E-Agriculture

“E-Agriculture” is an emerging field in the intersection of agricultural informatics, agricultural

development and entrepreneurship, referring to agricultural services, technology dissemination,
and information delivered or enhanced through the Internet and related technologies. More
specifically, it involves the conceptualization, design, development, evaluation and application of
new (innovative) ways to use existing or emerging information and communication technologies
(ICTs). E-Agriculture goes beyond technology, to promote the integration of technology with
multimedia, knowledge and culture, with the aim of improving communication and learning
processes between various actors in agriculture locally, regionally and worldwide. Facilitation,
support of standards and norms, technical support, capacity building, education, and extension
are all key components to e-Agriculture. There are several types of activity related to e-
agriculture applications that are widely recognized around the world today. The delivery of
agricultural information and knowledge services (i.e. market prices, extension services, etc)
using the Internet and related technologies falls under the definition of e-Agriculture. More
advanced applications of e agriculture in farming exist in the use of sophisticated ICTs such as
satellite systems, Global Positioning Systems (GPS), advanced computers and electronic systems
to improve the quantity and quality of production. In India agriculture is a major occupation for
most part of population. Most rural population depends upon agriculture as their important
occupation.

Current scenario of agriculture sector

The occupational structure of India is dominated by the “agricultural sector” and the
“manufacturing sector” and the “service sector” is lagging far behind in this context. This shows
that India is predominantly an agricultural economy and hence it requires strongest protection
and development of its “agricultural resources”. India is facing certain “Agricultural Challenges”
that must be resolved as soon as possible. The major challenges to “Agriculture Sector in India”
are:

1) Insufficient agricultural infrastructure and support facilities,

2) Insufficient institutional capacity to deliver farmers specific services,
3) Lack of awareness regarding suitable agricultural methods among the farmers,
4) Agricultural content development and its up gradations,
5) Ownership issues of the public and government generated data,
6) Inadequate use of Public-Private Partnerships in India,
7) Lack of “Common Platforms” for the farmers in India,
8) Absence of an “Agricultural Think-Tank” in India,
9) Insufficient use of ICT for agricultural purposes, etc.
Information and communication technology in agriculture (ICT in agriculture), also known
as e-agriculture, focuses on the enhancement of agricultural and rural development through
improved information and communication processes. More specifically, e-agriculture involves
the conceptualization, design, development, evaluation and application of innovative ways
to use information and communication technologies (ICTs) in the rural domain, with a
primary focus on agriculture. ICT includes devices, networks, mobiles, services and
applications; these range from innovative Internet-era technologies and sensors to other pre-
existing aids such as fixed telephones, televisions, radios and satellites. Provisions of standards,
norms, methodologies, and tools as well as development of individual and institutional
capacities, and policy support are all key components of e-agriculture.

Many ICT in agriculture or e-agriculture interventions have been developed and tested around
the world to help agriculturists improve their livelihoods through increased agricultural
productivity and income, or by reducting risks.

ICT interventions

Many ICT interventions have been developed and tested around the world, with varied degrees
of success, to help agriculturists improve their livelihoods through increased agricultural
productivity and incomes, and reduction in risks.

Wireless technologies: - Wireless technologies have numerous applications in agriculture. One

major usage is the simplification of closed-circuit television camera systems; the use of wireless
communications eliminates the need for the installation of coaxial cables.

Global Positioning System (GPS):- In agriculture, the use of the Global Positioning
System provides benefits in geo-fencing, map-making and surveying. GPS receivers dropped in
price over the years, making it more popular for civilian use. With the use of GPS, civilians can
produce simple yet highly accurate digitized map without the help of a professional cartographer.

Geographic information systems:- Geographic information systems, or GiS, are extensively

used in agriculture, especially in precision farming. Land is mapped digitally, and pertinent
geodetic data such as topography and contours are combined with other statistical data for easier
analysis of the soil. GIS is used in decision making such as what to plant and where to plant
using historical data and sampling.
Computer-controlled devices (automated systems):- Automatic milking systems are computer
controlled stand alone systems that milk the dairy cattle without human labor. The complete
automation of the milking process is controlled by an agricultural robot, a complex herd
management software, and specialized computers. Automatic milking eliminates the farmer from
the actual milking process, allowing for more time for supervision of the farm and the herd.
Farmers can also improve herd management by using the data gathered by the computer. By
analyzing the effect of various animal feeds on milk yield, farmers may adjust accordingly to
obtain optimal milk yields. Since the data is available down to individual level, each cow may be
tracked and examined, and the farmer may be alerted when there are unusual changes that could
mean sickness or injuries.

Smartphone mobile apps in agriculture:- The use of mobile technologies as a tool of

intervention in agriculture is becoming increasingly popular. Smartphone penetration enhances
the multi-dimensional positive impact on sustainable poverty reduction and identify accessibility
as the main challenge in harnessing the full potential in agricultural space. The reach of
smartphone even in rural areas extended the ICT services beyond simple voice or text messages.
Several smartphone apps are available for agriculture, horticulture, animal husbandry and farm
machinery.

RFID :- RFID is an acronym for “radio-frequency identification” and refers to

a technology whereby digital data encoded in RFID tags or smart labels are captured by a reader
via radio waves. The Veterinary Department of Malaysia's Ministry of Agriculture introduced a
livestock-tracking program in 2009 to track the estimated 80,000 cattle all across the country.
Each cattle is tagged with the use of RFID technology for easier identification, providing access
to relevant data such as: bearer's location, name of breeder, origin of livestock, sex, and dates of
movement. This program is the first of its kind in Asia, and is expected to increase the
competitiveness of Malaysian livestock industry in international markets by satisfying the
regulatory requirements of importing countries like United States, Europe and Middle East.
Tracking by RFID will also help producers meet the dietary standards by the halal market. The
program will also provide improvements in controlling disease outbreaks in livestock.

E-commerce:- Online purchasing order of agri-inputs and agri-equipments is a subset of E-

commerce.
Advantage of ICT in Agriculture:

The benefits of ICTs for increased agricultural productivity and strengthening the
Agricultural sector include timely and updated information on agriculture related issues such as
new varieties release, emergence of new threats such as diseases, weather forecast, pricing
control, warning alerts etc.

1) It can initiate new agricultural and rural business such as e-commerce, real estate business for
satellite offices, rural tourism, and virtual corporation of small-scale farms.

2) It can support policy-making and evaluation on optimal farm production, disaster

management, agro-environmental resource management etc., using tools such as geographic
information systems (GIS).

3) It can improve farm management and farming technologies by efficient farm management,
risk management, effective information or knowledge transfer etc., realizing competitive and
sustainable farming with safe products. For example, farmer has to make critical decisions such
as what to plant? When to plant? how to manage pests?, while considering offfarm factors such
as environmental impacts, market access, and industry standards. IT-based decision support
system (DSS) can surely help their decisions.

4) It can provide systems and tools to secure food traceability and reliability that has been an
emerging issue concerning farm products since serious contamination such as chicken flu was
detected.

5) It can facilitate rural activities and provide more comfortable and safe rural life with
equivalent services to those in the urban areas, such as provision of distance education,
telemedicine, remote public services, remote entertainment etc.

6) Empowerment of Stakeholders (Government Officials, Research, Education & Extension

Scientists, farmers and other service providers such as Community Information centers.

7) Development of Knowledge Management, Decision Support and Advisory Systems to

strengthen Extension services and also used for Farmers Redressal system

8) Efficient management (Development, Conservation, allocation and utilization) of resources .

9) Improved productivity and profitability of farmers through better advisory systems.

ICT in enhancing agricultural productivity:
 Understanding and addressing global agriculture developments both advantageous and
disadvantages are critical to improving smallholder livelihoods, in which ICT can play a
major role.
 The continued increase in globalization and integration of food markets has intensified
competition and efficacy in the agriculture sector, and has brought unique opportunities to
include more smallholders into supply chains.
 Agriculture faces a range of modern and serious challenges, particularly in developing
countries exposed to price shocks, climate change, and continued deficiencies in
infrastructure in rural areas.
Use of ICT in Agriculture:
 Increasing efficiency, productivity and sustainability of small scale farms.
 Information about pest and disease control, especially early warning systems, new
varieties, new ways to optimize production and regulations for quality control.
 Better of markets resulting from informed decisions about future crops and commodities
and best time and place to sell and buy goods.
 Up-to-date market information on prices for commodities, inputs and consumer trends.
 Strengthen capacities and better representation of their constituencies when negotiating
input and output prices, land claims, resource rights and infrastructure projects.
 Reduce social isolation, widen the perspective of local communities in terms of national or
global developments, open up new business opportunities and allow easier contact with
friends and relatives.
Global Trends in E-Agriculture

A. Technology-based Solutions: - Applications of e-Agriculture in intensive agricultural

systems in developed countries are gearing towards using sophisticated technologies to improve
the quantity and quality of production, in order to maximize profits. This is the case in precision
agriculture in which farmers are harnessing computer and satellite technologies to cut costs,
improve yields and protect the environment; and e-commerce (or e-marketing) in which the
marketing and sale of agricultural products is conducted over electronic networks such as the
Internet and extranets. On the other hand in many developing countries farmers‟ access to
information is improved through grass root level initiatives of using ICTs as well as distance
education modalities to enhance the knowledge base among service providers.

B. Precision Agriculture:- In precision agriculture or site-specific farming, farmers are using

ICTs and other technologies to obtain more precise information about agricultural resources
which allow them to identify, analyze, and manage the spatial and temporal variability of soil
and plants for optimum profitability, sustainability, and protection of the environment .

Precision agriculture is described as:"A system to manage farm resources better.

Precision farming is an information technology based management system now possible because
of several technologies currently available to agriculture. These include global positioning
systems, geographic information systems, yield monitoring devices, soil, plant and pest sensors,
remote sensing, and variable rate technologies for application of inputs. Precision agriculture is
an advanced e-agriculture application. It makes use of five major components of technology: 1)
Geographical Information Systems (GIS) for analysis and management of spatial data and
mapping; 2) Remote Sensing (RS) to identify and 3) Global Positioning Systems (GPS) to locate
and define spatial features or activities that contribute to the quality of site-specific practices; 4)
Variable Rate Technology (VRT) allowing targeted, site-specific input applications; and 5) Yield
monitoring for recording crop productivity as an historical database for crop management.

C. E-Commerce in Agriculture:- Improved productions and high yields result in the need to
look for profitable markets beyond local communities, and electronic markets are providing an
opportunity to farmers to market and sell their produce to buyers at the global level. Electronic
commerce (ecommerce), simply defined as the general exchange of goods and services via the
Internet, is already having a significant impact on agriculture.
 Expert System for Decision Support in Agriculture
Introduction
Agricultural production system has been evolving into a complex business system
requiring the accumulation and integration of knowledge and information from many diverse
sources. In order to remain competitive, the modern farmer often relies on agricultural specialists
and advisors to get information for decision making. Unfortunately assistance of the agricultural
expert is not always available when the farmer needs it. In order to alleviate this problem, expert
systems were identified as a powerful tool with extensive potential in agriculture.
An Expert System (ES), also called a Knowledge Based System (KBS), is a computer
program designed to simulate the problem-solving behavior of an expert in a narrow do main or
discipline. The expert system could be developed for decision-making and location specific
technology dissemination process. An expert system is software that attempts to reproduce the
performance of one or more human experts, most commonly in a specific problem domain, and
is a traditional application and/or subfield of artificial intelligence.5Expert systems helps in
selection of crop or variety, diagnosis or identification of pests, diseases and disorders and taking
valuable decisions on its management.

Expert system meanings

An Expert System is a computer program that stimulates the judgment and behaviour of a
human (or) an organization that has expert knowledge and experience in a particular field. It is
program that emulates the interaction a user might have with a human expert to solve a problem.
An Expert System is a problem solving and decision making system based on knowledge of its
task and logical rules or procedure for using knowledge. Both the knowledge and the logic are
obtained from the experiences of a specialist in the area.
Expert System are recognized as an appropriate technology because they address the
problem of transferring knowledge and expertise from highly qualified specialists to less
knowledgeable personnel. In agriculture, this transfer is always taking place from research to
extension, from extension to farmers, and even from farmers to farmers. Expert system present
excellent tools for relieving the increasing pressure on the limited expertise available in
developing nations. It must be recognized that knowledge, the very foundation of expertise, is a
scarce resource in developing nations. Expert System can help expand this vital resource by
making available, in specific situations, vital knowledge that increase the effectiveness of less
experienced personnel.
The Expert System uses a hierarchical classification and a mix of the text description;
photographs and artistic pictures. The system involves two main sub tasks, namely diagnosis and
management. The system designed and developed using visual basic as front- end and Microsoft
Access as back- end software.
An Expert System is a computer program normally composed of a knowledge base,
influence engine and user-interface. Expert system in the area of agriculture and describes the
design and development of the rule based expert system, using the shell ESTA (Expert System
for Text Animation). The designed system is intended for the diagnosis of common diseases
occurring in the rice plant. ESTA programming is based on logic programming approach. The
system integrates a structured knowledge base that contains knowledge about symptoms and
remedies of diseases in the rice plant appearing during their life span.
An Expert System is defined as “ a computer program designed to model the problem
solving ability of a human expert ”. It is also defined as “a system that uses human knowledge
captured in a computer to solve problems that ordinarily require human expertise”. Expert
System increases the probability, frequency and consistency of making good decisions, additive
effect of knowledge of many domain experts, facilitates real time, low – cost expert level
decisions by the non- expert enhance the utilization of most of the available data and free the
mind time of the human expert to enable him or her to concentrate on creative activities. Expert
System offers an environment where the good capabilities of humans and the power of computer
can be incorporated into overcome many of the limitations.
Importance of Expert System
The complexity of problems faced by the farmers are yield loses, soil erosion, selection of crop,
increasing chemical pesticides cost, pest resistance, diminishing market prices from international
competition and economic barriers hindering adoption of farming strategies.
Expert System are computer program that are different from conventional computer programs as
they solve problems by mimicking human reasoning process, relying on logic, belief, rules of
thumb opinion and experience.
In agriculture Expert System are capable of integrating the perspectives of individual desciplines
such as plant pathology, entomology, horticulture and agricultural meteorology into a framework
that best address the type of ad hoc decision making required of modern farmers. Expert system
can be one of the most useful tools for accomplishing the task of providing growers with day to
day integrated decision support needed to grow their crops.
EXPERT SYSTEM SHELL DEVELOPMENT
An expert system is an interactive computer-based decision tool that uses both facts and
heuristics to solve difficult decision making problems, based on knowledge acquired from an
expert. An expert system is a model and associated procedure that exhibits, within a specific
domain, a degree of expertise in problem solving that is comparable to that of a human expert.
An expert system relies on two components: a knowledge base and an inference engine. A
knowledge base is an organized collection of facts about the system‟s domain. An inference
engine interprets and evaluates the facts in the knowledge base in order to provide an answer.
Typical tasks for expert systems involve classification, diagnosis, monitoring, design,
scheduling, and planning for specialized endeavours.
Facts for a knowledge base must be acquired from human experts through interviews and
observations. This knowledge is then usually represented in the form of “if-then” rules
(production rules): “If some condition is true, then the following inference can be made (or some
action taken).” The knowledge base of a major expert system includes thousands of rules. A
probability factor is often attached to the conclusion of each production rule, because the
conclusion is not a certainty.
An important feature of expert systems is their ability to explain themselves. Given that the
system knows which rules were used during the inference process, the system can provide those
rules to the user as means for explaining the results. By looking at explanations, the knowledge
engineer can see how the system is behaving and how the rules and data are interacting. This is
very valuable diagnostic tool during development.
Expert systems typically have three components viz., knowledge base, inference engine
and user interface. The knowledge base is the component that contains the knowledge obtained
from the domain expert. Normally, the way of representing knowledge is using rules. The
inference engine is the component that manipulates the knowledge found in the knowledge base
as needed to arrive at a result or solution.
The user interface is the component that allows the user to query the system and receive
the results of those queries. Many expert systems also have an explanation facility which
explains why a question was asked or how a result or solution was obtained.
There are several major application areas of expert system such as agriculture, education,
environment and medicine. These four applications are widely used among the practitioners. The
components and application of expert system for agriculture is same as that of other three
applications. The experience and knowledge of a human expert is captured in the form of IF-
THEN rules and facts which are used to solve problems by answering questions typed at a
keyboard attached to a computer on such diversified topics, for example, in pest control, the need
to spray, selection of a chemical to spray, mixing and application, optimal machinery
management practices, weather damage recovery such as freeze, frost or drought, etc. Now-a-
days expert system in agriculture is employed more for diagnosis and management of
economically significant pest problems like diseases and insects of crop plants.
Examples of expert system
Rice Expert System :- In India, Sarma et al. (2010) developed an expert system in order to
diagnose and manage the diseases occurring in rice crop.
Tomato Expert System: -A web based tomato crop expert information system was developed
by Babu et al.(2010) in India. The tomato crop expert advisory system is aimed at a collaborative
venture with eminent Agriculture Scientist and experts in the area of tomato plantation with an
excellent team of computer engineers, programmers and designers.
Rapeseed-Mustard Expert System: Vinod et al. (2008) developed an image based rapeseed-
mustard disease expert system in India. The diagnosis and control measures of economically
important diseases like Alternaria blight, white rust and white rot , downy mildew complex,
powdery mildew, white rot of rapeseed-mustard were effectively performed by using this expert
system.
Advantages of Expert System
The significant advantages in the above mentioned expert systems of different crops are given
below.
 The system can be used by extension personnel, researchers and farmers to identify crop
diseases and enable to proceed their management.
 User can easily identify the disease on the basis of photographs of symptoms and text
descriptions of disease.
 The user friendly software developed using windowing environment, thus provides
enough facilities to identify the disease and to suggest the remedy conveniently.
 Provide consistent answers for repetitive decisions, processes and tasks.
 Hold and maintain significant levels of information.
  Reduce employee training costs.
 Centralize the decision making process.
 Create efficiencies and reduce the time needed to solve problems.
 Combine multiple human expert intelligences.
 Reduce the amount of human errors.
 Review transactions that human experts may overlook.
Limitations of Expert System
Various limitations in the Expert Systems of different crops are listed out below.
 Many farmers in the country are illiterate and knowledge of computers in rural areas is
still unreached.
 If the picture used in expert system is poor quality, the confusion in diagnosis of the
problem will be happened and ultimately decision making will not be done properly.
 The complexities arising in managing rules for large knowledge base. It is difficult to
write knowledge-based rule and place them in proper sequence for larger number of
parameters. Verification of large numbers of rule-based system is difficult.
 Since the computer is lack of common sense, the programmer should develop the expert
system in efficient way. If he or she does mistake, everything will be collapsed.
 In the developing countries, lots of farmers are not competent to English language, such
expert systems need to be developed in regional languages.
 The expert systems are to be demonstrated to village area through blocks or village
administration unit so that farmers can get a chance to develop their own expertise.
 Adding speech interface to the system may be proved to be more beneficial for the
farmers of the remote area.
 Will not be able to give the creative responses that human experts can give in unusual
circumstances.
 Lack of flexibility and ability to adapt to changing environments.
 Not being able to recognize when no answer is available.
 Knowledge acquisition remains the major bottleneck in applying expert system
technology to new domains.
 Maintenance and extension of a rule base can be difficult for a relatively large rule base
(beyond 100 rules).
 Expert systems are not as compact as neural network and genetic algorithm systems. This
makes them harder to embed in other systems, as the inference engine and working
memory must be part of the system at run-time.
 Decision Support Systems

DSS is “a computer-based system that aid the processing or decision making”. DSS defined
roughly as interactive computer based systems that aid in making a quality decision. DSSs
typically have quantitative output and place emphasis on the end-user for final problem solution
and decision making irrespective of the expert system (ES), which uses qualitative reasoning to
solve a problem for decision making. Often ESs are developed around very specific and highly
detailed “Domains” and thus tend to be narrow in their range of knowledge.
DSSs exist for a wide range of applications including agriculture, water resources,
environment, organizational management, health and business. Within these sectors, they are
used to improve personal efficiency, expediting problem solving, facilitating interpersonal
communication, promoting learning and training and increasing organizational control. For
example, in the agricultural field, DSS can be designed for use by agronomists, soil scientists,
agricultural engineers, entomologists, weather experts, farmers, students and extension workers.
They can be further targeted for a variety of environments or agricultural commodities like
temperate or tropical conditions, rainfed or irrigated environments, upland or lowland areas,
watershed or field levels, fruits or grains, rice or wheat and others. Typical information that a
decision support application might gather and present would be comparative weather forecasting
between one week and next; projected yield of particular crop before harvest; the consequences
of different decision alternatives; optimal dosage of fertilizers for a given crop to maximize
yield; prediction of pathogen infestation based on the hypothetic climatic conditions; application
of water, temperature and fertilizer; and so on.
Capabilities of DSS
The key characteristics and capabilities are as follows:
1. Ability to support in semi-structured and unstructured problems, including human judgment
and computerized information.
2. Ability to support managers at all levels.
3. Ability to support individuals and groups.
4. Ability to present knowledge on ad hoc basic in customized way.
5. Ability to select any desired subset of stored knowledge for presentation or derivation during
problem solving.
6. Ability to support for interdependent or sequential decisions.
7. Ability to support intelligence, design, choice and implementation.
8. Ability to support variety of decision processes and styles.
9. Ability to support modelling and analysis.
10. Ability to support data access.
11. Benefits must exceed cost.
12. Allow modification to suit needs of user and changing environment.
13. Support quick decision-making using standalone, integration or web-based fashion DSSs
having maximum number of these key characteristics and capabilities can be more useful and
adoptable.
Components of Decision Support Systems
The most general architecture of DSS can be divided into four subsystems, namely Database
Management Subsystem, Knowledge-based Management Subsystem, User Interface Subsystem
and the User (Fig. 1). As far as technology levels are concerned they have suggested three levels
of hardware and software for DSS.
Level 1 (Specific DSS): This is the actual application that allows the decision makers to make
decisions in a particular problem area.
Level 2 (DSS Generator): This level allows development of easy specific DSS applications.
Level 3 (DSS Tools): This level includes special languages, functions, libraries and linking
modules.

Decision Support Systems in an Agricultural Perspective

Application of DSS in the area of agriculture takes the form of integrated crop
management decision support and encompasses fertilizer management, weed management, water
management, plant protection, soil erosion, land use planning, drought management, pollution
control, etc. These systems are addressing problems related to conservation and improving soil
fertility, local water balance, efficient agronomical practices, canopy management, pest and
insect management, reducing pre- and post-harvest losses, conservation of forests and global
environment change etc. Web-based DSSs are also playing important role in dissemination of
technology transfer related to crop management practices, irrigation scheduling, fertilizer
application, etc.
Nutrient Management:- Fertilizers and lime are increasingly expensive but are commonly
needed to grow high-yielding and good-quality crops. However, unnecessary use is wasteful,
reduces farm profits and increases the risk of diffuse nutrient pollution. To maximize profits and
avoid waste, farmers need to plan their use of nutrients for each field crop in each year. Organic
manures (farmyard manure, sewage sludge, slurries, etc.) contain large quantities of nutrients
which can often mean that large reductions are possible in the need for inorganic fertilizers.
Nutrient management can play an important role in many of the regulatory and non-regulatory
duties of farm related management, and can protect, restore and enhance the status and diversity
of all surface water ecosystems and ensure the progressive reduction of groundwater pollution.
For Nutrient management, different DSSs have been designed to recommend site-specific and
need-based parameters that result in an optimized fertilizer management strategy.
2 Insect and Pest Management:- Plant protection is definable as the reasoned application of
agronomic methods, products as well as chemicals to allow optimal productive factors, while
respecting the farm worker, the environment and the consumer. The concept of computerized
DSSs for pest management is not new. DSS models have been developed for diseases that could
expand very rapidly or those that should be controlled regularly.
3 Agricultural Land Use and Planning:- As population and human aspirations increase, land
becomes an increasingly scarce resource, calling for land use planning. Land use planning is
defined as a systematic assessment of land and water potential, alternatives for land use and the
economic and social conditions. It has become essential to mitigate the negative effects of land
use and to enhance the efficient use of resource with minimal impact on future generations. Land
use planning is becoming complex and multidisciplinary as planners face multiple problems that
need to be addressed within a single planning framework. Such problems include non-point-
source pollution, water allocation, urbanization, ecosystem deterioration, global warming,
poverty and employment, deforestation, desertification, farmland deterioration and low economic
growth. Many different DSS tools for land use-related decision-making have been designed.
4 Global Environment Change and Forecasting;- Global environment change is happening.
Human activities, including those related to the production, supply and consumption of food, are
responsible for changing worlds’ climate and giving rise to other, globally and locally important
environment changes.
5 Water and Drought Management:- Nearly one billion people worldwide are malnourished.
The majority of these people live in developing countries, where increasing water scarcity
complicates efforts towards food self-sufficiency. Huge amounts of water are needed to produce
more food and eradicate hunger among increasing populations. The current limited approaches to
increasing demands of water will not be enough to eradicate hunger, especially in areas with
growing populations and amidst dry climates in most developing countries. The central issue is
how to manage water for all the different functions for which it is needed. With the advent of
agronomic models that show how vegetation is likely to respond to climatic stress, with remote
sensing to monitor vegetations conditions from airborne and space borne platforms, and with
GIS to display spatial and temporal data in more comprehensible ways, it is now feasible to more
accurately assess the impacts of drought. Different DSSs have been developed to tackle with the
problems related to water and drought management.
6 Other Applications
TropRice, an integrated rice management system being used by researchers, extension workers
and some farmers has been used in Asia for irrigated rice areas. It provides some generic and
some site-specific information for rice cultivation. It is currently being used in China, India,
Indonesia, the Philippines, Thailand and Vietnam and is being translated or localized by national
collaborators for local conditions in these countries. Other applications of DSS in agriculture
range from conserving soil, local water balance, agronomical practices, canopy management
cropping system analysis, and conservation of forest and computer multimedia instruction. Web
based DSSs are assisting in forest management, resource management, and, agricultural
emergency response. DSS for farm mechanization using GIS based on linear programming
provides machinery selection and planning to minimize farmer mechanization has been
developed by Suarez de Cepeda et al. (2005). The system includes the natural factors (climate
and soil conditions), plot geographic site and the crop and machinery data. The GIS is part of the
system to carry out a spatial analysis of the farm results to make machinery grouping.