Abstract. Acknowledgments

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2 Citation: Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T. (eds.) GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India. Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 98 p p. ISBN Order code: CPE 127. Abstract "GIS Application in Cropping System Analysis - Case Studies in Asia" is a product of the hands-on training program for scientists f r o m Asian national agricultural research systems (NARS) on the use of geographic information system (GIS) in analysis of cropping systems. The main objective of this training program was to pave a progressive path towards greater understanding and use of GIS for meaningful interpretation of various datasets. This publication contains an overview and methodologies of GIS software. Participants f r o m Bangladesh, India, Nepal, and Sri Lanka analyzed datasets on rice-wheat and legumes using various GIS tools. The individual case studies showed changed scenario in crop productivity, diseases, and pests. A few case studies dealt w i t h thematic mapping for constraints analysis. Acknowledgments The authors and editors are particularly indebted to the ICRISAT GIS faculty w h o supervised and developed all the maps and most of the graphics of this publication. These are M Irshad A h m e d and M Moinuddin of the GIS Laboratory, under the supervision of F T Bantilan Jr; K S Prasad and M Ashok Reddy of Agroclimatology, under the supervision of S M V i r m a n i. The coordination process of this publication by C L L Gowda is gratefully acknowledged. Although many contributed to w o r d processing of original manuscripts and subsequent corrections, the coordinating role played by J Nalini and I Radha is particularly appreciated. A l l contributors to this volume are appreciative of the competent technical editing of V K Sheila to enhance the final output. The coordination of the production process by T R Kapoor is gratefully acknowledged. T h e research activities were supported by the Asian Development Bank a n d donors supporting ICRISAT's unrestricted core activities.

3 GIS Application in Cropping System Analysis - Case Studies in Asia Proceedings of the International W o r k s h o p on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region August 1997, ICRISAT, Patancheru, India Edited by S Pande, A K Maji, C Johansen, and F T Bantilan Jr ICRISAT I n t e r n a t i o n a l C r o p s R e s e a r c h I n s t i t u t e f o r t h e S e m i - A r i d T r o p i c s Patancheru , Andhra Pradesh, India

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5 Workshop Sponsors Cornell University Soil Management CRSP Rice-Wheat Consortium for the Indo-Gangetic Plain (RWC) International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) W o r k s h o p O r g a n i z i n g C o m m i t t e e S Pande, Chairperson C Johansen N P Saxena I P Abrol P K Joshi A Ramakrishna Members S M Virmani F T Bantilan Jr C L L Gowda B Diwakar The opinions expressed in this publication are those of the authors and not necessarily those of ICRISAT. The designations employed and the presentation of the materials in this publication do not imply the expression of any opinion whatsoever on the part of ICRISAT concerning the legal status of any country, territory, city, or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. Where trade names are used this does not constitute endorsement of or discrimination against any product by ICRISAT.

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7 Contents Preface v Part I: GIS - Overview O v e r v i e w of G I S 3 S M V i r m a n i, K S Prasad, a n d S Pande F u n d a m e n t a l aspects of G I S 7 F T Bantilan Jr, A K M a j i, a n d M I A h m e d I n t r o d u c t i o n t o P C A r c / I n f o 1 3 K S Prasad a n d S Pande A r c V i e w G I S 1 6 M I A h m e d U s e o f R a s t e r G I S - I D R I S I 2 0 F T Bantilan Jr a n d M Srinivas M a p s - t y p e s, l a y o u t, a n d designing 23 F T Bantilan Jr a n d K S Prasad G e o r e f e r e n c i n g a n d m a p p r o j e c t i o n s 2 7 M I A h m e d S p a t i a l a n d attribute d a t a i n G I S a n d t h e i r processing 3 3 F T Bantilan Jr, M I A h m e d, a n d A K M a j i D i g i t i z a t i o n using P C A r c E d i t 3 7 K S Prasad, M Srinivas, a n d S Pande C r e a t i n g w h e a t productivity m a p s i n A r c P l o t 4 5 K S Prasad

8 Part II: Case Studies Analysis of constraints and potential of the soils of the 53 Indo-Gangetic plains of Punjab using GIS G S S i d h u, A K M a j i, B K K h a n d p a l, S Pande, a n d M V e l a y u t h a m C h a n g e s i n w h e a t p r o d u c t i v i t y i n t h e I n d o - G a n g e t i c 6 1 plains o f I n d i a A K M a j i, S Pande, M V e l a y u t h a m, a n d MI A h m e d S p a t i a l analysis o f i n c i d e n c e o f p l a n t parasitic n e m a t o d e s 6 5 i n r i c e - w h e a t - l e g u m e s c r o p p i n g systems o f U t t a r P r a d e s h, I n d i a S Pande, M A s o k a n, S B S h a r m a, a n d P K Joshi P r o d u c t i o n t r e n d s o f l e g u m e s i n rice-based c r o p p i n g 6 8 system o f S r i L a n k a H B Nayakekorala, S Pande, K V Ravindra, Y S C h a u h a n, a n d R Padmaja U s e o f G I S i n studying t h e distribution p a t t e r n o f 7 3 c h i c k p e a i n rice a n d w h e a t c r o p p i n g systems i n N e p a l S P Pandey, K S a h, S Pande, a n d K S Prasad Diseases o f c h i c k p e a i n t h e r i c e - w h e a t c r o p p i n g 7 6 system o f N e p a l K Sah, S P Pandey, S Pande, K S Prasad, a n d M M o i n u d d i n W h e a t p r o d u c t i o n p e r f o r m a n c e i n B a n g l a d e s h : 8 0 A G I S analysis D N R Paul, U K D e b, F T Bantilan Jr, M M o i n u d d i n, a n d S Pande Participants 88

9 P r e f a c e Geographic information system (GIS) has p r o v e d to be an important analytical tool in various fields of agricultural research a n d natural resource management. In view of recent progress in the use of this tool, it was thought o p p u r t u n e to provide a training p r o g r a m for scientists f r o m Asian national agricultural research systems (NARS) on the "Use of G I S in analysis of cropping systems". This training course was designed to follow up a workshop on H a r m o n i z a t i o n of databases for G I S analysis of c r o p p i n g systems in the Asia region held d u r i n g August 1997 at ICRISAT, Patancheru, India. T h e proceedings of this workshop was published in 1999 as "GIS analysis of cropping systems". This publication "GIS Application in C r o p p i n g System Analysis - Case Studies in Asia" deals w i t h GIS software overview a n d methodologies (Part I) a n d is supplemented w i t h case studies in Asia (Part II). Participants f r o m Bangladesh, India, N e p a l, a n d Sri L a n k a h a d hands-on training in different GIS software a n d the relevant features of the individual case studies are presented. Datasets on rice-wheat a n d legumes were assembled in the GIS core a n d analyzed using various G I S tools. Analysis using 'overlay', 'reclassification', a n d statistical analysis were major applications. Case studies showed temporal a n d spatial changes in crop productivity a n d pests. A few case studies attempted thematic m a p p i n g for constraints analysis. S o m e advanced features of GIS have already been published in "GIS analysis of cropping systems". S o m e of the notable papers are on interfacing GIS a n d crop simulation modeling a n d application of statistics. GIS is fast progressing a n d m a n y advanced features are n o w c o m i n g to light. This training p r o g r a m is a humble beginning a n d paves a progressive p a t h towards use of GIS. It is anticipated that this program will ensure that the participants understand and use G I S for further objective specific interpretation of various datasets a n d i m p r o v i n g knowledge in their respective field of research. T h e Editors

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13 Overview of GIS S M Virmani, K S Prasad, and S Pande 1 Introduction Geographic information system (GIS) is a facility for diverse professionals such as geographers, foresters, environmentalists, planners, decision makers, a n d researchers. These professionals d e p e n d on the information related to an area or location for analyzing a n d implementing a developmental plan. Conventional data storage a n d management has failed to satisfy their needs. W i t h the advent of computer technology, database m a n a g e m e n t in terms of 'where' a n d 'what' has been classed through GIS. W h a t i s G I S? G I S has been variously defined as: A computer-assisted system for the capture, storage, retrieval, analysis, a n d display of spatial data w i t h i n a particular organization (Clarke 1986). A set of t o o l s f o r c o l l e c t i n g, s t o r i n g, r e t r i e v i n g, t r a n s f o r m i n g, a n d d i s p l a y i n g d a t a f r o m t h e r e a l w o r l d f o r a p a r t i c u l a r set o f p u r p o s e ( B u r r o u g h 1986). A n o r g a n i z e d c o l l e c t i o n o f h a r d w a r e, software, g e o g r a p h i c d a t a, a n d p e r s o n n e l d e s i g n e d t o efficiently c a p t u r e, store, u p d a t e, a n a l y z e, a n d display all forms of geographical referenced information ( E S R I ). A computer system capable of holding a n d using data describing a place on the earth's surface. 1. ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP V i r m a n i, S. M., Prasad, K. S., and P a n d e, S Overview of GIS. Pages 3-6 in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 3

14 T h e last definition is simple a n d needs little elaboration w h i c h highlights the components of a G I S. Computer hardware A G I S can be workstation (high end) or PC based a n d depends on the user's need. T h e m i n i m u m hardware requirement comprises a standard central processing unit (CPU) w i t h adequate m e m o r y, color monitor, keyboard, mouse, printer or plotter, a n d a digitizer. Software Software p e r f o r m the various functions in G I S. There are m a n y software prevalent in the market. T h e software is chosen based on the need a n d investment required. Spatial data a n d their attributes G I S analyzes spatial data a n d their attributes. Spatial data represent the earth surface a n d the attributes are the characteristics of the spatial data. Trained personnel GIS does not function by itself a n d it must be emphasized that a group of welltrained persons skilled in the technology are crucial components a n d are termed as " h u m a n w a r e ". U s e s o f G I S A G I S is n o t simply a computer for m a k i n g maps, a l t h o u g h it can create maps on different scales a n d in different projections. It is an analytical tool. T h e m a j o r advantage of a G I S is that it allows o n e to identify the spatial relationship between m a p features a n d the non-spatial data. Therefore, GIS can be used to answer the questions related to locations, conditions, a n d trends using temporal data a n d pattern. G I S can also provide answers to some assumptions a n d presumptions o n " w h a t will h a p p e n...? " a n d " w h a t i f...? " situations w h i c h are analyzed t h r o u g h 'modeling'. G I S can be used as a decision support tool in solving increasingly complex u r b a n a n d environmental problems, forestry a n d wildlife tracking, wasteland developments, agriculture a n d groundwater resource exploration, a n d water resource management. U r b a n application includes utilities management, site suitability analysis, a n d demographic studies. 4

15 T y p e s o f G I S G I S stores data in t w o types of data models. These are: 1. Raster Data M o d e l 2. Vector Data M o d e l Based on the use of data m o d e l, G I S is called raster-based G I S or vector-based GIS. However, present-day G I S facilitates analysis on b o t h data models. G I S S o f t w a r e Based on these t w o data models there are different G I S software packages available in the market. A m o n g these, IDRISl, SPANS, ILWIS, M a p l n f o, a n d PC Arc/Info are most popular. IDRISl, ILWIS, a n d S P A N S are raster-based a n d M a p l n f o, ISROGIS, a n d PC Arc/Info are vector-based G I S software. D a t a A n a l y s i s T o o l G I S is a p o w e r f u l tool for m a p analysis. Spatial data stored in digital f o r m a t in a G I S allows for r a p i d access for traditional as well as innovative purposes. Traditional i m p e d i m e n t s to the accurate a n d r a p i d measurement of area a n d to m a p overlay no longer exists. G I S has t h e ability to p r o v i d e m u l t i p l e a n d efficient cross-referencing a n d searching. D a t a D i s p l a y T o o l s Electronic display offers significant advantage over the paper m a p. Ability to browse across an area without interruption by mapsheet boundaries. Ability to z o o m a n d change scale freely. Display in "3 dimensions" w i t h "real t i m e " rotation of view angle. 5

16 Advanced GIS W i t h passage of time, advancement in i n f o r m a t i o n technology a n d experiences in the d o m a i n s are providing n e w dimensions in GIS application. M a n y n e w techniques in spatial m o d e l i n g a n d decision-support systems are b e c o m i n g available for c o m p l e x a n d multi-dimensional analysis. A d v a n c e m e n t in t e c h n o l o g y of G I S facilitates cross-cutting research activities. M o d e l i n g, p r o g r a m m i n g, a n d i n t e r f a c i n g m o d e l s w i t h G I S p r o v i d e vital research tools. G I S triggers unification of technologists in natural resource management, c r o p p i n g systems management, a n d environmental studies. G I S a n d global positioning system (GPS) integration have p a v e d a p a t h for m o r e accurate m a p p i n g. Integration of GIS a n d remote sensing favor better resources a n d environmental management. References B u r r o u g h, P. A Principles of geographic information system for land resource assessment. O x f o r d, UK: C l a r e n d o n Press. C l a r k e, K. C Recent trends i n g e o g r a p h i c a l i n f o r m a t i o n system. Geo-processing 3 : E S R I (Environmental Systems Research Institute) U n d e r s t a n d i n g G I S - the Arc/Iinfo m e t h o d. Redlands, California, U S A : ESRI. 6

17 Fundamental Aspects of GIS F T Bantilan Jr 1, A K Maji 2, and M I Ahmed 1 Introduction Recently there has been an increased need for timely response to p r o m o t e effective administration, planning, decision m a k i n g, a n d d e v e l o p m e n t processes. C o u p l e d w i t h the complexity a n d v o l u m e of today's data requirements, traditional paper-based data handling, spatial analysis, a n d display methods have p r o v e d inadequate. T h e introduction of computers has offered a solution to these deficiencies. T h e application of "Database" a n d " I n f o r m a t i o n System" technology has led to development of computerized geographic information system (GIS). T h e concept of G I S includes the collection, storage, analysis, a n d dissemination of integrated land-related information. G I S is defined as computerized database management system for capturing, storing, validating, maintaining, analyzing, displaying, a n d m a n a g i n g spatially referenced data for providing m a n a g e m e n t information or for the development of better understanding of the environment. 1. ICRISAT, Patancheru , Andhra Pradesh, India. 2. National Bureau of Soil Survey and Land Use Planning, Amravati Road, Nagpur , Maharashtra, India. ICRISAT Conference Paper no. CP B a n t i l a n J r., F. T., M a j i, A. K., and A h m e d, M. I Fundamental aspects o f GIS. Pages 7-12 in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 7

18 Views of GIS GIS can be viewed with different perspectives depending on the objectives of the users. However, in totality, GIS is viewed as given below (Maguire et al. 1991): Views Map perspective Database management system Spatial analysis tools Features Cartographic aspects Well-designed a n d Analysis and implemented database modeling Functions Map processing and Used for analytical Information display system operation system I m p o r t a n c e o f G I S G I S has b e c o m e o n e of the essential tools in spatial analysis a n d provides excellent display. G I S is popularly used for various reasons as outlined by R h i n d (1977): To m a k e existing maps m o r e quickly a n d m o r e cheap. To make m a p s for specific user needs. To m a k e m a p p r o d u c t i o n possible in situations w h e r e skilled staff are unavailable. To allow e x p e r i m e n t a t i o n w i t h different graphical representations of the same data. T o facilitate m a p m a k i n g a n d u p d a t i n g w h e n t h e d a t a are already i n digital f o r m. To facilitate analyses of data that d e m a n d interaction b e t w e e n statistical analysis a n d m a p p i n g. To create m a p s that are difficult to m a k e by h a n d, e.g., 3 - d i m e n s i o n a l maps or stereoscopic maps. To create m a p s in w h i c h selection a n d generalization p r o c e d u r e s are explicitly defined a n d consistently executed. Data in GIS G I S stores t w o types of data: the location of geographic features a n d the attributes of those features. There are t w o m a i n spatial data models in 8

19 representing b o t h types of geographic data: the vector m o d e l a n d the raster m o d e l. T h e vector data m o d e l represents geographic features the w a y paper maps do. An x,y (Cartesian) coordinate system references real-world locations. Points are recorded as a single coordinate. Lines are recorded as a series of ordered x,y coordinates. Areas are recorded as a series of x,y coordinates defining line segments that enclose an area. Each feature is assigned a unique id, number, or tag. T h e arc-node data structure in a vector m o d e l stores a n d references data so that nodes construct arcs a n d arcs construct polygons. Topology in a vector m o d e l explicitly defines spatial relationships, supporting the topological concepts of connectivity, area definition, a n d contiguity. In the vector data m o d e l, surfaces are represented as a series of isolines. T h e raster data m o d e l is more like a p h o t o g r a p h than a m a p. It has a regular grid or dots called cells or pixels, filled w i t h values. A p h o t o depicts a continuous surface. In the raster data m o d e l, the earth is treated as continuous surface. T h e numeric value of each cell or pixel m a y represent either a feature identifier, a qualitative attribute code, or a quantitative attribute value. T h e raster data m o d e l represents a discrete p o i n t as a value in a single cell, a linear feature as a series of connected cells that depict length, a n d an area feature as a group of connected cells depicting shape. In b o t h models, the attributes of geographic features are organized as records in attribute tables. Linking attributes to geographic features, the georelational m o d e l is by unique identifiers in b o t h the geographic features list a n d in the attribute table. It is possible to convert data f r o m raster to vector a n d vice versa, a n d thereby take advantage of the strengths of b o t h data models. Rasterization is the process of converting data f r o m raster to rector. Vectorization is the reverse process of converting data from vector to raster. GIS Software T h e capability of a GIS package depends on the m o d u l e (sub-program) it contains. These modules execute different functions in GIS. A modest G I S should support the following (Eastman 1990): Spatial a n d attribute database creation. M a p digitization. Analysis/transformation/manipulation. 9

20 Statistical analysis. Decision support system development. Data display a n d cartographic operations. Steps for Creating a GIS Database To create a vector p o l y g o n database in G I S core, three steps are followed: 1. Digitization of spatial data (map). 2. Creation of attribute database. 3. Linking steps 1 a n d 2. T h e flow diagram (Fig. 1) shows the creation of a topologically correct vector database. DATA COLLECTION ATTRIBUTE Link by Unique Identifier SPATIAL DATA REGISTRATION RECTIFICATION DIGITIZE Convert To Vector Input to Text File Visual Check EDIT Link Spatial Attribute Data Add Identifiers GIS DATABASE Figure 1. Steps in creating G I S database. 10

21 Common Operations in GIS T h e most c o m m o n operations carried o u t by GIS are database query, m a p algebra, a n d distance- a n d context-related analysis. These are described in 'spatial a n d attribute data a n d their processing in GIS'. Spatial overlay. T w o themes are c o m b i n e d to f o r m a n e w spatial feature (both geometric a n d attribute features are combined) k n o w n as spatial overlay. Three types of overlay can be p e r f o r m e d : p o l y g o n - p o l y g o n, line-polygon, a n d pointp o l y g o n. Buffer zone creation. Distance operator in GIS is used for buffer zone creation. It shows the proximity or nearness f r o m any point, line, or p o l y g o n. M a p generalization. M a p generalization is a function to dissolve or merge adjacent p o l y g on features. Feature extraction. Feature extraction is subset selection of a m a p, e.g., district soil m a p from state soil m a p. O t h e r F e a t u r e s o f G I S Digital terrain modeling Two-dimensional maps (chloropleth maps) cannot perceive the landform features w h i c h are having varying surface forms. A n y digital representation of the continuous variation of relief over space is k n o w n as Digital Elevation M o d e l (DEM) (Burrough 1986). T h e D E M is generated by the input data, data models, a n d related algorithms. Data f r o m satellite images a n d contour lines denoting elevation are used to generate Digital Terrain M o d e l ( D T M ). In D E M the terrain features are represented through x, y, a n d z coordinates to depict a 3-dimensional view. T h e D E M is useful in slope analysis of a terrain, a n d c o m b i n i n g thematic information w i t h relief, laying railway lines, roads, etc. Decision-support system A decision-support system (DSS) is a computerized analytical tool to solve complex problems. This helps the decision makers to define their problems in GIS environment and get a solution using mathematical models a n d hybrid formulations. Spatial Decision S u p p o r t System (SDSS) is explicitly designed to 11

22 support a decision research process for complex spatial problems. S D S S provides a framework for integrating Database M a n a g e m e n t System ( D B M S ) w i t h analytical models, graphic display, a n d tabular reporting capabilities a n d expert knowledge of decision makers (Densham 1991). Conclusion It is very difficult to provide details of G I S t h r o u g h a small write-up like this. Learners practicing the use of G I S will learn m u c h m o r e while interacting w i t h GIS software. It is advisable that apart f r o m the manuals of the software p r o v i d e d by the software manufacturers, the books authored by Maguire et al. (1991), B u r r o u g h (1986), a n d A r o n o f f (1989) m a y be referred. References A r o n o f f, S Geographic information systems: A m a n a g e m e n t perspective. Ottawa, USA: W D L Publication. B u r r o u g h, P. A Principles of geographical information system for land resource assessment. O x f o r d, UK: Clarendon Press. D e n s h a m, P. J Spatial decision support system in geographical information system. Pages in Geographical information system: principles a n d applications (Maguire, D.J., G o o d c h i l d, M. F, a n d R h i n d, D.W., eds.). N e w York, USA: L o n g m a n Scientific a n d Technical. E a s t m a n, J. R IDRISI: A grid based geographic analysis system. Massachusetts, U S A : Graduate S c h o o l of Geography, Clark University. M a g u i r e, D. J., G o o d c h i l d, M. F., and R h i n d, D. W Geographical information system: principles a n d applications. N e w York, USA: L o n g m a n Scientific a n d Technical. R h i n d, D C o m p u t e r aided cartography. Transactions of the Institute of British Geographers 2 :

23 Introduction to PC Arc/Info K S Prasad and S Pande 1 Introduction Geographic information system (GIS) evolved as a means of assembling a n d analyzing spatial d a t a. M a n y G I S software systems h a v e b e e n d e v e l o p e d. PC Arc/Info software w h i c h is being used at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) is described in brief (condensed f r o m PC Arc/Info user's m a n u a l for training purposes). This software is m a d e by the Environmental Systems Research Institute (ESRI), California, U S A. PC Arc/Info is a vector-based G I S software. In PC Arc/Info various maps are stored in layers called coverages. PC ArcEdit is one of the specialized PC Arc/Info modules designed to provide advanced capabilities for interactive, sophisticated graphic editing for coverage creation a n d update, a n d for final cartographic production. PC Arc/Info uses a c o m m a n d interface. An operation is performed by typing a c o m m a n d along w i t h c o m m a n d arguments. PC Arc/Info Modules PC Arc/Info is available w i t h its user guides for all its modules. It is necessary that a beginner must learn Disk operating system (DOS) a n d W i n d o w s operating system. PC Arc/Info includes Simple M a c r o Language (SML) to build macros. T h e different modules that are available in PC Arc/Info are: (1) Starter Kit, (2) ArcEdit, (3) ArcPlot, (4) Overlay, (5) Network, a n d (6) Data Conversion. 1. ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP Prasad, K.S., and P a n d e, S Introduction to PC Arc/Info. Pages in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., M a j i, A.K., Johansen, C, and Bantilan Jr., FT., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 13

24 Starter Kit PC Arc/Info Starter Kit is the basic p r o g r a m m o d u l e dealing w i t h digitizing, attribute table creation, host c o m m u n i c a t i o n, a n d plot system f u n c t i o n. PC ArcEdit PC ArcEdit is used for m o r e sophisticated interactive digitization a n d editing. It has all the capabilities of Arc Digitizing System a n d also allows to edit the attribute associated w i t h graphic features while these features are displayed on screen. These editing capabilities include m o v i n g, a d d i n g, a n d deleting individual vertices in arcs a n d reshaping a n d splitting features. A n n o t a t i o n (text) can be positioned interactively to follow the orientation of features. Features for editing can be selected either w i t h the cursor or by q u e r y i n g their attributes, thus allowing considerable flexibility in m a k i n g precise changes. PC ArcPlot PC ArcPlot allows interactive m a p creation a n d display, graphic query, a n d plotting of maps. PC Overlay PC Overlay is the most i m p o r t a n t m o d u l e for analysis such as all types of overlays a n d buffer generation. PC overlay features six overlay programs: C L I P, E R A S E C O U, IDENTITY, INTERSECT, U N I O N, a n d U P D A T E. PC Network PC N e t w o r k m o d u l e is used in o p t i m a l routing, allocation, districting, a n d address matching/geocoding. PC N e t w o r k analyzes networks, such as roads, rivers, a n d electric p o w e r grids, stored as Arc/Info coverages. PC Data Conversion PC Data Conversion m o d u l e provides special functions permitting data exchange between PC Arc/Info coverages a n d a variety of other data exchange formats. 14

25 Spatial Terms T h e various spatial terms applied in PC Arc/Info a n d used while m a p preparation are described below. M a p. A m a p is a combination of point, line, a n d closed p o l y g o n (area) features. A r c s. Arcs represent line features, the borders of polygons, or b o t h. O n e line feature m a y be m a d e up of m a n y arcs. Arcs are linked to their endpoints (nodes) a n d to the areas (polygons) on each side. N o d e s. Nodes represent endpoints of the arc a n d intersections of line features. P o l y g o n. A p o l y g o n is defined by the series of arcs w h i c h compose its border a n d by a label point positioned inside its border. T h e label point ID is used to assign the p o l y g o n a user-id. L a b e l points. Label points represent p o i n t features. They are also used to assign user-ids to polygons. T h e user-ids are used to associate tabular attribute data w i t h polygons. P o i n t features. Point features are locations defined by single x,y coordinate pairs. Each point feature is represented by a label point. T i c s. Tics are geographic registration or geographic control points for a coverage. T h e y represent k n o w n locations on the earth's surface. T h e y allow all coverage features to be registered to a c o m m o n coordinate system. Before beginning digitization the tic registration points should be located a n d a unique n u m b e r should be assigned. C o v e r a g e e x t e n t o r b o u n d a r y ( B N D ). Coverage extent represents m a p extent. It is a rectangle that defines the coordinate limits (extreme m i n i m u m a n d m a x i m u m coordinates) of coverage arcs a n d label points. GIS Functional Elements There are five essential elements that a G I S must contain: data acquisition, preprocessing data management, m a n i p u l a t i o n, analysis, a n d product generation. For any given application of a GIS it is important to learn these modules perfectly before planning to take up any GIS project. 15

26 ArcView GIS M I Ahmed 4 Introduction ArcView is a p o w e r f u l, easy-to-use tool that brings geographic information to the desktop. It gives the p o w e r to visualize, explore, query, a n d analyze data spatially. ArcView is m a d e by the E n v i r o n m e n t a l Systems Research Institute (ESRI), California, U S A. Arc/Info (which is also m a d e by ESRI) data can be used w i t h ArcView to access vector coverages, m a p libraries, grids, images, a n d event data. This brief description of ArcView is based on ESRI (1996). W o r k i n g S p a t i a l l y ArcView can be used to w o r k spatially. A key feature of ArcView is that it is easy to load tabular data, such as d B A S E files a n d data f r o m database servers, so that one can display, query, summarize, a n d organize this data geographically. While w o r k i n g w i t h ArcView a completely n e w w a y of visualizing for seeing patterns, understanding geographic relationships, a n d achieving new results will be possible. A r c V i e w ' s U s e r I n t e r f a c e ArcView provides a simple user interface w i t h views, tables, layouts, charts, a n d scripts stored in o n e file called a 'project', each c o m p o n e n t having a specific use a n d functionality. N o t more than one project can be o p e n e d at a time in 1. ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP A h m e d, M.I ArcView GIS. Pages in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for G I S Analysis of C r o p p i n g Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , A n d h r a Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 16

27 ArcView. Projects enable o n e to keep together all the a b o ve components that are needed for a specific task or application. T h e P r o j e c t W i n d o w A new project or an existing one lists all the components of the project a n d enables their management. A c o m p o n e n t can be o p e n e d by double-clicking it. A project c o m p o n e n t opens its o w n w i n d o w. A n y n u m b e r of w i n d o w s can be opened in ArcView, but at any o n e time there is only o n e 'active w i n d o w ' (the w i n d o w currently being w o r k e d with). W h e n an action in ArcView is performed, it applies to the active w i n d o w. ArcView's user interface changes according to w h a t is in the active w i n d o w. For example, w h e n the project w i n d o w is active, the buttons, tools, a n d menus for w o r k i n g w i t h projects can be seen. M e n u bar T h e m e n u bar along the t o p of the ArcView w i n d o w contains ArcView's p u l l d o w n menus. To choose a m e n u, the mouse or a k e y b o a r d shortcut can be used. S o m e keyboard shortcuts are listed in the menus. Others d e p e n d on the graphical user interface (GUI) system. T h e contents of the m e n u bar change according to w h a t is in the active w i n d o w. Button bar T h e button bar located beneath the m e n u bar in the ArcView w i n d o w contains buttons w h i c h give quick access to various controls. T h e contents of the button bar change according to w h a t is in the active w i n d o w. Toolbar T h e toolbar located beneath the b u t t o n bar in the ArcView w i n d o w contains various tools to w o r k w i t h. However, while w o r k i n g on the project w i n d o w or on a script, there is no toolbar. T h e tool remains selected until another is chosen. T h e T a b l e o f C o n t e n t s Each view has a "Table of Contents" that lists the themes in the view a n d shows w h a t symbols a n d colors they are d r a w n w i t h. 17

28 Views W i t h ArcView, geographic data can be explored in interactive m a p s called views. Every v i e w features ArcView's u n i q u e geographic "Table of Contents", m a k i n g it easy to understand a n d control w h a t is displayed. T a b l e s W o r k i n g w i t h tabular data in ArcView's tables shows the records of a v i e w a n d its attributes. T h e v i e w shows the features of the selected records. ArcView's tables also have a full range of features for obtaining s u m m a r y statistics, sorting, a n d querying. C h a r t s ArcView's charts offer a p o w e r f u l business graphics a n d data visualization capability that is fully integrated into ArcView's geographic environment. T h e user can simply click on features on a v i e w to a d d t h e m to the chart. ArcView allows the user to w o r k simultaneously w i t h geographic, tabular, a n d chart representations of data. L a y o u t s ArcView's layouts allow creation of high quality, full color maps by first arranging the various graphic elements on-screen in the w a y desired. H i g h quality results on a w i d e range of printers a n d plotters can be p r o d u c e d in ArcView. L a y o u t s are smart because they have a live link to the data they represent. W h e n a layout is printed, any changes to the data are automatically included, so that everything on the m a p can be updated. S c r i p t s ArcView scripts are macros written in A v e n u e (ArcView's p r o g r a m m i n g language a n d development environment). W i t h A v e n u e it is possible to customize almost every aspect of ArcView, f r o m a d d i n g a n e w b u t t o n to r u n n i n g a written script, to creating an entire custom application. 18

29 Analysis of Cropping Systems At the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), ArcView has been used as an exploratory tool in the analysis of cropping systems. It was primarily used to m a p o u t crop distribution (random) at the district level for the Indo-Gangetic Plain area. T h e d o t density technique was used to depict distribution of crops across the Indo-Gangetic Plain districts. Other maps, such as the disease distribution a n d nutrient deficiency distribution, were also m a d e. Resource inventory maps such as soil type, length of growing period, a n d precipitation were also used to analyze the cropping systems w i t h ArcView analysis capabilities. S o m e examples are available in the case studies described later in this proceedings. Reference E S R I (Environmental Systems Research Institute) ArcView G I S user guide, Using ArcView GIS. Redlands, California, USA: ESRI. 19

30 Use of Raster GIS -- IDRISI FT Bantilan Jr and M Srinivas 1 Introduction Geographic information system (GIS) software uses one of t w o m a i n data models: vector a n d raster. T h e raster m o d e l is m o r e convenient in h a n d l i n g a n d processing the data as the basic unit in this m o d e l is a cell or pixel. IDRISI for w i n d o w s makes use of elements f r o m b o t h data models (Eastman 1990). T h o u g h it is primarily a raster-based software, IDRISI for w i n d o w s uses vector data structures as a major f o r m of display a n d exchange as well as essential aspects of vector database management. Overview T h e system of IDRISI for w i n d o w s consists of several programs, a m a i n interface p r o g r a m (with a m e n u a n d toolbar system), a n d a collection of over 150 program modules that provide facilities for the input, display, a n d analysis of b o t h geographic a n d remotely sensed data. T h e spatial data in IDRISI are organized in m a p layers, each describing o n e single theme, such as roads, elevation, a n d soil type. Maps take on t w o basic forms: raster image layers a n d vector layers. Raster layers are excellent in representing spatially continuous data such as elevation, rainfall, etc. Vector layers describe distinct features w i t h sharp boundaries on the earth's surface such as roads, land parcels, a n d political districts. In addition, IDRISI for w i n d o w s has capabilities for the processing of remotely sensed image data, this performing as Image Analysis System (IAS) a n d GIS. 1. ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP Bantilan Jr., F.T., and Srinivas, M Use of Raster GIS - IDRISI. Pages in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 20

31 IDRISI for Windows Environment T h e w o r k i n g environment of IDRISI is similar to a n y other k i n d of software where the user enters into different user-defined menus to execute functions. Working with IDRISI There are several functions existing w i t h IDRISI. S o m e of the important functions of IDRISI in the f o r m of menus are as follows: E N V I R O N W o r k i n g directory is to be set. T h e files to w o r k w i t h a n d the output files are stored in this directory. F I L E File m e n u has a series of functions for file information a n d management. DIGITIZE T h e digitize b u t t o n provides for on-screen digitization w i t h raster b a c k g r o u n d a n d creation of vector files. D I S P L A Y IDRISI has variety of options for displaying the data. T h i s m e n u can be used to display raster, vector, a n d m a p composition data. R E F O R M A T R e f o r m a t h a s f u n c t i o n s t o c h a n g e t h e n a t u r e o f geographic data files. A N A L Y S I S Over 100 modules are devoted to the analysis of spatial data. DATA E N T R Y T h e data entry function integrates spatial a n d attribute data to get the required output w i t h suitable legend. Additional features of IDRISI Creation of digital elevation models. I m a g e analysis i n c l u d i n g c a l c u l a t i o n of N D V I ( N o r m a l i z e d Difference Vegetative Index). Statistical analysis of geographic data. M a p analysis. 21

32 Database Query T h e database query exercise explores the most f u n d a m e n t a l operation in GIS; " w h a t is at this location?" a n d "where are all locations that have this attribute?" T h e example deals with an area where local farmers practice w h a t is k n o w n as "recessional agriculture" by planting s o r g h u m (Sorghum bicolor) in the flooded areas after the waters recede. T h e conditions that m a k e an area suitable for recessional sorghum agriculture in this location are: (1) the area should be flooded (elevations less than 9 m ) ; a n d (2) it should be on clay soils. Each condition must be represented by an image, i.e., F L O O D a n d C L A Y S O I L. B E S T S O R G, the n a m e for suitable areas, is the result of c o m b i n i n g these t w o images such that the areas left are those that satisfy b o t h conditions. To do this, B o o l e a n images are p r o d u c e d that contain only values of one a n d zero, where a value of one indicates that a pixel meets the condition a n d a value of zero indicates that a pixel does not. To p r o d u c e the B o o l e a n image F L O O D, the elevation m o d e l of the w h o l e area called D E L E V is generated using R E C L A S S m o d u l e. To create B o o l e a n image C L A Y S O I L, the soil m a p n a m e d D S O I L, where the set of clay soils have a value of one a n d the rest have a value of zero, is chosen. A S S I G N m o d u l e is used to p e r f o r m to carry this out. Next, O V E R L A Y m o d u l e is used to p e r f o r m a multiplication operation between t w o B o o l e a n images, F L O O D a n d C L A Y S O I L, resulting in a logical e n d. T h e result is B E S T S O R G, where each pixel satisfies b o t h conditions. T h e area in hectares is calculated using the A R E A m o d u l e. D a t a R e f o r m a t t i n g, M a s k i n g, a n d I m a g e G r o u p F i l e s T h e data needed to carry o u t a GIS task in IDRISI for w i n d o w s usually come f r o m various sources a n d in different formats. It is then necessary to reformat the various datasets to a c o m m o n format before w o r k can proceed. Moreover, the area of interest m a y only be a subset of the available image; thus the technique of masking is needed. Lastly, the use of image g r o u p files is illustrated in the interactive querying of attributes in D I S P L A Y. This exercise allows exploration of the use of the various modules in IDRISI in manipulating available data. Reference E a s t m a n, J. R IDRISI: A grid based geographic analysis system. Massachusetts, USA: Graduate School of Geography, Clark University. 22

33 Maps - Types, Layout, and Designing F T Bantilan Jr and K S Prasad 1 Introduction Maps represent geographic features as points, lines, a n d areas. Points represent objects such as wells, telephone poles, buildings. Lines represent features such as rivers a n d roads. Areas represent the shape a n d location of geographic features considered homogeneous, such as states, soil types, or land use zones. Maps present descriptive information a b o u t geographic features using symbols a n d labels. T h e m a p readers interpret the relationships between geographic features a n d locations a n d derive i n f o r m a t i o n from the position of m a p p e d objects. Maps use plane coordinate system, a f ramework for defining real-world location of geographic features on the m a p. T h e process of "flattening" the earth's surface to represent it on paper is called projection. A discussion on projection is presented later in this proceedings. Types of Maps Topographic m a p A topographic m a p is a reference tool, s h o w i n g the outlines of selected natural a n d m a n - m a d e features of the earth. It often acts as a frame to other information. 1. ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP Bantilan Jr., F.T., and Prasad, K.S Maps - types, layout, and designing. Pages in G I S application in c r o p p i n g system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., M a j i, A. K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics 23

34 Thematic map A thematic m a p is a tool to c o m m u n i c a t e the geographic concepts such as the distribution of p o p u l a t i o n densities, climate, soils, a n d landmarks. Thematic maps are of t w o types: chloropleth a n d isopleth. C h l o r o p l e t h m a p. A chloropleth m a p uses reporting zones such as census tracts to s h o w data; e.g., average incomes a n d soil geology. I s o p l e t h m a p. An isopleth m a p shows an imaginary surface by means of lines called isolines that j o i n points of equal value (e.g., contours on a topographic m a p ). Characteristics of M a p s Often stylish a n d generalized a n d require careful interpretation. Usually o u t of date. S h o w o n l y static situation. Often highly elegant/artistic. Useful to answer certain types of questions. G I S F e a t u r e s A geographic information system (GIS) must p r o v i d e the same capabilities as the paper maps provide, a n d more. T h e p o w e r of G I S lies not only in the ability to store geographic data, but also, the ability to analyze it m o r e efficiently a n d m o r e conveniently t h a n is possible w i t h paper maps. PC ArcPlot provides full cartographic o u t p u t capabilities f r o m simple screen displays to high quality cartographic plots for reports a n d presentations. It facilitates interactive m a p creation a n d previewing maps on the monitor, sending m a p s to a printer or plotter, a n d using maps as graphic w i n d o w s of the database for interactive query a n d u p d a t i n g of attribute information. It has also facilities for creating cartographic symbols a n d a m a c r o language to create customized user applications. 24

35 Designing a Map M a p design means resolving a n u m b e r of technical a n d graphic issues regarding m a p such as scale of the m a p, colors, a n d symbols to be used. Features for scaling a n d positioning maps, specifying symbols, selecting, d r a w i n g a n d labeling coverage features, a d d i n g titles, legends, neatlines, scale bars, n o r t h arrows, etc. are available in PC Arc/Info. M a p L a y o u t Considerations for the creation of a successful m a p include the layout of the m a p components in relation to each other, the characteristics of the symbols a n d labels used, a n d the size a n d scale of the entire m a p. It is advisable to design an intended layout on paper before preparing any final m a p in ArcPlot. In addition to graphic considerations, there are technical factors w h i c h determine the a m o u n t, a n d detail of information that a m a p can display a n d still be easily understood. These include scale, resolution, a n d classification. In most cases, there is a trade-off between the a m o u n t of information that can be c o m m u n i c a t e d a n d the complexity of that information. M a p S c a l e a n d R e s o l u t i o n M a p scale is the extent of reduction required to display a p o r t i o n of the earth's surface on a m a p. It is expressed as a ratio of distance on the m a p page to distance on the g r o u n d. T h e appropriate scale at w h i c h any m a p is to be displayed depends on the information to convey. T h e use of terms 'small scale' a n d 'large scale' are often confusing. Large scale maps show features in greater detail but cover less area. Thus, important spatial relationships m a y not be s h o w n. Small scale maps show a larger area but the information is in less detail, w h i c h m a y reduce the usefulness of the m a p for certain applications. T h e resolution of a m a p determines h o w accurately b o t h the location a n d shape of geographic features are represented. Unlike m a p scale, resolution is determined at the time a m a p is created on paper or digitized a n d cannot be changed in the display process. T h e scale f r o m w h i c h a m a p is digitized affects the resolution of that m a p. In a large scale m a p, the location features are depicted m o r e closely a n d match the actual real-world coordinates because the extent of reduction f r o m g round to m a p coordinates is less a n d spatial relationships between features are more 25

36 closely preserved. Also, since m o r e detail can be s h o w n, less s m o o t h i n g of features occurs. Thus, features shape will m o r e accurately reflect the true shape of features represented. 26

37 Georeferencing and Map Projections M I Ahmed 1 Introduction Spatial data has been presented on paper for long until recently w h e n the computers took over the task of m a p m a k i n g (digital cartography). Geographic information system (GIS) provides w i t h the state-of-the-art technology in spatial analysis a n d is a very i m p o r t a n t tool for decision m a k i n g in natural resources management a n d planning. There are m a n y advantages of GIS such as customized display of m a p features, attachment of multiple attributes to the m a p features, a n d spatial analysis functions (e.g., buffers a n d overlay). However, the spatial representation of data in G I S remains tied to the m a p p i n g plane, i.e., a two-dimensional surface or paper. T h e only true representation of the earth, free of distortion, is a globe. T h e element of compromise is fundamental. A spherical surface cannot be p r o d u c e d on a flat paper; also the corresponding properties of positions a n d relative position w h i c h exist on that surface cannot be produced. Georeferencing Georeferencing refers to the m a n n e r in w h i c h locations in m a p (flat) are related to earth (curved or spherical) surface locations. This is possible by a process of transformation called m a p projections. 1. ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP A h m e d, M. I Georeferencing and map projections. Pages in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi- Arid Tropics. 2 7

38 Map Projection M a p projection is a mathematical transformation that is used to create a flat m a p sheet f r o m the spherical surface. It defines the m a p p i n g f r o m geographic coordinates on a sphere or the geodetic coordinates on a spheroid to a plane surface. Projection formulae convert data f r o m geographical location on a sphere or a spheroid to a representative location on a flat surface. Parameters Required for the Projection of a M a p Reference surface (ellipsoid or sphere) Reference surface is a mathematically (e.g., sphere or spheroid) or physically (geoid) defined surface to approximate the shape of the earth for referencing the horizontal and/or vertical position. T h r o u g h a l o n g history, the "shape of the earth" was refined f r o m flat-earth models to spherical models of sufficient accuracy to allow global exploration, navigation, a n d accurate m a p p i n g. Geodetic datum A d a t u m is a set of parameters defining a coordinate system, a n d a set of control points whose geometric relationships are k n o w n, either through measurement or calculation (Dewhurst 1990). A geodetic d a t u m is defined by a spheroid, w h i c h approximates the shape of the earth, a n d the spheroid's position relative to the center of the earth. It is a reference object that describes the position, orientation, a n d scale relationships of a reference ellipsoid to the earth. M o d e r n geodetic d a t u m are defined w i t h respect to the center of the earth, while historical geodetic d a t u m are defined w i t h respect to fundamental points on the surface of the earth. M e a s u r e m e n t s on t h e g l o b e. A l t h o u g h degrees of latitude a n d longitude can be used to locate exact positions on the surface of the globe, they are not u n i f o r m units of measure on the surface of the globe. O n l y along the equator does the distance represented by o n e degree of latitude approximate the distance represented by one degree of longitude. This is because the equator is the only parallel as large as the meridian. T h e degrees of latitudes a n d longitudes are not associated w i t h a standard length because this reference system measures angles f r o m the center of the earth, rather t h a n distances on the earth's surface. 28

39 P l a n a r m e a s u r e m e n t s. Because it is difficult to m a k e measurements in spherical coordinates, geographic data is projected into a planar coordinate system called Projection. O n c e it is projected o n t o a flat surface, the spherical values change. On a flat surface, locations are identified by x,y coordinates on a grid, w i t h the origin at the center of the grid. Each position has t w o values, the x-coordinate a n d the y-coordinate. T h e advantage of the planar system is that the measures of length, angle, a n d area are constant across the t w o dimensions. M a p projections and parameters Every flat m a p misrepresents the surface of the earth in some way. However, a m a p can show one or more b u t never all of the following: (1) True areas, (2) True shapes (conformality), (3) True directions, a n d (4) True distances. A n y representation of the earth's curved surface o n t o a two-dimensional surface involves distortion of at least one of the above parameters. Different projections produce different distortions. The characteristics of each projection m a k e them useful for some applications a n d not for others. Of the four basic properties, correct representation of area over the w h o l e m a p is the most easily achieved, since equivalent areas can be p r o d u c e d by an infinite variation of dimensions within the same shape or by e m p l o y i n g different shapes. A satisfactory balance of properties is not difficult to achieve over relatively small areas; it is w h e n the area covered by the projection increases that difficulties arise, a n d most projections make p o o r show at their extremes w h e n used to represent the w h o l e globe. M a p projections fall into four general classes (Snyder 1987): Cylindrical projections result from projecting a spherical surface o n t o a cylinder. - P r o j e c t i o n of a S p h e r e o n t o a C y l i n d e r (Tangent Case): W h e n the cylinder is tangent to the sphere, contact is a l o n g a great circle (the circle f o r m e d on the surface of the earth by a plane passing through the center of the earth). - Projection of a Sphere o n t o a Cylinder (Secant Case): In the Secant Case, the cylinder touches the sphere along t w o lines, b o t h small circles (a circle f o r m e d on t h e surface of t h e e a r t h by a p l a n e n o t passing through the center of the earth). 29

40 - Transverse P r o j e c t i o n of a S p h e r e o n t o a C y l i n d e r (Tangent Case): W h e n the cylinder u p o n w h i c h the sphere is projected is at right angles to the poles, the cylinder a n d resulting projection are transverse. - Oblique Projection of a Sphere o n t o a Cylinder (Tangent Case): W h e n the cylinder is at some other, non-orthogonal angle w i t h respect to the poles, the cylinder a n d resulting projection is oblique. C o n i c projections result f r o m projecting a spherical surface o n t o a c o n e. - Projection of a Sphere o n t o a C o n e (Tangent Case): W h e n the cone is tangent to the sphere, contact is along a small circle. - Projection of a Sphere o n t o a C o n e (Secant Case): In the Secant Case, the cone touches the sphere along t w o lines, a great circle a n d a small circle. A z i m u t h a l projections result f r o m p r o j e c t i n g a spherical surface o n t o a plane. - Projection of a Sphere o n t o a Plane (Tangent Case): W h e n the plane is tangent to the sphere, contact is at a single p o i n t on the surface of the earth. - Projection of a Sphere o n t o a Plane (Secant Case): In the Secant Case, the plane touches the sphere along a small circle if the plane does not pass through the center of the earth. Miscellaneous projections include unprojected ones such as rectangular, latitude a n d longitude grids, a n d other examples that do not fall into the cylindrical, conic, or azimuthal categories. Some Important Map Projections Conic projections Albers equal a r e a conic. The Albers equal area conic is a conic projection that distorts scale a n d distance except along standard parallels. Areas are proportional a n d directions are true in limited areas. It is used in U S A a n d other large countries w i t h a larger east-west than north-south extent. 30

41 Equidistant conic. In the equidistant conic, the direction, area, a n d shape are distorted away f r o m standard parallels. It is used for portrayals of areas near to, b u t on o n e side of, the equator. L a m b e r t c o n f o r m a l conic. In the L a m b e r t c o n f o rmal conic, the area a n d shape are distorted away f r o m standard parallels a n d the directions are true in limited areas. It is used for maps of N o r t h America. P o l y c o n i c. T h e polyconic projection was used for most of the earlier U S G S (United States Geological Survey) topographic quadrangles, neither conformal nor equal area. T h e central meridian a n d the equator are straight; other meridians are complex curves. T h e parallels are non-concentric circles. T h e scale is true along each parallel a n d along the central meridian. Azimuthal projections A z i m u t h a l equidistant. Azimuthal equidistant projections are sometimes used to show air-route distances. Distances measured f r o m the center are true. Distortion of other properties increases away f r o m the center point. L a m b e r t a z i m u t h a l e q u a l a r e a. T h e L a m b e r t azimuthal equal area projection is sometimes used to m a p large ocean areas. T h e central meridian is a straight line whereas others are curved. A straight line d r a w n through the center p o i n t is on a great circle. O r t h o g r a p h i c. Orthographic projections are used for perspective views of hemispheres. T h e area a n d shape are distorted. Distances are true along the equator a n d other parallels. S t e r e o g r a p h i c. Stereographic projections are used for navigation in polar regions. Directions are true from the center point a n d scale increases away from the center point as does distortion in area a n d shape. Miscellaneous projections U n p r o j e c t e d m a p s. Unprojected maps include those that are f o r m e d by considering longitude a n d latitude as a simple rectangular coordinate system. Scale, distance, area, a n d shape are all distorted w i t h the distortion increasing t o w a r d the poles. 31

42 S p a c e O b l i q u e M e r c a t o r. T h e Space Oblique Mercator is a projection designed to s h o w the c u r v e d ground-track of L A N D S A T images. There is little distortion along the ground-track b u t only w i t h i n the n a r r o w b a n d (about 15 ) of the L A N D S A T image. References D e w h u r s t, T T h e application o f minimum-curvature-derived surfaces in the transformation of positional data f r o m the N o r t h A m e r i c a n D a t u m of 1927 t o the N o r t h A m e r i c a n D a t u m o f N O A A Technical M e m o r a n d u m N O S N G S Rockville, M a r y l a n d, USA: N O A A. S n y d e r, J. P M a p projections: a w o r k i n g m a n u a l. U S G S Professional Paper Washington, D C, USA: United States G o v e r n m e n t Printing Office. 32

43 Spatial and Attribute Data in GIS and their Processing F T Bantilan Jr 1, M I Ahmed 1, and A K Maji 2 Introduction A m a p represents geographic features a n d related spatial p h e n o m e n a conveying information about locations w i t h their attributes graphically. Spatial information describes the position of a geographic feature on the earth's surface as well as the spatial relationships between features. Attribute (nonspatial) data describe characteristics of the geographic features represented such as the feature type, its name, abundance, a n d quantitative information [e.g., area (of a soil unit) a n d length (of a river)]. Geographic information system (GIS) uses raster a n d vector representations to m o d e l locations a n d provide linkages between spatial a n d non-spatial/attribute data. These linkages make GIS "intelligent" as the user can store a n d examine the information about 'where' a n d 'what' scenario. For example, a soil m a p w i t h m a p p i n g units can have not only the soil type as the theme for m a p display but also other physical a n d chemical properties tied to it. T h e linkage between the feature a n d its attribute data is established by giving each feature one unique means of identification, w h i c h is usually a n a m e or a n u m b e r called its ID. Non-spatial attributes of the feature are then stored, usually in one or more separate files towards this ID number. In other words, locational information is linked with additional information f r o m a database. 1. ICRISAT, Patancheru , Andhra Pradesh, India. 2. National Bureau of Soil Survey and L a n d Use Planning, Amravati Road, Nagpur , Maharashtra, India. ICRISAT Conference Paper no. CP Bantilan Jr., F.T., A h m e d, M. I., and M a j l, A. K Spatial and attribute data in GIS and their processing. Pages in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi- Arid Tropics. 3 3

44 Organization of Attribute Data T h e attribute data is as important as the spatial data a n d needs to be organized a n d stored in different formats a n d files as required by the GIS software in use. T h e most generic f o r m w o u l d be the ordinary delimited text format, w h i c h is accepted by software such as ArcView. Other methods are described below. Flat files or spreadsheets Flat files or spreadsheets is a simple m e t h o d of storing data. All data of one geographic feature is stored in o n e r o w (record), w i t h each attribute stored in each c o l u m n. All records in the database have the same n u m b e r of "fields" Individual records have different data in each field w i t h one field serving as the key to locate a particular record. As the n u m b e r of fields increase a flat file is cumbersome to search. Additionally, a d d i n g n e w records in it is time consuming. Other methods offer m o r e flexibility a n d responsiveness. Hierarchical files Hierarchical files store data in m o r e than one type of record. This m e t h o d is usually described as a "parent-child" or "one-to-many" relationship. O n e field is key to all records, but data in o n e record does not have to be repeated in another. This system allows records w i t h similar attributes to be associated together. T h e records are linked to each other by a key field. Each record, except for the master record, has a higher level record file linked by a key field "pointer". In other words, one record m a y lead to another a n d so on in a relatively descending pattern. An advantage is that w h e n the relationship is clearly defined, a n d queries follow a standard routine, a very efficient data structure results. Access to different records is available, or easy to deny to the user by not furnishing that particular file of the database. O n e of the disadvantages of these files is one must access the master records, w i t h the key field determinant, in order to link " d o w n w a r d " to other records. Relational files Relational files connect different files or tables w i t h o u t using internal pointers or keys. Instead, a c o m m o n link of data is used to j o i n or associate records. T h e link is n o t hierarchical. A "matrix of tables" is used to store the information. As l o n g as the tables have a c o m m o n link they m a y be c o m b i n e d by the user to f o r m n e w inquiries a n d data output. This is the most flexible system a n d is suited to S Q L (structured query language). Queries are not limited by a 34

45 hierarchy of files, b u t instead are based on relationships f r o m one type of record to another that the user establishes. Because of its flexibility, this system is the most popular database m o d e l for GIS. Geo-relational structure A geo-relational structure contains characteristics of b o t h geographic a n d relational databases. Geographic coordinates are not required for every file, b u t each data element must relate to geographic coordinates through key variables. Design of Attribute Database Data stored in the database m a n a g e m e n t system is by attribute n a m e. T h e storage format is invisible. Thus, for data exchange, c o m m o n formats are necessary. These should contain c o m m o n attribute names or a c o m m o n glossary of terms w h i c h must be maintained a n d updated. T h e same attribute name must be used for information of like kinds in all the files in the data management system. T h e attribute names should be as generic as possible a n d should represent only the substance a n d n o t the level of aggregation. Thus, for example, temperature should be called m a x i m u m temperature a n d m i n i m u m temperature, a n d not daily m a x i m u m a n d daily m i n i m u m. T h e names should be limited to eight characters. In addition to the names, the data management system should contain units; r e c o m m e n d e d levels of aggregation (e.g., geographic, temporal, taxonomic, etc.), a m i n i m u m level of significant digits, a n d a reference code for location of procedures a n d standards. Judicious selection of file names can improve the management a n d processing capabilities of relational data management system. A unique ID, w h i c h identifies each record, is used to access a single record f r o m all other records. W h e n compiling a data m a n a g e m e n t system, it is necessary to consider the data requirements that are needed to accomplish the intended objective a n d the above considerations should help in the achievement of the objective. Attribution Creating a spatial database requires careful planning a n d execution. This also involves decisions on the generation of unique identification codes for each feature so that the related attribute data can be linked. T h e task can be achieved 35

46 d u r i n g the initial stages of digitization of maps and/or after digitization. For example, a r o a d m a p (line features) can have different types of roads such as national highway a n d state highway. These varieties of r o a d features can be given unique identification (ID) while being digitized. O n c e the geographic features are c o d e d, a separate table of non-spatial attributes can be prepared to a d d m o r e data for analysis. Database Query, Analysis, and Modeling Analysis in GIS is basically a query on the spatial data. This m a y be a spatial query to find neighborhoods or adjacency of occurrence of an event or p h e n o m e n o n. There can also be a query on non-spatial data linked to the spatial data to infer on the spatial pattern of the associated non-spatial attributes. Processes can be m o d e l e d after the data has been analyzed for a long period of time to obtain successful results. Geo-processing Geo-processing involves the processing of spatial a n d attribute data for special objectives. This includes m a p overlay, m a p merging, subset selection a n d buffering, each of w h i c h gives a specific output. M a p overlay is the most important function of a G I S a n d also its strength. It facilitates the analysis of multiple layers at o n e time to find areas of homogeneous characteristics. ' M a p merging' is used w h e n smaller areas are to be a p p e n d e d to create a larger d o m a i n. Subset selection clips out features f r o m a larger database to z o o m into a selected w i n d o w. Buffering is a n e i g h b o r h o o d function to look into the possible area of expansion of the spatial p h e n o m e n o n or the diffusion of an event. For further reading, B u r r o u g h (1986) m a y be referred. Reference B u r r o u g h, P. A Principles of geographical information systems for land resource assessment. O x f o r d, UK: Clarendon Press. 36

47 Digitization Using PC ArcEdit K S Prasad, M Srinivas, and S Pande 1 Introduction O n e of the most important activities in geographic information system (GIS) is 'digitization'. In PC Arc/Info package, ArcEdit m o d u l e is used for digitization a n d data input. Digitization and Data Input Data input is a process of converting analog data (map features) into a digital f o r m to develop a spatial database for further analysis required for different applications. Data input is carried o u t t h r o u g h the process called digitization. Input data to the GIS can c o m e f r o m several sources such as digitized maps f r o m the output of the digitizing table; scanned maps f r o m a scanner; ASCII files containing point data w i t h associated coordinates f r o m G P S (global positioning system) surveys; text data f r o m published sources such as census departments; a n d thematic maps f r o m satellite imagery. Before digitization is carried o u t one must k n o w a few c o m m o n l y used terms. These are: D i g i t i z e r. Digitizer is the hardware device that is used to input coordinate data (e.g., hardcopy maps) into the computer. T h e m a p to be digitized is m o u n t e d on the digitizer (digitizing table) before digitizing. D i g i t i z i n g t a b l e t ( C u r s o r ). Digitizing tablet is the hand-held device that is used to enter coordinate data on the digitizer. It usually consists of a n u m b e r of 1. ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP Prasad, K. S., Srinlvas, M., and P a n d e, S Digitization using PC ArcEdit. Pages in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 3 7

48 buttons a n d a set of cross hairs used to p i n p o i n t locations on maps m o u n t e d on the digitizer. S e r i a l p o r t. Serial p o r t is an interface connector used to connect the PC w i t h devices such as digitizers, plotters, m o d e m s, or a host computer. T h e steps involved in digitizing a m a p are: Procure the maps to be digitized. M a r k clearly a n d n u m b e r all tics (at least f o u r k n o w n points covering the map) on the m a p to be digitized. G i v e a user-id to each feature on the m a p to be digitized. M a r k a starting p o i n t on all closed loops so that it is k n o w n where to begin a n d e n d w h i l e digitizing. P C D i g i t i z e r C o n f i g u r a t i o n After the m a p area is prepared for digitizing, it has to be checked a n d ensured that the PC a n d digitizer area are correctly set up to communicate w i t h each other. For this: T h e digitizer must be connected to o n e of the serial ports on the computer. T h e c o m m a n d C O N - D I G s h o u l d b e t y p e d before using the digitizer for coordinate input. This c o m m a n d identifies the digitizer a n d serial p o r t to be used. C O N - D I G { D I G I T I Z E R } { P O R T } { B A U D - R A T E } { P A R I T Y } { D A T A B I T S } { S T O P B I T S } To test the digitizer configuration, D I G T E S T is t y p e d a n d entered. A n u m b e r of tests will be executed as follows: Description on h o w the digitizer was configured in PC Arc/Info. T h e message "Initializing Digitizer" will determine the digitizer a n d the PC are c o m m u n i c a t i n g. For each cursor b u t t o n pushed, a line of data should appear at the b o t t o m of the screen. T h e third test is to verify that stream m o d e digitizing is working. T h e cursor should be m o v e d a r o u n d the digitizing tablet a n d o n e of the cursor buttons h e l d d o w n. T h e screen s h o u l d display c h a n g i n g x,y coordinates (as the cursor is being m o v e d ) a n d the cursor key n u m b e r for each digitizer cursor b u t t o n p u s h e d. 38

49 T h e next test involves entering cursor values in the order 0 t h r o u g h 9, A, a n d then B. Lastly, there will be a p r o m p t to enter t w o points approximately 10 inches apart. This is to test the ability a n d the accuracy of the system to calculate the distance between the points. D i s c o n n e c t i n g the digitizer w i t h t h e host c o m p u t e r is d o n e by t y p i n g C O N - D I G N O N E a n d enter. After ensuring that the PC a n d the digitizer are configured (connected), the first step involved in digitizing a m a p is to m o u n t it (prepared map) on the digitizer. While m o u n t i n g the m a p it is necessary to ensure that the area to be digitized is contained w i t h i n the active area of the digitizing board, i.e., avoiding the edges of the digitizer where the digitizing cursor is inoperable. Digitizing Digitization is the process of converting the spatial features, viz., point, line, a n d p o l y g o n features of a m a p in digital format. A p o i n t is represented by a single coordinate; a n d a line by a string of coordinates. O n e or more lines c o m b i n e d w i t h a label p o i n t inside m a k e a p o l y g o n. Thus digitizing is the procedure for capturing a series of points a n d lines. Points are used for t w o different purposes: to represent p o i n t features or to identify the presence of a p o l y g o n. To a v o i d confusion, b o t h kinds of points in the same coverage should not be digitized. There are t w o types of digitizing: discrete digitizing a n d spaghetti digitizing. Discrete digitizing refers to digitizing through several smaller arcs a n d creating a series of connected arcs by digitizing each intersection as a node. Spaghetti digitizing refers to digitizing through one l o n g arc ignoring the intersections of arcs a n d digitizing the outline as one l o n g arc. Coverage Creation with PC Arc/Info A coverage is a p r i m a r y means for storing a n d representing m a p data in PC Arc/Info. It is a digital version of a m a p. In PC Arc/Info coverage, m a p features are stored as simple points, lines, or polygons. Thematic descriptors for each point, line, or p o l y g o n are stored in feature attribute tables. 39

50 There are six basic steps used for coverage a u t o m a t i o n w i t h PC Arc/Info: 1. Digitize the m a p. 2. Identify a n d correct digitizing errors. 3. Define features a n d b u i l d topology. 4. Identify a n d correct topology errors. 5. Assign attributes to coverage features. 6. Identify a n d correct attribute-coding errors. Firstly, an empty coverage containing only tic points is created (c:\yourname) [arc] create ticcov. This produces an e m p t y coverage w i t h a n u m b e r of files including B N D ( b o u n d a r y file) a n d T I C (tic files) on it. If there are several sheets of m a p of the same area w i t h different information, the filename T I C C O V will contain the c o m m o n tic values of true coordinates of at least four control points f r o m the m a p ; these can later be used for generating other maps. T A B L E S a n d E n t e r a r e t y p e d t o s t a r t t h e t a b l e s f o r e n t e r i n g tic coordinates of the prepared m a p. (C:\yourname) [arc] Tables Select the ticcov.tic :sel ticcov.tic 0 records selected: (message appears). Type :add to start entering tic coordinate a n d Enter. Idtic 1 appears on the screen; enter x tic value a n d y tic value, a n d repeat the same for other three tics. Values to be a d d e d are given below: idtic:l idtic:2 idtic:3 idtic:4 xtic:72 xtic:96 xtic:72 xtic:92 ytic:36 ytic:36 ytic:12 ytic:12 - Enter all four ID tics. - T y p e L I S T to see w h e t h e r all tic values are registered correctly. For example, if one of the tic values is wrongly entered, proceed as follows to make correction. - Use the Update c o m m a n d a n d enter as: U P D A T E 40

51 Enter Record N u m b e r : (message appears) Type particular record n u m b e r in w h i c h corrections are to be m a d e a n d Enter. T h e following message appears: I D T I C X T I C Y T I C EDIT? T h e n m a k e a correction on I D T I C, X T I C, or Y T I C whichever is w r o n g by simply typing the correct value. T h e n press Enter a n d enter. If a w r o n g tic point w h i c h has to be r e m o v e d f r o m the list is r, follow the procedure given below: Select ticcov.tic 5 records selected (message appears). Reselect $ R E C N O = 5 1 record selected (message appears). Purge (to delete the particular selected record) Record n u m b e r 5 gets deleted. To check this, type list. LIST O n l y 4 tics appear on the screen. - Type Q to exit T A B L E S. - Use the c o m m a n d C R E A T E to copy the ticcov tics to India coverage. - For example: (c:\yourname) [arc] create India ticcov T h e India coverage has all the coordinate values. Coverage Management - Use the Describe c o m m a n d to list the contents of the coverage. e.g., [ARC] Describe India - C o p y the coverage into a floppy disk. e.g., [ARC] copycov India a:india - To change the name of our coverage. e.g., [ARC] renamecov India Indiast 41

52 B U I L D T H E C O V E R A G E Type B U I L D Following syntax appears on the screen. B U I L D [cover] [poly/line/point] B U I L D I N D I A P O L Y To see the p o l y g o n attribute file, type: List India.Pat $RECNO AREA PERIMETER INDIA INDIA-ID 1 XXX XXXX XXX XXXX XXX XXXX XXX XXXX XXX XXXX 4 4 T h e I N D I A m a p attribute table is seen; each district will be identified w i t h these ID numbers a n d each district here is referred as a p o l y g o n. D i g i t i z i n g E r r o r s "To err is h u m a n " is no exception in G I S a n d particularly in m a p digitization. W h i l e creating p o l y g o n coverage, a n u m b e r of errors can occur w h i c h should be identified a n d corrected. S o m e c o m m o n errors are undershoot a n d overshoot (geometric coordinate errors), too m a n y label points, a n d no label points. I d e n t i f i c a t i o n o f E r r o r s Pseudo node Pseudo n o d e is a n o d e at w h i c h only t w o arcs intersect (or a single arc connects w i t h itself). A n o t h e r w a y to consider pseudo nodes is that they identify locations where an otherwise contiguous arc is actually 'split' into smaller discrete arcs. Pseudo n o d e does n o t necessarily indicate an error or p r o b l e m. Pseudo nodes can be displayed in ArcEdit, DIGITIZE, EDIT, a n d E D I T P L O T. T h e y are always d r a w n w i t h a d i a m o n d symbol. 42

53 Dangling node Dangling node is the dangling e n d point of a dangling arc. It can either be an undershoot or an overshoot. Dangling nodes can be displayed in ArcEdit, DIGITIZE, EDIT, a n d EDITPLOT. T h e y are s h o w n w i t h a square b o x s y m b o l. T h e N O D E E R R O R S c o m m a n d can also list the dangling nodes present in the coverage. Too many label points If a p o l y g o n contains more than one label point it shows an error. A label error is also notified for that p o l y g o n if the labels have same I D. Multiple labels m a y also indicate unclosed polygons. This label error can be displayed in EDITPLOT. Multiple labels are always d r a w n with their p o l y g o n boundaries. T h e L A B E L E R R O R S c o m m a n d can be used to list the polygons, w h i c h contain too m a n y label points. No label points A p o l y g o n w h i c h does not contain a label point will be given a user-id of 0; a n d unless a polygon contains a label point, its user-id can never be changed f r o m 0. If a polygon does not have a user-id, PAT (Point Attribute Table) attributes cannot be maintained for that p o l y g o n. Polygons w i t h no label points can be displayed in E D I T P L O T. Their boundaries are always d r a w n, a n d a star symbol is placed inside t h e m. T h e L A B E L E R R O R S c o m m a n d can be used to list polygons that have no label points. N o t e. There should always be a label error star in the center of the coverage. This star indicates that the universe p o l y g o n that surrounds the coverages has no label points. If the star fails to appear, it means that a p o i n t lies outside the b o u n d a r y of the coverage. It is necessary to use C L E A N or B U I L D to create p o l y g o n topology before potential label errors can be identified. Sliver polygons Sliver polygons are f o r m e d w h e n the boundaries between t w o polygons overlap. They m a y f o r m d u r i n g coverage overlaying, a n d w h e n the boundaries between t w o polygons are digitized twice. This can be seen in E D I T P L O T c o m m a n d. 43

54 G a p s Gaps are parts where the boundaries between t w o polygons do n o t meet. T h e y are f o r m e d mainly d u r i n g coverage overlaying. This can be seen in E D I T P L O T c o m m a n d. W e i r d polygon Weird p o l y g o n is f o r m e d w h e n t w o overshooting arcs meet at the e n d (overshooting), enclosing some small area. This can be seen in E D I T P L O T c o m m a n d. 44

55 Creating Wheat Productivity Maps in ArcPlot K S Prasad 1 Introduction D u r i n g demonstration on various features of Arc/Info, a m o d u l e n a m e d ArcPlot was explained in detail. PC ArcPlot provides for using m a p s as graphic w i n d o w s to databases for interactive query a n d update of attribute i n f o r m a t i o n. It also provides facilities for interactively creating a n d previewing m a p s on the m o n i t o r screen, a n d sending maps to a printer or plotter. Generation of Maps T h e m a p coverage of India has been digitized a n d each district assigned w i t h an ID (identification n u m b e r ). Stepwise activities for the generation of w h e a t (Triticum aestivum) productivity maps are illustrated b e l o w in a sequential order. For this, A R C p r o m p t is first o p e n e d. C:\[ARC] list India.PAT. This c o m m a n d is used to enter the data required in the table given below. It will show the p o l y g o n attribute table w i t h records. Rec. no. Area Perimeter India_ India-id 1 Xxxxx xxxxxx Xxxxx xxxxxx Xxxxx xxxxxx Xxxxx xxxxxx ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP Prasad, K. S Creating wheat productivity maps in ArcPlot. Pages in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 45

56 W h i l e preparing a w h e a t productivity m a p, a database is created in d B A S E III+ on data of year-wise w h e a t yield assigning IDs of the districts of India. O n e has to k n o w the database thoroughly. For example, if the database is f r o m 1960 to 1995 the fields will be as given below: Field 1 District Name of district Field 2 India-id Code of the district Field 3 Ar 60 Area under wheat in 1960 Field 4 Prod 60 Production of wheat in 1960 Field 5 Yld 60 Yield of wheat in 1960 Likewise, data of each year is entered continually until all data up to 1995 are entered. T h e n, India coverage for wheat productivity is copied w i t h the following c o m m a n d. C : \ [ A R C ] C o p y c o v India W h e a t i n d (new coverage). C:\[ARC]Additem T h e n, a n e w syntax is seen as b e l o w : Usage: Additem[in-file] [out-file] [pitem-name] [item-width] [output-width] [item-type] [decimal-places] [start-item] A d d i t e m W h e a t i n d _pat W h e a t i n d _ p a t India-Id 6 6 N India-id will be a d d e d to W h e a t i n d. p a t C:\[ARC]Tables This c o m m a n d enables to see the content of the table. C:\[ARC]Select Wheatind.pat (wheatind.pat is the table name) records will be selected (message is displayed after the c o m m a n d execution). T h e n the following c o m m a n d is entered: Cal India-id = W h e a t i n d - i d [Same IDs of India-id will be transferred to W h e a t i n d - i d also.] In b o t h Wheat.dbf file a n d w h e a t i n d coverage files, India-id is c o m m o n. In W h e a t i n d coverage the following class items should be a d d e d : Ar60 Ar65 Ar70 Ar75 Prod60 Prod65 Prod70 Prod75 Yld60 Yld65 Yld70 Yld75 Ar80 Ar85 Ar90 Ar95 Prod80 Prod85 Prod90 Prod95 Yld80 Yld85 Ydl90 Ydl95 46

57 T h e procedure for adding these items is: C : \ [ A R C ] A d d i t e m Wheatind.Pat Wheatind.Pat Y l d N For a d d i n g other items, this exercise is repeated until all data up to the final item Y l d 9 5 is a d d e d. T h e n, in W h e a t i n d coverage different classes are seen. L i n k i n g t h e C o v e r a g e w i t h d B A S E Files To link the coverage with d B A S E files A R C P L O T is typed. T h e next c o m m a n d is: Join Wheatind.pat (coverage) W h e a t (dbf file) India-id (relate item) Linear N o w the coverage is linearly linked w i t h d B A S E file (Wheat.dbf) For selecting polygons the next c o m m a n d used is: Asel W h e a t i n d polys Rsel W h e a t i n d polys # a r 6 0 le 1000 Nsel W h e a t i n d polys Areas of more than or equal to 1000 will be selected. Classmanual W h e a t i n d polys # y l d Y l d 6 0 x Four break points should be entered: To prepare a m a p composition (final map) of wheat yield for 1960 in ArcPlot, the following steps are followed: M a p M a p e W Y L D 6 0 P W h e a t i n d Arclines W h e a t i n d 1 Arclines of W h e a t i n d appear on the screen. Polygonsh W h e a t i n d Y l d 6 0 Polygonshade with 5 classes appear on the screen. Box (frame for the map) is inserted. 47

58 To write text the following c o m m a n d s are used: Textsymbol 8 9 M o v e (position of text). W h e a t Productivity of India to be t y p e d a n d entered. B o x (frame for the title) is inserted. Next, M e n d to be t y p e d a n d entered. M a p e n d Q u i t (to exit f r o m ArcPlot). Prepare a key file in A S C I I : Edit C:\Yld60.key (to be created in text editor).1 below.08 t ha t ha t ha t ha -1 C : \ [ A R C ] A R C P L O T DISP 4 M A P Y l d 6 0 P A g a i n the entire m a p appears on the screen. A d d L e g e n d. M o v e Keybox.3.2 Keyspace.1.1 Text symbol 3 3 Keyshade Yld.key 48

59 L e g e n d will be placed where s h o w n by the cursor. M E N D M A P E N D A N D Q U I T T h u s the wheat productivity m a p of India for 1960 is p r o d u c e d. T h e same exercise can be repeated for other years. 49

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61 PART II: Case Studies

62

63 Analysis of Constraints and Potential of the Soils of the Indo-Gangetic Plains of Punjab Using GIS G S Sidhu 1, A K Maji 2, B K Khandpal 1, S Pande 3, and M Velayutham 2 Introduction Agricultural activities are related closely w i t h climate, soil, water, a n d biotic factors. Soil survey provides an in-depth look into these properties for proper a n d alternative land use plan. D u r i n g a systematic soil survey, huge a m o u n t of data is collected on the site characteristics, soil morphology, physical a n d chemical properties of the soils, crop yields, cropping pattern, a n d other relevant socio-economic features. In the past such vast databases were m a n a g e d manually to report the results t h r o u g h maps, charts, diagrams, a n d texts. W i t h the advent of advanced computerized technology, vast spatial databases have been best handled by geographic information system (GIS) w h i c h was first developed in the 1960s a n d has n o w further developed as a unique tool w i t h huge potential. M c C l o y (1995) has defined GIS as a means of 1. Regional Centre, National Bureau of Soil Survey and Land Use Planning, New Delhi , India. 2. National Bureau of Soil Survey a n d L a n d Use Planning, Amravati Road, Nagpur , Maharashtra, India. 3. ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP Sidhu, G. S., M a j i, A. K., K h a n d p a l, B.K., P a n d e, S., and V e l a y u t h a m, M Analysis of constraints and potential of the soils of the Indo-Gangetic plains of Punjab using GIS. Pages in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 5 3

64 storing, restricting, analyzing, a n d display of spatially related sets of resource data so as to provide m a n a g e m e n t i n f o r m a t i o n or to develop better understanding of environmental relationship. Material and Methods T h e data generation Interpretation of satellite data (IRS LISS II), field surveys for site a n d soil morphological characteristics, a n d laboratory analysis for physical a n d chemical properties of the soils f o r m e d the p r i m a r y database for the case study reported by S i d h u et al. (1995). T h e datasets were p u t into the G I S core to produce various thematic maps. Creation of the database T h e soil m a p polygons were digitized to f o r m the spatial database using m a n u a l 'table digitization' in the Arc/Info G I S system. T h e attribute database using 27 soil, site, a n d chemical properties were generated in the d B A S E IV environment. Generation of thematic m a p s T h e general procedure a d o p t e d for thematic m a p generation is the reclassification of basic soil m a p using appropriate dataset. A generalized flow diagram is s h o w n in Figure 1 w h i c h was applied to generate maps of Punjab, India. T h e single area analysis of each theme was d o n e to give the statistical information a b o u t these maps. 54

65 Soil Association 3-Tier A p p r o a c h Digitize B u i l d Layer S O I L A T T R I B U T E Association no. Soil Coverage Soil depth Texture Salinity Sodicity, etc D A T A B A S E I N T E G R A T I O N L I N K IDs A T T R I B U T E - W I S E M A P R E C L A S S I F I C A T I O N S O I L A T T R I B U T E T H E M A T I C M A P F i g u r e 1. F l o w d i a g r a m f o r g e n e r a t i o n o f t h e m a t i c m a p. 55

66 Results and Discussion O n e of the most c o m m o n a n d important activity in G I S is thematic m a p generation w h i c h helps in understanding various class/categorical aspects of the concerned theme w i t h reference to their spatial distribution a n d extent. O u t of m a n y maps, those i m p o r t a n t for constraint analysis are depicted through Figure 2 (soils), Figure 3 (salinity), Figure 4 (calcareousness), Figure 5 (particle size distribution), a n d Figure 6 (erosion). Statistical i n f o r m a t i o n pertaining to area was p r o d u c e d using "single m a p analysis" a n d is depicted in Table 1. Soils distribution at subgroup level is depicted in Figure 2. Entisols, Inceptisols, a n d Alfisols are the p r e d o m i n a n t soils of the state of P u n j a b in India. Salinity status is represented t h r o u g h the extent in area a n d the severity in terms of electrical conductivity (EC) of the soils. Extent. Class Class name Coverage of m a p units 1 Limited < l / 3 m Moderate 1/2-2/3 e Extensive >2/3 Severity. Class Class name EC (ds m -1 ) 1 Nil <0.8 2 Slight Moderate Strong >2.5 T h e c o m b i n a t i o n of these t w o forms the legend for salinity status (Figure 3). M a p analysis shows that about 1 8 % of area of Punjab is suffering f r o m different classes of salinity. Calcareousness is moderate in thousand k m 2 area. Particle size analysis shows that majority of the soils have fine loamy texture, followed by sandy soils. A b o u t 1 2 % area of the state is under threat of moderate soil erosion. It is obvious that such i n f o r m a t i o n gives a better understanding of the quality of the lands of the area. 56

67 Typic Udortients Typic Ustorthents Typic Ustipsamments Typtc Ustifluvents Udic Ustochrepts Typic Eutrochrepta Typtc Ustochrepts Fluventic Ustochrepts Natric Ustochrepts Vertic Ustochrepts Typic Haplustalfs Typic Natrustalfs Aeric Halaquepts Ustochreptic Camorthids Ustic Torripsamments Figure 2. Soils m a p of Punjab, India. Severity and extent Nil Sltght (Moderate) Slight (Extensive) Moderate (Moderate) Moderate (Extensive) Settlements Rajasthan Figure 3. Severity (and extent) of soil salinity in Punjab, India. 57

68 Figure 4. Calcareousness of soil in Punjab, India. Figure 5. Distribution of various soils differing in particle size in Punjab, India. 58

69 Strong Moderate None to Slight Settlements F i g u r e 6. S e v e r i t y o f soil e r o s i o n i n P u n j a b, I n d i a. 59

70 Table 1. Area analysis of thematic maps. Class Area (km 2 ) Area (%) Salinity status Others e m m e Calcareousness Nil Slight Moderate Others Particle size distribution Coarse loamy Fine loamy Sandy Sandy skeletal Loamy skeletal Fine silty Fine Others Erosion Strong Moderate None to slight Settlements References M c C l o y, K. R Resource m a n a g e m e n t information system: process a n d practice. UK: Taylor & Francis. S i d h u, G. S., W a l i a, C. S., T a r s e m, L a l, R a n a, K. P. C., and S e h g a l, J Soils of Punjab for optimising land use. N B S S Publication 4 5. Nagpur, India: National Bureau of Soil S u r v e y a n d L a n d Use Planning. 60

71 Changes in Wheat Productivity Indo-Gangetic Plains of India in the A K Maji 1, S Pande 2, M Velayutham 1, and M I Ahmed 2 Introduction T h e green revolution achieved t h r o u g h the interaction of i m p r o v e d cultivars, e x p a n d e d irrigation, a n d increased use of fertilizer has p r o m p t e d the f a r m i n g c o m m u n i t y of India to opt for a cropping system, the rice (Oryza sativa)-wheat (Triticum aestivum) system, w h i c h until n o w p r o v e d to be one of the most successful a n d sustainable programs. Singh a n d Paroda (1994) have reviewed the rice-wheat productivity in the Asia-Pacific region. However, at the later stage of practicing this cropping system in the Indo-Gangetic Plain (IGP) of India, several problems such as decline in yield, salinity, a n d sodicity causing concern are encountered. Therefore, it is necessary for analysis of the factors controlling the production a n d productivity of the rice-wheat system. The Uses of GIS Primary database creation of rice-wheat c r o p p i n g systems of the I G P has been accomplished by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT). Dataset pertaining to w h e a t productivity were used for analysis of temporal change from 1960 to 1990 using geographic information system (GIS) techniques in Arc/Info platform. 1. National Bureau of Soil Survey and L a n d Use Planning, Amravati Road, Nagpur , Maharashtra, India. 2. ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP M a j i, A. K., P a n d e, S., V e l a y u t h a m, M., and A h m e d, M.I Changes in wheat productivity in the Indo-Gangetic plains of India. Pages in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 61

72 A district p o l y g o n coverage was digitized for the I G P region in India covering the states of P u n j a b, H a r y a n a, Uttar Pradesh, Bihar, West Bengal, a n d part of H i m a c h a l Pradesh in Arc/Info G I S system. Database of w h e a t crop consisting area, p r o d u c t i o n, a n d yield (t ha -1 ) were created in d B A S E III f o r m a t for all the districts falling under the I G P region over a p e r i o d f r o m to This database is linked to the base coverage using "Relate Function" based on the c o m m o n IDs as district ID number. T h e coverage has been generalized based on the yield into several classes, i.e., < 0. 5 t ha - 1, t h a - 1, t ha - 1,... up to > 4. 0 t ha - 1 d e p e n d i n g u p o n the range of yield data. Using dissolve function, separate coverages were created for yield of a n d yield of "Vector overlay" was p e r f o r m e d for these t w o coverages to indicate the areas of change f r o m to These areas of change were aggregated to g r o u p into n e w classes. Results and Discussion T h e yield scenario of a n d a n d change in productivity are presented in Figure 1. T h e results s h o w that there was a steady increase in w h e a t productivity over time in the entire I G P region in India w i t h varying degree of enhancement in the yield. T h e areas having yield < 0. 5 t ha - 1 h a d increased f r o m 1.5% to 2. 5 % ; similarly 6 classes (class 4-9 ) were observed in the production class o f t ha - 1, s h o w i n g a change of productivity f r o m 1 t ha - 1 to 4 t ha - 1. T h e overall rate of change was also observed f r o m 1.5 t to as high as %. T h e analysis also reveals that t h o u g h lower IGP comprising of West Bengal, Bihar, a n d eastern Uttar Pradesh has lower productivity of wheat, in some areas increase (%) in productivity was fairly high. Highest rate of growth was recorded in H a r y a n a, Punjab, a n d part of western Uttar Pradesh. However, in some of the districts negative g r o w t h trend has been observed because of very high productivity in o n e year, b u t rest of the years h a d a lower productivity resulting in overall negative g r o w t h. Conclusion G I S application has been used in bringing o u t the different aspects of the ricewheat-based cropping system in the I G P of India. T h e G I S output can be utilized to identify the indicators for p r o d u c t i o n, intervention needs, a n d 62

73 1960 Yield (tons/ha) Data not available 0 (No wheat is grown) Yield (tons/ha) Data not available Growth rate Growth rate (%) F i g u r e 1. T e m p o r a l c h a n g e i n w h e a t p r o d u c t i v i t y i n t h e I n d o - G a n g e t i c p l a i n o f I n d i a d u r i n g ( S o u r c e : A g r i c u l t u r a l S i t u a t i o n i n I n d i a. 63

74 strategic p l a n n i n g procedure. However, G I S is a tool a n d the m a j o r success in bringing sustainability to rice-wheat system lies in interdisciplinary w o r k. Reference S i n g h, R. B., and P a r o d a, R. S Sustainability a n d productivity o f ricew h e a t system in the Asia-Pacific region: research a n d technology d e v e l o p m e n t needs. Pages 1-35 in Sustainability of rice w h e a t production system in Asia (Paroda, R.S., Terence W o o d h e a d, a n d S i n g h, R.B., eds.). Bangkok, T h a i l a n d : Regional Office for Asia a n d the Pacific (RAPA), Food a n d Agriculture Organization of the United Nations (FAO). 64

75 Spatial Analysis of Incidence of Plant Parasitic Nematodes in Rice-Wheat-Legumes Cropping Systems of Uttar Pradesh, India S Pande, M Asokan, S B Sharma, and P K Joshi 1 Introduction Geographic information system (GIS) is o n e of the most p o w e r f u l tools, n o w widely used, to understand various agricultural problems. Use of G I S in plant parasitic n e m a t o d e research is a n e w field of activity a n d database creation in GIS environment a n d their analysis on the nematode-plant relationship are scanty. T h e m a i n objective of this paper is to m a p the incidence a n d distribution of nematodes using GIS a n d to see their presence in different cropping systems. A case study from Uttar Pradesh in India is presented here as an example of G I S application in n e m a t o d e research. GIS application Spatial information on the occurrence of different plant parasitic nematodes was collected f r o m 13 districts of Uttar Pradesh a n d used in G I S to build a layer of nematode incidence. A n o t h e r layer s h o w i n g different cropping systems practiced in the area was generated. For analysis of incidence of nematodes in different cropping systems, these t w o layers were overlayed. T h e m a p s p r o d u c e d are presented in Figure ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP P a n d e, S., A s o k a n, M., S h a r m a, S. B., and Joshi, P.K Spatial analysis of incidence of plant parasitic nematodes in rice- wheat- legumes c r o p p i n g systems of Uttar Pradesh, India. Pages in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., E.T, eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 65

76 Cropping system Wheat-maize Wheat-pearl millet Rice-wheat Wheat-chickpea Incidence of nematode Low Medium High Figure 1. Nematode incidence in various cropping systems of Uttar Pradesh, India. 66

77 Results a n d Discussion A large n u m b e r of fungi, bacteria, viruses, plant parasitic nematodes, a n d mycoplasma-like organisms attack different crops (Sharma et al. 1996). However, there is a lack of data on the distribution of plant parasitic nematodes associated w i t h cropping systems of the region. This investigation provides macro-scale data a n d important b a c k g r o u n d information on the m u l t i - locational incidence of parasitic nematodes. It is observed that high incidence of n e m a t o d e attack is intensive in w h e a t (Triticum aestivum) -maize (Zea mays) a n d wheat-chickpea (Cicer arietinum) p r o d u c t i o n systems. Rice (Oryza sativa)- w h e a t a n d wheat-pearl millet (Pennisetum glaucum) production systems have l o w incidence of nematode attack. Various types of nematodes attack these cropping systems. Meloidogyne javanica, Ty/enchorhynchus spp, Pratylenchus s p p, Hoplolaimus s p p, Rotylenchulus reniformis, Heterodera cajani, a n d Helicotylenchus spp are important nematodes that affect different cropping systems of Uttar Pradesh. Reference S h a r m a, S. B., A l i, S. S., U p a d h y a y, K. D., and A h m e d, F Potential nematode constraints of pigeonpea in Uttar Pradesh in northern India. Afro- Asian J o u r n a l of Nematology 6(2):

78 Production Trends of Legumes in Rice-based Cropping System of Sri Lanka H B Nayakekorala 1, S Pande 2, K V Ravindra 2, Y S Chauhan 2, and R Padmaja 2 Introduction Rice (Oryza sativa) followed by rice is the m a j o r c r o p p i n g system traditionally practiced in Sri L a n k a, often on imperfectly- to poorly-drained soils. However, since the early 1930s, expansion of irrigated area resulted in inclusion of welldrained soils in this p r o d u c t i o n system. Expansion of irrigated area, however, resulted in changed hydrology, leading to insufficient year-round water availability to reliably practice a rice-rice cropping system. In the M a h a season (major rainy season f r o m m i d - O c t to mid-jan) all lands in c o m m a n d areas of almost all irrigation schemes have e n o u g h water for the rice crop. B u t in the Yala season (minor rainy season f r o m late A p r to Aug) only a p o r t i o n of the c o m m a n d area can be cultivated w i t h rice d u e to inadequate availability of water. D u e to the l o w water requirement of legumes a n d other u p l a n d crops c o m p a r e d to rice, these have been introduced into the rice-rice cropping systems since the early 1960s. Comparatively higher returns f r o m legumes a n d other field crops m a d e the cropping system of M a h a season rice followed by other field crops in the Yala season m o r e popular. 1. Natural Resources Management Centre, Department of Agriculture, Peradeniya, Sri Lanka. 2. ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP N a y a k e k o r a l a, H. B., P a n d e, S., Ravindra, K. V., C h a u h a n, Y. S., and P a d m a j a, R Production trends of legumes in rice-based cropping system of Sri Lanka. Pages in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 6 8

79 Rice-based Cropping Systems and Legumes In rice-based cropping systems, rice is g r o w n p r e d o m i n a n t l y in the M a h a season. L a n d preparation starts in late Oct to mid-nov. Varieties of 3 1 / 2-4 months duration are generally g r o w n in this system a n d harvested in Feb-Mar. T h e other field crops in rotation are g r o w n during late A p r to Sep. T h e most popular crops are chili (Capsicum a n n u u m ), o n i o n ( A l l i u m cepa), grain legumes, tobacco (Nicotiana tabacum), a n d vegetables. T h e highest returns are obtained f r o m chili a n d o n i o n. In rice-based cropping systems, the legumes g r o w n include mainly m u n g bean (Vigna radiata), c o w p e a (Vigna unguiculata), soybean (Glycine m a x ), g r o u n d n u t (Arachis hypogaea), black g r a m (Vigna m u n g o ), a n d pigeonpea (Cajanus cajan). M u n g bean a n d c o w p e a are the m a i n legumes g r o w n because they have a higher d e m a n d, mainly for breakfast f o o d. After the introduction of short-duration varieties, pigeonpea is b e i n g cultivated in m a n y areas. Studies using GIS A spatial database was created in Arc/Info for Sri L a n k a. T h e administrative m a p was used to examine the spatial distribution of p a d d y a n d legumes, a n d their changes over time a n d in relation to each other. C r o p statistics f r o m publications of the G o v e r n m e n t of Sri L a n k a were used to analyze the spatial variation in the crop area a n d production. Statistics were c o m p i l e d for each district a n d linked to the district m a p of Sri Lanka. T h e districts were classified into groups using ArcView a n d layouts prepared to print the maps. GIS Outputs Presently, legumes are g r o w n in rice-based cropping systems in areas a n d locations where water d e m a n d for p a d d y cannot be m e t a n d soil drainage conditions are favorable. T h e extent of legumes g r o w n in Sri L a n k a over different periods shows that the major growing areas were confined to 4 districts in , but increased to 8 districts in a n d to 11 d u r i n g (Figure 1). T h e total extent of legumes in the island was ha in , ha in , a n d ha in , on average per year. Districtwise extent of legumes following rice (% area) a n d changes over time are presented in Table 1. Results show that a steady increase in legume area has taken place over time in Sri L a n k a. S o m e districts (Mannar, Kurunegala, a n d Puttalam) show a declining trend for Twenty districts were in the 5% 69

80 T a b l e 1. E x t e n t o f l e g u m e s f o l l o w i n g rice a n d c h a n g e i n e x t e n t o v e r t i m e i n S r i L a n k a. Extent of legumes after rice (%) District Ampara Anuradhapura Badulla Batticaloa Colombo Galle Gampaha Hambantota Jaffna Kandy Kegalla Killinochi Kalutara Kurunegala Mannar Matale Matara Monaragala Mullaittivu Nuwaraeliya Polonnaruwa Puttalam Ratnapura Trincomalee Vavuniya

81 Figure 1. Extent of grain legumes following rice in Sri Lanka during different periods from to

82 range, 2 districts in the % range, 2 districts in the % range, 1 district in the % range, a n d 1 district was above the 4 0 % range in T h e decrease in legume extent d u r i n g the past decade can be d u e to switching of some of the farmers f r o m legumes to m o r e profitable crops such as chili a n d o n i o n. T h e above scenario m a y be attributed to various biotic a n d abiotic factors. A m o n g s t biotic factors, Agromyza sp, Maruca testulalis, a n d Helicoverpa armigera are the m a j o r insects of legumes. Collar rot, yellow mosaic virus, a n d cercospora leaf spot are the m a j o r diseases. Soil water deficit a n d p o o r soil fertility are the m a j o r abiotic constraints. C o n c l u s i o n It can be concluded that G1S can be used as a tool to study a n d analyze various aspects of cropping systems using suitable spatial a n d attribute datasets. A c k n o w l e d g m e n t T h e authors greatly acknowledge the contributions m a d e by M/s M I A h m e d, K S Prasad, K Srinivas, M Srinivas, a n d M M o i n u d d i n of ICRISAT in data processing a n d G I S analysis. 72

83 Use of GIS in Studying the Distribution Pattern of Chickpea in Rice and Wheat Cropping Systems in Nepal S P Pandey 1, K Sah 1, S Pande 2, and K S Prasad 2 Introduction Rice (Oryza sativa), wheat (Triticum aestivum), a n d grain legumes constitute m o r e than 8 0 % of total f o o d crops p r o d u c t i o n in Nepal. A m o n g the legumes, chickpea (Cicer arietinum) is an i m p o r t a n t crop g r o w n in rice-based c r o p p i n g systems. However, the area, p r o d u c t i o n, a n d yield of chickpea have considerably declined d u r i n g the past 10 years mainly d u e to severe epidemics of botrytis gray m o l d (caused by Botrytis cinerea). Chickpea, b e i n g a staple f o o d as well as a potential export c o m m o d i t y of the country, deserves intensive research a n d development towards i m p r o v e m e n t in production of this crop. Geographic information system (GIS) can be used to guide research a n d d e v e l o p m e n t efforts by clearly depicting spatial a n d temporal changes in chickpea area, production, a n d yield in relation to available i n f o r m a t i o n on relevant bio-physical factors controlling p r o d u c t i o n. G I S A p p l i c a t i o n G I S databases were created in Arc/Info environment following n o r m a l procedures. Base maps were digitized a n d polygons were assigned w i t h 1. NARC, PO Box 5459, Kathmandu, Nepal. 2. ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP Pandey, S. P., S a h, K., P a n d e, S., and Prasad, K. S Use of GIS in studying the distribution pattern of chickpea in rice and wheat cropping systems in Nepal. Pages in GIS application in c r o p p i n g system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 73

84 poly-ids. Polygons were attributed w i t h temporal series data of chickpea area on a district basis. As an example for this exercise t w o m a p s were generated to display changes in chickpea distribution over 10 years. These t w o m a p s were overlayed on the physiographic m a p of N e p a l to analyze the distribution of chickpea in relation to different physiographic regions (Fig. 1). R e s u l t s a n d D i s c u s s i o n Distribution of chickpea shows extensive spread in the Terai region ( < m) f o l l o w e d b y the Siwalik ( m ), a n d M i d d l e M o u n t a i n regions ( m ), w i t h little chickpea g r o w n i n the H i g h M o u n t a i n a n d H i g h H i m a l a y a n regions. T h e Terai region is an extension of the Indo-Gangetic Plain h a v i n g favorable soil a n d climatic conditions a n d thus favors chickpea g r o w t h. D u r i n g the past 10 years chickpea cultivation has declined in m a n y hill districts (Fig. 1). Also, a shift of chickpea cultivation has taken place f r o m the eastern Terai to western Terai districts. This p h e n o m e n o n relates to the greater incidence a n d severity of botrytis gray m o l d in the eastern part than the western p a r t of the country. C o n c l u s i o n Thus, G I S can be used to quickly a n d clearly identify changes in c r o p p i n g pattern a n d infer possible constraints causing such changes. W i t h G I S, it is possible to p r o d u c e information in concise, attractive, a n d accessible f o r m that w o u l d enable planners, decision makers, a n d other concerned users to understand c r o p p i n g situations in a better w a y a n d in the shortest possible time. 74

85 District boundary Physiographic region High Himalaya ( 2, 500-8, 848 m) High Mountain ( 2, 000-2, 500 m) Middle Mountain ( m) Siwalik ( m) Terai (<300 m) 1000 ha <1000 ha 1984/ ha <1000 ha 1994/ 95 Figure 1. T e m p o r a l variation of chickpea distribution in r e l a t i o n to physiographic regions in Nepal, / / 9 5 (Source: Agricultural Statistics Division, Ministry of Agriculture, His Majesty's Government, Nepal). 75

86 Diseases of Chickpea in the Rice-Wheat Cropping System of Nepal K Sah 1, S P Pandey 1, S Pande 2, K S Prasad 2, and M Moinuddin 2 Introduction G r a i n legumes are i m p o r t a n t in N e p a l as a part of the h u m a n diet for cheap protein, feed for animals, restoration of soil fertility, a n d as an export c o m m o d i t y. G r a i n legumes rank f o u r t h in area a n d fifth in p r o d u c t i o n after rice (Oryza sativa), maize (Zea mays), a n d w h e a t (Triticum aestivum). T h e m a i n grain legume crops of N e p a l are lentil ( L e n s culinaris), grass p e a ( L a t h y r u s sativus), pigeonpea (Cajanus cajan), black g r a m (Vigna m u n g o ), soybean (Glycine max), chickpea (Cicer arietinum), horse g r a m ( M a c r o t y l o m a uniflorum), m u n g bean (Vigna radiata), a n d cowpea (Vigna unguicu/ata). Lentil alone covers m o r e t h a n 5 0 % of the total legume crops area a n d contributes 5 5 % to the total p r o d u c t i o n of grain legumes. A large n u m b e r of s u m m e r a n d winter grain legumes are g r o w n in the varied agroecological zones of the country. Physiographic Regions N e p a l is divided into five distinct physiographic zones r u n n i n g parallel f r o m east to west w i t h v a r y i n g elevation. Terai region ( < m ), Siwalik region ( m ), Middle M o u n t a i n region ( m ), H i g h M o u n t a i n region 1. NARC, PO Box 5459, Kathmandu, Nepal. 2. ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP S a h, K., Pandey, S. P., P a n d e, S., P r a s a d, K. S., and M o i n u d d i n, M Diseases of chickpea in the rice-wheat cropping system of Nepal. Pages in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on H a r m o n i zation of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T, eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 76

87 ( m ), a n d H i g h H i m a l a y a n region ( m ). A m o n g these, the Terai region is cultivated to chickpea; M i d d l e M o u n t a i n region also grows chickpea a n d other legumes. R a i n f a l l D i s t r i b u t i o n T h e average precipitation is a b o u t mm per a n n u m b u t actual a m o u n t of rainfall at different places varies greatly according to topographical variation. T h e highest annual precipitation mm occurs in Pokhara, L u m l e area of Karki district. In general, the eastern p a r t has m o r e precipitation as c o m p a r e d to the western part of the country. A b o u t 8 5 % of total rainfall occurs during J u n to Sep. C a s e S t u d y U s i n g G I S Arc/Info software was used to generate physiographic a n d rainfall distribution m a p of N e p a l using n o r m a l procedure. Physiographic m a p was digitized as a separate theme a n d was superimposed on the district base theme. Rainfall distribution m a p was prepared using p o i n t data f r o m meteorological observations. A separate layer s h o w i n g location specific (point data) occurrence of different diseases of chickpea was prepared a n d overlayed on the above maps to study the relationship between occurrence of disease a n d rainfall a n d variation in altitude. R e s u l t s a n d D i s c u s s i o n T h e occurrence of different diseases of chickpea in different physiographic regions a n d in different rainfall zones are presented in Figure 1. M a p analysis shows that occurrence of diseases such as collar rot, d r y root rot, a n d alternaria blight are more prevalent in the eastern Terai region a n d eastern Siwalik region as compared to the same physiographic region in the western side. These diseases are also f o u n d in the M i d d l e M o u n t a i n region of eastern N e p a l. Botrytis gray m o l d, stunt, black rot, a n d d r y root rot have spread over the w h o l e of Terai region. T h e rainfall distribution pattern m a p shows that the diseases are prevalent in the rainfall zone of m m. 77

88 Physiography High Himalaya (2,500-8,848 m) High Mountain (2,000-2,500 m) Middle Mountain ( m) Siwalik ( m) Terai (<300 m) Country boundary District boundary Disease legend Botrytis gray mold Alternaria blight Black rot Collar rot Dry root rot Stunt Rainfall (mm) < >2500 Chickpea distribution Figure 1. Diseases of chickpea in Nepal in different regions (top) and rainfall zones (bottom). 78

89 Conclusion Geographic information system (GIS) helps in studying the interactive results of various themes t h r o u g h m a p overlay techniques. However, other layers of biotic a n d abiotic parameters m a y be considered for better interpretation on the incidence of diseases in different areas of N e p a l. 79

90 Wheat Production Performance A GIS Analysis in Bangladesh: D N R Paul 1, U K Deb 2, F T Bantilan Jr 2, M Moinuddin 2 and S Pande 2 Introduction Rice (Oryza sativa) a n d w h e a t (Triticum aestivum) are the t w o m a j o r cereal crops of Bangladesh. Information on p r o d u c t i o n performance of w h e a t in Bangladesh is very i m p o r t a n t since the crop occupied a b o u t 7 0 8, ha a n d the p r o d u c t i o n was 1.45 million t in / 9 8 ( B B S 1998). T h e g o o d performance of the crop is attributed to high-yielding varieties a n d i m p r o v e d m a n a g e m e n t practices. T h e magnitude of g r o w t h a n d variability in w h e a t p r o d u c t i o n has i m p o r t a n t implications for f o o d security in Bangladesh. T w o aspects of w h e a t p r o d u c t i o n are i m p o r t a n t for policy purposes. These are g r o w t h in p r o d u c t i o n a n d the situation of p r o d u c t i o n risk (variability in production). This analysis has been focused on g r o w t h a n d variability in area, p r o d u c t i o n, a n d yield of wheat in different regions of Bangladesh, for the pregreen revolution p e r i o d ( ) (period I), green revolution period ( ) (period II), a n d post-green revolution p e r i o d ( ) (period III). Objectives T h e objectives of this study are: To quantify the rate of g r o w t h in area, p r o d u c t i o n, a n d yield of w h e a t for different periods in different regions of Bangladesh; a n d 1. Agricultural Statistics Division, BRRI, Gazipur , Bangladesh. 2. ICRISAT, Patancheru , Andhra Pradesh, India. ICRISAT Conference Paper no. CP P a u l, D. N. R., D e b, U. K., Bantilan Jr, F.T., M o i n u d d i n, M., and P a n d e, S Wheat production performance in Bangladesh: A GIS analysis. Pages in GIS application in cropping system analysis - Case studies in Asia: proceedings of the International Workshop on Harmonization of Databases for GIS Analysis of Cropping Systems in the Asia Region, August 1997, ICRISAT, Patancheru, India (Pande, S., Maji, A.K., Johansen, C., and Bantilan Jr., F.T., eds.). Patancheru , Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics. 80

91 To estimate the level of variability in w h e a t area, p r o d u c t i o n, a n d yield for different periods in different regions of Bangladesh. Data and Methodology Secondary data were collected f r o m the Bangladesh B u r e a u of Statistics. In this study, the country was delineated into 17 regions coinciding w i t h the o l d 17 greater districts, viz., D h a k a, M y m e n s i n g h, Faridpur, Chittagong, Chittagong Hill Tracts, N o a k h a l i, Comilla, Sylhet, Rajshahi, Dinajpur, Rangpur, Bogra, Pabna, Khulna, Barisal, Jessore, a n d Kushtia. C r o p years 1971/72 a n d 1972/ 73 were excluded f r o m this study d u e to abnormality in p r o d u c t i o n in these t w o years. Changes in area, p r o d u c t i o n, a n d yield of w h e a t in Bangladesh were analyzed using statistical techniques a n d geographic i n f o r m a t i o n system (GIS) software. ArcView-GIS software was used to depict spatial changes in w h e a t productivity over time. W i t h the help of this software, associations between growth rate in wheat area a n d yield were m a p p e d for the three different periods focused in this study. Results a n d Discussion Analysis of contribution of different regions to total w h e a t area a n d p r o d u c t i o n in the three different periods s h o w e d that average a n n u a l w h e a t area in Bangladesh was 5 0, ha while total production was 3 0, t in period I. D u r i n g period II, b o t h the average area a n d p r o d u c t i o n of w h e a t increased to 187,000 ha a n d 2 8 8, t respectively. In period III, average w h e a t area a n d production increased to 5 8 1, ha a n d 1093,000 t respectively. Rajshahi, Rangpur, Dinajpur, Kushtia, Pabna, M y m e n s i n g h, C o m i l l a, Jessore, a n d Faridpur are the m a j o r wheat-growing regions of Bangladesh. These regions contribute a b o u t 9 0 % of the total w h e a t area a n d total w h e a t p r o d u c t i o n in Bangladesh. Chittagong, Chittagong Hill Tracts, N o a k h a l i, Sylhet, K h u l n a, a n d Barisal regions have a very small share in the total w h e a t area a n d p r o d u c t i o n. T h e yields of w h e a t in Bangladesh d u r i n g periods I, II, a n d III were 5 9 7, , a n d 1880 kg ha - 1 respectively. Yield levels in all the regions increased d u r i n g periods II a n d III (Fig. 1). 81

92 T h e estimated g r o w t h rates in area, p r o d u c t i o n, a n d yield of w h e a t in different regions of Bangladesh are presented in Table 1. Based on the a n n u a l c o m p o u n d rate of g r o w t h, study regions were classified into four categories: Category A (high growth) includes regions, w h i c h achieved a g r o w t h rate of 5% or above; Category B (moderate growth) comprises regions, w h i c h achieved a growth rate o f > 1 % b u t < 5 % ; Category C (slow growth) includes regions, w h i c h achieved positive g r o w t h rate up to 1 % ; a n d Category D (negative growth) encompasses regions that experienced negative g r o w t h rate in w h e a t area/production/yield in the reference period. D u r i n g p e r i o d I, 12 regions experienced high rate of g r o w t h in area a n d all these 12 regions except K h u l n a h a d a high g r o w t h rate in p r o d u c t i o n. K h u l n a region experienced moderate growth in p r o d u c t i o n d u r i n g p e r i o d I. A n u m b e r of regions experienced negative g r o w t h in area (3 regions), p r o d u c t i o n (2 regions), a n d yield (7 regions). Other regions h a d moderate g r o w t h in area, p r o d u c t i o n, a n d yield of w h e a t in period I. In p e r i o d II, 13 regions experienced high g r o w t h in area a n d all the regions experienced high g r o w t h in p r o d u c t i o n. All the regions except Comilla, Barisal, a n d Sylhet experienced high g r o w t h in yield d u r i n g this period. Faridpur region experienced negative g r o w t h in area while all the regions h a d positive g r o w t h in p r o d u c t i o n a n d yield. D u r i n g p e r i o d III, Chittagong, Barisal, a n d Pabna regions h a d high growth in area while only Barisal region h a d high g r o w t h in p r o d u c t i o n. Six regions faced negative growth in area while 8 regions h a d negative growth in p r o d u c t i o n. Other regions experienced moderate g r o w t h in area a n d p r o d u c t i o n. All the regions except Chittagong a n d Chittagong Hill Tracts h a d negative g r o w t h in yield during this period. These t w o regions h a d positive a n d moderate g r o w t h in yield. A n n u a l g r o w t h rate in area, p r o d u c t i o n, a n d yield was c o m p u t e d for each of the three periods m e n t i o n e d earlier. In all the three periods, Bangladesh as a w h o l e h a d positive g r o w t h in w h e a t area. It h a d positive g r o w t h in p r o d u c t i o n in periods I a n d II, b u t h a d a negative g r o w t h rate in period III. D u r i n g periods I a n d II, annual c o m p o u n d rate of g r o w t h in w h e a t yield in Bangladesh was % a n d % respectively. On the other h a n d, yield declined at the rate of % per a n n u m d u r i n g p e r i o d III. Thus, it appears that the g r o w t h in w h e a t production in Bangladesh in periods I a n d II was d u e to the expansion of area a n d increase in yield. Yield decline d u r i n g the period III poses a serious threat to 82

93 w h e a t production in Bangladesh, since there is unlikely to be an expansion in w h e a t area d u e to land scarcity. For clear understanding of the g r o w t h scenario, the association between g r o w t h rate in wheat area a n d yield are discussed. All regions under study were classified into four types of associations: AA - positive growth rate of area associated with positive growth rate of yield. AB - positive growth rate of area associated with negative growth rate of yield. BA - negative growth rate of area associated with positive growth rate of yield. BB - negative growth rate of area associated with negative growth rate of yield. Association AA indicates that w h e a t is either replacing other crops or is g r o w n in the newly cultivated area a n d the overall yield of wheat has increased. On the other h a n d, BA implies replacement of wheat by other crops or reduction in wheat cultivation a n d increase in yield in the remaining areas. Figure 2 shows the association between g r o w t h rate in area and yield. In period I, n u m b e r of regions falling under category A A, A B, B A, a n d BB were 8, 6, 2, a n d 1 respectively; for period II, it was 16, 0, 1, a n d 0 respectively, while the corresponding numbers in period III were 2, 6, 3, a n d 6. Bangladesh as a w h o l e h a d positive growth in area a n d yield d u r i n g periods I a n d II. However, d u r i n g period III, area growth has accompanied a negative rate of g r o w t h in yield. Analysis of the magnitude of relative variability in wheat yield in different regions of Bangladesh s h o w e d that the coefficients of variation (CV) in wheat yield, after detrending, d u r i n g periods I, II, a n d III were 1 0 %, 2 0 %, a n d 1 1 % respectively. M y m e n s i n g h, Faridpur, Chittagong Hill Tracts, N o a k h a l i, Rajshahi, Dinajpur, Rangpur, Pabna, Khulna, Jessore, a n d Kushtia h a d a significant increase (at 1% level of significance) in production variability. N o n e of the regions except Chittagong h a d any statistically significant decrease (at 5% level of significance) in yield variability during period II c o m p a r e d to period I. In period III compared to p e r i o d I, five regions (Chittagong Hill Tracts, Noakhali, Pabna, Barisal, a n d Jessore) h a d a statistically significant (at 1% level of significance) increase in CV of yield while eight regions (Dhaka, M y m e n s i n g h, Faridpur, Sylhet, Rajshahi, Dinajpur, Rangpur, a n d Bogra) experienced a statistically significant decrease in CV of yield. Other regions had no significant change in CV of yield. T h e regions that h a d an increase in CV of yield, contributed 1 8 % in total w h e a t area a n d 1 7 % of total wheat production 83

94 Table 1. Annual compound rate of growth (%) in area, production, and yield of wheat in different regions of Bangladesh for the periods (I), (II), and ( I I I ). Growth rate (%) in periods Area Production Yield Region I II III I II III I II III Dhaka Mymensingh Faridpur Chittagong Chittagong Hill Tracts Noakhali Comilla Sylhet Rajshahi Dinajpur Rangpur Bogra Pabna Khulna Barisal Jessore Kushtia Bangladesh

95 Average yield (Kg/ha) in period I Yield increase (%) in period II over preiod I < Yield increase (%) in period III over period Period I : Pre-green revolution period, Period II : Green revolution period, Period I I I : Post- green revolution period, Figure 1. Changes in wheat productivity in Bangladesh,

96 Period I ( ) Period II ( ) Period III ( ) Types of association AA - positive area growth, positive yield growth AB - positive area growth, negative yield growth BA - negative area growth, positive yield growth BB - negative area growth, negative yield growth Figure 2. Association between growth rate of area and yield of wheat. 86

97 in the country d u r i n g period III. On the other h a n d, those regions w h i c h h a d decrease in CV of yield, contributed 6 3 % in total wheat area a n d 6 2 % of total wheat production during period III. T h e implication of this finding is that reduction in yield fluctuation over time has increased the stability in w h e a t yield a n d thereby f o o d security. S u m m a r y a n d C o n c l u s i o n GIS can successfully be used to analyze the spatial a n d temporal changes in the production scenario of a crop. This study revealed that average area a n d production of wheat in Bangladesh has increased over time while contribution of total wheat area of different regions a n d production changed over time. Average yield of wheat in Bangladesh has progressively increased in succeeding periods as follows: 5 9 7, , a n d 1880 kg ha - 1 in periods I, II, a n d III respectively. Y i e l d in all the regions has increased in recent times as c o m p a r e d to the pre-green revolution period. In the pre-green revolution a n d green revolution periods, Bangladesh had positive growth in wheat area a n d production. B u t in the post-green revolution period, it had a negative growth rate in production, a n d yield declined at the rate of % per a n n u m. Thus, it appears that the growth in wheat production in Bangladesh in periods I a n d II was due to the expansion of area a n d increase in yield but negative growth in wheat production in period III was due to declining yield. So, yield decline rather than yield variability possesses a serious threat to wheat production in Bangladesh. Therefore, emphasis should be on research for yield enhancement rather than yield stabilization. R e f e r e n c e B B S (Bangladesh Bureau of Statistics) Yearbook of agricultural statistics D h a k a, Bangladesh: B B S, Ministry of Planning. 87

98 P a r t i c i p a n t s B B a j r a c h a r y a * S P K a m * International Center for Integrated M o u n t a i n D e v e l o p m e n t ( I C I M O D ) P O B o x Social Scientist Division International Rice Research Institute (IRRI) K a t h m a n d u P O B o x N e p a l 1099, M a n i l a Fax: / Philippines Phone: Fax: , Telex: 2439 ICIMOD NP Phone: Extn 592/387 Cable: ICIMOD, Kathmandu, Nepal s.kam@cgnet.com Telex: (ITT) RICE PM H E s w a r a n * Cable: RICEFOUND MANILA Director International Programs Division Natural Resources Conservation Services United States D e p a r t m e n t of Agriculture K a m a l S a h Scientist Planning a n d C o o r d i n a t i o n Division N e p a l Agricultural Research C o u n c i l (NARC) P O B o x P O B o x Washington, D C K a t h m a n d u, N e p a l U S A Fax: / Fax: Phone: Phone: di@mos.com.np Hari.Eswaran@ usda.gov Telex: 2262 Narani NP G H y m a n * C e n t r o Internacional d e Agricultura Tropical (CIAT) A p d o, Aereo Cali C o l o m b i a Fax: Phone: cgnet.com B K K h a n d p a l Regional Centre National Bureau of Soil Survey a n d L a n d Use Planning ( N B S S & L U P ) N e w Delhi India * Resource persons. 88

99 A K M a j i * Senior Scientist National Bureau of Soil Survey a n d L a n d Use Planning ( N B S S & L U P ) A m r a v a t i R o a d N a g p u r Maharashtra India Fax: Phone: /534545/ nbsslup@x400.nicqw.nic.in Telex: NBSL-IN Cable: SOILANDBRU M a s o o d A l i H e a d, Division of A g r o n o m y Indian Institute of Pulses Research (IIPR) K a n p u r , Uttar Pradesh India Fax: Phone: / iipr@x400.nicqw.nic.in Telex: IIPR IN Cable: DALHANSODH S P P a n d e y Senior Soil Scientist Soil Science Division N e p a l Agricultural Research C o u n c i l (NARC) P O B o x K a t h m a n d u N e p a l Fax: / Phone: di@mos,com.np Telex: 2262 Narani NP D N R P a u l Principal Scientific Officer Agricultural Statistics Division Bangladesh Rice Research Institute Gazipur Bangladesh Fax: (IRRI, Dhaka) (CIMMYT, Dhaka) Phone: Extn irri.dhaka@drik.bgd.toolnet.orq cm@cimmyt.bdmail,net Cable: RICERES H B N a y a k e k o r a l a Research Officer/Deputy Director Natural Resources Management Centre Department of Agriculture Peradeniya Sri L a n k a Fax: Phone: dgaqri@sri.lanka.net P R e i c h * G I S Expert Natural Resources Conservation Services United States D e p a r t m e n t of Agriculture P O B o x Washington, D C U S A 89

100 G S S i d h u Soil Scientist Regional Centre N a t i o n a l Bureau of Soil Survey a n d L a n d Use Planning ( N B S S & L U P ) N e w D e l h i India P T u l a c h a n * Farm Economist International Centre for Integrated M o u n t a i n D e v e l o p m e n t ( I C I M O D ) P O B o x K a t h m a n d u N e p a l Fax: / Phone: Telex: 2439 ICIMOD NP Cable: ICIMOD, Kathmandu, Nepal L V e n k a t a r a t n a m * G r o u p H e a d (Agriculture & Soils) National Remote Sensing Agency (NRSA) Balanagar H y d e r a b a d A n d h r a Pradesh India Fax: or Phone: Telex: NRSA IN Cable REMOSEN J W h i t e * Centra International de M e j o r a m i e n t o de Trigo ( C I M M Y T ) Lisboa 2 7, P O B o x M e x i c o D.F. M e x i c o Fax: Phone: or i.white@cgnet.com Telex: CIMTME Cable: CENCIMMYT I C R I S A T Patancheru A n d h r a Pradesh, India I P A b r o l * Facilitator, Rice-Wheat C o n s o r t i u m N e w D e l h i, India M I A h m e d * Scientific Officer (GIS) M A s o k a n Scientific Officer (SEPP) F T B a n t i l a n Jr* Senior Scientist (Environmental Physics) Geographic Information Systems y F R B i d i n g e r * Regional Executive Director ICRISAT Asia Region 90

101 S C h a n d r a * Senior Statistician R J K M y e r s Project Team Leader, S2 Y S C h a u h a n Senior Scientist (Physiology) R P a d m a j a Scientific Officer (GIS) U K D e b Scientist (SEPP) K S P r a s a d * Cartographer B D i w a k a r Program Leader Training & Fellowships Program C L L G o w d a Coordinator Cereals & Legumes Asia Network ( C L A N ) C J o h a n s e n * Project Team Leader, S4 P K Joshi Senior Scientist (Economics) A R a m a k r i s h n a Scientist ( A g r o n o m y / C L A N ) K V R a v i n d r a Scientific Officer (Agronomy) N P S a x e n a Project Team Leader, Chickpea S B S h a r m a Senior Scientist (Nematology) M Srinivas* Scientific Officer (GIS) J K u m a r Senior Scientist (Breeding) S u r e s h P a n d e * Senior Scientist (Pathology) M M o i n u d d i n * Associate (GIS) S M V i r m a n i * Project Team Leader, S3 91

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103 About ICRISAT The semi-arid tropics (SAT) encompasses parts of 48 developing countries including most of India, parts of southeast Asia, a swathe across sub-saharan Africa, much of southern and eastern Africa, and parts of Latin America. Many of these countries are among the poorest in the world. Approximately one-sixth of the world's population lives in the SAT, which is typified by unpredictable weather, limited and erratic rainfall, and nutrient-poor soils. ICRISAT's mandate crops are sorghum, pearl millet, finger millet, chickpea, pigeonpea, and groundnut; these six crops are vital to life for the ever-increasing populations of the SAT. ICRISAT's mission is to conduct research which can lead to enhanced sustainable production of these crops and to improved management of the limited natural resources of the SAT. ICRISAT communicates information on technologies as they are developed through workshops, networks, training, library services, and publishing. ICRISAT was established in It is one of 16 nonprofit, research and training centers funded through the Consultative Group on International Agricultural Research (CGIAR). The CGIAR is an informal association of approximately 50 public and private sector donors; it is co-sponsored by the Food and Agriculture Organization of the United Nations (FAO), the United Nations Development Programme (UNDP), the United Nations Environment Programme (UNEP), and the World Bank.

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