[Webinar recording in last slide or at https://youtu.be/taHHp3UbRZI, 28/2/2018] As part of its work on farmers’ data rights and following up on the face-to-face course on Farmers’ Access to Data organized in Centurion in November 2017, GFAR collaborates with the Global Open Data for Agriculture and Nutrition initiative (GODAN) and the Technical Center for Agricultural and Rural Cooperarion (CTA) on a series of webinars on data-driven agriculture, its opportunities and its challenges. Overview of webinar #2 Data becomes significant if it can be linked to information, knowledge and wisdom. Once processed it can be used to generate detailed insights into farm operations and the environment. It assists big and small holder farmers in making data-based operational decisions to optimize yield and boost revenue while minimizing expenses, the chances of crop failure, and environmental impact. For data driven agriculture to happen we have to distinguish the data streams in the food chain from pre-planting to consumption, for example: data collected and managed from the farm by farmers which can be either static or dynamic; data coming from external sources like market prices and data that is exported for aggregation by other farm service providers. However, farmers may not be in a position to realize those streams and possibly what data and information is required to answer the food chain questions, for example: What produce can I grow where I live? When should I sow/plant/harvest/market it? How should I sow/plant/harvest/market it? All these questions can be answered if the factual data or information is used or made available to the farmers. Webinar Goals Make the participants understand the different key data streams, flow and sources that are vital to agricultural value chains. Participants will be in position to identify the data they own or collect on their farms and its usefulness, understand the difference between human and machine farm data, identify the part in the agricultural value chain where data, and which data, is needed most. About the presenter Stephen Kalyesubula is a Computer Engineering and an agri-preneur from Makerere University. He is a graduate researcher at iLabs@Mak Project – Makerere University and his key technological interests include: Data science, robotics, Internet of things, AI and design thinking. He is among the directors of Youths In Technology and Development Uganda whose mission is to create tech communities of practice where appropriate use of technology promotes sustainable development in agriculture, health and education.

1. Website: www.yitedev.ml
WEBINAR: KEY DATA FOR FARMERS
By: Stephen Kalyesubula
@kal_stephen
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2. - Why Data
- Data, information and Knowledge
- Knowledge pyramid and the FAIR facets
- Data streams, flows and the key data involved
- Sampling corn in the food value chain to identify the key data
- Sample data for livestock keepers
- File types for Data and Information, Data and information sources
- Role of e-solutions in data driven agriculture
- Data related questions
The webinar Topics
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3. - Detailed insights into farm operations
& environment.
-Making data-driven operational
decisions to optimize yield and boost
revenue while minimizing expenses,
chances of crop failure, and
environmental impact.
The Essence of data to farmers
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4. To answer the 3 Q’s
What
produce can
I grow where
I live?
When should I
sow/plant/harves
t/market it?
How Should I
Sow/plant/harvest/
Market it?
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5. Data, Information And Knowledge
DECISIONS WISDOM

6. Data, Information And Knowledge
KNOWLEDGE
INFORMATION
DECISIONS
DATA
Crop field
boundaries data
Crop type
information
Legend
Free State 2007
CROP TYPE
DryBeans
FallowWeed
Groundnuts
Maize
MaizeWheatPivot
Pasture
Sorghum
SoyaBeans
Sunflower
Wheat
WinterGrazing
Images: GIS in AGRICULTURE, DAFF (Directorate: LUSM, Division: GIS & Monitoring)

7. 1The data knowledge Pyramid ..
Involves linking Data to
information to knowledge to
wisdom
Data Sources
Innovator
Decision Makers
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8. Twitter: @kal_Stephen Website: www.yitedev.ml
2FAIR FACETS
Provided under terms that permit
reuse & redistribution
including interoperability
Available and usable in a convenient
and modifiable form
Easy to compare within and between
sectors, across geographic locations, over
time in order to be most effective and
useful.
Open for use
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9. 9

10. Data and Information Flows in agri-food Systems
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11. 1ONFARMSOILDATA
*Physical, Chemical and Biological Soil properties
▪ Soil texture and Soil structure – Arrangement of
particles
▪ Soil color and humus content
▪ Soil Acidity and Alkalinity
pH scale grows from 0 to 14, pH – 7 is neutral, pH<7 is a
acidic and pH>7 is alkaline. pH influences disease
conditions, affects availability of nutrients
Generated and collected on the
farm as a result of caring farm
operations
Evaluation of
Humus content
Humus
Content %
Soil Color (Moist State)
Low < 1 Light brown, light grey
Slightly 1-2 Brown Grey
Medium 2-3 Dark brown, dark grey
High 3-5 Black and brown, black
and grey
Very High >5 Black in (lowlands) Grey-
brown (in hills)
DATA AND INFORMATION
STREAMS
Growers
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12. 1ONFARMCASHFLOW
DATA
Constantthroughout
▪ Initial Capital investments
▪ Costs for inputs: Labor expenses, Expenses
on fertilizers, Irrigation expenses, Costs for
farm tools,Transport and Communication
costs, Pesticides and weedicides costs,
storage costs among others
▪ Costs and Sales for the farm output (Yields,
Processed products and by products), Net
profits and losses,Total revenue
DATA AND INFORMATION STREAMS
Growers
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13. 1ONFARMOtherdata
▪ Yields per hectare or crop (Grading according to
quality)
▪ Seeding data
▪ Date of sowing and harvesting
▪ Pests and Disease Attacks (When and how they are
treated)
▪ Plant growth data (E.g..Texture of leaves)
▪ Customers and suppliers for farm inputs
▪ Amount and Types of nutrients used
▪ Irrigation schedules
▪ Machine data collected by tractors, Installed weather
and soil sensor systems, RFID etc.
DATA AND INFORMATION
STREAMS
Growers
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14. 2IMPORTED
DATAMarket
Usuallyownedand
Managedby3rd party
▪ Prices for farm outputs in regional and national
agricultural markets (Accessible Markets with favorable
prices)
▪ Market demand and supply projections
▪ Tax ratings, License and VAT rates
▪ Potential customers: Super markets,Whole and Retail
sellers, Restaurants, Hotels etc.
▪ Prices from trusted and genuine farm tools and farm
input sellers like quality seeds, pesticides and
weedicides.
▪ Costs for obtaining licenses, grants or loans from banks
and credit services
DATA AND INFORMATION STREAMS
Growers 14

15. 2IMPORTEDDATAcrop
data
Maindetailsaboutthe
selectedcrop
▪ Required pH and moisture levels
▪ Pests, Disease and weed control practices
▪ Nutrient Values probably per 100g of edible portion
▪ Weather reports and Water management practices
▪ Fertilization and intercropping methods
▪ Harvesting methods and Irrigation techniques
▪ Agro-food processing:Value add to the products/commodity
▪ Pollution and food waste control measures
▪ Machine data generated by advanced technologies such as
micro sensors, GPS, GIS, UAV and satellite imagery
DATA AND INFORMATION STREAMS
Growers 15

16. 3EXPORTEDDATA
▪ Normally used for aggregation by service providers like
the government, Innovators, Research organizations like
local and International organizations among many others.
▪ This data can contribute to the forecast of various
variables in Agricultural value chain for example: Market
demand and Supply among many others.
▪ Examples of Exported Data.:Total yields, Crop data
DATA AND INFORMATION STREAMS
Growers 16

17. Example: Maize crop in the food value chain
Maize is the most important cereal crop
in sub-saharan Africa. It is a staple food
for an estimated 50% of the population.
According to FAO data, Africa produced
7.5% of the 1, 037 million tonnes
produced worldwide in 37 million
hectares in 2014 (FAOSTAT, 2014).
Longe 5 (Nalongo) (QPM Maize), Photo credit: @yitedev
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18. Pre-planting
• Crop Data e.g.. Nutritional data, types
• Rain Forecasts and Irrigation
schedules
• Price and demand market projections
• Time to harvest and possible yield
• Access to credit
• Price Forecast for farm inputs
• Land selection, soil & Fertilizer
information
• Capital
• Availability of labor or Machines
Planting
• Land preparation
• Farm inputs
• Seed data (varieties,
seeding, selection
type and amount etc.)
• Irrigation schedules
• Soil characteristics
(Surface, Nutrient
levels etc.)
• Crop data, Cash flow
Cultivation
• Sensor data to monitor
the plant growth
(Stress levels of crop,
soil conditions)
• Pests and weed
density plus
herbicides
• Cash flow
• Pests and disease
control
• Water management
• Nature and method of
fertilization
Mostly Planning stage Waste management, food safety and quality practices
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19. Harvesting and Storage
• Harvesting date and method
(Optimum time when stalks
have dried and moisture of
grain as about 20-17%.)
• Grading the yields
• Amount of yields (probably
in Kgs)
• Main grain storage medium
for unprocessed maize
• Drying strategy (12%-15.5%
moisture content)
• Protection measures from
insect pests, rodents, molds,
birds and man.
Marketing/packaging/
Branding
• Packaging and
branding
• Markets with high
demand and good
prices
• Transport costs
• Whole sale and Retail
Food processing
• Adding value to
processed maize
(maize meal,
porridges, pastes and
beer)
• Best processing
methods
• Costs and availability
of milling machines
• Processing of by
products
• Packaging and
branding
• Prices and demand
Waste management practices, food safety measures and quality practices
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20. Livestock Data
Market
•Price forecasts for farm outputs
•Price for feeds
•Market demand projections
•Price for farm inputs like spraying tools, drugs and pesticides
•Price for livestock breeds
Farm
Management
•Drug spraying methods
•Pests and disease control like for cattle: Milk fever, Retained foetal membranes, Mastitis etc.
•Setting up infrastructure for farms i.e. Ventilation
•Feed formulas and alternative sources
•Climate and weather conditions
•Harvesting, Storage and preservation methods for farm outputs
•Breed performance monitoring practices like fertility rates
•Environment information (GHG emissions etc.) and protection measures
•Tracing of live stocks for security. (Can be sensor data from the trackers or cameras)
•Feed intake, Chewing activity, Temperature, Ruminant PH, Hoof health etc.
Other data
•Adding vale to the farm products
•Transport costs
•Processing costs for the farm outputs
•Breeding methods and species
•Processing of by products to biogas
•Immunization schedules
• Manure management (Deposited on pasture, burned, liquid or slurry, pit etc. )
Freegratepicture.com
Diarymaster.com
Kinawanswa Goat Farm
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21. File types for Data / information
Extension Description
.csv Comma Separated Values. Tabular data format like excel but stripped back to just contain data in
a simple structure.
.json JavaScript Object Notation. A hierarchical data format native to the JavaScript language which is
used widely on the web as it forms part of the HTML5 specification.
.xml eXtensible Markup Language. A markup specification that has a wide range of uses. Has been
criticised for its complexity and verbosity in comparison to JSON.
.rdf Although RDF (Resource description framework) should not be a data format (not covered here).
RDF defines a formal data structure which can be applied in xml, json and csv formats. Use of the
extension implies that the structure is used and most commonly the data itself is in XML format.
.rss Another specific XML structure that is often used for data feeds that regularly update such as
news and weather.
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22. Data sources
Organization Data Web links
FAO Production for crops and livestock ,Trade matrix and indices,
Food Balance, Food Security, Prices (Consumer and producer),
Inputs (Fertilizers by Nutrient or product, pesticides, Land use
etc. ) to mention but a few.
http://www.fao.org/faostat/en/#data
INFONET
BIOVISION
Crops, fruits Medicinal plants and Vegetables:1.
Geographical Distribution in Africa, 2.General Information and
Agronomic Aspects, 3.Information on Pests, 4. Information on
Diseases, 5. Information on Weeds, 6.Information Source Links,
7. Cultural practices
Human: Healthy foods, Nutrition Related diseases, Insect
transmitted diseases, Zoonotic diseases, Hygiene and
Sanitation
Animal: Animal Husbandry and welfare, Animal species and
commercial insects, Animal health and disease management,
Fodder production and Products.
Environmental: Agro ecological zones, water management,
soil management, sustainable and organic agriculture,
conservation agriculture, agroforestry, trees, processing and
value addition
http://www.infonet-biovision.org
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23. Data sources
Organization Data Web links
World Bank Health Nutrition and Population Statistics, commodity prices
etc.
http://databank.worldbank.org/data/datab
ases.aspx
FAO Offers data, metadata, reports, country profiles, river basin
profiles, regional analyses, maps, tables, spatial data,
guidelines, and other tools on:
• Water resources: internal, transboundary, total
• Water uses: by sector, by source, wastewater
• Irrigation: location, area, typology, technology, crops
• Dams: location, height, capacity, surface area
• Water-related institutions, policies and legislation
http://www.fao.org/nr/water/aquastat/main
/index.stm
RESAKS Agriculture information and growth http://resakss.org/
Can you think of other sources?
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24. Key Challenges
• Availability, Accessibility, Affordability
• Accuracy, Relevance, Usefulness, Data ownership
• Timeliness,Trustworthiness, Interoperability
Opportunities
Smart Phones
Photo: M-Farm
Smart Field IoT sensors
Collect the data on climatic condition soil
moisture & fertility, root & shoot growth,
profused leaves growth, photo-period
monitoring, floral & seed setting,
grain/fruit bearing, pest & deceases as
critical growth factors symptoms, harvest
readiness.
Photo: ASARECA
Data Driven Mobile
and web Apps
Internet
connectivity
• Farm Management Information
systems including DSS, GIS etc.
• ICT enabled learning and knowledge
exchange for example: Chatbot,
eWallets, eAgr-Calculators that act
like planning tools etc.
• Modelling solutions
• Sensory and proximity web data tools
• Online commerce tools
Drone Technology
Aerial photography and
remote sensing
Landsat.ug
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25. Source: eTransform Africa, Agricultural Sector Report, 2012, Deloitte
Smart data driven
agricultural e-Solutions
promote the use,
equitable sharing,
availability and access of
key data
These appropriate
solutions/applications
may be specific at one
level or on
multiple levels, but all
integrated/interconnecte
d contributing
to one end.
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26. Example: Crop planner tool by Dan
Data from the cropping calendar: Information on planting, sowing
and harvesting periods of locally adapted crops in specific agro-
ecological zones. It also provides information on the sowing rates of
seed and planting material and the main agricultural practices.
Prices, Land size etc.
Lookout for: Crop calendar designed by FAO
Link:
http://www.fao.org/agriculture/seed/cropcalendar/welcome.do
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27. Data related Questions
Question: Discovering Data
• Do I understand what the dataset is? Does the title and description match
the data itself?
• Am I able to access the data?
• Am I permitted to use the data?
• Do I understand the data itself?
• What questions can I answer using the data?
• Is the dataset supported long term?
• Is the data consistent?
• Is the data clean?
• How much effort would be required to make the data usable?
• Can I get support on the data and find what else it has been used for?
• Is the data too granular, too generic?
Re-Users Checklist
Technical
• Is the data available in a format appropriate for the content?
• Is the data available from a consistent location?
• Is the data well-structured and machine readable?
• Are complex terms and acronyms in the data defined?
• Does the data use a schema or data standard?
• Is there an API available for accessing the data?
Social
• Is there an existing community of users of the data?
• Is the data already relied upon by large numbers of people?
• Is the data officially supported?
• Are service level agreements available for the data?
• It is clear who maintains and can be contacted about the data?
Provenance Checklist
The checklist below will help established the provenance of a dataset and help establish the level of trust in that dataset.
• Is the data wholly owned and produced by the data provider?
• Does anyone else produce comparable data for cross checking?
• Is it clear if the data has been derived from other sources of data?
• Are the other sources of data clear?
• Are the other sources of data trustworthy and comparable with other data providers?
• Is it clear if and how any data has changed (from any source) prior to being made available as open data at your point of access?
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28. Twitter: @kal_Stephen Website: www.yitedev.ml
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29. Supports global efforts to make data
relevant to agriculture and nutrition
available, accessible, and usable for
unrestricted use worldwide.
Over 600 partners
Join at
http://www.godan.info/become-a-
godan-partner
CTA is at the forefront of the fight
against poverty and for
sustainable food security in the
African, Caribbean and Pacific
(ACP) Group of States and the
European Union (EU)
http://www.cta.int
The Global Forum on Agricultural
Research and Innovation’s partners
work to make agri-food research
and innovation systems more
effective, responsive and equitable
Over 550 partners
Join at
http://www.gfar.net/about-us/be-a-partner
Twitter: @kal_Stephen Website: www.yitedev.ml
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