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Opportunities for crowdsourcing approaches and
satellite images for yield gap analysis
Eskender Andualem Beza
May 12, 2015
GRS – Integration
Outline
Introduction
●Yield gap
●Definitions
What factors to collect
●Meta-analysis ●Result: Meta-analysis
How to collect factors ● Innovative data collection approaches
● Crowdsourcing
● Remote sensing
Introduction: Yield gap
(Van Ittersum and Rabbinge, 1997; Van Ittersum et al (2013)
o Anticipated world population by 2050 o Closing the ‘yield gap’ on currently available agricultural lands
Concept of production ecology
Definitions
Yield potential (Potential yield) - Yp: yield of a crop cultivar under defined weather conditions, when grown with no nutrient and water stress and biotic stresses effectively controlled
Water-limited yield - Yw: same as Yp, but water supply is limiting (and hence soil type and topography matter)
Actual yield – Ya: yield achieved by farmers in a given region under dominant management practices and soil properties
Yield gap – Yg: difference between Yp (or Yw) and Ya
Van Ittersum et al., 2013. Field Crops Research 143, 4-17.
Major steps for Yield gap analysis
Step 1: Measuring
Main reason for this step: To identify the potential
scope for raising average yields via management changes
Commonly used methods for Yp:
o Crop growth models o Field experiments o Best farmers yields o Highest recorded yields o Earth Observation o ....... Source: GYGA
Main reason for this step: To identify the key causes of the yield gap
Commonly used methods: Statistical models, qualitative analysis, Frontier methods ....
Explaining yield gap: Step 2
Which factors to collect?
Meta-analysis
Study locations included in Meta-analysis
270 records with unique ID
Results: Management factors
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Results: Edaphic factors
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Results: Farm characteristics
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Results: Socio-economic factors
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How to bridge the data gap? A potential answer—let the farmers tell us themselves!
Most of the management,
farm characteristics and
socio-economic factors that
explain yield gap cannot be
obtained by other means
other than asking the farmers
themselves- either by
traditional farm survey
methods or by through self-
reporting (e.g. SMS)
Example
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Farmers’ soil quality score “Clearly the farmers were aware of their
soil”
U.Sopheap et al., 2012
Crowdsourcing
o The term Crowdsourcing was first coined in 2006
o It commonly refers to outsourcing a function done locally to
a large, disconnected group of people.
In the Context of Agriculture:
“Crowdsourcing is when information is sourced from a group of people (e.g. farmers) in response to an open call, a request for specific information (e.g. crop/farm mgmt. information), or for an exchange, organized by a central organizer/organizing body. ” (USAID, 2013)
Typology of Crowdsourcing
Type How it works Kinds of problems
Examples
Knowledge Discovery and Management (KDM)
Finding and collecting information
into a common location and format
Information gathering, organization, and
reporting
Geo-wiki OpenStreetMap
RANET iCow
CCI-Bioversity
Distributed Human Intelligence Tasking
(DHIT)
For analysing large amounts of
information
For problems involving large-scale data
analysis
FoldIt, GalaxyZoo
Broadcast Search
Solving empirical problems
Ideation problems with empirically provable
solutions, such as scientific problems
Amazon Mechanical Turk
Peer-Vetted Creative
Production
For creating and selecting creative
ideas
Ideation problems where solutions are matters of taste or market support, such as design or aesthetic
problems
Istockphoto Threadless
Brabham, 2013
Case studies
Use of satellite images for Yield gap analysis
The use of satellite data for crop yield gap analysis : to estimate the actual yield
MSc topic: The use of smart phones and satellite
images for crop yield gap analysis
The main objective of this MSc topic is:
To explore the potential of satellite images to estimate important parameters (e.g. crop phenology, sowing date, actual yield etc.) that are relevant for yield gap analysis.
Thank you for your attention
Photo by Arun
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