is a PASCAL based programming

language for WINDOWS (BORLAND).

e) Others: MS-Office (MS-Word, MS-Excel, MS-Access), Endnote for

documentation and calculation.



4.2 Methodology

The following flowcharts Fig 4.1a & Fig 4.1b depict the overview of the methodologies

followed in the study to achieve the desired objectives of the study. The methodology was

designed on a two-tier approach.

Ancillary Soil DataKharif Season Satellite Data (Oct, 2005)

FSI Forest Cover Map

District Boundary

Soil sampling in dominant (NBSS & LUP) LU/ LC classes

(Lat/ Long)

Surface layer Sub-surface layer 0–25cm Georeferencing 25–50cm & 50–100cm

Soil Analysis Texture & Bulk Density

Forest & water bodies masked

District wise coverage clipped

Pedotransfer Function (Literature Survey)

Field Capacity & Training sites obtained Wilting Point for LU/ LC classes

Soil Depth

LU/ LC AWHC area statistics

FIG 4.1a: Methodology to prepare landuse/ land cover map and soil database



Climatic Data Soil Data Rainfed LUTs (28 stations for MP)

(1980 to 2003)

Soil & Land Characterization

Temperature (Daily)

Precipitation Soil Map Soil Characteristics LUT

Requirements(Daily) (1:500000) LUT’s

ParameterAttributes database

(Kc, RD, P, Ky)

PET Calculation Soil Texture & Depth

FAO Framework of (Thornthwaite Method) Land Evaluation

Soil Suitability for Spatial Modeler Soil Simulation (Summing) Rainfed Crops

Unit

GIS AnalysisWater Balance

(BUDGET Software)

Spatial LGP LGP Climatic Yield Potential Agro-Climatic

Suitability of (LUT Specific) (LUT Specific)

Rainfed LUTs

Fig 4.1b: Methodology to assess Agro-climatic suitability of rainfed land utilization types



4.2.1 Preparation of Landuse/ Land Cover

Kharif landuse/ land cover map prepared under the study. The satellite data of 1st

October, 2005 was used for the purpose. The landuse/ land cover mapping procedure has been in

detail illustrated in Fig 4.1a in the methodology.

4.2.1.1 Satellite Data Processing

(A) Acquisition and importing data to Image processing system - The required satellite data

for the kharif season was browsed from the archive of NRSA, NDC, Hyderabad website

www.nrsa.gov.in. A complete range of satellite imageries of the study area had cloud until 30th

September 2006. The imagery of 1st October, 2005 was found to be cloud free and thereby the

most dependable for the study. Therefore this data was selected for the landuse/ land cover

mapping. The scene was obtained in super – structured data format from NRSA Hyderabad.

Digital products have been supplied in LGSOWG (Landsat Ground Station Operators Working

Group) or Super Structure Format. Each imagery consisted of four bands, of which a set of two

bands were provided together in one CD and the remaining two in another. The bands were

separately provided as imagery files along with the Volume Directory File, the Leader File, the

Trailer File and the Null Volume File. Each of these bands were individually imported into

*.img format through ERDAS Imagine using the mentioned parameters (CD INFO) and zeroes

were ignored while computing the statistics. Finally, all the four bands were stacked together to

obtain the final satellite imagery.

(B) Geometric Correction – To meet the landuse/ land cover classification studies, the data

needs to be geo-rectified with location accuracy. The information in the raw image is not related

to the real world. Geometric correction is performed through georeferencing, which establishes

this relation between row/column numbers and real world coordinates. In the study, AWiFS

images were georeferenced using the UTM projection type, with WGS-84 spheroid and datum

names. This ensured the satellite imagery to have a spatial reference with the real world. The

satellite images were geometrically registered using the already georeferenced Landsat-TM

resampled at 56 m pixel size, using nearest neighbour resampling with first order polynomial

equation. An RMSE of 0.2970 was recorded for kharif scene after completion of the

georeferencing.


http://www.nrsa.gov.in/


(C) AOI of data – AWiFS image was covering a large area encompassing almost the entire of

the Madhya Pradesh state. Since the study area was only a part of the entire state, the next task

was to generate a subset from the data to obtain the area of interest of nine districts by clipping it

with available Madhya Pradesh district coverage.

(D) Preparation of data for pre-field interpretation –An unsupervised classification was

carried out to obtain the spectral classes representing various landuse/ land cover classes for

each district. For each of the nine districts 10 clusters, representing landuse/ land cover were

classified. These clusters represented the forest, water body, fallow, scrubland and two to three

agricultural land utilization types. These pre-field classifications were used to plan the field

survey for landuse/ land cover data collection. Based on this, a thorough field study was

performed and 280 training sites were collected out of which, 90 (10 for each nine districts)

were used as check points for accuracy assessment. The remaining 190 sites were used for image

classification using supervised classification.

(E) Training site collection/ ground truth for major landuse - The field survey was carried

out over a 20-day period beginning by end of September till mid - October 2007. Field work

encompasses a thorough study of the area (district wise) in the satellite imagery, SOI toposheets

and the classified (unsupervised) imagery to ensure representative site identification for landuse/

land cover data collection.

Major land use/ land cover information in the area were obtained with their geographical

locations. Total of 280 training sites with GPS readings for landuse/ land cover information were

collected. Few field photographs showing various landuse/ land covers are shown in Plate 1 to

Plate 8.



PLATE 1: Soil Profile Dug Out PLATE 2: Sugarcane Field Awaiting Harvest

1 2

PLATE 3: Intercropped Soyabean PLATE 4: Field under Soyabean cultivation (1)

and adjoining field ready for cultivation (2)

PLATE 5: Soyabean harvested field PLATE 6: Ripened Jowar crop

PLATE 7: Field being prepared for sowing PLATE 8: Rock outcrop

These observed training sites were used for supervised classification.



(F) Downloading of GPS data - GPS values were obtained in the projection system of

Geographic Lat/Long. These values were tabulated in MS - excel and then brought to ERDAS-

Imagine where the projection system was changed to UTM to match with satellite image

projection system. Coordinate Calculator, where the input projection and output projection were

first defined and then the values were fed to convert in desired coordinate system through

ERDAS. These UTM coordinates were then saved as a *.dbf file. This file was then called in

ARC-INFO and converted to *.shp file. This sample-gps vector file was later used for training

sites and the validation of the classification of landuse/ land cover.

(G) Supervised Classification for landuse/ land cover – Classes have to be

distinguished in an image and classification needs to have different spectral characteristics. The

principle of image classification is that a pixel is assigned to a specific class based on its feature

vector by comparing it to predefined clusters in the feature space. The supervised (Maximum

Likelihood), remote sensing classification methodologies was utilized for this project. The

classification procedure was carried out by executing the following steps:

(i) Masking of Forest cover and Water Bodies from FSI forest density map:

The agricultural land cover classes often have a tendency to mix up with the forest cover

within the study area. Thus, the forest landuse was beforehand masked out of the Kharif crop

(October). Before the landuse/ land cover mapping was done, a vegetation grid map was

obtained from the Forest Survey of India. These maps were obtained in *.img format, with

Polyconic/ Everest Projection System. The forest density maps were then mosaiced and

reprojected to UTM/ WGS-84 and forest cover and water bodies were masked before processing

the data for agricultural landuse and other classes’ classification. The study area was then

clipped out of the mosaiced forest density map using the district boundary coverage, to obtain

the forest map of the area (Fig 4.2).



Figure 4.2: Forest map of the study area

(ii) Clipping of each district:

After mosiacing, forest cover was clipped for each district. Masking was then performed

to mask out the forest and water bodies separately for every district. This ensured that the forest

and water bodies would not be influenced by the classification. As a result, the chances of

agricultural signatures mixing with the forest signatures were minimized. Only the non-forest

class was clipped out of the satellite data by specifying a code ‘1’, and the remaining classes not

needed for the classification ‘0’ was assigned. This non-forest class was processed under

supervised classification.



(iii) Training set generation for landuse classes:

Based on the field work, a set of 280 training sites with locational and landuse

information were obtained. These training sites were sorted out district wise. Out of them about

10 training sites from each district were taken for accuracy assessment. Using the remaining

190, signatures were generated for each class. The landuse classes that were considered for

generation of the training sites were soyabean, late sown soyabean, harvested soyabean, pigeon

pea, paddy, sugarcane, cotton, fallow and scrubland. For each of the classes, a sufficient amount

of training sites was used for the classification procedure.

(iv) Maximum Likelihood Classification:

The AWiFS October dataset was classified to obtain the kharif landuse. Supervised

classification of the masked FCC of October scene was performed. Multispectral classification

was carried out using MLC algorithm. This scheme was followed because of its ability to

convert spectral classes into information classes from remote sensing data. It proceeded through

the selection of training sites and decision boundary of maximum probability based on mean

vector, variance covariance and correlation matrix of the pixels. Effective classification of

remote sensing image data depends upon separating land cover types into sets of spectral

signatures that represent the data in a form suited to the particular classifier algorithm used.

Supervised classification processes involved the initial selection of training sets on the image,

which represented specific land classes to be mapped. Training sites are sets of pixels that

represent what is recognized as a discernable pattern, or potential land cover class. The

delineation of training sites representative of land cover types is most effective when an image

analyst has knowledge of the geography of a region and experience with the spectral properties

of the cover class. Initial training sites for signature generation were developed from points

obtained from the GPS ground truth data. These points were converted into vectors. However,

statistically, groups of single pixels do not make for good training sites. The more pixels that can

be used in training (within reason), the better the statistical representation of each spectral class

(Lillesand & Kiefer, 1987). In order to attempt to rectify this problem, each ground truth point

was treated as a seed pixel and by means of a proximity analysis was grown out one pixel on all

sides to create new training sites.



Signature separability is a statistical measure of distance between two signatures. It can

be calculated for any combination of bands used in the classification (Table 4.4). It was

determined to be good for all land categories. Separability was calculated employing the

Transformed Divergence measure which gives an exponentially decreasing weight to increasing

distances between the classes. The scale of the divergence values can range from 0 to 2000. As a

rule, if the result is greater than 1900, the classes can be considered good to be separated. Values

ranging between 1700 and 1900 are considered fairly well for separation, whereas below 1700,

the separation is poor.

Table 4.4: Signature separability matrix

Kharif 1

(Soyabean)

Kharif 2

(Harvested

Field)

Kharif 3

(Pigeon Pea)

Fallow Scrubland Water Bodies

Kharif 1 (Soyabean) - 1995 1998 2000 1990 2000

Kharif 2 (Harvested

Field)

1999 - 2000 1993 1992 2000

Kharif 3 (Pigeon Pea) 1994 1990 - 2000 2000 2000

Fallow 1980 1993 2000 - 1990 2000

Scrubland 1990 1980 2000 1999 - 2000

Water Bodies 2000 2000 2000 2000 2000 -

Once ‘good’ separation of the training sites was determined, a supervised, (full

Gaussian) maximum likelihood classification was reprocessed implementing the AWiFS scene

of October. This classification incorporated the pixel values (Digital Numbers) from Bands 4, 3,

1 based upon the signatures generated for each land cover category. The full maximum

likelihood classifier uses the Gaussian threshold stored in each class signature to determine if a

given pixel falls within the class or not. The maximum likelihood classifier is considered to give

better results, yet is a much slower process due to the large number of calculations. However,

the accuracy is largely dependent upon the quality of the signatures. For the scope of this

project, settlements were purposely not included in ground data collection and thus not

incorporated in the Maximum Likelihood Classification. An alternative ancillary vector layer

was incorporated to include the settlement landuse. Likewise, the classification procedure was

repeated for each of the nine districts. Once the classification was completed, they were

individually overlaid with the masked files to include the forest and water body class into the

classification scheme.



(v) Accuracy Assessment:

An accuracy assessment of the land-cover classification map for each district was

performed in ERDAS (Appendix 4). For each district, the set of 10 known training sites kept as

checkpoints for accuracy assessment, were invoked in the process. The classification was

assessed using these points for accuracy assessment of the classification.

It was an important step of the classification process. The goal was to quantitatively

determine how effectively pixels were grouped into the correct land cover classes. Predefined 10

pixels were selected. Then the original image was used along with ground truth to determine the

true land cover represented by each pixel. This ground truth was compared with the

classification map. When the ground truth and classification match, then the classification of that

pixel was considered accurate. Given that enough pixels were checked, the percentage of

accurate pixels gave a good estimate of the accuracy of the whole map. Classification accuracy

in a broad sense refers to the correspondence between classification of remotely sensed data and

actual observations on the field.

An important measure frequently used to assess classification accuracy is Kappa

Coefficient, which expresses the proportionate reduction in error generated by a classification

process compared with the error of a completely random classification. This measure is more

appealing as it considers all elements only, as in the case of overall classification accuracy.

4.2.2 Creation of Soil Database

Soils are one of the most valuable of non-renewable natural resources. To ensure their

sustained utilization, it is indispensable to know their nature, characteristics, extent, qualities,

productive capacity. In order to assess their potential and problems for rational land evaluation,

there is need to have a comprehensive and comparable, at the same time upgradeable

information. Fig 4.1a gives an overview of the process followed in soil database creation, which

have been explained in detail in the following section.

(i) Digitization of Soil map - Soil map (1:500,000 scale) obtained from NBSS & LUP was used

to create digital soil map coverage of the study area. The soil maps (hard copy) were scanned,

georeferenced and the digitized t create vector soil map.



(ii) Map unit wise attribute database generation of soil and land characteristics - The soil

and land characteristics of each of the mapping units were stored in the associated attribute table

(Appendix 9).

(iii) Review of literature to collect soil information of the area - Soil information of 65

number of soil data were also collected from relevant literatures including soil series reports of

Madhya Pradesh (NBSS & LUP), IMSD Soil Mapping Reports for different districts, Journal of

Indian Society of Soil Science, Agro-pedology Journals. A pedo-transfer equation too was

obtained from the literature, which was used for the Available Water Holding Capacity (AWHC)

computations for all the soil samples collected trough literature and fieldwork.

(iv) Collection of soil samples during fieldwork - Besides this, 29 locations were observed to

collect soil samples of 0 – 15cm, 15 – 30cm, 30 – 50cm and > 50cm soil depth.

(v) Soil Sample Analysis - These samples were analysed for soil texture. Soil clods of surface

soils were also collected from these sites to analyze bulk density of soil.

(a) Bulk Density – the procedure to obtain the bulk density is described as below:

•

•

•

•

•

•

The clods collected from filed were oven dried for 24 hours at 28° C

These clods were weighed and then tied up and dipped in hot wax

A measuring cylinder of 500 ml was taken and filled with water till 300ml mark

The wax covered clod was immersed into the cylinder and raised water level was

recorded

The difference in the initial and final reading of water in the cylinder was the volume of

soil clod

Finally bulk density was calculated as: )()(

ccclodofVolumegmclodofMass

BD =

(b) Soil Texture – Texture analysis had been performed by hydrometer method in the following

steps:

•

•

Hydrogen peroxide treatment was done to remove the soil organic carbon

15 ml of sodium hexa-meta-phosphate solution was added to the samples



•

•

•

They were then electrically stirred and transferred to 1000 ml cylinders

The first reading (‘IR’ by Hydrometer) was noted down for sand percentage calculation.

i.e. sand % = 2*(50-IR). After 2 hours the second reading (IIR) was noted down for the clay

content i.e. clay % = 2*IIR. The temperature correction factor was not considered, as it was less

than 20° C throughout observation.

Silt percentage was calculated by [100 – (sand % + clay %)].

(vi) Available Water Holding Capacity (AWHC)

In order to obtain the water retention characteristics, to estimate the available water

holding capacity (AWHC), a pedo-transfer equation was used. The pedo-transfer equations used

in the study have been described by Gajbhiye (1990) for the vertisol soils of Maharashtra.

This equation was used to obtain the water retention characteristics of all the soil

samples that have been collected through literature survey and field survey. Once this was

accomplished, the available water was then computed by differencing the water at wilting point

from the field capacity. The available water holding capacity (AWHC) was estimated by:

AWHC (mm/ cm) = (FC – WP)* Depth of soil (cm)*10

The available water holding capacity for the soil series were computed. The depth class

considered in the computation was derived from the NBSS & LUP report, with some

modifications. The depth classes taken were 0 – 25 cm, 25 – 75 cm, 75 – 100 cm and 100+ cm.

The class of 50 – 75 cm as given by the report was clubbed along with 25 – 50 cm class, as its

occurrence was over a very limited area. Thus the final AWHC computation was done using

these four depth classes. For each soil series, the layer wise AWHC values were added up.

Based on similar depth class and texture property of the soil series, the AWHC values were then

averaged out. These average values were then allocated to the soil-mapping units, based on the

location specific depths and texture.



(vii) Generating Simulation Units

The soil-mapping units compiled were further refined to generate simulation units to be

used for further study. Firstly, those soil units were identified, which were likely to be

influenced by the same weather station. This was based on the proximity of a station to the

concerned soil unit. Altogether 13 weather stations were identified and under each station, a

group of soil-mapping units was identified. Depending on the texture and depth similarities, the

final simulation units were narrowed down (Appendix 15).

4.2.3 Climatic Database

The raw data for daily maximum and minimum temperature, rainfall data were obtained

for twenty eight stations for the entire Madhya Pradesh from the IMD, New Delhi.

Meteorological elements like temperature, wind speed, humidity, sunshine govern the reference

evapotranspiration (ET0), but due to unavailability of all the parameters for all the concerned

stations, only the temperature data has been considered for computation of the potential

evapotranspiration (PET) based on the Thornthwaite method.

(a) Database creation in Excel format

The daily temperature and rainfall data over a period of 24 years (from 1980 to 2003)

were converted to *.txt files to make them compatible for use in MS-Excel or MS-Access

depending upon the data volume. The parameters were then organized in MS-Excel for all the 28

stations.

The rainfall data was compiled to decadal (10 days) for each of the 28 stations for a

period of 24 years (1980 to 2003) and then a 24 years average decadal rainfall data was

compiled for the decadal rainfall over the entire state.

The daily temperature data (max & min) was then compiled to mean monthly

temperature data for computation of PET. The daily rainfall data was compiled to mean monthly

data by adding up.



(b) Calculation of PET

Monthly PET for a period of 24 years from 1980 to 2003, for all the 28 stations was

calculated using the standard Thornthwaite method. Then a 24 years average PET was also

compiled. It is a widely used method for estimating potential evapotranspiration was derived by

Thornthwaite (1948), who correlated mean monthly temperature with evapotranspiration as

determined from a water balance for valleys where sufficient moisture water was available to

maintain active transpiration. In order to clarify the existing method, the computational steps of

the Thornthwaite equation are now discussed.

Step 1: the annual value of the heat index I is calculated by summing monthly indices over a 12

month period. The monthly indices are obtained using:

i = (T/5)1.514

I = ∑=

12

1jji

in which ij is the monthly heat index for the month j (which is zero when the mean monthly

temperature is 0 °C or less), Ta (°C) is the mean monthly air temperature, and j is the number of

months (1–12).

Step 2: The Thornthwaite general equation calculates unadjusted monthly values of potential

evapotranspiration ET0p (mm) based on a standard month of 30 days, with 12 hours of sunlight

per day.

PET = ( )aIT10××16

T = mean air temperature (°C)

a = 492390017920000077100000006750 23 .... +×+×−× III

The value of the exponent ‘a’ in the preceding equation varies from zero to 4.25 (Jain and Sinai,

1985), the annual heat index varies from zero to 160, and ET0 is zero for temperatures below 0

°C.



The PET (mm/ month) was estimated using the Thornthwaite Equation, which is one of

the best methods of PET estimation when the lack of numerous data sets for PET computation

becomes an obstacle. This equation considers the maximum and minimum air temperature as the

minimum data requirement for PET estimation. To obtain the daily PET, the following formula

was used:

PET (mm/ day) = [k * PET (mm/ month)] / Number of days in a month

k = the latitude-wise correction factors for which values are given by Michael (1978).

This factor is determined by the maximum possible duration of sunlight in Northern

Hemisphere and is expressed in units of 30 days of 12 hours each (Table 4.5). The adjustment

coefficients at 5° interval was used to compute the adjustment co-efficients at 1° interval by

interpolation (Table 4.6).

Table 4.5: Adjustment Co-efficients at 5° interval (k)

Latitude Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

20° N 0.95 0.90 1.03 1.05 1.12 1.11 1.14 1.11 1.02 1.00 0.93 0.94

25° N 0.93 0.89 1.03 1.06 1.15 1.14 1.17 1.12 1.02 0.99 0.91 0.91

Source: Land and Water management Engineering (V.V.N. Murthy, 2002)

Table 4.6: Adjustment Co-efficients at 1° interval (k)

Latitude Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

20° N 0.95 0.90 1.03 1.05 1.12 1.11 1.14 1.11 1.02 1.00 0.93 0.94

21° N 0.946 0.898 1.03 1.052 1.126 1.116 1.146 1.112 1.02 0.998 0.926 0.934

22° N 0.942 0.896 1.03 1.054 1.132 1.122 1.152 1.114 1.02 0.996 0.922 0.928

23° N 0.938 0.894 1.03 1.056 1.138 1.128 1.158 1.116 1.02 0.994 0.918 0.922

24° N 0.934 0/892 1.03 1.058 1.144 1.134 1.164 1.118 1.02 0.992 0.914 0.916

25° N 0.93 0.89 1.03 1.06 1.15 1.14 1.17 1.12 1.02 0.99 0.91 0.91

ET is commonly estimated from ground meteorological data with available land cover

information through the conventional “reference ET - crop coefficients” approach. The

meteorological variables driving the physical process of ET are readily available through routine

monitoring networks such as National Climatic Data Center (NCDC). Established methods

(Jensen et al. 1990) exist to calculate point-specific reference ET from point meteorological

data. With the support of GIS technology, it is possible to extend these methods to get spatially

distributed ET over a region.



The monthly PET was then used to derive a 24 years average decadal (10 day) PET for

each 28 stations over the entire state. All together 36 decads of PET and rainfall were obtained.

c) Spatial Interpolation of PET and Rainfall data

Climatic station data were stored as point data in GIS software. These point information

were interpolated to prepare spatial climatic database (PET and rainfall). The interpolation of the

scattered spatial data is a general problem with specific solutions. They allow the provision of an

interpolated value of a variable for any location within the region of interest. In the study, FAO

New LocClim 1.03 was used for the spatial interpolation of climatic data.

Fig 4.3: FAO New LocClim 1.03

The technique adopted for the previously mentioned study was Thin Plate Splines, which

is a radial basis function, which gave the mean squared difference in the observed values as a

function of the distance between the locations of observations. The main condition is the

minimum of curvature of the deterministic part. It refers to a physical analogy involving the

bending of a thin sheet of metal. It gives a smooth interpolation. FAO New LocClim allows only

Thin Plate Splines to reproduce the observations exactly. The Workbench mode was selected as

it allows interpolation of agro-climatic data. The spatial climatic database creation is explained

as follows:



(i) 24 years average monthly PET and Rainfall (by combining year-wise monthly PET &

Rainfall) were arranged for each of the 28 stations as the input. The input files have been

imported from the created *.csv files. In this case, the column order of “Longitude, Latitude,

Altitude, 12 Values” was chosen. The values are usually calculated for a location with pre-

defined grid size. Once the data has been evoked, the grid specifications and the interpolation

methods were finalized. The study area extents were demarcated for the entire Madhya Pradesh

state and the grid size was given to be 0.085°, which is approximately 10 km. This interpolated

the data for 12 months. The interpolated gridded data (output *.csv files) were saved as ASCII

files (*.dat) to create surfaces from them in ERDAS imagine. This resulted in 12 PET surfaces

and 12 rainfall surfaces. These surfaces were then projected to Geographic Lat/ Long, WGS 84

and then re-projected to UTM (WGS-84) and the study area was clipped out of these re-

projected surfaces one by one.

Fig 4.4 : Input parameter specification in FAO New LocClim 1.03

(ii) For the given area, the 36 ten-day composite of PET (24 years average) and Rainfall

database for all the stations were arranged individually as decadal inputs. Finally 36 ten-day

composite PET input files and Rainfall input files were arranged. The input files have been

imported from already created *.csv files. Here the column order of “Longitude, Latitude,

Altitude, Value” possibility was chosen as it most aptly suited the input data structure.



The interpolated files were again saved as ASCII files (*.dat) to create surfaces from

them in ERDAS imagine. This resulted in 36 PET surfaces and 36 rainfall surfaces. These

surfaces were re- projected to Geographic Lat/ Long, WGS 84 and then re-projected to UTM

(WGS-84) and the study area was clipped out of these re-projected surfaces one by one.

4.2.4 Data Analysis

a) Length of Growing Period Estimation

The growing period defines the period of the year when both moisture and temperature

conditions are suitable for crop production. The growing period provides a framework for

summarizing temporally variable elements of climate, which can then be compared with the

requirements and estimated responses of the plant. Such parameters as temperature regime, total

rainfall and evapotranspiration and the incidence of climatic hazards are more relevant when

calculated for the growing period, when they may influence crop growth, rather than averaged

over the whole year. The estimation of growing period is based on a water balance model, which

compares rainfall (P) with potential evapotranspiration (PET). If the growing period is not

limited by temperature, the ratio of P/PET determines the start, end and type of growing period.

The determination of the beginning of the growing period is based on the start of the rainy

season. The first rain falls on soil which is generally dry at the surface and which has a large soil

moisture deficit in the soil profile. LGP analysis is based either on average climatic data or on

historic data for individual years. Most early AEZ studies calculated LGP based on average

monthly rainfall and PET. While this approach may be acceptable for broad scale regional

studies.

(i) Spatial LGP

The estimation of growing period is based on a water balance model, which compares

rainfall (P) with potential evapotranspiration (PET). The determination of the beginning of the

growing period is based on the start of the rainy season.



Figure 4.5: Length of Growing Period (Source: FAO Soils Bulletin, 73) [B = beginning of growing period; BH = beginning of humid period; EH = end of humid period

ER = end of rainy season; E = end of growing period; P = rainfall; ET = potential evapotranspiration]

There are obvious differences in plant response depending on whether the growing

period is continuous, or whether it is broken into shorter periods of moisture availability

separated by dry periods. Fig 4.5 demonstrates a graphical method of computing LGP under

normal conditions. The number of LGPs is therefore an important consideration in agro-

ecological zone definition. LGP analysis is based either on average climatic data or on historic

data for individual years. The long-term climatic data for the stations spread throughout the

state, published by IMD were used to work out the water balance and calculating a general LGP

for the area.



CLIMATIC DATA (Max - Min Temp & Rainfall)

(IMD) 24 Years Average 24 Years Average

Monthly PET Daily Rainfall

36 Ten-Day 36 Ten-Day Composite PET Composite Rainfall

INTERPOLATION IN NEW-LOCCLIM(Thin Plate Splines)

10 Km Grid Size

PET Surfaces (ERDAS IMAGINE)

Rainfall Surfaces (ERDAS IMAGINE)

Spatial Modeler (“PET Surface / 2”)

36 Ten-Day Composite 0.5*PET

Spatial Modeler [(Rainfall >= 0.5*Pet) 1, (Rainfall < 0.5*Pet) 0]

36 Ten-Day CompositeLGP Surface

Spatial Modeler(Summing)

SPATIAL LGP SURFACE

Fig 4.6: Methodology of spatial LGP estimation



From the previously calculated daily PET, a ten-day composite PET (mm) was

computed. This was done for 28 stations throughout Madhya Pradesh and for a 24-years average

since 1980 until 2003. Fig 4.6 elucidates the complete methodology to arrive at the desired

spatial LGP. The ten-day composite Rainfall (mm) was however, estimated from the daily

rainfall values as provided by the IMD climatic dataset. For each year, 36, ten-day composite

values were computed for each station. Then for each station, a 24 years average ten-day

composite was estimated. Once the computations were completed, these station estimates for the

ten-day composite Rainfall and PET were interpolated in New Loclim using the Thin Plate

Splines method for an approximately 10km grid size. The output were saved as ASCII files

(*.dat) to generate Rainfall and PET surfaces for 36, ten-day composite, using ERDAS. These

surfaces were then reprojected to UTM/ WGS 84. These surfaces were converted to 0.5*PET

surfaces using the spatial modeler in ERDAS (Fig 4.7).

FIG 4.7: Model for determining 0.5* PET



This resulted in 36, ten-day composite of 0.5*PET surfaces. Next, a simple water balance

procedure was applied (Fig 4.8), which is a balance between water supply (Rainfall) and water

need (PET), to compute the LGP. For each of the 36, ten-day composite period, the LGP as

computed using the basic concept of,

RF >= 0.5*PET, then LGP = 1

RF < 0.5*PET, then LGP = 0

This condition was run through a model in spatial modeler, to compute LGP for each of the 36,

ten-day composite.

FIG 4.8: Model for determining LGP

Once this was completed, all the 36 resultant LGP surfaces were added up to obtain a

general spatial LGP map. The LGP map was classified to obtain five classes and one pixel (10

km grid size) representing each of the classes was chosen to plot the LGP graphically.



(ii) Crop Specific LGP

The effectiveness of early rains increases considerably once rainfall is equal to, or

exceeds, half of Potential Evapotranspiration. The growing period continues beyond the rainy

season, when crops often mature on moisture reserves stored in the soil profile. Soil moisture

storage must therefore be considered in defining the length of the growing period. While

standardization among crops may be permissible in a regional study where a number of crops

are considered, information on soil available water holding capacity (AWHC) can usually be

inferred from the soil inventory, and its inclusion in the moisture balance would improve the

accuracy of LGP prediction.

Fig 4.9: BUDGET Program



in the study consisted of monthly PET and Rainfall observations.

Using the BUDGET program (Fig 4.9), the monthly data were processed and the daily actual

evapotranspiration (ETa) and potential evapotranspiration were computed (ETpot). These

computations were used to derive the crop specific LGP by a simple program run in MS-Excel.

By visualizing the variation of the soil water content in the profile during the simulation process,

the program is also valuable as a didactic tool.

The input consists of:

1. Climatic Data: for each day of simulation period, the program requires information

- total length of growing period

- Kc for mid season and late s

The climatic data

a) Input

concerning the weather conditions. Those conditions determine the amount of water that

can be extracted from the soil profile, by soil evaporation and crop transpiration. The

climatic conditions are given by –

• evaporative demand of the atmosphere, which is given by PET;

• rainfall;

The input data consisted of mean monthly PET (24 years average) and Rainfall

observations. In the study, simulations run with climatic data of mean average, not linked to any

specific year. The 24 years average monthly data have been specified and saved as ‘ET0’ and

‘PLU’ files for PET and rain data respectively. At run time these monthly data were processed to

derive daily data.

2. Crop Data: to specify the crop parameters, new sets were created for specific crops.

Each of these was saved as files with extension ‘CRO’. Parameters describing crop

development and root water uptake were as follows

- class of crop type (annual or perennial)

eason (Appendix 17).



-

tivity class. It determines the Readily Available

Water, which is the maximum amount that a crop can extract from its root zone without

water stress.

e of ground cover at maximum crop canopy: Under normal field

conditions, complete groundcover is valid for most crops. Under this condition, the soil

evaporation and the maximum crop canopy [LAI] at a balance. This factor would affect

the partitioning of ETcrop in soil evaporation and crop transpiration.

- Class of sensitivity to soil salinity: It is considered from the indicative values for various

crops as suggested. Salts in water solution reduce crop transpiration by making the soil

wat

- Sen ages and yield response factors: The yield response to water deficit for

various sensitivity stages has been specified. This is used to convert the water stress in

nce, flowering and early yield formation than during vegetative period or

ripening. Indicative values for Ky factor for various crops and sensitivity stages have been

con

for Ky

Specification and selection of the appropriate parameters mentioned above completes the

cre o

the simulation units, by combining the

soil-mapping units (NBSS & LUP) having same texture and depths. For each of the 13

weather station, these simulation units have been considered as the soil files saved with

‘SOL’ extension.

Class of rooting depth: For fully developed crops, the classes range from shallow rooted

crops to very deep-rooted crops. The rooting depth has been specified during crop file

creation, from the indicative values for the crops (Appendix 19).

- Class of sensitivity to water stress: The crop specific ‘P’ values that are the fraction of

Total Available Water have been considered from the indicative values and then the crop

has been assigned to the specific sensi

- Class of degre

er less ‘available’ for plant root extraction.

sitivity st

estimates of yield depression. For each stage, the length and corresponding Ky factor are

specified. The higher the Ky value, the more sensitive the crop is to water deficit.

As Doorenbos and Kasam (1979) remarked, crops are more sensitive to water deficit

during the emerge

sidered from the given Appendix 18. In case of data unavailability, default value ‘1’ is used

for each stage.

ati n of crop parameters.

3. Soil Parameters: The soil files were generated as



parame

layers each of the layers. Soil

typ a

4. Program Parameters: The soil compartments and effective rainfall parameters were

adjusted before the simulation was begun.

- depth, the profile was split into compartments with corresponding thickness

-

5. opped period. Here

n the water content would be close to

Specifications and selection of appropriate parameters creates a complete set of soil

ters. The parameters that have been addressed are the soil layer, where the number of soil

has been specified along with the soil texture and thickness for

e w s selected from a list, which is associated with typical hydraulic properties.

Based on the

for each compartment, which had the hydraulic property of one soil layer.

Effective rainfall was determined by the USDA-SCS procedure.

Simulation: In this section, the simulation period was linked to the cr

the initial conditions for the simulation run were adjusted. The initial soil water content

of the total profile had been set at wilting point. This was keeping in mind the fact that

the simulation began with the monsoon, whe

wilting point. The initial conditions in the soil are strongly determined by the climatic

conditions.



Water Balance Calculation Procedure adopted in BUDGET Program

In the simplified model, root zone (Fig 4.10) is considered as a single r

eservoir. By

keeping track of the incoming and outgoing water at the edges of root zone, the amount of water

retained was calculated by means of a soil water balance.

Figure 4.10: Root zone as a reservoir

To explain the retention, movement and uptake of water in the soil profile throughout the

growing season, simulation models commonly divide both the soil profile and time into small

portions. As such, the one-dimensional vertical water flow and root uptake was solved using a

finite difference technique (Carnahan et al., 1969; Bear, 1972). A mesh of grid lines with

spacing ∆z and ∆t (Fig. 4.11) is established throughout the region of interest occupied by the

independent variables: soil depth (z) and time (t). The depth increment in the program is by

default 0.1m and the time increment is fixed at one day. The water extraction by plant roots is

solved for each node at different depths zi and time levels tj so that the dependent variable – the

moisture content θ i, j is determined for each node of the solution mesh and for every time step.



Figure 4.11: A time (t) - depth (z) grid [Source: BUDGET Reference Manual]

The differential flow equation is replaced by a set of finite difference equations, written

in terms of dependent variable θ (Fig. 4.12). The simulation begins with the drainage of the soil

profile.

Since the soil water variation calculated depends on the actual soil water content, the

calculation sequence might theoretically influence the simulation results. However, as the time

step was confined to a day and the response of the process of water flow at particular soil water

ontent is different from the res ke and water extraction, it was

assumed that the sequence has nearly any affect on the simulation results. In a well-watered soil,

when drainage is important, evaporation and transpiration will be at their potential rate, and

hence independent from the soil water content. In dry soil, the evapotranspiration rate depends

on the actual soil water content but at that moment, soil water flow is extremely insignificant.

c ponse of the process of water upta



Figure 4.12: Calculation scheme of the model [Source: BUDGET Reference Manual]

stimation:

Runoff E

The estimation of amount of rain lost by surface runoff is based on the curve number

method developed by the US Soil Conservation Service (USDA, 1964; Rallison, 1980;

Steenhuis et al., 1995):

SPRO

]).([ 20 2−=

1100254

CN

SSP ).( 20−+

−=S

Where, RO = amount of water (mm) lost by runoff

P = rainfall amount (mm) (0.2) S = amount of water that can infiltrate prior to runoff

S = potential maximum storage (mm) CN = Curve Number

++ += θ i, j-1 θ i, j Evaporation Sub-routine ∆θi, dt

E (act)

E (pot)

Transpiration Sub-routine ∆θi, dt

T (act)

T (pot)

Drainage Sub-routine∆θi, dt

Runoff Sub-routine

Rain

Runoff

Drain



soil gets igh

CN will hav runoff.

By

isture classes

f from all

Effective R

Effective rainfall is that part of rainfall that is stored in the root zone and not lost by

runoff. As t e period is

unknown and the water lost cannot be determined by water balance equation on daily basis.

Rain that falls on unsaturated soil infiltrates, incr e soil water content until the

saturated (P=0.2S), after which additional rainfall becomes runoff. A soil with a h

e a small potential storage (S) and may loose a large amount of rainfall as

The storage capacity of a soil is larg ller CN value) if it is, dry than when it is wet.

linear interpolation between corresponding CN values at various antecedent mo

(AMC), CN was adjusted to the wetness of topsoil. When calculating the wetness, the default

CN value corresponding to AMC-II is considered. It is the preferred value as the AMC-I and

AMC-III are considered to be rr to esc tu of s of

sources, including soil moisture.

ainfall Estimation:

easing th

er (sma

‘E or bands’ d ribe depar re urface run

he rainfall data consists of monthly values, the rainfall distribution over th

Figure 4.13: Partitioning of rainfall in effective rainfall, surface runoff & deep percolation [Source: BUDGET Reference Manual]

After subtracting rainfall amount lost by runoff, the effective rainfall is estimated by the USDA-

storage capacity of the root zone was used to determine the effective rainfall (Appendix 16).

Simulations with rainfall data from various climatic zones indicate that the procedure predicts

effective rainfall with an accuracy of ± 20%.

SCS procedure. A daily soil water balance incorporating crop evapotranspiration, rainfall and



Crop Evapotranspiration:

ET crop is the evapotranspiration from a well-watered soil under given climatic

conditions. It is calculated by multiplying ETo by the crop coefficient Kc. The Kc integrates the

effect of characteristics that distinguish the cropped surface from reference surface. It combines

the effect of soil evaporation and crop transpiration.

In the program, ETcrop was calculated by assuming that the soil surface is wet from rain.

ETcrop is the sum of the maximum amount of water that can be lost by soil evaporation (Epot)

and by crop transpiration (Tpot):

ET crop (ETpot) = Epot + Tpot

he ma

Where, f and c are regression coefficients and LAI is the Leaf Area Index (m2.m2). With f = 1

and c = 0.6 to 0.7 acceptable estimates of the potential soil evaporation may be obtained. The

potential crop transpiration (Tpot) was calculated by subtracting Epot from ETcrop.

Water lost at the soil surface by evaporation is extracted from the topsoil. Since water at

surface is lost faster that at the bottom, weighting factors (fwz) were introduced. The sum of the

weighting factors is equal to one. These factors determine the part of the atmospheric demand

(Epot) that might be extracted from a particular soil depth. If at that depth the soil is wet, the

requested amount extracted (α = 1). However, if the water content drops below threshold, water

cannot b

water content at wilting point. The actual soil evaporation (Eact) is obtained by integrating the

T ximum amount of water that might be lost by soil evaporation (Epot) was estimated by

means of a Ritchie-type equation (Belmans et al., 1983):

ETcropefEpot cLAI ** −=

e extracted at that depth (α = 0). The threshold value, which is air dry, is half of the soil

following equation over the entire topsoil (z).

dzEpotEact **fw* z∫= α



tion is extracted out of root zone. The root extraction term, S, is

the amount of water that is extracted by the roots per unit of bulk volume of soil, per unit of time 3.m-3

Where, Si = sink term (m .m .day ) of soil compartment i

Ks = water stress factor (dimensionless) Smax = maximum sink term (m .m .day )

quation over the entire rooting depth, the actual transpiration

rate (Tact) was obtained. The sum Σ(Si*dz) can never exceed Tpot. In this program, uniform

water uptake by plant roots over entire root zone was selected to define the maximum sink term.

culated Smax should always be smaller

than specified maximum value. This implies that the water extraction over the entire root zone is

uniform (when Ks = 1).

ce it is rare

that rainfall is homogenously distributed over all the days of the month, some processing was

required to determine the amount of rainfall that is stored in the top soil as effective rainfall, lost

by surface runoff. The water stress factor (Fig. 4.14) is equal to one in the range between the

anaerobiosis point and threshold soil water content as in the following figure. When the soil

water content is above the anaerobiosis point, the root zone is water logged and Ks is smaller

than one (after a time lag determined by the user).

Water lost by transpira

(m .day-1). S, depends on a maximum sink term, Smax, and on the water stress factor Ks:

Si = Ks*Smax

3 -3 -1

3 -3 -1

By integrating the above e

Feddes et al., (1978), proposed this solution method. In this case, the Smax is calculated by

dividing the Tpot with actual rooting depth. However, cal

By weighing the evapotranspiration rates in previous month, daily ETo rates were

obtained in the BUDGET program. The calculation was based on the interpolation technique

proposed by Gommes (1983). The same holds for the rainfall data too. However, sin



[Source: BUDGET Reference Manual]

The threshold value below which Ks becomes smaller than one is determined by the soil

The actual crop evapotranspiration (ETact) is the sum of the actual soil evaporation and actual

crop transpiration:

ET act = Eact + Tact

ETact is smaller than or equal to ET crop (ETpot).

Figure 4.14: Water stress factor (Ks)

water depletion fraction for no stress (p). The p factor divides the Total Available soil Water

(TAW), in two parts: water that can be extracted without stress (RAW) and water that is more

difficult to extract. The threshold value is not a unique value but varies as a function of the

evaporation demand. If the demand is low, p is somewhat larger then when it is high and hence

more soil water can be transpired without inducing crop water stress. When the soil water

content is below the threshold value, the water extracted is limited (Ks < 1) and the crop will be

under water stress. The crop stress increases as the soil water content decreases. Water

extraction becomes zero at Wilting Point.



b) Relative Yield Estimation:

Doorenbos and Kassam (1979) empirically derived yield-response factors (Kyi) for

individual growth stages (i.e. establishment, vegetative, flowering, yield formation and ripening

period) and for the total growing period. These factors are yield response factors for water stress

in specified growth stage (i) and over total growing period of crops and are given by:

−=

−

i

ii ETcrop

KyYmYa 11 ETact

Where, Ya is actual harvested yield, Ym is the maximum crop yield under given management

conditions that can be obtained when water is not limiting, ETacti is the actual

evapotranspiration and ETcropi is the evapotranspiration for non-limiting water conditions

during the ith stage of growth a r to water stress. This equation

is valid for most crops for water deficits in the range (1 – ETact/ ETcrop≤0.5).

avoid this affect, water was not considered when the potential transpiration rate is smaller than

the specified value. As water stress could not be considered as constant throughout the growing

inadequate to produce the expected total relative yield depression. In order to combine the effect

of several periods of water stresses over a growing period, the effects were combined by means

nd Kyi is the yield response facto

In the study, water stress was expressed as relative transpiration (Tact/ Tpot) it may

result in large effects of water stress on yield during periods of low transpiration. In order to

period, but occurs with different magnitude at different periods, the previous equation would be

of the multiplicative approach (Jensen, 1968; Hanks, 1974):

N

∏

−−=ia

iy ETET

KYmYa 11 ,

, =

Where, П stands for the product of the N functions (sensitivity stages) between the square

brackets.

i ic1 ,



LGP Estimation

With the given inputs already described and for the initial conditions, the program

simulated information on:

- Actual amount of water lost by soil evaporation (Eact)

- The potential amount of water that can be lost by soil evaporation (Epot)

- Actual amount of water lost by crop transpiration (Tact)

Half-Potential

Evapotranspiration,"1","0")}. Then using this value as a logical test, true values identified by

- The potential amount of water that can be lost by crop transpiration (Tpot)

Once the simulations were completed, the soil water balance files were saved as excel

files. At first, half of actual ET was computed. Then the growing period was computed by using

a simple function of “IF” syntax {IF (Actual Evapotranspiration >=

“1” and false values identified by “0” were obtained. Finally, these values were added up to get

the total period for which the water will be available in soil to encourage crop growth. The soil-

mapping unit wise LGP was then assigned, based on these computations of LGP for the

simulation units and a crop specific LGP was spatially generated using four classes (< 75 days,

75 – 125 days, 125 – 150 days and > 150 days).



The following flow diagram (Fig 4.15) shows how the crop specific LGP was estimated

Fig 4.15: Methodology of estimating LUT (crop) specific LGP

4.2.5 Climatic Potential Yield Estimation (Water Limited Yield Potential)

Crop production under optimum condition of adequate water supply determines the yield

potential of crop. Crop production determined by water availability can be described in terms of

“water-limited yield”. Among the potential yield estimation, water-limited yield potential is

more pragmatic indicator of biophysical yield potential. It can be quantified as possible

attainable yield under varying water scarcity, considering all other factors of production at their

optimum level. It followed multiplicative Stewart’s formula to assess relative yield potential

assuming production potential as 100. It indicated how well a crop cycle fits within the available

total LGP and how well crop water requirements were met by ratio of ETa and ETpot.

by using the program simulated output of ET-pot and ET-act.

LUTs (KHARIF)

(Max - Min Temp & Rainfall) (IMD)

CLIMATIC DATA

Thornthwaite Method

Simulation Units (Grouping of Weather Station wise Similar Depth and Textured

ETact (Daily) ETpot (Daily)

0.5 * ETpot

LUT SPECIFIC LGP

CONDITION IF (ETact >= 0.5 *ETpot,"1","0")

Soil – Water Balance

MS-EXCEL

Crop Coefficient (Kc)

BUDGET SOFTWARE

Soils)

SOIL DATA (NBSS & LUP)

Sorghum Pigeon Pea Soyabean

PET (mm/ month)

Rainfall (mm/ month)



A crop soil-water balance program was used in the study to compute actual

evapotranspiration for simulation units along with their Expected Relative Crop Yield (Y %). for

the three specific crops. The moisture-limited yields of annual rainfed crops have been

calculated by applying crop stage specific and total growth period yield reduction factors in

accordance with procedures developed by FAO. As the program gene Response

percentage to water stress, an average water limited yield potential was determined for each of

the crop specific simulation unit. Based on these, the soil-mapping units were allocated

respective yield potential percentages. Using the same, a crop specific spatial water limited

poten sui ap was generated using three yield p 5 % -

Highly Suitable; 55 – 75 % - Moderately Suitable; < 55 % - Marginally Suitable). This

outcome gave complete sequence of the that is essential for the crops under

consideration. Thus, it was used as the p itability factor to be accorded along

with the soil-terrain suitability factor, to establish the final crop suitability analysis.

4.2.6 Soil Suitability Evaluation

Soil is the most important resource base for crop production in rainfed agro-ecosystem.

As each crop requires definite soil and sit um growth, the analysis of

soil site suitability for specific crop becomes a necessity. The objectives of soil-site suitability

evaluation are in perspective to predict and classify land for crop growth. Based on the FAO

(1976) Land Evaluation Framework, a so luation was performed.

a) Soil Productivity

lity for a defined

type of use in a specific way. An interesting example of systematic land-quality upgrading and

grading is the ‘Soil Productivity Rating’ introduced by FAO. An ‘Index of Productivity’ is

rates Yield

otential classes (> 7tial yield tability m

climatic suitability

erfect climatic su

e conditions for its optim

il-site suitability eva

Index:

Land quality is a complex attribute of land that influences land suitabi

calculated on amount of various physical and chemical factors making up the quality.



A soil productivity index of the study area was prepared by using the Riquier index

model (1976). In this, rating nine properties were taken into account, each being rated on a scale

from 0-100. It’s an index obtained by multiplying the productivity ratings by each other. A soil

database stored in MS-excel was used to rate the soil characteristics based on the criteria

described by Riquier (1976). Soil attribute for each soil-mapping unit was stored in tabular

database. For each mapping unit the ratings for individual parameters have been enlisted.

SPI = H*D*P*T*N or S*O*A*M

Where:

H: soil moisture rating; D: drainage conditions rating; P: effective soil depth rating

T: texture rating; N: base saturation rating; S: soluble salts rating

O: organic matter rating; A: CEC of clay mineral rating; M: mineral reserve rating

The soil moisture (H) has been rated (0-100) based on the Length of Growing Period

Table 4.7: Rating for Soil Moisture Factor (H)

3 30 – 60 20

60 – 90 40

90 – 120 50

6 120 – 150 60

The drainage factor

prevailing over the study area (Table 4.7).

Sl.No. Length of Growing Period (Days) Ratings

1 < 15 5

2 15 – 30 10

4

5

7 150 – 180 70

8 180 – 210 80

9 210 – 240 90

10 > 240 100

(D) rated based on the soil drainage parameter. The soil drainage

quantification was done based on those available for the soil units of the area as described in the

NBSS & LUP soil report. Based on this classification, the drainage ratings were assigned as

follows (Table 4.8).



Table 4.8: Rating for Drainage Factor (D)

Sl.No. Description Code Drainage Class Rating

1 Marked Water Logging 0, 1 Extremely Poor, Very Poor 10

3 Moderate Water Logging 3 Imperfect 40

4 Water Logging for (8 days – 2 months) 4, 6, 7 Moderately Well, Somewhat Excessive, Excessive 80

5 Good Drainage 4, 6, 7 Moderately Well, Somewhat Excessive, Excessive

2 Soil Flooded for (2 – 4 months) 2 Poor 40

80

6 Water Logging for < 8 days 5 Well 90

While rating the soil depth factor

(P), the following depth ratings were taken into

s per NBSS & LUP (Table 4.9). The soil depth affects the drainage, aeration and water

Fa or (P)

De

2 Very Shallow Soil 1 10 – 25 20

4 75 – 100 5 > 100 100

The soil texture factor

account a

retention properties.

Table 4.9: Rating for Soil Depth ct

Sl.No. Description Code pth (cm) Rating 1 Rock Outcrop with Extremely Shallow Soil 0 < 10 10

3 Shallow Soil 2 25 – 50 50 4 Slightly Deep soil

Moderately Deep Soil 3 50 – 75 80

5 Deep Soil

(T) provided a measure for the permeability and water retention

capacity. The stoniness affects soil workability. In order to rate the texture factor (Table 4.10),

the particle size class along th the surface s ss parameters were also put into use.

Table 4.10: Rati exture Factor (T)

Sl.N D ting 1 Fragmental 2 Sandy Skeletal 3 Loamy Skeletal % stoniness 4 Loamy Skeletal stoniness 5 Clayey Skeletal with > 30 % stoniness 6 Clayey Skeletal with 15 – 30 % stoniness 30 7 Sandy with > 30 % stoniness 20 8 Sandy with < 30 % stoniness 30

10 Coarse Loamy 50

12 Coarse Silty 70

14 Clay 90 15 Fine 100 16 Very Fine 100

wi tonine

ng for T

o. escription Ra10 10 20 with > 30

with 15 – 30 % 30 20

9 Loamy 40

11 Fine Loamy 60

13 Fine Silty 80



The soil unit wise base saturation (N) was rated (Table 4.11). Base saturation varying

from < 15 % to > 75 % or soil with > 20 – 30 % CaCO3 is scaled from 0 to 100. However since

e area under study has < 15 % calcareousness, it has not been considered while rating.

Table 4.11: Rati B

o. Descr Rating Soil with se sa 5 % 40

wit at3 Soil wit at4 Soil wit at

Soil with se sa n > 75 % 100 6 Soil excessively calcareous (> 20 – 30 %) 80

th

ng for ase Saturation Factor (N)

Sl.N1

iption turation < 1 ba

2 Soil h base s uration 15 – 35 % 50 h base sh base s

uration 35 – 50 % 60 uration 50 – 75 % 80

5 ba turatio

The CEC factor (A) was rated from 40–100 with range from < 5 m.e. % to > 50 m.e. %.

Table 4.12: Rating for CEC factor (A)

Sl.No. Description Rating 1 Exchange capacity < 5 m.e. % 40 2 3 Exchange capacity 20 – 50 m.e. % 80 4 capacity > 50 m.e

Once the ratings were arranged, the productivity index of each unit was found using the

following equation.

SPI = (H/100 * D/100 * P/100 * T/100 * N/100 * A/100) * 100

productivity index assessment. In the given study, mineral reserves (M) and the organic matter

(O) were not considered as parameters. The reason for not taking organic matter into

consideration was the fact that proper management practices

ue to which the results ed might be ed to a great . Once the productivity

index was computed for e h m , they were classified into soil productivity classes.

This index obtained for ch d i range ratings and then

assigned the following productiv

Exchange capacity < 20 m.e. % 60

Exchange . % 100

In the current study, only the above stated six parameters were taken into account for soil

it is a factor that is affected a lot by

d obtain maneuver extent

ac apping unit

ea soil-mapping unit were clubbe nto

ity classes (Table 4.13).



Table 4.13: Soil Productivity Classes

Index Range Classes Productivity Classes

34.1 - 64 2 Good 19.1 – 34 rage 7.1 –

0 – r

b) FAO Framework of L d Eval ty ana

utilization type are compared with landuse requirement to obtain an overall suitability

assessment of landuse for each ps. The method was utilized to

assess land suitability of th harif crop on Pea, S and Soyabean dominant

over the study area. The lan se re ) are ven in the Appendix 7. The

necessary soil characteristic long ues nst each soil-mapping unit

31 m

onsidering requirement of crop to find the suitability class of each mapping

unit. Different parameters that have been taken into account are the soil physical conditions

(texture, depth, phic limitation

(slope percentage, erosion) of each soil-mapping unit.

- Texture

- Stoniness

- Moisture Availability in crop growing

- Availability of foothold for plant growth

development

- Drainage - oxygen availability to roots

- Slope

sion

- workability

Source: NBSS & LUP, Technical Bulletin 129 (2006)

64.1 – 100 1 Excellent

3 Ave19 4 Poor

7 5 Extremely Poo

an uation for crop suitabili lysis

In the FAO land evaluation procedure, land quality/ land characteristic of each land

of land utilization types i.e. cro

ree k s viz., Pige orghum

du quirements (crop specific gi

s a with their analytical val agai

(1 apping units) for specific crops were tabulated. Then, land quality parameters (Table

4.14) were assessed c

and stoniness), soil wetness limitation (drainage), and topogra

Table 4.14: Soil-site characteristics with corresponding land quality

Soil – Site Characteristics Related Land Quality

Physical Conditions of Soil (s)

- Depth

season

- Availability of foothold for root

Wetness Conditions (w) Available Moisture

Topographic Conditions (e) - Erosion

Resistance to Ero



Crop suitability classes w eter based on the FAO criteria

and ratings of soil and terra ac g nts. The suitability class of

each land quality was observ max um ity observed was assigned to the unit

as the overall suitability wit ication of the land quality at the sub-class level (soil

conditions-“s”, wetness conditions-“w” & topographic conditions-“e”) that decided the

and terrain qualities of

individual crops. This final soil suitability for each crop for each soil-mapping unit was

mpil

itability were successively modified in

correlation to edaphic suitabilities, to provide an overall crop suitability. The matching of land

ualities with crop requirements included soil-physiographic and climatic evaluation. The first

step referred to the nd physio with crop requirements.

Based on the degree and num r tatio as established as S1,

S2, S3 and N for specific cro . cl ith that of the crop

requirements. For this, specific soil and e limited yield potential was

calculated, using precipita e crop as obtained from PET,

crop factor and moisture storage capacity. The analysis was designed by following the simple

steps as outlined in the following section:

ere generated for each soil param

in qualities cordin to crop requireme

ed and the im suitabil

h specif

suitability class. Thus, an overall suitability was computed for the soil

co ed together (Appendix 10). Three crop suitability maps were obtained. This suitability

map was based on the soil and terrain properties. The sub-classes were finally combined and

merged to obtain a linear suitability class including Highly Suitable (S1), Moderately Suitable

(S2), Marginally Suitable (S3) and Not Suitable (N) for each of the three crops.

4.2.7 Analysis of Agro-climatic crop suitability assessment

Crop suitability is a result of both agro-climatic and agro-edaphic evaluation. Since agro-

climatic ratings are independent of soil limitations and edaphic ratings do not consider climatic

limitations, the two components were combined to arrive at the final suitability classification.

Fig 4.1b gives a schematic preview to the methodology adhered to in fulfilling the above stated

objective. Therefore, the results of agro-climatic su

q

comparison of soil a graphic properties

be of limi ns identified, suitability class w

ps Then the imatic condition was compared w

crop-linked moistur

tion data, consumptive use of water of th



(a) Landuse analysis in relation to soil productivity (SPI)

The spatial soil productivity was analysed with the landuse/ land cover generated for the

study area. The productivity class map was combined with the landuse/ land cover map and

productivity class associated with each class were identified.

(b) Integration of FAO soil suitability with crop specific climatic yield potential

The program generated water limited yield potential was linked to spatial data in a GIS

platform, for spatial analysis of land evaluation. An attempt was made to arrive at a final crop

suitability frame (soil suitability combined with climatic suitability) by comparing the results

from traditional soil suitability study based on FAO framework with water limited yield

suitability study based on BUDGET Program – “soil-water balance program”.

This accomplished the soil and climatic data analysis for crop suitability assessment in

the rainfed agro-ecosystem. The FAO frame work-based soil suitability raster and the climatic

suitability raster were combined to obtain a refined suitability. The decision to modify the

suitability analysis was based on an integration of the soil’s physical characteristics (based on

FAO framework of land evaluation) with climatic parameter (Water Limited Yield Potential). If

the soil suitability was found to be low and yield potential as high, the final suitability was

upgraded to one class higher suitability. This enabled improving the suitability assessment by

incorporating complete bio-physical information of resources. However, it implies that by

initiating improved management, the areas under marginal and moderate suitability can be

further improved on. The modified suitability assessment thus would lead to deciding as to

which quarter of the soil management has to be attended to in order to get the maximum yield

out of it.



Chapter 5 RESULTS & DISCUSSION

ent of technologies to increase crop

productivity. This in turn will be initiated through improved resources used efficiently. An

attempt was made to create a digital database of climate, soils and landuse/ land cover and to

GIS acts as a useful media to provide a spatial dimension. At the same time, it integrates

maps from various sources encompassing various vistas. Spatial assessment of agro-climatic

suitability analysis requires an integration of soil resource potential, climatic potential and

landuse/ landcover in GIS environment to generate resource constraints and potential information

The study area comprises of nine districts falling within two agro-ecological sub-regions

namely AESR 10.1 (Hot Sub Humid Dry Eco- Region) and AESR 5.2 (Hot Moist Semi-Arid

Eco-Region). The AESR zone map was obtained keeping the natural boundary of the districts as

the AESR zone demarcation. Table 5.1 enumerates the features of the AESR zones in the study

area,

LGP (120 – 150 Days) 10 Central Highlands (Malwa &

Bundelkhand) Hot Arid Climate,

Low to Medium AWC LGP (60 – 90 Days)

AWC = Available Water Capacity Source: Sehgal. et.al.1992

The major predicament in today’s scenario is to effectively feed the population from the

infertile soil in a fragile world. This will necessitate developm

assess their potential for agricultural landuse planning.

for sustainable landuse planning. Various outputs were generated in both tabular and map forms.

The following section deals with the major results and outcome of the study.

Table 5.4: Features of Agro – ecological zone covering the study area

Zone No. Indian AESR Zones Features 5 Central Highlands (Malwa), Gujrat

Plains and Kathiawar Peninsula Hot Moist Semi Arid Climate,

Medium to High AWC



The AWiFS scene of October 2005 covering the study area was classified to obtain

landuse - land cover for the kharif season of the area. Satellite data was clipped into districts and

Table 5.5: Landuse/ land cover (Kharif Season) distribution in the study area

4. Kharif 4 (paddy) 1604.89 3.21

9. Scrubland 4221.20 8.45 10. Forest 12087.43 24.19

pie-graph in Fig 5.1 to show the landuse distribution pattern all over the area.

5.1 Landuse / Land Cover Mapping

separate training sets were generated for each district. A supervised classification was performed

to obtain the land use/ land cover information classes for the major landuse. The areal extents of

various landuse/ land cover in the study area are given in Table 5.2.

Sl. No. Kharif Landuse Area (sq. km) Area (%) 1. Kharif 1 (soyabean) 15070.81 30.17 2. Kharif 2 (harvested field) 2942.68 5.89 3. Kharif 3 (pigeon pea) 1272.15 2.55

5. Kharif 5 (sugarcane) 970.32 1.94 6. Kharif 6 (cotton) 507.25 1.02 7. Plantation/ Orchards 172.36 0.34 8. Current Fallow 10050.45 20.12

11. Water Body 924.30 1.85 12. Settlement 134.94 0.27

The area-wise landuse/ land cover of kharif season was computed and then plotted on a

8

24.19 1.85

30.17

0.34

20.125.89

1.02

Kharif 1 (mainly soyabean) Kharif 2 (harvested f ield) Kharif 3 (pigeon pea)Kharif 4 (paddy) Kharif 5 (sugarcane) Kharif 6 (cotton)Plantation/ Orchard Current Fallow ScrublandForest Water Body Settlement

0.27

.45

2.553.211.94

Figure 5.3: Landuse distribution (Area %)



The majority of the area is under land utilization type (LUT-3) soyabean cropping

system, with nearly 36% of the total area being occupied by both early as well as late sown

soyabean. Pigeon pea is grown over nearly 2.55% of the area. A vast extent of the area is found

to be kept currently fallow, to be taken up for a good cultivation of early rabi crop like gram or

wheat. They appear very distinctly on the satellite data. Nearly a quarter of the area is under

forest cover and about 9% under scrublands. Detailed crop acreage tabulation in all the nine

districts for LUT-2 (sorghum) and LUT-3 (soyabean) has been provided in the Appendix 2 and

ppendix 3. A



The landuse/ land cover map Fig 5.2 gives an overview of the kharif season landuse/

land cover in the area of interest. As the district boundaries are overlaid on the map, the district

wise variations can be observed.

Figure 5.4: Landuse/ Land cover map of Kharif season



ussed in the following section.

Table 5.6: District wise distribution of area (%) under the dominant landuse types

Landuse Types

Districts Kharif-1 Area %

Kharif-2 Area %

Kharif-3 Area %

Kharif-4 Area %

Kharif-5 Area %

Kharif-6 Area %

Current Fallow Area %

Overall Accuracy

% Bhopal 37.44 4.63 18.28 - - - 19.19 74.29 Dewas 27.96 8.77 - - - 7.31 17.73 74.14 Harda 47.31 - - - - - 9.30 75.00 Hoshangabad 24.12 - - 2.10 - - 25.75 76.67 Indore 64.46 - - - - - 10.93 74.00 Narsimhapur 8.04 - 11.35 - 15.44 - 25.81 76.00 Raisen 5.64 - 2.15 17.18 - - 30.67 76.00 Sehore 33.44 20.61 18.28 - 2.65 - 11.38 75.51 Ujjain 53.84 14.06 - - - - 18.80 75.00

Kharif-1: Dominantly Soyabean Kharif-2: Dominantly Harvested Field Kharif-3: Dominantly Pigeon Pea Kharif-4: Dominantly Paddy Kharif-5: Dominantly Sugarcane Kharif-6: Dominantly Cotton

Bhopal

In the following Table 5.3 the area distribution for major landuse/ land cover types in the

districts have been enumerated. It gives a vivid look into the district wise dominant landuse. The

district wise landuse has been disc

has a soyabean dominant landuse. As evident from the classified image and the

generated statistics, more than 40 % area is under the soyabean crop. The other land use was

pigeon pea. On ground truthing, a dominant aspect noticed was floriculture and lemon orchards.

The region having 2 – 3 % slope restricts the soil development to great depths. This shallow

depth in the area encourages the farmers to take up fodder crops like jowar, which can survive in

such conditions. Dewas is a predominantly soyabean growing district with sparse jowar

occurrences. It is a double-cropped area where wheat or gram is taken as rabi crop. Jowar is

mostly grown with soyabean as intercropping. Cotton is grown in this part, which is supported

by the soil type dominant here. Even the agricultural statistics show that it is the sole district,

which contributes substantial cotton production among the nine districts under study. Nearly 7

% area belongs to the cotton cultivation.



Harda and Hoshangabad: have soyabean dominant landuse. The slope constraint restricts

deep soils. As a result, the water balance is low and eventually this affects the “force ripening”

crop behaviour. The soyabean is adapted to a mechanism to ripen early. This explains the earlier

harvestin soyg dates of abean along this belt. On the contrary, Indore has a trend of late sown

oyabean. The soyabean crop here is harvested at least 10 – 12 days after the harvest of normal

crop in egi s

s

other r ons. Nar imhapur district having ac ess to so e irrigat n facilit reflects

e are ently w, w e u irri he the seaso

en district only area is under soyabean as a kha crop. In the kharif season fields are

left fallow, so that the farm can early rabi crops, somewhere around first week of

Octo

p is t en ar aximum use of the

oil mo llow in the kharif

5.6% rif

ers take

ber.

c m io y the

difference in trend, with more area under sugarcane crop. It accounts for nearly 15.4 % area,

whereas, pigeon pea occupies nearly 11 % and soyabean follows with about 8 % area. Majority

of th a is curr fallo which ill tak p the gated w at in rabi n. In

Rais

In dominant areas, a single cro ak ound the year to make m

s isture to ensure good crop. They take either soyabean or keep it fa

season and wheat/ gram in the rabi season. Fallow is taken in areas where bores are not

available. The Barna water reservoir sprawling over 77-sq.km area supports paddy too. It is

dominant around the stretches where irrigation is ensured; away from these patches are the

pigeon pea and soyabean areas. Pigeon pea is found mostly in the eroded (gully) areas, where

they are grown for self-consumption mostly. Sehore is a double cropped area with soyabean

being the dominant crop, followed by pigeon pea and sugarcane. The prominent fallows include

both the current fallow as well as scrubland, which have been noticed during the field work.

These are mainly due to the land acquisitions, which are lying unused in and around 20 – 25 km

from Bhopal. From Bhopal towards Dewas via Sehore, jowar too had been identified. This belt

being a rugged terrain, where water availability is scarce to support soyabean, is highly suitable

for the jowar cultivation. Ujjain is marked by dominance of late sown soyabean. Jowar is mostly

grown as an intercrop, dominantly to serve the purpose of livestock fodder. Pigeon pea is grown

too. The management practices are highly developed in this belt.



The kharif landuse/ land cover extent in the area was studied with respect to the AESR

regions. The distribution of crops in each of the agro-ecological sub regions has been depicted

in Table 5.4. They give a vivid insight into the crop dominance of the individual AESR zones

covering the study area.

Table 5.7: Areal distribution of Landuse (Kharif) in AESR zones 10.1 (Hot Sub-Humid Dry Eco-Region) and 5.2 (Hot Moist Semi-Arid Eco-Region)

Agro-ecological Sub Region (Area %) Sl.No. Landuse/ Land Cover

AESR 10.1 (Hot Sub Humid Dry

Eco- Region)

AESR 5.2 (Hot Moist Semi-Arid

Eco-Region) 1. Kharif 1 (soyabean) 22.13 45.75

3. Kharif 3 (pigeon pea) 3.86 0.00

2. Kharif 2 (harvested field) 4.48 8.62

4. Kharif 4 (paddy) 4.87 0.00

5. Kharif 5 (sugarcane) 2.94 0.00

8. Current Fallow 21.97 16.53

In the AESR zone10.1, overall 27 % area approximately goes to the soyabean cultivation,

followed by nearly 5% area that goes to paddy cultivation. The paddy cultivation may be

attributed to the irrigation facilities available in this belt. Pigeon pea, which is an important pulse

crop grown in these area, contributes nearly 3.86%. This crop was found to be grown mostly for

self consumption although marketing is also done. This AESR zone accounts for nearly 29.2%

area under forest cover. The area under current fallow accounting to 22% area approximately, is

mostly taken under gram or wheat in the rabi season. In the AESR zone 5.2, nearly 54%

approximately area is under soyabean cultivation. Cotton is found in this belt, where 2.98% area

contributes to this crop.

6. Kharif 6 (cotton) 0.00 2.98

7. Plantation/ Orchards 0.00 1.01

9. Scrubland 8.07 9.19

10. Forest 29.18 14.52

11. Water Body 2.38 0.82

12. Settlement 0.11 0.57



5.2 Preparation of Digital Soil Database

5.2.1 Preparation of Digital Soil Map

The vector coverage of the soil map (NBSS & LUP) was prepared using the ARC-GIS

9.1 Prio ea

ontains 131 soil-mapping units. The salient characteristics of soil (viz. soil texture, depth, pH,

base saturation etc.) for mapping units were stored in the excel database. A soil map (Appendix

– Fig 1) has been generated for the study nt s along with

their salient characteristics are given in Appendix 1. The study area was broadly classified into

eight physiographic units. The most dominant physiographic unit being plains.

Table 5.8: Distribution of physiographic units in various districts

Phys hic Units (Area %) Sl. No Districts ual

s Plateau

eau Plains Und g

pValleys Flood

plains 1 Bhop 6.39 40.66 6 14.92 14.77 − 2 Dewas 8.17 10.82 18.92 25.66 27.03 − − 3 Harda − − − − 100.00 − − − 4 Hos ba − − 39.11 5 47.13 − − 5 Indore 28.63 21.55 9 0.39 36.97 − − 6 Nars pu − 42.93 43.66 13.41 − − 7 Raisen 3.18 − 32.24 3.75 52.21 1.77 4.23 2.62 8 Sehore 3.88 2.24 46.22 0.06 32.68 12.45 2.46 9 Ujjain 39.92 29.15 4.74 − 1.34 24.84 − −

The soil information provided was at the sub-group level. The major taxonomic groups

mostly loam to loamy skeletal with shallow depth soils. The soil mapping units were grouped

based on the soil texture and soil depth classes to prepare soil texture and soil depth maps

respectively (Fig 5.3).

software. r to this, the soil maps were scanned and then georeferenced. The study ar

c

area. The domina physiographic unit

iograpHill

range Resid

hillUndulating

platulatinlains

al 12.49 6.4 4.31 9.41

hanga d 7.4 6.31 4.07 8.3

imha r − −

Plains and plateaus mostly dominate the area. Table 5.5 reflects that the district of Ujjain

has nearly 40% area under the hills and about 30% area under residual hills, whereas Harda was

found to be completely under plains (100%).

were found to be lithic ustorthents, typic haplustalfs, vertic ustochrepts. The plains were

dominated by the deep soils with extensive heavy clay soils. The soils in the hilly terrains were



Figur Texture d Dep p

availability in the soils. A complete picture of how

the texture and depth have been varying over the entire terrain can be synoptically viewed here.

ery deep soils do make them sustain crops with lesser potential.

e 5.5: Soil an th ma

The texture and depth dominant over the area of interest was mapped to understand their

inter-relationship in influencing the moisture

The area shows a dominance of clay soils with clay content of more than 60% in cases of fine

soil types. These were found mostly in the central, eastern and southern belts. However, in the

rugged western terrains the loamy soils were found to be more prominent. Interestingly, in terms

of depth too the western fringes seem to be lagging behind. They were found to be vulnerable

due to dominance of soils with less than 25cm depth. However, in the eastern parts of Indore and

Ujjain occurrences of v



5.2.2 Available Water Holding Capacity:

A major function of soils is to store moisture and supply to crops during crop growth.

Available water holding capacity of soils provides a buffer to determine a crop’s capacity to

withstand the dry conditions. Soil’s ability to hold water is primarily determined by the soil

texture and depth. As a result, the study of soil texture becomes important. AWHC is the amount

of water held in soil between the field capacity and wilting point. It represents the quantity of

moisture available to crops. The AWHC estimation of the soils in the study area was done.

NBSS & LUP soil map provided the soil textural class (at family level) of each soil-

mapping unit. AWHC for these units were estimated using a pedo-transfer function (Eq. 1)

developed for vertisols soils. The estimation required information of sand, silt and clay fractions

in soils. For this purpose, soil samples from 29 locations of surface (0 – 15cm) and sub-surface

(15 – 30cm, 30 – 50cm and 50 – 75cm) were collected in selected soil textural class. Besides this,

47 number of soil data were collected through literature survey from journals of Indian Society of

soil science, IMSD reports of M.P. and 18 soil series data were collected from the Soil Series of

M.P, NBSS Publication with their water retention characteristics (FC and WP). For all the other

soil data, the dominant textural classes were obtained and AWHC was estimated.

Practically clay and actio n soils of Madhya Pradesh

and as such, the combined affect of both the pa ameters would result in a positively significant

relationship. Hence the prediction of water retention based on interrelationship of these two

ariabl

Y = - 6.960 + 0.162 silt + 0.435 clay, for Wilting Point (1500 kPa) [Eq. 1]

silt size fr ns are intricately mixed i

r

v es, may be more meaningful for adoption. The multiple regression equation applied for

the study is:

Y = - 15.801 + 0.185 silt + 0.997 clay, for Field Capacity (33 kPa)

[Y = water (kg/kg)]



samples of the study area, the coarse fragments

have not been taken into account. This is because of the fact that the soils mainly being in-situ in

While estimating the AWHC for the soil

nature have insignificant percentage contribution of coarse fragments. All these AWHC data

were grouped based on soil textural class and soil depth (Appendix 5). The dominant soil texture

along with their depth information were compiled for the mapping units and then the computed

AWHC were averaged out to assign to each of the mapping unit.

C

C0.40

a (

in 1

sep

th

Scl Sc Cl

Sic

0.15

0.20

0.25

0.30

0.35

whc

m)

moi

l d

Clay %

31 - 40 41 - 50 51 - 60

Figure 5.4: Relationship of soil texture to water retention in 1m soil depth

As is evident from Fig: 5.4, there is a distinct variation in water retention and in turn

AWHC among soil textural classes. Coarse textured soils have lower retention and low AWHC

than finer ones. The soils of same textural class vary in this respect depending upon the extent of

clay content. This could be attributed to the physico-chemical properties of soil as is evident

from the positive correlation coefficient between moisture retention indices and soil parameters.



Figure 5.5: Available Water Holding Capacity map

AWHC estimated were assigned to soil map units and thus a spatial AWHC map was

generated (Fig 5.5). It gives clear picture of the spatial distribution of the available water holding

capacity over the study area. District wise AWHC distribution is also evident from the above

representatio te , due to the

depth limitations and textural oddity. The plains in the southern and eastern parts of the area

The lowest AWHC class with < 50mm/ cm occupies about 24% area. This was found mostly

along the western and central areas where the ruggedness explains the problematic situation of

low AWHC. Whereas, nearly 31% area is under very high water holding capacity. The districts

of Hoshangabad, Harda, Raisen and Dewas mostly falls to this class. However, for the

remaining classes, around 27% is contributed by the areas under 150 – 200mm AWHC and 12%

by areas under 100 – 150mm.

n. The hilly rrain of Hoshangabad district shows very low AWHC

show a distinctly high AWHC. The very low water holding capacity trends in the Indore and

Ujjain districts were found to be due to the dominance of shallow soils in these areas.

The overall areal distribution of the available water holding capacity can be clearly

understood from Fig 5.6. It gives a quantitative view to the dominance of the AWHC classes.



23.54%

6.62%12.02%

26.58%

31.23%

Very Low Low Moderate High Very High

Figure 5.6: Areal distribution of AWHC in the study area

District wise areal distribution of AWHC was also compiled in Table 5.6, to give a

descriptive overview to the trends. Bhopal shows equity in the distribution. About 25% area was

found to have AWHC 50

d to have

nearly 72% area with very high AWHC mostly being influenced by texture and depth.

Districts Very Low Low Moderate High Very High

Narsimhapur 9.09 1.60 22.33 56.22 10.76 9.08 8.82 30.30 45.82

Sehore 22.86 2.71 17.34 39.13 17.95 Ujjain 68.86 12.68 0.47 17.99 −

less than mm. The district is more or less equally endowed with

moderate (26%) and very high (22%) water holding capacity. Indore and Ujjain show a high

areal coverage of 54% and 69% respectively under very low AWHC. This may be attributed to

the ravinous physiographic conditions of the area. In case of Ujjain, the low trends of AWHC

were found to be due to the dominance of hill ranges and residual hills. Harda shows tremendous

sustenance in terms of AWHC as 80% of its area enjoys more than 200mm water holding

capacity. This was found due to the total coverage by plains with more than 100cm deep soils.

The area also was found to be rich in terms of soil texture. Hoshangabad was foun

Table 5.6: District wise distribution of area (%) under AWHC classes

AWHC Classes

(< 50mm) (50 – 100mm) (100 – 150mm) (150 – 200mm) (> 200mm) Bhopal 24.59 17.22 26.31 9.91 21.97 Dewas 24.00 6.65 15.64 28.08 25.63 Harda − − − 17.17 82.83

Hoshangabad 9.62 1.58 16.12 0.73 71.94 Indore 53.91 11.94 1.14 33.01 −

Raisen 5.99



The soil-mapping unit wise estimated AWHC values have been grouped according to the

physiographic units they comply to. Broadly, around eight major physiographic units have been

identified within the study area and the AWHC variations have been studied in relation to the

mean along with the standard deviation of the AWHC estimations. The average and the standard

deviation of the physiographic unit wise AWHC has been represented in Table 5.7.

Table 5.7: Mean (−X ) and SD (δ ) of the estimated AWHC over the Physiographic Units

AWHC (mm)

Physiography Mean Std Dev

Plains 217.46 46.58

Valleys 117.89 77.74

s greater

weightage to h rtio wer standard

eviations indicate that the actual observations have allegiance to the mean observations and that

they do not deviate mu

Hill range 61.08 58.74 Residual hills 75.49 54.93 Plateau 109.31 85.09 Undulating plateau 118.01 62.28

Undulating plains 125.49 81.28

Flood plains 75.32 -

The maximum AWHC was estimated to be in the plains followed by valleys, undulating

plateaus and plateaus. Whereas, the lowest AWHC average was estimated to be in the residual

hills. The constant hazard of being worn down by one or the other natural causes results in the

soils being extremely vulnerable against attaining higher water holding capacities. The same

trend is identified in the hilly regions, where the landform stability is questionable. The SD (δ) is

a measure of how extensively the values are dispersed from the mean values. It give

igher disto ns about the average value. In the residual hills, the lo

d

ch.



5.3 Climatic Data Analysis: Spatial Climatic Database

ters. Spatial

interpolation was the GIS technique used to interpolate climatic data. FAO New LocClim 1.03

softwa ed in ud here

observations were not available. It gave the possi rpolate user-defined data at any

spatial resolution. Spatial interpolations may result in extrapolations if the group of stations used

is all on one side of the grid po curs at argins of the selected area if no stations

outside the area are taken into c ration. To s is, limatic data was interpolated for

the whole of Madhya Pradesh a n the study as d out.

Climate data (daily rai daily m m and maximum temperature) for the

Climate is highly variable in time and space. Climate data were obtained for specific

locations. Estimation of climate at a given location was necessary to investigate the climatic

constraints and compare different locations. Spatial distribution of climatic data (rainfall &

temperature) was required to analyse its spatial influence on crop growth parame

re was us the st y to estimate the climatic condition spatially at locations w

bility to inte

int. This oc the m

onside olve th the c

nd the area w clippe

nfall and inimu

period of 1980 to 2003 for twenty-eight IMD stations all over Madhya Pradesh were compiled.

The data were computed for mean monthly rainfall, temperature (minimum & maximum) and on

a decadal basis to compute Length of Growing Period (LGP). Monthly and decadal PET was

computed for all the 28 stations. The monthly PET (mm/ month) was then converted to (mm/

day) using the latitude wise correction factor determined by the sunlight duration.

12

15

0

3

6

9

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

PET

(mm

/ day

)

Indore Narsimhapur Pachmarhi Raisen Ujjain

Figure 5.7: 24 years average PET distribution over five selected stations in the study area.



The 24 years average (1980 to 2003) PET distribution over the study area has been

synoptically presented by plotting its distribution for five sample stations spread evenly over the

study area as shown in Fig 5.7. It ranges from as low as about 0.86 mm/ day in the month of

December in Raisen, to as high as nearly 12.0 mm/ day for the month of May for Narsimhapur

station. Indore, Raisen, Narsimhapur and Ujjain seem to have the most variable of PET

distribution of the stations chosen to be presented here. For Pachmarhi, the PET variation was

observed to be very smooth seldom crossing 6.5 mm/ day. This is because this area experiences

the moistest climatic conditions due to rainfall conditions.

500

600

infa

(mm

200

300

400

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Ra

ll)

0

100

Indore Narsimhapur Pachmarhi Raisen Ujjain

Figure 5.8: 24 years average Rainfall distribution over five selected stations in the study area.

The 24 years average rainfall (1980 – 2003) for the five selected stations were plotted to

depict the overall rainfall variations over the study area as seen in Fig 5.8. Raisen experiences

maximum rainfall during august. Pachmarhi shows a steady high rainfall through June until mid-

October. However, for the rest of the year, the area is under no rainfall.

The monthly and annual average of PET (mm/ day) (Appendix 11) and the monthly

Rainfall (Appendix 13) over the 28 climatic stations in Madhya Pradesh were used to generate

spatial PET surfaces. The 24 years average PET and Rainfall for January and June interpolated

in the FAO New LocClim has been shown in Fig 5.9 and Fig: 5.10.



Figure 5.9: 24 years average Rainfall & PET in January for Madhya Pradesh

The spatial 24 years average rainfall and PET surfaces for the month of January has been

depicted in this figure. The study area’s PET surface was clipped out of this surface map.

Figure 5.10: 24 years average Rainfall & PET in June for Madhya Pradesh

The spatial 24 years average rainfall and PET surfaces for the month of June has been

depicted in this figure. The study area’s PET surface was clipped out of this surface map.

Similarly the 24 years average decadal PET and rainfall were also generated.



5.3.1 Length of Growing Period

The concept of the growing period is essential as it provides a way of including

seasonality in land resource appraisal. The growing period defines the period of the year when

both moisture and temperature conditions are suitable for crop production. As crop suitability

assessment is a prime objective of the study, hence the concept of length of growing period

holds immense importance. As a result, the estimation of LGP was given a detailed attention. In

this study, a dual approach to determine the length of growing period has been adopted to

analyse the variations in the outcome. On the first hand, a climate-based LGP was estimated,

where the crop parameters were not taken into account. This took into consideration the spatial

distribution l an hand, an

endeavour to estimate a crop specific LGP was taken using simple soil water balance model.

Henceforth, the results obtained are discussed below:

5.3.1.1 Spatial LGP Analysis

Most of the early AEZ studies calculated LGP based on average monthly Rainfall and

PET. A trend of the general length of growing period spanning over the study area was

estimated using the climatic parameters of ten-day composite PET and Rainfall. This procedure

has taken into account the simple LGP concept irrespective of the soil parameter. The state’s

LGP map was clipped out of the resultant LGP outcome along with the area of interest too.

The LGP map was converted to days by multiplying with ‘10’ and then was reclassified

into five classes of LGP. For the entire state, the following classes have been identified; 60 – 90

days, 90 – 120 days, 120 – 150 days, 150 – 180 days and 180 – 200 days. In case of the study

area, the classes have been identified as, 90 – 100 days, 100 – 120 days, 120 – 140 days, 140 –

150 days and day LGP classes

belonging to the study area was chosen to reflect the graphical LGP.

half of potential

evapotranspiration.

of rainfal d PET over a period of 24 years average. On the other

150 – 160 s. A single pixel be onging to each of the above stated l

The graph in Fig: 5.11 give a graphical overview as to how the LGP could be estimated

manually on basis of a graphical representation of precipitation and



0

60

100

80

20

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21222324252627282930 313233343536

LGP (10-day composite)

(mm

)

Rain0.5 PET

Figure 5.11: LGP (90 - 100 days) for a 10 km grid

The LGP precisely begins at the 18th dekad and ends around the 28th dekad, which adds

upto nearly 90 to 100 days when the climatic conditions are ideal to support crop growth.

20

80

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21222324252627282930 313233343536

(

0.5 PET

40

60

mm

)

Rain

0

LGP (10-DAY COMPOSITE)

Figure 5.12: LGP (100 - 120 days) for a 10 km grid

This graph in Fig: 5.12 is a representation of the second cla

ss of LGP with 100 to 120

days that was generated. Here the growing period gets an additional ten days to its previously

discussed counterpart. As such, this zone is ideal for the crops requiring nearly 120 days of

growing period.



0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21222324252627282930 313233343536

GP

(mm

)

Rain0.5 PET

L (10-DAY COMPOSITE)

(

The LGP within this grid begins at the 16 dekad ending somewhere in the 28 dekad,

nd a short duration of 10 days at the beginning also amounts to the LGP. Thereby it allows

duration of nearly 130 days moisture sufficiency to plants.

Figure 5.13: LGP 120 - 140 days) for a 10 km grid

th th

a

0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 212223 2425262728 2930 313233 343536

(mm

)

Rain0.5 PET

LGP (10-DAY COMPOSITE)

Figure 5.14: LGP (140-150 days) for a 10 km grid

This grid representing an LGP of 140 to 150 days reflects the most desired conditions as

the moisture availability for crop growth will be adequate without any stress.



The distribution of LGP over the entire state as well as the study area has been shown in

relation to the AESR zones dominant over the region (Table 5.8). As identified, Madhya Pradesh

is broadly divided into the following AESR zones with specified length of growing period (in

days).

Table 5.8: AESR zone-wise spatial LGP (days) with district coverage

AESR Zone Description LGP (days) Districts

4.3 Hot Moist Semi Arid 120 – 150 1

4.4 Hot Moist Semi Arid 120 - 150 5

5.2 Hot Moist Semi Arid 120 - 150 12

10.1 Hot Dry Sub Humid 150 - 180 14

10.2 Hot Dry Sub Humid 150 – 180 1

ot

In the following section a spatial representation of the general LGP has been displayed,

hich has been obtained as per the procedures explained in the previous chapter.

10.3 H Dry Sub Humid 150 – 180 7

10.4 Hot Moist Sub Humid 180 - 210 5

Source: NBSS & LUP, 1995

w

Figure 5.15: Length of Growing Period (Madhya Pradesh)



As is evident from Fig: 5.15, the estimated length of growing period is falling in place

with the existing LGP prevalent over the area. The area distribution of each class of LGP over

the entire state is given in Table 5.9. The areas under hot moist sub humid zone (south eastern

parts) have an estimated LGP ranging from 120 to 180 days. In the western and north-western

belt, areas under moist semi-arid and hot sub humid zone have LGP ranging between 60 to 120

days. Whereas adhy ne has a LGP

f 90 to 120 days. In rn be t sem e h P ranging from 90 to

150 days.

Table 5 A l L adhy adesh

LGP km) ) < 90 61700 19.95

90 12 150 - 180 43400 14.04

> 1 0

, central M a Pradesh mostly under hot dry sub humid AESR zo

o the northe lt, the mois i arid zon as a LG

.9: real extent of spatia GP over M a Pr

(days) Area (sq. Area (%

- 120 117800 38.100 - 150 83200 26.91

80 3100 1.0

Figure 5.16: Length of Growing Period (Study Area)



In case of the study area, two major AESR zones (Fig: 5.16) have been identified as hot

sub humid dry (10.1) and hot moist semi arid (5.2). For the former, the LGP is mostly ranging

from 100 to 120 days, followed by 90 to 100 days; whereas for the latter, LGP was found to

range from 90 to 120 days. However, in the former zone the LGP ranging from 150 to 160 days

was found to be almost negligile.

Table 5.10: Areal extent of Spatial LGP (days) over study area

LGP (days) Area (sq. km) Area % < 100 16000 32.06

100 – 120 30000 60.12 1140 – 150 1300 2.61

There has been a sizeabl tency n the ated LGP over the area and the

LGP as specified by the AESR e area bution of each class of LGP over the entire

tudy area is given in Table 5.10.

5.3.1.2 Crop Specific LGP Analysis

The water-balance describes climate as it is sensed by crops, as the interaction of energy

and water in the environment. Distribution of land utilization types is more correlated with

water-balance (ET-actual & ET-potential) than with only the climatic measures (temperature &

rainfall). The BUDGET program used in the study computes crop specific ET-actual and ET-

potential a daily basis for the soil simulation units. First, the mapping units likely to be

influenced by the same weather station were delineated. Totally 13 weather stations were

identified and under each of the 13, a group of soil-mapping units were identified. The

simulation units were developed by grouping soil-mapping units of similar depth along with

similar textures. Eighty four simulation units were generated.

The program has used monthly rainfall and PET inputs to compute pseudo daily

estimates. Considering these daily estimates, the ET-actual and ET-potential were added up to

obtain the growth-stage specific ET-actual and ET-potential. Three different simulation units of

one-meter deep clay was selected for the three crops, having different LGPs.

20 – 140 2500 5.01

> 150 100 0.20

e consis betwee estim

zone. Th distri

s



0

100

200

Establishment Flow YieldFormati

Ripening

Growth Stage

mm

ETactETpot

PIGEON PEA

300

400

Vegetative eringon

Figure 5.17: LUT-1 (Pigeon pea) growth-stage specific ET-actual and ET-potential in clay soil

In Fig 5.17, the variation of actual evapotranspiration (ETact) and potential

cted on a clay sample for the LUT-1 (pigeon pea crop)

The critical period was found to be the yield formation. The disparity between the actual

crop water-use, at this juncture indicates that the crop might suffer a yield loss. At the ripening

stage, actual ET, matching the optimum ET conditions indicate the drying up of soil and hence

creating difficulty for the crop to extract water from soil.

evapotranspiration (ETpot) has been proje

with 127 days LGP. The temporal distribution has been over the growth stages corresponding to

the crop. The evapotranspiration is influenced by growth stages. Pigeon pea reflects the

maximum ETact at the vegetative stage, when soil water is not limited. Throughout the growth

stage, the actual evapotranspiration has not exceeded the potential. The moisture conditions were

sufficient for the crop’s survival.



SORGHUM

0

100

200

300

400

Establishment Vegetative Flow ering YieldFormation

Ripening

Growth Stage

mm

ETact

ETpot

Figure 5.18: LUT-2 (Sorghum) growth-stage specific ET-actual and ET-potential in clay soil

in higher ETact, especially early in the growing season.

Optimum moisture range is from field capacity to 40 % of water availability. Sorghum can

In the above graph (Fig: 5.18), the variation of actual evapotranspiration and potential

evapotranspiration has been projected on a clay sample for LUT-2 (sorghum crop) with 155

days LGP. In case of sorghum, the maximum ETact, which is at close quarters with the optimum

conditions (ETpot), is found to be around the vegetative growth stage. This implies the crop

water use at its peak. This is because of the fact that the crop is taken just after the rains

commence. When the soil or crop surface is wet especially after rains, the evaporation portion is

significantly increased, resulting

extend rooting upto 1.5m. Pre-flowering and grain development is critical stages in respect of

moisture.



SOYABEAN

0

100

200

300

400

500

Establishment Vegetative Flow ering YieldFormation

Ripening

Growth Stage

mm

ETactETpot

Figure 5.19: LUT-3 (soyabean) growth-stage specific ET-actual and ET-potential in clay soil

In Fig 5.19, the variation of actual evapotranspiration and potential evapotranspiration

has been projected for LUT-3 (soyabean crop) with 162 days LGP. At the vegetative stage,

when the rains have just been settled down, the soil evaporation is at its maximum, resulting in

the higher ETact at the onset of the growing season itself. Very low ETact in the yield formation

period indicates a threat to the crop growth. It implies a limitation in the soil water for being

used by the crop development. This might lead to a hampered yield. However, in the ripening

stage, the negligible ETact explains the fact that as the growing season has advanced, the crop

canopy has increased, and evaporation from the wet soil surface gradually decreases.

The crop specific LGP (days) have been estimated for the soil simulation units using the

software derived Et-actual and ET-potential. This LGP has been spatially represented through a

GIS platform, to reflect a general trend of the crop specific growing days.



(i) LUT-1 (Pigeon pea): The distribution of the length of growing period for pigeon pea crop

over the study area has been illustrated in Figure: 5.20. LUT-1 has nearly 48% area with LGP of

125 to 150 days. This reflects a very conducive condition for the landuse type. About 30% area

was observed to be under 75-125 days of LGP.

(ii) LUT-2 (Sorghum): A spatial representation of the length of growing period for the sorghum

crop was attempted here (Fig 5.21). LUT-2 (sorghum) has 42% area with more than 150 days of

LGP and about 29% area with 125-150 days LGP. Nearly 16% area was found to have < 75 days

of LGP.

(iii) LUT-3 (Soyabean): In case of LUT-3 (soyabean) (Fig: 5.22), the area shows a liberal trend

towards supporting the crop throughout the area. Approximately 38% areas have m re than 150

days abo

LGP Classes

o

of LGP and ut 29% area was observed to have 75-125 days of LGP.

Table 5.11: Distribution of area (%) in various LGP classes for various LUTs

Landuse Types

< 75 days 75-125 days 125-150 days > 150 days LUT 1 (Pigeon pea) 12.52 29.27 47.80 10.41 LUT 2 (Sorghum) 16.05 12.99 28.55 42.41 LUT 3 (Soyabean) 12.52 29.27 20.41 37.79

Table 5.11 gives a complete look into the areal distribution of LGP classes for each of

the land utilization types selected for the study. This LGP trend however reflects only the

climatic influences.

Crop water better known as evapotranspiration, is the water used by a crop for growth

and cooling purposes. Different crops having different water requirements, respond uniquely to

water stress, which again varies from one growth stage to another. The throughout trend of ET-

actual being lesser than the ET-potential implies that the crops under this soil condition can be

taken up for the specified period of growth (LGP) without being supported by irrigation.



Figure 5.20: LGP (days) map for LUT-1 (pigeon pea crop)

Figure 5.21: LGP (days) map for LUT-2 (sorghum crop)

Figure 5.22: LGP (days) map for LUT-3 (soyabean crop)



5.4 Soil Resource Appraisal for Crop Suitability Assessment

5.4.1 Soil Productivity Assessment

Land Evaluation is used to describe the process of collating and interpreting basic

inventories of soil, vegetation, climate etc. in order to identify and compare landuse alternatives.

The basis of land evaluation for defined type of landuse is the comparison of appropriate land

qualities with relevant landuse requirements. Soil Productivity Index, also known as FAO

productivity rating has served as rational indicator to landuse planners and decision makers for

sustainable use of soil resources. It takes into account nine factors viz., soil moisture, depth,

drainage, texture, ba ral reserves. In the

given study, mineral reserves (M) and the organic carbon factors have not been considered. Soil

organic carbon being overlooked because of the fact that it is mostly influenced by the

management practices. Each factor was rated on a scale from 0 to 100 and the soils were rated

according to the above properties. The actual factor-wise score was multiplied by each and

conveyed as a percentage to arrive at the final index. For each mapping unit the ratings for

individual parameters have been enlisted.

A soil is more fertile having more rooting volume available (P), the higher the base

saturation (N), the more readily penetrable it is to roots (T). These factors (P, T, N) are further

corrected by (A): a higher CEC augments nutrient retention. Finally, soil moisture (H) and the

xcess water (D) factors clearly affect productivity. Once the ratings have been arranged the

productivity index of each unit was computed (Appendix 6) using the following equation.

SPI = (H/100 * D/100 * P/100 * T/100 * N/100 * A/100) * 100

In the current study, only the above stated six parameters were taken into account for soil

productivity index assessment. The ratings obtained for each soil-mapping unit have been

clubbed into range ratings. The resultant productivity index is classified into following four

classes namely, excellent (> 64), good (34-64), average (19-34), poor (7-19) and extremely poor

(< 7).

se saturation, organic matter, CEC of clay, salts & mine

e



Figure 5.23: Soil Productivity Map

The map (Fig 5.23) reflects the

various productivity classes in the study area. The

riverine zone however has an average productivity, whereas the rugged mountainous terrain has

shown good productivity. The areal coverage under each of the SPI class has been tabulated in

Fig 5.24.

60

30

50

AREA % 53.86 14.95 7.84 23.34

Poor

0

10

20

Good Average Poor Extremely

40

Figure 5.24: Area under various soil productivity classes

SOIL PRODUCTIVITY RATING CLASSES





5.4.1.1 Analysis of Soil Productivity in relation to land use/ land cover

On analyzing the soil productivity in relation with the landuse/ land cover of the study

area it has been found that the good soil productivity with only 53.86% area supports the forests,

whereas the extremely poor productivity areas with 23.34 % coverage, are dominated by the

soyabean crops. These are the areas which are kept as fallow in the rabi season. This also covers

the poor productivity with nearly 8 % area coverage.

Table 5.12: Percent areal extent of soil productivity with respect to various physiographic units

Soil Productivity Rating Classes Physiography Good Average Poor Extremely Poor

Hill range − 22.87 9.44 67.69 Residual hills − 3.39 32.50 64.12 Plateau 38.68 28.56 0.66 32.10 Undulating plateau

0.12 0.26 0.35 0.27

Plains 96.57 0.34 0.26 2.84 Undulating plains 49.20 21.34 17.01 12.46 Valleys − Flood plains − 100.00 − −

ty ratings. Whereas, in the hill ranges and residual hills each more than 60% soils

were identified to have extremely poor soils. This may be attributed to poor drainage conditions

and rooting problem of the area. The soils in hese belts being extremely fragile and under

developed result in such poor productivity standards.

Table 5.13: District wise areal distribution in percentage of soil productivity

Soil Productivity Index (Area in percentage) Districts Good Average Poor Extremely Poor

Bhopal 51.47 12.29 16.12 20.12 Dewas 40.29 16.25 10.38 33.08 Harda 89.84 − − 10.16 Hoshangabad 71.57 18.80 5.87 3.75 Indore 19.27 13.73 28.32 38.68 Narsimhapur 65.43 22.33 − 12.23 Raisen 79.94 11.17 2.78 6.11 Sehore 58.92 9.75 7.75 23.58 Ujjain 1.62 24.02 8.37 65.98

90.35 5.41 4.24

A broad distribution pattern of the soil productivity in relation to the major

physiographic units have been depicted in Table 5.12. The flood plains in the eastern parts of the

study area rated as average soils. Nearly 97% of the plain soils were found to have the good

productivi

t



A detailed tabulation of the district wise soil productivity (Table 5.13) gives a clear view

little above 50%. The minimum incidence

of good productive soils can be found in districts of Dewas, Indore and Ujjain. Average

pro s ar m

Harda, which has no this cat nearly 24% of average

productive soils, followed by Narsimhap wit . a and mhapur show no accounts

of poor productive s as, In e h this category. Ujjain,

records about 66% of the extremely po ils

terrain, gullied lands predominant ove re Th sults i th constraints, as a result

of which the severe poor conditions are dom e trend is noticed for both Indore

(38.68%) as well as Dewas with 33%, both of which share the similar terrain conditions.

Table 5.14: Distribution of Kharif landuse/ land cover with their soil productivity

(sq. km) (sq. km) (sq. km) (sq. km)

Kharif 1 (Late Sown 628.83 2.34 389.40 5.21 128.78 3.28 1072.37 9.21

Kharif 2 (Harvested Field) 980.03 3.64 381.23 5.10 377.96 9.64 1261.16 10.83 .55 84.43 2.15 200.05 1.72

Kharif 4 (Paddy) 1271.24 4.72 210.49 2.82 20.09 0.51 102.41 0.88 Kharif 5 (Sugarcane 0.66 30.60 1.12

harif 6 (Cotton) 146.42 0.54 138.04 1.85 36.36 0.93 184.97 1.59 lantation/ Orchards 37.94 0.14 58.12 0.50

Current Fallow 539 20.02 2227.01 19.13 Scrubland 2020.86 7.50 7 10 1075.07 9.24 Forest 2 32.02 34 15.17 898.29 22.91 1416.55 12.17 Water Body 6 2.45 28. 22.10 0.56 110.64 0.95

of the character of the available soils and their potential to support the agricultural practices in

the study area. Soil productivity index considered in area percentage seems to be dominated by

the good productivity in Harda with nearly 89%, followed by Raisen with 79% approximately

and 72% in Hoshangabad. These conditions may be attributed to the handsome depth conditions

of the soils. Bhopal registers a good productivity of a

ductive soil e however more or less evenly distributed all over the area of study apart fro

soil under egory. Ujjain records for

ur h 22% Hard Narsi

oils. Where dor as nearly 28% soil under

or so . This is due to the presence of immensely rugged

r this gion. is re n dep

inant. The sam

SPI Classes

Good Average Poor Extremely Poor Landuse/ Land cover Area Area % Area Area % Area Area % Area Area %

Kharif 1 (Soyabean) 5969.01 22.17 1917.68 25.65 1279.34 32.62 3802.05 32.66

Soyabean)

Kharif 3 (Pigeon Pea) 738.82 2.74 265.47 3

) 46 1.71 362.67 4.85 12.03 0.31 1KP 68.64 0.92 6.58 0.17

92 22.0.08 1678

.78 .28

64 .53

727.17 18.54328.50 8.38

8623.660.2

3 111

.19 49 1.72



of the

soils are occupied by the forests in this region. Whereas, 19 % of the poor soils are assigned to

current fa

of landus he extremel oil producti ave much

pondered over. More than 50 % of total area of extremely poor soils is under soyabean in this

cupy ne 2 % ese nd 1 is oc by the current fallow.

le 5.15 ESR wis utio oil pr ivity in %

ESR Z Clas AESR e Wis roduc rea i

ood 365erage 312 18.45 oor 23ely 78

ot Moist Sem -Regio

TA 169Good 23249.01 70.43

Average 4343.11 13.16 Poor 1579.59 4.79

Extremely Poor 3839.17 11.63

Hot Sub Humid Dry Eco- Region

TOTAL 33010.88

The landuse/ land cover in relation to the soil productivity has been depicted in a tabular

format as shown in Table 5.14. From the overall scene, we can confer that the area under the

good productive zone is mainly dominated by the forests contributing nearly 32 % of the total

land cover followed by the soyabean with 28 % and current fallow with 20 %. However, the

major crops like paddy contribute about 5 % and pigeon pea with 3 % area of each landuse to

the good productivity.

The average productivity shows a great deal of variety in its distributions. 35 % of the

total area under soyabean belongs to average soil productivity followed by nearly 23 % of the

total area of current fallow. About 5 % of the soil with average productivity goes to sugarcane

and nearly 3.55 % to pigeon pea. Nearly 16 % of the average productivity soils are devoted to

the forest cover in this belt.

The area contribution of different crops shows much variation when it comes to poor soil

productivity index. The soyabean occupies more than 40 % of the poor soils. Nearly 23 %

llow areas.

The trends e in t y poor s vity h variety to be

zone. Forests oc arly 1 of th soils a 9 % cupied

Tab : A zone e distrib n of s oduct (Area )

A one SPI ses Zon e Soil P tivity(A n sq.km)

8.03

Area (%)

21.59 GAv 6.67

P 38.83 13.80 Extrem

O Poor L

22.50 4

46.16

H i-Arid Econ

T 6.03



The agro-ecological sub region wise distribution of the soil productivity depicts an

overall trend of dominance of extremely poor and good soil productivity (Table 5.15). In the

AESR region 5.2, about 46.16% area is extremely poor in terms of soil productivity. It is a

reflection of the fact that these areas in the western parts of the study area have highly erosive

soils. Moreover, this area is dominated by the eroded and rugged terrain. This necessitates the

soils in this zone to be managed very carefully and efficiently in order to ensure that their

existing productivity is retained and at the same time, management practices have to be

trodu

mands a significant amount of management support to preserve the already

in ced in order to improve the quality of soil for further intensive uses. This involves proper

landuse planning for the region in concern so that the objectives can be attained. This is more

important keeping in view that this particular study will henceforth be dealt in detail to prepare

landuse plan incorporating the soil moisture availability and soil suitability to the different crops

in the study area.

The AESR region 10.2 has more than 70 % area under good soil productivity. This

particular trend is mainly due to the dominance of the plains and plateaus in these regions. This

may be attributed to the existence of the riverine actions encouraging better organic matter

breeding. This too de

available productive soil for better yields. Thus, efficient landuse planning is a prime concern for

this part of the study area too. The average productivity soils have 13% area under

consideration.



5.4.2 FAO framework of Land Evaluation for Crop Suitability Analysis

FAO Framework of land evaluation describes a scheme for land suitability classification.

According to it, land suitability is fitness of a given land for specific use. The following three

levels of decreasing generalization are defined:

2) Land suitability class: degree of suitability within orders, Highly suitable (S1);

Moderately suitable (S2); Marginally suitable (S3); Not suitable (N)

3) Land suitability sub-class: degree of limitation within class, s-texture, depth, stoniness

& slope; w-d

1) Land suitability order: kind of suitability, S or N

rainage; e-erosion.

vel. The soil-terrain suitability rating used in the study captured the factors that

influence production sustainability.

i) Suitability assessment for LUT-1 (Pigeon pea crop)

Pigeon Pea is an important pulse crop grown in the study area as inter crop and mixed

crop. It is a highly drought resistant crop. The crop may be grown on very deep, well-drained

soils.

In the present study, three major landuse types namely, LUT-1 (pigeon pea), LUT-2

(sorghum) and LUT-3 (soyabean) predominant over the study area have been chosen for the

soil-terrain suitability analysis. The soil-terrain factors required for specific landuse types,

considered for the study have been soil texture, depth, stoniness, slope, erosion and drainage or

wetness factor Appendix 7. Based on these parameters suitable for a landuse type (crop), a

suitability study has been performed and their outcomes are presented at a sub-class

classification le



7%

32.83%

Highly Suitable Moderately Suitable Marginally Suitable Not Suitable

51.21%

14.81.09%

Figure 5. 25: Areal extent of suitability class for LUT-1 (Pigeon pea)

framework. Land suitability of each soil-mapping unit was assessed by comparing soil and land

characteristics with crop requirement criteria. The suitability class of the map-units are given in

The Pigeon pea suitability map in Fig 5.28 gives an illustrated insight into the

ost of the areas along the plains were

Table 5.16: Distribution of suitability class for LUT-1 (Pigeon pea)

Sl No Suitability Class Area % 1 Highly Suitable (S1) 1.09 2 Moderately Suitable (S2) 51.21 3 Marginally Suitable (S3) 32.83 4 Not Suitable (N) 14.87

The suitability of Pigeon pea crop was assessed using the FAO land evaluation

Appendix 10. The areal distribution of pigeon pea suitability has been shown in Fig 5.25. In case

of Pigeon Pea crop suitability, the classes of S1, S2, S3 and N were identified. A little over 1%

of the area was found to be highly suitable for the crop. This is predominantly in the flood plains

of Raisen district. Nearly 52 % area was found to be moderately suitable. In the marginally

suitability about 32 % area is encompassed. Around 15 % of the study area is however not

suitable for the landuse type. The majority of the non suitable areas were found to be in the

highly eroded gully areas on the western fringe.

distribution of the suitability pattern over the study area. M

found to be moderately suitable (S2), for the landuse type. An extensive patch over the rugged

western margins were found to be marginally suitable (S3) for pigeon pea.



Area distribution for suitability of pigeon pea crop in relation to physiographic units has

been depicted in Table 5.17. It shows clearly that nearly 24% of non-suitable areas are within the

hill ranges. Whereas, the plains have about 60% area of moderate suitability for pigeon pea.

Interestingly the undulating plains, flood plains have respectively 50% and 40% area of highly

suitable category for the cultivation of the crop. This is due to the highly favourable soils in

terms of texture, depth and chemical characteristics prevalent over the area.

Table 5.17: Areal distribution for LUT-1 (Pigeon pea) suitability based on physiographic units

Pigeon Pea Suitability (Area %) Physiography S1 S2 S3 N

RaResidual Hill − 0.52 17.28 10.96

Undulating Plateau − 1.50 13.10 1.52

Sorghum is the third major food grain crop of India. It is a greatly drought resistant crop.

It is predominantly rainfed.

Hill nge − 3.49 8.29 24.91

Plateau − 17.81 30.63 54.41

Plains − 58.09 16.84 0.00 Undulating Plain 50.06 18.49 8.69 8.21 Valley 9.14 0.10 5.17 − Flood Plains 40.80 − − −

ii) Suitability assessment for LUT-2 (Sorghum crop)

30.55%

Highly Suitable Mo rginally S Not Suitable

24.19%14.87%

30.40%

derately Suitable Ma uitable

Ar l extent of suitab for LU

Figure 5.26: ea ility class T-2 (Sorghum)



The suitability of Sorghum crop was assessed using the FAO land evaluation framework.

For Sorghum crop suitability, all the four classes S1, S2, S3 and N were identified and their

extents tabulated in Table 5.18. The highly suitable class occupies only nearly a quarter (24%)

of the total area. In the moderate and marginal suitability category, 60% of the study area was

covered, with equal share to each class. The similar areas in the rugged west and sparsely in the

eastern belt were found to be not suitable for the crop to be undertaken.

T

Sui1 Highly S le (S1) .19

Moderate uitab ) 55 Marginal uitab ) 0

4 Not Suita (N) 7

Table 5.1 a deta tab tion ph ographic unit wise area

distribution under different suitability class. The sorghum suitability map (Fig 5.29) shows the

spatial distribution of the suitability. The plains seem to occupy 66% of total area under highly

o 49% area were found to be under the

plains, 22% under the undulating plateaus. The plateaus occupy about 54% of non-suitable

areas, mostly dominant in the western areas, characterized by the rugged gullied terrains.

Considerably 33% of the marginally suitable areas were found to be under the plateaus.

Table 5.19: Areal distribution for LUT-2 (Sorghum) suitability based on physiographic units

Sorghum Suitability (Area %) Physiography S1 S2 S3 N

Hill Range − 1.34 13.49 24.91 Residual Hill 0.51 0.47 18.66 10.96 Plateau 12.25 20.16 33.08 54.41 Undulating Plateau 2.64 0.43 14.15 1.52 Plains 66.08 48.70 14.51 0.00 Undulating Plain 18.40 21.74 5.85 8.21 Valley 0.12 5.70 0.26 − Flood Plains − 1.46 − −

able 5.18: Distribution of suitability class for LUT-2 (Sorghum)

Sl No tability Class Area % uitab 24

2 ly S le (S2 30.3 ly S le (S3 30.4

ble 14.8

9 shows iled ula of ysi

suitable category. In the moderate suitability category to



iii) Suitability assessment for LUT-3 (Soyabean crop)

Soyabean is grown both as a pulse as well as oilseed crop. Generally grown in the kharif

season it is a typical rainfed crop. It however does not tolerate drought. Heavy clays are best, if

avoided for soyabean cultivation.

54.75%

1.09% 15.33%

28.82%


Figure 5.27: Areal extent of suitability class for LUT-3 (soyabean)

The suitability of Soyabean crop was assessed using the FAO land evaluation

framework. The suitability analysis (Fig 5.27) reveals that the classes of S2, S3 and N are

dominant. As evident from Table 5.20, meager 1% of the area was found to be highly suitable

for soyabean. The moderately suitable class (S2) however, occupies the maximum area of 55 %

of the total area. In the marginally suitable category (S3) 28% area is covered.

Table 5.20: ion o Soyabean)

Suita ty C %Highly S e (S1) 9

2 Modera ta 5 ina tab 2

Suit ) 3

The areal distribution of Soyabean suitability classes under each of the major

physiographic units have been described in Table 5.21. The plains, undulating plains too seem to

dominate the highly suitable and moderately su able classes. These trends were found to be in

coherence with the soil’s favourable characteristics to support the crop growth. The soyabean

suitability map gives an overview of the pattern of suitability distribution (Fig 5.30).

Distribut f suitability class for LUT-3 (

Sl No bili lass Area 1 uitabl 1.0

tely Suil i

ble (S2)l )

54.73 Marg4 Not

ly Suable (N

e (S3 28.815.3

it



Table 5.21: Areal distribution for LUT-3 (Soyabean) suitability based on physiographic units

Soyabean Suitability Area % Physiography S1 S2 S3 N

u − 1.40 13.31 4.50 − 56.87 14.35 0.00

Undulating Plain 50.06 17.37 9.76 7.96 Valley 9.14 3.05 0.27 − Flood Plains 40.80 − − −

Hill Range − 3.26 9.45 24.16 Residual Hill − 0.49 19.68 10.62 Plateau − 17.55 33.18 52.76 Undulating PlateaPlains



Figure 5.28: LUT-1 (Pigeon pea) suitability map

Figure 5.29: LUT-2 (Sorghum) suitability map

Figure 5.30: LUT-3 (Soyabean) suitability map



5.4.3 Soil Productivity Index based crop suitability assessment

Analysis by integration of SPI with the suitability class facilitates for its further

classification to identify suitability unit based on their productive capacity. Map units with same

suitability class and different SPI ratings will not have similar response to sub-class map units

and their soil productivity.

(i) Suitability of LUT-1 (Pigeon pea crop) based on SPI class

Table 5.22: Di uita arious SPI classes SPI (Area %)

stribution of s bility for LUT-1 (Pigeon pea) under v

Soil Suitability Class Good Average Poor Extremely Poor

S1 − 1.09 − − S2 45.33 4.10 − 1.78 S3 8.33 8.65 5.87 9.98 N − 1.31 1.31 12.24

Areas of high suitability were found to have an average productivity of soil (Table 5.22).

Overall, maximum area is under moderate suitability with good productivity. It accounts for

45% of the area. However, 10% of the area under extremely poor productivity was marginally

suitable for pigeon pea crop. The 12% area of extremely poor productivity may be one of the

reasons for it being unsuitable for the crop.

1.09

45.33

4.101.78

8.33 8.655.87

9.98

1.31 1.31

12.24

0

10

20

30

40

50

Aver

age

Goo

d

Aver

age

Extre

mel

yPo

or Goo

d

Aver

age

Poor

Extre

mel

yPo

or

Aver

age

Poor

Extre

mel

yPo

or

HighlySuitable

Moderately Suitable Marginally Suitable Not Suitable

Area

%

Figure 5.31: LUT-1 (Pigeon pea) suitability distribution under various SPI classes



The information derived here may help in prioritizing area for sustained crop production

31, for LUT-1, areas of

n extremely poor soil productivity class.

:

ea %)Suitability Clas Good Average Poor Extrem Poor

Hig uitable (S1) 22.80 − − 1.39 Moderately Suitable 25.31 3.67 1.57 Marginally Suitable ( 5.55 10.03 4.83 Not Suitable (N) − 1.31

d no area was having extremely poor productivity.

About 13% of the unsuitable area was having extremely poor productivity as seen in Fig 5.32.

as well as improving the crop management practices. As seen in Fig 5.

high suitability were found to have only average soil productivity. In case of moderately suitable

areas, nearly 45% areas have good productive soils. Areas marginally suitable show a

considerably significant contribution from extremely poor soils. Similarly the areas which are

not suitable also have show

(ii) Suitability of LUT-2 (Sorghum) based on SPI class

Table 5.23 Distribution of LUT-2 (Sorghum) suitability under various SPI classes

SPI (Ar s ely

hly S (S2) −

S3) 9.98 1.31 12.24

Sorghum was reflected to be the ideal crop to be taken up in an intensive manner in the

study area to make sure the resources suitable for its sustenance, were optimally used (Table

5.23). Nearly 23% of the area with high suitability for sorghum was highly productive. Whereas,

only 1% was found to have extremely poor productivity. Similarly, for the moderate suitability,

25% area was having good productivity an

22.80

1.39

25.31

3.671.57

5.55

10.03

4.83

9.98

1.31 1.31

12.24

0

10

20

30

Goo

d

Extre

mel

yPo

or Goo

d

Aver

age

Poor

Goo

d

Aver

age

Poor

Extre

mel

yPo

or

Aver

age

Poor

Extre

mel

yPo

or


Area

%

Figure 5.32: LUT-2 (Sorghum) suitability distribution under various SPI classes



The suitability distribution under various productivity classes would provide valuable

information to delineate areas for prolonged crop production along with improved management

practices.

(iii) Suitability of LUT-3 (Soyabean) based on SPI class

Table 5.24: Distribution of LUT-3 (Soyabean) suitability under various SPI classes

rea %) Poor Extremely Poor

Highly Suitable (S1) − 1.09 − −

Marginally Suitable (S3) 5.28 8.16 5.87 9.52 Not Suitable (N) − 1.31 12.70

Soyabean s a er the average productivity area as seen in

Table 5.24. Although nearly 50% of the ea was found, to be under the

SPI (ASoil Suitability Class Good Average

Moderately Suitable (S2) 48.38 4.59 − 1.78

1.31

uitable soils occupy meag 1% of

moderately suitable ar

good productive soils. These would be ideal to take up soyabean, with proper management

strategy ensured. They might in turn lead on to be highly suitable if the management practices

were carefully executed. Fig 5.33 shows the graphical display of the areal extents.

50

60

1.09

48.38

4.59 1.785.28 8.16 5.87

9.52

1.31 1.31

12.70

0

10

20

30

40

Aver

age

Goo

d

Aver

age

Extre

mel

yPo

or Goo

d

Aver

age

Poor

Extre

mel

yPo

or

Aver

age

Poor

Extre

mel

yPo

or

HighlySuitable

Moderately Suitable Marginally Suitable Not Suitable

Are

a %

Figure 5.33: LUT-3 (Soyabean) suitability distribution under various SPI classes



5.4.4 Climatic Yield Potential (Water Limited Yield Potential) Suitability Analysis

oth temperature and rainfall factors,

which play significant role in crop growth. Temperature as a factor influences agricultural

roductio tem g early

flowering. The sho eld. Increasing temperatures

ensure increased transp ion th y ind ing yield to fall. If the growing

period is shorter le e cr ere is loss of yield. LGP larger than the

crop growing cyc d loss too hese l ope throug

climatic factors. Water stress affects the crop growth, yield formation. The yield reducing effects

ion calculated

for fully optimized production situation is normally greater than the production realized from

farming. It is not the actual production, rather the biophysical potential production, which is

determined by temperature and water availability.

The areal extents for LUT-1 (pigeon pea) of water limited yield potential classes have

been tabulated in Table 5.25. About 3% area with > 70% yield potential were found to be most

dominant over the Pachmarhi area and in strips over Ujjain and Indore. The extreme eastern area

with nearly 36.5% coverage, were found to be reflecting stressful conditions for crops due to <

35% water limited yield potential. The central and southern parts were found to have low to

moderate water limited yield potential.

Table 5.25: Distribution of water limited yield potential classes for LUT-1 (Pigeon pea

Sl No Potential (%) Area (%)

1 High (> 70) 3.42 2 Moderate (50 - 70) 23.45 3 Low (35 - 50) 36.60 4 Very Low (< 35) 36.54

It is a complete climatic parameter that includes b

p n. Higher peratures reduce total duration of a crop cycle by inducin

rter the crop cycle, the lesser the relative yi

evaporation and irat ereb uc s

than the growth cyc of th op, th

le implies yiel . T osses rate h yield reducing effects of

of water stress varies from crop to crop.

Water-limited yield potential under normal conditions is a realistic indicator of

biophysical possibilities i.e. production potential. As a reference value, it is preferred to

biophysical yield potential. It can be taken as land quality indicator. The product

)

Water Limited Yield



43% area was highly suitable with more than 85% yield potential. This is because the crop can

withstand harsh conditions and even then survive. About 31% area was found to have moderate

Table 5.26: Distribution of water limited yield potential classes for LUT-2 (Sorghum)

4 Very Low (< 55) 7.70

like the pigeon pea crop. The areal distribution of the

crop’s yield potential under various classes have been provided in Table 5.27. Less than 1% area

0.99 2 Moderate (50 - 70) 23.63 3 Low (35 - 50) 21.06

36% area had less than 35% yield potential. Whereas, for

LUT-2 (sorghum), more than 40% area was found to have more than 85% water-limited yield.

he water-limited yield potential for all the three crops behaved in terms of the duration of the

crop cycle. The longer the cycl h r yield potential. The soil depth

prevalent over the area has also affected the yield. Areas with shallow soils have reflected lesser

yields, in spite of having modera to h

In case of sorghum, however, the trend is different. As is evident from the Table 5.26,

water-limited yield potential. However, nearly 8% area was found to have very low water-

limited yield potential.

Sl No Water Limited Yield

Potential (%) Area (%)

1 High (> 85) 43.00 2 Moderate (70 - 85) 30.70 3 Low (55 - 70) 18.60

Soyabean crop has similar trends

was found highly suitable for the crop in terms of water-limited yield potential, whereas, about

54% area has less than 35% water-limited yield potential.

Table 5.27: Distribution of water limited yield potential classes for LUT-3 (Soyabean)

Sl No Water Limited Yield Potential (%) Area (%)

1 High (> 70)

4 Very Low (< 35) 54.32

From the yield potential suitability study, it was found that the yield potential coverage

has varied from one land utilization type to another. As has been observed, for LUT-1 (pigeon

pea) and LUT-3 (soyabean), more than

T

e, the igher was the wate -limited

te igh LGP.



having deep soils (> 75 cm) and the other having shallow soils (< 25

cm). In this case, Hoshangabad, with soils deeper than 75 cm and Bhopal with less than 25 cm

oils were chosen.

Apart from short and excessively long LGPs, the dominant soil depth in an area also

affects the water-limited yield potential. The BUDGET program being a simple water balance

model, considered the soil depths while estimating the relative yield. In order to study the impact

of soil depth in water-limited yield potential computation, in high rainfall areas, two stations

were selected, one was

s

120

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

- D d)

mm

)

25

125

Rai

n

PET (mm)30

60

90

PET

(

50

75

100

fall

(mm

)

0 0Rain (mm)

10 ay Period (Hoshangaba

ist ution over Ho

Figure 5.34: PET and Rainfall d rib shangabad with high rainfall and deep soil conditions

The ten-day composite PET (Appendix 12) and rainfall (Appendix 14) plotted (Fig 5.34)

for the station reflects that the high decadal rains of more than 120 mm, with moderate PET

result in medium LGP, which is conducive for crop growth. This area being ideal in terms of

climatic as well as soil factors was found to have variable yield potentials for varied land

utilization types (crops).



1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

10 - Day Period (Bhopal)

0

60

80

100

120

fa

PET (mm)Rain (mm)

0

20

40

60

80

100

120

PET

(mm

)

20

40 Rai

nll

(mm

)

Figure 5.35: PET and Rainfall distribution over Bhopal with high rainfall and shallow soil conditions

For Bhopal, the shallow soils played a significant role. The decadal PET and rainfall in

Fig 5.35 gives an indication to the LGP trend prevalent in the area. The soils being a constraint

along with high PET rates and rainfall result in low to very-low moisture-limited yield potential.

The impact of soil depth was also analysed taking into account the stations under low

rainfall areas. Raisen with soils deeper than 75cm and Ujjain with shallow soils were chosen for

this part.

60

80

100

120

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

E 40

80

120

160

Rai

nfal

l (m

m)

Rain (mm)0

20

40

10 - Day Period (Raisen)

PT

(mm

)

0

PET (mm)

Figure 5.36: PET and Rainfall distribution over Raisen with low rainfall and deep soil conditions

For a low rainfall region like Raisen with high PET rates, the yield potential was found

to be ranging from 35% to more than 85%. The LGP for all crops, in this area too was found to

be more than 100 days as seen in Fig 5.36. This is because the soil conditions were ideal for the

moderate to high water limited yield potential.



0

20

40

60

80

100

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

10-Day Period (Ujjain)

PET

(mm

)

0

20

40

60

80

100

Rai

n (m

m)

PET (mm)Rain (mm)

Figure 5.37: PET and Rainfall distribution over Ujjain with low rainfall and shallow soil conditions

looking into the trends of rainfall and PET distribution (Fig 5.37). Because of low LGP available

for the crops, the yield potentials were also found to be low to very low. Soils in these areas

dominantly being less than 25 cm deep were also responsible for the low yields.

Ujjain being endowed with shallow soils have constrained water-limited yield potential.

The LGP observed over the area was found to be about 100 days as could be observed by



Integrating climatic yield potential suitability with FAO soil suitability to generate modified suitability

The resultant soil-terrain suitability classes reflect interactions between characteristics of

land. Hence, this method of determining land suitability is mostly qualitative. However, there is

an increasing demand for more quantification in land evaluation. In the agro-climatic suitability

assessment, degree of water-limited yield potential must be taken into account. This necessitated

inclusion of objective relationship between biophysical data; more precisely water-limited yield

potential.

l

uitability classes facilitates for further classification of it to identify suitability unit based on

their yield potential. Map units with same suitability class and different yield potential will have

Soil Suitability Climate Suitability Modified Suitability

S1 S1 S1

S2 S2

S2 S1 S1

S2 S2

S3 S2

S3 S2 S2

S3 S3

N N

N S3 S3

N N

The decision was based on the criteria that if the soil suitability is of a lower order (S2)

and water limited yield is of higher order (S1), then the resultant suitability would be upgraded

to one class higher suitability (S1). This would ensure that with proper management practices

the constraint posed by the lower order suitability could be mitigated, under climatically suitable

conditions.

Analysis by integration of FAO soil-terrain suitability with the climatic yield potentia

s

different response to sub-class map units and their yield potential.

The following decision criteria was adopted for integration of the soil suitability with the

climate suitability to arrive at the final modified suitability for a specific crop.



(i) Suitability of Pigeon pea by integrating climatic yield potential with soil suitability

Integrating information of climatic as well as edaphic potentials would lead to better

understanding of the natural resources at our disposal. This would ensure an improvement in the

suitability assessment studies with detailed intricacies.

Water Limited Yield Potential (Area %) Soil Suitability

> 75 % 50 – 75 % < 50 % S1 − 0.10 0.99

bution of the soil suitability

improved upon by integration of climatic yield potential parameter has been illustrated in Fig

5.38. In areas of high suitability less than 1% of the total area was found to have 50-75% and

less than 50% water-limited yield potential. This was found in the plateaus in the Pachmarhi

area and along the Nar ha M y areas dominant over

southern Indore, Dewas, northern Harda, central and eastern Hoshangabad and parts of Sehore

and Bhopal occupy about 17% area having 50-75% water-limited yield potential and 35% area

having less than 50% water-limited yield potential. They were m tly dominant in the plains and

plateaus of the study area. The areas with m inal suitability class have nearly 24% area with

less than 50% water-limited yield potential. Interestingly, less than 1% of the total area under

marginal and non-suitable conditions for pigeon pea was found to have more than 75% water-

limited yield potential. About 14% of the tot rea under non-s ble conditions have less than

50% water-limited yield potential.

Table 5.29: Distribution of LUT-1 suitability modified with climatic yield potential suitability

4 S3 S1*/ S2 S2 8.70 5 S3 S3 S3 24.13 6 N S3 S3 13.38 7 N S1*/ S2* N 1.48

Table 5.28: Distribution of soil suitability for LUT-1 (Pigeon pea) under water limited yield potential classes

S2 − 16.59 34.62 S3 0.79 7.91 24.13 N 0.20 1.28 13.38

The area percentage of each suitability class assigned to each of the water-limited yield

potential classes have been tabulated in Table 5.28. The distri

mada in Hos ngabad district. oderate suitabilit

os

arg

al a uita

Sl No Soil Suitability Climate Suitability Modified Suitability Area %

1 S1 S2/ S3* S2 1.09 2 S2 S2 S2 16.59 3 S2 S3 S3 34.62



The integration of the broader FAO soil suitability classes and water limited yield

potential suitability classes have resulted in the final areal extents in each suitability classes for

and determined the water limited yield

po r

igeon pea cultivation. Whereas, significant 72% area was found to have marginal suitability.

(ii) Suitability of Sorghu by integr ing clim yield potential with soil suitability

Sorghum is an importa crop in entral s of Ind a result, it was undertaken

for the various suitability analysis. The areal distribution of water-limited yield under each soil

suitability category has been tabulated in Table 5.30. It shows that a considerable amount of the

area with high suitability index has higher yield potentials too.

N 1.48 12.45 0.93

The distribution of the soil suitability improved upon by integration of climatic yield

potential parameter has been illustrated in Fig 5.39.

1) areas, 24% of the total area was found to have more than

75% of water limited yield potential. These areas were most dominant in the plains along the

armada. Parts of central Indore, north-eastern Ujjain have highly suitable areas for sorghum

cultivation. The entire belt stretching from rn Harda and tending till Narsimhapur in the

east have a sm th linea in the suita ity pattern for the crop. Areas with moderate

suitability (S2) dominant over Ujjain and Bhopal occupy nearly 30% of the total area. Areas of

marginal suitability (S3) predominant in the me western Ujjain and Raisen have 14% area

Pigeon pea crop as seen in Table 5.29. The (*) indicates areas where the soil suitability does not

match the climatic suitability as the soils in these areas being a constraint, the BUDGET

program gave more weightage to the climatic factors

tential in tune with the climatic parameters. Nearly 26% area has moderate suitability fo

p

m at atic

nt the c part ia. As

Table 5.30: Distribution of soil suitability for LUT-2 (Sorghum) under water limited yield potential classes

Water Limited Yield Potential (Area %) Soil Suitability

> 75 % 55 – 75 % < 55 % S1 24.19 − − S2 27.05 3.50 − S3 14.31 9.31 6.77

In case of highly suitable (S

N

northe ex

oo rity bil

extre



with very high water-limited yield potential. Interestingly, about 13% of the area under non-

suitable conditions for pigeon pea growth was found to be having 55-75% water-limited yield

potential.

Table 5.31: Distribution of LUT-2 suitability modified with climatic yield potential suitability

Sl No Soil Suitability Climate Suitability Modified Suitability Area % 1 2 S2 S1 S1 27.05

S1 S1 S1 24.19

3 S2 S2 S2 3.50 4 S3 S1* S2 14.31

S3 S3 S3 6.77 7 N S2*/ S3 S3 13.38

ly suitable (S1) for sorghum. In

case of moderately suitable class, nearly 27% area appeared to be ideal for the crop to be

undertaken.

(iii) Suitability of Soyabean by integr lim p al with soil suitability

Soyabean is the most important pulse crop gr

was one of the most probable choices for the suitability assessment study. The areal distribution

f water-limited yield under each soil suitability category has been tabulated in Table 5.32. The

area statistics reveal that moderately suitable soils have more than 50% yield potentials.

oyabean) under water limited yield potential classes

Water Limited Yield Potential

> 75 % 50 – 75 % < 50 %

5 S3 S2 S2 9.31 6

8 N S1* N 1.48

Final suitability analysis for the sorghum crop has been done by integrating the soil and

climatic suitability outputs. The area statistics of the modified suitability category have been

tabulated in Table 5.31. About 51% area turned out to be high

ating c atic yield otenti

own in the central parts of India. Thus it

o

Table 5.32: Distribution of soil suitability for LUT-3 (S

(Area %) Soil Suitability

S1 − 0.10 0.99 S2 − 16.59 38.17 S3 0.79 6.83 21.21 N 0.20 0.11 15.01

The distribution of the soil suitability improved upon by integration of climatic yield

potential parameter has been illustrated in Fig 5.40.



The area has nearly 25% area that was realized to have moderate suitability (S2) for the

soyabean crop covering parts of Indore, western Ujjain, flood plains in Raisen and in parts of

Hoshangabad and Harda. Majority of the area under soyabean was found to have marginal

suitability (S3) for its development. Nearly 74% of the area mostly concentrated in Ujjain,

dore, Raisen and Narsimhapur and along the river course was found to be ideally suitable.

Table 5.33: Distribution of LUT-3 suitability modified with climatic yield p l suitability

Soil Suitability Climate Suitability Modified Suitability A S 3* 1 3 S 2

5 S3 S3 S3 21.21 6

(*): Indicates that the climatic suitability generated by the BUDGET program stressed more on

the climatic parameters than the soil conditions.

In

otentia

Sl No rea % 1 S1 2/ S S2 1.09 2 S2 S2 S2 6.59 3 S2 S3 S3 8.17 4 S3 1*/ S S2 7.62

N S3 S3 15.01 7 N S1*/ S2* N 0.32



Figure 5.38: UT-1 (Pi a) suitabili ap by integratin imatic yiel tial with soil suitability

L geon pe ty m g cl d poten

Figure 5.39: LUT-2 (Sorghum) suitability map by integrating climatic yield potential with soil suitability

Figure 5.40: LUT-3 (Soyabean) suitability map by integrating climatic yield potential with soil suitability



The matching of land qualities with crop requirements includes soil-physiographic and

climatic evaluation. Sufficient agricultural exploitation of the climatic potential and maintenance

of land productivity largely depend on the soil characteristics and its management on an

ecologically sustained basis. Soil’s physical attributes influence its ability to retain and supply

water, so that crops can fully utilize climatic resources of a given location. An understanding of

soils is essential for the effective exploitation of climate, terrain and crop resources. Soil

suitability analysis was based on the knowledge of crop requirements under prevailing soil

conditions. In other words, the soil suitability classifications quantify the extent to which soil

conditions match crop requirements.



Chapter 6 SUMMARY & CONCLUSIONS

. In process of achieving agro-climatic soil suitability

assessment for rainfed land utilization types using FAO approaches, the other objectives

addressed were preparation of landuse/ land cover inventory of kharif season, developing a long

term database of climate and soil. The specific requirements for growth of three selective rainfed

[LUT-1 (pigeon pea), LUT-2 (sorghum) and LUT-3 (soyabean)] were matched with the soil and

land characteristics to assess its suitability for sustained production. The various parameters used

in conjunction with the FAO framework of land evaluation, to refine the suitability analysis

included Soil Productivity Index (SPI) and Climatic Yield Potential (water limited yield

potential). Suitability assessment provides the basis for estimation of potential productivity of

land resources.

The present study area including nine districts of Madhya Pradesh was carefully chosen

keeping in mind its variability in terms of the agro-ecological sub-regions (AESR) namely

AESR 5.2 (Hot moist semi arid) and AESR 10.1 (Hot dry sub-humid). The AESR 5.2 occupies

the western districts of Indore, Ujjain and Dewas, while AESR 10.1 comprises of Bhopal,

Raisen, Sehore, Harda, Hoshangabad and Narsimhapur. The study area stretches from west to

central Madhya Pradesh, covering nearly 49, 958 sq.km of south central Madhya Pradesh,

falling in rainfed agro-ecosystem.

A single date AWiFS scene of October, 2005 was used to prepare a kharif season

landuse/ land cover inventory to study general landuse pattern in the study area. The majority of

the area was found to be dominated by Soyabean (Kharif 1), with nearly 36% of the total area

coverage. It was followed by Pigeon pea (Kharif 3), which was grown over nearly 2.55% of the

Soil and climate based agro-ecological approach enables to identify zones with unique

combination of homogenous climate and soil factors for crop production. The present study was

undertaken to assess the soil and land potential in general and in unison with climatic suitability

to improve the crop suitability assessment to identify area for sustainable crop production in

rainfed agro-ecosystem. The main objective was to characterize soil and climate to address agro-

climatic crop suitability assessment



area. Sorghum was mostly grown as an intercrop with soyabean. Paddy (Kharif 4) was grown

1.94% and 1.02%. Nearly 24% area is under forest cover.

The vector coverage of soil map of NBSS & LUP at 1: 250, 000 scale was prepared

textural and depth constraints in these areas. These areas being extensively

degraded and erosive in nature, thus the soils have very low AWHC. Nearly 24% area was

found to have very low (< 50mm) AWHC. The plains were found to have distinctly high AWHC

(150-200 mm). Nearly 27% of the area was found to be having high AWHC whereas 31% area

was under very high (> 200mm) AWHC.

ed from IMD, for a period of 1980 to 2003 of 28 stations of

Madhya Pradesh were compiled on a decadal and monthly basis. Using the monthly average

temperature, the 24-years average monthly PET was computed using Thornthwaite method. 24-

years average monthly and decadal rainfall and PET were computed. The climatic station data

were then interpolated using FAO New LocClim to represent the climatic variability spatially

using GIS software. Spatial monthly and decadal rainfall and PET were analysed to estimate

over 3% area. Sugarcane (Kharif 5) and cotton (Kharif 6) were also found to be grown in nearly

using ARC GIS. The study area was found to have 131 soil-mapping units. The area was

broadly classified into eight major physiographic units, the most dominant being the plains. The

area of interest was found to be dominated by Vertisols with lithic ustorthents, typic haplustalfs,

vertic ustochrepts, typic ustochrepts, chromic haplusterts etc. The western areas were found to

have shallow and skeletal soils, whereas the soils in the rest of the area were highly favourable

in terms of depth as well as texture. Storage of moisture and its supply to crops during growing

period is a major function of soils in rainfed region. Thus, the study of Available Water Holding

Capacity (AWHC) became important in the study. In order to obtain area specific available

water holding capacity, the application of a unique pedo-transfer function, developed for the

Vertisols soils was selected as it neglected the generalizations introduced by the universal pedo-

transfer function based on general texture patterns. The soil-mapping unit wise AWHC was

estimated based on the AWHC of the soil series collected from field, various literature and Soil

Series of Madhya Pradesh (NBSS & LUP Publication No 78). It was observed that the western

areas comprising of Indore, Ujjain and Dewas were deficit, with less than 50mm AWHC. This

was due to the

Spatial distribution of climatic data (rainfall and PET) was required to analyse its spatial

influence on crop growth parameters. In the study, the daily climatic data (rainfall, minimum

and maximum temperature), obtain



LGP variation over the study area. As crop suitability assessment is a prime objective of the

study, hence the concept of LGP holds immense significance. In the present study, a climate

based spatial LGP was estimated. A trend of general LGP was estimated using the 10 day

composite PET and rainfall.

study area. Soil suitability analysis based on the FAO

amew

tic

arameter that includes both climate (temperature and rainfall factors) and soil factors (depth

An endeavour to estimate a crop specific LGP was taken using simple soil-water balance

program; BUDGET. The LGP was estimated for each of the three LUTs namely pigeon pea,

sorghum and soyabean, with respect to the soil simulation units, using the BUDGET program

that derive actual evapotranspiration and potential evapotranspiration. The soil simulation units

were derived by combining soil mapping units of same depth and texture classes. A total of 84

simulation units were obtained.

Considering the physical and chemical characteristics of the soil-mapping units, the soil

productivity index (SPI) (Requier model, 1976) or the FAO productivity rating was assessed.

The parameters of soil depth, texture, moisture, drainage, base saturation and CEC were used to

assess soil productivity ratings. Soils of the plains, valleys and undulating plains were found to

have good SPI (> 35). The good productive class occupied 54% of the total area. The average

productive rating of soils was over 15% of the area, whereas nearly 24% area was rated

extremely poor for the areas of highly erosive gullied lands. The study area has eight major

physiographic units namely hills, residual hills, plains, undulating plains, plateaus, undulating

plateaus, valleys and flood plains. Among them, the plains were observed to have good

productive soils.

There are three major rainfed crops (LUTs) namely pigeon pea, sorghum and soyabean

which are grown predominantly over the

fr ork of land evaluation was assessed for these crops. The soil characteristics considered

for the study were soil texture, depth, stoniness, slope, erosion and drainage or wetness factor. In

case of LUT-1 (pigeon pea), 51% area was found to be moderately suitable (S2), followed by

32% area marginally suitable (S3) and nearly 15% area was unsuitable. Suitability analysis for

LUT-2 (sorghum) showed about 25% area to be highly suitable (S1), followed by 31%

moderately suitable (S2) and 30% marginally suitable (S3). LUT-3 (soyabean) was observed to

have 55% area under moderately suitable class (S2) and 29% area to be marginally suitable (S3).

Climatic yield potential expressed as, water-limited yield potential is a complete clima

p



and texture) in determining crop growth. Under normal conditions it is a realistic indicator of

biophysical possibilities i.e. production potential.

The water-limited yield potential was estimated by the BUDGET program, which takes

into account monthly rainfall, PET, crop parameters (rooting depth, depletion factor, growth

ve more dominance to the climatic parameters. The program

reflected that shorter the crop cycle, the lesser was the yield potential. At the same time, areas

er, seldom has the FAO framework of land evaluation been improved upon

with information like water-limited yield potential for a regional level analysis. Thus, the

tempt

stage’s length, Kc values and yield response factor), soil parameter (depth, texture). For each

simulation unit, the daily influence of water stress on crop growth was computed. Climatic yield

potential for each simulation unit (combination of climate and soil types) was estimated for each

LUT (viz, pigeon pea, sorghum and soyabean). It was observed that water-limited yield potential

varied with the climatic variability with same type of soils. The BUDGET program being a

simple water balance model, ga

with shallow soils even with medium crop cycle have shown very low yield potential.

In the study, an attempt was made for the improvement of the traditional suitability

assessment of landuse types by considering climatic yield potential. An improved suitability

assessment was suggested for the landuse types, by the integration of both the soil and climatic

potential. It was observed that for LUT-1 (pigeon pea), 26% area was moderately suitable (S2)

followed by 72% area of marginally suitable (S3) class. In case of LUT-2 (sorghum), 51% area

was observed to be highly suitable (S1) and 27% to be moderately suitable (S2). Various studies

have been made to assess the land evaluation or soil productivity or water limited yield

potential. Howev

at has been a unique endeavour to do a spatial agro-climatic suitability assessment at a

regional scale. Climatic yield potential presumed to be a useful indicator of land quality and can

possibly be used in better assessment of land resource for sustained crop production in the

rainfed agro-ecosystems. Thus, in conclusion it can be summarized that remote sensing and GIS

are useful tools in generating natural resource database to integrate and assess their potential on

spatial basis. Integration of GIS with water balance model in the present study was found to be

highly useful to assess agro-climatic suitability of crops in rainfed areas for better landuse

planning.



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i

APPENDIX

Fig 1: Soil map of the study area (NBSS & LUP)

ii

Fig 1.1: Soil mapping units

iii

Appendix 1: Soil mapping unit characteristics

Sl.No. Physiography SMU Taxonomy LGP (days)

Drainage Class

Depth (cm) Texture BSP OM

(%) CEC

1 Hill Ranges 124 Lithic Ustorthents 90-120 6-S.Ex. 10-25 Loamy 82-88 0.9 20-22

2 Undulating Plain

126 Typic Haplustalfs

90-120 5-well >100 Fine-loamy

88-92 0.2 32-38

3 Hill Ranges 128 Typic Haplustolls 90-120 4-M.well >100 Fine-

loamy 82-89 0.5 18-29

4 Undulating Plain 130 Typic

Ustochrepts 90-120 5-well 25-50 Loamy 82-89 0.9 18-29

5 Undulating Plain 131 Vertic

Ustochrepts 90-120 5-well 75-100 Fine 82-89 0.5 18-29


Haplustalfs 90-120 5-well >100 Fine-loamy 82-89 0.5 32-36

7 Undulating Plain

133 Typic Haplustalfs

90-120 5-well >100 Fine-loamy

81-91 0.9 10-23


Ustorthents 90-120 6-S.Ex. <10 Loamy-skeletal 81-91 0.9 10-23

9 Undulating Plain 135 Lithic

Ustorthents 90-120 6-S.Ex. <10 Loamy 81-91 0.9 10-23

10 Residual Hills 140 Lithic Ustochrepts 120-150 6-S.Ex. 25-50 Clayey 89-92 0.9 14-15

11 Residual Hills 142 Vertic Ustochrepts 120-150 5-well >100 Fine 71-88 0.5 17-20

12 Residual Hills 143 Typic Ustochrepts

120-150 5-well 25-50 Loamy 71-88 0.5 17-20

13 Residual Hills 144 Vertic Ustochrepts 120-150 4-M.well >100 Fine 71-88 0.5 17-20

14 Residual Hills 149 Lithic Ustochrepts 120-150 5-well 25-50 Loamy 80-85 0.9 24-34

15 Residual Hills 153 Typic Ustorthents 120-150 5-well 10-25 Clayey 80-85 0.9 24-34


Haplusterts 120-150 5-well >100 Fine 96-98 0.9 59-60


Ustochrepts 120-150 5-well 50-75 Fine 96-98 0.5 59-60


Ustochrepts 120-150 5-well 10-25 Loamy-skeletal 90-97 0.5 34-59

19 Undulating Plain 165 Chromic

Haplusterts 120-150 4-M.well >100 Fine 90-97 0.2 34-59


Ustochrepts 120-150 5-well >100 Fine 90-97 0.5 34-59




Ustochrepts 120-150 6-S.Ex. 25-50 Clayey 92-98 0.9 45-50

23 Plains 177 Typic Ustochrepts

150-180 5-well 25-50 Loamy 92-95 0.9 32-44

24 Plains 178 Typic Haplusterts 150-180 4-M.well >100 Fine 92-95 0.5 32-44




28 Plains 183 Typic Haplusterts

150-180 4-M.well >100 Fine 90-95 0.5 35-45


30 Plains 185 Vertic Ustochrepts 150-180 5-well >100 Fine 90-95 0.5 35-45

…contd.

iv


Drainage Class


(%) CEC


150-180 4-M.well >100 Fine 90-95 0.2 35-45



34 Plains 194 Vertic Ustochrepts 189-210 4-M.well 75-100 Fine 85-95 0.5 32-40



189-210 4-M.well >100 Fine 85-95 0.5 32-40

37 Flood Plain 197 Fluventic Ustochrepts 120-150 5-well >100 Fine-

loamy 85-90 0.9 32-40

38 Plateau 202 Lithic Ustorthents 90-120 6-S.Ex. 10-25 Loamy-

skeletal 58-67 0.2 12-15

39 Hill Ranges 203 Lithic Ustochrepts 90-120 5-well 25-50 Loamy 58-67 0.9 12-15

40 Hill Ranges 204 Lithic Ustorthents 90-120 6-S.Ex. <10 Loamy-

skeletal 58-67 0.9 12-15

41 Residual Hills 21 Lithic Ustorthents

90-120 6-S.Ex. 25-50 Loamy-skeletal

64-93 0.9 20-25

42 Residual Hills 210 Typic Ustochrepts 90-120 5-well 25-50 Loamy 85-88 0.2 10-18

43 Hill Ranges 218 Typic Ustochrepts 90-120 4-M.well >100 Fine 60-63 0.5 32-35

44 Hill Ranges 219 Vertic Ustochrepts 90-120 4-M.well >100 Fine 60-63 0.2 32-35

45 Residual Hills 22 Typic Ustropepts 90-120 6-S.Ex. 25-50 Clayey 64-93 0.9 20-25


47 Residual Hills 221 Lithic Ustochrepts 90-120 2-poor 25-50 Loamy-

skeletal 60-73 0.2 32-35

48 Residual Hills 224 Lithic Ustochrepts 90-120 5-well 25-50 Loamy 80 0.2 10-15

49 Residual Hills 228 Typic Ustochrepts 90-120 5-well >100 Fine-

loamy 46-56 0.2 18-22

50 Valleys 238 Typic Ustochrepts 150-180 7-Ex. 25-50 Loamy 91-95 0.2 24-34

51 Valleys 239 Typic Ustochrepts 150-180 5-well >100 Fine-

loamy 91-95 0.5 24-34

52 Undulating Plateau 248 Typic

Ustochrepts 120-150 5-well 25-50 Loamy 80-90 0.5 24-30


Haplustalfs 120-150 5-well 75-100 Fine-loamy 50-70 0.5 17-24

54 Residual Hills 26 Lithic Ustorthents 90-120 6-S.Ex. 10-25 Loamy 96 0.2 18-44

55 Plateau 266 Typic Haplustalfs 120-150 5-well >100 Fine-

loamy 78-98 0.2 29-45

56 Plateau 269 Typic Haplustalfs 120-150 4-M.well >100 Fine 78-98 0.5 29-45

57 Residual Hills 27 Typic Ustorthents 90-120 5-well 10-25 Loamy 96 0.5 18-44

58 Valleys 275 Typic Haplusterts 150-180 4-M.well >100 Fine 90-98 0.9 33-40

59 Plains 283 Typic Haplusterts 180-210 5-well >100 Fine 94-98 0.2 30-40

60 Plains 288 Typic Haplustalfs 180-210 5-well >100 Fine 94-98 0.2 30-40

…contd.

v


Drainage Class


(%) CEC

61 Hill Ranges 299 Lithic Ustochrepts

90-120 5-well 25-50 Clayey 60-70 0.9 30-35


Ustochrepts 90-120 5-well 25-50 Loamy-skeletal 60-70 0.5 30-35

63 Plains 301 Vertic Ustochrepts 120-150 5-well 50-75 Fine 92-95 0.2 42-52


65 Valleys 303 Vertic Ustochrepts 120-150 4-M.well >100 Fine 98 0.9 50

66 Valleys 304 Typic Ustochrepts

120-150 5-well 50-75 Fine 98 0.5 50

67 Hill Ranges 305 Typic Ustochrepts 150-180 5-well >100 Fine-

loamy 60-70 0.5 12-16


69 Plateau 311 Vertic Ustochrepts 120-150 5-well >100 Fine 92-98 0.5 38-48

70 Plateau 314 Typic Ustochrepts 120-150 5-well 75-100 Fine 92-95 0.2 40-45


180-210 4-M.well >100 Fine 88-97 0.5 47-55

72 Plateau 317 Typic Haplusterts 180-210 4-M.well >100 Fine 89-93 0.9 41-49


74 Plateau 322 Lithic Ustorthents 90-120 6-S.Ex. <10 Loamy-

skeletal 78-86 0.9 22-35

75 Plateau 323 Lithic Ustorthents 90-120 6-S.Ex. 10-25 Loamy-

skeletal 78-86 0.2 22-35

76 Plateau 324 Lithic Ustorthents 90-120 6-S.Ex. 10-25 Loamy 78-86 0.2 22-35

77 Plateau 325 Typic Ustorthents 90-120 6-S.Ex. 25-50 Loamy 78-86 0.9 22-35


Ustorthents 90-120 5-well 25-50 Clayey -skeletal 76-95 0.5 26-40


Haplustolls 90-120 6-S.Ex. 10-25 Clayey -skeletal 76-95 0.9 26-40


Haplustolls 90-120 5-well 25-50 Clayey 76-95 0.9 26-40

81 Undulating Plateau 332 Lithic

Ustochrepts 90-120 5-well 25-50 Clayey 76-95 0.9 26-40



83 Plateau 338 Typic Ustochrepts 120-150 5-well 25-50 Clayey 95-97 0.9 40-50



86 Plateau 344 Lithic Ustorthents 120-150 6-S.Ex. <10 Loamy 95-97 0.5 40-50


88 Plateau 354 Lithic Ustorthents 120-150 5-well 10-25 Loamy 84-98 0.9 36-42


90 Plateau 361 Vertic Ustochrepts 150-180 5-well >100 Fine 92-97 0.2 38-50

…contd.

vi

Sl.No. Physiography SMU Taxonomy LGP (days) Drainage Class


(%) CEC

91 Plateau 363 Typic Haplusterts

150-180 4-M.well >100 Fine 87-89 0.9 40-42

92 Plateau 365 Lithic Ustorthents 90-120 6-S.Ex. <10 Loamy-

skeletal 84-87 0.9 30-35

93 Plateau 366 Lithic Ustorthents 90-120 6-S.Ex. <10 Clayey 84-87 0.5 30-35




180-210 4-M.well >100 Fine 90-98 0.2 42-55


98 Plains 379 Chromic Haplusterts 180-210 4-M.well >100 Fine 90-98 0.2 42-55


100 Undulating Plateau 382 Typic Haplusterts 180-210 4-M.well >100 Fine 92-98 0.2 42-50

101 Undulating Plateau 384 Typic Haplusterts

180-210 4-M.well >100 Fine 92-98 0.2 42-50

102 Undulating Plateau 385 Typic Haplusterts 180-210 4-M.well >100 Fine 92-98 0.2 42-50

103 Undulating Plateau 386 Vertic Ustochrepts 180-210 4-M.well >100 Fine 92-98 0.5 42-50

104 Undulating Plain 391 Chromic Haplusterts 180-210 4-M.well >100 Fine 96-98 0.9 40-50

105 Undulating Plain 392 Typic Haplusterts 180-210 4-M.well >100 Fine 96-98 0.9 40-50




109 Plains 398 Vertic Ustochrepts 180-210 4-M.well >100 Fine 95-99 0.2 44-54



112 Plateau 400 Vertic Ustochrepts 150-180 4-M.well >100 Fine 94-97 0.2 34-40




116 Plains 405 Chromic Haplusterts 150-180 4-M.well >100 Fine 87-97 0.5 42-52


118 Plateau 409 Vertic Ustochrepts 120-150 5-well 75-100 Fine 92-95 0.5 35-45

119 Plateau 411 Lithic Ustochrepts 120-150 5-well 25-50 Clayey 92-95 0.2 35-45


…contd.

vii

Sl.No. Physiography SMU Taxonomy LGP (days) Drainage Class


(%) CEC

121 Undulating Plateau 42 Lithic Ustorthents

90-120 5-well 10-25 Clayey 85-96 0.9 35-48

122 Undulating Plateau 450 Lithic Ustochrepts 90-120 5-well 25-50 Loamy 84-86 0.9 24-36

123 Plateau 52 Vertic Ustochrepts 120-150 4-M.well 75-100 Fine 89-97 0.5 36-45

124 Plateau 57 Typic Ustropepts 120-150 5-well 25-50 Clayey 88-97 0.9 52-60

125 Plateau 6 Lithic Ustorthents 90-120 6-S.Ex. <10 Loamy 56-97 0.9 25-38

126 Plateau 64 Lithic Ustorthents

120-150 6-S.Ex. 10-25 Loamy 88-97 0.9 52-60

127 Plateau 66 Vertic Ustropepts 150-180 4-M.well >100 Fine 91-97 0.2 34-45


129 Plateau 68 Typic Ustropepts 150-180 4-M.well >100 Fine 91-97 0.2 34-45

130 Plains 75 Typic Ustropepts 150-180 4-M.well 50-75 Fine 82-92 0.2 27-40

131 Plains 79 Vertic Ustochrepts

150-180 4-M.well >100 Fine 82-92 0.5 27-40

viii

Appendix 2: District wise Area Distribution under Soyabean Crop

Soyabean

Area ('000 Ha) District 2002 - 2003 2003 - 2004 2004 - 2005 2005-2006 2006-2007 (FFC)

Bhopal 84.69 86.4 89.70 90.60 85.80 Dewas 268.75 271.1 274.20 280.70 285.40 Harda 150.51 152.3 153.00 162.20 157.70 Hoshangabad 204.30 200.5 204.00 188.50 186.80 Indore 219.58 218.5 220.10 209.90 223.20 Narsimhapur 70.27 62.2 60.20 53.10 78.30 Raisen 73.30 69.3 77.60 70.60 83.30 Sehore 246.48 255.3 275.70 269.80 373.10 Ujjain 414.65 364.0 400.10 421.70 422.00

Appendix 3: District wise Area Distribution under Sorghum/ Jowar Crop

Jowar

Area ('000 Ha) District 2002 - 2003 2003 - 2004 2004 - 2005 2005-2006 2006-2007 (FFC)

Bhopal 2.50 3.20 2.70 - - Dewas 18.10 20.80 17.80 - - Harda 1.00 1.40 1.50 0.10 0.10 Hoshangabad 1.60 1.40 1.40 N N Indore 1.70 5.10 1.80 - - Narsimhapur 6.10 4.60 5.20 - - Raisen 1.00 1.30 1.40 0.10 0.10 Sehore 5.40 4.90 4.00 0.10 0.10 Ujjain 6.20 52.90 19.70 - -

Source: Ministry of Agriculture, Govt. of India/ Commissioner, Land Records & Settlement, Govt. of Madhya Pradesh/ Indian Harvest (N = Negligible < 50 ha, FFC = Final Forecast)

ix

Appendix 4: District wise Accuracy Assessment

District Landuse Producer's Accuracy

(%) User's Accuracy

(%) Overall Accuracy

(%) Kappa

Statistics Bhopal Kharif 1 (Soyabean) 47.06 80.00 74.29 0.7 Harvested Field 87.50 70.00 Kharif 3 (Pigeon Pea) 54.55 60.00 Current Fallow 90.91 100.00 Scrubland 85.71 60.00 Water Body 100.00 70.00 Forest 88.89 80.00 Dewas Kharif 1 (Soyabean) 58.33 63.64 74.14 0.7 Kharif 2 (Harvested Field) 66.67 85.71 Kharif 6 (Cotton) 45.45 83.33 Plantation 100.00 100.00 Current Fallow 100.00 66.67 Scrublan d 100.00 57.14 Water Body 100.00 66.67 Forest 88.89 80.00 Harda Kharif 1 (Soyabean) 83.33 52.63 75 0.68 Kharif 1 (Late Sown Soyabean) 63.64 70.00 Current Fallow 41.67 100.00 Scrubland 71.43 83.33 Water Body 100.00 100.00 Forest 100.00 89.47 Hoshangabad Kharif 1 (Soyabean) 62.50 100.00 76.67 0.72 Kharif 4 (Paddy) 80.00 80.00 Current Fallow 100.00 60.00 Scrubland 50.00 40.00 Water Body 100.00 80.00 Forest 83.33 100.00 Indore Kharif 1 (Soyabean) 86.67 76.47 74 0.67 Kharif 1 (Late Sown Soyabean) 66.67 85.71 Current Fallow 100.00 28.57 Scrubland 50.00 100.00 Water Body 100.00 60.00 Forest 87.50 87.50 Narsimhapur Kharif 1 (Soyabean) 50.00 66.67 76 0.72 Kharif 3 (Pigeon Pea) 100.00 71.43 Kharif 5 (Sugarcane) 44.44 57.14 Current Fallow 80.00 88.89 Scrubland 83.33 83.33 Water Body 100.00 80.00 Forest 100.00 80.00 Raisen Kharif 1 (Soyabean) 50.00 66.67 76 0.71 Kharif 3 (Pigeon Pea) 80.00 80.00 Kharif 4 (Paddy) 57.14 50.00 Current Fallow 75.00 90.00 Scrubland 80.00 66.67 Water Body 100.00 80.00 Forest 100.00 90.00

x

Sehore Kharif 1 (Soyabean) 75.00 60.00 75.51 0.71 Kharif 2 (Harvested Field) 55.56 62.50 Kharif 5 (Sugarcane) 66.67 100.00 Current Fallow 77.78 100.00 Scrubland 80.00 66.67 Water Body 100.00 60.00 Forest 88.89 88.89 Ujjain Kharif 1 (Soyabean) 62.50 50.00 75 0.7 Kharif 1 (Late Sown Soyabean) 50.00 100.00 Kharif 2 (Harvested Field) 85.71 85.71 Current Fallow 100.00 87.50 Scrubland 100.00 42.86 Water Body 100.00 100.00 Forest 100.00 66.67

xi

Appendix 5: Soil-mapping unit wise AWHC (mm)

Sl.No. Soil Mapping Unit Available Water Holding

Capacity (mm) 1 124 11.14 2 126 75.32 3 128 75.32 4 130 47.40 5 131 192.05 6 132 75.32 7 133 75.32 8 134 11.14 9 135 11.14

10 140 135.10 11 142 170.44 12 143 47.40 13 144 170.44 14 149 47.40 15 153 58.88 16 155 170.44 17 157 137.92 18 164 11.14 19 165 183.33 20 166 170.44 21 171 185.15 22 175 151.59 23 177 137.92 24 178 185.15 25 179 254.69 26 180 254.69 27 182 254.69 28 183 254.69 29 184 254.69 30 185 254.69 31 186 254.69 32 191 254.69 33 192 254.69 34 194 231.70 35 195 185.15 36 196 254.69 37 197 75.32 38 202 5.32 39 203 47.40 40 204 5.32 41 21 47.40 42 210 47.40 43 218 153.15 44 219 153.15

xii


C apacity (mm) 45 22 151.59 46 220 5.32 47 221 47.40 48 224 47.40 49 228 75.32 50 238 47.40 51 239 75.32 52 248 47.40 53 252 75.32 54 26 5.32 55 266 75.32 56 269 153.15 57 27 5.32 58 275 153.15 59 283 153.15 60 288 153.15 61 299 135.10 62 300 47.40 63 301 231.70 64 302 254.69 65 303 254.69 66 304 58.88 67 305 75.32 68 308 5.32 69 311 254.69 70 314 231.70 71 315 254.69 72 317 254.69 73 319 5.32 74 322 5.32 75 323 5.32 76 324 5.32 77 325 47.40 78 328 137.92 79 330 58.88 80 331 137.92 81 332 137.92 82 333 153.15 83 338 134.75 84 339 134.75 85 343 5.32 86 344 5.32 87 348 137.92 88 354 5.32

xiii


Capacity (mm) 89 360 135.10 90 361 153.15 91 363 153.15 92 365 5.32 93 366 58.88 94 371 153.15 95 372 254.69 96 377 254.69 97 378 254.69 98 379 153.15 99 380 254.69 100 382 254.69 101 384 254.69 102 385 254.69 103 386 254.69 104 391 254.69 105 392 254.69 106 394 254.69 107 395 153.15 108 397 153.15 109 398 153.15 110 399 153.15 111 4 47.40 112 400 153.15 113 402 153.15 114 403 153.15 115 404 153.15 116 405 153.15 117 407 153.15 118 409 59.78 119 411 135.10 120 412 135.10 121 42 58.88 122 450 47.40 123 52 231.70 124 57 135.10 125 6 5.32 126 64 5.32 127 66 254.69 128 67 135.10 129 68 254.69 130 75 231.70 131 79 254.69

xiv

Appendix 6: Soil Mapping Unit wise Soil Productivity Index

Sl.No. SMU Productivity Index Production Class

1 124 2.56 Extremely Poor 2 126 21.60 Average 3 128 19.20 Average 4 130 7.20 Poor 5 131 28.80 Average 6 132 21.60 Average 7 133 16.20 Poor 8 134 0.48 Extremely Poor 9 135 0.96 Extremely Poor

10 140 12.96 Poor 11 142 32.40 Average 12 143 6.48 Extremely Poor 13 144 28.80 Average 14 149 8.64 Poor 15 153 7.78 Poor 16 155 54.00 Good 17 157 43.20 Good 18 164 2.59 Extremely Poor 19 165 38.40 Good 20 166 43.20 Good 21 171 38.40 Good 22 175 17.28 Poor 23 177 10.08 Poor 24 178 44.80 Good 25 179 44.80 Good 26 180 44.80 Good 27 182 44.80 Good 28 183 44.80 Goo d 29 184 44.80 Good 30 185 50.40 Good 31 186 44.80 Good 32 191 51.20 Good 33 192 51.20 Good 34 194 40.96 Good 35 195 51.20 Good 36 196 51.20 Good 37 197 25.92 Average 38 202 0.77 Extremely Poor 39 203 4.32 Extremely Poor 40 204 0.38 Extremely Poor 41 21 3.20 Extremely Poor 42 210 5.40 Extremely Poor 43 218 25.60 Average 44 219 25.60 Average 45 22 11.52 Poor

xv

Sl.No. SMU Productivity Index Production Class 46 220 2.05 Extremely Poor 47 221 1.28 Extremely Poor 48 224 5.40 Extremely Poor 49 228 12.96 Poor 50 238 8.96 Poor 51 239 30.24 Average 52 248 8.64 Poor 53 252 16.59 Poor 54 26 2.56 Extremely Poor 55 266 25.92 Average 56 269 38.40 Good 57 27 2.88 Extremely Poor 58 275 44.80 Good 59 283 57.60 Good 60 288 57.60 Good 61 299 12.96 Poor 62 300 2.88 Extremely Poor 63 301 34.56 Good 64 302 38.40 Good 65 303 38.40 Good 66 304 34.56 Good 67 305 18.14 Poor 68 308 2.56 Extremely Poor 69 311 43.20 Good 70 314 34.56 Good 71 315 64.00 Good 72 317 51.20 Good 73 319 2.56 Extremely Poor 74 322 0.64 Extremely Poor 75 323 1.92 Extremely Poor 76 324 2.56 Extremely Poor 77 325 6.40 Extremely Poor 78 328 5.40 Extremely Poor 79 330 1.28 Extremely Poor 80 331 16.20 Poor 81 332 16.20 Poor 82 333 32.00 Average 83 338 19.44 Average 84 339 19.44 Average 85 343 3.07 Extremely Poor 86 344 1.54 Extremely Poor 87 348 19.44 Average 88 354 3.46 Extremely Poor 89 360 22.68 Average 90 361 50.40 Good

xvi

Sl.No. SMU Productivity Index Production Class 91 363 44.80 Good 92 365 0.96 Extremely Poor 93 366 2.88 Extremely Poor 94 371 51.20 Good 95 372 51.20 Good 96 377 51.20 Good 97 378 57.60 Good 98 379 51.20 Good 99 380 51.20 Good 100 382 51.20 Good 101 384 51.20 Good 102 385 51.20 Good 103 386 51.20 Good 104 391 51.20 Good 105 392 51.20 Good 106 394 51.20 Good 107 395 51.20 Good 108 397 51.20 Good 109 398 51.20 Good 110 399 51.20 Good 111 4 5.12 Extremely Poor 112 400 44.80 Good 113 402 44.80 Good 114 403 44.80 Good 115 404 50.40 Good 116 405 44.80 Good 117 407 44.80 Good 118 409 34.56 Good 119 411 19.44 Average 120 412 19.44 Average 121 42 6.48 Extremely Poor 122 450 7.20 Poor 123 52 30.72 Average 124 57 24.30 Average 125 6 1.02 Extremely Poor 126 64 3.84 Extremely Poor 127 66 44.80 Good 128 67 22.68 Average 129 68 44.80 Good 130 75 35.84 Good 131 79 44.80 Good

xvii

Appendix 7: FAO Criteria Ratings for Evaluating Soil Site Suitability for Crops

Crop Soil Site

Characteristics Suitability Class

Highly Suitable

(S1) Moderately Suitable

(S2) Marginally Suitable

(S3) Not Suitable

(N) Pigeon Pea Texture sicl, l, sil c, sc csl, scl, s - Depth (cm) > 100 75 - 100 25 - 75 < 25 Surface Stoniness 0 - 15 % 15 - 40 % 40 - 75 % > 75 % Drainage Well/ Mod. Well Imperfect Poor/ S. Ex Excessive Slope 0 - 3 % 3 - 5 % 5 - 8 % > 8 % Erosion e1 e2 e3 e4 Sorghum Texture f, fine loam, c cl, l s, sk fragmental Depth (cm) > 75 50 - 75 25 - 50 < 25 Surface Stoniness 0 - 15 % 15 - 30 % 30 - 60 % > 60 % Drainage Well/ Mod. Well Imperfect Poor/ S. Ex Excessive Slope 0 - 3% 3 - 5% 5 - 8 % > 8 % Erosion e1 e2 e3 e4 Soyabean T exture cl, sil, l, sl c, ls s - Depth (cm) >75 50-75 25-50 <25 Surface Stoniness 0-10 % 10-25 % 25-35 % >35 % Drainage Well/ Mod. Well Imperfect/ Poor Excessive/ S.Exc V. Excessive Slope 0 - 3 % 3 - 5 % 5 - 8 % > 8 % Erosion e1 e2 e3 e4

Source: Manual Soil-Site Suitability Criteria for Major Crops (NBSS & LUP Publ. 129) Technical Bulletin, 2006

Appendix 8: Soil Characteristics Code

CODE Depth Class depth(cm)

0 Extremely Shallow <10 1 Very Shallow 10 - 25 2 Shallow 25 - 50 3 Slightly Deep 50 - 75 4 Moderately Deep 75 - 100 5 Deep >100

CODE Erosion Class Symbol 0 No Erosion e0 1 Slight e1 2 Moderate e2 3 Severe e3 4 Very Severe e4 Stoniness Class

CODE coverage % 0 Slight <15% 1 Moderate 15 - 30% 2 Strong 30 - 60% 3 Very Strong >60%

Source: Man ual Soil-Site Suitability Criteria for Major Crops (NBSS & LUP Publ. 129) Technical Bulletin, 2006

xviii

Appendix 9: Soil and Land Characteristics of Soil-Mapping Units

SMU Texture Class Depth Class Stoniness

(%) Drainage Class Slope Class

(%) Erosion Class

124 l Very Shallow 30 - 60 Somewhat excessive > 8 e3 126 l Deep < 15 Well 3 - 8 e2 128 l Deep 30 - 60 Moderately Well 3 - 8 e2 130 l Shallow < 15 Well 3 - 8 e3 131 c Moderately Deep < 15 Well 3 - 8 e2 132 l Deep < 15 Well 1 - 3 e2 133 l Deep < 15 Well > 8 e2 134 l Extremely Shallow 30 - 60 Somewhat excessive > 8 e3 135 l Extremely Shallow < 15 Somewhat excessive > 8 e3 140 c Shallow > 60 Somewhat excessive > 8 e3 142 c Deep < 15 Well 3 - 8 e2 143 l Shallow < 15 Well 3 - 8 e3 144 c Deep > 60 Moderately Well 3 - 8 e2 149 l Shallow < 15 Well 3 - 8 e3 153 c Very Shallow 30 - 60 Well 3 - 8 e3 155 c Deep < 15 Well 3 - 8 e2 157 c Slightly Deep < 15 Well 3 - 8 e3 164 l Very Shallow < 15 Well > 8 e3 165 c Deep < 15 Moderately Well 3 - 8 e2 166 c Deep 15 - 30 Well 3 - 8 e2 171 c Deep < 15 Moderately Well 1 - 3 e2 175 c Shallow < 15 Somewhat excessive 3 - 8 e2 177 c Shallow < 15 Well 3 - 8 e3 178 c Deep < 15 Moderately Well 1 - 3 e2 179 c Deep < 15 Moderately Well 1 - 3 e2 180 c Deep < 15 Moderately Well 1 - 3 e2 182 c Deep < 15 Moderately Well > 8 e2 183 c Deep < 15 Moderately Well > 8 e2 184 c Deep < 15 Moderately Well > 8 e2 185 l Deep 15 - 30 Well 3 - 8 e2 186 c Deep < 15 Moderately Well > 8 e2 191 c Deep < 15 Moderately Well 1 - 3 e2 192 c Deep < 15 Moderately Well 1 - 3 e2 194 c Moderately Deep < 15 Moderately Well 3 - 8 e3 195 c Deep < 15 Moderately Well 3 - 8 e2 196 c Deep < 15 Moderately Well 1 - 3 e2 197 l Deep < 15 Well 1 - 3 e2 202 l Very Shallow 30 - 60 Somewhat excessive > 8 e3 203 l Shallow < 15 Well 1 - 3 e2 204 l Extremely Shallow 30 - 60 Somewhat excessive > 8 e4 21 l Shallow 30 - 60 Somewhat excessive > 8 e3

210 l Shallow < 15 Well > 8 e3 218 c Deep < 15 Moderately Well 3 - 8 e2 219 c Deep < 15 Moderately Well 3 - 8 e2 22 c Shallow 15 - 30 Somewhat excessive > 8 e3

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(%) Erosion Class

220 l Very Shallow > 60 Somewhat excessive > 8 e3 221 l Shallow 30 - 60 Poor 3 - 8 e3 224 l Shallow <15 Well > 8 e3 228 l Deep <15 Well 3 - 8 e3 238 l Shallow 30 - 60 Excessive > 8 e4 239 l Deep <15 Well 1 - 3 e2 248 s Shallow <15 Well > 8 e3 252 l Moderately Deep <15 Well 3 - 8 e2 26 l Very Shallow > 60 Somewhat excessive > 8 e3

266 l Deep <15 Well 1 - 3 e2 269 c Deep <15 Moderately Well 3 - 8 e2 27 l Very Shallow > 60 Well 3 - 8 e2

275 c Deep <15 Moderately Well 3 - 8 e2 283 c Deep <15 Well 3 - 8 e2 288 l Deep <15 Well > 8 e2 299 c Shallow <15 Well 3 - 8 e3 300 l Shallow 30 - 60 Well 3 - 8 e3 301 c Slightly Deep <15 Well 3 - 8 e2 302 c Deep <15 Moderately Well 3 - 8 e2 303 c Deep <15 Moderately Well 1 - 3 e2 304 c Slightly Deep <15 Well 3 - 8 e2 305 l Deep <15 Well 3 - 8 e2 308 l Very Shallow <15 Somewhat excessive > 8 e3 311 c Deep <15 Well 3 - 8 e2 314 c Moderately Deep <15 Well 3 - 8 e2 315 c Deep <15 Moderately Well 3 - 8 e2 317 c Deep <15 Moderately Well 1 - 3 e2 319 l Very Shallow 15 - 30 Somewhat excessive > 8 e4 322 l Extremely Shallow 30 - 60 Somewhat excessive > 8 e4 323 l Very Shallow <15 Somewhat excessive > 8 e3 324 l Very Shallow 15 - 30 Somewhat excessive > 8 e4 325 l Shallow <15 Somewhat excessive > 8 e3 328 c Shallow <15 Well 3 - 8 e3 330 c Very Shallow 30 - 60 Somewhat excessive > 8 e4 331 c Shallow 30 - 60 Well 3 - 8 e3 332 c Shallow 30 - 60 Well 1 - 3 e2 333 c Shallow <15 Moderately Well 1 - 3 e2 338 c Shallow 30 - 60 Well > 8 e3 339 c Shallow <15 Well 3 - 8 e2 343 l Very Shallow 15 - 30 Somewhat excessive > 8 e4 344 l Extremely Shallow <15 Somewhat excessive > 8 e3 348 c Shallow <15 Well 3 - 8 e2 354 l Very Shallow 15 - 30 Well > 8 e3 360 c Shallow <15 Well 1 - 3 e2 361 c Deep <15 Well 1 - 3 e2

xx



(%) Erosion

Class 363 c Deep < 15 Moderately Well 1 - 3 e2 365 l Extremely Shallow < 15 Somewhat excessive > 8 e3 366 c Extremely Shallow < 15 Somewhat excessive 1 - 3 e2 371 c Deep < 15 Moderately Well 3 - 8 e2 372 c Deep < 15 Moderately Well 3 - 8 e2 377 c Deep < 15 Moderately Well 1 - 3 e2 378 c Deep < 15 Well 1 - 3 e2 379 c Deep < 15 Moderately Well 3 - 8 e2 380 c Deep < 15 Moderately Well 3 - 8 e2 382 c Deep < 15 Moderately Well 1 - 3 e2 384 c Deep < 15 Moderately Well 1 - 3 e2 385 c Deep < 15 Moderately Well 1 - 3 e2 386 c Deep < 15 Moderately Well 1 - 3 e2 391 c Deep < 15 Moderately Well 1 - 3 e2 392 c Deep < 15 Moderately Well 3 - 8 e2 394 c Deep < 15 Moderately Well 1 - 3 e2 395 c Deep < 15 Moderately Well 1 - 3 e1 397 c Deep < 15 Moderately Well 1 - 3 e2 398 c Deep < 15 Moderately Well 1 - 3 e2 399 c Deep < 15 Moderately Well 1 - 3 e2

4 l Shallow 15 - 30 Somewhat excessive > 8 e3 400 c Deep < 15 Moderately Well 3 - 8 e2 402 c Deep < 15 Moderately Well 1 - 3 e2 403 c Deep < 15 Moderately Well 3 - 8 e2 404 c Deep < 15 Well 1 - 3 e2 405 c Deep < 15 Moderately Well 3 - 8 e2 407 c Deep < 15 Moderately Well 1 - 3 e2 409 c Moderately Deep 30 - 60 Well 3 - 8 e2 411 c Shallow > 60 Well > 8 e3 412 c Shallow < 15 Well 3 - 8 e2 42 c Very Shallow < 15 Well 3 - 8 e2

450 l Shallow < 15 Well > 8 e2 52 c Moderately Deep < 15 Moderately Well 3 - 8 e2 57 c Shallow 30 - 60 Well 3 - 8 e3 6 l Extremely Shallow 30 - 60 Somewhat excessive > 8 e3

64 l Very Shallow < 15 Somewhat excessive 3 - 8 e3 66 c Deep < 15 Moderately Well 3 - 8 e2 67 c Shallow 15 - 30 Well 3 - 8 e3 68 c Deep < 15 Moderately Well 3 - 8 e2 75 c Slightly Deep < 15 Moderately Well 3 - 8 e2 79 c Deep < 15 Moderately Well 3 - 8 e2

Source: Soils of Madhya Pradesh for Optimising Landuse, (NBSS & LU P Publ. 59) 1996

xxi

Appendix 10: Soil Mapping Unit wise Crop Suitability based on FAO framework

Sl No SMU Pigeonpea_Suitability Sorghum_Suitability Soyabean_Suitability

1 124 N N N 2 126 S2 S2 S2 3 128 S3 S3 S3 4 130 S3 S2 S3 5 131 S2 S2 S2 6 132 S1 S2 S1 7 133 S3 S3 S3 8 134 N N N 9 135 N N N

10 140 N N N 11 142 S2 S2 S2 12 143 S3 S3 S3 13 144 N N N 14 149 S3 S3 S3 15 153 N N N 16 153 S2 S2 S2 17 155 S3 S2 S2 18 157 N N N 19 164 S2 S2 S2 20 165 S2 S2 S2 21 166 S2 S1 S2 22 171 S3 S3 S3 23 175 S3 S3 S3 24 178 S2 S1 S2 25 179 S2 S1 S2 26 180 S2 S1 S2 27 182 S3 S3 S3 28 183 S3 S3 S3 29 184 S3 S3 S3 30 185 S2 S3 S2 31 186 S3 S3 S3 32 191 S2 S1 S2 33 192 S2 S1 S2 34 194 S2 S2 S2 35 195 S2 S2 S2 36 196 S2 S1 S2 37 197 S1 S2 S1 38 202 N N N 39 203 S3 S3 S3 40 204 N N N 41 21 S3 S3 S3 42 210 S3 S3 S3 43 218 S2 S3 S2 44 219 S2 S3 S2 45 22 S3 S3 S3

xxii

Sl No SMU Pigeonpea_Suitability Sorghum_Suitability Soyabean_Suitability 46 220 N N N 47 221 S3 S3 S3 48 224 S3 S3 S3 49 228 S2 S1 S2 50 238 S3 S3 S3 51 239 S1 S2 S1 52 248 S3 S3 S3 53 252 S2 S2 S2 54 26 N N N 55 266 S1 S2 S1 56 269 S2 S2 S2 57 27 N N N 58 275 S2 S2 S2 59 283 S2 S2 S2 60 288 S3 S3 S3 61 299 S3 S3 S3 62 300 S3 S3 S3 63 301 S3 S2 S2 64 302 S2 S2 S2 65 303 S2 S1 S2 66 304 S3 S2 S2 67 305 S2 S2 S2 68 308 N N N 69 311 S2 S2 S2 70 314 S2 S2 S2 71 315 S2 S2 S2 72 317 S2 S1 S2 73 319 N N N 74 322 N N N 75 323 N N N 76 324 N N N 77 325 S3 S3 S3 78 328 S3 S3 N 79 330 N N N 80 331 S3 S3 S3 81 332 S3 S3 S3 82 333 S3 S3 S3 83 338 S3 S3 S3 84 339 S3 S3 S3 85 343 N N N 86 344 N N N 87 348 S3 S3 S3 88 354 N N N 89 360 S3 S3 S3 90 361 S2 S1 S2

xxiii

Sl No SMU Pigeonpea_Suitability Sorghum_Suitability Soyabean_Suitability 91 363 S2 S1 S2 92 365 N N N 93 366 N N N 94 371 S2 S2 S2 95 372 S2 S2 S2 96 377 S2 S1 S2 97 378 S2 S1 S2 98 379 S2 S2 S2 99 380 S2 S2 S2

100 382 S2 S1 S2 101 384 S2 S1 S2 102 385 S2 S1 S2 103 386 S2 S1 S2 104 391 S2 S1 S2 105 392 S2 S2 S2 106 394 S2 S1 S2 107 395 S2 S1 S2 108 397 S2 S1 S2 109 398 S2 S1 S2 110 399 S2 S1 S2 111 4 S3 S3 S3 112 400 S2 S2 S2 113 402 S2 S1 S2 114 403 S2 S2 S2 115 404 S2 S1 S2 116 405 S2 S2 S2 117 407 S2 S1 S2 118 409 S3 S3 S3 119 411 N N N 120 412 S3 S3 S3 121 42 N N N 122 450 S3 S3 S3 123 52 S2 S2 S2 124 57 S3 S3 S3 125 6 N N N 126 64 N N N 127 66 S2 S2 S2 128 67 S3 S3 S2 129 68 S2 S2 S2 130 75 S3 S2 S2 131 79 S2 S2 S2

xxiv

Appendix 11: PET (mm/ day)

Station Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Anl_Avg Betul 1.43 2.01 3.90 6.60 9.17 7.29 4.59 3.84 3.91 3.31 2.00 1.43 4.12 Bhopal 1.09 1.75 3.83 7.25 10.65 8.78 5.46 4.41 4.59 3.84 2.21 1.27 4.59 Datia 0.63 1.22 3.43 7.64 12.44 11.79 7.94 6.26 5.67 4.04 1.89 0.84 5.32 Guna 0.85 1.47 3.51 7.43 11.50 10.45 6.16 4.95 4.98 3.94 2.02 1.02 4.86 Gwalior 0.51 1.07 3.02 7.18 12.32 13.06 8.73 6.86 6.22 4.07 1.72 0.69 5.46 Hoshangabad 1.22 1.85 4.32 8.30 11.96 10.10 5.71 4.52 4.76 4.05 2.28 1.25 5.03 Indore 1.25 1.88 3.94 7.17 9.96 8.17 5.14 4.22 4.31 3.78 2.31 1.38 4.46 Jabalpur 0.99 1.70 3.82 7.87 11.98 9.87 6.04 5.04 5.06 4.01 2.00 1.06 4.95 Khajuraho 0.67 1.31 3.42 8.28 13.63 13.33 8.29 6.52 5.94 4.21 1.90 0.85 5.70 Khandwa 1.49 2.32 5.17 10.23 14.06 10.21 6.01 4.87 5.22 4.56 2.54 1.46 5.68 Malanjkhand 1.18 1.80 3.43 6.19 8.70 7.42 4.88 4.43 4.19 3.19 1.81 1.03 4.02 Narsimhapur 1.12 1.83 4.10 8.69 12.71 10.97 6.43 5.06 5.39 4.36 2.26 1.20 5.34 Nimach 0.97 1.69 3.83 7.57 10.81 9.46 5.89 4.88 4.91 4.53 2.26 1.12 4.83 Nowgong 0.65 1.26 3.20 7.71 12.65 12.34 7.82 6.03 5.68 4.19 1.84 0.82 5.35 Pachmarhi 1.13 1.55 2.70 4.57 6.53 5.13 3.62 3.00 3.32 2.47 1.51 0.99 3.04 Panna 0.77 1.25 3.07 6.39 9.81 8.85 5.92 4.99 4.43 3.36 1.75 1.04 4.30 Raisen 0.90 1.49 3.40 7.71 10.93 9.79 5.68 4.87 4.93 3.64 1.60 0.86 4.65 Rajgarh 0.94 1.65 3.96 8.46 13.48 11.62 6.78 5.11 5.13 4.09 2.07 1.06 5.36 Ratlam 1.17 1.91 4.31 7.87 11.22 8.76 5.49 4.40 4.49 4.67 2.44 1.36 4.84 Rewa 0.71 1.34 3.24 7.18 10.93 10.62 6.99 5.89 5.37 3.98 1.96 0.85 4.92 Sagar 1.06 1.78 4.08 8.09 11.53 9.39 5.54 4.42 4.53 4.14 2.39 1.32 4.86 Satna 0.75 1.39 3.45 7.74 12.37 11.54 7.32 6.00 5.45 4.14 1.94 0.91 5.25 Seoni 1.36 2.40 5.00 9.14 12.38 8.88 5.43 4.42 4.77 4.21 2.58 1.55 5.18 Shajapur 1.02 1.56 3.58 7.76 11.29 9.35 6.02 4.75 4.88 3.89 2.02 1.08 4.77 Shivpuri 0.65 1.29 3.07 6.45 10.62 10.39 6.69 5.21 5.03 3.90 1.75 0.86 4.66 Sidhi 0.78 1.44 3.45 7.92 12.10 11.51 7.46 6.37 5.76 4.24 2.01 0.93 5.33 Ujjain 1.10 1.62 3.34 6.44 9.52 8.21 5.30 4.33 4.50 3.70 2.11 1.22 4.28 Umaria 0.93 1.60 3.41 7.05 10.18 8.59 5.16 4.44 4.30 3.23 1.70 0.91 4.29

Appendix 12: 10 day composite PET (mm)

Station Dek

1,2,3 Dek 4,5,6

Dek 7,8,9

Dek 10,11,1

2

Dek 13,14,1

5

Dek 16,17,1

8

Dek 19,20,2

1

Dek 22,23,

24

Dek 25,26,27

Dek 28,29,

30

Dek 31,32,

33

Dek 34,35,

36 Betul 14.30 20.12 38.95 66.01 91.73 72.87 45.94 38.36 39.10 33.09 20.00 14.33 Bhopal 10.90 17.52 38.33 72.46 106.48 87.85 54.55 44.14 45.93 38.37 22.14 12.66 Datia 6.30 12.20 34.34 76.44 124.41 117.90 79.42 62.57 56.69 40.41 18.95 8.39 Guna 8.50 14.68 35.14 74.27 115.04 104.49 61.57 49.51 49.77 39.40 20.25 10.18 Gwalior 5.07 10.69 30.24 71.81 123.22 130.63 87.29 68.63 62.16 40.70 17.23 6.94 Hoshangabad 12.19 18.54 43.21 83.05 119.62 101.01 57.08 45.20 47.64 40.47 22.79 12.47 Indore 12.46 18.76 39.43 71.73 99.61 81.73 51.41 42.22 43.07 37.83 23.05 13.84 Jabalpur 9.93 17.02 38.19 78.67 119.83 98.68 60.44 50.35 50.63 40.13 20.01 10.59 Khajuraho 6.72 13.11 34.25 82.81 136.26 133.30 82.91 65.22 59.39 42.08 19.03 8.54 Khandwa 14.93 23.20 51.67 102.28 140.64 102.09 60.13 48.69 52.22 45.63 25.39 14.57 Malanjkhand 11.77 17.97 34.27 61.87 87.01 74.19 48.76 44.32 41.92 31.94 18.10 10.33 Narsimhapur 11.25 18.29 40.97 86.88 127.14 109.74 64.33 50.61 53.94 43.64 22.61 11.99 Nimach 9.71 16.92 38.27 75.72 108.05 94.58 58.88 48.80 49.10 45.25 22.55 11.22 Nowgong 6.46 12.65 32.02 77.09 126.55 123.40 78.16 60.32 56.82 41.88 18.43 8.24 Pachmarhi 11.33 15.55 26.97 45.70 65.32 51.35 36.16 30.02 33.18 24.66 15.11 9.90 Panna 7.72 12.46 30.66 63.88 98.12 88.53 59.24 49.92 44.32 33.60 17.48 10.41 Raisen 9.02 14.87 33.98 77.14 109.27 97.93 56.82 48.67 49.27 36.37 16.03 8.64 Rajgarh 9.43 16.51 39.59 84.63 134.84 116.16 67.75 51.07 51.35 40.88 20.70 10.57 Ratlam 11.68 19.07 43.10 78.74 112.20 87.58 54.86 44.03 44.86 46.75 24.43 13.63 Rewa 7.13 13.36 32.44 71.79 109.25 106.22 69.91 58.91 53.72 39.81 19.60 8.53 Sagar 10.64 17.76 40.79 80.92 115.27 93.94 55.37 44.21 45.32 41.42 23.94 13.22 Satna 7.48 13.93 34.54 77.39 123.71 115.37 73.25 60.01 54.51 41.40 19.38 9.06 Seoni 13.63 24.00 49.98 91.45 123.85 88.82 54.29 44.21 47.66 42.15 25.80 15.51 Shajapur 10.22 15.63 35.79 77.64 112.90 93.46 60.24 47.49 48.76 38.93 20.21 10.81 Shivpuri 6.48 12.90 30.66 64.53 106.20 103.95 66.87 52.05 50.33 38.95 17.47 8.58 Sidhi 7.78 14.44 34.55 79.25 120.99 115.09 74.57 63.75 57.63 42.38 20.10 9.30 Ujjain 11.01 16.18 33.39 64.43 95.24 82.06 52.97 43.27 45.03 36.96 21.12 12.22 Umaria 9.28 16.01 34.09 70.46 101.77 85.90 51.56 44.44 43.00 32.35 17.01 9.10

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Appendix 13: Rainfall (mm)

Station Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Anl_Avg Betul 13.22 24.05 11.01 8.62 12.82 167.24 299.69 355.83 186.17 61.65 12.77 11.99 97.09 Bhopal 13.25 10.40 9.11 4.34 15.93 144.37 335.03 359.12 147.70 35.72 9.61 7.68 91.02 Datia 9.40 11.53 3.81 4.80 7.78 91.58 239.18 282.72 152.55 23.94 2.74 3.20 69.43 Guna 11.55 12.45 6.11 3.73 9.09 87.53 296.97 344.79 157.83 39.17 8.52 4.71 81.87 Gwalior 9.95 14.75 5.04 8.04 14.03 66.62 226.13 220.94 148.60 35.99 6.66 6.40 63.60 Hoshangabad 11.75 13.37 19.51 8.62 12.35 153.68 375.65 412.06 164.59 31.71 16.64 6.33 102.19 Indore 5.75 6.30 1.97 3.58 11.76 195.26 276.09 266.62 132.16 39.91 4.75 3.16 78.94 Jabalpur 19.85 29.02 14.17 5.18 11.37 193.94 337.06 403.25 188.15 22.60 8.72 9.46 103.56 Khajuraho 21.64 33.17 8.52 4.84 11.24 109.81 314.19 412.35 209.50 28.99 7.21 4.98 97.20 Khandwa 6.01 5.19 4.02 1.69 5.58 116.75 214.69 204.74 144.53 35.28 7.61 7.89 62.83 Malanjkhand 25.64 25.03 20.68 6.18 15.78 187.38 337.97 332.51 180.74 45.89 15.94 10.78 100.38 Narsimhapur 22.82 18.78 10.10 1.51 12.84 144.92 289.83 369.57 206.39 29.62 12.69 11.23 94.19 Nimach 1.55 1.20 0.65 2.71 6.06 83.93 208.82 257.85 93.58 20.69 5.16 22.31 58.71 Nowgong 15.55 14.48 6.77 6.93 9.35 84.38 300.19 367.38 199.69 23.57 1.91 4.94 86.26 Pachmarhi 8.67 5.90 9.65 1.81 24.72 177.92 434.09 456.16 228.19 45.91 11.17 13.41 118.13 Panna 20.16 18.16 13.55 4.77 4.85 145.94 376.03 421.11 249.34 44.11 5.45 6.79 109.19 Raisen 14.44 13.69 3.14 0.27 9.45 114.45 244.39 514.36 154.26 31.50 5.33 13.31 93.22 Rajgarh 8.85 3.53 0.94 3.16 7.12 73.36 179.02 245.96 67.46 27.12 7.04 3.00 52.21 Ratlam 5.89 2.48 0.75 2.32 8.64 109.70 330.44 346.74 89.30 45.13 4.14 2.69 79.02 Rewa 17.51 22.57 6.95 5.47 8.61 118.18 312.98 309.23 209.02 41.07 6.12 6.00 88.64 Sagar 18.75 21.68 9.13 7.20 18.95 134.58 335.13 411.08 191.52 28.47 12.18 11.15 99.98 Satna 17.64 24.43 10.55 6.21 15.22 131.73 312.84 334.18 231.17 31.19 5.28 6.25 93.89 Seoni 23.58 27.70 19.88 7.92 16.41 195.32 330.01 320.28 161.57 54.31 11.79 10.27 98.25 Shajapur 6.90 5.57 0.98 2.38 9.06 101.64 203.87 293.75 124.91 24.61 6.01 5.34 65.42 Shivpuri 10.06 11.10 6.04 4.87 11.86 78.83 289.22 262.57 137.77 36.28 10.13 4.84 71.96 Sidhi 19.30 24.33 11.78 5.88 13.08 118.16 346.85 317.02 274.35 38.67 8.62 7.42 98.79 Ujjain 9.60 2.91 3.12 3.74 7.99 123.19 304.41 253.19 145.07 30.88 10.65 5.90 75.05 Umaria 27.13 21.95 13.28 4.43 13.63 129.26 306.77 354.81 266.88 37.71 9.95 10.09 99.66

Appendix 14: 10 day composite Rainfall (mm)

Station Dek 1 Dek 2 Dek 3 Dek 4 Dek 5 Dek 6 Dek 7 Dek 8 Dek 9 Dek 10 Dek 11 Dek 12 Betul 3.45 5.36 4.41 6.68 10.02 7.35 2.93 4.67 3.41 2.66 3.31 2.65 Bhopal 3.33 5.50 4.42 2.74 5.47 2.19 2.13 4.04 2.94 1.00 1.78 1.56 Datia 1.98 4.13 3.29 3.84 3.84 3.84 0.91 1.97 0.93 1.14 1.90 1.76 Guna 2.85 4.85 3.85 3.73 5.15 3.57 1.65 2.56 1.90 1.20 1.39 1.14 Gwalior 2.21 4.32 3.32 4.84 5.56 4.35 1.62 1.78 1.64 2.68 2.91 2.45 Hoshangabad 1.56 6.27 3.92 3.87 5.46 4.04 6.66 6.99 5.86 2.73 3.13 2.76 Indore 0.89 2.94 1.92 1.31 3.10 1.89 0.60 0.78 0.59 1.25 1.23 1.10 Jabalpur 4.62 9.60 5.63 9.41 10.67 8.94 4.26 5.38 4.53 1.39 2.10 1.69 Khajuraho 6.54 9.21 5.89 10.55 12.06 10.56 2.59 3.26 2.67 1.47 1.91 1.46 Khandwa 2.00 2.00 2.00 1.60 2.73 0.86 1.39 1.68 0.95 0.42 0.76 0.51 Malanjkhand 6.55 9.87 9.22 8.20 9.34 7.49 6.76 7.18 6.74 1.69 2.56 1.93 Narsimhapur 6.99 9.47 6.36 6.24 7.26 5.28 3.21 3.79 3.10 0.34 0.75 0.42 Nimach 0.46 0.52 0.57 0.31 0.80 0.09 0.19 0.25 0.21 0.78 1.12 0.81 Nowgong 5.48 6.89 3.18 5.06 5.83 3.59 2.11 2.57 2.09 1.96 2.98 1.99 Pachmarhi 2.11 4.89 1.67 1.45 2.97 1.48 3.16 3.54 2.95 0.46 0.86 0.49 Panna 5.60 7.99 6.57 5.19 7.05 5.92 4.19 5.17 4.19 1.47 1.79 1.51 Raisen 3.36 6.98 4.10 3.92 5.56 4.21 0.81 1.35 0.98 0.09 0.08 0.09 Rajgarh 2.75 3.67 2.43 0.44 2.18 0.91 0.27 0.39 0.28 1.06 1.21 0.89 Ratlam 1.78 2.54 1.57 0.38 1.62 0.48 0.25 0.26 0.24 0.77 0.77 0.77 Rewa 5.67 6.50 5.34 6.57 8.52 7.48 1.79 2.97 2.19 1.75 1.93 1.79 Sagar 6.19 7.35 5.21 6.47 8.23 6.98 3.02 3.17 2.94 2.06 2.87 2.27 Satna 5.98 6.23 5.43 7.65 9.14 7.64 3.35 3.92 3.28 2.07 2.17 1.97 Seoni 6.15 9.94 7.49 8.48 10.23 8.99 6.58 6.89 6.41 2.45 2.95 2.52 Shajapur 1.72 3.54 1.64 1.18 2.86 1.53 0.29 0.38 0.31 0.78 0.89 0.71 Shivpuri 2.66 4.35 3.05 2.91 4.70 3.49 1.95 2.09 2.00 1.40 1.86 1.61 Sidhi 5.33 7.93 6.04 7.29 9.11 7.93 3.86 4.52 3.40 1.96 2.01 1.91 Ujjain 2.41 4.20 2.99 0.80 1.24 0.87 0.60 1.52 1.00 1.21 1.37 1.16 Umaria 8.30 10.04 8.79 6.73 8.32 6.90 4.09 4.98 4.21 1.34 1.68 1.41

…contd.

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Station Dek 13 Dek 14 Dek 15 Dek 16 Dek 17 Dek 18 Dek 19 Dek 20 Dek 21 Dek 22 Dek 23 Dek 24

Betul 4.27 4.57 3.98 55.74 55.79 55.71 99.90 100.00 99.79 118.61 118.83 118.39

Bhopal 5.24 5.87 4.82 48.13 48.18 48.06 111.52 111.89 111.62 119.71 119.97 119.44

Datia 2.19 3.20 2.39 30.51 30.58 30.49 79.22 80.33 79.63 94.24 94.48 94.00

Guna 2.79 3.43 2.87 29.08 29.48 28.97 97.98 101.00 97.99 114.93 115.00 114.86

Gwalior 4.26 5.29 4.48 22.22 22.43 21.97 74.74 76.43 74.96 73.65 73.86 73.43

Hoshangabad 4.06 4.39 3.90 51.16 51.29 51.23 125.22 126.13 124.30 137.24 137.53 137.29

Indore 3.72 4.28 3.76 65.08 65.21 64.97 92.03 92.49 91.57 88.87 89.01 88.74

Jabalpur 3.69 3.99 3.69 64.62 64.73 64.59 112.35 112.49 112.22 134.42 134.69 134.14

Khajuraho 3.60 3.95 3.69 36.45 36.83 36.53 104.72 104.94 104.53 137.45 137.95 136.95

Khandwa 1.79 2.10 1.69 38.11 39.92 38.72 71.46 71.84 71.39 68.25 68.43 68.06

Malanjkhand 5.15 5.72 4.91 62.44 62.53 62.41 112.69 113.22 112.06 110.84 111.00 110.67

Narsimhapur 3.93 4.83 4.08 48.30 48.43 48.19 96.26 96.99 96.58 123.19 123.27 123.11

Nimach 2.01 2.10 1.95 28.00 28.12 27.81 69.29 70.00 69.53 85.95 86.10 85.80

Nowgong 3.01 3.18 3.16 27.02 29.33 28.03 99.74 101.05 99.40 122.46 122.63 122.29

Pachmarhi 7.64 8.94 8.14 59.33 59.64 58.95 144.51 145.02 144.56 152.05 152.17 151.94

Panna 1.51 1.89 1.45 47.84 49.65 48.45 124.99 126.00 125.04 140.37 140.53 140.21

Raisen 3.05 3.35 3.05 38.15 38.35 37.95 81.34 81.76 81.29 171.45 171.59 171.32

Rajgarh 2.21 2.74 2.17 23.25 25.98 24.13 59.18 60.27 59.57 81.99 82.03 81.94

Ratlam 2.84 2.94 2.86 36.42 37.00 36.28 110.15 110.26 110.03 115.58 115.92 115.24

Rewa 2.84 2.89 2.88 38.40 41.39 38.39 104.26 104.59 104.13 103.08 103.15 103.00

Sagar 6.28 6.38 6.29 44.50 45.46 44.62 111.54 112.10 111.49 137.03 137.23 136.82

Satna 4.99 5.26 4.97 43.75 44.13 43.85 104.28 104.43 104.13 111.39 111.56 111.23

Seoni 5.42 5.56 5.43 65.00 65.37 64.95 109.33 110.90 109.78 106.76 107.02 106.50

Shajapur 2.98 3.10 2.98 33.82 34.10 33.72 67.96 68.00 67.91 97.92 98.10 97.73

Shivpuri 3.93 3.99 3.94 26.12 26.74 25.97 96.32 96.53 96.37 87.52 87.63 87.42

Sidhi 4.22 4.56 4.30 38.78 40.11 39.27 115.41 116.01 115.43 105.67 106.00 105.35

Ujjain 2.58 2.87 2.54 41.25 41.39 40.55 101.34 101.68 101.39 84.40 84.59 84.20

Umaria 4.53 4.59 4.51 42.94 43.38 42.94 101.63 103.16 101.98 118.27 118.37 118.17

…contd.

xxvii

Station Dek 25 Dek 26 Dek 27 Dek 28 Dek 29 Dek 30 Dek 31 Dek 32 Dek 33 Dek 34 Dek 35 Dek 36 Betul 63.41 62.19 60.57 22.67 19.75 19.23 4.58 4.16 4.03 4.00 4.14 3.85 Bhopal 51.13 48.89 47.68 12.39 11.91 11.42 3.60 3.20 2.81 2.56 2.69 2.43 Datia 51.03 50.69 50.83 10.70 6.93 6.31 1.02 0.87 0.85 1.07 1.11 1.02 Guna 52.97 52.49 52.37 16.25 12.06 10.86 3.19 2.79 2.54 1.57 1.63 1.51 Gwalior 50.00 49.41 49.19 13.68 11.86 10.45 2.79 2.01 1.86 2.13 2.24 2.03 Hoshangabad 56.00 54.86 53.73 12.78 10.01 8.92 7.01 5.60 4.03 2.11 2.19 2.03 Indore 47.00 41.11 44.05 15.75 12.86 11.30 2.19 1.40 1.16 1.05 1.10 1.01 Jabalpur 65.00 61.80 61.35 10.94 6.83 4.83 3.82 2.70 2.20 3.15 3.21 3.10 Khajuraho 73.83 68.41 67.26 11.79 9.19 8.01 2.90 2.41 1.90 1.54 1.66 1.78 Khandwa 51.59 47.09 45.85 12.84 11.24 11.20 3.16 2.28 2.17 2.48 2.69 2.72 Malanjkhand 61.27 60.27 59.20 17.29 14.79 13.81 6.28 5.01 4.65 3.47 4.30 3.01 Narsimhapur 71.24 67.50 67.65 11.54 9.21 8.87 4.92 3.98 3.79 3.41 3.74 4.08 Nimach 33.37 31.19 29.02 7.56 6.36 6.77 1.95 1.62 1.59 7.27 7.44 7.60 Nowgong 68.19 65.49 66.01 9.34 7.64 6.59 0.83 0.56 0.52 1.54 1.65 1.75 Pachmarhi 78.48 75.47 74.24 17.37 14.55 13.99 4.29 3.68 3.20 4.35 4.47 4.59 Panna 86.11 82.84 80.39 15.87 14.70 13.54 2.17 1.72 1.56 2.14 2.26 2.39 Raisen 53.42 51.26 49.58 11.94 9.99 9.57 1.99 1.65 1.69 4.21 4.44 4.66 Rajgarh 25.93 21.05 20.48 10.05 8.90 8.17 3.27 2.30 1.47 0.91 1.00 1.09 Ratlam 31.05 29.29 28.96 15.99 14.77 14.37 1.79 1.35 1.00 0.83 0.90 0.96 Rewa 71.06 69.67 68.29 15.39 13.49 12.19 2.23 2.00 1.89 1.84 2.00 2.16 Sagar 65.47 63.84 62.21 10.62 9.49 8.36 4.95 4.00 3.23 3.41 3.79 3.95 Satna 78.83 76.85 75.49 11.74 9.68 9.77 1.93 1.69 1.66 2.00 2.08 2.17 Seoni 55.69 52.89 52.99 20.81 17.30 16.20 4.60 3.91 3.28 3.18 3.42 3.67 Shajapur 42.09 41.64 41.18 12.91 6.43 5.27 2.18 1.94 1.89 1.53 1.78 2.03 Shivpuri 47.93 45.92 43.92 14.29 11.00 10.99 4.14 3.03 2.96 1.49 1.61 1.74 Sidhi 93.29 91.12 89.94 14.47 12.74 11.46 3.29 2.94 2.39 2.21 2.47 2.74 Ujjain 50.23 47.95 46.89 12.58 9.89 8.41 4.80 3.10 2.75 1.75 1.97 2.18 Umaria 90.65 88.89 87.34 15.28 11.96 10.47 4.46 3.10 2.39 3.10 3.36 3.63

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Appendix 15: Simulation Units derived from Soil-Mapping Units’ Texture and Depth

Code Weather Station

SMU Texture Class Depth Class Tex-Dep. Code

Simulation Unit Code

A Bhopal 124 L 1 j Aj 220 L 1 j 343 L 1 j 344 L 1 j 26 L 1 j 27 L 1 j 338 C 2 b Ab 339 C 2 b 348 C 2 b 299 C 2 b 22 C 2 b 317 F 3 f Af 363 F 3 f 179 F 3 f 283 F 3 f 275 F 3 f 303 F 3 f 315 F 3 f 371 F 3 f 288 F 3 f 301 F 2 e Ae 304 F 2 e 300 K 2 h Ah 221 K 2 h 248 L 2 k Ak 238 L 2 k 228 M 3 o Ao 252 M 3 o 239 M 3 o B Dewas 124 L 1 j Bj 319 L 1 j 157 F 2 e Be 153 C 1 a Ba 322 K 1 g Bg 134 K 1 g 164 K 1 g 348 C 2 b 328 P 2 q Bq 177 L 2 k Bk 203 L 2 k 143 L 2 k 149 L 2 k 130 L 2 k 178 F 3 f Bf 131 F 3 f 218 F 3 f 317 F 3 f 219 F 3 f 142 F 3 f 144 F 3 f 166 F 3 f 155 F 3 f 171 F 3 f 128 M 3 o Bo 126 M 3 o

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Code Weather Station SMU Texture Class Depth Class Tex-Dep. Code Simulation Unit Code C Harda 324 L 1 j Cj 325 L 2 k Ck 328 P 2 q Cq 330 P 1 p Cp 331 C 2 b Cb 332 C 2 b 179 F 3 f Cf 180 F 3 f 182 F 3 f 183 F 3 f 184 F 3 f 185 F 3 f 377 F 3 f 378 F 3 f 379 F 3 f

D Hoshangabad 52 F 3 f Df 314 F 3 f 361 F 3 f 179 F 3 f 165 F 3 f 171 F 3 f 183 F 3 f 371 F 3 f 377 F 3 f 184 F 3 f 185 F 3 f 195 F 3 f 196 F 3 f 363 F 3 f 365 K 1 g Dg 202 K 1 g 322 K 1 g 324 L 1 j Dj 360 C 2 b Db 412 C 2 b 300 K 2 h Dh 301 F 2 e De 304 F 2 e E Indore 218 F 3 f Ef 333 F 3 f 179 F 3 f 166 F 3 f 171 F 3 f 371 F 3 f 21 K 2 h Eh 140 C 2 b Eb 153 C 1 a Ea 130 L 2 k Ek 210 L 2 k 344 L 1 j Ej 164 K 1 g Eg

xxx

Code Weather Station SMU Texture Class Depth Class Tex-Dep. Code Simulation Unit Code F Narsimhapur 4 L 2 k Fk 400 F 3 f Ff 402 F 3 f 379 F 3 f 397 F 3 f 398 F 3 f 399 F 3 f 404 F 3 f 405 F 3 f 407 F 3 f 391 F 3 f 395 F 3 f 403 F 3 f 409 F 3 f 411 C 2 b Fb 412 C 2 b

G Pachmarhi 52 F 3 f Gf 311 F 3 f 183 F 3 f 185 F 3 f 186 F 3 f 191 F 3 f 371 F 3 f 372 F 3 f 377 F 3 f 391 F 3 f 392 F 3 f 394 F 3 f 314 F 3 f 57 C 2 b Gb 412 C 2 b 6 L 1 j Gj 202 K 1 g Gg 42 C 1 a Ga 450 L 2 k Gk

H Raisen 299 C 2 b Hb 412 C 2 b 308 L 1 j Hj 79 F 3 f Hf 315 F 3 f 192 F 3 f 302 F 3 f 194 F 3 f 68 F 3 f 317 F 3 f 195 F 3 f 196 F 3 f 303 F 3 f 304 F 2 e He 301 F 2 e 197 M 3 o Ho 305 M 3 o 239 M 3 o

xxxi

Code Weather Station SMU Texture Class Depth Class Tex-Dep. Code Simulation Unit Code I Ratlam 124 L 1 j Ij 343 L 1 j 204 K 1 g 203 L 2 k Ik 210 L 2 k 224 L 2 k 21 K 2 h Ih 221 K 2 h 22 C 2 b Ib 140 C 2 b 153 C 1 a Ia 228 M 3 o Io 202 K 1 g Ig 371 F 3 f If J Sagar 64 L 1 j Jj 67 C 2 b Jb 202 K 1 g Jg 382 F 3 f Jf 79 F 3 f 315 F 3 f 379 F 3 f 380 F 3 f 391 F 3 f 384 F 3 f 385 F 3 f 68 F 3 f 386 F 3 f 75 F 2 e Je 197 M 3 o Jo

K Shajapur 124 L 1 j Kj 135 L 1 j 142 F 3 f Kf 131 F 3 f 134 K 1 g Kg 322 K 1 g I Ratlam 124 L 1 j Ij 126 M 3 o Ko 132 M 3 o 133 M 3 o L Sehore 22 C 2 b Lb 338 C 2 b 348 C 2 b 360 C 2 b 266 M 3 o Lo 269 F 3 f Lf 178 F 3 f 179 F 3 f 371 F 3 f 171 F 3 f 317 F 3 f 361 F 3 f 365 K 1 g Lg 323 K 1 g 324 L 1 j Lj 354 L 1 j 325 L 2 k Lk 366 C 1 a La 328 P 2 q Lq 304 F 2 e Le

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Code Weather Station SMU Texture Class Depth Class Tex-Dep. Code Simulation Unit Code M Ujjain 124 L 1 j Mj 344 L 1 J 203 L 2 K Mk 130 L 2 K 210 L 2 K 204 K 1 G Mg 322 K 1 G 131 F 3 F Mf 166 F 3 F 219 F 3 F 21 K 2 H Mh 221 K 2 H 22 C 2 B Mb 126 M 3 O Mo 128 M 3 O

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Appendix 16: Effective Rainfall for various levels of ETcrop

Monthly ETcrop [mm/ month]

30 60 90 120 150 180 210 240 Monthly Rain [mm/ month]

Effective Rainfall [%]

10 58 62 66 71 75 81 86 92

20 63 68 72 77 82 88 94 100

30 63 67 72 77 82 88 94 100

40 62 66 71 76 81 86 92 99

50 61 65 70 74 79 85 91 97

60 60 64 68 73 78 83 89 95

70 59 63 67 72 77 82 88 93

80 58 62 66 71 76 81 86 92

90 57 61 65 70 74 80 85 91

100 56 60 64 69 73 78 84 90

120 55 59 63 67 72 77 82 87

140 54 58 61 66 70 75 80 85

160 53 56 60 64 69 74 79 84

180 52 55 59 63 68 72 77 82

200 51 55 58 62 67 71 76 81

Source: USDA. National Engineering Handbook

Appendix 17: Crop Specific Kc Values

Crop Kc ini Kc mid Kc end

Soyabean - 1.15 0.50

Sorghum - 1.10 0.55

Pigeon Pea - 1.15 0.30

Source: FAO Irrigation and Drainage Paper 24 (Doorenbos & Pruitt, 1977)

Appendix 18: Crop Specific Ky Values

Crop Vegetative Period

Early Late Total Flowering Yield Formation Ripening Total Growing

Period

Soyabean - - 0.20 0.80 1.00 - 0.85

Sorghum - - 0.20 0.55 0.45 0.20 0.90

Pigeon Pea 0.20 - 0.90 0.70 0.20 1.15

Source: FAO Irrigation and Drainage Paper 33 (Doorenbos & Kassam, 1979)

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Appendix 19: Crop Specific Effective Rooting Depth, Salt Tolerance Levels and Maximum Depletion Factor

Crop Rooting Depth (m) Depletion Factor (P) Max ECe (dS/m)

Soyabean 0.6 – 1.3 0.50 10

Sorghum 1.0 – 2.0 0.55 18

Pigeon Pea 0.6 – 1.0 0.40 9

Source: BUDGET Reference Manual

Appendix 20: Crop Specific Length of Growth Stages (days)

Crop Establishment Vegetative Flowering Yield Formation Ripening

Soyabean 10 30 – 40 25 – 35 30 – 40 10 - 15

Sorghum 15 - 20 20 - 30 15 - 20 35 - 40 10 - 15

Pigeon Pea 10 - 25 25 - 30 15 - 20 20 - 25 15 - 20

Source: FAO Irrigation and Drainage Paper 33 (Doorenbos & Kassam, 1979)

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