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Presented by:
CPA Stephen Omondi Okoth
Agribusiness and Financial Management Consultant
Elison Decision Support Services
(Institute of Logistics and Supply Chain Management)
Victor House 7th Floor, Kimathi Street,
P.O. Box 53353, Nairobi -00200
Phone: +254786707360
Harnessing the power of convergence of ICTs, Data Science and Management Science
in Agriculture.
Providing you with evidence based, technology driven and solutions oriented insights
for sound management of agricultural projects and enterprises.
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Farm Data and DSS for enhanced agricultural productivity
Outline:
• Enhanced Agricultural Production
• Agricultural Production Decisions
• Information and Data
• Decision Support Systems
• DSS architecture
• DSS rationale, use and users
• DSS Components and Structure
• DSS process
• Distributed DSS
• Status and trends
• Prospects
• A case for the dairy goat industry
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Enhanced Agricultural Production
Efficient Production through increased output from
units of resources applied in production
• Enhanced production efficiency as measured by
metrics like crop yield per acre, yield per shilling,
milk yield per lactation day, livestock weight gain
par day, fecundity, survival/mortality, herd size
growth per season, egg production per day
• Adoption of efficiency measures for example
precision farming with low or zero tolerance to
resource wastage
• Optimal combination of farm resources
• Optimal combination of enterprises
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Enhanced Agricultural Production:
Effective Production:
• Meeting agricultural production goals e.g. food
and nutritional security, improved household
incomes, employment
• Contribution towards growth of returns to firms
in the agricultural sector thereby enhanced
national income
Results from better choices on what to produce,
how to produce and how much to produce, store,
distribute and where to sell.
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Agricultural Production Decisions
Choosing from amongst alternatives: which
agricultural enterprise(s) to engage in, how to
produce and how much to produce, store,
distribute and whom to produce for.
• Different approaches to decision making with
the same basic objective of effective decisions
• Quantitative approach is concerned with
identifying relevant data, measuring the data
values, uncertainties, other factors relevant in
a given decision and the resulting optimal
decision. Statistical tools and methods are
available to organize evidence, evaluate risks,
and aid in decision making.
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Agricultural Production Decisions
Most of decision theory is normative or
prescriptive, i.e. it is concerned with identifying
the best decision to take, assuming an ideal
decision maker who is fully informed, able to
compute with perfect accuracy, and fully rational.
Choosing from amongst alternatives in order to
make the best possible decisions, involve the
following analytic approaches:
• What-If Analysis • Sensitivity Analysis • Goal
Seeking Analysis • Optimization Analysis
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Data and Information
Making right production decisions is usually
based on the:
• quality of data;
• ability to sift through and analyze the data to
find informative trends; upon which
• solutions and strategies for growth can be
formulated.
Decision Support Systems provide efficient and
effective platform and tools for converting raw
data into information for decision making
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Decision Support Systems(DSS)
• DSS is a class of computer-based information
systems including knowledge-based systems
that support decision-making activities.
• A DSS provides a platform and tools to analyze
and model data into information useful in
making quality decisions based upon the data.
• Decision support systems do not make
decisions. The objective is to allow the
manager to consider a number of alternatives
and evaluate them under a variety of potential
conditions.
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DSS Archtecture
FARM DATA
USER
INTERFACE
DSS DATA
BASE
EXTERNAL
DATA
DSS SOFTWARE SYSTEM
MODELS
OLAP TOOLS
DATA MINING TOOLS
USERS
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Types of DSS
• A model-driven DSS emphasizes access to and manipulation of a
statistical, financial, optimization, or simulation model. Model-driven
DSS use data and parameters provided by users to assist decision
makers in analyzing a situation; they are not necessarily data intensive.
• A communication-driven DSS supports more than one person
working on a shared task; examples include integrated tools like
Microsoft's NetMeeting or Groove
• A data-driven DSS or data-oriented DSS emphasizes access to and
manipulation of a time series of internal company data and,
sometimes, external data.
• A document-driven DSS manages, retrieves and manipulates
unstructured information in a variety of electronic formats.
• A knowledge-driven DSS provides specialized problem solving
expertise stored as facts, rules, procedures, or in similar structures.
[Our aim is to develop a data-driven DSS for agriculture industry.
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Rationale, use and users
DSS is extensively used in business and management to provide performance
analytics and executive dashboards for improved decisions about better
allocation of business resources.
• Improves efficiency and effectiveness in decision making
• Facilitates communication with evidence based insights
• Promotes learning from farm data
• Increases organizational control
• Creates a competitive advantage over competition
• Encourages exploration and discovery on the part of the decision makers
• Reveals new approaches to thinking about the problem space
Useful for decisions at primary production, secondary production, service
provision, and policy levels in the agricultural sector.
Applicable to the decision making roles by farmers, farm managers, farmers
cooperatives, extension services, investors, policy makers an regulators,
development partners, service providers e.g. banks, insurers
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Components
• Database and database management System
• Library of Decision models that can be used to predict
and forecast possible outcomes of alternative decisions
• Report Engine/Generator and interface to allow users
interact with the system and assist in analysis of
outcomes
• Users
Typically, four major options are used in decision support
namely: analytic models; specialized databases like data
warehouse; decision-makers own insights and judgments;
an interactive computer-based modeling process to
support the making of semi-structured and unstructured
decisions.
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Process
From Data inputs through analysis, modeling and
information outputs into decisions
• Inputs: factors, numbers, and characteristics to
analyze,
• User Knowledge and Expertise: Inputs requiring
manual analysis by the user assumptions,
scenarios
• Outputs: Transformed data from which DSS
"decisions" are generated
• Decisions: Results generated by the DSS based on
user criteria
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Distributed DSS
• Internet and mobile telephony with related
software applications has enabled distribution
of DSS
• User can input decision scenarios through
own desktop or smartphone and gets report
back on the front-end user managed device
• The database, analytical models, rules housed
in the backend are communicated with
through OLAP tools to generate predictions
and forecasts
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Status and trends
• DSSAT4 package, developed through financial
support of USAID during the 80's and 90's, has
allowed rapid assessment of several agricultural
production systems around the world to facilitate
decision making at the farm and policy levels.
• A number of Agricultural DSSs in use in Australia,
US, Israel and Europe.
• In Eastern Africa, the use is still at infancy and
majorly supply driven
• The limited use is a pointer to constraints to the
successful adoption on DSS in agriculture.
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Prospects
The demand will be driven by farming increasingly being
engaged in as a business and attracting investments and
attention from sectors like insurance and banking
On the other hand, supply will be generated by:
• ICT as an enabler of business and decisions through
cellular network and mobile telephony is reaching
everywhere
• The ICT savvy youth is increasingly getting attracted
into the agricultural industry
• Infrastructure projects for energy, water, roads, optic
fiber lines are increasingly covering rural areas
• A large pool of graduates with competence in data
science and ICTs
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Case:Risk & Investment Analysis
Business needs(Problem): Evidence based risk analysis
and investment appraisal for dairy goat enterprises
User needs: Projection for herd growth and
ascertainment of ROI, NPV thereby outlining investment
criteria; mortality risk forecasts for insurance
Data needs: breed, birthdate, sex, daily data on
temperature, humidity, rainfall, milk production, livestock
sales, feed type and quantities, mortality, fecundity, labor
hours, veterinary interventions, costs, economic
indicators
Gaps: Projections only based on deterministic theoretical
models ignoring risks and uncertainties
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Case continued
• Solution: Data Driven Model oriented DSS
• Database built from a longitudinal study for the
first 8 years since import of a breed, 8 years being
the useful life.
• Database improved by data from subsequent
cohorts
• Probability models built based on age specific
survival and fecundity rates. Stochastic NPV
model built based on
• User inputs size of herd purchased, date of
purchase, cost, and age and sex composition and
gets a projection of herd size at any given future
time and related NPV.
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Case continued
Issues:
• Poor record keeping hence poor availability of
bio-economic data
• Elaborate data collection components not inbuilt
on project components
• Poor or erratic electricity collection interrupts
usage of computers and phones in remote farms
• Unavailability of tested homegrown bio-economic
models
• Who pays for the DSS? Is it a public good through
government, NGO?
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Opportunities:
“Providing solutions to problems and resolutions
of issues are the parents for opportunities.”
Thank you!
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