Notice Board


Monday, January 23, 2017



Vehicular Pollution Control


Human activities generate three broad sources of air pollution: stationary or point, mobile, and indoor. In developing countries especially in the rural area, indoor air pollution from using open fires for cooking and heating may be a serious problem. Industries, power plants etc. are the cause of stationary air pollution. But in urban areas –both developing and developed countries, it is predominantly mobile or vehicular pollution that contributes to overall air quality problem. In Delhi, the data shows that of the total 3,000 metric tonnes of pollutants1 belched out everyday, close to two-third (66%) is from vehicles. Similarly, the contribution of vehicles to urban air pollution is 52% in Bombay and close to one-third in Calcutta.2 Katz (1994) has estimated that in Santiago, Chile, wherever pollution concentration exceeds ambient standards, mobile sources or vehicles are the cause. Similarly, in case of Budapest, Hungary, transport is the dominant source of emissions except sulphur dioxide (SO2), contributing 57% of Oxides of Nitrogen (NOx), 80% of lead (Pb), 81% of carbon monoxide (CO) and 75% of hydrocarbon (HC) emissions (Lehoczki, 2000).

A number of countries have targeted vehicles and associated sectors (such as, fuel) to curb the menace. Notable successful initiatives are: conversion of public transport from diesel to CNG in Delhi, switching of Vikrams (tuk-tuks) from diesel to electricity in Kathmandu valley, shifting from leaded to unleaded gasoline in many countries etc. Still the pollution problem in urban cities may continue to loom large due to ever-burgeoning vehicular population, which is outpacing any such measure and road network development. Following data gives a glimpse of such skewed growth. Against 1.9 million vehicular population in 1990 in Delhi, it rose to nearly 3.6 million in the year 2001 (i.e., an increase of nearly 87%). During the same period, Delhi’s population has increased by only 43% (from 9.5 million to 13.8 million) and road-length by merely 14% (from 22,000 Km to 25,000 Km) respectively. Situation is similar across a number of cities in India and the developing world. This indicates the exigency of controlling vehicular pollution.

The worst thing about vehicular pollution is that it cannot be avoided as the emissions are emitted at the near-ground level where we breathe. Pollution from vehicles gets reflected in increased mortality and morbidity and is revealed through symptoms like cough, headache, nausea, irritation of eyes, various bronchial problems and visibility. The pollution from vehicles are due to discharges like CO, unburned HC, Pb compounds, NOx, soot, suspended particulate matter (SPM) and aldehydes, among others, mainly from the tail pipes. A recent study reports that in Delhi one out of every 10 school children suffers from asthma that is worsening due to vehicular pollution.3 Similarly, two of the three most important health related problems in Bangkok are caused by air pollution and lead contamination, both of which are contributed greatly by motor vehicles.4 Situation is same in a number of other mega-cities across the globe – be it Mexico City, Sao Paulo and Santiago in Latin America or Bangkok, Jakarta, Manila, Dhaka in Asia or Ibadan and Lagos in Africa or in cities of Eastern Europe, the erstwhile USSR and the Middle East.

According to the World Health Organisation (WHO), 4 to 8% of deaths that occur annually in the world are related to air pollution and of its constituents, the WHO has identified SPM as the most sinister in terms of its effect on health.

The SPM is not homogeneous. It has a number of constituents. As a result, it is measured and characterised in various ways: (i) TSP (Total suspended particulates) with particle diameters < 50-100 μm is the fraction sampled with high-volume samplers. (ii) PM: Inhalable particles having a diameter <10 μm penetrates through the nose, by breathing. (iii) Thoracic particles: are approximately equal to PM particles. (iv) PM: ‘Fine fraction’ with a diameter <2.5 μm penetrates to the lungs; and (v) Black smoke: a measure of the blackness of a particle sample gives a relative value for the soot content of the sample. Due to their high health damaging potential10102.5 recent studies have started paying more attention to PM10 and PM2.5 particles.

The different air pollutants due to vehicles can have effects at all the three levels – local (e.g., smoke affecting visibility, ambient air, noise etc.), regional (such as smog, acidification) and global (i.e., global warming). The vehicles besides being the prominent source of air pollutants also account for a number of external effects, such as congestion, noise, accidents, road wear and tear, and ‘barrier effects.
Under this background this note investigates what is the economics of vehicular pollution control and what policy instruments / initiatives can be employed to control the vehicular pollution. For a prescription to yield desired results, it should hit the right source of pollution. Section 2 gives in brief the contribution of different sources to vehicular air pollution problem. This is followed by the economics of vehicular pollution control in Section 3. The section also explores the instruments that can be applied to control vehicular pollution. 
The major difference between developing and developed countries lies in the fact that institutions are in place and information of health impacts are known to the policy makers. For developing countries, the challenge rests on devising suitable policy instruments that fully take into account the damage caused by the polluting source. A discussion of complexity involved in estimating the damage function is given in Section 4. Section 5 gives under what conditions a particular instrument will be more appropriate, especially in the case of mega cities of developing world followed by India’s experience in combating vehicular pollution. The concept note concludes in Section 6. It is to be stated at the outset that the note covers mainly the environmental consequences of transport and does not investigate the other important external effects of the sector such as barrier effect, congestion effect etc.


Vehicular pollution sources are not homogenous, as there is a complete range of technological mix. The mix could be in terms of fuel used – gasoline or diesel or natural gas; or engine type – 2-stroke or 4-stroke and/or a combination of these. Emissions from Gasoline Vehicles Gasoline-powered engines are of two types: 4-stroke and 2-stroke.  gives the various sources of emissions in the two cases. The exhaust emissions from gasoline-run vehicles consist of CO, HC, NOx, SO2, and partial oxides of aldehydes, besides particulate matters including lead salts.


Motor vehicle exhaust emissions are a significant source of pollution, including carbon monoxide, nitrogen oxides and hydrocarbons. These pollutants can be harmful to human health and the environment and lead to the formation of ground level ozone (smog). Exhaust emissions from cars and trucks are one of the single greatest sources of air pollution in the Chicago and Metro-East St. Louis areas.
The Illinois EPA's vehicle emissions inspection program plays an important role in improving air quality and public health in Illinois. The federal Clean Air Act (42 U.S.C. § 7511a) requires vehicle emissions inspection programs in large, urbanized areas that do not meet the National Ambient Air Quality Standards (NAAQS) for ozone. Although Illinois has made significant strides to clean its air, levels of air pollution in the Chicago and Metro-East St. Louis areas still exceed the ozone NAAQS. Additionally, the Illinois Vehicle Emissions Inspection Law of 2005 (625 ILCS 5/13C) requires a vehicle emissions inspection program to reduce air pollution from motor vehicles in these areas of Illinois. For these reasons, the vehicle emissions inspection program is part of Illinois EPA’s strategy to reduce air pollution in Illinois and bring the Chicago and Metro-East areas into attainment of the ozone NAAQS.
Through the On-Board Diagnostic (OBD) test, vehicle emissions inspections in Illinois identify malfunctioning emission control systems that often result in vehicles exceeding federal emission standards. Requiring repairs on such vehicles helps clean the air while improving the vehicle's performance and fuel economy.
Most 1996 and newer gasoline-powered passenger vehicles are subject to emissions inspections after they are four years old (e.g. 2012 vehicles are being inspected in 2016 for the first time). The inspection month coincides with the expiration date of the vehicle license plate. Typically, even model-year vehicles are inspected during even years, and odd model-year vehicles are inspected in odd years.
The Illinois EPA oversees its vehicle emissions inspection program that is operated by its contractor. The Illinois EPA enforces the vehicle emissions inspection requirement by partnering with the Illinois Secretary of State’s Office to deny vehicle license plate registrations to non-complying vehicles.
Automobile pollution sources effect and control of automobile pollution Air Pollution from Motor Vehicles Standards and Technologies for Controlling Emissions Controlling Emissions from In-Use Vehicles Inspection and maintenance (I/M) measures to control emissions from in-use vehicles are an essential complement to emission standards for new vehicles. Although difficult to implement, an effective inspection and maintenance program can significantly reduce emissions from uncontrolled vehicles. I/M programs are also needed to ensure that the benefits of new-vehicle control technologies are not lost through poor maintenance and tampering with emission controls. I/M programs for gasoline vehicles commonly include measurement of hydrocarbon and carbon monoxide concentrations in the exhaust. These have limited effectiveness but can identify gross malfunctions in emission control systems. Newer programs such as the IM240 procedure developed in the United States use dynamometers and constant volume sampling to measure emissions in grams per kilometer over a realistic driving cycle. Inspection and maintenance of high-technology, computer-controlled vehicles can be enhanced substantially with on-board diagnostic systems. For diesel vehicles, smoke opacity measurements in free acceleration are the most common inspection method. This approach also has limited effectiveness but can identify serious emission failures. Opacity measurements can also be used to control white smoke emissions from twostroke motorcycles.On-road emission checks can improve the effectiveness of periodic I/M programs. Checks for smoke emissions from two-stroke and diesel vehicles can be made more effective by visual prescreening. The effectiveness of on-road checks for hydrocarbons and carbon monoxide can be enhanced by remote sensing the concentrations of these pollutants in the vehicle exhaust.
There are two main types of I/M programs: centralized programs, in which all inspections are done in high-volume test facilities operated by the government or contracted to competitively-selected private operators, and decentralized programs, in which both emissions testing and repairs are done in private garages.

Automobile Engineering Previous Years Question Papers


what is mis ? Decision support system


Management information systems encompass a broad and complex topic. To make this topic more manageable, boundaries will be defined. First, because of the vast number of activities relating to management information systems, a total review is not possible. Those discussed here is only a partial sampling of activities, reflecting the author's viewpoint of the more common and interesting developments. Likewise where there were multiple effects in a similar area of development, only selected ones will be used to illustrate concepts. This is not to imply one effort is more important than another. Also, the main focus of this paper will be on information systems for use at the farm level and to some lesser extent systems used to support researchers addressing farm level problems (e.g., simulation or optimization models, geographic information systems, etc.) and those used to support agribusiness firms that supply goods and services to agricultural producers and the supply chain beyond the production phase.

Secondly, there are several frameworks that can be used to define and describe management information systems. More than one will be used to discuss important concepts. Because more than one is used, it indicates the difficult of capturing the key concepts of what is a management information system. Indeed, what is viewed as an effective and useful management information system is one environment may not be of use or value in another.
Lastly, the historical perspective of management information systems cannot be ignored. This perspective gives a sense of how these systems have evolved, been refined and adapted as new technologies have emerged, and how changing economic conditions and other factors have influenced the use of information systems.

Before discussing management information systems, some time-tested concepts should be reviewed. Davis offers a commonly used concept in his distinction between data and information. Davis defines data as raw facts, figures, objects, etc. Information is used to make decisions. To transform data into information, processing is needed and it must be done while considering the context of a decision. We are often awash in data but lacking good information. However, the success achieved in supplying information to decision makers is highly variable. Barabba, expands this concept by also adding inference, knowledge and wisdom in his modification of Haechel's hierarchy which places wisdom at the highest level and data at the lowest. As one moves up the hierarchy, the value is increased and volume decreased. Thus, as one acquires knowledge and wisdom the decision making process is refined. Management information systems attempt to address all levels of Haechel's hierarchy as well as converting data into information for the decision maker. As both Barabba and Haechel argue, however, just supplying more data and information may actually be making the decision making process more difficult. Emphasis should be placed on increasing the value of information by moving up Haechel's hierarchy.

Another important concept from Davis and Olsen is the value if information. They note that “in general, the value of information is the value of the change in decision behavior caused by the information, less the cost of the information.” This statement implies that information is normally not a free good. Furthermore, if it does not change decisions to the better, it may have no value. Many assume that investing in a “better” management information system is a sound economic decision. Since it is possible that the better system may not change decisions or the cost of implementing the better system is high to the actual realized benefits, it could be a bad investment. Also, since before the investment is made, it is hard to predict the benefits and costs of the better system, the investment should be viewed as one with risk associated with it.Another approach for describing information systems is that proposed by Harsh and colleagues. They define information as one of four types and all these types are important component of a management information system. Furthermore, the various types build upon and interact with each other.Descriptive information can also be used as inputs to secure other needed types of information.


“what is” information is needed for supplying restraints in analyzing farm adjustment alternatives. It can also be used to identify problems other than the “what is” condition. Descriptive information is necessary but not completely sufficient in identifying and addressing farm management problems.

The second type of information is diagnostic information, This information portrays this “what is wrong” condition, where “what is wrong” is measured as the disparity between “what is” and “what ought to be.” This assessment of how things are versus how they should be (a fact-value conflict) is probably our most common management problem. Diagnostic information has two major uses. It can first be used to define problems that develop in the business. Are production levels too low? Is the rate earned on investment too low? These types of question cannot be answered with descriptive information alone (such as with financial and production records). A manager may often be well supplied with facts about his business, yet be unable to recognize this type of problem. The manager must provide norms or standards which, when compared with the facts for a particular business, will reveal an area of concern. Once a problem has been identified, a manager may choose an appropriate course of action for dealing with the problem (including doing nothing). Corrective measures may be taken so as to better achieve the manager’s goals. Several pitfalls are involved for managers in obtaining diagnostic information. Adequate, reliable, descriptive information must be available along with appropriate norms or standards for particular business situations. Information is inadequate for problem solving if it does not fully describe both “what is” and “what ought to be.”As description is concerned with “what is” and diagnostics with ”what is wrong,” prediction is concerned with “what if...?” Predictive information is generated from an analysis of possible future events and is exceedingly valuable with “desirable” outcomes. With predictive information, one either defines problems or avoids problems in advance. Prediction also assists in analysis. When a problem is recognized, a manager will analyze the situation and specify at least one alternative (including doing nothing) to deal with it. Predictive information is needed by managers to reduce the risk and uncertainty concerning technology, prices, climate, institutions, and human relationships affecting the business. Such information is vital in formulating production plans and examining related financial impacts. Predictive information takes many forms. What are the expected prices next year? What yields are anticipated? How much capital will be required to upgrade production technologies? What would be the difference in expected returns in switching from a livestock farm to a cropping farm? Management has long used various budgeting techniques, simulation models, and other tools to evaluate expected changes in the business.

Without detracting from the importance of problem identification and analysis in management, the crux of management tasks is decision making. For every problem a manager faces, there is a “right” course of action. However, the rightness of a decision can seldom, if ever, be measured in absolute terms. The choice is conditionally right, depending upon a farm manager’s knowledge, assumptions, and conditions he wishes to impose on the decision. Prescriptive information is directed toward answering the “what should be done” question. Provision of this information requires the utilization of the predictive information. Predictive information by itself is not adequate for decision making. An evaluation of the predicted outcomes together with the goals and values of the manger provides that basis for making a decision. For example, suppose that a manager is considering a new changing marketing alternative. The new alternative being considered has higher “predicted” returns but also has higher risks and requires more management monitoring. The decision as to whether to change plans depends upon the managers evaluation of the worth of additional income versus the commitment of additional time and higher risk. Thus, the goals and values of a farm manager will ultimately enter into any decision.

The importance of management information systems to improve decision making has long been understood by farm management economists. Financial and production records have long been used by these economists as an instrument to measure and evaluate the success of a farm business. However, when computer technology became more widely available in the late 1950s and early 1960s, there was an increased enthusiasm for information systems to enhance management decision processes. At an IBM hosted conference, Ackerman, a respected farm management economist, stated that:

“The advances that have taken place in calculating equipment and methods make it possible to determine the relationship between ultimate yields, time of harvest and climatic conditions during the growing season. Relationship between the perspective and actual yields and changing prices can be established. With such information at hand the farmer should be in a position to make a decision on his prediction with a high degree of certainty at mid-season regarding his yield and income at harvest time.”

This statement, made in 1963, reflects the optimism that prevailed with respect to information systems. Even though there was much enthusiasm related to these early systems they basically concentrated on accounting activities and production records. Examples include the TelFarm electronic accounting system at Michigan State University and DHIA for dairy operations. These early systems relieved on large mainframe computers with the data being sent to a central processing center and the reports send back to the cooperating businesses. 


limited data analysis capabilities beyond calculating typical ratios (e.g., return on assets, milk per cow, etc.). By the mid 1960s it became clear that the accounting systems were fairly effective in supplying descriptive and diagnostic information but they lacked the capacity to provide predictive and prescriptive information. Thus, a new approach was needed – a method of doing forward planning or a management information system that was more model oriented. Simulation models for improving management skills and testing system interaction were developed. As an example, Kuhlmann, Giessen University, developed a very robust and comprehensive whole farm simulation model (SIMPLAN) that executed on a mainframe computer. This model was based on systems modeling methods that could be used to analyze different production strategies of the farm business. To be used by managers, however, they often demanded that the model developer work closely with them in using the model.
Another important activity during this period was the “Top-Farmer Workshops” developed by Purdue University. They used a workshop setting to run large linear-programming models on mainframe computers (optimization models) to help crop producers find more efficient and effective ways to operate their business.

As mainframe timeshare computers emerged in the mid-1960's, I became possible to remotely access the computer with a terminal and execute software. Systems such TelPlan developed by Michigan State University made it possible for agricultural producers to run a farm related computer decision aids. Since this machine was shared by many users, the cost for executing an agriculturally related decision aid was relatively inexpensive and cost effective. These decision aids included optimization models (e.g., least cost animal rations) budgeting and simulation models, and other types of decision aids. These decision aids could be accessed by agricultural advisor with remote computer terminals (e.g., Teletype machine or a touch-tone telephone). These advisors used these computer models at the farm or at their own office to provide advice to farm producers.

These were exciting times with many people becoming involved in the development, testing, refining, and implementation of information systems for agriculture. Computer technology continued to advance at a rapid pace, new communication systems were evolving and the application of this technology to agriculture was very encouraging. Because of the rapid changes occurring, there were international conferences held where much of the knowledge learned in developing these systems was shared. One of the first of these was held in Germany in the mid-1980s.

It was also clear from these early efforts that the data oriented systems where not closely linked to the model oriented systems. Information for the data oriented systems often did not match the data needed for the model oriented systems. For example, a cash-flow projection model was not able to directly use financial data contained in the accounting system. In most cases, the data had to be manually extracted from the accounting system and re-entered into the planning model. This was both a time consuming and error prone process
Because of the lack of integration capabilities of various systems, they were devoid of many of the desirable characteristics of an evolving concept describes as decision support systems (DSS). These systems are also known as Executive Support Systems, and Management Support System, and Process Oriented Information Systems . The decision support system proposed by Sprague and Watson (House, ed.) Has as its major components a database, a modelbase, a database/modelbase management system and a user interface (see Figure 3). The database has information related to financial transactions, production information, marketing records, the resource base, research data, weather data and so forth. It includes data internally generated by the business (e.g., financial transactions and production data) and external data (e.g., market prices). These data are stored in a common structure such that it is easily accessible by other database packages as well as the modelbase.

The modelbase component of the system has decision models that relate to operational, tactical and strategic decisions. In addition, the modelbase is able to link models together in order to solve larger and more complex problems, particularly semi-structured problems. The database/modelbase management system is the bridge between database and modelbase components. It has the ability to extract data from the database and pass it to the modelbase and vice versa. The user interface, one of the more critical features of the system, is used to assist the decision maker in making more efficient and effective use of the system. Lastly, for these systems to be effective in supporting management decision, the decision maker must have the

Decision Support System

skills and knowledge on how to correctly use these systems to address the unique problem situation at hand.
Several follow-up international conferences were held to reflect these new advances in management information systems. The first of these conferences focused on decision support systems was held in Germany. This conference discussed the virtues of these systems and the approach used to support decisions. Several prototype systems being developed for agriculture were presented. From these presentations, it was clear that the decision support systems approach had many advantages but the implementation in agriculture was going to be somewhat involved and complex because of the diversity of agricultural production systems. Nevertheless, there was much optimism for the development of such systems.

A couple of years later, another conference was held in Germany that focused on knowledge-based systems with a major emphasis on expert systems and to a lesser extent optimum control methods and simulation models. Using Alter’s scheme to describe information systems, for the
most part these would be described as suggestion models. It was interesting to note that the prototype knowledge-based systems for the most part did not utilize the concepts of decisions support systems which was the focus of the earlier conference. Perhaps this was related to the fact that many of the applications were prototypes.
The international conference that followed in France focused on the low adaption rate of management information systems. This was a topic of much discussion but there were few conclusions reached except the systems with the highest adaption rate were mainly data-oriented ones (e.g., accounting systems, field record systems, anaimal production and health records, etc.) which provide mainly descriptive and diagnostic information.
The international conferences that followed had varying themes. One of the major themes was precision agriculture with several conferences held. These conferences extolled the use of geographic information systems (GIS) in conjunction with geographic positioning systems (GPS) to record and display data regarding cropping operations (e.g., yields obtained) and to control production inputs (e.g., fertilizer levels). Other conference addressed the use of information systems to more tightly control agriculture production such as those developed for greenhouse businesses.
To briefly summarize the historical developments, there have been significant efforts devoted to improving the management information systems from the early computerized activities forty years earlier. The decision aids available have grown in number and they are more sophisticated. There has been some movement toward integration of the data oriented systems and the model oriented systems. An examination of our current usage of management information systems, however, suggests that we have not nearly harnessed the potential of the design concepts contained modern management information systems.


The current status of management information systems is remains dynamic. Several adoption surveys and personal experiences lead to some interesting observations. These observations will be reviewed in the context of a decision support system as defined by Spraque and Watson.
On-Farm Information Systems -- Computer Hardware
The percentage of farms owning a computer continues to grow. Most commercial farms now own a computer and have access to the Internet, many with high speed connections. Most of the computers are of recent vintage with large data storage and memory capacity. It is safe to state that the hardware is not the bottleneck with respect to management information systems.

On-Farm Database and Modelbase Applications

The decision support system literature stressed that the database and modelbase remain separate entities. They should be bridged by the database/modelbase management system. In examining much of the software developed for on-farm usage, it appears that most of it does not currently employ this design concept. Indeed most of the software is a stand-alone product with the database an integral part of the modelbase. However, some packages have the ability to export and import data, allowing for the sharing of data across the various packages, but these data sharing features are usually rather narrow in scope and flexibility.


The most common software packages used by agricultural producers are data oriented with the most common being one designed for financial accounting. Accounting packages explicitly designed for agricultural businesses and general business accounting packages are used for keeping the financial records. Because of their rather low cost relative to the agricultural specific packages, the general purpose packages are growing in market share. These financial accounting systems are used beyond completing tax documents. They are also important for providing information to creditors and for planning and control. Production management also accounts for a significant proportion of computer usage. There are many software packages available that address livestock problems. Some are database programs to keep track of animal related data and/or feed inventories. There are models to address operational and tactical decisions such as ration balancing, culling decisions, alternative replacements options, etc.
However, many livestock producers also use off-farm production records processing such as using the DHIA service bureau for processing dairy records. These service bureaus provide a downloading feature so the data can be moved to the on-farm computer.
For cropping operations, there are similarities in software availability. Database systems are available for keeping track of information on fields and sub-fields, particularly fertilizers and pesticides applied, varieties planted and yields achieved.
Though there is increasing interest in geographic information systems by agricultural producers, the main usage is for yield monitoring and mapping. This approach is used to evaluate the effectiveness of alternative management practices employed in the production of the crop (e.g., comparison of varieties, seeding rates, pest control measures, tillage systems, etc.) and to identify field problems (e.g., soil compaction, drainage problems, etc.). This yield monitoring approach is finding the greatest acceptance and this may be in part because the yield monitoring and mapping systems are common option on grain harvesting equipment. One of the real concerns with using yield monitoring and mapping systems relates to the issue of arriving at the correct inference of what causes the variation in yields noted. The potential layers of data (e.g., pH, precious crops grown, soil structure, planting date, nutrients applied, variety grown, pesticides used, rainfall, etc.) has been suggested to exceed 100. To be able to handle the large number of data layers in an effective manner would suggest a full-feature geographic information system (GIS) might be needed. However, few agricultural producers have access to a full-feature GIS and/or training to utilize these systems, and there are substantial costs related to capturing and storing various data layers. Nevertheless, the more obvious observations originating from these systems (e.g., such as poor drainage and soil compaction) have resulted in sound investments being made in corrective measures.
To a limited extent, some agricultural producers are starting to make use of remote sensing data to identify problems related to the growing crop such as an outbreak of a disease. Those using remote sensing feel they are able to more quickly identify the problems and take corrective action, minimizing the damage done.
Precision agriculture applied to the animal industries is on a different scale. Information systems are playing a major role on the integrated mega-farms. When using information systems to carefully track genetic performance, balance rations, monitor health problems, facilities scheduling, control the housing environment and so forth, it is generally acknowledged that it is possible to achieve a fairly significant reduction in cost per unit of output (10-15%) over that of more traditional, smaller farming operations. These are proprietary information systems and the information from these systems are used to gain a strategic competitive advantage.

Lastly, the general purpose spreadsheet is the most common software used for planning purposes. Some of these applications are very sophisticated and address complex problems.
User Interface .The user interface has improved in greatly in quality. Most agricultural software now uses the windowing environment. This environment makes it easier for the user to use and access data and information, and to move data from one application to another or to link applications. However, this still remains a user-initiated task and in some cases can be complex. Also most of the data contained in the software package is unique to that package and not easily shared with other software packages. Thus, from a DSS viewpoint there are still significant shortcomings. 

The Decision Maker An often overlooked component of a decision support system is the decision maker. Prior surveys suggest that the primary user of the on-farm computer system is the farm operator. Operators that are younger and college educated were much more likely to routinely use the computer. Also large farms were more likely to utilize a computer in their farming operation. It is also observed that there is a fair amount of “learning cost” related to use of on-farm information systems. These cost can be large enough to hinder the adoption of management information systems.


There is increased interest and excitement about the role external information systems available to agricultural producers, particularly Internet and satellite data transmission systems. Each of these technologies is a vast resource of data which can be used to enhance the various levels (e.g., information, intelligence, knowledge, wisdom) of Haechel's Hierarchy for an individual or organization. Another information source is the outside advisor. As the complexity and breadth of the farm level decision process has increased, the use of consultants and advisors has grown. This is particularly true of the larger farming operations.
The growth in Internet is phenomenal. The growth in its use by agricultural producers is also phenomenal. Email is a common communication tool used by agricultural business. The same is true for the world-wide-web (WWW). They made extensive use of the web to find information that fit their unique requirement. Even though they find it a major source of information for their operation, it takes good skills to locate the information desired. One of the common complaints is the amount of time it takes to utilize the Internet effectively and the lack of depth of information. One of the critical questions relates to how effective Internet is in addressing the higher levels of Haechel's hierarchy.
Other Internet resources available to agriculture include sites for downloading agricultural software. Much of the economic data compiled by the government is now available on-line. Lastly, in some cases it is being used as a marketing tool for products produced by the business.
Satellite Data Transmission Systems

The satellite data transmission systems are widely used by producers. These systems are passive data acquisition systems from the user's viewpoint. Data is continuously broadcast to the leased data terminal from a satellite. The data is automatically stored in the data terminal and can be accessed by a menuing process. These systems provide current data/information on a number of topics. Amounts and types of data/information received depends upon the options purchased. The basic subsystem provides for the latest market prices and news, weather maps (e.g., rainfall, jet streams, severe weather, crop soil moisture index, soil temperature, air temperature, etc.), government reports on market developments, long- and short-term weather forecasts, political developments that pertain to agriculture, and product information. Premium service options add even more features.
Outside Advisors
Several recent studies suggest that use of outside advisory services by farmers to enhance and supplement their on-farm information systems was fairly prevalent. The tax preparer is the

advisory most commonly used. Other important sources of information include the local Extension agents, veterinary consultants, accountants, crop/pest management consultants, and livestock management advisors (e.g., a nutritionist).

The outside advisors utilize many different software packages to help provide advice to producers. FINPAK developed by the University of Minnesota is an example of a software package widely used by outside advisors with farmers. This financial analysis and related projection package helps evaluate the financial process being made by the farm and compares alternative future business options. This package (an accounting type model) is widely used in the U.S.


Predicting the future is not an exact science. But with the structural changes occurring in agriculture today, the management problems are significantly different from the problems of yesterday. Earlier emphasis in information systems was on improving production management decisions. Today, major issues that are commonly faced in management relate to financial, human resource, and marketing management. These management areas and their importance are identified in the strategic management workshops I have conducted with agricultural producers. Thus, managers will have less time to address production issues because more time and effort are being focused in the other management areas. This will have an impact on information systems to address production management.
Addressing Structured Decisions In the future information systems to address production management will likely be of five general types: 1) software for systems analysis, 2) theory testing, software for teaching purposes, 3) software for advisors, 4) software for use by producers, and 5) software to control and monitor the supply chain.
Software for systems analysis and theory testing will be developed with the primary objective of defining the structure and studying the dynamics and interaction of the various system components. Its main use is in research. These models are fairly complex and often have robust data requirements. Their utilization often depends upon availability of the developers to run the model or assist in the use of the model. This software is very useful in testing various hypotheses regarding system dynamics 

These models play a vital role in generating a better understanding of the overall system and can give valuable insight on how to manage the system. They are also useful in identifying areas for further research. The results from these models are communicated in various ways (e.g., journal articles, trade journals, and advisory service publications and conferences) and these communicated results are often used by producers to adjust production practices. However, direct use by producers to evaluate their own unique situations is not common with these models. There are several reasons for this limited use including a poor user interface or lacking the data to drive the model. Also, it is generally unlikely that transformation of a model of this nature into one that is to be used by the producers will be successful.

Software developed for teaching purposes is likely to continue. Sometimes these software packages are referred to as simulation games. Because these models teach concepts and principles, they are often a simplification of reality. They tend to use the case analysis approach, making it difficult to use the model to analyze various options and alternatives utilizing actual business data. The models are often used in an interactive mode (e.g., in a classroom or workshop environment) where knowledge is gained by testing “what if” questions, then observing the results. These models can be very powerful teaching tools, but are rarely used to analyze actual business situations. Producers often lose interest in using this software because it is too simplistic, takes too much time and effort to extract knowledge for better decision-making, or it does not adequately reflect the reality of the business.
Software for advisors is a class of software that is used by agricultural advisors (e.g., Extension staff, consultants, and agribusiness firms) to assist producers in making decisions. The advisor is a necessary intermediary, because the software could demand a thorough understanding of a difficult set of concepts (e.g., long range planning) or it may be rather demanding of the user’s time and effort (e.g., a large amount of data has to be collected, entered and analyzed), or the time and effort to become proficient in the use of the model is considered excessive. This type of software will grow in importance as the use of outside consultants and advisory services by agricultural businesses grows. These outside advisors and consulting services will increasingly use many different software packages to help provide advice to the producer. The package they use depends upon their area of specialization. For instance, those that are offering production advise may use one of several production decision aid models.

Advisors also serve as an intermediary to extracting information from Internet (external data). They often subscribe to threaded discussion groups. They use these groups for posting problems and receiving back suggested solutions. They also learn from the exchange of ideas between others using the system. Also, advisors more readily see the merit of using a software program designed for systems analysis for enhancing their personal knowledge and skills and solving problems for their clients. This is particularly true if the software has a good user interface.
Software for use by producers is and will continue to be some of the most demanding software to develop. As indicated earlier, a large amount of software has been written, but much of it has fallen short of expected usage rate. One reason is the decision makers have found the software fails to address their problems. The software must be fairly easy to utilize, and the producer expects it to provide information that has a perceived value greater than the cost of attaining that information.


Software being used by producers can be grouped into two subcategories. The first subcategory is used to process transaction data and meet regulatory requirements. These are the software applications most used by the actual businesses. They must keep accounting, personnel and crop production records (e.g., pesticides used) because of government regulation. They also use software to reduce the time, effort and cost of processing the transaction records. This is why payroll packages, and shipping and billing systems are commonly employed on these operations. This usage will continue to grow in importance.
The other subcategory of software is used for management purposes. This currently accounts for a lesser portion of the computer usage. A large growth in this usage of this software is unlikely. The time and effort to master this software is major commitment. Since management time is being diverted to areas other than production management, they will have less and less time to become proficient in the use of this software. Thus, very thorough and sophisticated systems (e.g., the SAP software system) currently being employed by large companies are not likely to be common on farm businesses because of their complexity and cost.

Software for process control is used to control and automate many of the structured-operational decisions of the business enterprises, such as controlling temperature, light, irrigation and fertility in greenhouses. These models are generally of a closed-loop optimal control design. The process control models are generally knowledge based systems and have been developed using knowledge from many sources including the systems analysis models discussed earlier. The use of process control systems will grow in importance and acceptance. This acceptance implies that the managers have confidence in the models and that they improve the efficiency and effectiveness of the business. These models also free them to concentrate on more complex decisions.

Software to control and monitor the supply chain will greatly grow in importance. The will be many factors driving this grow including concerns about food safely, country of origin labeling, organic foods, foods to meet special dietary requirements, and concerns about product liability suits. In will likely become commonplace that a food item purchased by the consumer at the retail level will have attached its entire history, including identity preservation and traceability, included with the purchase. The new advances in RFID chips and the requirements by certain major retailers to label all products with these chips will impact agricultural businesses including those engaged in producing farm products. The system imposed upon the entire supply chain will likely be designed by the retailers and the entire chain will need to adjust to the defined information structure. To adapt to the defined information structure may mean a major restructuring of the information system currently being used by the business with substantial costs associated with the conversion.
-tructured and Unstructured Decisions
To address the management areas related to human resources, finances, and marketing, suggest information systems that can address ill-structured or unstructured problems. Some would state that we are in the process of moving from the “old economy” to “new economy.” With this

paradigm shift, among the changes is a movement from resource based to idea based wealth creation, from a stable comparative advantage to a dynamic one, from investment in physical assets to investment in human capital, from protected to open markets, from subsidies to encouragement to adapt, from hierarchal organizations to strategies alliances and partnerships. In addition agriculture will move from commodity markets to product markets and it will become more environmentally friendly, concerned with food safety, and quality and supply coordination.

If this transition from the “old economy” to the “new economy” occurs for agriculture, then the information systems of the past will not be adequate for the future. They will need to be much broader and more comprehensive than the current systems. 
The future systems must:

• address the larger scope of financial management rather than financial record keeping, tax reporting, and analysis.

• help define marketing strategies and alliances.

• help identify potential niche markets rather than supplying data on current commodity market trends;
• support the creation of new ideas.
• nurture the growth of knowledge since this will become a major source of wealth creation.
• deal with the many dimensions and complexity of human resource management.

• signal needed production changes in an overall system of supply chain management.

• assist in negotiating contractual arrangements.
• help the producer adopt to an economic climate that has more risk and uncertainty because of less government intervention in markets.

• provide the capacity to track the identify of a product from its genetics to the consumer.

• assist in producing a product that meets customer desires rather than the production of a commodity.

Developing farm-level information systems to fulfill these needs will be a major challenge. It will take a major rethinking with regard to the role of management information systems. It will involve more than enhancing hardware, communications infrastructure, and software components of the information system. An equally important consideration will be the analytical skills, knowledge, wisdom, and interests of the agricultural decision maker.

The information system of the future will need to concentrate more on the upper levels of Haechel's hierarchy -- knowledge and wisdom. As Honaka and Hirotaka observe, knowledge has two forms, tacit (subjective) and explicit (objective). Tacit knowledge is gained from experiences and practice, whereas explicit knowledge is based more on theory and rationality. As decision makers address problems, they convert knowledge between the two forms. An information system that focuses only on one form will have shortcomings. The information system of the future must have both forms of knowledge, and encourage the conversion of knowledge between the forms as a continuous process. Only by this process will the manager's knowledge base grow in size and function.

Information systems of the past have tended to concentrate on explicit knowledge (e.g., linear programming to balance a ration) and, to lesser extent tacit knowledge. Many of the problems of the future will involve tacit knowledge. The challenge will be designing information systems that will allow for an easier and more effective means of sharing tacit knowledge. The Internet will no doubt play a key role in meeting this challenge. Perhaps a system for documenting experiences (e.g., structured case studies) can be used to enhance the sharing of tacit knowledge.