SC2 Workshop 2: CGIAR Open Data and Big Data Efforts
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CGIAR Open Data and Big Data Efforts Medha Devare GODAN Workshop 30 September 2016
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Transcript of SC2 Workshop 2: CGIAR Open Data and Big Data Efforts
CGIAR Open Data and Big Data Efforts
Medha Devare
GODAN Workshop30 September 2016
Grassini et al., 2013
Devkota et. al; Field Crops Res 179:81-94
Intensification requires
layered options based on
multi- dimensional
analysis, user
knowledge, preferences
Challenges
http://guides.library.queensu.ca/infoneeds
Get from this… To this:
Requires access to data sets and harmonization on…
Challenges
interoperability (standards, ontologies…)
tools/platformsincentives/culture
http://www.breidt.net/scripts/pics/hochb.jpg
Global discovery
Federated search across centers
Categorized content type
Faceted results
Contents referenced via standard geo-coordination (ISO)
Machine-readable
Human + machine readable content
Collection of tools
Toolkit for analytics
Improved access, reuse
High precision, integration via controlled vocabularies, ontologies
Learning from other domains: The aspiration
Credit: Soonho Kim (IFPRI) and NCBI
Bricks and rivets…
Center+ repositories, dbs
Interoperability
Ontologies, vocabularies
Harmonized data/info
Data management
Agronomy-breeding mgmt. systems
CGIAR technology catalog
Analytics/tools
Technology mapping
Research discovery
Decision support, visualization
M+E
Infrastructure (LOD enabled)
CGIAR plans, budgets, approaches aligned
across units[leadership, legal, HR,
Proj Mgmt, IT, KM-DM]
Aligned CG-donor policies,
guidelines/DMPs…
OA-OD capacity, support, visibility
Metadata, SOPs
phase I OA/OD, build in phase II
phase II OA/OD / Big Data
Apps – links to telcos(to/from farmer)
Data quality, workflows
Genebanks
Other interoperable
platforms
Genetic Gains
Hey Cigi, when should I plant my maize? How should I manage my crop?
Real-time decision support system for farmers
Easy natural language as an interface
Smart artificial intelligence trained by CGIAR and partners
Leveraging open, harmonized and interoperable multiple databases
Credit: Jawoo Koo (IFPRI)
The aspiration…
Bricks and rivets: CG core metadata schema
Bricks and rivets: Agronomy Management System
Plot size: 5 x 5 sq. m (or as close to it as possible) = 8 rows in line-sown plots EXTRA PLOTS: T9 and T10
T1 T2 T3 T4 T9
OPV = Arun 2 OPV = Arun 2 OPV = Arun 2 OPV = Arun 2 OPV = Rampur Composite
broadcast line sown line sown broadcast line sown
farmer NPK farmer NPK 120:60:60 NPK 120:60:60 NPK farmer NPK
T5 T6 T7 T8 T10
hybrid hybrid hybrid hybrid OPV = Rampur Composite
broadcast line sown line sown broadcast line sown
farmer NPK farmer NPK 120:60:60 NPK 120:60:60 NPK 120:60:60 NPK
Stage of trial Protocol
Pre-planting * Perform germination tests with hybrid and OPV; estimate germination percent
Tillage * Note what farmers did to prepare each field/rep; try to get a realistic estimate of time and
labor required
Crop establishment * Record sowing date for each plot
Seed rate * 40 kg/ha; calculate the rate for your plots, be sure to adjust for germination percent!
Sowing * Note dates, time taken, labor required for planting each plot.
Line-sown
* Calculate number of rows per plot, and amount of seed required per row, and prepare
separate containers/bags per row containing seed
* Rows should be spaced 60 cm apart (i.e. 8 rows in line-sown plots)
* Use jab planter to sow line-sown plots, making sure that seed are deep enough to find
moisture and germinate. Note average time taken for line-sowing plot.
Data harmonization via AgrO-based Agronomy Management System + Fieldbook (similar to the BMS)
Adaptive Research Data 2011 Wheat
ART Name: Intercropping in Sugarcane
Yield Data of Rajma in sugarcane intercropping
REP TRT Seeding Date/Variety Flowering daysMaturity days PHT(cm)F pod/Plant UF pod/Plant Grain/pod TGW(g) YLD kg Area(m²) Seed Yield(kg/m2)
S1 S2 S3
1 1 25-Nov..
1 2 1-Dec. 124 90 1.177 1.481 1.352
1 3 1-Dec. 47 110 31.24 10 3 2.7 346 17.0 180 0.128 0.148 0.157
1 4 1-Dec. 44 65 40 6 1 6 283.5 6.5 180 0.083 0.079 0.054
Farmer field trial
Experiment station trial
Greenhouse trial
Farmer field demonstration
Experiment station
demonstration
Technology adoption study
Other
Trial, demo type
Farmer field trial Farmer managed
Researcher managed
Other
Farmer field trial
Farmer managed
Personnel
PI
Field supervisor
Data collector
Site
Country
State
District
Sub-district
Town
Village
Municipality
Ecozone Altitude
Latitude
Longitude
Goal: Pre-loaded,
authoritative lists
