Véronique Bellon-Maurel de Montpellier SupAgro présentent comment les TIC peuvent contribuer à l'évaluation environnementale.

1. Information & Technologies
for Agro-Processes
Agro-
How ICT can contribute to
environmental assessment
Pr V. Bellon-Maurel
TIC & Agriculture – 4-5 Novembre 2010
Information &
Technologies
for Agro-Processes
Agro- TIC & Agriculture – 4-5 Novembre 2010

2. Why carrying out
Environmental Assessment?
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Agro- TIC & Agriculture – 4-5 Novembre 2010

3. A big challenge
If you can not measure it, you can not improve it!
Improving (reducing) our environmental footprint
William Thomson (1868)
Also named….
Lord Kelvin
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4. How carrying out
Environmental Assessment?
Several methods
=>
Life Cycle Assessment is global and covers the whole cycle
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5. Life Cycle Assessment (ISO 14040 and 14044)
MID-POINT
EMISSION ENVIR. IMPACTS
-In water - Global warming
-In air
-In soil - Stratosph ozone
depletion
2- LCI:
Inventory - Photochemical
oxidation
of emitted/
consumed - Acidification
substances
CONSUMPTION
- Eutrophication
1- Modelling - Energy
the studied - Raw material - Toxicity,ecotox
system - Land
- Resource
depletion
HUGE NEED OF DATA 3- Convert
flux into
impacts DAMAGES
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6. How to get such amount of data?
A old story…
- In 1999, SETAC created the “Streamlined LCA” group (Todd & Curran, 1999)
⇒ LC Inventory (LCI) is the most demanding phase of LCA.
-Agricultural system are susceptible to natural variability
=> generalisation is more difficult and
=> reliable emissions data hard to collect (Lewis et al., 1999).
-In 2009 Finnveden et al. describe recent developments in LCA
=> confirm that LCI data acquisition remains one of the most labour-
and time-intensive stages of LCA.
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7. Two ways of collecting data in agriculture
1- From global (farm level) to specific (one product): the ACCOUNTING APPROACH
Input Output Prod 1
Prod 2
Prod 3
Prod 4
Prod 5
Allocation !!
Input Output
2- From each step to each product : the ANALYTICAL APPROACH ICT
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8. Different types of data must be recorded
Input Output
Data about the process and conditions are to be recorded
Emissions vary a lot depending on the environmental conditions, technologies etc
Input -> Model (Environmental conditions, technology) -> Emissions
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9. Variability of emissions with regard to the conditions
Example: fertilization / Langevin et al, 2010
Emission levels depends on the technology and on the conditions
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10. Different types of data must be recorded
Input Output
Data about the process and conditions are to be recorded
Emissions vary a lot depending on the environmental conditions, technologies etc
Input -> Model (Environmental conditions, technology) -> Emissions
Various steps X various inputs, conditions, technologies => Huge amount of data
Example in viticulture
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11. Viticulture
Shoot shredding Tillage (cultivation)
Pre-Pruning + Pruning
Grass Mowing
Deeper Tillage
Tillage (cultivation)
Fertilisation
(Bud-removal)
Lifting
Harvest
Tillage (cultivation)
Grass Mowing
Tillage (cultivation)
OPERATIONS
Vine topping
Pesticide Spraying
On-the-row cultivation Pesticide spraying Fertilisation Tillage
(7 times-14 spraing) Vine management
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12. Example: Fertilizing in viticulture
SOIL
Fuel consumption/
emissions
NH3
N2O Soil moisture
Air moisture
Fertilizer burying
Soil-slurry pH TYPE & AMOUNT OF FERTILIZER
Leaching
NO3-
Soil , Rain
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13. How to feed a LCI database?
Manual input SOIL T°
Fuel consumption/
Sensor input Wind emissions
One-shot input
NH3
Weather input
Model output N2O Soil moisture
Air moisture
Fertilizer burying
Soil-slurry pH TYPE & AMOUNT OF FERTILIZER
NO3-
Soil type Rain
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14. Where can ICT contribute ?
Manual input
Sensor input Mobile technologies for data collection
One-shot input Embedded sensors for data collection
Weather input
Model output Automatic data collection & mapping
Data collection and Modelling
Databasis
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Agro- TIC & Agriculture – 4-5 Novembre 2010

15. Mobile technologies for data acquisition
• Today: Pocket PCs, PDA, EDA, mobile phones
- Pb: Acceptation by farmers
- Mobile phones have the interest of having well penetrated the
market anywhere (5 billion subscriptions in 2010).
• Towards: more automatic data collection?
- Bar codes, RFId (ex: pesticide recognition, see Peets et al
2009)
- Voice recognition (ex: Talkman for forestry, see Rumble et al,
2009)
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Agro- TIC & Agriculture – 4-5 Novembre 2010

16. Embedded sensors for data acquisition
• Coupling sensor and GPS outputs
- Sensors for the operation: digital gauges (fuel), flowmeters (pesticide
spraying, irrigation), etc
- Sensors for the conditions: soil moisture, air moisture, wind…
• Isobus standard
- Helps to seamlessly record data from different types of machines as well as
working times (Sat-trace);
- Very interesting for generating huge amount of data on farm operations; See
Steinberger et al. (2009)
• Mapping is not a must
GPS is necessary for recognising the field but a unique value –not a map- is
needed for LCI.
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Agro- TIC & Agriculture – 4-5 Novembre 2010

17. Automatic data collection and mapping
• Deals with weather data but also with environmental condition
data (soil moisture, texture etc)
• Use of « regional » weather stations or of local
sensor networks.
• Mapping increases the precision but is not
compulsory (the nearest station data can be used)
• Such data are used to surrogate the lack of other
data which are more difficult to measure but necessary for impact
assessment; for instance soil T°can be modelled fro m air T°
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18. Databases
• Farmers are more and more pushed to
record data for traceability purposes: compulsory (ex:
pesticide, fertilizers…) and voluntary traceability (ex: );
• Use of digital databases
• LCI generation would not require too much
additional data (environmental data to
generate pollution emission from input use)
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19. Conclusion
• ICTs have a great interest for environmental assessment, in
particular to generate LCI data (automatic or streamlined data
acquisition)
• Not only knowledge of input (and technology) is needed but also
knowledge of the environmental conditions:
Input -> Model (Environmental conditions, technology) -> Emissions
• So far, ICTs have not penetrated enough the agricultural
market but 3 main drivers exist:
- Low cost GPS
- Smart phones
- Compulsory traceability
Only few additional data (environmental data) must be added to traceability data ti
generate emission data
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20. Thank you for your attention
2 publications are currently being prepared on that subject:
- Streamlining life cycle inventory data generation in agriculture using
traceability data and information and communication technologies –
Part I: General concepts .
- Streamlining life cycle inventory data generation in agriculture using
traceability data and information and communication technologies –
Part II: Specifications to build up LCI-relevant databases in viticulture.
ACKNOWLEDGEMENT
This work was carried out as part of a travelling scholarship supported by the
European Commission (IRSES program, IRSES project nr 235108), the
Languedoc Roussillon Council (Regional Plat-form GEPETOS – ECOTECH-
LR) and PEER (Partnership for European Environmental Research).
Information &
Technologies
for Agro-Processes
Agro- TIC & Agriculture – 4-5 Novembre 2010