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Valued Grain Chain in different farm businesses
Liisa Pesonen
Luke
Janne Rundqvist
AgroVäst
This project has received funding
from the European Union’s
Horizon 2020 research and innovation
programme under grant agreement
Nº 818182.
2
Value from data in
grain chain
3
Data sources
• FMIS
• Machine data
• Sensor data
• Remote sensing data
• Dedicated Apps
• Activity data (time, location)
From farm data to valuable product information
Operations planning
• C, N, P, € balance
• Working time
• Biodiversity
• Chemical use
Product data for
grain marketplace
• Carbon footprint
• Nitrogen footprint
• Traceability/trust
• Handling history
• ’Pesticide free’
Product data for
Consumer
• Climate impact
• Environment impact
• Traceability/trust
• Safety
• Health impact
• Wellness/fairness
4
5
Valued Grain Chain
concept
6
Steps 1-2 in the Valued Grain Chain
1. Identifying quality
areas in fields before
harvest
• Satellite images,
weather, soils, input
data
2. Confirming the
quality areas in
field
• Measuring
protein content
from the ripened
growth
7
3. Logistics plan for the harvest.
4. Harvest with site-specific information,
unique IDs for harvest batches.
5. Creating unique batch-specific IDs in
all steps where a new batch is made;
trailer, dryer, silo, sale. Keeping book of
IDs. A sales batch ID includes all previous
Ids.
Steps 3-5 in the Valued Grain Chain
8
6. Sales batch to an electronic
marketplace, attached with unique ID
and measured and calculated batch-
specific product information.
7. Farmer is aware of the production
costs of the sales batch when selling.
Steps 6-7 in the Valued Grain Chain
9
Tackling complexity
• Several technical/digital systems, each farm have their own
set of tools.
- Need to integrate existing systems for data flows
• Aiming at:
• Technology serves the farmer, not vice versa. Integrated
automation ’behind the panel’.
• Farmer focuses on his/her own farm operations. Usability of user
interfaces and creation of situational awareness are important.
• Cost efficiency by defined roles and data sharing between the
digital systems.
10
Valued Grain Chain partners
AgroIntelli AsP, DK
AgroVäst, SE
Cinia Oy, FI
DataVäxt AB, SE
GrainSense Oy, FI
Hedåkers Säteri, SE
Knehtilän tila, FI
Luke, FI
Similän tila, FI
Suomen Viljakauppa Oy, FI
Suonentieto Oy, FI
11
Farm-specific
business
approaches
12
Farm fields
13
• Large field blocks
• Homogenising yield quality already in
the fields by precision farming
technology
• Sorting and mixing homogne sales
batch after harvest
➢ High quality wheat for local bread
Farm fields – Focus on farm level
14
• Many small but heterogenious field
blocks
• Focusing on different qualities based
on field properties
• Selecting field blocks to harvest as
homogenous quality batches for sale
➢ Malt barley & enzyme malt barley
Farm fields – Focus on field level
15
• Large field blocks
• High soil variation; soil type, moisture,
organic matter…
• Selecting field zones for high-quality
grain batches for special products
➢ Sustainable oat granola
or barley for a micro brewing plant
Farm fields – Focus on field zone level
16
Data
Data processing
All three business approaches
can use the same concept,
only resolution and organizing
of data vary.
17
By Business Model Alchemist
http://www.businessmodelalchemist.com/tools, CC BY-SA 1.0,
https://commons.wikimedia.org/w/index.php?curid=11892574
18
Hard values
- Protein
- Purity
- Ecotoxins
- Volume weight
Soft values
- Climate
- Environment
- Healt
- Fairness
- Trust
Processing industry
Food industry
Consumers
Digital tools
Data
Digital media
Digital technologies play a key role when establishing
the connection between farm data, soft values and consumers
in order to create and realize value!
19
Integrated digital tools and systems
create competitive capacity to farms.
Farm’s business approach is a mean to
have a competitive edge on top of that.
Valued Grain Chain
20
Thank you!

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Teknologiapaiva 13012021-valued grain chain in different farm businesses

  • 1. 1 Valued Grain Chain in different farm businesses Liisa Pesonen Luke Janne Rundqvist AgroVäst This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nº 818182.
  • 2. 2 Value from data in grain chain
  • 3. 3 Data sources • FMIS • Machine data • Sensor data • Remote sensing data • Dedicated Apps • Activity data (time, location) From farm data to valuable product information Operations planning • C, N, P, € balance • Working time • Biodiversity • Chemical use Product data for grain marketplace • Carbon footprint • Nitrogen footprint • Traceability/trust • Handling history • ’Pesticide free’ Product data for Consumer • Climate impact • Environment impact • Traceability/trust • Safety • Health impact • Wellness/fairness
  • 4. 4
  • 6. 6 Steps 1-2 in the Valued Grain Chain 1. Identifying quality areas in fields before harvest • Satellite images, weather, soils, input data 2. Confirming the quality areas in field • Measuring protein content from the ripened growth
  • 7. 7 3. Logistics plan for the harvest. 4. Harvest with site-specific information, unique IDs for harvest batches. 5. Creating unique batch-specific IDs in all steps where a new batch is made; trailer, dryer, silo, sale. Keeping book of IDs. A sales batch ID includes all previous Ids. Steps 3-5 in the Valued Grain Chain
  • 8. 8 6. Sales batch to an electronic marketplace, attached with unique ID and measured and calculated batch- specific product information. 7. Farmer is aware of the production costs of the sales batch when selling. Steps 6-7 in the Valued Grain Chain
  • 9. 9 Tackling complexity • Several technical/digital systems, each farm have their own set of tools. - Need to integrate existing systems for data flows • Aiming at: • Technology serves the farmer, not vice versa. Integrated automation ’behind the panel’. • Farmer focuses on his/her own farm operations. Usability of user interfaces and creation of situational awareness are important. • Cost efficiency by defined roles and data sharing between the digital systems.
  • 10. 10 Valued Grain Chain partners AgroIntelli AsP, DK AgroVäst, SE Cinia Oy, FI DataVäxt AB, SE GrainSense Oy, FI Hedåkers Säteri, SE Knehtilän tila, FI Luke, FI Similän tila, FI Suomen Viljakauppa Oy, FI Suonentieto Oy, FI
  • 13. 13 • Large field blocks • Homogenising yield quality already in the fields by precision farming technology • Sorting and mixing homogne sales batch after harvest ➢ High quality wheat for local bread Farm fields – Focus on farm level
  • 14. 14 • Many small but heterogenious field blocks • Focusing on different qualities based on field properties • Selecting field blocks to harvest as homogenous quality batches for sale ➢ Malt barley & enzyme malt barley Farm fields – Focus on field level
  • 15. 15 • Large field blocks • High soil variation; soil type, moisture, organic matter… • Selecting field zones for high-quality grain batches for special products ➢ Sustainable oat granola or barley for a micro brewing plant Farm fields – Focus on field zone level
  • 16. 16 Data Data processing All three business approaches can use the same concept, only resolution and organizing of data vary.
  • 17. 17 By Business Model Alchemist http://www.businessmodelalchemist.com/tools, CC BY-SA 1.0, https://commons.wikimedia.org/w/index.php?curid=11892574
  • 18. 18 Hard values - Protein - Purity - Ecotoxins - Volume weight Soft values - Climate - Environment - Healt - Fairness - Trust Processing industry Food industry Consumers Digital tools Data Digital media Digital technologies play a key role when establishing the connection between farm data, soft values and consumers in order to create and realize value!
  • 19. 19 Integrated digital tools and systems create competitive capacity to farms. Farm’s business approach is a mean to have a competitive edge on top of that. Valued Grain Chain