Paper presented at "International Federation of Information Processing 9.4 Conference", Bangalore, India, 28-31 May 2002. Conference hosted by Indian Institute of Management, Bangalore, http://www.iimb.ernet.in/ifip

Jonathan Woods, Buenafe R. Abdon, and Robert T. Raab

Introduction

Agriculture is one of the most important economic sectors in developing countries in Asia as well as the main employer, yet this sector is under increasing pressure due to rising population, consumer affluence, pressure from globalisation, and a shrinking resource base. Previous production challenges in developing Asia have been met largely through improvements in traditional agricultural inputs such as seeds and agrochemicals, but the present challenges need different solutions. Better management, which involves higher degrees of information and knowledge, is widely seen as a way to address these challenges. This 'knowledge-intensive management' has a potentially large contribution to make towards improving agriculture in developing Asia.

Although knowledge-intensive management is widely seen as a solution, delivering the requisite information and skills to farmers in developing Asia is fraught with difficulties. In this paper, we argue that Information and Communication Technologies (ICTs) are an excellent way to deliver knowledge-intensive management to the agricultural sector because this management strategy is information-based and dynamic. We then argue that agricultural professionals can be effective intermediaries for delivery because ICT infrastructure and human resources development in developing Asia are currently insufficient to deliver knowledge-intensive management strategies directly to farmers. We go on to suggest that e-learning, a form of distance education that uses ICTs, is an appropriate medium for the delivery of knowledge-intensive management strategies to agricultural professionals.

e-learning offers many advantages for delivering knowledge-intensive management strategies to agricultural professionals. Of the many e-learning tools available, simulations hold particular promise. Below, we review how simulations can contribute to current efforts in e-learning, and detail an example simulation which demonstrates their utility in delivering knowledge-intensive management to agricultural professionals. We then argue that simulations can help farmers better manage agriculture in the face of current challenges in developing Asia, especially if they are developed and deployed locally. Finally, we point out that as ICT infrastructure and human resource development improves in developing Asia, farmers will increasingly have direct access to e-learning tools, and simulations will be at the forefront of delivering knowledge-intensive management skills directly to Asian farmers.

The Agricultural Sector in Developing Asia

Despite ongoing industrialization and the rising importance of service and knowledge-based economies, agriculture remains a key sector of Asian economies. In 1999, farm-gate agricultural production (including fisheries and forestry) accounted for 27% of the GDP of South Asian developing countries, and 14% of the GDP of East Asian and Pacific developing countries (World Bank, 2001a). By comparison, agricultural output for EMU countries accounts for a mere 2% of those countries GDP, and only 2% of the American economy is agricultural (CIA Factbook, 2001).

Perhaps more significantly, a majority of the workforce in developing Asia work in agriculture. According to 1990 statistics, 63% of the South Asian and 69% of the East Asian and Pacific workforce are farmers (Maxwell and Percy, 2001). By contrast, a mere 2.5% of the US workforce is involved in agriculture and approximately 6% of the EMU (CIA Factbook, 2001; World Bank, 2001b). This largely rural and low-income majority is not only a driving force for developing Asian economies, but the social backbone of these societies as well.

Pressures on the Agricultural Sector in Developing Asia

For many urban Asians, agriculture is perceived as a stable, if somewhat backwards sector of the economy. The timelessness of the rice harvest and the apparent abundance of food in urban markets contribute to the view that Asian agriculture will continue to feed Asian consumers far into the future. But this perception is far from the truth.

In actuality, Asia's farmers are under increasing pressure from both domestic and international forces. The region's population is growing rapidly, and is expected to increase by 142% (South Asia) and 120% (East Asia and Pacific) by 2025 (Population Division, United Nations, 2001). According to projections by the International Food Policy Research Institute, by 2020 demand for cereals will grow by 50% and demand for meat will almost double in developing Asian countries. This is due both to increasing population size and the increasing affluence of that population. Similar demands will be placed on the production of non-food and export crops, such as cotton, rubber, and tropical fruits. Farmers in developing Asia will be expected to meet this additional demand (Rosegrant et al., 2001).

Unfortunately, this increased production will depend on an already overexploited natural resource base. Large areas of the most fertile agricultural land are being converted to non-agricultural uses through industrialization and urbanization. What remains is threatened by degradation from erosion, nutrient mining, water logging and salinisation. Water availability per capita in the region is decreasing rapidly as urban, industrial, and agricultural users compete for this resource. Given this situation,

"increases in yields will be difficult to accomplish. The challenge of increasing agricultural production is even more difficult in Asia where cropping intensities are already the highest in the developing world. The potential for yield increases is further limited by poor agricultural resource management practices that result in unsustainable farming systems" (Nath, 1999).

Globalisation means that Asian farmers must compete with farmers the world over for a share of the market. In tandem with the opening of global markets ha been the withdrawal of price supports, commodity protection, and government marketing programs, as well as a reduction in research and extension services. Trade liberalisation may actually increase potential farmer profits because domestic commodity prices will rise. However, many farmers in developing Asia do not have the skills, knowledge, or market access to take advantage of these new business opportunities (Rosegrant et al., 2001).

In summary, Asian farmers face three main challenges.

• Economic. Asian farmers need to improve their economic performance (i.e. maximise profits) in a deregulated, largely unsubsidised global market.
• Production. Asian farmers need to increase production to meet their own subsistence needs as well as the food, fibre, and other agricultural demands of a rapidly growing local population.
• Environmental. Asian farmers must meet the first two demands without degrading their natural resource base.

Agriculture is becoming a Knowledge-Intensive Industry

That knowledge is becoming a much more important factor in the success of most industries is not in question. As Moe (2000) states,

"Technology is the driver of the New Economy, and human capital is its fuel. In today's world, not only does knowledge make the difference in how an individual performs, but it also makes the difference in how well a company performs and, for that matter, how well a country performs."

Numerous recent authors echo this view (Houghton and Sheehan, 2000; Skyrme, 1997; Abell and Oxbrow, 1999; Atkinson and Randolph, 1998).

The Knowledge Economy Report, a submission to the New Zealand Government by their IT Advisory Group, provides a well-researched independent view of the factors that lead to the effective development of a knowledge economy (IT Advisory Group, 1999). It talks about the growth and development of a global knowledge society where

"individuals who are well-educated, self-motivated, and linked into information networks, are the most likely to live prosperous and fulfilling lives. Enterprises that are attuned to their customers' requirements, employ educated workers, encourage innovation through their workplace organisation, and know more and learn faster than their competitors, are the most likely to succeed and grow".

The Foresight Project, a strategic visioning initiative of the New Zealand Ministry of Research, Science and Technology, likewise recognizes the importance of knowledge in the success of economies (The Foresight Project, 1999). It notes that

"Knowledge is one of the main drivers of prosperity and well-being. Knowledge includes information in any form, know-how and know-why. It involves the way we interact, as individuals and as a community. Knowledge can be embodied in people, as 'human capital', and in technology."

Knowledge and information-based technologies are as important for the Asian agriculture sector as they are for other sectors and industries. A recent survey of FAO employees found that information and new technology were perceived as pressing needs in world agriculture.

"What is clearly established from past experiences and has been repeatedly stated by respondents is the overwhelming need to focus on the demand for new technology and on training people in using technology. In rural areas specific work has to be done to establish accessibility to communications media and information: how are information needs determined; how can the capacity of the NARS to promote Agricultural Information Systems be enhanced; how can stakeholders participation be promoted; how can effectiveness of information systems on stated objectives be measured."(FAO, 2001)

But information and technology on their own cannot address the challenges of agriculture in developing Asia. New computers in every village or advanced disease diagnostic kits are useless if farmers are unable to apply the information that these technologies provide. Information-based technologies in agriculture (including ICTs) need to be used within a framework that gathers, synthesizes, analyses, interprets, and applies the information. Knowledge-intensive management provides such a framework. As noted by Price and Balasubramanian (1996),
"Knowledge-intensive resource management and technology is an approach to fine-tuning farmer management to enhance profitability and environmental integrity in high-productivity systems."
Knowledge-intensive management brings together disparate pieces of information (e.g. cold weather, 10 days till harvest, 12 harmful insects per rice plant) to make intelligent decisions (e.g. the insects won't multiply before harvest-time due to cold weather therefore no need to spray).

"Much of what we currently see as mismanagement…is the farmer response to lack of appropriate knowledge in managing the Green Revolution changes to cropping systems. The "blanket recommendation approach" gave farmers information without understanding--it provided information but it did not expand knowledge"(Price and Balasubramanian 1996).

While previous advances in agricultural productivity in developing Asia were largely due to improvement of traditional inputs such as seeds, fertilizers, and pesticides, future increases will be realized mainly through knowledge-intensive management.

"By the late 1980s, the most advanced 'post-Green Revolution' areas of Asia, such as the Punjab of India and Central Luzon of the Philippines, had reached a point of sharply diminishing returns to further intensification and had entered a second "post-Green Revolution" phase characterized by the substitution of better knowledge and management skills for higher levels of input use (Byerlee and Pingali 1994). Productivity gains accrued to farmers from differences in the way inputs were used; that is, the timing and method of input use improved production (Byerlee 1987; Pingali, Moya, and Velasco 1990). Over time, farmer technical knowledge and management skill become the primary determinants of differences in productivity and profits between farmers" (Pingali and Heisey, 1999, p. 7).

The agriculture sector in developing Asia needs to become more knowledge-intensive so that new information-based technologies can be applied effectively to management of looming economic, production, and environmental challenges.

Meeting the Economic Challenge
Economic principles are the basis for most knowledge-intensive management in agriculture. A farm must make a profit in order to survive, and therefore management decisions must ultimately refer to the economic bottom line. Many knowledge-intensive management strategies, such as Integrated Pest Management (IPM), take into account a variety of environmental, agronomic, and economic factors when making farming decisions, but ultimately make decisions that will make a farming operation profitable in a deregulated and largely unsubsidized global market. The experience of Indonesian rice producers in after the introduction of IPM is an excellent example of the difference knowledge-intensive management strategies can make in agriculture in Asia. In fact,
"between 1987 and 1990, the introduction of an IPM program on rice in Indonesia resulted in a 50 percent decrease in use of pesticides, a 15 percent increase in yields, an increased net profit per farmer per season of US $18, and a government savings of US $120 million a year in pesticide subsidies." (Schillhorn van Veen et al., 1997, p 22)
Improvements in the exchange of information between producers, consumers, and marketing systems will allow adequately prepared farmers to incorporate local and global economic information into their management strategies, helping them to prosper under changing economic conditions.

Meeting the Production Challenge

The "yield gap" is a commonly acknowledged phenomenon in agriculture and is defined in two ways. One is to describe the difference between the attainable yields that agricultural scientists at research stations achieve, and the actual yields obtained by farmers using the same seeds and inputs. Alternatively, yield gap may also refer to the difference between the mean yield of large plot demonstrations or the top 10% of farmers (using the presently available improved technologies and management practices in the best possible combination) in a given location and the average yield of all farmers for that location (FAO, 2000). While many factors contribute to the yield gap, one of the main ones is differential access to information and the skills needed to apply it. Given the same seeds, fertilizers, pesticides, irrigation access, and labour, a farmer who uses knowledge-intensive management to make production decisions will likely harvest a larger or higher-value crop than one who does not. Productivity gains accrue to farmers from differences in the way inputs are used; that is, the timing and method of input use (Byerlee 1987; Pingali, Moya, and Velasco 1990). The farmer makes more profit, while at the same time producing more food to help meet increasing demand.

Meeting the Environmental Challenge

"The move toward knowledge-intensive management will be stimulated by environmental concerns as well as by the need to reduce costs and remain competitive under market liberalization" (The World Bank, undated).

Ultimately farming systems have to be sustainable. Sustainability means that agricultural practices should produce useful, healthy products, remain economically viable, maintain a good quality of life for those involved directly or indirectly, and have a positive effect on the environment. In theory, truly sustainable agriculture can support human societies ad infinitum. Contrary to popular belief, sustainability and prosperity are not necessarily at odds, and sustainable farming practices can be driven by, rather than compromised by, economic forces.

While sustainability is more difficult to define in practice, knowledge-intensive management that is designed to improve economic performance can also improve agricultural sustainability.

"Integrated nutrient-organic matter management and pest management approaches are receiving increasing attention as pathways to sustainable high-production agriculture and reduction of off-site problems". (Matson et al., 1997)

For example, Integrated Pest Management (IPM) is a fairly complex set of ideas that aims to maximise profits by balancing the cost of controlling pests with the potential economic damage those pests may cause. Pest damage is tolerated up until a critical point at which the cost of controlling the pest is less than the value of the damage the pest will inflict. While the explicit purpose of IPM is to improve profits, and the decision to spray or not is based on economics, a highly desirable side effect of IPM is that economic considerations force potentially harmful inputs (e.g. pesticides) to be used in a judicious way. IPM tends to result in reduced pesticide use, and a marked reduction in pesticide residues in the environment and on crops, even though decision-making is primarily economic.

Delivering Knowledge-Intensive Management

It follows from the above that delivery of knowledge-intensive management skills to farmers is critical. Nobel Peace Prize winner Dr. Norman Borlaug points out that

"Ways must be found to improve access to information by less-educated farmers-because of equity reasons and also to facilitate accelerated adoption of the newer knowledge-intensive technologies." (Borlaug and Dowswell, 2001)

But many are finding out that dissemination of knowledge-intensive management approaches is much more complex than the delivery of a traditional agricultural input. Even the World Bank's most recent topic brief on natural resource management research stated that,

"The appropriate mechanisms to organize and manage research and technology dissemination for knowledge-intensive agriculture is still being debated" (The World Bank, undated).

Traditional agricultural inputs tend to come in discrete packages, such as a seed. This package is static and therefore unalterable once it reaches the farmer - if a gene or piece of DNA in the seed is faulty, the farmer cannot request a new gene be sent, nor can the farmer adjust the gene on their own. The information that came from the research and development effort that produced the seed is largely imbedded in the seed itself. Little if any explicit information, such as cultural requirements, is included.

On the other hand, knowledge-intensive management strategies such as IPM encompass thousands of distinct pieces of information about pests, pest controls, the damages they cause, etc. This information is dynamic because practitioners all over the world are constantly making new findings and recommending changes to existing practices. Finally, the information is explicit - farmers use information directly to make knowledge-intensive management decisions.

ICTs are an obvious and appropriate medium for information delivery and even expanding farmers' knowledge - information with understanding.

Knowledge-intensive management has been delivered through numerous modalities, including farmer participatory research, farmer field schools, mass media campaigns, and traditional distance education. Although many of these were successful, their impact and coverage have been limited by high-costs, small program sizes, or other factors. The emergence and expansion of cost-effective ICT networks in Asia offer the potential to deliver information and the skills needed to apply it to agriculture via a medium that is itself information based. This idea of delivering knowledge-intensive management over ICTs has excited many in the agricultural sector.

The Future?
In an ideal world, every farmer would be connected to the rest of the world through a user-friendly and inexpensive computer. The software on the computer would quickly search and summarize information requested by the farmer and present it in a culturally and context-sensitive multimedia environment. The farmer would be able to read or hear the information in his or her own language, be able to direct queries and comments to experts and expert systems, participate in discussions about his or her agricultural interests, and use the computer as a tool to improve their farm. Technicians, agricultural scientists, social scientists, educators and others would support the network by solving technical problems, digesting new research findings, and creating learning activities for network users.
"Farmers will use the Internet to quickly access small, useable, and timely chunks of information that can help them make key decisions. Information via the Internet and web will be accessed by farmers through cellular phones, handheld digital notebooks, and even in the cabs of tractors. (Shutske, 1999)
In fact, this situation is already a reality in many developed countries, where a significant number of farmers routinely use ICTs to access the information they need for the knowledge-intensive management of their farms.

The Present.

Unfortunately, the situation in developing Asia today is a different story. ICT's are largely unknown to the majority of Asians in developing countries, and even if massive ICT infrastructure was built, most farmers in developing Asia do not have the requisite skills to take advantage of it. Agricultural information is widespread yet difficult to access, and little scope for communication exists.

"Much information is unavailable or inaccessible, particularly to poor farmers, many practical lessons have been learnt but not shared, and there are few opportunities for dialogue to enable concerns to be resolved." (FAO, 2000)

Until massive investment in ICTs, human resources, education, and rural development occurs, farmers will continue to rely on agricultural professionals to help them access information, learn about knowledge-intensive management, and obtain traditional inputs. Defined functionally, agricultural professionals play the critical role of linking technology sources to technology end users - the farmers. This definition goes beyond the traditional role of extension workers to include assessment and articulation of farmers' technology needs, research and development of new technology, testing and evaluation of new technology, and transferring it to farmers. In particular, agricultural professionals have a crucial role to play in bridging the technology gap that exists between the existing scientific knowledgebase and information and knowledge in the hands of farmers.
"Despite all the sophisticated information delivery systems available today, the hands-on, one-on-one, field-by-field service provided by trained professionals is by far the most effective method for helping a farmer adopt new management systems in response to new information. Now, more than ever, farmers need professional advice to develop and implement economically and environmentally sustainable plans which will meet their unique needs, and be practical on their farms." (Jones, 2000)
Even in the United States, agricultural professionals (crop consultants) continue to play a significant and valued role in farming.
"A survey conducted by Doane Agricultural Services in 1993 demonstrated that independent crop consultants (those who have no connection to product sales) were consulting on 49% of the cotton, 50% of the vegetables, 39% of the rice, 19% of the corn, and 12% of the soybeans." (Jones, 2000)

e-learning for Agricultural Professionals

ICTs promise to play an important role in the delivery of information and knowledge-intensive management skills to agricultural professionals. Perhaps the most exciting aspect of the application of these technologies for agricultural education is the emerging field of e-learning. e-learning is the most recent evolution of distance learning - a learning situation where instructors and learners are separated by distance, time or both. e-learning (sometimes also defined as 'Internet-enabled learning'), uses network technologies to create, foster, deliver, and facilitate learning, any time and anywhere.

"e-learning is characterized by speed, technological transformation, and mediated human interactions" (Stokes, 2000).

e-learning is becoming increasingly important in developing Asia. In fact, the 6 largest open universities in the world are located in Asia (China, India, Indonesia, Korea, Thailand, and Turkey) and Asia has by far the largest number of distance learners (Tam, 1998). A major recent study of the impact of ICTs in developing countries pinpoints distance education as a demonstrated success with much potential for the future. (Digital Opportunity Initiative, 2001). An urgent need to bridge the education gap and the lower relative cost of e-learning has thrust e-learning into the spotlight, as noted by Bollag (2001) and others (Digital Opportunity Initiative, 2001; Farrel, 1999; 2001; Tam, 1998).

Capper (2001) lists the benefits to learning online that are unique to the medium:

Any time. A participant can access the learning program at any time that is convenient - not just during the specific 1-3 hour period that is set for a conventional course. The episodes can be quick snatches at odd times or long late-night sessions. Cross-time-zone communication, difficult to arrange in real time, is as easy as talking to someone across town when using the Internet.

Any place. The participants do not have to meet. That means they can be anywhere. International sharing is feasible. Individuals can log on at work, home, the library, in a community learning center or from their hotel when travelling.

Asynchronous interaction. Unlike face-to-face or telephone conversations, electronic mail does not require participants to respond immediately. As a result, interactions can be more succinct and to-the-point, discussion can stay more on-track, and people can get a chance to craft their responses. This can lead to more thoughtful and creative conversations.

Group collaboration. Electronic messaging creates new opportunities for groups to work together, creating shared electronic conversations that can be thoughtful and more permanent than voice conversations. Sometimes aided by on-line moderators, these net seminars can be powerful for learning and problem-solving.

New educational approaches. Many new options and learning strategies become economically feasible through online courses. For instance, the technology makes it feasible to utilize faculty anywhere in the world and to put together faculty teams that include master teachers, researchers, scientists, and experienced professional developers. Online courses also can provide unique opportunities for teachers to share innovations in their own work with the immediate support of electronic groups and expert faculty.

E-learners use a variety of tools while learning. For example, e-mail, e-mail newsletters, listservs, discussion groups, chat, instant messaging, and internet broadcasts can be used for communication (White, 2001), while hyperlinked web pages, downloadable documents, multimedia, interactive forms, and simulations are used to engage and involve learners with content. Whether to use and how to use these different tools is an important consideration of instructional design for e-learning. In the section below, the characteristics of one type of tool, simulations, is discussed with reference to the applicability of this tool in delivering knowledge-intensive management strategies to agricultural professionals in Asia.

Simulations

Computer simulations and their recreational counterpart, computer games, allow users to 'try out' aspects of the real world while controlling or easing many of the complexities that the real world represents. Anyone who uses a GUI-based computer (e.g. Windows) uses a simulation of a desktop often without even thinking about it (Turkle, 1997). Early computer simulations often had to be downloaded and installed as programs on a user's computer, but new technologies such as Java and Macromedia Flash have made it relatively easy to deploy simulations on-line (Fishwick, 2000; Chargel, 2001).

Simulations are used in education because they are safer, faster, simpler, and more economic than the real world. A familiar example of a simulation used for learning are the flight simulators used for training pilots. Flight simulators are generally cheaper than real airplanes, they allow pilots to practice dangerous manoeuvres without endangering lives, and they can be reset much faster than a real airplane can be checked and refuelled. However, even the best flight simulator cannot replace a real airplane, and simulator training is always considered to be a supplement to actual flight experience (de Moura Castro, 2000).

Simulations in agriculture have some of the same advantages and disadvantages. They are engaging, cheap, fast, and safe to use, and they can be used again and again. On the other hand, because simulations are not the real thing, they cannot hope to replace all aspects of what they are simulating. Some of the advantages and disadvantages of learning about knowledge-intensive management strategies through simulations are listed here (after Jackson and Jones, 2001).

Advantages of Simulations

• Engaging. Simulations are more entertaining, and realistic than textual, graphic, or mathematical descriptions of agricultural processes. They allow a user to 'experience' data in a more familiar form than spreadsheets or graphs.
  
• Cheap. An agricultural experiment takes land, labour, and money to run properly. While actual experiments are important for advancing the agricultural knowledgebase, simulations at a fraction of the cost can adequately mimic the experiential learning aspects of experimentation.
  
• Fast. Seasonal or annual processes such as crop growth can be experienced in minutes instead of months.
  
• Safe. Learners can experiment with dangerous or destructive phenomena without endangering themselves, others, or the environment. For example, a learner may apply dangerously high levels of pesticides using a simulation in order to understand what the consequences are without actually experiencing them.
  
• Clear. Simulations can simplify complex processes so that they are more easily understood.
  
• Graded. Simplified simulations can have layers of complexity added as learner understanding expands. For example, a learner can experiment with the effects of fertiliser input on crop growth in isolation before learning about the interaction of fertiliser input on both crop growth and pest infestations.
  
• Reusable. Simulations can be easily rewound, restarted, reset, and retried as many times as necessary.

Disadvantages of Simulations

• Unrealistic. The demonstrated concept may be so oversimplified that the learner is unable to apply them to the real world. For example, a simulation could allow a learner to solve a problem by taking actions that would be impossible in real life.
  
• Misunderstood. Learners may get the simulation to 'work' without actually understanding why it works. Without a good idea of what the simulation is meant to represent, the learner cannot effectively learn from it, even if the learner manipulates the simulation correctly.
  
• False Confidence. Because they allow dangerous or destructive decisions to be made, simulations may encourage rash behaviour in the field.
  
• Gaming. Simulations can become mere games if not conceptualised within the instructional design of a learning module.

An Example - Using a Simulation to Learn about Pesticide Resistance

IPM is one of the most exciting knowledge-intensive management strategies in agriculture today because of its potential to prevent misuse of pesticides, increase farmer profits, and improve the sustainability of world agriculture. International and local NGOs, government agriculture departments, universities, and industry have all expressed interest in and support for the dissemination of this knowledge-intensive management strategy. IPM includes a broad and detailed knowledgebase, and some of the key concepts of IPM are complex and difficult to understand and explain. While there is clearly a large demand for agriculture professionals with IPM in their repertoire, the conceptual difficulties in learning and then sharing this information with others limits their numbers.

One example of an IPM concept that confounds many learners is how to manage the problem of pests becoming resistant to the effects of pesticides. A common experience in agriculture has been that the initial effectiveness of a new pesticide quickly wears off after a few seasons of continuous use. In order to maintain the same kill rate, farmers have to apply higher and higher doses of pesticide, which impacts the farmers' health, their profits, and the local environment. The mechanism of resistance build-up is that resistance is genetic, and genetically resistant individuals will survive pesticide applications and contribute more offspring to the next generation. Because resistant individuals are reproducing more than non-resistant individuals, genetic drift towards a more resistant pest population occurs. The result is that fewer individuals are susceptible to the control, and therefore the pesticide is less effective. And that is how pesticide resistance occurs.

The above explanation of this phenomenon may be slightly confusing, as it assumes a certain degree of literacy, an understanding of biology, and an ability to conceptualise real-life occurrences from a textual description. And this is a layman's explanation of what happens - a population geneticist or entomologist would include many additional variables and idiosyncratic vocabulary in their description of the mechanism of pesticide resistance. More complex theories of how pesticide resistance occurs have been modelled, but a textual description of how they work would take several pages of difficult, mathematical text. Textual descriptions of the pesticide resistance phenomena are probably not the most effective way to deliver this knowledge to learners.

The Pesticide Resistance Simulator

The mechanism and management of pesticide resistance is an example of a concept that can be learned more easily using a simulation. In our Pesticide Resistance Simulator, a population of 'bugs' is animated on the screen. They crawl around, reproduce, and die according to a Leslie matrix, a life-table model from population ecology (Leslie, 1945). In the simulation learners can adjust some of the variables, such as how many bugs have the resistance gene and the susceptibility of non-resistant bugs to the pesticide, but the simulation calculates all the other model variables in the background. When the bug population seems to have gotten out of control, the learner can 'spray the field', reducing the number of bugs according to the underlying model. Over time, as in nature, the effectiveness of the spray is reduced.

The simulation is used as part of an Introductory IPM course offered through e-learning. In the course, the learner is guided through a series of experiments that use the simulation as a tool to answer specific questions. The experiments ask the learner to vary the strength of the pesticide, to use more than one kind of pesticide, to spray only when the pests reach a certain density, etc. The learner is also encouraged to invent new experiments and to test problems from their real-world experience using the simulation. The learner can apply dangerous amounts of pesticide season after season, run hundreds of seasons worth of experiments in a single afternoon, experience and see the results of a complex mathematical model without concerning themselves with its derivation, and avoid spending money on real pesticides (or losing real crops).

Advantages and disadvantages of the pesticide resistance simulation

The pesticide resistance simulation capitalizes on the advantages laid out by Jackson and Jones (2001). The animated bugs crawling across the screen are eye-catching and engaging, attracting learners in a way that text cannot. The simulation is cheap to run compared to a lab or field experiment, although the costs of developing the simulation must also be accounted for. Several generations of bugs are born, reproduce, and die in a matter of minutes, providing a much faster experience for the learner than a traditional experiment. Because the learner is using 'virtual pesticide', the simulation is safe, removing the risks inherent in real-life demonstrations or experiments that involve pesticides. A simplified algorithm brings the concept of the simulation to the forefront, presenting a clear narrative of pesticide resistance that is unhampered by obfuscating factors that can confound a traditional experiment. In the Introductory IPM course, the simulation is presented in the context of a graded exercise, where the learner begins by understanding the basic concept and then applies the concept to a series of experiments that require altering simulation settings. The simulation can be reused as many times as the learner deems necessary, repeating, rewinding, or trying out new ideas.

There are several drawbacks of using a simulation to teach pesticide resistance, however. The algorithm that underlies the simulation is by necessity a simplification of existing models of pesticide resistance, which in turn are clumsy approximations of the real world. Because the simulation is unrealistic in so many subtle ways, the possibility that learners will be unable to apply their learning experiences to the real world exists. Also, because the simulation is relatively simple and the user controls only a few variables, there is a chance that the learner will misunderstand what is actually occurring in the simulation. For example, the learner could draw the wrong conclusions about the effects of the spray, concluding that the population becomes more resistant because the spray has somehow changed the nature of the bugs rather than selectively killing them. This simulation allows unrealistic behaviours such as spraying two or three times per day. There is also a tight relationship between learner actions and the results obtained. The simulation does not caution users about excess spraying or the potential health and environmental impacts, nor does it realistically portray the stochastic nature of biological systems. When faced with an actual field situation, learner false confidence in the efficacy or safety of pesticides could encourage economically damaging and dangerous behaviours in the field. Finally, the simulation can be easily 'gamed', leading to irrelevant activities such as trying to kill every bug on the screen or attempting to invoke resistance rather than prevent it.

Placing the simulation within the context of a course exercise, publishing learning objectives, and providing feedback from knowledgable facilitators or instructors can effectively offset these potential drawbacks (Vincent and Shepherd, 1998, deJong and van Joolingen, 1996). In isolation, the pesticide resistance simulator may be confusing to learners, but when these learners are prompted by a well-designed exercise and guided by facilitators the simulator becomes a useful learning tool. Simulation-based learning that relies heavily on learner intuition is, in general, less effective than simulation-based learning that is guided or directed. In fact, pure simulation learning can sometimes be less effective than tutorial-based instruction. Placing simulations in a learning context with human facilitators to guide and direct learning is probably the best way to offset the potential disadvantages of simulation-based learning (Schank and Cleary, 1994).

Simulations and the delivery of knowledge-intensive management skills

Simulations show much promise for delivering knowledge-intensive management skills to agricultural professionals in developing Asia. Well-designed and deployed simulations can transmit learning across language and cultural barriers where text-based content would be inappropriate. They also breach conceptual barriers for many learners, offering new opportunities for understanding through experience and experimentation (Vincent and Shepherd, 1998; deJong and van Joolingen, 1996).

At issue, though, is whether this type of experiential learning can be effectively passed from agricultural professionals to farmers. Do simulations improve skills and knowledge in a way that allows knowledge transfer? Or are simulations only useful for first-hand learning?

Studies on the effectiveness of simulation-based learning show that there is no clear advantage over rote learning in terms of knowledge gained but that there is a clear advantage in terms of higher-order skills acquisition (Gokhale, 1996; Ziegler, 2001). Learners who use simulations acquire better problem-solving skills and a higher-level understanding of the concept at hand, in addition to the basic facts. This enables them to apply the facts to new situations and incorporate new facts into the their conceptual understanding - an important ability if they are expected to transfer these skills to new problems or other learners (Gokhale, 1996; Vincent and Shepherd, 1998);

Agricultural professionals who learn using simulations should have enhanced higher-order understanding of the concepts, allowing them to transfer knowledge and skills to farmers from a position of relative enlightenment. In addition, the portability and web-distributed nature of such simulations make it possible to deliver the actual simulation to the farmer, with the agricultural professional acting as a guide or facilitator. Whether or not this actually transpires in the field is currently unknown, but we believe that the potential for a more effective transfer of knowledge-intensive management skills increases the potential for success.

The future of simulations in agriculture in developing Asia

Currently, simulations are in their infancy as an e-learning tool, especially for agriculture, and particularly in the developing world. The technology needed to create and deploy simulations is neither especially expensive nor inaccessible, and individuals interested in creating simulations should be encouraged to try. A critical mass of simulation programmers in developing Asia is needed in order to exchange the ideas, code, and techniques that can make simulations great tools for e-learning. Perhaps a listserv would be an appropriate first step towards developing such a community. Many general resources already exist on the web, but the need to localize and adapt this technology to the specific problems of agriculture in developing Asia is emphasized here.

Simulations are an excellent example of an application that, when delivered via the ICT infrastructure, can have a positive impact on the agricultural challenges facing developing Asia. We feel that the promise of simulations as a component tool of e-learning should not be ignored, and strongly encourage interested parties to contact the authors to further discuss their potential.

Summary and Conclusions

The agricultural sector in developing Asia must be included in any effort to leverage development through the application of ICTs. Because it is a significant contributor to GDP and also the main employer in the region, small improvements made in agriculture by using ICTs can have a large net effect on the region. We have suggested here that e-learning is an appropriate and viable medium for delivering the information and skills needed to manage agricultural operations effectively. Such 'knowledge-intensive management' skills should be targeted at agricultural professionals, as they are widely acknowledged as two-way conduits of agricultural knowledge and can help farmers to access this information. By delivering knowledge-intensive management skills and information, ICTs can play a major role in addressing the present challenges in the agricultural sector in developing Asia.

Simulations are just one example of the many component tools of e-learning. When well designed and appropriately presented, simulations offer many advantages over more traditional content presentations. Using the simulation described in this paper, learners can gain understanding about how to manage a complex agricultural phenomenon (pesticide resistance) through interaction and experimentation. Although presently uncommon, we expect such simulations to become an important part of agricultural e-learning in the future, and contribute to solving some of agriculture's more pressing problems.
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