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An Irrigation Ontology and Its Use for Localized, Illustration-Based Educational Materials


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AN IRRIGATION ONTOLOGY AND ITS US E FOR LOCALIZED, ILLUSTRATIONBASED EDUCATIONAL MATERIALS By CAMILO CORNEJO A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by Camilo Cornejo

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I dedicate this work to my whole family and to Carolina.

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iv ACKNOWLEDGMENTS This dissertation work would not have been completed without th e help of several people whom I wish to thank. First, I thank my chair, Dr. Dorota Haman, for all her help and support, interest, knowledge problem solving and advice. Without the help of Dr. Howard Beck, this work could have been co mpleted. Thanks go to Dr. Fedro Zazueta for his help during the ontology m odeling stages. I thank Dr. Sandra Russo and Dr. Nick Place whose comments and edits contributed s ubstantially to my research and to the completion of this document. I would also like to thank to all the people from PROMIPAC in El Salvador for helping me with the field research pr esented in this study. Special thanks go to my friends, who always helped me when needed. Finally, I would like to thank the very special people in my life, Carolina and my family, for their support.

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v TABLE OF CONTENTS Page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES...........................................................................................................viii LIST OF FIGURES...........................................................................................................ix ABSTRACT......................................................................................................................x ii CHAPTER 1 INTRODUCTION........................................................................................................1 Education, Literacy, and Agricultural Development....................................................1 Literacy....................................................................................................................... ..2 Literacy in Latin America.....................................................................................4 Literacy in Africa..................................................................................................5 Literacy in the Arab States....................................................................................5 Literacy in Asia.....................................................................................................6 Teaching Agriculture to Adults....................................................................................6 Agricultural Education Using Images..........................................................................8 Justification.................................................................................................................. .9 Overall Objectives of the Study..................................................................................11 Methodology...............................................................................................................12 Expected Outcomes....................................................................................................13 Organization of the Dissertation.................................................................................13 2 IRRIGATION ONTOL OGY MODELING...............................................................14 Introduction.................................................................................................................14 Thesauri...............................................................................................................14 Ontology..............................................................................................................17 Ontology Classification.......................................................................................19 Ontology Languages............................................................................................20 Ontology Editors.................................................................................................20 Objectives...................................................................................................................21 Methodology...............................................................................................................21 Ontology Specification........................................................................................22 Ontology Conceptualization................................................................................23 Ontology Formalization and Implementation.....................................................24

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vi Ontology Documentation, Evaluation, and Maintenance....................................24 Application of Modeling Methodology to Development of Irrigation Ontology.......25 Conclusions.................................................................................................................32 3 IRRIGATION ONTOLOGY FORMALIZ ATION AND IMPLEMENTATION.....34 Introduction.................................................................................................................34 Objectives...................................................................................................................37 Ontology Formalization..............................................................................................37 Ontology Implementation...........................................................................................41 Conclusions.................................................................................................................54 4 EDUCATIVE ILLUSTRATIONS.............................................................................56 Introduction.................................................................................................................56 Graphical Communication..........................................................................................57 Educational Materials.................................................................................................60 Experiments with Vectorizing Images, Options for Creating Vector Graphics..60 Scalable Vector Graphics....................................................................................62 GraphicsEditor.....................................................................................................67 Composing Educational Materials......................................................................72 Presentation Generation.......................................................................................74 Conclusion..................................................................................................................76 5 EVALUATION OF EDUCATIONAL DRAWINGS IN EL SALVADOR, CENTRAL AMERICA..............................................................................................78 Introduction.................................................................................................................78 Materials and Methods...............................................................................................81 Results........................................................................................................................ .83 Contour Planting or Farming...............................................................................85 Earth Basins.........................................................................................................87 Rain and Drainage...............................................................................................88 Retention Ditches................................................................................................89 Stone Terraces (Lines).........................................................................................89 Connectors...........................................................................................................90 Conclusions.................................................................................................................91 6 SUMMARY AND CONCLUSIONS.........................................................................96 Summary and Conclusions.........................................................................................96 Future Work................................................................................................................99

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vii APPENDIX A TERMS IN THE IRRIGATION ONTOLOGY.......................................................100 B DOCUMENTATION FOR THE IRRIGATION ONTOLOGY..............................103 LIST OF REFERENCES.................................................................................................109 BIOGRAPHICAL SKETCH...........................................................................................115

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viii LIST OF TABLES Table page 2-1 Comparison by topics of various sources vs. irrigation ontology............................31 2-2 Comparison of various s ources vs. irrigation ontology............................................32 5-1 Literacy rates of small farmer s interviewed in El Salvador.....................................84 5-2 Age groups of small farmers interviewed in El Salvador........................................84 5-3 Type of educational materials us ed by farmers in El Salvador................................85 5-4 Drawings’ connectors selected by farmers in El Salvador.......................................91

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ix LIST OF FIGURES Figure page 2-1 View of the AGROVOC Thesaurus.........................................................................15 2-2 View of the NAL Thesaurus....................................................................................16 2-3 Diagram representing the conceptualization process...............................................27 2-4 Main topics covered by the Irr igation Ontology in ObjectEditor............................29 3-1 View from the ObjectEditor.....................................................................................38 3-2 Evapotranspiration term and its gloss (short definition)..........................................40 3-3 Definition of concept in English..............................................................................41 3-4 Definition of concept in Spanish..............................................................................41 3-6 Association relationship properties..........................................................................44 3-7 Use of part-of type of relationship...........................................................................44 3-8 Use of generalization type of relationship................................................................45 3-9 Use of generalization type of relationship................................................................45 3-10 Use of generalization type of relationship................................................................45 3-11 Use of the sequence relationship..............................................................................46 3-12 Sample of the soil module........................................................................................48 3-13 Sample of the water sources module........................................................................49 3-14 Drainage module, sub-classes with generalization relationships.............................50 3-15 A small section of the system design module..........................................................51 3-16 A section of the system design module....................................................................52 3-17 Partial view of the irriga tion system management module......................................53

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x 3-18 A section of the irrigation equipment and structures module..................................54 4-1 Communication model ad apted from Funch (1995)................................................59 4-2 Interferences on the communicati on model modified from Funch 1995.................60 4-3 Vectorization using Flash and original digital picture..........................................61 4-4 Pattern recognition using GIMP and original digital picture................................61 4-5 Sample of localization with Scalable Vector Graphics (SVG)................................64 4-6 Sample of localization with Scalable Vector Graphics (SVG)................................64 4-7 Module “Cleaning Irrigation Filters” from Object Editor........................................65 4-8 SVG presentation “Cleaning Ir rigation Filters” in English......................................66 4-9 SVG presentation “Cleaning Ir rigation Filters” in Spanish.....................................67 4-10 Maize instance within the plant topic in the irri gation ontology..............................68 4-11 Context and gloss for the maize instance.................................................................69 4-12 Groups that constitu te the maize graphic.................................................................70 4-13 Example of a person graphic....................................................................................71 4-14 Skin color term associated to “person” term............................................................71 4-15 Different skin colors depending on the origin of the person....................................72 4-16 Irrigation Training Materials module template........................................................73 4-17 Example of print file generated from the ontology management system.................76 4-18 Example of educational draw ings on irrigation techniques.....................................76 5-1 Map of El Salvador and locati on of communities visited (CIA, 2004)....................79 5-2 Section of drawings representing contour planting..................................................86 5-3 Drawings representing earth basins..........................................................................87 5-4 Drawing representing rain and drainage..................................................................88 5-5 Drawing representi ng a drainage ditch.....................................................................89

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xi 5-6 Drawing showing stone terraces or lines..................................................................90 5-7 Connectors................................................................................................................9 0

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xii Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy AN IRRIGATION ONTOLOGY AND ITS US E FOR LOCALIZED, ILLUSTRATIONBASED EDUCATIONAL MATERIALS By Camilo Cornejo May 2006 Chair: Dorota Z. Haman Cochair: Howard W. Beck Major Department: Agricultura l and Biological Engineering There is little doubt that economic and soci al development, and the benefits that accrue such as improved nutrition and health, requires an educated populace. However, illiteracy affects 860 million people as of 2005, without including a larger number of adults with low level of formal education. Mo st illiterates are poor, farmers, and female, living in rural areas. In agricu lture, education is essential to improve food security, rural employment, and to reduce poverty. It is difficult to transmit information to people that cannot und erstand traditional text based educational materials. An option is the use of illustration-based materials in which the information is represented using graphics. Manual development of graphical materials, even using traditional computer graphics packages, is a very time consuming process. Those materials usually are general a nd do not reflect the cu ltural conditions of the target audience. The approach presented in this work aims at producing illustration based educational materials using an ont ology based system. This methodology allows

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xiii for development of illustrations that can be adju sted (localized) to specific characteristics of the audience. Ontology is a formal, explicit specification of a conceptualizati on within a domain, where conceptualization refers to an abstra ct model of some phenomenon. An irrigation ontology was developed to orga nize data and organize concep ts in the irrigation and water management domains, while allowing browsing, search, tagging and classification of information. This ontology consists of mo re than 270 terms and 300 relationships. The irrigation ontology also stores ve ctor graphics that can be lo calized. This means that they can be adapted to represent more properly the conditions of the audience that will use the educational materials. Trial versions of th e illustration based educational materials were evaluated in El Salvador. Th e levels of understanding of the message being transmitted by the illustrations, as well as each illustration (e.g., color, size, level of detail), were evaluated. The main advantage of using an irrigati on ontology to model and manage irrigation and water management information is that th e content can be separated from the format, meaning that the same content can be presented in multiple formats like web pages, printed text, presentations, or PDF files.

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1 CHAPTER 1 INTRODUCTION Education, Literacy, and Agricultural Development As a means of production, way of life and s ource of food, agriculture in developing countries has been suffering irreparably over the last decade. While this is happening, there is an increasing rea lization that our rural farm ers, NGOs, governments and researchers simply cannot afford to conti nue wasting resources pursuing development and research goals that cannot tangibly change the lives of rural farmers and become a permanent part of their lives (Mukhwana, 2000). There is a need to find more sustainable methods, approaches and technologies of food production that can increase agricultural productivity and inco me while protecting and enhancing the environment (Mukhwana, 2000). The total population in West Africa tripled between 1950 and 2000. In 1950, the urba n/rural population ratio was 1:10, in 1990 it was 1:3.4 and in 2005, 42% of the populat ion was living in urban areas (UNFPA, 2005). With the exception of Burkina Faso, per capita food intake is diminishing. Increasing population density and pressure on the land have altere d traditional production patterns, and sustained agricultural production is being threatened (Lindley et al. 1996). What matters most for economic development in Africa is the capability of rural people to be efficient producers given their natura l resource base. There is little doubt that economic and social development, and the bene fits that accrue such as improved nutrition and health, require an educated populace (Li ndley et al. 1996). No country has become

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2 developed without well-educated people and a strong agricultural base that provides food security (Lindley et al. 1996). The improvement of a country's human resource capacity for productivity is a prerequisite for social and economic development. In the agricultural sector, formal and nonformal education are both essential for re ducing poverty, for improving food security and rural employment and reducing poverty (Lindl ey et al. 1996). Non-formal agricultural education, often provided by both public and pr ivate extension services, is needed for training of farmers, farm families and workers and for capacity building in a wide range of rural organizations and groups (Lindley et al. 1996). It is increasingly clear that extension workers need bette r training in both technical agriculture and the extension methods necessary to disseminate production technologies to the thousands of small-scale farmers who n eed them (Lindley et al. 1996). Most of the available empirical data that testify to the link among educa tion, literacy and agricultural productivity are based on studies of formal schooli ng (UNESCO, 1994; Lauglo 2001, Wilfred Monte, 2002). Education is an e ssential prerequisite for reducing poverty, improving agriculture and the living conditions of rural people and building a food-secure world (ERP, 2005). Literacy The term “literacy” has always been used to denote a certain ability or inability. For example, a person who cannot use a computer will be referred to as being “computer illiterate or a person who cannot use money pr operly will be referred to as “economically illiterate.” These examples suggest that the term illiteracy can be seen as being relative to a certain situation (Williams, 2001).

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3 The American Federal National Literacy Act of 1991 defines literacy as “having an ability to read, write, speak English, com pute, and solve problems to achieve and function in a job and in society” (Williams, 2001). Adult literacy can be defined in different ways. Two definitions will be used in this work. The United Nations defini tion states that a literate ad ult is “a person aged 15 or over who can read and write” (UNESCO, 2000 ). The Central Intelligence Agency’s (CIA, 2004) Factbook considers a literate a dult to be “a person over 10 years who can read and write”. Because developing countries are struggling to keep up with citizens demands for basic needs, being literate becomes a luxury in a situation of scarce resources (Williams, 2001). Estimates and projections collated by the UNESCO’s (2000) Institute for Statistics show a steady fall in the number of illiterat e adults from 22.4% of the world's population in 1995 to 20.3% in 2000. This means that the number of illiterate adults fell from an estimated 872 million in 1995 to 862 million in 2000. Based on current trends, the Institute estimates this should drop to 824 million, or 16.5%, by 2010. Still these numbers are high, and most of the illit erate are poor, farmers, and fe male, living in areas away from the urban centers, with little or no access to educati on, not to say technology (i.e., electricity, telecommunications). The problems of literacy relate not only to the governments’ organizational structure, teaching material, languages barrie rs, subjects matter, teaching and the training of facilitators, but more importantly to the way literacy is conceptualized (UNESCO, 1997). In any development activity people need to attain these successive levels of skill,

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4 and work out --with those in charge of the financial or institutional elements-a pedagogy by which people can acquire a skill, apply it and acquire the next skill (UNESCO, 1997). Despite the increase in the world populat ion, great strides have been made to increase literacy, though th ere are sharp differences between industrialized and developing countries. The growth in the numbe r of literate men and women in the world is expected to continue for the foreseeable future. Nevertheless the number of illiterate adults has remained at about 885 million si nce 1980, with females still outnumbering males (UNESCO, 1997). Literacy in Latin America Today nearly 90% of Latin American/Cari bbean adults can read and write but poor education systems continue to generate new illiterates. According to the latest estimations by the UNESCO’s (1999) Institute for Statistics, the region's overall illiteracy rate is 1%, compared to 40% in sub-Saharan Africa and 45% in South Asia. Latin America and the Caribbean's relatively good performance, how ever, masks huge disparities within and between nations. Countries like Argentina, Trinidad and Tobago, Bahamas, Cuba and Uruguay have illiteracy rates of less than 5%. But 13% of Brazilians a nd almost a third of Guatemalan adults cannot read or write. A glance at absolute numbers reveals th e millions of men and women who, because they have not mastered basic reading and writ ing skills, are deprived of the opportunity to enter the labor market or become full-fle dged citizens. Some 39 million adults in the region are illiterate, and Brazil's 13% illiteracy rate actually represents roughly 16 million people. The bulk of these illiterates can be found in rural areas, among ethnic minorities and the poor. Particular emphasi s must be given to dealing w ith issues of marginalization

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5 and equity, such as those affecting girls a nd women, and people in rural areas (UNESCO, 1997). Literacy in Africa As in many developing nations, illiteracy is very high in sub-Saharan Africa. In fact it is a region with the world’s highest illiteracy rate (54%). However, there is a considerable difference from one country to a nother. In 1997, in countries such as Kenya, Tanzania, Zimbabwe, Botswana and South Africa the literacy rate is about 70%, while in countries such as Uganda, Malawi, Burundi and Rwanda the literacy rates are below 49%. Southern African countri es, as with many Third World countries, expanded their education systems rapidly in the 1960s and 1970s (Walters and Watters, 2001). Is a lot of enthusiasm in literacy work and a growing r ealization that literacy is crucial in the context of integrated programs for im parting messages on population, health, and agriculture and in the struggle to esca pe poverty (UNESCO, 1997). In geographical terms, the northern region is the poorest. Food security is likely to be a problem to poor households despite the statements, which refe r to fertile lands a nd abundant food supplies (World Bank Report, 1995). Literacy in the Arab States Illiteracy remains a seri ous problem in the Arab region, where the number of illiterate adults reaches more than 65 milli on people. For men the rate has fallen from 45% in 1980 to 23% in 1995; for women it has fallen from 71% to 56%, though several of the less developed Arab States are stil l encountering difficulties (UNESCO, 1997). On average only about 63% of th e total adult populati on in the Arab States can read and write. This is one of the lowest adult literacy rates in the world. Literacy levels are below the regional average in Egypt, Mauritania, Morocco, the Sudan and Yemen, and are about

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6 90% or higher in Jordan, the Palestinia n Autonomous Territories and Qatar (EFA, 2006a). What makes the matter worse is the ex isting and increasing gender inequality in access to education. Literacy in Asia Asia is the largest continent. By 2000 the population of Asia was 3,688 million, about 60% of the world's total population ( UNESCO, 2000). The literacy data for Asia are divided in three major regions: Central Asia with an adult liter acy rate of 99%, East Asia and the Pacific at 91%, and South and We st Asia at 58%. As with the Arab states, the intra-region variation is high. Some of the causes of the differences are economic development, previous and past socio-pol itical conditions, and to a lesser degree geographic situation (EFA, 2006b). The illiteracy rate in Asia is higher than the world average and other regions except for Africa. Teaching Agriculture to Adults The modernization theory advocated, in the early 1960s, a large expansion of schooling based on the human capital theor y, which saw education as a productive investment essential for economic growth. Th is view reinforced the understanding that less developed countries were undeveloped b ecause of their basic characteristics, including their poor education and skills le vels (Walters and Watters, 2001). Adult education is embedded in the political, social cultural and economic processes of society. The information above suggests that the nature of adult ed ucation policies, programs and practices reflects the interests and values of different social groups, and the distribution of power and influence in the soci ety (Walters and Watters, 2001). In the last 20 years most developing c ountries have embarked on numerous adult education programs that focused on skills development in both the formal and informal

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7 economies. Within the context of globalized economies, economic development and adult education, or adult learning, become even more urgent and complex (Walters and Watters, 2001). Agricultural education projects are ba sed on teaching a topic to a determined audience. Teaching, like other forms of in formation transmission, is a communication process. Usually the teacher sends a verbal message, which contains some information to the learners who are expected to receive it and integrate it into their existing knowledge. This process is not so simple. First, teachers have to encode their thoughts into words and/or other forms of comm unication. Then students have to decode the message; this means they have to make sense of it (Blum, 1996). To make sure that this actually happens, teachers can do two things: strengthen their verbal messages by additional means such as visual teaching aids, thus enabling students to receive the message over two or more parallel communication lines (the ear and the eye). However, the two parallel messa ges must be matched in order to have an amplifying effect. If they are not, they crea te confusion ("noise," in the language of communication) (Blum, 1996). Performing the ac tivity and the educational materials can help the learner remember the concepts ta ught, these are the parallel messages when dealing with illiterate audiences. Agricultural teachers have an advantage when teaching in the field. Students can observe by themselves and through different ch annels of perception a situation that the teacher might find difficult to put (encode) into words. Messages that are received by the students are filtered and stored temporarily in the short-term memory. They are forgotten after about 30 seconds if they cannot be ke pt in mind or transferred to the long-term

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8 memory. Thus, we forget casual telephone num bers very quickly unless we make an intellectual effort to remember them. The long-term memory receives new information better when it fits into an already existing framework of concepts. Incomprehensible and unclear messages are not easily stored in the long-term memory and they are quickly forgotten. Competing verbal and audiovisual messages are difficult to cope with. Showing something to students and talki ng about something different weakens the transmission of the message (Blum, 1996), he nce, the importance of content-relevant educational materials that are easy to comprehend to the audience (learners). The transfer of technical skills seems to be even more restricted. In most cases it was found that, with practice, the speed and qua lity of a given tec hnical task could be improved, but that this does not help to impr ove other practices. However, the transfer of practical training can be enhanced to some ex tent when students understand the principles that underlie the practices. In agriculture, th is means that we can enhance the teaching of practices when we make sure that students understand why they shoul d do things the way they are taught (Blum, 1996). When learning needs reinforcement, edu cators can use an array of educational materials. However, when dealing with illi terate audiences the available materials are hard to find, and if available they are not always relevant to the audience. Illustrationbased educational materials are the best opti on when trying to provide support materials for illiterate people. Agricultural Education Using Images Most of the communication means containing images are especially appropriate for a public that has received little or no formal education. Its visual natu re attracts attention

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9 and helps the message to be transmitted at a glance (De Paolis, 1994). The images have to generate participation and iden tification of the observer with the subject or object shown. When referring to images there has to be a distinction be tween photographs and illustrations or drawings. That difference will be fundamental to differentiate the iconic cultures; some cultures may id entify photographs more easily than drawings or vice versa (De Paolis, 1994). However, that separation mi ght also be economical and technological given the differences in printing costs a nd equipments needed for photographs and drawings. The different elements of the me ssages have to be composed in a way to contribute to producing certain effects on the recipient (audience). Justification A difficulty commonly encountered in th e preparation of illustration-based educational materials is the preparation of th e artwork, especially when the material is intended for an ethnic or language group or groups other than the one creating the material. Over the years, attempts have been made to supply visual models that might make the job of drawing visuals easier fo r workers with limited training. Another problem often encountered is the difficulty in finding experienced personnel to prepare the materials quickly and easily. It has also pr oven difficult to adapt (localize) materials which have been successful in one region or co untry to another ethnic ally or culturally different one because the models do not le nd themselves to change: instead, project workers use available material s that are not appropriate ,or have to create educational materials from scratch. With the increasing num bers of computers being used in the field there is growing demand for a simpler and more direct system to produce appropriate materials.

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10 The user should be able to choose among the images that convey the desired message, adapt them as necessary and print th em out in a very short time. The images should be realistic and as detailed as necessary. The main advantage of such a system is that the same images could be easily changed for a variety of different uses and formats. Any changes on the illustrations can be easily and quickly done on the computer screen. There is no need to make entirely new drawings when the audience changes. The image bank can be made available to several organizations. The general steps that have to be followed to develop educational illustrations are; 1. Decide on the form, context, and use of the illustrations based on an understanding of the audience (their att itudes and practices) and the development of strategies aimed at changing behavior in line with the established goals of the project. 2. Collect images (the easiest would be to use images already existing in an “image bank,”) from other materials with the respec tive permission, or scenes that could be photographed. 3. Adapt the existing materials to make them culturally appropriate for the audience’s needs, interest and conditions. 4. Prepare the educational materials containi ng the illustrations in the desired format, and reproduce the materials. Presently, this process is mostly done manually and can be very time consuming. Another drawback is that resources are wasted since materials developed in other projects are not adapted or reused in ot her applications. The use of a computer assisted process to produce, store, and manage the illustrations, a nd then develop the educational materials, would make this process easier and more efficient. The potentials of this type of system to produce educational materials are just being recognized. Aside from saving time and mone y, using the computer also allows the production of specialized illustrations from the image bank. Handouts and flyers can be

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11 produced from images in the bank and produced in small numbers on a printer or photocopy machine. High quality small editions of training materials can be produced easily and quickly for workshops and seminars. The use of computer graphics to produce project support materials could simplify a co stly and complex task. This would bring enormous potential benefits to projects de veloping educational and instructional support materials (Tisa, 1991). Overall Objectives of the Study The main objective of this study is to develop an ontology for the irrigation and water management domain. Ontologies have be en proposed to solve problems that arise from using different terminology to refer to th e same concept or using the same term to refer to different concepts (Beck and Pint o, 2002). Ontologies can be used to organize metadata and to order concepts in a gi ven domain, while allowing browsing, search, tagging and classification of documents. Secondary objectives of this study are: To adapt a process to develop ontologies in the agricultural domain. Create an ontology for irrigation related materials. The information contained in the ontology will focus on the knowledge needed in developing countries to improve irrigation practices. To present the information on more than one language (i.e., English, Spanish) and other characteristics that will allow locali zation of the materials to be developed. To find a technology capable of creating didactic manuals “on-the-fly” for extension education, and to prin t those materials “on-demand.” To evaluate the illustration-based educational materials on the field. To demonstrate the necessity of a tool to create didactic manuals especially for people with low literacy/educat ion levels, or little knowle dge of the topics. This will include, illiterate people (mainly in developing countries), people without knowledge of English (i.e., foreign agri cultural workers), children (i.e., 4-H).

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12 Methodology This project focuses on the creation of an irrigation domain ontology. The ontology includes text, pictures and drawings related mainly to irrigation and water management. The content is organized by topics (i.e., su rface irrigation, water conservation, etc). The use of this tool potentially will avoid many of the delays, costs, and inventory issues associated with traditional development of educ ational materials. This could facilitate the transfer of information from the extension spec ialists to extension agents to final client. The base for a successful implementation of an ontology system is a narrow and clearly defined knowledge domain; the choice of subject is crucia l to a successful implementation. The process to create the ontol ogy includes the collection of all relevant information for all topics that the irrigation ontology will cover. The information includes text and other visual aids (e.g., pictures, diagrams, drawings). Since the objective of this work is to cr eate educational mate rials for people with low levels of education, the next step is to de velop drawings that will be used to explain some processes and ideas to people that are not able to read. A big challenge to this project is to achieve the automation of th e process of creating situation and culture specific drawings from digital pictures. The end product will be a t ool that facilitate s the creation, storage and management of content in multiple formats. The ontologybased tool will also allow localization of multiple properties of graphics and text. Finally, it will permit the development of educational materials ranging from manuals c ontaining just text a nd technical language for the extension agents, and educational materi als with visual aids and some brief text for illiterate learners.

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13 The field evaluation of the illustrations wa s done in a rural area in El Salvador. It consisted of the use of closed and open-ende d questions. The data collected was helpful making changes and improvements in the content, illustrations and format of the educational materials. The comments and results were incorporated in the final development of the graphic materials. Expected Outcomes The main outcome of this project was th e creation of an irri gation ontology that allows the storage of multili ngual text and visual aids; the data stored should be easily manipulated (allow for localization) in or der to create easy to understand and reproduce educational materials. Organization of the Dissertation This dissertation is organized in six chapte rs. Chapter 1 includes a literature review of the main concepts utilized, also in th is chapter the justif ication, objectives, and methodology for the study are presented. The ontology modeling methodology is presented in two chapters; sp ecification, conceptualization, and evaluation are explained in Chapter 2, while formalization and implem entation are presented in Chapter 3. Chapter 4 shows the incorporation of vector graphics into the i rrigation ontology to allow the development of illustration-based educational materials. The field evaluation of the educational materials conducted in El Salvador (Central America) is presented in Chapter 5. Finally a summary of the dissertation and conclusions are presented in Chapter 6.

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14 CHAPTER 2 IRRIGATION ONTOLOGY MODELING Introduction An increasing number of information re sources require improved information management systems. There are several appro aches to organize information; the most common are glossaries and thesauri. Glossaries are lists of terms with their meanings specified as natural language statements. Th esauri provide descri ptions and additional semantics between terms like synonym and antonym relationships. A basic ontology can be very similar to a thesaurus. However, the ontology is not limited to the types of relationships present in a thesaurus; instead it has a series of features that improve its search and conceptu al capabilities. An ontology can be regarded as a particular knowledge base, describing fact s assumed to be true by a group of users of a certain domain. Thesauri Thesauri provide only very basic mode ling paradigms and no knowledge can be extracted from a thesaurus except simple keyword relationships (Lauser, 2004). A thesaurus is a networked collection of contro lled vocabulary terms based on hierarchical, equivalent and associative relationships. Th esauri are limited in the inter concepts relationships that can be re presented. Hence, the specific information that can be extracted is also limited. A thesaurus is ba sed on concepts expressed as terms and some relationships among those terms. Term is a word or expression that has a precise meaning in some science, art, profession, or subject. The types of relationshi ps available for the

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15 thesaurus may require for the terms to be arra nged in categories that do not form a logical hierarchy. Two examples are presented, AGROVOC (2005) from the United Nations’ Food and Agricultural Organi zation (Figure 2-1), and th e United States National Agricultural Library Thesauri (NALT, 2005) (Figure 2-2). Figure 2-1. View of the AGROVOC Thesaurus In Figure 2-1, in the left column a list of terms in different languages that correspond to the term “irrigation” can be obs erved. In the right column another set of

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16 terms is presented, preceded by NT, or RT. NT is used for narrower term, this means that it is a term more specific than irrigation. RT m eans related term; it is a term that is not too closely related to “irrigation.” The existing relationships are designed to gi ve the terms semantic logic, rather than to indicate relationships like “part of,” or “belongs to,” that are common in ontologies. The basic relationships that can be encountered in a Thesaurus are hierarchical “Broader Term” (BT) and “Narrower Term” (NT), equivalent “Use Preferred Term” (USE) and “Used for” (UF), and associative relationshi ps “Related Terms” (RT) (Hassen, et al., 2004). Figure 2-2. View of the NAL Thesaurus

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17 Apart from the display of the related te rms, the only difference between the NAL thesaurus and AGROVOC is that the former offers a brief definition for some of the terms that it contains. The main objective of a thesaurus is to cr eate a hierarchy of related terms. Terms could be defined as the “names” of the c oncepts. A thesaurus basically takes taxonomies (a classification that arranges the terms into a hierarchy) and extends them allowing other statements to be made about the terms. Thesau ri allow the search of terms in a structured manner; they also allow the search of rela ted terms relatively easily, since all related terms should be located close to each other. Ontology Ontology is a formal, shared, explicit spec ification of a concep tualization within a domain (Gruber, 1993). Conceptualization re fers to an abstract model of some phenomenon. Shared means that an ontology captures consensual knowledge accepted by a group (Benjamins et al., 2002). The features contained in an ontology are classes, subclasses, instances, properties, and comp lex (inter) relationshi ps between terms. Ontology is a more complete structure to describe a dom ain’s concepts as well as multiple relationships among those concepts. An ontology formally describes a domain (Antoniou and van Harmelen, 2004); it provides a generic way to reuse and share content across applications and groups (Pinto and Ma rtins, 2001). However, it is important to remark that the model can only be considered an ontology if it is a shared and consensual knowledge model agreed upon by a community (Hassen, et al., 2004; Antoniou and van Harmelen, 2004). A formal ontology is a controlled vocabulary expressed in an ontology representation language, a model for describi ng the world that consists of a set of

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18 concepts, descriptions or properties, and re lationships. This language has a grammar for using vocabulary terms to express something meaningful within a specified domain of interest. An ontology representa tion of the domain should try to be a resemblance of the real world complexities. The interrelations present between terms in an ontology allow the search tool to produce a list of rela ted and relevant terms. All the associated information related to the term being search is retrieved. An ontology typically is shared or built with the collaboration of dom ain experts (Pinto and Martins, 2001). Ontologies are widely used in Knowledge Engineering, Artificial Intelligence, and Computer Science, in applic ations related to knowledge management, natural language processing, e-commerce, intelligent integra tion information, information retrieval, database design and integration, and e ducation (Gmez-Prez et al., 2004). Ontologies have been proposed to solve pr oblems that arise fr om using different terminology to refer to the same concept or using the same term to refer to different concepts (Beck and Pinto, 2002). The term “ ontology” is a branch of Philosophy that deals with the nature and organization of realit y. Aristotle first define d it as “the science of being as such” (Guarino and Giaretta, 1995 ). All type of communications, including the internet with its great cap acity to disseminate informa tion, need a shared vocabulary. Even a simple list of terms can be viewed as an ontology, since it is a set of definitions that helps to better understa nd a topic (Passin, 2004). The Semantic Web is based on ontologie s for organizing large collections of knowledge. Ontologies allow searching inform ation distributed across multiple sites on the web, and in different languages (Beck and Pinto, 2002). The Semantic Web provides a common framework that allows data to be shared and reused across application,

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19 enterprise, and community boundaries. It is an extension of th e current web and it contains the information which is given we ll-defined meaning, better enabling computers and people to work in cooperation (W3C, 2001a ). The way that knowledge is stored and organized influences the retrieval prob lem (Beck and Pinto, 2002). Conventional information retrieval technologies like the one s used in web search engines are not as precise and do not always retr ieve relevant information. The knowledge organization in concepts and the relationships among those concepts within a domain is what improve s the searching capabil ities of an ontology (Passin, 2004). Information resources are att ached to the ontology terms to create a complete database. As a result, users can perform queries to re trieve the specified information (Beck and Pinto, 2002). Research ontologies are becoming more common, as a tool to describe a vocabulary’s meaning and the relations am ong those meanings. The simplest ontology describes a hierarchy of concepts relate d by assumed relationships. They aim at improving the communication between com puters and humans. Ontologies have applications in software development, rese arch, and database applications. Reusability means that the ontology should allow knowledge sharing and reuse. An ontology can be used to organize metadata and to provide an order to concepts in a given domain, while allowing browsing, search, tagging and cl assification of documents. Knowledge acquisition permits the ontology to model the do main of the applica tion. Reliability and maintenance allows consistency check for software development. Ontology Classification According to their accuracy in characteri zing the conceptualization to which they commit, the ontologies are divided into fine-gra ined and coarse. For this project a coarse

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20 ontology was developed. This means that th e ontology is based on terms and concepts already agreed by users, and it is designed to support limited and specific services. Ontologies can also be classified by the level of generality as top-level ontologies, domain and task ontologies, and applicati on ontologies (Guarino, 1998). The irrigation ontology built here is a domain ontology; this means that it describes a vocabulary related to a generic domain (irriga tion) on which it focuses. Ontology Languages A couple of the languages (Beck and Pi nto, 2002; Passin, 2004) used to define ontologies are the Resource Description Framework (RDF), and DARPA, the Agent Markup Language & Ontology Interchange Language. RDF (W3C, 2002) has developed on top of the extensible markup language (XML) (W3C, 2004b) for the purpose of describing web resources. The DARPA Agent Markup Language & Ontology Interchange Language (DAML+OI L) that is being develope d for building more complex ontologies (DAML, 2004; W3C 2001c). Both are based in semantic networks, however, some of them differ in their level of expr essiveness, and this affects the kinds of inferences that can be applied. Ontology Editors Ontology editors (or builders) were develope d to help create ontologies in different domains. Some of the ontology editors ar e OntoBroker, Protg-2000, Ontolingua, and ObjectEditor. OntoBroker created by the In stitute for Applied Computer Science and Formal Description Methods (OntoBroker, 2004) uses HTML, XML, and RDF. Protg2000 developed by the Knowledge Modeling Group (KMG) at Stanford University, allows the user to create a domain on tology. Ontolingua Server (http://www-kslsvc.stanford.edu:5915/) is widely used. It main tains a large library of ontologies that can

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21 be reused, and permits collaboration among various authors (Far quhar et al., 1995). The tool used to construct the irrigation ontology was the ObjectEditor, a Web-based tool for constructing ontologies within specific domains (http://orb.ifas.ufl.edu/Obj ectEditor/index.html) develope d in the Department of Agricultural and Biological Engineering at the University of Florida (Beck, 2003a; 2003b). Objectives The objectives of this chap ter are 1) to select a modeling methodology for a domain ontology, 2) to use this methodology to define and model an irrigation ontology, and 3) to compare the irrigation ontology with some existing thesauri. Methodology There are several methodologies to build ontol ogies, however the on e that best fits the irrigation domain ontology is presented be low. There are some typical steps that should be followed to construct an ontol ogy (Uschold and King, 1995; Pinto and Martins, 2001): Specification Conceptualization Formalization Implementation Evaluation, maintenance, and documentation. These steps are represented in the ontology life cycle diag ram (Figure 2-2); they are related to most software e ngineering activities. Various authors have developed some variations of the life cycle. One of the most accepted is the evolving prototyping life cycle (or evolutionary cycle).

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22 Figure 2-2. Activities of the ont ology development life cycle In this cycle the developer can go back from any stage to any stage of the development process. This means that the ontology can be modified until the evaluation is satisfactory and all the objectives of the ont ology are met (Beck and Pinto, 2002). Ontology Specification Ontology specification refers to the defi nition of the scope of the ontology. The scope of the ontology presented in this work is irrigation knowledge domain, related to small farmers’ irrigation systems. One question that should be asked at this point is: Why develop an ontology? Some of the reasons for the development of an ontology for specific domain are (McGui nness and Fridman Noy, 2001): To provide a common structure of information within a domain To make domain assumption explicit To allow the reuse of domain knowledge To analyze domain knowledge Conceptualization Implementation Evaluation Formalization Specification

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23 Ontology Conceptualization One of the basic applications of ontologies is having an agreed set of terms and concepts organized in order to facilitate information use by humans and computers. Uschols and Gruninger (1996) recommend having brainstorming sessions to compile relevant terms and phrases that may late r constitute concepts in the ontology. The structure of the ontology becomes apparent by grouping the terms in related areas. It is also important to consider closely related or equivalent terms to avoid duplication of concepts. An ontology should effectively minimize ambiguity and if possible, all definitions should be defined in natural language. In some cases examples may be needed to clarify definitions (Usc hols and Gruninger, 1996). Knowledge acquisition, the next step after the definiti on of the domain and scope of the ontology is the definition of classe s that describe concepts in the domain (McGuinness and Fridman Noy, 2001). A topdown approach was selected, over a bottom-top or a combination, to define the hier archy of classes and subclasses, this means that the classes (more general terms) were first defined and then the subclasses (more specialized terms) and so on. This structure implies that work should start in the most fundamental terms before moving to the more abstract terms within a domain (Uschols and Gruninger, 1996). Once appropriate terms we re defined, then their properties were determined to describe the internal structure of the concepts. All terms have to be related to other classes (as concep ts are related to other c oncepts within the domain); ObjectEditor allows four types of relations hips: “association,” “part,” “sequence,” and “generalization.”

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24 Ontology Formalization and Implementation Chapter 3 covers the formalization and implementation process. A detailed explanation of all the processe s is given; examples from th e irrigation ontology are used to illustrate the ideas and some modeling issues. Ontology Documentation, Evaluation, and Maintenance Continuous evaluation of the ontology is im portant in order to avoid problems or make corrections before it is too costly to do it. The following evaluation guidelines should be considered (McGuinness and Fr idman Noy, 2001; Uschols and Gruninger, 1996): Develop a natural language (e.g., Eng lish) definition of the ontology Use common and agreed terms (e.g., standards, dictionaries) Notice relationships with other terms (synonyms referring to the same concept) Avoid circular reference when defining terms Use clear and concise definitions Provide examples to explain concepts when needed Guidelines to document the ontology are desirable. All important assumptions about the main concepts defined in the ontology should be documented (Uschols and Gruninger, 1996). This documentation could be then used as metadata. Maintenance is a constant process with any ontology. Ontol ogies are continuously confronted with evolution problems, and maintenance is neces sary to ensure the reliability of the ontology. The irrigation ontology was also eval uated against the NALT, AGROVOC and IWMI descriptors. This evaluation was conducted to check how well the irrigation ontology covers the terms within the irrigation/water manageme nt domain. A list with all the ontology terms divided by topics was co mpared against the terms contained in AGROVOC, NALT, and IWMI descriptor list.

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25 Application of Modeling Methodology to Development of Irrigation Ontology The modeling of the irrigation ontology wa s conducted using previously described general steps: specificati on, conceptualization, formalization, implementation, and evaluation. Developing an irriga tion ontology is not a goal in its elf. The main objective is the use of the defined sets of terms and th eir structure for a particular purpose. As a consequence there is not unique ontology of a specific domain (i rrigation in this case). An ontology is an abstraction of a particular domain, and there are always alternatives. What was included in the ir rigation ontology was determined by the final use of the ontology. However, the irrigation ontology is still general enough to allow expansion and shareability. To help with the specification of the ir rigation ontology some questions have to be asked; the answers to these questions gui de the rest of the modeling process: 1) Why an ontology? An ontology offers versatility that other knowledge management systems (e.g., thesauri) cannot provide. Onto logies can be modeled to fit the user necessities while being malleable enough to be adapted and shared for other uses. Ontologies offer a better way to organi ze information, and manage content. 2) What will be the objectives (main and secondary) of the irrigation ontology? The objectives for development if irriga tion ontology were early defined as: Evaluate if the ontology can be used to develop educational materials. Collect and store irrigati on and water management related information, mainly focus at the development of educational materials for small farmers with low levels of literacy. Store this information with a common st ructure that can be reused in other applications. Offer tools for the development of multi -format educational materials for broad audiences.

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26 3) What is the scope of the irrigation ontology? The irrigation and water management domain is very broad, so limitations have to be created for the ontology. The objectives of the ontology help limit the ontology’s scope in this case subtopi cs that are closely related to small farm irrigation. Examples can be water harvesting, soil conservation, low cost irrigation systems, etc. During the specification process it is im portant to remember that the final application defines the domain of the ontol ogy. Limitations out of the control of the experts and modelers should al so be considered; in the ca se of the irrigation ontology available time and labor were the limits. By having these factors in mind the ontology development process can be guided toward the ontology’s objectives. Conceptualization covers the process of collecting the information (knowledge) that will be part of the ontol ogy’s content. At this point it is important to remember that the ontology has to have a finite scope and purpose for its content. The modeling methodology aims at representing the “real world” in logical terms using a given ontology software editor, in this case ObjectEd itor. A flow chart of the conceptualization process is presented in Figure 2-3; it explai ns the flow from data acquisition to the incorporation of the term into the ObjectEditor. Considering the objectives and limits of the irrigation ontology, the first step toward the actual definition of the irrigati on ontology was to write down an unstructured list of all the relevant terms expected to appe ar in the ontology. The list of terms relevant to the irrigation domain was developed with in formation extracted from sources such as the Land and Water Development Division of the Food and Agricultural Organization (LWD, 2005), American Society of Agricu ltural and Biological Engineers (ASABE)

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27 (ASABE, 2005), United States National Agricultural Librar y Thesaurus (NALT) (NATL, 2005), and the Extension Data Information Sour ce (EDIS) from the University of Florida (EDIS, 2005). A group of specialists from the Un iversity of Florida was also involved in the knowledge modeling process. It is importa nt to mention that every individual had a personal ontology; meaning that each one had a particular perception of the knowledge about the irrigation domain. In order to cr eate a common ontology fr om the perceptions of individual experts, much group discussion was required to arrive to a common set of terms and their definitions. Figure 2-3. Diagram representing the conceptualization process This complex process is illustrated with a simplified example. The first step was to select a representative sample of irriga tion related terms from the literature: Water management Precipitation Evapotranspiration Soil Aquifer Irrigation scheduling Microirrigation Infiltration River The next step was to group the common terms together (conceptual clustering). Data Acquisition Data Sources Data Process List o f Terms Object Editor

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28 precipitation, evapotranspiration soil, infiltration aquifer, river water management, irrigation scheduling From here groups of related terms were created. Each group was named by the general concept it represents. The groups we re be modeled as “modules” in ObjectEditor to facilitate its display. Weather Soil Water Resources Water Management The groups were created according to the relevance they have to the ontology developers and modelers. As new terms ente red the collection, new groups were defined if the existing ones we re not adequate. After the identification of the relevant terms, these terms were organized in a taxonomic hierarchy. The irrigation ontol ogy modeling methodology followed a topdown approach (Prieto-Diaz, 2002). This means that the more general terms were placed higher in the hierarchy, and the terms became mo re specific towards the lower levels of the ontology. The irrigation ont ology was classified following existing classification from the literature, and by agreem ent among the experts involved in the modeling of the irrigation ontology. For topics like “system design” or “irriga tion efficiency,” the process of selecting the terms, definitions, and the determinati on of relationships among those concepts was iterative, meaning that the process had to be repeated multiple times until all the experts agreed on a common irrigation ontology. For ot her topics like “weather,” “plant,” or

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29 “soil,” the classification process was much simpler, having only to follow pre-existing classifications, from the sources sited above. Figure 2-4. Main topics covered by the Irrigation Ontology in ObjectEditor The documentation part consisted of reco rding the sources of the information collected and incorporated into the irrigation ontology. It also included any comments made during the modeling process. Evalua tion and maintenance of the ontology were interrelated. Below are some questions that were used during the evaluation process: Does a selected term fit the ontology specification? Does the location of a term in the hierarchy make sense? Do the gloss and definition of the term are in sync with the specific ontology domain? Were there any errors during the ontol ogy implementation process? (e.g., was the correct relationship used?)

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30 Errors were corrected as encountered duri ng the evaluation process. This facilitated the maintenance of the irrigation ontol ogy. The continuous evaluation and correction process helped avoid the necessity of larger modifications at the end of the modeling process. The irrigation ontology was compared to the NALT, AGROVOC thesauri, and IWMI descriptors. At the time of this ev aluation the irrigation ontology contained around 270 terms from the irrigation and water mana gement domain. NALT refers to the United States National Agricultural Library Thesaurus (NALT, 2005). The 2006 edition is the fifth edition of the NAL Agricultural Thesau rus, first released in 2002. The total number of terms contained in the NATL is 66,417 with definitions for 2,038 terms. AGROVOC is a multilingual, structured and controlled vocabulary designed to cover the terminology of all subject fields in agri culture, forestry, fisheries, f ood and related domains. Currently, it works in the following languages: English, Fr ench, Spanish, Arabic and Chinese. Other national versions include Czech, Portuguese Japanese and Thai language versions. German, Italian, Korean, Hungarian, and Sl ovak language versions of AGROVOC are under construction. As an example, the English version has 28,127 terms, while the Spanish version has 28,123 terms. It was developed by the United Nations’ Food and Agricultural Organization (AGROVOC, 2005) An unpublished list of 2,388 irrigation related descriptors provided by the Internati onal Water Management Institute (IWMI) in Sri Lanka was also analyzed during this study. NALT, AGROVOC thesauri, and IWMI desc riptors were used to check how well the irrigation ontology covers th e terms within the irrigation/water management domain. A manual search of the terms stored in th e following sources was performed. The search

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31 was conducted using the online search tools for each of the datasets described above. For the evaluation the terms were divided into ni ne main areas or topics: Irrigation Water Sources, Weather, Plant, Soil, Drainage Chemigation, Irrigation System Design, Irrigation System Management, and Irrigation Equipment and Structures. A list with all the terms included in the irri gation ontology divided by topics was compared against the terms (or synonyms) in AGROVOC, NALT, an d IWMI descriptors. The results are presented in Table 2-1 below. Table 2-1. Comparison by topics of va rious sources vs. irrigation ontology Topics Irrigation Ontology AGROVOC IWMI NALT Total # of terms % % % Irrigation Water Sources 45 20.00 26.67 28.89 Weather 13 53.85 46.15 53.85 Plant 16 68.75 25.00 37.50 Soil 31 64.52 48.39 87.10 Drainage 20 15.00 15.00 10.00 Chemigation 16 6.25 12.50 18.75 Irrigation System Design 41 4.88 2.44 9.76 Irr. System Management 30 23.33 0.00 10.00 Irr. Equip. and Structures 59 0.00 0.00 16.95 Source: Cornejo, 2006 % = # Terms1 / # Terms in Ontology 100 Equation 2-1 1 Number of terms from AGROVOC or IWMI matching terms in the irrigation ontology. From Table 2-1 can be observed th at AGROVOC, IWMI, and NALT contain a higher percentage of the same terms as the ir rigation ontology in three main topics. Those topics are soil, plant, and weather with values ranging from 37.5% to 87%. For the topics more relevant to irrigation like system design, system management, and irrigation equipment, the values range from 0% to 23.3%. Irrigation equipment and structures are

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32 the topic where less matches occurred, the only da tabase that had any terms related to this topic was the NALT with 16.95% of the terms. In Table 2-2 a more general comparison is presented. The total number of terms found in each of the datasets compared to th e total number of terms from the irrigation ontology is shown. Again using Equation 2-1 th e matched terms from each of the sources were compared to the total number of term s from the Irrigation Ontology (271). IWMI contained 15.8% of the terms, AGR OVOC 22.1%, and NALT 27.68%. Even so, the IWMI descriptors in theory should have more irrigation and water management concepts; this set is the one that ha s fewer of the terms containe d in the irrigation ontology Table 2-2. Comparison of various sources vs. irrigation ontology Topics Irrigation Ontology AGROVOC IWMI NALT Total number of terms 271 60 43 75 Percentage from Irr. Ontology 22.14 15.87 27.68 Source: Cornejo, 2006 Conclusions The presented methodology for ontology deve lopment seems to work well for the irrigation ontology. This framework is generi c enough to be used to create other domain ontologies especially within the agricultu ral field. The irriga tion ontology developed using this methodology should fulfill requiremen ts for compatibility and shareability with other ontologies. The above a pproach made the modeling proc ess very straight forward and it was easily followed by the experts that had little experience with ontology modeling. As stated in the objectives, the domain of the irrigation ontology was very limited. Because of the narrow domain of the irrigation ontology, it was possible to do all the modeling manually. The final irrigation onto logy developed in this project has more than 270 terms and 300 relationships howev er the process was time consuming and

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33 required multiple brainstorming sessions for the experts to agree in the final ontology. For larger ontologies an automatic m odeling methodology should be developed to expedite this process.

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34 CHAPTER 3 IRRIGATION ONTOLOGY FORMALIZ ATION AND IMPLEMENTATION Introduction Ontologies can be used to support a great variety of task s in diverse research areas such as knowledge representation, natural language processing, info rmation retrieval, databases, knowledge management, online database integration, digital libraries, geographic information systems, and visual re trieval or multi agent systems. Ontologies enable shared knowledge and reuse where in formation resources can be communicated between human or software agents. Semantic relationships in ontologi es facilitate making statements and asking queries about a subject domain due to the use of conceptualization. Domain ontologies are reusable in a given specif ic domain (medical, engineering, law, irrigation, etc.). These ontologies provi de vocabularies about concepts within a domain and the relationships among those con cepts, about the activities taking place in that domain, and about the theories and princi ples presented in that domain. There is a clean boundary between domain ontologies and upper-level on tologies. The concepts in domain ontologies are usually specializations of concepts already defined in top-level ontologies, and the same might occur with th e relationships (Mizoguc hi et al., 1995; van Heijst et al., 1997). Ontologies offer ways of better managing the vast educational resources that have been and are still being developed by organizations such as the U.S. Cooperative Extension Service and United Nati ons Food and Agricultural Organization. Issues involved in educational resource management include properly identifying (cataloging) each resource, where large numbers of resources exist at many levels of

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35 granularity ranging from entire training curricu lums to individual lessons or modules to the content of those modules including individual text fr agments, images, and other multimedia resources. New authoring tools for generating this content in the context of ontologies, and tools for automatically generatin g presentations in different formats from shared content are needed. Learning object t echnologies and standards such as SCORM (Godwin-Jones, 2004) addresses ways of bette r packaging educational resources into reusable components. SCORM provides a metadata standard for describing learning objects, and includes tags that can referen ce taxonomic subject classification systems including ontologies (although SC ORM itself is not a standard for ontologies). Content management systems are database management systems for storing content in the form of text and other multimedia resources. They store content in a presentation-independent way, and are capable of generating particul ar presentations from content according to different customizable styles. Combining c ontent management syst ems with ontologies and with learning object standards leads to an ontology management system that can better organize educational content, facilitate content development, and automate the process of generating educational material s. By using ontologies the information publishing process can be greatly facilitated (Clark, et al 2004). This approach is being used at the Univ ersity of Florida on a range of projects, including one on developing educ ational extension materials to help farmers with limited formal education understand basic principles of irrigation. These educational materials rely heavily on graphic images to illustrate ir rigation principles such as creation of water retention structures or general layout of ir rigation systems (text is limited or optional because many of the farmers using these mate rials are illiterate). Furthermore these

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36 materials must be adapted to fit local cultur al environments. For example, illustrations should change to show crops and agricultural systems local to the area where they are applied, and people should be presented in gender and culturally specific contexts. An ontology can be the basis of a fully operational database management system (Beck, in press). The concepts and relationships in the ontology also contain primitive data such as text, images, and other mu ltimedia resources that provide additional definition of the concepts. The ontology manage ment system includes a formally defined ontology language which also acts as a data modeling (data definition) language for the database, tools for inspecting and editing the ontology, operations for manipulating the ontology (reasoners), and secondary storage management to support efficient processing of these operations. An ontology manager was used to construct th e irrigation ontology along with associated educational content for the domain. Facil ities that are part of this system for automatically generating presentati ons from content are used to create Webbased and printed educational materials. This process is described below. The process of building the irrigation ont ology is an important first step in facilitating shared ontologies for this domain. The proce ss of building working, shared ontologies is still in its in fancy. Although established standards for building ontologies now exist, and formal methodologies are well developed, there is a need to build working examples and demonstrate their utility. The technology for content management, l earning objects, and authoring tools for creating educational resources likewise is in a rapid state of evolution. Conventional presentation tools (Microsoft PowerPoint Adobe Acrobat, and Macromedia Breeze) while widely used, do not attempt to repres ent content in a pres entation independent

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37 format, and make no attempt at classifying content in any context, let alone one as sophisticated as an ontology. Building e ducational materials within an ontology management system hopefully shows the advant ages of this approach to better organize educational resources, and gain flexibility in automatically presenting educational materials to meet individual learning styles, native language s, and respect local cultural contexts. Water management and irrigation is a ma jor component in agricultural technology. Currently no known ontology on irrigation exists Irrigation ontology was constructed to provide a framework for organi zing materials within this sp ecific domain. This ontology can become a starting point for a larger ontol ogy covering irrigation concepts in general. This chapter presents the methodology used to construct the irri gation ontology, briefly describes the tools and environment used to construct the ontol ogy, and provides details of the resulting irrigation ontology including the top-level concepts, and some examples of small domains within the ontology. A complete list of the terms and concepts appearing in the ontology is included in Appendix A. Objectives The objectives of this chapter are: 1) Fo rmalization of the irrigation ontology using ObjectEditor. 2) Implementation of the ir rigation ontology as part of the ontology modeling process presented in Chapter 2 us ing ObjectEditor. 3) Identification and discussion of modeling issues encountere d during the implementation process. Ontology Formalization The steps in the ontology mode ling methodology are specification, conceptualization, formalization, implemen tation, and evaluation. In this chapter

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38 formalization and implementation are discussed in detail. The other st eps of the irrigation modeling methodology for the irriga tion ontology were explained in detail in Chapter 2. The irrigation ontology was constructed using ObjectEditor a graph-based, Webbased tool (http://orb.ifas.ufl.edu/ObjectEd itor/index.html) for constructing ontologies within specific domains developed at the University of Florida, USA (Beck, 2003a, 2003b). Figure 3-1. View from the ObjectEditor ObjectEditor can be run on-lin e in any Web browser (utiliz ing a Java plug-in) that communicates to a remote server hosting an ob ject-oriented database management system (ObjectStore) (Figure 3-1). Obj ectEditor’s interface enables users to interact with the ontology in order to define content objects a nd represent how the objects in a domain are interrelated. ObjectEditor provides a comp lete ontology management system for editing,

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39 viewing, managing physical storage, managi ng multiple users, and providing reasoning and query processing facilities. Apart from the knowledge modeling that the irrigation ontology represents, ObjectEd itor allows the storage and management of ontology content. Content can include text, graphics, and mathematical equations. This content can be rendered in multiple formats depending on the method of presentation. For example, Web pages for personal computers and PDA’s, and files (e.g., PDF) for printed media. The separation of content from format typical for an ontology increases the flexibility at publication time, reducing time and work n eeded to reproduce the same content in different media. The process of developing an irrigation ontology us ing ObjectEditor is presented here. ObjectEditor defines its ow n formalization of definiti ons and constraints for the terms and relationships used to implemen t the irrigation ontol ogy. In the irrigation ontology the concepts are represented as classe s. Each class can have multiple properties; ObjectEditor supports simple st ring, rich text, integer, float, range, and images, as data types for the properties. Associations repr esent relationships be tween objects. All the subclasses inherit the propertie s and associations of their s uperclasses. Each term (class) has a short description or gloss (Figure 3-2); this facilitates the definition of the sense of each concept. The gloss can be expressed in multiple languages. The irrigation ontology is implemented in English and Spanish.

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40 Figure 3-2. Evapotranspiration term and its gloss (short definition). Each term (class) presented in the irri gation ontology has its definition; this ObjectEditor property allows the inclusion of a textual definition of the term in multiple languages. The terms in the irrigation ontology have definitions in English (Figure 3-3) and Spanish (Figure 3-4).

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41 Figure 3-3. Definition of concept in English Figure 3-4. Definition of concept in Spanish Ontology Implementation For the irrigation ontology, the main irrigati on related topics (ter ms) selected are: Irrigation Water Sources, Weat her, Plant, Soil, Drainage, Irrigation System Design, Irrigation System Management, and Irrigation Equipment and Structures. ObjectEditor

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42 allows the creation of “modul es” that permit the division of the ontology in sub areas; this permits a less cluttered, more focused pr esentation of the terms and relationships in the ontology. Modules are a visualization tool available in ObjectEd itor; they are not a part of the ontology modeling language. The mo dules do not contain th e same number of related terms nor do they have the same le vel of detail. During ontology implementation the concepts and the relationships among concepts were defined. The implementation process makes use of a top-down approach for knowledge modeling using the above defined modules as nine major irri gation and water management topics. As defined earlier, an ontology consists of the basic terms (concepts) and relations between those terms. A domain specific terminology (set of concepts) was first assembled in a vocabulary, then that vo cabulary was organized according to the objectives of the irrigation ontology (Chapter 2) and placed into nine well defined modules Modeling process consists of identifyi ng rules, definitions and relationships between terms and relations within a ontology. ObjectEditor has predefined rules in how to create terms and how to use relationshi ps (Beck, 2003a). Object Editor provides four types of relationships: genera lization, part-of, associati on, and sequence. Generalization is used to represent superc lass/subclass relationships; a “p ine tree” is a subclass of the class “tree.” Part-of is used for objects that are physically a part of larger composite objects; the class “tire” is a phys ical part of the class “vehicle.” Sequence is used to indicate that a concept follows another; in a sequence of classes, “socks” are worn before “shoes.” Association is used between two ot herwise related concepts were none of the three previous relationship types apply. Thes e different relationships are graphically

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43 represented in ObjectEditor by different types of vectors. Th e types of the relationships are identified in Figure 3-5, with in dashed-line rectangles. Figure 3-5. Relationships supported by ObjectEditor The association type of relationship has a ssociation properties that can be modified by the user. An association name and a gloss have to be defined to give sense to the association (Figure 3-6). The process of defining and associ ation relationship depends on the terms to be related. For exam ple, it is known that a “pipeline” has a “pressure rating” and a “pipe sizing” (Figure 3-5). This association is defined as has for this type of relationships. The association name is provi ded to give more sense to the relation between two terms, than a general relationship could give. Association Generalization Sequence

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44 Figure 3-6. Association relationship properties In Figure 3-7, the “part of” relationship is used for physical parts like the classes “manifold,” “lateral,” and “distribution equipmen t” that are parts of the class “pressurized irrigation system.” Another case represen ts the use of rela tionships of the “generalization” type; this re lationship used to relate sub-classes to a more general concept or class. For example: “semi-circul ar,” “ridge,” and “tri angular” are all subclasses of the more general term (class) “bund;” and “bund” itself is a sub-class of “contour farming,” and so on (Figure 3-8). Figure 3-7. Use of part-o f type of relationship Pressurized Irri g ation S y ste m Lateral Manifold Distribution E q ui p men t

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45 Figure 3-8. Use of generaliza tion type of relationship Most of the modeling issues are related to the selection of a wrong type of a relationship for the association among term s. In Figure 3-9 the “conveyance system design” with the terms surface, ground, and harv ested water as parts of it are presented. Figure 3-9. Use of generaliza tion type of relationship The main issue with this design is that surface, ground, and harvested water should be sub-classes of “water sour ces,” associated through the ge neralization relationship with “water sources,” This design is presented in Figure 3-10. Figure 3-10. Use of generalization type of relationship Micro-catchmen t Contour Farmin g Earth Basin Plantin g Pi t Stone Lines Bund Rid g e Trian g ula r Semi-circula r Surface Water Harvested Water G r ound Water Conve y ance S y st. Water Sources Surface Wate r Ground Wate r Harvested Wate r Water Sources

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46 In an example of a sequence, a prim ary channel is followed by a secondary channel, and secondary channel by a distribution channel. In th is case the particular order is important. In a real irrigation project the secondary channel can only be present after a primary channel, and a delivery channel should go after a secondary ch annel, and this is reflected in Figure. 3-11. Ne vertheless, a delivery channel can sometimes go directly after a primary channel if the secondary channel does not exis t in the particular system. Figure 3-11. Use of the sequence relationship Often concepts need clarification. Using the concept “p recipitation” as the source of water directly available to the plant is erroneous. However “precipitation” includes rain, snow, and hail, and of these three concep ts only rain is the precipitation that is directly available to the plant. Relocation of entire groups of terms may be necessary to make an ontology more functional. As an example, originally the “irrigation system design” concept included concepts related to system selection and equipment selection. System design includes concepts like terrain conditions, soil characte ristics, crop requirements, and others. All these factors will influence the choice of system and are used in the calculations involved in the irrigation system design. However, afte r initially incl uding all those terms in the design it was decided that equipment select ion was related to ir rigation system design, but it would be better lo cated within the irrigation equipment topic. Following is a general descrip tion of the relevant points of each of the main topics (Figure 2-4), known as modules in ObjectEditor. In this ont ology all the terms are defined Primar y Channel Secondar y Channel Deliver y Channel

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47 in the context of their relation to irrigation. The “weather” module includes terms that are indirectly related to irrigati on like “wind,” “radiation,” “tem perature” and “precipitation.” All of them contribute to “evaporation” to whic h they are related via associations. Evaporation is also part-of “evapotranspira tion” so the ontology will relate those two terms. Since the methods to calculate eva potranspiration are important to determine irrigation requirements the ontology also incl udes the “Pan,” “Penman-Monteith,” and “Blaney-Criddle” methods of estimating refere nce evapotranspiration. It is important to clarify that not all the terms related to weat her are included; the irrigation ontology is not intended to include all terms in any topic, just those relevant to th e limited domain of the ontology. However, ObjectEditor permits the shar ing of the ontology so it can be edited and expanded as needed for other applications. The topic “plant” includes terms related to the plant physiology and also to the water use by the plant. Basic concepts lik e “root,” “stem,” and “leaf” are all physical parts of the plant and are related as such. Terms related to “plant type,” and “growth season” are also included si nce those concepts are rela ted to “transpiration” and “evapotranspiration” that are used to estimate “water requi rement” terms that are also included in this module. The two examples presented above show how the topics “weather” and “plant” are related thru the te rm “evapotranspiration” demonstrating that all the ontology is interconnected. The “soil” module is formed by five main groups of terms, “soil available water,” “soil chemistry,” “structure,” “texture,” a nd “topography.” One example is presented in Figure 3-12, where “texture” is associated by the content of “clay,” “sand,” and “silt,” and “loam” is the combination of spec ific proportions of those materials.

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48 Figure 3-12. Sample of the soil module Other section of the same figure shows the term “soil moisture retention” and how it depends on “mulch,” “cover crop,” and “conser vation tillage,” practices that affect the soil capacity of retaining moisture. It does not appear on the figure but “soil moisture retention” is also related to “texture” and “structure.” The “water sources” module includes the main sources of water used for irrigation. Those sources include “surface water,” “gr ound water,” and “harvested water;” all of them receive some water from “precipitation” then they are associated to it. Another source in this module is “recla imed water” (Figure 3-13).

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49 Figure 3-13. Sample of the water sources module Also related to “water sour ces” but not subclasses of it ar e “water quality” and “water quantity.” Water quality is a smaller module that includes terms like “water hardness,” “electrical conductivity,” “pH,” and “total dissolved solids.” Under the “drainage” topic the terms include “drainage considerations,” “drainage clogging” and “drainage design ;” the design includes vari ous sub-classes like “tile drainage” and “ditches.” All of these sub-cl asses are related with “drainage” via the generalization relationship (Figure 3-14).

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50 Figure 3-14. Drainage module, sub-classes with generalization relationships The “system design” module is the most co mplex of all the modules included in the ontology. It has more than 50 terms and ar ound 60 relationships. A view of a small selection of this module is show in Figure 3-15. This module is related to most of the other modules like “weather,” “p lant,” “soil,” “irrigation eq uipment and structures,” and irrigation system management.” Some of the relationships and examples of complexities of this module are presented below.

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51 Figure 3-15. A small section of the system design module System design is related to the soil module through “soil characteri stics,” and to the “plant” module via “irriga tion requirement” and “crop requ irement.” Relations of the system design with some other modules (t opics) are presented below in Figure 3-16. Irrigation system layout rela tes to the “plant” module th rough the terms “planting system,” and “spacing.” Pumping system desi gn is associated with “irrigation equipment and structures” by way of “pumping equipmen t.” Similarly, “conveyance system design” and “pipeline.” Pumping, “conveyance,” and “distribution efficiencies” have also relationships with terms in the “irr igation system management” module.

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52 Figure 3-16. A section of the system design module The next module comprises “irrigation syst em management” which is related to “irrigation scheduling,” “irrig ation system maintenance,” and “chemigation.” Irrigation system maintenance includes topics like “ pump check,” “pressuri zed irrigation,” and “surface irrigation” (Figure 3-17). The ontology aims at containing some of the practices that a farmer should follow to maintain an irrigation system. For example “pressurized

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53 irrigation” includes “check lines for leaks,” “clean lines or pipes,” “clean filters,” “irrigation system calibration,” and “uniformity test.” Figure 3-17. Partial view of the ir rigation system management module The last module presented in Figure 318, is the “irriga tion equipment and structures” with the following sub-classes: “system control,” “filtration equipment,” “conveyance equipment,” “pumping equipmen t,” “distribution equipment,” “system controllers,” and “chemigation equipment.” Th e difference between “system control” and

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54 “system controllers” is that the first refers to equipment like “valve,” “flow meter,” “pressure regulator”; and the second refers to sensors and automatic controllers. Figure 3-18. A section of the irriga tion equipment and structures module Conclusions The implementation of the ontology is what allows the presentation and sharing of the knowledge modeled and contained in the irrigation ontology. In order to create a common irrigation ontology, a logical fram ework and classification of terms was

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55 developed with the help of experts in th e domain. The terms included in the ontology are clear and with definitions that are easy to understand. The resulting irrigation ontology includes formal definition of concepts and, wh en need the relationshi ps are also defined. A structured and reusable vocabulary wa s developed for the irrigation ontology. Building ontologies manually requires a lot of time, especially during the conceptualization and implementation pr ocesses. The modeling process can be complicated and may be difficult to reach consen sus in some of the terms, their definition and relationships. At this time the Irrigation Ontology consists of more than 270 terms, and around 300 relationships among those te rms (Appendix A). Future work should include the development or incorporation of an automated implementation process in order to work with larger datasets.

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56 CHAPTER 4 EDUCATIVE ILLUSTRATIONS Introduction In many rural development programs in Latin America and Africa, field-level training for small farmers is the most appropriate mean s of communicating new ideas and practices. In many rural areas poverty and illiteracy are common and people cannot use training materials containing only textual information and even graphical information may not be understood if it is too technical or abstract. Printed educat ional materials are important since people could use them to reinforce the concepts and remember what they have learned. However, staff respons ible for conducting the traini ng often have few resources to help them with the process. Appropriate resources usually have not been developed to fit the conditions of a particular audience. More work is needed in the development of applications that could facilitate the production of personalized trai ning materials as a way to transmit information. This project is a response to this problem as it aims at development of an application to produce appropriate training manua ls for non-literate users. Th e focus of the project is on Africa and Latin America, which contain a num ber of countries with a high rate of illiteracy. This project uses an ontology based system that is designed for storing content, which includes concepts, media such as text and images (i.e., pictures, drawings, and diagrams) and can also include animati ons, sounds, and video. There are many advantages in utilizing an ontology system fo r storing educational resources. Immediate

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57 advantages are that educational materials can be more easily pr oduced compared with conventional tools (e.g., PowerPoint, PDF, a nd Flash). Information can be shared and reused in ways that are not possible using conventional tool s due to proprietary restrictions. For example, conventional software is not designed to re trieve content from a database. This means that if something change s in the content, then, the presentation has to be changed manually. Flash permits li nking to artwork stored in a database. However, Flash is a proprietary language th at does not allow easy manipulation of the file that composes the graphic; instead a new graphic has to be developed to include each desired variation. There is also a need for low cost devel opment tools and the possibility of cooperative work that can be done by worki ng with an online tool. These are just some of the reasons why a new system to produce illustration based edu cational materials is needed. The use of an ontology will facilita te the updating of information in various formats and different localized presentations (e.g., print, Web-based) can be created automatically from the same content. This work presents an approach for ma naging information and producing localized educational materials by using an ontology system to manage content. It is applied to irrigation and water management information topics and produces illustration (drawing) based training materials fo r non-literate farmers. Graphical Communication Systems of communication based on graphi cs have been successfully employed (e.g., Chinese, Egyptian, Mayan) (Yazdani a nd Barker, 2000). Nevertheless, to convey information thru graphics, they have to be simple enough to be easily understood by people with low educational levels, and at the same time those graphics have to transmit complex information. Line drawings are a good tool to produce simple graphics.

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58 However, to convey complex information just re levant information has to be included in the drawing, avoiding the inclus ion of unnecessary details. The graphics have to also be relevant for the conditions of the people that are going to use them. Development of educational materials that are culturally sensitive is called localization. Localization is the process of targeting a product to a local clientele by “translating” the produc t and adding local, specific feat ures where applicable (Luong et al., 1995). In the case of illustration based e ducational materials, these have to be developed in such a way that the clientele us ing them can associate themselves with the actions being presented in the manual’s drawi ngs (e.g., race, gender, tools, environment, etc). In this project, culture is considered as the collectively held set of attributes (e.g., values, believes and basic assumptions) and behaviors, which is dynamic and changing over time. Culture affects many elements of communication such as, language, colors, graphics, icons, date, time, numbers, currencie s, units, and personal titles (Dahl, 2003). In other words, culture affects the way people perceive things, and knowledge. When an educational manual has to be produced for multiple audiences, for example, in Africa, Latin America, and As ia, the conventional approach results in duplication of efforts and pos es the challenge of producing and updating the information in different formats that are concordant to the individual realities of each culture. The inclusion of localization is focu sed at improving communication among facilitators and learners. Communication is th e transfer of a message from one person to another, so that it is under stood, and hopefully, so that it invo kes a response (Figure 4-1). There is always a sender and a receiver in any communication. At least there is an

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59 intended receiver. Sender and receiver have di fferent personal and cultural realities. The use of localization should increase the success of a message being transmitted among parties. Figure 4-1. Communication model adapted from Funch (1995) Senders and receivers each ha ve their own reality formed by their experiences, their perceptions, their ideas, etc. (Funch, 1995). Due to this background they will perceive, experience, and interpret things differently. Each individual will always perceive the same event a little different. The message in western societies is often verbal, something that is being expressed in language, spoken or written. But there is also a non-verbal portion, covering everything else most notably body language th at is represented thru images. Nevertheless, in any communicati on process it cannot be granted that the receiver will interpret the message the same way as the sender intended it. In the usual communication process among people, many fa ctors have an effect on the message, influencing what the receiver perc eives from the sender (Figure 4-2). Sender Message Receiver Encodes Decodes and responds with nextmessa ge Sender Message Receiver Educationallevel Religion Culture Agedifference Gender

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60 Figure 4-2. Interferences on the communi cation model modified from Funch 1995. When cultural differences are included in th e communication process, this becomes more complex. Language complications and cultural differences affect the transmission and interpretation of a message. The inclusi on of localization in the development of educational materials should help reduci ng miscommunication issues. To have an efficient intercultural communication process, cultural sensitive materials could be used to avoid prejudices and to facilitate the adop tion of local cultural characteristics into the communication process. Educational Materials Once the topic of the training relevant to the community has been identified, localized educational materials can be devel oped. To accomplish this, characteristics of the population have to be considered. It is possible to use some of the information provided by the local extension agents or traine rs, but more often data has to be collected through questionnaires and interviews (see futu re work as described in the conclusion section below). Then this information is in corporated in the desi gn of the educational manuals. Experiments with Vectorizing Images, Op tions for Creating Vector Graphics Various techniques were tested to convert digital pictures to line (vector) drawings in order to represent them using vector graphi cs. A vectorization tool that replaces pixels patterns with vectors from Flash (Fla sh, 2004) provided some good visual results (Figure 4-3), still, the resulting drawings were too complex (too many colors, and vectors) to be easily changed as required for this project. Furtherm ore, the vectorization tool only performed well on simple, well-defined patterns.

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61 Figure 4-3. Vectorization using Flas h and original digital picture Another technique was to use pattern r ecognition software, to extract the main features from a digital picture. The softwa re used was GIMP (GIMP, 2004), and results are presented in Figure 4-4. The complication wa s that to have a fairly clear pattern, the background of the picture needed to be a plain color, and without shadows. Figure 4-4. Pattern recogn ition using GIMP and orig inal digital picture The use of Flash and GIMP to transfor m digital pictures to vectors was very time consuming, due to the time needed to ed it the pictures manuall y, plus the processing time required by the software. The proce ss also required high quality photographs,

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62 without any objects in the bac kground. Even the cloth used as a background in Figure 4-4 caused problems with the vectorization process. Scalable Vector Graphics In order to handle localizati on of drawings, scalable vector graphics (SVG) format has to be utilized. SVG format was de veloped by the World Wide Web Consortium (SVG Working Group, 2004; W3C, 2004a). It handles vector gr aphic display and animation based on the extensible markup language (XML Working Group, 2004). It is a text-based language that is re solution independent. Localizati on applied to scalable vector graphics means that imagery and text can be easily converted to different languages and cultural settings. Changing just one XML tag can modify graphics to adhere to local needs. This means that if the color of the sk in has to be changed, ju st the portion of code that affects the skin color has to be modifi ed. This can be done automatically by linking the SVG graphics to the object database thru the data-handling feature in SVG that can be used to create dynamic graphics. In the SVG sample code below, notice that only one part of the code (in bold and marked with an arrow) needs to be changed in order to alter the color of the skin. This method can be used to modify some feat ures of the graphics. When other characters need to be added (i.e cloths, tools) those can be switch on and off using the SWITCH tag available in the SVG code. SVG Sample code:

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63 (represents the skin color: arms and face) ]>
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64 Figure 4-5. Sample of localization with Scalable Vector Graphics (SVG) In Figure 4-6 a new item has been added to the original drawing without the necessity of modifying the existing one. The desired feature is just switched on or off depending on what is needed for the manual. Figure 4-6. Sample of localization with Scalable Vector Graphics (SVG) An example of a specific task, such as clean ing of an inline filt er in the irrigation system, was selected to demonstrate the pro cess of transferring the information into a

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65 graphical form that can be used in a manua l. A module showing the flow of the steps needed to complete the activity for whic h the training is conducted was produced. Photographs were used to repres ent each step (Figure 4-7). Figure 4-7. Module “Cleaning Irriga tion Filters” from Object Editor These photographs were treated as the conten t to be utilized by the ObjectEditor. All the components (represented by photographs ) of the process being demonstrated are

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66 linked depending on the relationship among them These linkages determine the order in which the images have to appear in the fi nal printed manual in order to transmit the activity in a logical way. At a later stage in th is project, drawings will be available as well as photographs to describe the different training activities. Figure 4-8. SVG presentation “Cleani ng Irrigation Filters” in English The next step was to extract the inform ation contained in the ontology to create presentation based on SVG technology that can be seen in any internet browser capable of opening SVG files. What the SVG render does is to navigate and select the different components of the educational material li ke introduction, materials, activity, and

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67 comments; then it arranges them in a predef ined format created also with SVGs. An example of parts of the presentation in Eng lish and Spanish is presented in Figures 4-8 and 4-9 respectively. Figure 4-9. SVG presentation “Cleani ng Irrigation Filters” in Spanish GraphicsEditor GraphicsEditor is a tool in corporated into ObjectEditor that permits the creation of objects that have vector graphics as content. This tool faci litates the combination of the graphic’s properties with the properties of the objects they help repr esent. GraphicsEditor was used to create the graphics for the objects to be used in the educational materials.

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68 This application allows the creation of vector graphics based on the scalable vector graphics (SVG) standard by the World Wide Web Consortium. GraphicsEditor allows the creation of graphics using lines and polygons. When a graphic is completed all its parts (e.g., lines and polygons) can be selected to form a group. Group is a function of GraphicsEditor that gives the possi bility of adding properties to the graphic. An example will be us ed to explain this process. The first step was the creation in the irriga tion ontology of the in stance called Maize (Figure 4-10) this instance is a subclass of the Monocot cl ass under the term pl ant classification. Figure 4-10. Maize instance within the plant topic in the irrigation ontology The second step is to enter a short defin ition (gloss) for the instance; this gloss helps to identify the instance. A context is also given to the instance, in the case of maize

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69 the context is related to the plant module (Figur e 4-11). It is important to observe that the name of the instance and the gloss are given in English and Spanish. Figure 4-11. Context and gloss for the maize instance The third step was to create the vect or graphic using the GraphicsEditor incorporated in ObjectEditor. The maize plant graphic was constructed from multiple polygons. Next, the polygons are selected a nd associated in a group. Using the group function properties, names were given to each group (i.e., Maize plant, and corn) (Figure 4-12). These properties are the base to have localizable graphics.

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70 Figure 4-12. Groups that constitute the maize graphic One of the properties incorpor ated into the GraphicsEditor creates a path from the of the vector graphic to the object it represent in the irri gation ontology. For example, the skin color in the graphic of a person (Figure 4-13), follows a path to the skin color term associated to the “person” term in the irrigation ontology (Figure 4-14).

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71 Figure 4-13. Example of a person graphic This means that the skin color property of the vector graphic is associated with the “skin color” term in the irrigation ontology. Figure 4-14. Skin color term a ssociated to “person” term

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72 For example, to create a graphic for an African person, the quer y ‘African person’ could be used. The result of that query woul d be a person graphic with black skin. The information to change the color of the skin in the graphic comes from the irrigation ontology (Figure 4-15). There is a module in the ontology that specifies the color of the skin for a person from a given geographical region. Figure 4-15. Different skin colors depending on the origin of the person There can be an African person with white skin; however, to fac ilitate the design of this system the simplification pr esented in Figure 4-15 was used. Composing Educational Materials To facilitate the design of the educationa l materials a new template module called Irrigation Training Materials was created in ObjectEditor. In this template the topic of the

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73 training material was defined, as well as the introduction, materials, activity, and comments (Figure 4-16). In the activity sec tion the steps of the educational activity should be laid out. Each step has a gra phical representation as well as a textual description of the action depicted. The steps were organized sequentially using relationships of the sequence type among themse lves, meaning that step two has to occur after step one o ccurs, and so on. Figure 4-16. Irrigation Training Materials module template Sequence Graphic Description

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74 Two options where considered for where to include a description for each graphic. The first option was to add the description in the same instance as the graph. The second one was to add a separate category (class) c ontaining the description. The last option was selected since that one allows the use of the same graphic for different circumstances (educational materials with diffe rent topics). Instead of havi ng to change the graphic and its description, only a new and i ndependent description have to be created and associated to the existing graphic (Figure 4-16). Presentation Generation The ontology management system used to create the irrigation ontology also stores content (multimedia content in the form of text, images, sound, video, and other content) associated with each ontology concept. Thus it provides content management with the ontology acting to integrate the content. The content also enhances the concept definitions (although not compatible with and essentially ignored by the reasoner, such content provides us eful annotations). Presentations can be automatically gene rated from this content by specifying a mapping from content objects to the desire d presentation. This mapping, which can be implemented using XSL style sheet technol ogy, specifies how content objects are to appear (for example, fonts and colors) a nd also manages policies on how they can be arranged. Mappings can generate presentations in a variety of different Web page styles, slide show formats (such as PowerPoint) and printed layouts (such as PDF or EPS formats). A sample for a printed publication on irrigation appears in Figure 4-17, and a sample of a slide-style presentation appears in Figure 4-18. The elements in the printed publication were created using XML FO (Formatting Objects) and a commercial

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75 rendering package, RenderX (RenderX, 2004). The process involved 1) generate an XML document from the content for the publica tion stored in the on tology manager, 2) convert the XML source document to an XM L FO based on style specified in an XML style sheet (XSLT), and 3) rendering the XML FO to a printable publication (PDF format) using RenderX. The approach to the graphics example in Figure 4-8 is to store elements of the graphic using vector graphics. The vect ors are stored in the ontology management system as database objects. Larger graphi cs are composed from smaller elements, much like image libraries in conventi onal graphics packages. Howeve r, the ontology is used to enhance the description of th e graphic elements. It not only improves search and retrieval of specific elements, but enables lo calization at the level of concepts. For example, graphic elements appearing in Figure 4-12 can be changed based on crops grown in a particular location, and people can appear differently (race, gender, and clothing can change) based on local conditions. In other software environments (such as Scalable Vector Graphics) these features mu st be changed at the level of individual graphic primitives (lines, polygons) but in the ontology management system these primitive elements are given meaning as obj ects (a plant, a crop, a person with a particular skin color).

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76 Figure 4-17. Example of print file genera ted from the ontology management system. Figure 4-18. Example of educational drawings on irrigation techniques Conclusion Any kind of educational material is ba sed on the transmission of information, presenting knowledge in different media like books, audiovisuals, etc. Nevertheless, presenting information to a non-literate audience is more difficult. To “transform” very complex knowledge to a basic representati on requires a different approach in the

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77 development of educational materials. To de velop educational materi als easily a better understanding of the learning process for non-literate people is needed. To develop culturally specifi c educational materials, in formation about the culture of the clientele has to be collected. The lo calized data has to be included during the development of the cultural sensitive educati onal materials. For this project, information about who (gender, age) performs each irrigatio n or water management activity is critical for appropriate localization. Also what tool s are used, and other complementary factors like clothing, and time when the action is performed, will help to communicate the topic of the educational material to the local conditions. Scalable vector graphics (SVG) have th e qualities needed to produce localized graphics. They can be modified without the need of producing a new drawing, while maintaining all the qualities of a vect or graphic. SVG format is supported by GraphicsEditor in order to have a fully func tional localization tool for graphics working in conjunction with the object database. Future work should include the automatic generation of educational materials from the content in the irrigation ontology. The application should be able to generate presentations on-the-fly in multiple formats for any topic in the irrigation domain based on user queries.

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78 CHAPTER 5 EVALUATION OF EDUCATIONAL DRAWINGS IN EL SALVADOR, CENTRAL AMERICA Introduction Worldwide, the number of illi terate adults in 2000 wa s 862 million. According to current trends that number should drop to 824 million by the year 2010 (UNESCO, 2002). The United Nations definition of a literate adult is a person aged 15 or over who can read and write (UNESCO, 2000). In 1995, th e literacy rates for El Salvador were 73% for males and 70% for females (UNESCO, 1999). The data highlights the necessity of alternative educational materials, whic h reduce the effects of illiteracy in the transmission of information. Most of the e ducational materials in developing countries, with few exceptions, are overwhelmingly printoriented. In addition, most of the printed materials available are written at a level that makes them inaccessible to individuals with a low education (Hynak-Hankison, 1989; Stemmerman, 1991). A computer tool to facilitate the prod uction of illustratio n-based educational materials could make this task much easier and more effective. To accomplish this task, a group at the University of Florida’s De partment of Agricultural and Biological Engineering researched the possibility of de veloping a tool and a series of educational materials in the areas of water manageme nt and irrigation. The illustration-based materials under development are audience-o riented (from here on referred to as localized ). For example, if the illustrations are showing a Hispanic person for Latin America, this person can easily be changed to a Black person for African regions to make

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79 it more relevant to the local audience. Adju stments can be achieved just by selecting certain attributes from the database, without the necessity of crea ting a completely new drawing. However, the process of developing a product that meets audience needs, helps accomplish a teaching goal, or solves a problem, is sometimes challenging and may present hidden complexities (Bly, 1989). For the illustration-based materials to be useful, the initial ideas for the drawings were tested with a target populat ion of low-resources small farmers in El Salvador, Central America. El Salvador is located in Central Americ a; its borders are with Guatemala to the northwest, Honduras to the northeast, and the Pacific Ocean to the south (Figure 5-1). From around 1980, El Salvador was involved in a 12-year ci vil war, which cost about 75,000 lives. The war was brought to an end in 1992 when the government and leftist rebels signed a treaty that provided for military and political reforms. Figure 5-1. Map of El Salvador and locat ion of communities visited (CIA, 2004). El Salvador was selected for this study because of the social and agricultural conditions of the country. The a dult literacy estimated rates are considered high – 70% in 2000 (UNESCO, 2002), and 80% in 2003 (CIA, 2004), with a difference of around 10%

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80 between men and women. Illiteracy is more not iceable among elder adults in rural areas, and especially, in the eastern regions of th e country (Departments of San Miguel and La Union). Agricultural production in El Salvador is reduced and rudimentary. Most of the fresh fruits and vegetables that El Salvador consumes are imported from Guatemala and Honduras. The production is limited to staple foods like maize and beans. Rohr-Ruendaal (1997) highlights the n ecessity of evaluating educational materials; however, she also notices that th is is more important when the educational drawings have been developed without direct participation of the fi nal users. Since this work was a first step in the development of a tool to produce on-demand educational materials, the materials were developed away from the clientele; hence, it was important to test them. The aim of this first field test of the manuals was to identify the level of understanding of the drawings by small farmers in El Salvador; gather their views on the value of the material, and to incorporate any additional material, alterations, or deletions, which would help the farmers to better unders tand the educational materials. The data collected from this evaluation process would be useful to understand how these specific farmers interpret the educational drawings, a nd what has to be included to make the manuals practical in a training process. It wa s important to demonstr ate the necessity of testing illustration-based educational material s with a sample of the target audience before they are distributed to a larger popula tion. As the need to develop publications for people with low educational levels continues to grow, so does the importance of special considerations in the content and desi gn process (Ingram, et al., 2004). Audience background (e.g., culture, race) and experience should be considered in all phases of

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81 development of the materials. It is also important to understand the differences among people from different cultures, and to l earn how people understand the message from educational materials based on drawings. As stated by Rohr-Ruendaal (1997), some people in Africa have never seen a picture or a drawing, and as a resu lt, they are not used to interpreting illustrations as most other people do in everyday life (Clarkson and Johnson, 2001; and AMDM, 1997). Materials and Methods The data collection in El Salvador was conducted during July 2004, with the support of PROMIPAC-El Zamorano (Integrated Pest Management Program for Central America) in El Salvador. To collect the da ta, personal inte rviews were conducted with 63 small farmers in five different communiti es: El Peon, Huertas, Tunas, Singuil, and Pasacarrera. These “caserios” (groups of less th an 50 families) were distributed in three departments, Santa Ana in the West, and San Miguel and La Union in the East. The farmers that participated in the eval uation were part of the farmers’ field schools (FFS) in integrated pest management (IPM) supported by PROMIPAC. These schools meet once a week or once every two weeks for 3 to 5 hours. The topics where mainly focused upon crop production and inte grated pest management (IPM). This is important to note since it could be reflected in some of the answers given in the evaluation process. For this evaluation it was necessary to determ ine the literacy of small farmers in the visited communities in El Salvador. The samp le population selected for this evaluation consisted of people with a low level of education or illiterates. Sixty-three small farmers from five different communities participat ed in the evaluation. All the farmers are

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82 participants of the FFS in na tural resources conservation, basic grains production, and IPM. The questionnaire used in the evaluation cont ained a set of questions related to any previous training received by the farmer and specifically, to water management practices, and a set of questions on th e illustration-based educational materials. In the second section the farmers had to evaluate the clarity of each picture, the message carried by the pictures, and the arrangement of the pictur es explaining each activity. For example, the farmers were given five sets of materials (representing: contour planting, earth basins, rain and drainage, retention ditches, and st one lines). There was no oral explanation of the actions represented in each set of drawi ngs to avoid influencing the answers. The questionnaire was developed at the University of Florida by pr ofessors with experience in extension work, and water management practi ces. The questions were evaluated in the field with five farmers and changes were made to accommodate the questionnaire to make it more understandable. The drawings to be evaluated were devel oped using scalable v ector graphics (SVG) (W3C, 2001b). Scalable vector graphics is a platform for development of twodimensional graphics. Scalable Vector Graphics are used in many business areas including Web graphics, animation, user in terfaces, graphics interchange, print and hardcopy output, mobile applications a nd high-quality design (W3C, 2004). This graphics language is an open source (royalty free) standard; it is based in the extensible markup language (XML), which was also developed by the World Wide Web Consortium (W3C, 2004b). This allows the inte roperability of SVGs, as well as the use

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83 of this standard in conjunction with ontol ogies, and object-oriented databases. Further explanation on this topic is av ailable via Bada l et. al., (2004). The interviews were conducted in groups of fi ve or less people, since this facilitates greater participation and discussion among peopl e. The data was collected individually for each farmer. The structured questionnaire consisted of 15 questions, some general questions about the local conditions releva nt to agriculture, and water management practices. However, most of the questions were related to the educational drawings presented to the farmers for evaluation. The educational drawings to be evaluate d were grouped by topics. For example, the drawings related to contour planting were grouped togeth er. The idea was to have a product representing all the steps of a process, similar to an educational manual. Five sets of drawings were presented to the farmers: Contour planting or farming Earth basins Rain and drainage Retention ditches Stone lines The drawings were presented in black a nd white and color versions. This was done in order to compare if there is any significant difference in the interpretation of color versus black and white materials. Also, most of the printed educational material used in extension work is in black and white because it is less expensive than materials printed in color; and the availability of colo r printing technology is also limited. Results After analyzing the literacy data collected from the sample population (63), the values for women are above 80%, and for me n are almost 90% (Table 5-1). Literate

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84 individuals had from 2 to 6 years of basic schooling, which is considered a low literacy level. Illiterates did not attend school at all. Illiterate peop le in this study were 30 years or older and 50% of them where 40 years or older (Table 5-2). This shows a recent improvement in basic educa tion in the rural areas. Table 5-1. Literacy rates of small farmers interviewed in El Salvador. Individuals % Women % Men % Illiterate 8 13 3 17 5 11 Literate 55 87 15 83 40 89 Total 63 100 18 100 45 100 Source: Cornejo, 2004. However, young people still withdraw early from school to help with the economic activities of the family. More than 60% of young adults interviewed in this study had only few years of basic education. The assessm ent of the literacy level of the sample population could be an important factor in determining how small farmers are able to understand the educational drawings. Table 5-2. Age groups of small farmers interviewed in El Salvador. Age Groups Individuals Percentage 15-19 1 2 20-29 6 10 30-39 32 51 40-49 11 17 50+ 13 21 Total 63 100 Source: Cornejo, 2004. The objective of this work was to eval uate understanding by the farmers of the educational drawings developed for this proj ect. If effective educational materials are to be developed, then it is important that the information contained in those materials is conveyed to the audience (small farmers in th is case). An importan t thing to consider

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85 during the evaluation is if the farmers had been familiar with any type of educational materials. From Table 5-3, it can be noticed that 94% of the farmers had used text based educational materials. And all of the farm ers interviewed have used some kind of educational material, including photographs and video, during different training opportunities. As for all the e ducational materials tested, 63 small farmers evaluated the drawings. Table 5-3. Type of educational materials used by farmers in El Salvador. Educational Material Have used this type of materials: Individuals Percentage of total sample Posters 26 41 Text manuals 59 94 Photographs 7 11 Videos 7 11 Source: Cornejo, 2004. Contour Planting or Farming Contour farming (Figure 5-2) consisted of a set of five drawings aimed to represent the use of contour planting. The objective was to show “contour” lines to the farmers evaluating the drawings. Since these are twodimensional drawings, to show curves and differences in distance can be difficu lt (to enable prope r perspective).

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86 Figure 5-2. Section of drawings representing contour planting In these drawings, farmers identified the crop as being sorghu m (Figure 5-2). The farmers at first were more interested in th e crop variety, and trying to identify the other objects resembling plants. Some of the farmers sa id that “A” was a weed or an aloe plant, and that “B” were eggs laid by some insects, as seen in Figure 5-2. These interpretations confirm the recommendations given by Rohr-R uendaal (1997), that the drawings should be kept simple, and that all unnecessary deta ils should be avoided. This eliminates wrong interpretations, and it helps people focus on th e main aspects of the drawings, the objects that transmit the message that needs to be conveyed. Slightly more than 56% of the farmers identify the drawings as representing contour planting. Nevertheless, all of the pe ople interviewed recognized them as some type of drainage or land cons ervation practice. However, it is worth noting that all the farmers had had some training in land conservation practices given by different organizations, according to what the farm ers said during the interview process. Another drawing in this set shows a fiel d without contour pl anting or any other practice to reduce soil erosion or promote water conservation. In the black and white version, farmers identified the runoff “C” sometimes as water and in some cases as soil or A B C

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87 mud. In the color version, the wa ter “C” was easily id entified as such. This demonstrates the important that colors may have in the interpretation of illustrations. Earth Basins In the second set of drawings, representi ng earth basin construction, again some of the farmers were more concerned about the type of plant presented to them in the drawing. The origin of this problem could be that no explanation was given about the drawings, since that could influence the answ ers during evaluation, a nd the plants are of main interest to the farmer. From the size of the plants (A) the farmer s determined that some vegetables were grown (Figure 5-3) in the field. Then, the answers to the evaluation were related to vegetable production, like land preparat ion and planting of seedlings. Figure 5-3. Drawings repr esenting earth basins. Most of the farmers did not have any pr oblem identifying the plantain or banana tree (B). They also identify this practice as the use of intercropping. The object representing the water retained in the basi ns was easily recognized in the materials presented in colors. When presented in black and white, the farmers interpreted it as soil, mulch, and humid soil. It can be concluded that the color drawing was quite important A B

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88 for proper interpretation as it was in Figur e 5-2. None of the farmers interviewed recognized the use of earth basins for water re tention. However, it is important to clarify that these educational drawings are not inte nded to be used by themselves, but as a reinforcement of a comprehensive educationa l program, with the re spective expl anations. These materials should be intended to be used as an aid to the farmers to remember the information given during more extensive trainings. Rain and Drainage Two of the most difficult things to repres ent in drawings are abstract ideas and objects with changing scale or relative size. As a result, the drawings in Figure 5-4 were included in the evaluation. With the exception of 5 farmers, the majority recognized the entire sequence of drawings, from cloud forma tion to the rain, to the damage to the crop. Only one farmer interpreted the damaged crop (A) as being caterpillars, and this was in the black and white copies. The respondents even noticed the water running through the furrows (B). Figure 5-4. Drawing represen ting rain and drainage. A B

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89 Retention Ditches This set of drawings represents the use of ditches to collect rain water (runoff) and retain irrigation water in the dry season (Figure 5-5), as well as to serve as drainage during the rainy season. Some farmers confused the ditch or channel (A) with a road. This shows the problem in representi ng three-dimensional objects in a twodimensional drawing. This could also be a scale problem, and maybe showing a larger field would help in the interpretation of th ese drawings. A possible solution would be the use of shadows and colors to accentuate the f eatures of objects such as this. With the black and white examples of this manual, farmers had problems recognizing the water (B) flowing in the ditch. Figure 5-5. Drawing represen ting a drainage ditch Stone Terraces (Lines) There were two problems with how the farm ers interpreted this set of drawings (Figure 5-6). First, they thought that the line or barrier of trees (A) was a caterpillar. Second, they identified the stones (B) that try to represent a barrier (terrace) as insects’ eggs. Those answers could be related to the tr aining that the participants were recently receiving in integrated pest management. Ne vertheless, the farmers that have received A B

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90 training in soil conservation di d not have too much problem recognizing the objectives of these drawings. That means that with some explanati on, these drawings easily could serve as materials for recollection of more comprehensiv e training as it was in tended. This point is important, because it helps to highlight that no educational tool is intended to be used alone. These illustration-based materials are designed to complement the training given by an extension agent, within a well structured educational program. They are intended as a tool that will help the farmers remember the concepts learned during the training sessions. Figure 5-6. Drawing showing stone terraces or lines Connectors All the manuals use connectors to join one drawing to the next. The idea is that those connectors can show the flow of the id ea that the drawings are trying to convey to the learner. The figures used to represent connectors in the manuals are presented in Figure 5-7. A dice, an arrow, pointing hand, sticks, and numbers were tested. 2 Figure 5-7. Connectors A B

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91 Different connection symbols were eval uated for use in the tested manuals. The majority of farmers that could not read sele cted the hands, the dice and lines or sticks. However, there was some confusion with th e pointing hands, and arrows. Some farmers thought that arrows and/or hands were being used to point at some thing specific in the drawings. The people that could read selected the numbers, and lines or sticks as a way to denote order (Table 5-4). It is also important to notice that farmers in El Salvador have had contact with multiple types of educationa l materials and did not fit the literature model of farmers from some regi ons of Africa (Rohr-Ruendaal, 1997). Table 5-4. Drawings’ connectors sel ected by farmers in El Salvador. Signs Individuals % Hands 17 27 Arrows 12 19 Dice 3 5 Lines 17 27 Numbers 14 22 Total 63 100 Source: Cornejo, 2004. A main problem in this and other manuals is how to correctly represent actions being performed by people. Since movement is hard to represent in static graphics, the posture of the person in the graphic could he lp to represent movement. More work is needed in this area, to find a better way to represent movement and actions in the drawings. Conclusions Most of the farmers were able to recogni ze the objects and actions presented in the illustrations. However, some details were diffi cult to recognize, for example, rows of stones in a barrier were frequently confused with insects’ eggs. Also, a line of trees was misinterpreted as a caterpillar. Recent traini ng in Integrated Pest Management conducted

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92 with some groups could have contributed to th is interpretation. Interestingly, illiterate farmers often asked for some text to be incl uded with the drawings. The reason was that they could ask their children or neighbors to read the text, and that would help them remember the information. It was also observed that the majority of the farmers tend to focus on small details (i.e., shoes of the person, variety of the plants) that are not relevant to the educational message of the materials. It was concluded that caution is needed when including such details sin ce people who produce the materials might not consider important. All of the farmers that participated in the evaluation process were asked to give their comments in the overall quality and usef ulness of the drawings. It is interesting to note that the great majority of farmers who had a problem with understanding a large part or the whole manual were wo men. Women constituted around 27% of the participants. We can speculate that this c ould be associated with the lim ited participation of women in training related to agricultural practices and technologies. Most of the training targeting women in El Salvador is focused towards co mmercialization of agri cultural produce, and other household activities. A common recomm endation given by the farmers was to use colors instead of black and white drawings. As stated earlier bot h color and black and white drawings were evaluated. Farmers stated that colors would faci litate the recognition of some features as crops, wa ter versus soil, and insects. However, when the educational materials are to be developed in field offices by training agents, the availability of color printers or copiers is limite d, and in most cases, when av ailable, the costs are still prohibitive for most training programs in deve loping countries. The manuals were printed in 22.5 cm x 28 cm (8.5 in x 11 in) paper with four frames per page. Some farmers with

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93 vision problems recommended the use of larger drawings. Again, this shows the necessity of considering all the factor s that could improve the pe rception of the educational message. The most relevant comment is that all the farmers prefer to have text explaining the actions shown in the drawings. Even the peopl e with lower levels of formal education and illiterates supported this – as these farmers stated that their children or a neighbor could read to them. Also the farmers confir med the notion that the manuals need to be accompanied by an explanation of the processes being represented. Manuals presented without any explanation often would lead to misinterpreta tion and confusion. This can carry serious consequences depending on the topic of the educational materials (i.e., agrichemicals and toxic material applications). With the number of illiterates still high worl dwide, and most of them being farmers in developing countries (UNESCO, 2002), ther e is a necessity of alternatives to textbased educational materials. The use of illustra tion-based materials is not new. However, they are usually not relevant to the specific characteristics of the target audience, or have not been tested to assess how well they are understood. This project has confirmed the necessity of evaluating educational materials to be used in the training of farmers with low levels of formal education. Good illustrationbased materials have to be understood by the audience to be considered useful, and the only way of assuring this is through the us e of field evaluation. Too often extension program specialists ignore th is fact and produce educationa l materials that are not well suited for the specific conditions of the farmers.

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94 Some general points to follow when de veloping illustrations for educational materials are defined. Educational materials are supplemental to the learning proces s facilitated by an extension agent or other educator. When possible the audience should be in cluded in the de velopment of the educational materials and the illustrations to be included. This can increase the inclusion of local features the impr ove understanding of the information. Some people are not used to recogniz ing actions conveyed using drawings, especially in remote rural areas. Is important to understand how the audi ence interprets the message from the illustrations. The use of conventional symbols like arrows, crosses, has to be analyzed in a local context. Distinction of cultural differences (races, clothes, tools) are important hence the necessity of localization. Even if the audience has a lo w level of education or is illiterate, the inclusion of one sentence defining each action is recommended. The number of illustrations needed to express a simple task can be large. There has to be a compromise between the number of illustrations and the clarity of the message. The shape, size, color of the materials can be important to attract the attention of the audience. The evaluation of educational materials pe rformed in El Salvador, could improve the quality of the materials developed us ing a computer tool for production of personalized illustration-based extension manuals. It could also allow the development of evaluation guidelines for educational mate rials produced using this new tool. This could enable the inclusion of variables like language, culture gender, and other factors that affect how the farmers interpret and accept the information provided using educational materials.

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95 The types of educational materials pres ented here are not only important for farmers in developing countries but also in developed nations. M iller (2001) states that in the order of 40 million Americans age 16 years and older have low literacy skills (U.S. Congress, Office of Technology Assessment, 1993). Moreover, the importance of the evaluation of educational mate rials and the development of new tools to produce them is not limited to agriculture; they could be applied to the educ ation of children, and adults with disabilities.

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96 CHAPTER 6 SUMMARY AND CONCLUSIONS Summary and Conclusions The irrigation ontology acts as a database for organizing and st oring content. One of the uses for this ontology is organizing ir rigation information consisting of documents, images, and other media. The irrigation ontology was used for physical storage, manipulation of content, terms and rela tionships. The irriga tion ontology provides a conceptual map to which media can be attach ed and which people can navigate to find information. Other advantages of incor porating an ontology include better ways of representing concepts, ability to support natura l language-based references to objects, graphic browsing based on data visualization of ontologies, and ontol ogy assisted search. The irrigation ontology should allow for easier collaboration among specialists by using a common set of terms in order to exchan ge information or to produce collaborative publications, with the capability of reusing ex isting information. It should also allow the management of a larger amount of data wh ile providing improved searching capabilities compared to the actual browsers. In this di ssertation a methodology to create an irrigation ontology and to develop educational materials based on vector graphics coupled to this ontology were presented. Th e ontology modeling methodol ogy was presented in two chapters; specification, conceptu alization, and evaluation were explained in Chapter 2; while formalization and implementation ar e presented in Chapter 3. The irrigation domain ontology was used as an example to demonstrate all the modeling process. Chapter 4 illustrated the incor poration of vector graphics into the irrigation ontology to

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97 allow the development of illustration-based educational materials. In Chapter 5, the field evaluation of the educational materials conducte d in El Salvador (Central America) was presented. Finally, a summary of the disserta tion and conclusions is presented here in Chapter 6. The main objective of this dissertation was to develop an ontology for the irrigation and water management domain. Seconda ry objectives of this study were: To demonstrate the necessity of a tool to create didactic manuals especially for people with low literacy/edu cation levels, or little know ledge of the topics. To adapt a process to develop ontologies in the agricultural domain. To present the information on more than one language (i.e., English, Spanish) and other characteristics that will allow locali zation of the materials to be developed. To find a technology capable of creating didactic manuals “on-the-fly” for extension education, and to prin t those materials “on-demand.” To evaluate the illustration-based educational materials in the field. During this project it was learned that ont ologies can be used to solve problems related to the terminology used within a given domain. Ontologies can be used to organize metadata and to order concepts in a given domain, while allowing browsing, search, tagging and classification of documents It was shown that the irrigation ontology can be the basis for a system aimed at the de velopment of localized educational materials for people with low levels of formal education. In Chapter 1, a process was adapted to model the irrigation ontology. This methodology process shown to be an effi cient method to develop a domain ontology manually. Collaboration among various specialis ts in irrigation and knowledge modeling was necessary, and should be encourage, for the development of a common ontology. Specification and conceptualization are im portant to the entire ontology modeling

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98 process. During these two steps the foundati on for the ontology was laid. The domain, objectives, and limitations of th e irrigation ontology were defined. This helped to guide the rest of the modeling process presented in Chapter 3. Formalization and implementation were presented in Chapter 3. This was the actual creation of the ontology, where all the terms and relationships were implemented using ObjectEditor. The main issues presented during these steps are the correct definition of terms, as to maintain the semantic sense of the irrigation ontology. The other main issue is to include all the terms c overed by the ontology’s domain. At this time the Irrigation Ontology consists of more than 270 terms, and around 300 relationships among those terms (A ppendix A). A comparison was conducted between the irrigation ontology, and the following thesauri AGROVOC, NALT and IWMI descriptors. The terms in the irrigati on ontology were matched to the same terms (or synonyms) from each of the other da tasets. IWMI cont ained 15.8%, AGROVOC 22.1%, and NALT 27.68% of the terms included in the irrigation ontology. Even so, the IWMI descriptors in theory should have more irrigation and water management concepts; this set is the one that ha s fewer of the terms containe d in the irrigation ontology. In Chapter 4 after the evaluation of some vector graphics so ftware and formats available, scalable vector graphics (SVG) was selected as the format that better conforms to the necessities of this proj ect. The characteristics of S VG are that they can be easily localized. GraphicsEditor was the tool used to create the vector graphics; this tool incorporated into ObjectEditor permitted the localization of vector graphics. The composition of the illustrations used in the educational materials was also done using GraphicsEditor.

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99 A preliminary field evaluation of the illustration-based educational materials was conducted in El Salvador (C hapter 5). The results of the evaluation showed that localization affects the interpretation of the information presented through the illustrations. Also, it became clear that the input from the audience improves the design and understanding of the educational material s. A methodology shoul d be developed to evaluate the efficiency and efficacy of illu stration based educational materials. A standard evaluation methodology would reduce the time spent collecting data about the audience and their understanding of the illu stration-based educational materials. In conclusion, this work demonstrated th at a domain ontology can be used to store and manage the domain’s information. The comb ination of the capabilities of an ontology with the characteristics of scalable vector graphics permits the creation of localized graphics by using the content st ored in the irrigation ontology. Future Work The irrigation ontology can be the founda tion that underpins the development of other projects that require knowledge modeling, organization, and management capabilities. The ontology can be the basis fo r a system to manage information resources in the irrigation and water management domain. It could be incorpor ated or expanded to meet needs for projects that cover different sub-domains of irrigation. By making use of the knowledge contained in this domain-sp ecific terminology and concepts, better information management for the web environment can be supported. A user friendly interface should be developed to expand the us e of the irrigation ontology as a foundation for the development of educational materials.

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100 APPENDIX A TERMS IN THE IRRIGATION ONTOLOGY Weather: wind, radiation, temperatur e, precipitation, evapotranspiration, transpiration, evapotranspiration methods, Pan, Penman-Monteith, Blanney-Criddle, crop coefficient, pan coefficient. Plant: leaf, stem, root, root depth; plant type, planti ng system, spacing; growth season, growth stage, phenological stages, nu trient requirement, climatic requirement, cold resistance, toxicity resi stance, salinity resistance. Soil: soil chemistry: sodicity, salinity, soil pH, nutrients, electric conductivity; topography: erosion, wind, water, water moveme nt; soil available water: field capacity, permanent wilting point; structure: bulk densit y, compaction; soil moisture retention: mulch, cover crop, conservation tillage; textur e: clay, sand, silt, loam; organic matter, permeability: hydraulic conductivity, infiltration. Water Sources: water quantity, water qua lity, reclaim water, municipal sources; surface water, water bodies: lake, river, re servoir; groundwater: uns aturated, saturated; aquifer: confined, unconfined, artesian well, well; root zone: hygroscopic water, capillary water; cost, slope, soil, ha rvested water: basin wide harvesting, macro-catchment, floodwater; on-farm water harvesting: rooft op, micro-catchment: na tural depressions, natural rock dams, retention ditch, planting pit; contour farming: stone lines, terrace, bund, semi-circular, ridge, triangular; earth basin: meskat, negarim. Drainage: drainage design: coll ector ditches, tile drainage, lateral ditches, perimeter ditch & dike, beds & water furrows, drain tile clogging; drainage c onsiderations: spacing,

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101 alignments, drain depth, drain capacit y, outlets, connections; flooding damage, summer/winter time interval s; drain clogging, ochre depos itions, sulfur slimes. System Design: site characteristics, irrigation system layout: spacing, planting system; economic considerations, system us es: crop cooling, freeze protection, irrigation requirement: crop requirement, system re quirement: leaching requirement, system efficiency; drainage, system selection, pumping system design: pumping equipment, pumping equipment selection; pumping system efficiency; conveyance system design: ditch, primary channel, secondary channel, pi peline, main line, delivery channel, pipe sizing; water hammer, pressure rating, pressu re losses, conveyance system efficiency; distribution system design, dist ribution system efficiency: gravity irrigation, seepage irrigation, seepage irrigation efficiency, su rface irrigation, surface irrigation efficiency; pressurized irrigation, pressu rized irrigation efficiency. Irrigation System Management: irrigation sc heduling: timing of i rrigation, rainfall measurement, field water budget, soil moistu re monitoring; scheduling methods: visual appearance of the plant, wate r budget, long-term average irri gation requirements, climatic data, direct measurement, soil moisture se nsor; irrigation system maintenance: check pump: pressure settings, parts, lubrication; surface irriga tion, clean canals; pressurized irrigation: check lines for leaks, clean lines or pipes, clean filters, uniformity test, irrigation system calibration; chemigation: vol umetric flow rate measurement, calibration of injection systems, calculati ng fertilizer injection rates. Irrigation Equipment and Structures: system control: flow meter, pressure gauge, valve: gate valve, ball valve, vacuum re gulator, pressure regulator, automatically controlled valve; filtration equipment: cartridge filter, media filter, disc filter, screen

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102 filter, centrifugal filter ; conveyance equipment, pipeline, pipe fittings: adapter, coupling, cross, elbow, tee, plug; distribution equipmen t: sprinklers, guns, emitter: microsprinklers, drippers, bubblers; pumping equipment: dynamic pump, positive displacement pump: reciprocating pump: piston pump, diaphragm pu mp; rotary pump: flexible impeller pump, vane pump, lobe pump, gear pump, screw pum p; chemigation equipment: backflow prevention, chemical flow meters, pressu rized mixer tanks; chemical injection equipment: suction side injection, venturi in jectors; system cont rollers: computer controller, soil moisture sensors: TDR, diel ectric sensors, neut ron probe, resistance blocks, tensiometer.

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103 APPENDIX B DOCUMENTATION FOR THE IRRIGATION ONTOLOGY As presented in Chapter 2, documentati on is a segment of the ontology modeling process. Documentation it’s used to keep a record of the modeling process. The format used for the irrigation ontology records the term s in the left column, and any other data in the right column. The terms are written in a hierarchical order, starting with the most important term. In this case the terms are organized according to the irrigation ontology modules as created in Object Editor (Chapter 3). Those m odules are: Irrigation Water Sources, Weather, Plant, Soil, Drainage, Irrigation System Desi gn, Irrigation System Management, and Irrigation Equipment and Structures. In the right column multiple information is recorded, from source of info rmation (e.g., literature ) related to a given term. Also any comment or explanation about a term can be included; any details that can help guide the ontology modeling, or later on the evaluation and maintena nce processes. Other use of the irrigation ontology do cumentation is to help other people interested in adapting or re using the irrigation ontology for their own applications. Having a record of the ontology can help user s to share information. It also helps any recovery efforts that may be necessary if part of the ontology is damaged.

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104Term Description, Source, Comments Irrigation System Water Source Well Drilled Driven Dug Dam River (diverted) Water Harvesting Rooftop Micro-catchment = External Catchment Natural Depressions Retention Ditch Contour Farming Terrace Bund = Ridge Semicircular (instance) Triangular (instance) Planting Pit = Eyebrow Terraces Earth Basin Meskat (instance) Negarim (instance) Macro-catchment Floodwater Conveyance Canal Lining Irrigation Design http://edis.ifas.ufl.edu/BODY_AE064; http://www.ces.uga.edu/pubcd/b894-w.html Maintenance Well: attributes: Diameter, Depth, Casing http://edis.ifas.ufl.edu/WI002 Type of Lining could be an attribute of Canal: concrete; concrete blocks, bricks or stone masonry; sand cement; pl astic; and compacted clay. Possible benefits of lining a canal in clude: water conservation; no seepage of water into adjacent land or roads; reduced canal dimensions; and reduced maintenance. ftp://ftp.fao.org/agl/aglw/fwm/Manual7.pdf Metal and Composite could be attributes of pipeline

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105 Pipeline = Pressurized Friction loss Fittings Type Surface Irrigation Basin Furrow Border Sprinkler Irrigation Portable Sprinkler Central Pivot Lateral Moving Fixed Sprinkler Irrigation Microirrigation Drip Micro Sprinkler Chemigation Soil Moisture Retention Conservation Tillage Cover Crop Mulching Vegetative Residue Plastic Drainage Surface = External Sub-surface = Internal Bio-drainage Salinity Management Reduction of water pressure as water travels over distance and through any kind of restriction http://edis.ifas.ufl.edu/WI007 http://edis.ifas.ufl.edu/WI010 http://edis.ifas.ufl.edu/WI004 Removal of excess surface and subsurface water from land, including removal of soluble salts from the soil, to enhance crop growth. Evacuation of excess water from cultivated areas ; generally used to describe artificially installed drainage. The flow of water towards deeper layers or lateral outflow from an irrigation scheme; naturally present and sustains irrigation for a limited area and often with a time horizon.

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106Leaching Uses the evapotranspirative power of vege tation, especially trees, to keep groundwater tables deep. http://www.fao.org/ag/agl/iptrid/is_pa _03/is_pa_03_agriculture.pdf Irrigation Equipment Pump Centrifugal End-suction In-line Double suction Vertical multistage Horizontal multistage Submersible Self-priming Axial-flow Regenerative Positive Displacement Reciprocating Power Steam Rotary Pipe Material Polyethylene Diameter Length Thickness Fittings = Couplings Tees Elbows Maintenance could be an attribute of all the objects that need it. Operation and Installation sa me as case as Maintenance Pump attributes: Sitting Installation Operation and Maintenance Pump Power can be an attribute that includes Motorized and Manual, as well as the type of displacement. And only the Type of pump has to be an object. Attributes: Energy Supply: Electric, Di esel, Gas, Solar, Hydraulic Manual Size and Material of Pipe could be an attribute of Pipe. http://edis.ifas.ufl.edu/WI006 http://edis.ifas.ufl.edu/WI011 http://edis.ifas.ufl.edu/WI009

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107Filter Type Screen Disc Cartridge Media Centrifugal Valve Pressure Regulator = Reducing Gate Ball Electric Butterfly Air Vacuum Relief = Regulator Backflow Prevention Check Pressure Gauge Flow Meter = Water Meter Sprinkler Risers Head styles Dripper http://edis.ifas.ufl.edu/WI008 Valves control the flow of water to sprinklers and can be mechanical, hydraulic, electric or a hybrid http://edis.ifas.ufl.edu/WI005 A device used to measure the quantity of water that flows through a pipe Irrigation Water Quality Surface Ground Soil Composition Structure Texture Particles size Water is essential for plant life proces ses. A odor-less, tasteless liquid. What relations they have with irrigation? Soil provides the mechanical and nutrient support necessary for plant growth. Soil is a mixture of mineral matter, organic matter, and pores. http://www.oznet.ksu.edu/library/ageng2/L904.PDF Soil structure is the shape and arrangem ent of soil particles into aggregates. Soil texture is determined by the size of the particles that make up the soil. Particles sizes for various textural groups

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108Available water Density Porosity Topography Slope Runoff Water Content Saturation Field capacity Wilting point Oven dried Water Logging Salinity Infiltration Environment or Weather Precipitation Radiation Temperature Evapotranspiration Plant Type Resistance Drought Salinity Root Leaf Transpiration Available water for various soil types Soil bulk density is a measurement of the porosity of the soil. Porosity of a soil is defined as the volume of pores in a soil. http://www.uwsp.edu/geo/faculty/ritter/geog10 1/modules/soils/soil_development_soil_pro perties.html List important climatic events: Flood, Drought, Season http://soils.usda.gov/sq i/files/Infiltration.pdf Water is transferred from the su rface to the at mosphere through evaporation, the process by which wa ter changes from a liquid to a gas http://www.wcc.nrcs.usda.gov/nrcsirrig/i rrig-handbooks-part652-chapter4.html Water used by a crop (plant) for growth and cooling purposes Water is extracted from the soil root zone by the root system = Crop water use http://edis.ifas.ufl.edu/AE021

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109 LIST OF REFERENCES African Internet Connectivit y. 2003. Continental Connectivity Indicators, Jul 2003. South Africa. October 2003 from: http://www3.sn.apc.org/africa. AGROVOC. 2005. AGROVOC thesaurus. Food a nd Agricultural Organization of the United Nations. August 2005 from: http: //www.fao.org/scripts/ agrovoc/frame.htm Antoniou, G. and van Harmelen, F. 2004. A Se mantic Web Premier. Massachusetts: The MIT Press. Cambridge, MA. American Society of Agricultural and Biol ogical Engineers (ASABE). 2006. Publications Catalog. St. Joseph, MI. November 2005 from: http://www.asabe.org/pu bs/PubCat02/pubcat.html AMDM. 1997. Illustrated Manuals for Non-litera te Farmers, Project Evaluation. Gambia. Badal, R., C. Cornejo, and H. Beck. 2004. A Database Approach for Developing, Integrating, and Deploying E ducational Material on the Web. World Conference on E-Learning in Corp., Govt., Healt h, & Higher Ed., Vol. 2004, (1):475-481. Beck, H. and H.S. Pinto. 2002. Overview of Approach, Methodologies, Standards, and Tools for Ontologies. The Agricultural Ontology Service. UN FAO. Rome. Beck, H.W. (In Press). The ro le of ontologies in eLearning. Educational Technology Magazine. Beck, H.W. 2003a. Object Editor, an online tool for creating multimedia application. May 2005 from: http://orb.at.ufl.edu/ObjectEditor. Beck,H.W. 2003b. Integrating ontologies, object databases, and XML for educational content management. Proc of E-Learn 2003. Phoenix, AZ. Benjamins, R.V., D. Fensel, S. Decker and A. Gomez-Perez. 2002. KA: Building ontologies for the internet: a mid term report. Intern ational Journal of HumanComputer Studies. Vol. 51, (3) 687-712. Bly, R. 1989. The six most deadly causes of direct mail disaster. Direct Marketing, 52(14) 13-20.

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112 McGuinness, D.L., and N. Fridman Noy. 2001. Ontology development 101: A guide to creating your first on tology. June 2003 from: http://ksl.stanford.edu/people/onto logy101/ontology101-noy-mcguinness.html Miller, J.E. 2001. How to Write Low Literacy Materials. J. of Extension 39(1). May 2005 from: http://www.joe.org Mizoguchi, R., J. Vanwelkenhuysen, and M. Ikeda. 1995. Task ontologies for reuse of problem solving knowledge. In N. J. I. Ma rs (ed.), Towards Very Large Knowledge Bases, IOS Press. Mukhwana, E. J. 2000. The dilemma of agricult ural led development in Africa; The role of participatory on-farm research. SA CRED-AFRICA, Sustainable Agriculture Centre for Research and Development in Africa NALT. 2005. United States National Agri cultural Library Thesaurus. National Agricultural Library, Agricultural Resear ch Service, U. S. Department of Agriculture. October 2003 from: http ://agclass.nal.usda.gov/agt/agt.shtml Object Store. 2004. Official web site for Object Store Corporation. May 2004 from: http://www.objectstore.net OntoBroker. 2004. OntoBroker Project. Appl ied Computer Science and Formal Description Methods (AIFB), Universit t Karlsruhe (TH), Karlsruhe, Germany. November 2004 from: http://ontobroke r.aifb.uni-karlsruhe.de/index_ob.html. Ontolingua. 2006. Stanford Universit y. July 2004 from: http://www-kslsvc.stanford.edu:5915/ Passin, T.B. 2004. Explorers guide to th e semantic web. Manning Publications. Greenwich, CT. Pinto, H.S. and J.P. Martins. 2001. A met hodology for ontology integration. K-CAP. British Columbia, Canada. Prieto-Diaz, R. 2002. A faceted approach to building ontologies. Commonwealth Information Security Center. James Madison University. RenderX. 2004. PDF conversion. RenderX, Inc. Available at: http://www.renderx.com Rohr-Ruendaal, P. 1997. Where there is no artist, Developm ent drawings and how to use them. Intermediate Technology Publications. London, UK. Stemmerman, M. G. 1991. Readability of select ed public health information materials. Tri-State. Huntington, WV.

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113 SVG Working Group. 2004. Scalable Vector Gr aphics (SVG), XML Graphics for the Web. World Wide Web Consortiu m (W3C). December 2003 from: http://www.w3.org/Graphics/SVG. Tisa, B. 1991. Computer graphics for educationa l materials. Computer graphics used in the development of educational materials for African Studies. November 2005 from: http://www.africa.upenn.edu/Audi o_Visual/Computer_Graphics_10564.html United Nations Educational, Scientific, and Cultural Organi zation (UNESCO). 1995. Adult literacy rates by Region and Country. UNESCO Institute for Statistics. UNESCO. 1997. Literacy in the World. Fi fth International Conference on Adult Education (CONFINTEA V). UNESCO In stitute for Education. Hamburg. UNESCO. 1999. UNESCO Statistical Yearbook 1999. February 2004 from: http://www.uis.unesco.org/statsen/s tatistics/yearbook/YBIndexNew.htm UNESCO. 2000. World Education Forum, E ducation for All 2000 Assessment. Dakar, Senegal. February 2004 from: http://www.uis.unesco.org /ev.php?ID=5063_201&ID2=DO_TOPIC UNESCO. 2002. Statistics show slow progress towards universal literacy. UNESCO Press. Paris. United Nation Population Fund (UNFPA). 2005. State of World Population 2005. February 2006 from: http://www.unfpa.org/swp/swpmain.htm Uschold, M. and M. King. 1995. Towards a methodology for building ontologies. Workshop on Basic Ontological Issues in Knowledge Sharing. Montreal, Canada. Uschols, M. and M. Gruninger. 1996. Ontol ogies: principles, met hods, and application. Knowledge Engineering Review. 11(2). U.S. Congress, Office of Technology Asse ssment. (1993). Adult literacy and new technologies: tools for a lifetime, OTA-SET-550. Washington, DC: U.S. Government Printing Office. van Heijst, G., A. Th. Schreiber, and B. J. Wielinga. 1997. Using explicit ontologies in KBS development. International Journal of Human-Computer Studies, 46(2/3):183292. W3C. 2001a. Semantic Web. World Wide Web Consortium. November 2004 from: http:www.w3.org/2001/sw. W3C. 2001b. Scalable Vector Graphics (SVG) 1.1 Specification, W3C Recommendation 14 January 2001. World Wide Web Consortium. November 2004 from: http://www.w3.org/TR/SVG11/concepts.html

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114 W3C. 2001c. DARPA Agent Markup Language, and the ontology interchange language (DAML+OIL). World Wide Web C onsortium. November 2004 from: http://www.w3.org/TR/daml+oil-reference. W3C. 2002. Resource Description Framework (RDF). World Wide Web Consortium. November 2004 from: online at http:www.w3.org/RDF. W3C. 2004a. Scalable Vector Graphics (SVG) World Wide Web Consortium. November 2004 from: http://www.w3.org/Graphics/SVG/About W3C. 2004b. Extensible Markup Language (XML). World Wide Web Consortium. November 2004 from: http://www.w3.org/XML/ Walters, S. and Watters, K. 2001. Twenty years of adult education in Southern Africa. University of Western Cape. Int. J. of Lifelong Education, Vol. 20, No. 1/2 (JanuaryApril 2001), 100. Wilfred Monte, K. 2002. Adult literacy a nd adoption of agricultural yechnologies. reflections on the dimensions, applications and implications of a technology policy framework. Ministry of Gender, Labor a nd Social Development, Kampala, Uganda Williams, H. 2001. The Literate Illiterates of the Northern Cape Province of South Africa an empirical account. Intl. In form. & Libr. Rev (2001), 33, 261-274 doi:10.1006/iilr.2001.0173 Available online at http://www.idealibrary.com World Bank. 1995. Uganda: The challenge of growth and poverty reduction; World Bank (1993) Uganda: Agriculture, World Bank Country Study. Washington, D.C. XML Working Group. 2004. XML Extensible Ma rkup Language). January 2004 from: http://www.w3.org/XML. Yazdani, M. and Barker, P. 2000. Iconic communication. Intellect Books. Bristol, UK.

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115 BIOGRAPHICAL SKETCH Camilo Cornejo Dvila born in July 11, 1978, in Quito, Ecuador. He attended high school at Colegio Salesiano Sanchz y Cifuen tes in Ibarra, Ecuador, graduating in 1996. He attended the Escuela Agricola Panameri cana El Zamorano in Honduras, and later received his Agronomo degree in December 1999. He continued further studies at the University of Florida, College of Agricultura l and Life Sciences, obtaining the Bachelor of Science degree in May 2001; the Master of Science and Doctor of Philosophy degrees in agricultural and biolog ical engineering in May 2003, and May 2006 respectively.


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AN IRRIGATION ONTOLOGY AND ITS USE FOR LOCALIZED, ILLUSTRATION-
BASED EDUCATIONAL MATERIALS
















By

CAMILO CORNEJO


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2006


































Copyright 2006

by

Camilo Comejo

































I dedicate this work to my whole family and to Carolina.















ACKNOWLEDGMENTS

This dissertation work would not have been completed without the help of several

people whom I wish to thank. First, I thank my chair, Dr. Dorota Haman, for all her help

and support, interest, knowledge, problem solving and advice. Without the help of Dr.

Howard Beck, this work could have been completed. Thanks go to Dr. Fedro Zazueta for

his help during the ontology modeling stages. I thank Dr. Sandra Russo and Dr. Nick

Place whose comments and edits contributed substantially to my research and to the

completion of this document. I would also like to thank to all the people from

PROMIPAC in El Salvador for helping me with the field research presented in this study.

Special thanks go to my friends, who always helped me when needed. Finally, I would

like to thank the very special people in my life, Carolina and my family, for their support.
















TABLE OF CONTENTS
Page

A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TABLES .................................................... ............ .............. viii

LIST OF FIGURES ......... ......................... ...... ........ ............ ix

ABSTRACT .............. ............................................. xii

CHAPTER

1 IN TR OD U CTION ............................................... .. ......................... ..

Education, Literacy, and Agricultural Development...................................................1
L literacy ................................................................................................ . 2
Literacy in Latin Am erica .................................. .....................................4
L literacy in A frica ....................................................... 5
Literacy in the A rab States ............................................................................. 5
L literacy in A sia ...............................................................6
Teaching Agriculture to A dults ............................................................................. 6
Agricultural Education U sing Im ages ........................................ ....... ............... 8
Justification ......................................................... ................... ....... ..... 9
Overall O objectives of the Study................................................................... ...... 11
M methodology ..................................... ................................ ........... 12
E expected O utcom es .......................................... .. .. ......... ..... .. ... 13
Organization of the D issertation...................................................................... 13

2 IRRIGATION ONTOLOGY MODELING .............. .........................................14

In tro d u ctio n ........................................................................................................... 14
T h e sa u ri ............................................................................................................... 1 4
O n to lo g y .....................................................................................................1 7
O ntology C lassification .................................................................. ....... ....... 19
Ontology Languages..................... ...... ......................... 20
Ontology Editors .................................. .. .. .. ........ ...............20
O b j e c tiv e s ........................................................................................................2 1
M eth o d ology .................. .................................................................................2 1
O ntology Specification .............................................. ..... ........................ 22
Ontology Conceptualization ......................... .. .......................................... 23
Ontology Formalization and Implementation ............................................. 24


v









Ontology Documentation, Evaluation, and Maintenance....................................24
Application of Modeling Methodology to Development of Irrigation Ontology.......25
C o n clu sio n s..................................................... ................ 3 2

3 IRRIGATION ONTOLOGY FORMALIZATION AND IMPLEMENTATION .....34

In tro d u ctio n ........................................................................................................... 3 4
O bjectiv es .................................................................... ........................... 3 7
O ntology F orm alization ...................................................................... .................. 37
Ontology Im plem entation............................................ ............... 41
C o n c lu sio n s........................................................................................................... 5 4

4 EDUCATIVE ILLUSTRATION S ........................................ ........................ 56

In tro d u ctio n ............. .... ......... .....................................................................5 6
G raphical C om m unication ............................................................... .....................57
E educational M materials .......... .... ...... ...... .... .. .... .......... ..... ............ .... 60
Experiments with Vectorizing Images, Options for Creating Vector Graphics..60
Scalable V ector G graphics ............................................................................. 62
GraphicsEditor .................. ...... ... ............. ......... ........... 67
Composing Educational M materials ............................. ..... ...................... 72
P presentation G generation ............................................................ .....................74
Conclusion .............. ...... ............................................ ................. ....... 76

5 EVALUATION OF EDUCATIONAL DRAWINGS IN EL SALVADOR,
CEN TR A L A M ER ICA ........................................... .................. ............... 78

Introduction ............. .... .............................. ....... ................. 78
M materials and M methods ....................................................................... ..................8 1
R e su lts ................... ............ ................. ......................................8 3
Contour Planting or Farming ................... .......... .................. 85
Earth Basins ............ ... .......... ...............................87
Rain and Drainage .............. ........... ........ ......................... 88
R detention D itches ........................ ................ .. .. .... ........ ..... .... 89
Stone T erraces (L ines).......................................... ...... ..............89
Connectors .................. ......... .................... ............ ......... 90
C o n c lu sio n s..................................................... ................ 9 1

6 SUMMARY AND CONCLUSIONS............... ............. ........................96

Sum m ary and C onclu sions .............................................................. .....................96
F u tu re W o rk ...................................................... ................ 9 9









APPENDIX

A TERMS IN THE IRRIGATION ONTOLOGY ............... .................. ............100

B DOCUMENTATION FOR THE IRRIGATION ONTOLOGY ............................103

L IST O F R E F E R E N C E S ............................................... ....................... ..................... 109

BIOGRAPH ICAL SKETCH .............. ......................... ................... ............... 115
















LIST OF TABLES


Table page

2-1 Comparison by topics of various sources vs. irrigation ontology ............................31

2-2 Comparison of various sources vs. irrigation ontology.......................................32

5-1 Literacy rates of small farmers interviewed in El Salvador..................................84

5-2 Age groups of small farmers interviewed in El Salvador. .......................................84

5-3 Type of educational materials used by farmers in El Salvador.............................85

5-4 Drawings' connectors selected by farmers in El Salvador............... ...............91
















LIST OF FIGURES

Figure pge

2-1 View of the AGROVOC Thesaurus......... ................................15

2-2 View of the NAL Thesaurus ..................................... 16

2-3 Diagram representing the conceptualization process................... ............. 27

2-4 Main topics covered by the Irrigation Ontology in ObjectEditor ............................29

3-1 V iew from the O bjectE ditor......................................................................... ...... 38

3-2 Evapotranspiration term and its gloss (short definition). .......................................40

3-3 D definition of concept in English ........................................ ........................ 41

3-4 Definition of concept in Spanish.............. ........................... ... .. ............... .... 41

3-6 A association relationship properties ........................................ ....... ............... 44

3-7 Use of part-of type of relationship .......... .... ... ............................ ............... 44

3-8 Use of generalization type of relationship............................................. 45

3-9 Use of generalization type of relationship............................................. 45

3-10 Use of generalization type of relationship .................................. ... ............ 45

3-11 Use of the sequence relationship .......................... ......................... .. 46

3-12 Sam ple of the soil m odule ............................ ............... ................. ............... 48

3-13 Sample of the water sources module...................... ........................... 49

3-14 Drainage module, sub-classes with generalization relationships.............................50

3-15 A small section of the system design module ..................................................51

3-16 A section of the system design module................................................................. 52

3-17 Partial view of the irrigation system management module.............. ...................53









3-18 A section of the irrigation equipment and structures module ..............................54

4-1 Communication model adapted from Funch (1995) .............................................59

4-2 Interferences on the communication model modified from Funch 1995 ...............60

4-3 Vectorization using Flash and original digital picture .......................................61

4-4 Pattern recognition using GIMP and original digital picture.............................61

4-5 Sample of localization with Scalable Vector Graphics (SVG) .............................64

4-6 Sample of localization with Scalable Vector Graphics (SVG) .............................64

4-7 Module "Cleaning Irrigation Filters" from Object Editor................. ........... 65

4-8 SVG presentation "Cleaning Irrigation Filters" in English................ .......... 66

4-9 SVG presentation "Cleaning Irrigation Filters" in Spanish ...................................67

4-10 Maize instance within the plant topic in the irrigation ontology............................68

4-11 Context and gloss for the maize instance.........................................69

4-12 Groups that constitute the maize graphic ...................................... ............... 70

4-13 Example of a person graphic ............. ... ................................. 71

4-14 Skin color term associated to "person" term ................................. ...... ............ ...71

4-15 Different skin colors depending on the origin of the person...............................72

4-16 Irrigation Training M materials module template ................................ ............... 73

4-17 Example of print file generated from the ontology management system .............76

4-18 Example of educational drawings on irrigation techniques ...................................76

5-1 Map of El Salvador and location of communities visited (CIA, 2004)....................79

5-2 Section of drawings representing contour planting.......... .......... ............... 86

5-3 D raw ings representing earth basins...................................... ........................ 87

5-4 Drawing representing rain and drainage. ..................................... ............... 88

5-5 Drawing representing a drainage ditch............... ...................................89









5-6 Drawing showing stone terraces or lines....................................... ...............90

5 -7 C o n n ecto rs .................................................................................................... .... 9 0















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

AN IRRIGATION ONTOLOGY AND ITS USE FOR LOCALIZED, ILLUSTRATION-
BASED EDUCATIONAL MATERIALS

By

Camilo Corejo

May 2006

Chair: Dorota Z. Haman
Cochair: Howard W. Beck
Major Department: Agricultural and Biological Engineering

There is little doubt that economic and social development, and the benefits that

accrue such as improved nutrition and health, requires an educated populace. However,

illiteracy affects 860 million people as of 2005, without including a larger number of

adults with low level of formal education. Most illiterates are poor, farmers, and female,

living in rural areas. In agriculture, education is essential to improve food security, rural

employment, and to reduce poverty.

It is difficult to transmit information to people that cannot understand traditional

text based educational materials. An option is the use of illustration-based materials in

which the information is represented using graphics. Manual development of graphical

materials, even using traditional computer graphics packages, is a very time consuming

process. Those materials usually are general and do not reflect the cultural conditions of

the target audience. The approach presented in this work aims at producing illustration

based educational materials using an ontology based system. This methodology allows









for development of illustrations that can be adjusted (localized) to specific characteristics

of the audience.

Ontology is a formal, explicit specification of a conceptualization within a domain,

where conceptualization refers to an abstract model of some phenomenon. An irrigation

ontology was developed to organize data and organize concepts in the irrigation and

water management domains, while allowing browsing, search, tagging and classification

of information. This ontology consists of more than 270 terms and 300 relationships. The

irrigation ontology also stores vector graphics that can be localized. This means that they

can be adapted to represent more properly the conditions of the audience that will use the

educational materials. Trial versions of the illustration based educational materials were

evaluated in El Salvador. The levels of understanding of the message being transmitted

by the illustrations, as well as each illustration (e.g., color, size, level of detail), were

evaluated.

The main advantage of using an irrigation ontology to model and manage irrigation

and water management information is that the content can be separated from the format,

meaning that the same content can be presented in multiple formats like web pages,

printed text, presentations, or PDF files.














CHAPTER 1
INTRODUCTION

Education, Literacy, and Agricultural Development

As a means of production, way of life and source of food, agriculture in developing

countries has been suffering irreparably over the last decade. While this is happening,

there is an increasing realization that our rural farmers, NGOs, governments and

researchers simply cannot afford to continue wasting resources pursuing development

and research goals that cannot tangibly change the lives of rural farmers and become a

permanent part of their lives (Mukhwana, 2000).

There is a need to find more sustainable methods, approaches and technologies of

food production that can increase agricultural productivity and income while protecting

and enhancing the environment (Mukhwana, 2000). The total population in West Africa

tripled between 1950 and 2000. In 1950, the urban/rural population ratio was 1:10, in

1990 it was 1:3.4 and in 2005, 42% of the population was living in urban areas (UNFPA,

2005). With the exception of Burkina Faso, per capital food intake is diminishing.

Increasing population density and pressure on the land have altered traditional production

patterns, and sustained agricultural production is being threatened (Lindley et al. 1996).

What matters most for economic development in Africa is the capability of rural people

to be efficient producers given their natural resource base. There is little doubt that

economic and social development, and the benefits that accrue such as improved nutrition

and health, require an educated populace (Lindley et al. 1996). No country has become









developed without well-educated people and a strong agricultural base that provides food

security (Lindley et al. 1996).

The improvement of a country's human resource capacity for productivity is a pre-

requisite for social and economic development. In the agricultural sector, formal and non-

formal education are both essential for reducing poverty, for improving food security and

rural employment and reducing poverty (Lindley et al. 1996). Non-formal agricultural

education, often provided by both public and private extension services, is needed for

training of farmers, farm families and workers and for capacity building in a wide range

of rural organizations and groups (Lindley et al. 1996).

It is increasingly clear that extension workers need better training in both technical

agriculture and the extension methods necessary to disseminate production technologies

to the thousands of small-scale farmers who need them (Lindley et al. 1996). Most of the

available empirical data that testify to the link among education, literacy and agricultural

productivity are based on studies of formal schooling (UNESCO, 1994; Lauglo 2001,

Wilfred Monte, 2002). Education is an essential prerequisite for reducing poverty,

improving agriculture and the living conditions of rural people and building a food-secure

world (ERP, 2005).

Literacy

The term "literacy" has always been used to denote a certain ability or inability. For

example, a person who cannot use a computer will be referred to as being "computer

illiterate or a person who cannot use money properly will be referred to as "economically

illiterate." These examples suggest that the term illiteracy can be seen as being relative to

a certain situation (Williams, 2001).









The American Federal National Literacy Act of 1991 defines literacy as "having an

ability to read, write, speak English, compute, and solve problems to achieve and

function in a job and in society" (Williams, 2001).

Adult literacy can be defined in different ways. Two definitions will be used in this

work. The United Nations definition states that a literate adult is "a person aged 15 or

over who can read and write" (UNESCO, 2000). The Central Intelligence Agency's

(CIA, 2004) Factbook considers a literate adult to be "a person over 10 years who can

read and write".

Because developing countries are struggling to keep up with citizens demands for

basic needs, being literate becomes a luxury in a situation of scarce resources (Williams,

2001). Estimates and projections collated by the UNESCO's (2000) Institute for Statistics

show a steady fall in the number of illiterate adults from 22.4% of the world's population

in 1995 to 20.3% in 2000. This means that the number of illiterate adults fell from an

estimated 872 million in 1995 to 862 million in 2000. Based on current trends, the

Institute estimates this should drop to 824 million, or 16.5%, by 2010. Still these numbers

are high, and most of the illiterate are poor, farmers, and female, living in areas away

from the urban centers, with little or no access to education, not to say technology (i.e.,

electricity, telecommunications).

The problems of literacy relate not only to the governments' organizational

structure, teaching material, languages barriers, subjects matter, teaching and the training

of facilitators, but more importantly to the way literacy is conceptualized (UNESCO,

1997). In any development activity people need to attain these successive levels of skill,









and work out --with those in charge of the financial or institutional elements-- a pedagogy

by which people can acquire a skill, apply it and acquire the next skill (UNESCO, 1997).

Despite the increase in the world population, great strides have been made to

increase literacy, though there are sharp differences between industrialized and

developing countries. The growth in the number of literate men and women in the world

is expected to continue for the foreseeable future. Nevertheless the number of illiterate

adults has remained at about 885 million since 1980, with females still outnumbering

males (UNESCO, 1997).

Literacy in Latin America

Today nearly 90% of Latin American/Caribbean adults can read and write but poor

education systems continue to generate new illiterates. According to the latest estimations

by the UNESCO's (1999) Institute for Statistics, the region's overall illiteracy rate is 1%,

compared to 40% in sub-Saharan Africa and 45% in South Asia. Latin America and the

Caribbean's relatively good performance, however, masks huge disparities within and

between nations. Countries like Argentina, Trinidad and Tobago, Bahamas, Cuba and

Uruguay have illiteracy rates of less than 5%. But 13% of Brazilians and almost a third of

Guatemalan adults cannot read or write.

A glance at absolute numbers reveals the millions of men and women who, because

they have not mastered basic reading and writing skills, are deprived of the opportunity to

enter the labor market or become full-fledged citizens. Some 39 million adults in the

region are illiterate, and Brazil's 13% illiteracy rate actually represents roughly 16 million

people. The bulk of these illiterates can be found in rural areas, among ethnic minorities

and the poor. Particular emphasis must be given to dealing with issues of marginalization









and equity, such as those affecting girls and women, and people in rural areas (UNESCO,

1997).

Literacy in Africa

As in many developing nations, illiteracy is very high in sub-Saharan Africa. In fact

it is a region with the world's highest illiteracy rate (54%). However, there is a

considerable difference from one country to another. In 1997, in countries such as Kenya,

Tanzania, Zimbabwe, Botswana and South Africa the literacy rate is about 70%, while in

countries such as Uganda, Malawi, Burundi and Rwanda the literacy rates are below

49%. Southern African countries, as with many Third World countries, expanded their

education systems rapidly in the 1960s and 1970s (Walters and Watters, 2001). Is a lot of

enthusiasm in literacy work and a growing realization that literacy is crucial in the

context of integrated programs for imparting messages on population, health, and

agriculture and in the struggle to escape poverty (UNESCO, 1997). In geographical

terms, the northern region is the poorest. Food security is likely to be a problem to poor

households despite the statements, which refer to fertile lands and abundant food supplies

(World Bank Report, 1995).

Literacy in the Arab States

Illiteracy remains a serious problem in the Arab region, where the number of

illiterate adults reaches more than 65 million people. For men the rate has fallen from

45% in 1980 to 23% in 1995; for women it has fallen from 71% to 56%, though several

of the less developed Arab States are still encountering difficulties (UNESCO, 1997). On

average only about 63% of the total adult population in the Arab States can read and

write. This is one of the lowest adult literacy rates in the world. Literacy levels are below

the regional average in Egypt, Mauritania, Morocco, the Sudan and Yemen, and are about









90% or higher in Jordan, the Palestinian Autonomous Territories and Qatar (EFA,

2006a). What makes the matter worse is the existing and increasing gender inequality in

access to education.

Literacy in Asia

Asia is the largest continent. By 2000 the population of Asia was 3,688 million,

about 60% of the world's total population (UNESCO, 2000). The literacy data for Asia

are divided in three major regions: Central Asia with an adult literacy rate of 99%, East

Asia and the Pacific at 91%, and South and West Asia at 58%. As with the Arab states,

the intra-region variation is high. Some of the causes of the differences are economic

development, previous and past socio-political conditions, and to a lesser degree

geographic situation (EFA, 2006b). The illiteracy rate in Asia is higher than the world

average and other regions except for Africa.

Teaching Agriculture to Adults

The modernization theory advocated, in the early 1960s, a large expansion of

schooling based on the human capital theory, which saw education as a productive

investment essential for economic growth. This view reinforced the understanding that

less developed countries were undeveloped because of their basic characteristics,

including their poor education and skills levels (Walters and Watters, 2001). Adult

education is embedded in the political, social, cultural and economic processes of society.

The information above suggests that the nature of adult education policies, programs and

practices reflects the interests and values of different social groups, and the distribution of

power and influence in the society (Walters and Watters, 2001).

In the last 20 years most developing countries have embarked on numerous adult

education programs that focused on skills development in both the formal and informal









economies. Within the context of globalized economies, economic development and adult

education, or adult learning, become even more urgent and complex (Walters and

Watters, 2001).

Agricultural education projects are based on teaching a topic to a determined

audience. Teaching, like other forms of information transmission, is a communication

process. Usually the teacher sends a verbal message, which contains some information, to

the learners who are expected to receive it and integrate it into their existing knowledge.

This process is not so simple. First, teachers have to encode their thoughts into

words and/or other forms of communication. Then students have to decode the message;

this means they have to make sense of it (Blum, 1996).

To make sure that this actually happens, teachers can do two things: strengthen

their verbal messages by additional means such as visual teaching aids, thus enabling

students to receive the message over two or more parallel communication lines (the ear

and the eye). However, the two parallel messages must be matched in order to have an

amplifying effect. If they are not, they create confusion ("noise," in the language of

communication) (Blum, 1996). Performing the activity and the educational materials can

help the learner remember the concepts taught, these are the parallel messages when

dealing with illiterate audiences.

Agricultural teachers have an advantage when teaching in the field. Students can

observe by themselves and through different channels of perception a situation that the

teacher might find difficult to put (encode) into words. Messages that are received by the

students are filtered and stored temporarily in the short-term memory. They are forgotten

after about 30 seconds if they cannot be kept in mind or transferred to the long-term









memory. Thus, we forget casual telephone numbers very quickly unless we make an

intellectual effort to remember them. The long-term memory receives new information

better when it fits into an already existing framework of concepts. Incomprehensible and

unclear messages are not easily stored in the long-term memory and they are quickly

forgotten. Competing verbal and audiovisual messages are difficult to cope with.

Showing something to students and talking about something different weakens the

transmission of the message (Blum, 1996), hence, the importance of content-relevant

educational materials that are easy to comprehend to the audience (learners).

The transfer of technical skills seems to be even more restricted. In most cases it

was found that, with practice, the speed and quality of a given technical task could be

improved, but that this does not help to improve other practices. However, the transfer of

practical training can be enhanced to some extent when students understand the principles

that underlie the practices. In agriculture, this means that we can enhance the teaching of

practices when we make sure that students understand why they should do things the way

they are taught (Blum, 1996).

When learning needs reinforcement, educators can use an array of educational

materials. However, when dealing with illiterate audiences the available materials are

hard to find, and if available they are not always relevant to the audience. Illustration-

based educational materials are the best option when trying to provide support materials

for illiterate people.

Agricultural Education Using Images

Most of the communication means containing images are especially appropriate for

a public that has received little or no formal education. Its visual nature attracts attention









and helps the message to be transmitted at a glance (De Paolis, 1994). The images have to

generate participation and identification of the observer with the subject or object shown.

When referring to images there has to be a distinction between photographs and

illustrations or drawings. That difference will be fundamental to differentiate the iconic

cultures; some cultures may identify photographs more easily than drawings or vice versa

(De Paolis, 1994). However, that separation might also be economical and technological

given the differences in printing costs and equipment needed for photographs and

drawings. The different elements of the messages have to be composed in a way to

contribute to producing certain effects on the recipient (audience).

Justification

A difficulty commonly encountered in the preparation of illustration-based

educational materials is the preparation of the artwork, especially when the material is

intended for an ethnic or language group or groups other than the one creating the

material. Over the years, attempts have been made to supply visual models that might

make the job of drawing visuals easier for workers with limited training. Another

problem often encountered is the difficulty in finding experienced personnel to prepare

the materials quickly and easily. It has also proven difficult to adapt (localize) materials

which have been successful in one region or country to another ethnically or culturally

different one because the models do not lend themselves to change: instead, project

workers use available materials that are not appropriate ,or have to create educational

materials from scratch. With the increasing numbers of computers being used in the field

there is growing demand for a simpler and more direct system to produce appropriate

materials.









The user should be able to choose among the images that convey the desired

message, adapt them as necessary and print them out in a very short time. The images

should be realistic and as detailed as necessary.

The main advantage of such a system is that the same images could be easily

changed for a variety of different uses and formats. Any changes on the illustrations can

be easily and quickly done on the computer screen. There is no need to make entirely

new drawings when the audience changes. The image bank can be made available to

several organizations.

The general steps that have to be followed to develop educational illustrations are;

1. Decide on the form, context, and use of the illustrations based on an understanding
of the audience (their attitudes and practices) and the development of strategies
aimed at changing behavior in line with the established goals of the project.

2. Collect images (the easiest would be to use images already existing in an "image
bank,") from other materials with the respective permission, or scenes that could be
photographed.

3. Adapt the existing materials to make them culturally appropriate for the audience's
needs, interest and conditions.

4. Prepare the educational materials containing the illustrations in the desired format,
and reproduce the materials.

Presently, this process is mostly done manually and can be very time consuming.

Another drawback is that resources are wasted since materials developed in other projects

are not adapted or reused in other applications. The use of a computer assisted process to

produce, store, and manage the illustrations, and then develop the educational materials,

would make this process easier and more efficient.

The potentials of this type of system to produce educational materials are just being

recognized. Aside from saving time and money, using the computer also allows the

production of specialized illustrations from the image bank. Handouts and flyers can be









produced from images in the bank and produced in small numbers on a printer or

photocopy machine. High quality small editions of training materials can be produced

easily and quickly for workshops and seminars. The use of computer graphics to produce

project support materials could simplify a costly and complex task. This would bring

enormous potential benefits to projects developing educational and instructional support

materials (Tisa, 1991).

Overall Objectives of the Study

The main objective of this study is to develop an ontology for the irrigation and

water management domain. Ontologies have been proposed to solve problems that arise

from using different terminology to refer to the same concept or using the same term to

refer to different concepts (Beck and Pinto, 2002). Ontologies can be used to organize

metadata and to order concepts in a given domain, while allowing browsing, search,

tagging and classification of documents.

Secondary objectives of this study are:

* To adapt a process to develop ontologies in the agricultural domain. Create an
ontology for irrigation related materials. The information contained in the ontology
will focus on the knowledge needed in developing countries to improve irrigation
practices.

* To present the information on more than one language (i.e., English, Spanish) and
other characteristics that will allow localization of the materials to be developed.

* To find a technology capable of creating didactic manuals "on-the-fly" for
extension education, and to print those materials "on-demand."

* To evaluate the illustration-based educational materials on the field.

* To demonstrate the necessity of a tool to create didactic manuals especially for
people with low literacy/education levels, or little knowledge of the topics. This
will include, illiterate people (mainly in developing countries), people without
knowledge of English (i.e., foreign agricultural workers), children (i.e., 4-H).









Methodology

This project focuses on the creation of an irrigation domain ontology. The ontology

includes text, pictures and drawings related mainly to irrigation and water management.

The content is organized by topics (i.e., surface irrigation, water conservation, etc). The

use of this tool potentially will avoid many of the delays, costs, and inventory issues

associated with traditional development of educational materials. This could facilitate the

transfer of information from the extension specialists to extension agents to final client.

The base for a successful implementation of an ontology system is a narrow and

clearly defined knowledge domain; the choice of subject is crucial to a successful

implementation. The process to create the ontology includes the collection of all relevant

information for all topics that the irrigation ontology will cover. The information includes

text and other visual aids (e.g., pictures, diagrams, drawings).

Since the objective of this work is to create educational materials for people with

low levels of education, the next step is to develop drawings that will be used to explain

some processes and ideas to people that are not able to read. A big challenge to this

project is to achieve the automation of the process of creating situation and culture

specific drawings from digital pictures.

The end product will be a tool that facilitates the creation, storage and management

of content in multiple formats. The ontology-based tool will also allow localization of

multiple properties of graphics and text. Finally, it will permit the development of

educational materials ranging from manuals containing just text and technical language

for the extension agents, and educational materials with visual aids and some brief text

for illiterate learners.









The field evaluation of the illustrations was done in a rural area in El Salvador. It

consisted of the use of closed and open-ended questions. The data collected was helpful

making changes and improvements in the content, illustrations and format of the

educational materials. The comments and results were incorporated in the final

development of the graphic materials.

Expected Outcomes

The main outcome of this project was the creation of an irrigation ontology that

allows the storage of multilingual text and visual aids; the data stored should be easily

manipulated (allow for localization) in order to create easy to understand and reproduce

educational materials.

Organization of the Dissertation

This dissertation is organized in six chapters. Chapter 1 includes a literature review

of the main concepts utilized, also in this chapter the justification, objectives, and

methodology for the study are presented. The ontology modeling methodology is

presented in two chapters; specification, conceptualization, and evaluation are explained

in Chapter 2, while formalization and implementation are presented in Chapter 3. Chapter

4 shows the incorporation of vector graphics into the irrigation ontology to allow the

development of illustration-based educational materials. The field evaluation of the

educational materials conducted in El Salvador (Central America) is presented in Chapter

5. Finally a summary of the dissertation and conclusions are presented in Chapter 6.














CHAPTER 2
IRRIGATION ONTOLOGY MODELING

Introduction

An increasing number of information resources require improved information

management systems. There are several approaches to organize information; the most

common are glossaries and thesauri. Glossaries are lists of terms with their meanings

specified as natural language statements. Thesauri provide descriptions and additional

semantics between terms like synonym and antonym relationships.

A basic ontology can be very similar to a thesaurus. However, the ontology is not

limited to the types of relationships present in a thesaurus; instead it has a series of

features that improve its search and conceptual capabilities. An ontology can be regarded

as a particular knowledge base, describing facts assumed to be true by a group of users of

a certain domain.

Thesauri

Thesauri provide only very basic modeling paradigms and no knowledge can be

extracted from a thesaurus except simple keyword relationships (Lauser, 2004). A

thesaurus is a networked collection of controlled vocabulary terms based on hierarchical,

equivalent and associative relationships. Thesauri are limited in the inter concepts

relationships that can be represented. Hence, the specific information that can be

extracted is also limited. A thesaurus is based on concepts expressed as terms and some

relationships among those terms. Term is a word or expression that has a precise meaning

in some science, art, profession, or subject. The types of relationships available for the










thesaurus may require for the terms to be arranged in categories that do not form a logical

hierarchy. Two examples are presented, AGROVOC (2005) from the United Nations'

Food and Agricultural Organization (Figure 2-1), and the United States National

Agricultural Library Thesauri (NALT, 2005) (Figure 2-2).


AGROVOC Thesaurus


Last Update: November 2005


AGROVOC is a multilingual, structured and controlled vocabulary designed to
cover the terminology of all subject fields in agriculture, forestry, fisheries,
food and related domains (e.g. environment).

Search term: [ Search
v, starting with 0 containing text 0 exact match


EN : Irrigation

FR : Irrigation

ES Rieo

AR : 3
ZH :??
PT :Irricacgo

CS : zavlaha

3A : ?
TH : nitdraiii vml


NT: Irrigation continue

NT : Arrosage

NT : Irrigation par rotation

NT: Epandage des eaux usees

NT: Irrigation en hauteur

NT: Irrigation a la demand

NT: Irrigation de complement

NT : Irrigation fertilisante
RT: Riz inonde

RT: Rbseau d'irriqation
RT: Gestion des eaux

RT: Salinisation du sol

RT: Culture irricuee

RT : Matriel d'irrigation

RT: Hydraulicue aqricole


Figure 2-1. View of the AGROVOC Thesaurus


In Figure 2-1, in the left column a list of terms in different languages that

correspond to the term "irrigation" can be observed. In the right column another set of










terms is presented, preceded by NT, or RT. NT is used for narrower term, this means that

it is a term more specific than irrigation. RT means related term; it is a term that is not too

closely related to "irrigation."

The existing relationships are designed to give the terms semantic logic, rather than

to indicate relationships like "part of," or "belongs to," that are common in ontologies.

The basic relationships that can be encountered in a Thesaurus are hierarchical "Broader

Term" (BT) and "Narrower Term" (NT), equivalent "Use Preferred Term" (USE) and

"Used for" (UF), and associative relationships "Related Terms" (RT) (Hassen, et al.,

2004).


USDA United States Department o Agriculture
SNational Agricultural Library






SNew Search | Show Hierarchy Use your term for SEARCH in: Select Database


irrigation

Scope Note
Application of water to soil for the purpose of plant production.
Used For
herbigation *
watering *
Broader Term
irrigation and drainage
soil management
Narrower Term
irrigation canals
irrigation management
irrigation systems
irrigation water
Related Term
chemigation
fertigation
irrigated conditions
irrigated farming
Figure 2-2. View of the NAL Thesaurus









Apart from the display of the related terms, the only difference between the NAL

thesaurus and AGROVOC is that the former offers a brief definition for some of the

terms that it contains.

The main objective of a thesaurus is to create a hierarchy of related terms. Terms

could be defined as the "names" of the concepts. A thesaurus basically takes taxonomies

(a classification that arranges the terms into a hierarchy) and extends them allowing other

statements to be made about the terms. Thesauri allow the search of terms in a structured

manner; they also allow the search of related terms relatively easily, since all related

terms should be located close to each other.

Ontology

Ontology is a formal, shared, explicit specification of a conceptualization within a

domain (Gruber, 1993). Conceptualization refers to an abstract model of some

phenomenon. Shared means that an ontology captures consensual knowledge accepted by

a group (Benjamins et al., 2002). The features contained in an ontology are classes,

subclasses, instances, properties, and complex (inter) relationships between terms.

Ontology is a more complete structure to describe a domain's concepts as well as

multiple relationships among those concepts. An ontology formally describes a domain

(Antoniou and van Harmelen, 2004); it provides a generic way to reuse and share content

across applications and groups (Pinto and Martins, 2001). However, it is important to

remark that the model can only be considered an ontology if it is a shared and consensual

knowledge model agreed upon by a community (Hassen, et al., 2004; Antoniou and van

Harmelen, 2004).

A formal ontology is a controlled vocabulary expressed in an ontology

representation language, a model for describing the world that consists of a set of









concepts, descriptions or properties, and relationships. This language has a grammar for

using vocabulary terms to express something meaningful within a specified domain of

interest. An ontology representation of the domain should try to be a resemblance of the

real world complexities. The interrelations present between terms in an ontology allow

the search tool to produce a list of related and relevant terms. All the associated

information related to the term being search is retrieved. An ontology typically is shared

or built with the collaboration of domain experts (Pinto and Martins, 2001).

Ontologies are widely used in Knowledge Engineering, Artificial Intelligence, and

Computer Science, in applications related to knowledge management, natural language

processing, e-commerce, intelligent integration information, information retrieval,

database design and integration, and education (G6mez-Perez et al., 2004).

Ontologies have been proposed to solve problems that arise from using different

terminology to refer to the same concept or using the same term to refer to different

concepts (Beck and Pinto, 2002). The term "ontology" is a branch of Philosophy that

deals with the nature and organization of reality. Aristotle first defined it as "the science

of being as such" (Guarino and Giaretta, 1995). All type of communications, including

the internet with its great capacity to disseminate information, need a shared vocabulary.

Even a simple list of terms can be viewed as an ontology, since it is a set of definitions

that helps to better understand a topic (Passin, 2004).

The Semantic Web is based on ontologies for organizing large collections of

knowledge. Ontologies allow searching information distributed across multiple sites on

the web, and in different languages (Beck and Pinto, 2002). The Semantic Web provides

a common framework that allows data to be shared and reused across application,









enterprise, and community boundaries. It is an extension of the current web and it

contains the information which is given well-defined meaning, better enabling computers

and people to work in cooperation (W3C, 2001a). The way that knowledge is stored and

organized influences the retrieval problem (Beck and Pinto, 2002). Conventional

information retrieval technologies like the ones used in web search engines are not as

precise and do not always retrieve relevant information.

The knowledge organization in concepts and the relationships among those

concepts within a domain is what improves the searching capabilities of an ontology

(Passin, 2004). Information resources are attached to the ontology terms to create a

complete database. As a result, users can perform queries to retrieve the specified

information (Beck and Pinto, 2002).

Research ontologies are becoming more common, as a tool to describe a

vocabulary's meaning and the relations among those meanings. The simplest ontology

describes a hierarchy of concepts related by assumed relationships. They aim at

improving the communication between computers and humans. Ontologies have

applications in software development, research, and database applications. Reusability

means that the ontology should allow knowledge sharing and reuse. An ontology can be

used to organize metadata and to provide an order to concepts in a given domain, while

allowing browsing, search, tagging and classification of documents. Knowledge

acquisition permits the ontology to model the domain of the application. Reliability and

maintenance allows consistency check for software development.

Ontology Classification

According to their accuracy in characterizing the conceptualization to which they

commit, the ontologies are divided into fine-grained and coarse. For this project a coarse









ontology was developed. This means that the ontology is based on terms and concepts

already agreed by users, and it is designed to support limited and specific services.

Ontologies can also be classified by the level of generality as top-level ontologies,

domain and task ontologies, and application ontologies (Guarino, 1998). The irrigation

ontology built here is a domain ontology; this means that it describes a vocabulary related

to a generic domain (irrigation) on which it focuses.

Ontology Languages

A couple of the languages (Beck and Pinto, 2002; Passin, 2004) used to define

ontologies are the Resource Description Framework (RDF), and DARPA, the Agent

Markup Language & Ontology Interchange Language. RDF (W3C, 2002) has developed

on top of the extensible markup language (XML) (W3C, 2004b) for the purpose of

describing web resources. The DARPA Agent Markup Language & Ontology

Interchange Language (DAML+OIL) that is being developed for building more complex

ontologies (DAML, 2004; W3C 2001c). Both are based in semantic networks, however,

some of them differ in their level of expressiveness, and this affects the kinds of

inferences that can be applied.

Ontology Editors

Ontology editors (or builders) were developed to help create ontologies in different

domains. Some of the ontology editors are OntoBroker, Protege-2000, Ontolingua, and

ObjectEditor. OntoBroker created by the Institute for Applied Computer Science and

Formal Description Methods (OntoBroker, 2004) uses HTML, XML, and RDF. Protege-

2000 developed by the Knowledge Modeling Group (KMG) at Stanford University,

allows the user to create a domain ontology. Ontolingua Server (http://www-ksl-

svc.stanford.edu:5915/) is widely used. It maintains a large library of ontologies that can









be reused, and permits collaboration among various authors (Farquhar et al., 1995). The

tool used to construct the irrigation ontology was the ObjectEditor, a Web-based tool for

constructing ontologies within specific domains

(http://orb.ifas.ufl.edu/ObjectEditor/index.html) developed in the Department of

Agricultural and Biological Engineering at the University of Florida (Beck, 2003a;

2003b).

Objectives

The objectives of this chapter are 1) to select a modeling methodology for a domain

ontology, 2) to use this methodology to define and model an irrigation ontology, and 3) to

compare the irrigation ontology with some existing thesauri.

Methodology

There are several methodologies to build ontologies, however the one that best fits

the irrigation domain ontology is presented below. There are some typical steps that

should be followed to construct an ontology (Uschold and King, 1995; Pinto and Martins,

2001):

* Specification
* Conceptualization
* Formalization
* Implementation
* Evaluation, maintenance, and documentation.


These steps are represented in the ontology life cycle diagram (Figure 2-2); they are

related to most software engineering activities. Various authors have developed some

variations of the life cycle. One of the most accepted is the evolving prototyping life

cycle (or evolutionary cycle).












Evaluation Specification






Conceptualization
Implementation



SFormalization

Figure 2-2. Activities of the ontology development life cycle

In this cycle the developer can go back from any stage to any stage of the

development process. This means that the ontology can be modified until the evaluation

is satisfactory and all the objectives of the ontology are met (Beck and Pinto, 2002).

Ontology Specification

Ontology specification refers to the definition of the scope of the ontology. The

scope of the ontology presented in this work is irrigation knowledge domain, related to

small farmers' irrigation systems. One question that should be asked at this point is: Why

develop an ontology? Some of the reasons for the development of an ontology for

specific domain are (McGuinness and Fridman Noy, 2001):

* To provide a common structure of information within a domain
* To make domain assumption explicit
* To allow the reuse of domain knowledge
* To analyze domain knowledge









Ontology Conceptualization

One of the basic applications of ontologies is having an agreed set of terms and

concepts organized in order to facilitate information use by humans and computers.

Uschols and Gruninger (1996) recommend having brainstorming sessions to compile

relevant terms and phrases that may later constitute concepts in the ontology. The

structure of the ontology becomes apparent by grouping the terms in related areas. It is

also important to consider closely related or equivalent terms to avoid duplication of

concepts. An ontology should effectively minimize ambiguity and if possible, all

definitions should be defined in natural language. In some cases examples may be needed

to clarify definitions (Uschols and Gruninger, 1996).

Knowledge acquisition, the next step after the definition of the domain and scope

of the ontology is the definition of classes that describe concepts in the domain

(McGuinness and Fridman Noy, 2001). A top-down approach was selected, over a

bottom-top or a combination, to define the hierarchy of classes and subclasses, this means

that the classes (more general terms) were first defined and then the subclasses (more

specialized terms) and so on. This structure implies that work should start in the most

fundamental terms before moving to the more abstract terms within a domain (Uschols

and Gruninger, 1996). Once appropriate terms were defined, then their properties were

determined to describe the internal structure of the concepts. All terms have to be related

to other classes (as concepts are related to other concepts within the domain);

ObjectEditor allows four types of relationships: "association," "part," "sequence," and

"generalization."









Ontology Formalization and Implementation

Chapter 3 covers the formalization and implementation process. A detailed

explanation of all the processes is given; examples from the irrigation ontology are used

to illustrate the ideas and some modeling issues.

Ontology Documentation, Evaluation, and Maintenance

Continuous evaluation of the ontology is important in order to avoid problems or

make corrections before it is too costly to do it. The following evaluation guidelines

should be considered (McGuinness and Fridman Noy, 2001; Uschols and Gruninger,

1996):

* Develop a natural language (e.g., English) definition of the ontology
* Use common and agreed terms (e.g., standards, dictionaries)
* Notice relationships with other terms (synonyms referring to the same concept)
* Avoid circular reference when defining terms
* Use clear and concise definitions
* Provide examples to explain concepts when needed

Guidelines to document the ontology are desirable. All important assumptions

about the main concepts defined in the ontology should be documented (Uschols and

Gruninger, 1996). This documentation could be then used as metadata. Maintenance is a

constant process with any ontology. Ontologies are continuously confronted with

evolution problems, and maintenance is necessary to ensure the reliability of the

ontology.

The irrigation ontology was also evaluated against the NALT, AGROVOC and

IWMI descriptors. This evaluation was conducted to check how well the irrigation

ontology covers the terms within the irrigation/water management domain. A list with all

the ontology terms divided by topics was compared against the terms contained in

AGROVOC, NALT, and IWMI descriptor list.









Application of Modeling Methodology to Development of Irrigation Ontology

The modeling of the irrigation ontology was conducted using previously described

general steps: specification, conceptualization, formalization, implementation, and

evaluation. Developing an irrigation ontology is not a goal in itself. The main objective is

the use of the defined sets of terms and their structure for a particular purpose. As a

consequence there is not unique ontology of a specific domain (irrigation in this case).

An ontology is an abstraction of a particular domain, and there are always alternatives.

What was included in the irrigation ontology was determined by the final use of the

ontology. However, the irrigation ontology is still general enough to allow expansion and

shareability.

To help with the specification of the irrigation ontology some questions have to be

asked; the answers to these questions guide the rest of the modeling process:

1) Why an ontology? An ontology offers versatility that other knowledge management

systems (e.g., thesauri) cannot provide. Ontologies can be modeled to fit the user

necessities while being malleable enough to be adapted and shared for other uses.

Ontologies offer a better way to organize information, and manage content.

2) What will be the objectives (main and secondary) of the irrigation ontology? The

objectives for development if irrigation ontology were early defined as:

* Evaluate if the ontology can be used to develop educational materials.

* Collect and store irrigation and water management related information, mainly
focus at the development of educational materials for small farmers with low levels
of literacy.

* Store this information with a common structure that can be reused in other
applications.

* Offer tools for the development of multi-format educational materials for broad
audiences.









3) What is the scope of the irrigation ontology? The irrigation and water management

domain is very broad, so limitations have to be created for the ontology. The objectives

of the ontology help limit the ontology's scope, in this case subtopics that are closely

related to small farm irrigation. Examples can be water harvesting, soil conservation, low

cost irrigation systems, etc.

During the specification process it is important to remember that the final

application defines the domain of the ontology. Limitations out of the control of the

experts and modelers should also be considered; in the case of the irrigation ontology

available time and labor were the limits. By having these factors in mind the ontology

development process can be guided toward the ontology's objectives.

Conceptualization covers the process of collecting the information (knowledge)

that will be part of the ontology's content. At this point it is important to remember that

the ontology has to have a finite scope and purpose for its content. The modeling

methodology aims at representing the "real world" in logical terms using a given

ontology software editor, in this case ObjectEditor. A flow chart of the conceptualization

process is presented in Figure 2-3; it explains the flow from data acquisition to the

incorporation of the term into the ObjectEditor.

Considering the objectives and limits of the irrigation ontology, the first step

toward the actual definition of the irrigation ontology was to write down an unstructured

list of all the relevant terms expected to appear in the ontology. The list of terms relevant

to the irrigation domain was developed with information extracted from sources such as

the Land and Water Development Division of the Food and Agricultural Organization

(LWD, 2005), American Society of Agricultural and Biological Engineers (ASABE)









(ASABE, 2005), United States National Agricultural Library Thesaurus (NALT) (NATL,

2005), and the Extension Data Information Source (EDIS) from the University of Florida

(EDIS, 2005). A group of specialists from the University of Florida was also involved in

the knowledge modeling process. It is important to mention that every individual had a

personal ontology; meaning that each one had a particular perception of the knowledge

about the irrigation domain. In order to create a common ontology from the perceptions

of individual experts, much group discussion was required to arrive to a common set of

terms and their definitions.


Data
Sources
Datae r~ List of Data Object
S Acqutin Terms ProcessEdi







Figure 2-3. Diagram representing the conceptualization process

This complex process is illustrated with a simplified example. The first step was to

select a representative sample of irrigation related terms from the literature:

* Water management
* Precipitation
* Evapotranspiration
* Soil
* Aquifer
* Irrigation scheduling
* Microirrigation
* Infiltration
* River

The next step was to group the common terms together (conceptual clustering).









* precipitation, evapotranspiration
* soil, infiltration
* aquifer, river
* water management, irrigation scheduling

From here groups of related terms were created. Each group was named by the

general concept it represents. The groups were be modeled as "modules" in ObjectEditor

to facilitate its display.

* Weather
* Soil
* Water Resources
* Water Management

The groups were created according to the relevance they have to the ontology

developers and modelers. As new terms entered the collection, new groups were defined

if the existing ones were not adequate.

After the identification of the relevant terms, these terms were organized in a

taxonomic hierarchy. The irrigation ontology modeling methodology followed a top-

down approach (Prieto-Diaz, 2002). This means that the more general terms were placed

higher in the hierarchy, and the terms became more specific towards the lower levels of

the ontology. The irrigation ontology was classified following existing classification from

the literature, and by agreement among the experts involved in the modeling of the

irrigation ontology.

For topics like "system design" or "irrigation efficiency," the process of selecting

the terms, definitions, and the determination of relationships among those concepts was

iterative, meaning that the process had to be repeated multiple times until all the experts

agreed on a common irrigation ontology. For other topics like "weather," "plant," or









"soil," the classification process was much simpler, having only to follow pre-existing

classifications, from the sources sited above.


Weather Water Sources System Design











Plant Irrigation Irrigation System Manage...
Plant






Person




Soil Irrigation Equipment and ..
Drainage

Figure 2-4. Main topics covered by the Irrigation Ontology in ObjectEditor

The documentation part consisted of recording the sources of the information

collected and incorporated into the irrigation ontology. It also included any comments

made during the modeling process. Evaluation and maintenance of the ontology were

interrelated. Below are some questions that were used during the evaluation process:

* Does a selected term fit the ontology specification?
* Does the location of a term in the hierarchy make sense?
* Do the gloss and definition of the term are in sync with the specific ontology
domain?
* Were there any errors during the ontology implementation process? (e.g., was the
correct relationship used?)









Errors were corrected as encountered during the evaluation process. This facilitated

the maintenance of the irrigation ontology. The continuous evaluation and correction

process helped avoid the necessity of larger modifications at the end of the modeling

process.

The irrigation ontology was compared to the NALT, AGROVOC thesauri, and

IWMI descriptors. At the time of this evaluation the irrigation ontology contained around

270 terms from the irrigation and water management domain. NALT refers to the United

States National Agricultural Library Thesaurus (NALT, 2005). The 2006 edition is the

fifth edition of the NAL Agricultural Thesaurus, first released in 2002. The total number

of terms contained in the NATL is 66,417 with definitions for 2,038 terms. AGROVOC

is a multilingual, structured and controlled vocabulary designed to cover the terminology

of all subject fields in agriculture, forestry, fisheries, food and related domains. Currently,

it works in the following languages: English, French, Spanish, Arabic and Chinese. Other

national versions include Czech, Portuguese, Japanese and Thai language versions.

German, Italian, Korean, Hungarian, and Slovak language versions of AGROVOC are

under construction. As an example, the English version has 28,127 terms, while the

Spanish version has 28,123 terms. It was developed by the United Nations' Food and

Agricultural Organization (AGROVOC, 2005). An unpublished list of 2,388 irrigation

related descriptors provided by the International Water Management Institute (IWMI) in

Sri Lanka was also analyzed during this study.

NALT, AGROVOC thesauri, and IWMI descriptors were used to check how well

the irrigation ontology covers the terms within the irrigation/water management domain.

A manual search of the terms stored in the following sources was performed. The search









was conducted using the online search tools for each of the datasets described above. For

the evaluation the terms were divided into nine main areas or topics: Irrigation Water

Sources, Weather, Plant, Soil, Drainage, Chemigation, Irrigation System Design,

Irrigation System Management, and Irrigation Equipment and Structures. A list with all

the terms included in the irrigation ontology divided by topics was compared against the

terms (or synonyms) in AGROVOC, NALT, and IWMI descriptors. The results are

presented in Table 2-1 below.

Table 2-1. Comparison by topics of various sources vs. irrigation ontology
Topics Irrigation AGROVOC IWMI NALT
Ontology
Total # of
terms % % %
Irrigation Water Sources 45 20.00 26.67 28.89
Weather 13 53.85 46.15 53.85
Plant 16 68.75 25.00 37.50
Soil 31 64.52 48.39 87.10
Drainage 20 15.00 15.00 10.00
Chemigation 16 6.25 12.50 18.75
Irrigation System Design 41 4.88 2.44 9.76
Irr. System Management 30 23.33 0.00 10.00
Irr. Equip. and Structures 59 0.00 0.00 16.95
Source: Corejo, 2006


% = # Terms / # Terms in Ontology 100 Equation 2-1

1 Number of terms from AGROVOC or IWMI matching terms in the irrigation

ontology.

From Table 2-1 can be observed that AGROVOC, IWMI, and NALT contain a

higher percentage of the same terms as the irrigation ontology in three main topics. Those

topics are soil, plant, and weather with values ranging from 37.5% to 87%. For the topics

more relevant to irrigation like system design, system management, and irrigation

equipment, the values range from 0% to 23.3%. Irrigation equipment and structures are









the topic where less matches occurred, the only database that had any terms related to this

topic was the NALT with 16.95% of the terms.

In Table 2-2 a more general comparison is presented. The total number of terms

found in each of the datasets compared to the total number of terms from the irrigation

ontology is shown. Again using Equation 2-1 the matched terms from each of the sources

were compared to the total number of terms from the Irrigation Ontology (271). IWMI

contained 15.8% of the terms, AGROVOC 22.1%, and NALT 27.68%. Even so, the

IWMI descriptors in theory should have more irrigation and water management concepts;

this set is the one that has fewer of the terms contained in the irrigation ontology

Table 2-2. Comparison of various sources vs. irrigation ontology
Topics Irrigation AGROVOC IWMI NALT
Ontology
Total number of terms 271 60 43 75
Percentage from Irr. Ontology 22.14 15.87 27.68
Source: Corejo, 2006

Conclusions

The presented methodology for ontology development seems to work well for the

irrigation ontology. This framework is generic enough to be used to create other domain

ontologies especially within the agricultural field. The irrigation ontology developed

using this methodology should fulfill requirements for compatibility and shareability with

other ontologies. The above approach made the modeling process very straight forward

and it was easily followed by the experts that had little experience with ontology

modeling. As stated in the objectives, the domain of the irrigation ontology was very

limited. Because of the narrow domain of the irrigation ontology, it was possible to do all

the modeling manually. The final irrigation ontology developed in this project has more

than 270 terms and 300 relationships however the process was time consuming and









required multiple brainstorming sessions for the experts to agree in the final ontology.

For larger ontologies an automatic modeling methodology should be developed to

expedite this process.














CHAPTER 3
IRRIGATION ONTOLOGY FORMALIZATION AND IMPLEMENTATION

Introduction

Ontologies can be used to support a great variety of tasks in diverse research areas

such as knowledge representation, natural language processing, information retrieval,

databases, knowledge management, online database integration, digital libraries,

geographic information systems, and visual retrieval or multi agent systems. Ontologies

enable shared knowledge and reuse where information resources can be communicated

between human or software agents. Semantic relationships in ontologies facilitate making

statements and asking queries about a subject domain due to the use of conceptualization.

Domain ontologies are reusable in a given specific domain (medical, engineering,

law, irrigation, etc.). These ontologies provide vocabularies about concepts within a

domain and the relationships among those concepts, about the activities taking place in

that domain, and about the theories and principles presented in that domain. There is a

clean boundary between domain ontologies and upper-level ontologies. The concepts in

domain ontologies are usually specializations of concepts already defined in top-level

ontologies, and the same might occur with the relationships (Mizoguchi et al., 1995; van

Heijst et al., 1997). Ontologies offer ways of better managing the vast educational

resources that have been and are still being developed by organizations such as the U.S.

Cooperative Extension Service and United Nations Food and Agricultural Organization.

Issues involved in educational resource management include properly identifying

(cataloging) each resource, where large numbers of resources exist at many levels of









granularity ranging from entire training curriculums to individual lessons or modules to

the content of those modules including individual text fragments, images, and other

multimedia resources. New authoring tools for generating this content in the context of

ontologies, and tools for automatically generating presentations in different formats from

shared content are needed. Learning object technologies and standards such as SCORM

(Godwin-Jones, 2004) addresses ways of better packaging educational resources into

reusable components. SCORM provides a metadata standard for describing learning

objects, and includes tags that can reference taxonomic subject classification systems

including ontologies (although SCORM itself is not a standard for ontologies). Content

management systems are database management systems for storing content in the form of

text and other multimedia resources. They store content in a presentation-independent

way, and are capable of generating particular presentations from content according to

different customizable styles. Combining content management systems with ontologies

and with learning object standards leads to an ontology management system that can

better organize educational content, facilitate content development, and automate the

process of generating educational materials. By using ontologies the information

publishing process can be greatly facilitated (Clark, et al 2004).

This approach is being used at the University of Florida on a range of projects,

including one on developing educational extension materials to help farmers with limited

formal education understand basic principles of irrigation. These educational materials

rely heavily on graphic images to illustrate irrigation principles such as creation of water

retention structures or general layout of irrigation systems (text is limited or optional

because many of the farmers using these materials are illiterate). Furthermore these









materials must be adapted to fit local cultural environments. For example, illustrations

should change to show crops and agricultural systems local to the area where they are

applied, and people should be presented in gender and culturally specific contexts.

An ontology can be the basis of a fully operational database management system

(Beck, in press). The concepts and relationships in the ontology also contain primitive

data such as text, images, and other multimedia resources that provide additional

definition of the concepts. The ontology management system includes a formally defined

ontology language which also acts as a data modeling (data definition) language for the

database, tools for inspecting and editing the ontology, operations for manipulating the

ontology reasonerss), and secondary storage management to support efficient processing

of these operations. An ontology manager was used to construct the irrigation ontology

along with associated educational content for the domain. Facilities that are part of this

system for automatically generating presentations from content are used to create Web-

based and printed educational materials. This process is described below.

The process of building the irrigation ontology is an important first step in

facilitating shared ontologies for this domain. The process of building working, shared

ontologies is still in its infancy. Although established standards for building ontologies

now exist, and formal methodologies are well developed, there is a need to build working

examples and demonstrate their utility.

The technology for content management, learning objects, and authoring tools for

creating educational resources likewise is in a rapid state of evolution. Conventional

presentation tools (Microsoft PowerPoint, Adobe Acrobat, and Macromedia Breeze)

while widely used, do not attempt to represent content in a presentation independent









format, and make no attempt at classifying content in any context, let alone one as

sophisticated as an ontology. Building educational materials within an ontology

management system hopefully shows the advantages of this approach to better organize

educational resources, and gain flexibility in automatically presenting educational

materials to meet individual learning styles, native languages, and respect local cultural

contexts.

Water management and irrigation is a major component in agricultural technology.

Currently no known ontology on irrigation exists. Irrigation ontology was constructed to

provide a framework for organizing materials within this specific domain. This ontology

can become a starting point for a larger ontology covering irrigation concepts in general.

This chapter presents the methodology used to construct the irrigation ontology, briefly

describes the tools and environment used to construct the ontology, and provides details

of the resulting irrigation ontology including the top-level concepts, and some examples

of small domains within the ontology. A complete list of the terms and concepts

appearing in the ontology is included in Appendix A.

Objectives

The objectives of this chapter are: 1) Formalization of the irrigation ontology using

ObjectEditor. 2) Implementation of the irrigation ontology as part of the ontology

modeling process presented in Chapter 2 using ObjectEditor. 3) Identification and

discussion of modeling issues encountered during the implementation process.

Ontology Formalization

The steps in the ontology modeling methodology are specification,

conceptualization, formalization, implementation, and evaluation. In this chapter










formalization and implementation are discussed in detail. The other steps of the irrigation

modeling methodology for the irrigation ontology were explained in detail in Chapter 2.

The irrigation ontology was constructed using ObjectEditor a graph-based, Web-

based tool (http://orb.ifas.ufl.edu/ObjectEditor/index.html) for constructing ontologies

within specific domains developed at the University of Florida, USA (Beck, 2003a,

2003b).



Projects Windows Help
t ~j[:L: [] -- English v 4 EDIT
Mr.nicipal Soi.irrec

Pre-ipitation


Rerlaimed v'ater









Water Qualityl
"ItI al ,t








Soroe'- anre S.sttem


Figure 3-1. View from the ObjectEditor

ObjectEditor can be run on-line in any Web browser (utilizing a Java plug-in) that

communicates to a remote server hosting an object-oriented database management system

(ObjectStore) (Figure 3-1). ObjectEditor's interface enables users to interact with the

ontology in order to define content objects and represent how the objects in a domain are

interrelated. ObjectEditor provides a complete ontology management system for editing,









viewing, managing physical storage, managing multiple users, and providing reasoning

and query processing facilities. Apart from the knowledge modeling that the irrigation

ontology represents, ObjectEditor allows the storage and management of ontology

content. Content can include text, graphics, and mathematical equations. This content can

be rendered in multiple formats depending on the method of presentation. For example,

Web pages for personal computers and PDA's, and files (e.g., PDF) for printed media.

The separation of content from format typical for an ontology increases the flexibility at

publication time, reducing time and work needed to reproduce the same content in

different media. The process of developing an irrigation ontology using ObjectEditor is

presented here.

ObjectEditor defines its own formalization of definitions and constraints for the

terms and relationships used to implement the irrigation ontology. In the irrigation

ontology the concepts are represented as classes. Each class can have multiple properties;

ObjectEditor supports simple string, rich text, integer, float, range, and images, as data

types for the properties. Associations represent relationships between objects. All the

subclasses inherit the properties and associations of their superclasses. Each term (class)

has a short description or gloss (Figure 3-2); this facilitates the definition of the sense of

each concept. The gloss can be expressed in multiple languages. The irrigation ontology

is implemented in English and Spanish.










k Object Editor
Projects Windows Help


t EI [1 1 1 T E,.,.T,.,

E" 11:,,:l1 il,,rr l


le,,,, i i.,,t',: l-j e ., I :..- _,: 'I' ll,',ll,:.: ,,,_ -ler'
C L L .
E ,at Fele Te,,,
i |E : l r,' .il"El'.: .l .


F ; r, i,' .-e n i.- ie r L


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IE ,.]i h I1 ,


[] |E 'p'' 3p ''ar:pii.:cri so" prh





Iv
[ .JJ [ L;.etli J [ ioi J

IDGo.e.. Lrig
IThe e : ,' I 1. e. .ipiai.-,rn 5rjd liiar-pi 51i.-,r, Er,,jl,'t I
La -u ,'h de e ap'-., .--l', A hpcj ,pi hr-piia io'l' i-





Iv,
[ i-JJ | [ i'eille J


I -dd Fi..pellry


Elitelel l:bie.:II


Figure 3-2. Evapotranspiration term and its gloss (short definition).

Each term (class) presented in the irrigation ontology has its definition; this

ObjectEditor property allows the inclusion of a textual definition of the term in multiple

languages. The terms in the irrigation ontology have definitions in English (Figure 3-3)

and Spanish (Figure 3-4).


()biect [ditor
Projects Windows Help


I


























Figure 3-3. Definition of concept in English




Term Subclasses Instances Dethnihin Style
J % B u V := aa a V A'c
English :'Spanl; German

Evaporacion e3 ei process por ei cual agua en estado liquido
es cornvertid en vapor de agua (v.aporizaioni y removida de la
superficie de evaporacion (remocion de vapor) El agua se
evapora de mucaias superficies como lagos, nos.. pavimento,
suelo vegetation humeda


Figure 3-4. Definition of concept in Spanish

Ontology Implementation

For the irrigation ontology, the main irrigation related topics (terms) selected are:

Irrigation Water Sources, Weather, Plant, Soil, Drainage, Irrigation System Design,

Irrigation System Management, and Irrigation Equipment and Structures. ObjectEditor


Term Subclasses Instances Definihlnl Style
B U V A' O
English i Spanish German

Evaporation is the process whereby, liquid water is converted to
'rater vapour (vaporization) and removed from the e..,aporating
surface (vapour rerro'..'al i. V'.ater evaporaLes from a ./ariety of
surfaces, such as lakes, rivers, pa'/ements, soils and wet
vegetation









allows the creation of "modules" that permit the division of the ontology in sub areas;

this permits a less cluttered, more focused presentation of the terms and relationships in

the ontology. Modules are a visualization tool available in ObjectEditor; they are not a

part of the ontology modeling language. The modules do not contain the same number of

related terms nor do they have the same level of detail. During ontology implementation

the concepts and the relationships among concepts were defined. The implementation

process makes use of a top-down approach for knowledge modeling using the above

defined modules as nine major irrigation and water management topics.

As defined earlier, an ontology consists of the basic terms (concepts) and relations

between those terms. A domain specific terminology (set of concepts) was first

assembled in a vocabulary, then that vocabulary was organized according to the

objectives of the irrigation ontology (Chapter 2) and placed into nine well defined

modules

Modeling process consists of identifying rules, definitions and relationships

between terms and relations within a ontology. ObjectEditor has predefined rules in how

to create terms and how to use relationships (Beck, 2003a). ObjectEditor provides four

types of relationships: generalization, part-of, association, and sequence. Generalization

is used to represent superclass/subclass relationships; a "pine tree" is a subclass of the

class "tree." Part-of is used for objects that are physically a part of larger composite

objects; the class "tire" is a physical part of the class "vehicle." Sequence is used to

indicate that a concept follows another; in a sequence of classes, "socks" are worn before

"shoes." Association is used between two otherwise related concepts were none of the

three previous relationship types apply. These different relationships are graphically







43


represented in ObjectEditor by different types of vectors. The types of the relationships

are identified in Figure 3-5, within dashed-line rectangles.



---------------
I
'^ l rl'l' 12 lll HI I -lDitCc h F l 'iil 'l l / ': I l 1 'lr i "---? .: .:.l r.:l .r\, Jl d l l l
-----------

I- -1-|-'. -' /- -







I I
I I


F re: ire R tinlr

Prec -,.Ire LO .,r : :


Figure 3-5. Relationships supported by ObjectEditor

The association type of relationship has association properties that can be modified

by the user. An association name and a gloss have to be defined to give sense to the

association (Figure 3-6). The process of defining and association relationship depends on

the terms to be related. For example, it is known that a "pipeline" has a "pressure rating"

and a "pipe sizing" (Figure 3-5). This association is defined as has for this type of

relationships. The association name is provided to give more sense to the relation

between two terms, than a general relationship could give.














AocDtai9ric Nsrre aTteteri [ 9rcn ]

Enliirh

T TPEITLIE
FrTPerrrl Term ro: onte- Lrg'iar.lge
I lrIIi- t:. q I IEr. I': h I

v


,I:1,-.L L rig,.Ige
".TT.7tl t1 f rri'l ite Er .'-ri h I|





|ri Ne 'S etI 'ii'te C C iclr

Figure 3-6. Association relationship properties


In Figure 3-7, the "part of' relationship is used for physical parts like the classes

"manifold," "lateral," and "distribution equipment" that are parts of the class "pressurized

irrigation system." Another case represents the use of relationships of the

"generalization" type; this relationship used to relate sub-classes to a more general

concept or class. For example: "semi-circular," "ridge," and "triangular" are all sub-

classes of the more general term (class) "bund;" and "bund" itself is a sub-class of

"contour farming," and so on (Figure 3-8).

Pressurized Irrigation System


Manifold


Lateral


Distribution Equipment
Figure 3-7. Use of part-of type of relationship

























Figure 3-8. Use of generalization type of relationship

Most of the modeling issues are related to the selection of a wrong type of a

relationship for the association among terms. In Figure 3-9 the "conveyance system

design" with the terms surface, ground, and harvested water as parts of it are presented.


Surface Water

Water Sources --- Conveyance Syst. Ground Water

Harvested Water

Figure 3-9. Use of generalization type of relationship

The main issue with this design is that surface, ground, and harvested water should

be sub-classes of "water sources," associated through the generalization relationship with

"water sources," This design is presented in Figure 3-10.



SSurface Water


Water Sources Ground Water

Harvested Water

Figure 3-10. Use of generalization type of relationship









In an example of a sequence, a primary channel is followed by a secondary

channel, and secondary channel by a distribution channel. In this case the particular order

is important. In a real irrigation project the secondary channel can only be present after a

primary channel, and a delivery channel should go after a secondary channel, and this is

reflected in Figure. 3-11. Nevertheless, a delivery channel can sometimes go directly

after a primary channel if the secondary channel does not exist in the particular system.


Primary Channel --- Secondary Channel --- Delivery Channel

Figure 3-11. Use of the sequence relationship


Often concepts need clarification. Using the concept "precipitation" as the source

of water directly available to the plant is erroneous. However "precipitation" includes

rain, snow, and hail, and of these three concepts only rain is the precipitation that is

directly available to the plant.

Relocation of entire groups of terms may be necessary to make an ontology more

functional. As an example, originally the "irrigation system design" concept included

concepts related to system selection and equipment selection. System design includes

concepts like terrain conditions, soil characteristics, crop requirements, and others. All

these factors will influence the choice of system and are used in the calculations involved

in the irrigation system design. However, after initially including all those terms in the

design it was decided that equipment selection was related to irrigation system design,

but it would be better located within the irrigation equipment topic.

Following is a general description of the relevant points of each of the main topics

(Figure 2-4), known as modules in ObjectEditor. In this ontology all the terms are defined









in the context of their relation to irrigation. The "weather" module includes terms that are

indirectly related to irrigation like "wind," "radiation," "temperature" and "precipitation."

All of them contribute to "evaporation" to which they are related via associations.

Evaporation is also part-of evapotranspirationn" so the ontology will relate those two

terms. Since the methods to calculate evapotranspiration are important to determine

irrigation requirements the ontology also includes the "Pan," "Penman-Monteith," and

"Blaney-Criddle" methods of estimating reference evapotranspiration. It is important to

clarify that not all the terms related to weather are included; the irrigation ontology is not

intended to include all terms in any topic, just those relevant to the limited domain of the

ontology. However, ObjectEditor permits the sharing of the ontology so it can be edited

and expanded as needed for other applications.

The topic "plant" includes terms related to the plant physiology and also to the

water use by the plant. Basic concepts like "root," "stem," and "leaf' are all physical

parts of the plant and are related as such. Terms related to "plant type," and "growth

season" are also included since those concepts are related to "transpiration" and

evapotranspirationn" that are used to estimate "water requirement" terms that are also

included in this module. The two examples presented above show how the topics

"weather" and "plant" are related thru the term evapotranspirationn" demonstrating that

all the ontology is interconnected.

The "soil" module is formed by five main groups of terms, "soil available water,"

"soil chemistry," "structure," "texture," and "topography." One example is presented in

Figure 3-12, where "texture" is associated by the content of "clay," "sand," and "silt,"

and "loam" is the combination of specific proportions of those materials.











I EDIT

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I IIIT



rTi rifri


r.,lu li 1,










311Uli L ie Eul- Denr; it- I


Figure 3-12. Sample of the soil module

Other section of the same figure shows the term "soil moisture retention" and how

it depends on "mulch," "cover crop," and "conservation tillage," practices that affect the

soil capacity of retaining moisture. It does not appear on the figure but "soil moisture

retention" is also related to "texture" and "structure."

The "water sources" module includes the main sources of water used for irrigation.

Those sources include "surface water," "ground water," and "harvested water;" all of

them receive some water from "precipitation" then they are associated to it. Another

source in this module is "reclaimed water" (Figure 3-13).











.Object Editor
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rl i"I ', 11 i. rrr .



F re, rit inri


R;- I 3l1 led 1 ; [,J


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Water Quality






',-,r, e .lnr, r- t- -rr,

Figure 3-13. Sample of the water sources module

Also related to "water sources" but not subclasses of it are "water quality" and "water

quantity." Water quality is a smaller module that includes terms like "water hardness,"

"electrical conductivity," "pH," and "total dissolved solids."

Under the "drainage" topic the terms include "drainage considerations," "drainage

clogging" and "drainage design;" the design includes various sub-classes like "tile

drainage" and "ditches." All of these sub-classes are related with "drainage" via the

generalization relationship (Figure 3-14).











Projects Windows Help
1 English v 4 EDIT a

Free FIlo virn
c 1, I r ;e Deo- iltic-, i,



C ontrolleci

Eir ,ir, Clogging ELIfl 31iimes

I Ii. .i.Irlflq ei? r ,irnnie



Tile DI A'ii i ,e euigieijt-

Figure 3-14. Drainage module, sub-classes with generalization relationships

The "system design" module is the most complex of all the modules included in the

ontology. It has more than 50 terms and around 60 relationships. A view of a small

selection of this module is show in Figure 3-15. This module is related to most of the

other modules like "weather," "plant," "soil," "irrigation equipment and structures," and

irrigation system management." Some of the relationships and examples of complexities

of this module are presented below.







51


501i
Fre'. Ipitatiori






-'PiiiLJ Ei c io-













| le in eli il 'lion

IrrilM.tirn FPeniirieriert Chemigation



Op ReI 41jIr eleiil

Figure 3-15. A small section of the system design module

System design is related to the soil module through "soil characteristics," and to the

"plant" module via "irrigation requirement" and "crop requirement." Relations of the

system design with some other modules (topics) are presented below in Figure 3-16.

Irrigation system layout relates to the "plant" module through the terms "planting

system," and "spacing." Pumping system design is associated with "irrigation equipment

and structures" by way of "pumping equipment." Similarly, "conveyance system design"

and "pipeline." Pumping, "conveyance," and "distribution efficiencies" have also

relationships with terms in the "irrigation system management" module.











Projects Windows Help


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Planting 73 .tem -:p,mIJ


v 4 EDIT








Furlnpin Equllrnernt


Conri e? rn.:e 3 'temr Effi I


Pipelirie


[Cl'trihi.tion a Termr Effti


Figure 3-16. A section of the system design module

The next module comprises "irrigation system management" which is related to

"irrigation scheduling," "irrigation system maintenance," and "chemigation." Irrigation

system maintenance includes topics like "pump check," "pressurized irrigation," and

"surface irrigation" (Figure 3-17). The ontology aims at containing some of the practices

that a farmer should follow to maintain an irrigation system. For example "pressurized


Projects Windows Help


[--Y!=ter


Furnrping I3 .tern Efloieinl/


Fipe SIzIrni


_-3 '* I-l i e i l-i: l i. io


L-J










irrigation" includes "check lines for leaks," "clean lines or pipes," "clean filters,"

"irrigation system calibration," and "uniformity test."


v i,'l l.J l :lr l:er, r- r,,: I:. ti'ih ."

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L.On-terrrm -. er.ge Irrigati

"i lirriill: E-,, t-

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C Ihehk Funril


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: le.r Linec or Pipe.


C le an '_- 3313


1Ce31 Filter


i.liriil rrrilr Tei t


Irrirn tirn *- term C: alibr ti

Figure 3-17. Partial view of the irrigation system management module

The last module presented in Figure 3-18, is the "irrigation equipment and

structures" with the following sub-classes: "system control," "filtration equipment,"

"conveyance equipment," "pumping equipment," "distribution equipment," "system

controllers," and "chemigation equipment." The difference between "system control" and


II iIloIl, 3.leobulIll


I_-;V!;tp-r-ri ~mariririe


SEvile rn i i r'l .


P re -- 5,.Irl_. d Irr l-:'llr,










"system controllers" is that the first refers to equipment like "valve," "flow meter,"

"pressure regulator"; and the second refers to sensors and automatic controllers.


- uctin c '3de Irnecti n


,C lerri.. al llileitijri Eq u'ip


S i Uli [I l'. ,: t :

TE P


C lern i' llioCi E':ui t'I i'ent


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K i:ri[r~IIi~r


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Ei rinter


liri l rier


B,.irinlerT


Fi.mping' EqT.ilprmernt


El. I arlil, Pl.J iip


Figure 3-18. A section of the irrigation equipment and structures module

Conclusions

The implementation of the ontology is what allows the presentation and sharing of

the knowledge modeled and contained in the irrigation ontology. In order to create a

common irrigation ontology, a logical framework and classification of terms was


|Tools]


iI Ilrtig lbt in Eui


_ lelri ot ollil ,


f, rli'i.jlion E,:1,.l:pm e- rt,









developed with the help of experts in the domain. The terms included in the ontology are

clear and with definitions that are easy to understand. The resulting irrigation ontology

includes formal definition of concepts and, when need the relationships are also defined.

A structured and reusable vocabulary was developed for the irrigation ontology.

Building ontologies manually requires a lot of time, especially during the

conceptualization and implementation processes. The modeling process can be

complicated and may be difficult to reach consensus in some of the terms, their definition

and relationships. At this time the Irrigation Ontology consists of more than 270 terms,

and around 300 relationships among those terms (Appendix A). Future work should

include the development or incorporation of an automated implementation process in

order to work with larger datasets.














CHAPTER 4
EDUCATIVE ILLUSTRATIONS

Introduction

In many rural development programs in Latin America and Africa, field-level training for

small farmers is the most appropriate means of communicating new ideas and practices.

In many rural areas poverty and illiteracy are common and people cannot use training

materials containing only textual information and even graphical information may not be

understood if it is too technical or abstract. Printed educational materials are important

since people could use them to reinforce the concepts and remember what they have

learned. However, staff responsible for conducting the training often have few resources

to help them with the process. Appropriate resources usually have not been developed to

fit the conditions of a particular audience.

More work is needed in the development of applications that could facilitate the

production of personalized training materials as a way to transmit information. This

project is a response to this problem as it aims at development of an application to

produce appropriate training manuals for non-literate users. The focus of the project is on

Africa and Latin America, which contain a number of countries with a high rate of

illiteracy.

This project uses an ontology based system that is designed for storing content,

which includes concepts, media such as text, and images (i.e., pictures, drawings, and

diagrams) and can also include animations, sounds, and video. There are many

advantages in utilizing an ontology system for storing educational resources. Immediate









advantages are that educational materials can be more easily produced compared with

conventional tools (e.g., PowerPoint, PDF, and Flash). Information can be shared

and reused in ways that are not possible using conventional tools due to proprietary

restrictions. For example, conventional software is not designed to retrieve content from a

database. This means that if something changes in the content, then, the presentation has

to be changed manually. Flash permits linking to artwork stored in a database.

However, Flash is a proprietary language that does not allow easy manipulation of the

file that composes the graphic; instead a new graphic has to be developed to include each

desired variation. There is also a need for low cost development tools and the possibility

of cooperative work that can be done by working with an online tool. These are just some

of the reasons why a new system to produce illustration based educational materials is

needed. The use of an ontology will facilitate the updating of information in various

formats and different localized presentations (e.g., print, Web-based) can be created

automatically from the same content.

This work presents an approach for managing information and producing localized

educational materials by using an ontology system to manage content. It is applied to

irrigation and water management information topics and produces illustration (drawing)

based training materials for non-literate farmers.

Graphical Communication

Systems of communication based on graphics have been successfully employed

(e.g., Chinese, Egyptian, Mayan) (Yazdani and Barker, 2000). Nevertheless, to convey

information thru graphics, they have to be simple enough to be easily understood by

people with low educational levels, and at the same time those graphics have to transmit

complex information. Line drawings are a good tool to produce simple graphics.









However, to convey complex information just relevant information has to be included in

the drawing, avoiding the inclusion of unnecessary details. The graphics have to also be

relevant for the conditions of the people that are going to use them.

Development of educational materials that are culturally sensitive is called

localization. Localization is the process of targeting a product to a local clientele by

"translating" the product and adding local, specific features where applicable (Luong et

al., 1995). In the case of illustration based educational materials, these have to be

developed in such a way that the clientele using them can associate themselves with the

actions being presented in the manual's drawings (e.g., race, gender, tools, environment,

etc).

In this project, culture is considered as the collectively held set of attributes (e.g.,

values, believes and basic assumptions) and behaviors, which is dynamic and changing

over time. Culture affects many elements of communication such as, language, colors,

graphics, icons, date, time, numbers, currencies, units, and personal titles (Dahl, 2003). In

other words, culture affects the way people perceive things, and knowledge.

When an educational manual has to be produced for multiple audiences, for

example, in Africa, Latin America, and Asia, the conventional approach results in

duplication of efforts and poses the challenge of producing and updating the information

in different formats that are concordant to the individual realities of each culture.

The inclusion of localization is focused at improving communication among

facilitators and learners. Communication is the transfer of a message from one person to

another, so that it is understood, and hopefully, so that it invokes a response (Figure 4-1).

There is always a sender and a receiver in any communication. At least there is an









intended receiver. Sender and receiver have different personal and cultural realities. The

use of localization should increase the success of a message being transmitted among

parties.

Sender I Message Receiver


Encodes Decodes and
responds with
next mesifnle

Figure 4-1. Communication model adapted from Funch (1995)

Senders and receivers each have their own reality formed by their experiences, their

perceptions, their ideas, etc. (Funch, 1995). Due to this background they will perceive,

experience, and interpret things differently. Each individual will always perceive the

same event a little different. The message in western societies is often verbal, something

that is being expressed in language, spoken or written. But there is also a non-verbal

portion, covering everything else, most notably body language that is represented thru

images. Nevertheless, in any communication process it cannot be granted that the

receiver will interpret the message the same way as the sender intended it. In the usual

communication process among people, many factors have an effect on the message,

influencing what the receiver perceives from the sender (Figure 4-2).



Educational level
a] I e ] R eligion
SGender


Sender I Message I Receiver









Figure 4-2. Interferences on the communication model modified from Funch 1995.

When cultural differences are included in the communication process, this becomes more

complex. Language complications and cultural differences affect the transmission and

interpretation of a message. The inclusion of localization in the development of

educational materials should help reducing miscommunication issues. To have an

efficient intercultural communication process, cultural sensitive materials could be used

to avoid prejudices and to facilitate the adoption of local cultural characteristics into the

communication process.

Educational Materials

Once the topic of the training relevant to the community has been identified,

localized educational materials can be developed. To accomplish this, characteristics of

the population have to be considered. It is possible to use some of the information

provided by the local extension agents or trainers, but more often data has to be collected

through questionnaires and interviews (see future work as described in the conclusion

section below). Then this information is incorporated in the design of the educational

manuals.

Experiments with Vectorizing Images, Options for Creating Vector Graphics

Various techniques were tested to convert digital pictures to line (vector) drawings

in order to represent them using vector graphics. A vectorization tool that replaces pixels

patterns with vectors from Flash (Flash, 2004) provided some good visual results

(Figure 4-3), still, the resulting drawings were too complex (too many colors, and

vectors) to be easily changed as required for this project. Furthermore, the vectorization

tool only performed well on simple, well-defined patterns.






















Figure 4-3. Vectorization using Flash and original digital picture

Another technique was to use pattern recognition software, to extract the main

features from a digital picture. The software used was GIMP (GIMP, 2004), and results

are presented in Figure 4-4. The complication was that to have a fairly clear pattern, the

background of the picture needed to be a plain color, and without shadows.















Figure 4-4. Pattern recognition using GIMP and original digital picture

The use of Flash and GIMP to transform digital pictures to vectors was very

time consuming, due to the time needed to edit the pictures manually, plus the processing

time required by the software. The process also required high quality photographs,


177.'









without any objects in the background. Even the cloth used as a background in Figure 4-4

caused problems with the vectorization process.

Scalable Vector Graphics

In order to handle localization of drawings, scalable vector graphics (SVG) format

has to be utilized. SVG format was developed by the World Wide Web Consortium

(SVG Working Group, 2004; W3C, 2004a). It handles vector graphic display and

animation based on the extensible markup language (XML Working Group, 2004). It is a

text-based language that is resolution independent. Localization applied to scalable vector

graphics means that imagery and text can be easily converted to different languages and

cultural settings. Changing just one XML tag can modify graphics to adhere to local

needs. This means that if the color of the skin has to be changed, just the portion of code

that affects the skin color has to be modified. This can be done automatically by linking

the SVG graphics to the object database thru the data-handling feature in SVG that can be

used to create dynamic graphics. In the SVG sample code below, notice that only one

part of the code (in bold and marked with an arrow) needs to be changed in order to alter

the color of the skin. This method can be used to modify some features of the graphics.

When other characters need to be added (i.e. cloths, tools) those can be switch on and off

using the SWITCH tag available in the SVG code.

SVG Sample code:



"http://www.w3.org/TR/2001/REC-SVG-20010904/DTD/svgl0.dtd" [
















"http://ns.adobe.com/GenericCustomNamespace/1.0/">



















f l (represents the skin color: arms and
face)

]>


In figure 4-5, an example of the basic use of SVG internationalization is presented.

The sample code (SVG) presented above was modified in order to change the color of the

skin and hair, without the necessity of altering any other components of the code or


graphic.




























Figure 4-5. Sample of localization with Scalable Vector Graphics (SVG)

In Figure 4-6 a new item has been added to the original drawing without the

necessity of modifying the existing one. The desired feature is just switched on or off

depending on what is needed for the manual.






IN1














Figure 4-6. Sample of localization with Scalable Vector Graphics (SVG)

An example of a specific task, such as cleaning of an inline filter in the irrigation

system, was selected to demonstrate the process of transferring the information into a








graphical form that can be used in a manual. A module showing the flow of the steps

needed to complete the activity for which the training is conducted was produced.

Photographs were used to represent each step (Figure 4-7).


Intioduction


Rele-'-I e :Preis'..rie ii Filler


IJiie iie'./Filelel Cap,


r13te a ls3 fci Filtei Cleanin c


Feri'il e Fillei


iemr, a Fillel
Erea, Filter
E n.lcsh FIite r


U
B?


@


V.3ill FIllei Itl 'fl Cilol'jile


To'lp", Activity


Ii ; n il F il-i i i: -'


Srr \ Filter ":. 1i',


rn


T, Irri /llter ,'iri


Figure 4-7. Module "Cleaning Irrigation Filters" from Object Editor
These photographs were treated as the content to be utilized by the ObjectEditor.

All the components (represented by photographs) of the process being demonstrated are


Materials


Comments


Turli AateiI C'fi










linked depending on the relationship among them. These linkages determine the order in

which the images have to appear in the final printed manual in order to transmit the

activity in a logical way. At a later stage in this project, drawings will be available as well

as photographs to describe the different training activities.


Unescrew Fileter Cap

Unscrew filter cap to extract filter.


Remove Filter

Remove filter from the container.


Figure 4-8. SVG presentation "Cleaning Irrigation Filters" in English

The next step was to extract the information contained in the ontology to create

presentation based on SVG technology that can be seen in any internet browser capable

of opening SVG files. What the SVG render does is to navigate and select the different

components of the educational material like introduction, materials, activity, and










comments; then it arranges them in a predefined format created also with SVGs. An

example of parts of the presentation in English and Spanish is presented in Figures 4-8

and 4-9 respectively.


Limpiar El Filtro

Lavar el flitro con agua. Remover todas
las parlticulas de suelo.


Cepillar Filtro

User un cepillo suave para Ilmplar todo
el sudclo do los discos del illtro.


Figure 4-9. SVG presentation "Cleaning Irrigation Filters" in Spanish

GraphicsEditor

GraphicsEditor is a tool incorporated into ObjectEditor that permits the creation of

objects that have vector graphics as content. This tool facilitates the combination of the

graphic's properties with the properties of the objects they help represent. GraphicsEditor

was used to create the graphics for the objects to be used in the educational materials.









This application allows the creation of vector graphics based on the scalable vector

graphics (SVG) standard by the World Wide Web Consortium.

GraphicsEditor allows the creation of graphics using lines and polygons. When a

graphic is completed all its parts (e.g., lines and polygons) can be selected to form a

group. Group is a function of GraphicsEditor that gives the possibility of adding

properties to the graphic. An example will be used to explain this process. The first step

was the creation in the irrigation ontology of the instance called Maize (Figure 4-10) this

instance is a subclass of the Monocot class under the term plant classification.



.Object Editor
Projects Windows Help
1 [ D E T" f English EDIT



Plant Classification



Planting System Monocot



Dicot
Sorgurn


Spacing

Bean Banana

Figure 4-10. Maize instance within the plant topic in the irrigation ontology

The second step is to enter a short definition (gloss) for the instance; this gloss

helps to identify the instance. A context is also given to the instance, in the case of maize









the context is related to the plant module (Figure 4-11). It is important to observe that the

name of the instance and the gloss are given in English and Spanish.



Term Graphic Style

INSTANCE


Add Propery


Delete Obiect


Figure 4-11. Context and gloss for the maize instance

The third step was to create the vector graphic using the GraphicsEditor

incorporated in ObjectEditor. The maize plant graphic was constructed from multiple

polygons. Next, the polygons are selected and associated in a group. Using the group

function properties, names were given to each group (i.e., Maize plant, and corn) (Figure

4-12). These properties are the base to have localizable graphics.


Gloa I Lang..
Maize Plant ErEngli h lA
Planta de M aDiz C pani;h







[ Add ] [ Delete










Term Graphic Style


1 4 L:::: Q / D | | 1 I1 Line Thickness: Filled


Figure 4-12. Groups that constitute the maize graphic

One of the properties incorporated into the GraphicsEditor creates a path from the

of the vector graphic to the object it represent in the irrigation ontology. For example, the

skin color in the graphic of a person (Figure 4-13), follows a path to the skin color term

associated to the "person" term in the irrigation ontology (Figure 4-14).












I Term Graphic ] Style i



SGroups
I New Group








S r~c






Figure 4-13. Example of a person graphic

This means that the skin color property of the vector graphic is associated with the

"skin color" term in the irrigation ontology.


Figure 4-14. Skin color term associated to "person" term


OLiII r H i ci


Pu iticril'









For example, to create a graphic for an African person, the query 'African person'

could be used. The result of that query would be a person graphic with black skin. The

information to change the color of the skin in the graphic comes from the irrigation

ontology (Figure 4-15). There is a module in the ontology that specifies the color of the

skin for a person from a given geographical region.


E.Ionci H3ir



























Figure 4-15. Different skin colors depending on the origin of the person

There can be an African person with white skin; however, to facilitate the design of

this system the simplification presented in Figure 4-15 was used.
EI Il Hilr


























Composing Educational Materials
Eljc,- Shn















Figure 4-15. Different skin colors depending on the origin of the person

There can be an African person with white skin; however, to facilitate the design of

this system the simplification presented in Figure 4-15 was used.

Composing Educational Materials

To facilitate the design of the educational materials a new template module called

Irrigation Training Materials was created in ObjectEditor. In this template the topic of the










training material was defined, as well as the introduction, materials, activity, and

comments (Figure 4-16). In the activity section the steps of the educational activity

should be laid out. Each step has a graphical representation as well as a textual

description of the action depicted. The steps were organized sequentially using

relationships of the sequence type among themselves, meaning that step two has to occur

after step one occurs, and so on.



Windows Help
|:.E D *- T Eng-h v i EDIT '

Ir tr or l1.I'Ctic n
|Desciptiion Giapic



f-rnph 1 'e-. rl InTorI ;irp o Training Material 1







iriapt D-E- : Ii ti.ri -3teE of Traim rig r.1?terlil :-



I-ira ,ln 4 De cri.ption -_-'ep 1: Triin iri" rlteri l 4



r a[..thi C- .e : rllr l:r, l o: TI-litir n ular,-ll rl 1 .



:.r-ph r De n-rirtion tep o Tr ,rinrin r.i r teri l e.

: ornmernnt

Figure 4-16. Irrigation Training Materials module template









Two options where considered for where to include a description for each graphic.

The first option was to add the description in the same instance as the graph. The second

one was to add a separate category (class) containing the description. The last option was

selected since that one allows the use of the same graphic for different circumstances

(educational materials with different topics). Instead of having to change the graphic and

its description, only a new and independent description have to be created and associated

to the existing graphic (Figure 4-16).

Presentation Generation

The ontology management system used to create the irrigation ontology also stores

content (multimedia content in the form of text, images, sound, video, and other content)

associated with each ontology concept. Thus it provides content management with the

ontology acting to integrate the content. The content also enhances the concept

definitions (although not compatible with and essentially ignored by the reasoner, such

content provides useful annotations).

Presentations can be automatically generated from this content by specifying a

mapping from content objects to the desired presentation. This mapping, which can be

implemented using XSL style sheet technology, specifies how content objects are to

appear (for example, fonts and colors) and also manages policies on how they can be

arranged. Mappings can generate presentations in a variety of different Web page styles,

slide show formats (such as PowerPoint) and printed layouts (such as PDF or EPS

formats).

A sample for a printed publication on irrigation appears in Figure 4-17, and a

sample of a slide-style presentation appears in Figure 4-18. The elements in the printed

publication were created using XML FO (Formatting Objects) and a commercial









rendering package, RenderX (RenderX, 2004). The process involved 1) generate an

XML document from the content for the publication stored in the ontology manager, 2)

convert the XML source document to an XML FO based on style specified in an XML

style sheet (XSLT), and 3) rendering the XML FO to a printable publication (PDF

format) using RenderX.

The approach to the graphics example in Figure 4-8 is to store elements of the

graphic using vector graphics. The vectors are stored in the ontology management

system as database objects. Larger graphics are composed from smaller elements, much

like image libraries in conventional graphics packages. However, the ontology is used to

enhance the description of the graphic elements. It not only improves search and

retrieval of specific elements, but enables localization at the level of concepts. For

example, graphic elements appearing in Figure 4-12 can be changed based on crops

grown in a particular location, and people can appear differently (race, gender, and

clothing can change) based on local conditions. In other software environments (such as

Scalable Vector Graphics) these features must be changed at the level of individual

graphic primitives (lines, polygons) but in the ontology management system these

primitive elements are given meaning as objects (a plant, a crop, a person with a

particular skin color).










BUL245


UNIVERSITYY OF
- 'FLORIDA


IFAS EXTENSION


Microirrigation On Mulched Bed Systems: Components,
System Capacities, And ManagementI


Gary A. Clark, Craig D. Stanley, and Allen G. Smajstrla2


Microirrigation involves the slow application of
water on, above, or below the soil surface. This
encompasses trickle irrigation including drip, line
source, bubbler, and micro-spray ill i,-ation systems.
Water may be applied in drops, small streams, or
sprays at discrete locations or continuously along the
irrigation tube lateral. Placement of the lateral and
proper scheduling can allow precise application of
water to the active root system of a crop. Therefore


implement to hold a reel of tubing. Laterals are
placed Jilectrl Il'dr iIte mulch. Some tubing
manufacturers recommend burying the tube 1- to
2-inches below the soil surface. The adjustment to
current cultural practices can be minimal.
WIDTH PLASTIC h,*
L-, MULCH FACE
HEIGHT
/ /////// 7


Figure 4-17. Example of print file generated from the ontology management system.


Figure 4-18. Example of educational drawings on irrigation techniques

Conclusion

Any kind of educational material is based on the transmission of information,

presenting knowledge in different media like books, audiovisuals, etc. Nevertheless,

presenting information to a non-literate audience is more difficult. To "transform" very

complex knowledge to a basic representation requires a different approach in the


4*









development of educational materials. To develop educational materials easily a better

understanding of the learning process for non-literate people is needed.

To develop culturally specific educational materials, information about the culture

of the clientele has to be collected. The localized data has to be included during the

development of the cultural sensitive educational materials. For this project, information

about who (gender, age) performs each irrigation or water management activity is critical

for appropriate localization. Also what tools are used, and other complementary factors

like clothing, and time when the action is performed, will help to communicate the topic

of the educational material to the local conditions.

Scalable vector graphics (SVG) have the qualities needed to produce localized

graphics. They can be modified without the need of producing a new drawing, while

maintaining all the qualities of a vector graphic. SVG format is supported by

GraphicsEditor in order to have a fully functional localization tool for graphics working

in conjunction with the object database.

Future work should include the automatic generation of educational materials from

the content in the irrigation ontology. The application should be able to generate

presentations on-the-fly in multiple formats for any topic in the irrigation domain based

on user queries.














CHAPTER 5
EVALUATION OF EDUCATIONAL DRAWINGS IN EL SALVADOR,
CENTRAL AMERICA

Introduction

Worldwide, the number of illiterate adults in 2000 was 862 million. According to

current trends that number should drop to 824 million by the year 2010 (UNESCO,

2002). The United Nations definition of a literate adult is a person aged 15 or over who

can read and write (UNESCO, 2000). In 1995, the literacy rates for El Salvador were

73% for males and 70% for females (UNESCO, 1999). The data highlights the necessity

of alternative educational materials, which reduce the effects of illiteracy in the

transmission of information. Most of the educational materials in developing countries,

with few exceptions, are overwhelmingly print-oriented. In addition, most of the printed

materials available are written at a level that makes them inaccessible to individuals with

a low education (Hynak-Hankison, 1989; Stemmerman, 1991).

A computer tool to facilitate the production of illustration-based educational

materials could make this task much easier and more effective. To accomplish this task,

a group at the University of Florida's Department of Agricultural and Biological

Engineering researched the possibility of developing a tool and a series of educational

materials in the areas of water management and irrigation. The illustration-based

materials under development are audience-oriented (from here on referred to as

localized). For example, if the illustrations are showing a Hispanic person for Latin

America, this person can easily be changed to a Black person for African regions to make










it more relevant to the local audience. Adjustments can be achieved just by selecting

certain attributes from the database, without the necessity of creating a completely new

drawing. However, the process of developing a product that meets audience needs, helps

accomplish a teaching goal, or solves a problem, is sometimes challenging and may

present hidden complexities (Bly, 1989). For the illustration-based materials to be useful,

the initial ideas for the drawings were tested with a target population of low-resources

small farmers in El Salvador, Central America.

El Salvador is located in Central America; its borders are with Guatemala to the

northwest, Honduras to the northeast, and the Pacific Ocean to the south (Figure 5-1).

From around 1980, El Salvador was involved in a 12-year civil war, which cost about

75,000 lives. The war was brought to an end in 1992 when the government and leftist

rebels signed a treaty that provided for military and political reforms.




GUATEMALA H ON DU R AS

s CIhalatenango.
*,Santa Ana
Ahbuadhapdn Sensunlepaque,
Nueva
Sonsonate. an SAN SALVADOR

Acajuua San Vicente
*La Liberad .SanMigu

Pulerto La Unn "
.El Triurfo Puert
... Cutuco
0 15 30m I
S5" 3 NICARAGUA"

Figure 5-1. Map of El Salvador and location of communities visited (CIA, 2004).

El Salvador was selected for this study because of the social and agricultural

conditions of the country. The adult literacy estimated rates are considered high 70% in

2000 (UNESCO, 2002), and 80% in 2003 (CIA, 2004), with a difference of around 10%









between men and women. Illiteracy is more noticeable among elder adults in rural areas,

and especially, in the eastern regions of the country (Departments of San Miguel and La

Union). Agricultural production in El Salvador is reduced and rudimentary. Most of the

fresh fruits and vegetables that El Salvador consumes are imported from Guatemala and

Honduras. The production is limited to staple foods like maize and beans.

Rohr-Rouendaal (1997) highlights the necessity of evaluating educational

materials; however, she also notices that this is more important when the educational

drawings have been developed without direct participation of the final users. Since this

work was a first step in the development of a tool to produce on-demand educational

materials, the materials were developed away from the clientele; hence, it was important

to test them.

The aim of this first field test of the manuals was to identify the level of

understanding of the drawings by small farmers in El Salvador; gather their views on the

value of the material, and to incorporate any additional material, alterations, or deletions,

which would help the farmers to better understand the educational materials. The data

collected from this evaluation process would be useful to understand how these specific

farmers interpret the educational drawings, and what has to be included to make the

manuals practical in a training process. It was important to demonstrate the necessity of

testing illustration-based educational materials with a sample of the target audience

before they are distributed to a larger population. As the need to develop publications for

people with low educational levels continues to grow, so does the importance of special

considerations in the content and design process (Ingram, et al., 2004). Audience

background (e.g., culture, race) and experience should be considered in all phases of









development of the materials. It is also important to understand the differences among

people from different cultures, and to learn how people understand the message from

educational materials based on drawings. As stated by Rohr-Rouendaal (1997), some

people in Africa have never seen a picture or a drawing, and as a result, they are not used

to interpreting illustrations as most other people do in everyday life (Clarkson and

Johnson, 2001; and AMDM, 1997).

Materials and Methods

The data collection in El Salvador was conducted during July 2004, with the

support of PROMIPAC-El Zamorano (Integrated Pest Management Program for Central

America) in El Salvador. To collect the data, personal interviews were conducted with 63

small farmers in five different communities: El Pefion, Huertas, Tunas, Singuil, and

Pasacarrera. These "caserios" (groups of less than 50 families) were distributed in three

departments, Santa Ana in the West, and San Miguel and La Union in the East.

The farmers that participated in the evaluation were part of the farmers' field

schools (FFS) in integrated pest management (IPM) supported by PROMIPAC. These

schools meet once a week or once every two weeks for 3 to 5 hours. The topics where

mainly focused upon crop production and integrated pest management (IPM). This is

important to note since it could be reflected in some of the answers given in the

evaluation process.

For this evaluation it was necessary to determine the literacy of small farmers in the

visited communities in El Salvador. The sample population selected for this evaluation

consisted of people with a low level of education or illiterates. Sixty-three small farmers

from five different communities participated in the evaluation. All the farmers are









participants of the FFS in natural resources conservation, basic grains production, and

IPM.

The questionnaire used in the evaluation contained a set of questions related to any

previous training received by the farmer and specifically, to water management practices,

and a set of questions on the illustration-based educational materials. In the second

section the farmers had to evaluate the clarity of each picture, the message carried by the

pictures, and the arrangement of the pictures explaining each activity. For example, the

farmers were given five sets of materials (representing: contour planting, earth basins,

rain and drainage, retention ditches, and stone lines). There was no oral explanation of

the actions represented in each set of drawings to avoid influencing the answers. The

questionnaire was developed at the University of Florida by professors with experience in

extension work, and water management practices. The questions were evaluated in the

field with five farmers and changes were made to accommodate the questionnaire to

make it more understandable.

The drawings to be evaluated were developed using scalable vector graphics (SVG)

(W3C, 2001b). Scalable vector graphics is a platform for development of two-

dimensional graphics. Scalable Vector Graphics are used in many business areas

including Web graphics, animation, user interfaces, graphics interchange, print and

hardcopy output, mobile applications and high-quality design (W3C, 2004). This

graphics language is an open source (royalty free) standard; it is based in the extensible

markup language (XML), which was also developed by the World Wide Web

Consortium (W3C, 2004b). This allows the interoperability of SVGs, as well as the use









of this standard in conjunction with ontologies, and object-oriented databases. Further

explanation on this topic is available via Badal et. al., (2004).

The interviews were conducted in groups of five or less people, since this facilitates

greater participation and discussion among people. The data was collected individually

for each farmer. The structured questionnaire consisted of 15 questions, some general

questions about the local conditions relevant to agriculture, and water management

practices. However, most of the questions were related to the educational drawings

presented to the farmers for evaluation.

The educational drawings to be evaluated were grouped by topics. For example, the

drawings related to contour planting were grouped together. The idea was to have a

product representing all the steps of a process, similar to an educational manual. Five

sets of drawings were presented to the farmers:

* Contour planting or farming
* Earth basins
* Rain and drainage
* Retention ditches
* Stone lines


The drawings were presented in black and white and color versions. This was done

in order to compare if there is any significant difference in the interpretation of color

versus black and white materials. Also, most of the printed educational material used in

extension work is in black and white because it is less expensive than materials printed in

color; and the availability of color printing technology is also limited.

Results

After analyzing the literacy data collected from the sample population (63), the

values for women are above 80%, and for men are almost 90% (Table 5-1). Literate









individuals had from 2 to 6 years of basic schooling, which is considered a low literacy

level. Illiterates did not attend school at all. Illiterate people in this study were 30 years

or older and 50% of them where 40 years or older (Table 5-2). This shows a recent

improvement in basic education in the rural areas.

Table 5-1. Literacy rates of small farmers interviewed in El Salvador.
Individuals % Women % Men %
Illiterate 8 13 3 17 5 11
Literate 55 87 15 83 40 89
Total 63 100 18 100 45 100
Source: Corejo, 2004.


However, young people still withdraw early from school to help with the economic

activities of the family. More than 60% of young adults interviewed in this study had

only few years of basic education. The assessment of the literacy level of the sample

population could be an important factor in determining how small farmers are able to

understand the educational drawings.

Table 5-2. Age groups of small farmers interviewed in El Salvador.
Age Groups Individuals Percentage
15-19 1 2
20-29 6 10
30-39 32 51
40-49 11 17
50+ 13 21
Total 63 100
Source: Corejo, 2004.


The objective of this work was to evaluate understanding by the farmers of the

educational drawings developed for this project. If effective educational materials are to

be developed, then it is important that the information contained in those materials is

conveyed to the audience (small farmers in this case). An important thing to consider









during the evaluation is if the farmers had been familiar with any type of educational

materials.

From Table 5-3, it can be noticed that 94% of the farmers had used text based

educational materials. And all of the farmers interviewed have used some kind of

educational material, including photographs and video, during different training

opportunities. As for all the educational materials tested, 63 small farmers evaluated the

drawings.

Table 5-3. Type of educational materials used by farmers in El Salvador.

Educational Material Have used this type of materials:
Individuals Percentage of total sample
Posters 26 41
Text manuals 59 94


Photographs 7 11
Videos 7 11
Source: Corejo, 2004.

Contour Planting or Farming

Contour farming (Figure 5-2) consisted of a set of five drawings aimed to represent

the use of contour planting. The objective was to show "contour" lines to the farmers

evaluating the drawings. Since these are two-dimensional drawings, to show curves and

differences in distance can be difficult (to enable proper perspective).




















A / I


Figure 5-2. Section of drawings representing contour planting

In these drawings, farmers identified the crop as being sorghum (Figure 5-2). The

farmers at first were more interested in the crop variety, and trying to identify the other

objects resembling plants. Some of the farmers said that "A" was a weed or an aloe plant,

and that "B" were eggs laid by some insects, as seen in Figure 5-2. These interpretations

confirm the recommendations given by Rohr-Rouendaal (1997), that the drawings should

be kept simple, and that all unnecessary details should be avoided. This eliminates wrong

interpretations, and it helps people focus on the main aspects of the drawings, the objects

that transmit the message that needs to be conveyed.

Slightly more than 56% of the farmers identify the drawings as representing

contour planting. Nevertheless, all of the people interviewed recognized them as some

type of drainage or land conservation practice. However, it is worth noting that all the

farmers had had some training in land conservation practices given by different

organizations, according to what the farmers said during the interview process.

Another drawing in this set shows a field without contour planting or any other

practice to reduce soil erosion or promote water conservation. In the black and white

version, farmers identified the runoff"C" sometimes as water and in some cases as soil or









mud. In the color version, the water "C" was easily identified as such. This demonstrates

the important that colors may have in the interpretation of illustrations.


Earth Basins

In the second set of drawings, representing earth basin construction, again some of

the farmers were more concerned about the type of plant presented to them in the

drawing. The origin of this problem could be that no explanation was given about the

drawings, since that could influence the answers during evaluation, and the plants are of

main interest to the farmer.

From the size of the plants (A) the farmers determined that some vegetables were

grown (Figure 5-3) in the field. Then, the answers to the evaluation were related to

vegetable production, like land preparation and planting of seedlings.














Figure 5-3. Drawings representing earth basins.

Most of the farmers did not have any problem identifying the plantain or banana

tree (B). They also identify this practice as the use of intercropping. The object

representing the water retained in the basins was easily recognized in the materials

presented in colors. When presented in black and white, the farmers interpreted it as soil,

mulch, and humid soil. It can be concluded that the color drawing was quite important