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‘Transforming (space) data into knowledge and decision support’
(to improve production systems per crop,
per grower and per field)
Roelof Kramer, Head of Agriculture
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Introduction: Background Data
Knowledge Decisions
TSB feasibility study in 2014
• BS: End of EU beet production quota in 2017
• Rezatec: Use of remote sensing in agriculture
• RFL: Sustainable intensification
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Introduction: Global challenge Data
Knowledge Decisions
3
Sustainable production of more food with the same or less resource
• Climate change
• Environment
• Supply & demand
• Productivity
• Competitiveness
• Financial risk
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Systematic approach: Multiple users and challenges
Data Knowledge Decisions
4
Stakeholder Locations Crops
• Growers & processors
• Inputs, services & equipment
• Traders and retailers
• Financials, government & NGO’s
• Soil • Climate • PESTLE
• Cereals • Root crops • Oilseeds • Plantation
crops • Vegetables
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Balanced approach: Provider challenges Data
Knowledge Decisions
5
Data collection: Parameter and phenotyping
Knowledge creation: Data products
Decision support: Value added
• Timing • Quantity • Quality • Cost • Method
• Modeling • Crop stages • Analysis • Data library • Presentation
• Crops • Users • Solutions • Route to
market • IP/FTO
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Focused approach: Crops, customers, decisions and geographies
Data Knowledge Decisions
Crops Customers Decisions
• Sugar beet • Sugar cane • Wheat • Grass • Potato • Vegetables • OSR • Plantation
• Processors • Grower
groups • Seed industry • Government • Insurance • Retailers
• Supply chain • Insurance • Investment • Environment • Productivity • Crop monitor
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..of parameters affecting crop growth stages
Rezatec collects & processes big data per field, per grower and per crop..
Data Knowledge Decisions
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..by populating models with parameter data
Crop growth stages are simulated.. Data
Knowledge Decisions
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..to correct for missing, abnormal or changing parameters
• Cost of data versus model accuracy
• In season: Management and input variability
• Long term: Climate, inputs, soil and management
Simulations are calibrated with phenotyping.. Data
Knowledge Decisions
Canopy Cover and Biomass: Tablet, sensors, satellite data
Leaf Area Index: Handheld sensors
Crop stress: Near Infra Red with
satellite and radar
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Calibrated simulations reflect actual crop status..
Data Knowledge Decisions
Establishment: Development: Production: Harvest&storage:
Model estimated
Tablet derived
Satellite derived
..and gap analysis against actual and historical parameters provides insight in production efficiency
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Overview “Knowledge breeding” Data
Knowledge Decisions
1. Growth parameters • Soil • Management • Inputs • Environment
2. Phenotyping data • Emergence • Development • Production • Harvest &
Storage
3A. Simulation (Mathematical modeling) • Improve algorithms of public domain models • Decrease data/phenotyping cost • Increase data/phenotyping accuracy • Improve modeling accuracy
3B. Calibrated crop status - Crop data products • Emergence • Development • Production • Harvest & Storage
3C. Crop performance & parameter analysis: • Historical • Potential (in current process) • Optimal (ideal scenario) • Forecast - trends
4. Decision support - Key challenges • Productivity, Investment, Crop Monitoring • Value Chain, Insurance, Environment
Crop performance data products for online interrogation
Parameter data products and forecasts for online interrogation
Decision support alerts by phone and periodic reports via web portal
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Data products per crop stage
Data Knowledge Decisions
Establishment Development Production Harvest & Storage
• Emergence
speed
• Plants/ha
• Homogeneity
• Leaf area index
(LAI)
• Canopy cover
• Leaf / root
biomass
• Number of
flowers
• Period of
flowering
• NDVI/chlorophy
ll
• Kg/ hectare of
leaf, root,
bean, kernel
• Ripening
speed
• Dry mass root
/ leafs
• Sugar content
• NDVI/chlorop
hyll
• Harvest losses
• Post harvest
losses
• Quality /
quantity
reduction
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Decisions support modules Data
Knowledge Decisions
Scope Multiple fields Single field
Module 1. Value chain
2. Crop monitor
3. Insurance
4. Investment
5. Environment
6. Productivity
Objective
Optimize harvest,
transport and storage
Supply & demand
balancing
Field, farmer specific
insurance
Opportunity cost across
seasons
CO2 emission carbon food
Productivity increase in
season
Customer Processor and
grower
Processor, government,
trader
Insurance firms
Growers and processors
Grower, government
Grower, R&D service &
input providers
