1. DSS as a Catalyst in Indian
Agriculture
TEAM : PANDAVAS
SAMAGRA KUMAR | SIDDHANT SANJAY | VISHAL DHAWAN | MANISH CHAUDHARY| GUNJAN KUMAR VERMA
IIT KHARAGPUR

2. CONTENTS
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• Problem Statement
• Proposed Solution
• Implementation of the Solution
• Impact of the Solution
• Challenges & Mitigation Factors

3. PROBLEM AT A GLANCE
 India’s GDP showed a rapid decline from 30% to
14.5% in the last two decades.
 The growth rate for the agricultural sector
during the 11th FYP was 3.5% whereas the
economic growth rate was 8.2%.
 India’s productivity is only 3.1 ton/hectare
versus the global average of 4.2 ton/hectare.
OVERVIEW CAUSES
 Primary sector of Economy.
 Over 65% of population is involved in this sector
 Importance in International Trade
 Contribution to Foreign Exchange Resources
 Vast Employment Opportunities
 Source of Government Income
 Basis of Economic Development
REASONS FOR SELECTING THE CAUSE
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 Overcrowding
 Poor Technique of Production
 Inadequate Irrigation Facilities
 Inefficient Government Policies
 Inadequate Storage Facilities
 Illiteracy & Poor Economic Condition
RECENT TREND

4. PROPOSED SOLUTION
DECISION SUPPORT SYSTEM (DSS)
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The Proposed Framework of DSS Building
Decision Support System (DSS) is an interactive, flexible, and adaptable computer based informat
ion system that utilizes decision rules, models, and model base coupled with a comprehensive
database and the decision makers own insights, leading to specific, implementable decisions in
solving problems that would not be amenable to management science models. Thus, a DSS supports
complex decision making and increases its effectiveness
MEANING AND CONCEPT

5. PROPOSED SOLUTION
• To enhance the interface between the agricultural, meteorological and socio-economic
communities and farmers and other stakeholders;
• To establish the forecasting needs for decision making in crop production;
• To develop the capacity for integrated climate and agricultural simulation and prediction for
decision making for a range of farming systems; and
• To demonstrate the capability and value of improved climate prediction for improving crop
production at farm to national scales as a proof of concept.
OBJECTIVES
5
MERITS
• Time Saving
• Enhance Effectiveness
• Cost Reduction
• Promote Learning
• Increase Organizational Control
• Encourages exploration and discovery on the part of the decision maker
• Create Innovative ideas to speed up the performance

6. IMPLEMENTATION OF PROPOSED SOLUTION
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Data Gathering
Data Processing
Data Analysis
and Design
Decision
Making
Data
Implementa
tion
Deliberation
Stakeholders review and interpret
data, inform scientists and
policymakers
Scenario Analysis
with feedback and requirement of
stakeholders and decision makers
Evaluation
Performed by stakeholders, policy
makers, Coop Extension
Analysis
Scientists, Engineers &
Policymakers, collect analyze data
present to stakeholder
Implementation
Decision made will be conveyed to
stakeholder, he’ll use this knowledge, to
give favourable result.

7. 7
Various Components of Decision Support System (DSS) for
Crop Productivity Management
IMPLEMENTATION OF PROPOSED SOLUTION

8. 8
Components of Decision Support System for
policy decisions
Tool and technology that handles
various spatial databases, and is a young
area of information technology
Examine and analyze a wider range of
agricultural related resources such as
soil, weather, hydrology, various socio-
economic variables simultaneously and
accurately.
 DSS with GIS, organize and analyze
spatial data, address the problems
related to spatial and temporal
variability of various natural resources
on which the performance of agricultural
systems depends.
GIS [Geographic Information System]
Sources of Funding
1.) Department of Agriculture
2.) Indian Council of Agricultural Research
3.) Department of Science & Technology
4.) Various NGOs
IMPLEMENTATION OF PROPOSED SOLUTION

9. 9
IMPACT OF DECISION SUPPORT SYSTEM
Criteria to Measure the Impact of DSS
•Crop productivity
•Soil Fertility
•Land Use
•Irrigation
•Crop wastage
•Amount of Fertilizers used
•Livestock productivity
Scalability of DSS
Monitoring Mechanism Sustainability of DSS
•Initiation in few districts till Panchayat level .
•An official ,appointed; will be responsible for
providing required information & training to
farmers.
• NGOs will be consulted to create awareness at a
large scale .
•After development , expansion will be done at
national scale.
•A panel will be formed which will be responsible
only for proper management of DSS at every level.
•This panel will consist of specialist from every field
such as scientists, software engineers, water
management personnel, finance department
personnel, etc.
•Feedback forms will be circulated among everyone
related to DSS ranging from analysts to farmers.
•Collected feedback forms will be analyzed and
proper steps will be taken for the development of
DSS.
•Peasants will be trained regularly to take
maximum advantage of DSS.
•Technological amendments & advancements will
be done regularly.

10. 10
CHALLENGES & MITIGATION FACTORS
CHALLENGES
Social
Essential for climate, agriculture and social
scientists to collaborate at all stages
Client-community expectation and
understanding.
Need to maximize local participation.
Ability of farmers to cope with
climate variability and perception of factors.
Quality data availability and accessibility a
necessary condition for site selection
Farmers are often short of time (to learn and use DSS)
Technological
Develop approaches to predict crop prediction
and resource use at field and
regional scale.
Many farmers are not computer oriented.
Systems analysis of key decisions; factors
influencing decision-making and attitudes to
climate risks.
Current knowledge, perceptions and
practices about how climate variability
influence crop production.
Economical & Political
Need to consider scaling issues in working
from sub-national to national scales.
Distribution of Funding at various scales.
Credit Availability to Farmers.
Political constraints in various states against DSS.

11. 11
CHALLENGES & MITIGATION FACTORS
MITIGATION MEASURES
•Case studies about capability in climate forecasting
can be used to facilitate improved decision-making
about crop production.
•Interim workshops with scientists and end-user
communities.
•Final top-level conference to present science
and engage policy-makers and other
end-users.
•Building scientific capacity to conduct
interdisciplinary
studies that involve the
participation of clients.
•Evaluation of the value of improved climate
variability prediction
Criteria describing popularity of DSS
•Widespread problems need to be addressed;
•These products need to be location specific;
•There needs to be strong support from initial users;
•Relevance, simplicity, effectiveness, and low cost are key attributes;
•Products other than computer-based products should be considered; and14
•Users need to be closely involved in the development of these products.
Impact of DSS (Production vs Time Graph)