OLCA-Pest Workshop Slides - DTU

1

OLCA-Pest 2020 Stakeholder Workshop [back-to-back with LCAFood]13 October 2020

Peter Fantke, Technical University of Denmark

OLCA-Pest 2020 Stakeholder Workshop

- Project overview -

2

Pesticidefield applications

LCA goal = eco-labelling, eco-design of processed products, benchmarking, lifestyle LCA, etc.

Agriculture = background activity incl. processing,logistics, packaging and cooking

LCA practitioners of agrifood systems and food products

LCA goal = diagnosis and eco-designof new agricultural practices

Agronomists

Need to differentiate archetypes of food productions:• Average pesticide application practices• Average pedoclimatic conditions (for detailed emission processes)

Need to differentiate in detail agricultural practices related to pesticide application for specific

pedoclimatic conditions (site specific)

probably more than 90% of users of agricultural LCAs

Conditions for Assessing Pesticides in LCA

3

• Different developments for

– LCI databases

– Pesticide emission modelling (environment)

– Pesticide residues modelling (food)

– LCIA methodology for human/eco-toxicity

• Lead to

– Ambiguous boundaries between LCI and LCIA

– Gaps and overlaps between LCI and LCIA under- or overestimation of impacts

– Inconsistent assignment of LCI outputs to LCIA inputs

Related Challenges in LCA for Pesticides

4

Main 5 Recommendations

1. Use default pesticide emission fractions for background systems

2. Apply PestLCI Consensus web-tool for foreground systems

3. Include crop residues as pathway for human toxicity

4. Follow our approach for coupling emission and impact results and models (PestLCI Consensus, USEtox, dynamiCROP)

5. Implement additional emission compartments in LCI databases

5

Main outputs and projects

Consensus-building meetings

Path to Consensus for Pesticides in LCA

6

Pesticide application

LCILife Cycle Inventory

Carried out by LCA practitioners

Glasgow consensus* with general

recommendations but no guidance or tools

LCIALife Cycle Impact Assessment

Embedded in LCIA models

and LCA software as characterisation

factors

Pedo-climatic

conditions

Technologies

and practices

Aquatic ecotoxicity

Human toxicity

Resulting potential impacts

(Characterisation factors)

LCA State-of-the-Art Pesticide Assessment

• Fate

• Exposure

• Effects

100 % Soil

In most of the cases

(Ecoinvent, etc.)

*https://doi.org/10.1007/s11367-015-0871-1

https://doi.org/10.1007/s11367-015-0871-1

7

PestLCI Consensus

% Water

% Soil

% Air

% Crop


LCI LCIA

Pedo-climatic

conditions

Technologies

and practicesDistribution within the

environment

(Emission fractions)

Resulting potential impacts

(Characterisation factors)

New pesticides characterised in USEtox (input data and CFs)

Consistent coupling between dynamiCROP and PestLCI Consensus for

crop residue-related human toxicity

Guidance for different levels of expertise (model coupling, interpretation, etc.)

Operationalisation of Glasgow Consensus

New emission model with adjustable parameters (practices, pedoclimatic, etc.)

for advanced practitioners

Averaged emissions fractions for most common situations needed by LCA

practitioners and LCI databases

Improvements from OLCA-Pest

Aquatic ecotoxicity

Human toxicity

• Fate

• Exposure

• Effects

Human toxicity

dynamiCROP• Crop residues

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OLCA-Pest 2017-2020: Team

Peter Fantke (PI)

Carlos Melero

Philippe Roux

Michèle Egea

Emmanuel Maillard

Assumpció Antón

Nancy Peña

Thomas Nemecek

Gérard Gaillard

Christel Renaud-Gentié

Aurélie Perrin

Claudine Basset-Mens

Céline Gentil-Sergent

Charles Mottes

Pierre Naviaux

Sandra Perez-Jimenez

Contracted by

Vincent Colomb

Associate Partners

Serenella Sala

Joël Aubin

OLCA-PestOperationalizing

Life Cycle Assessment of

Pesticides

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OLCA-Pest: Focus Areas

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Fantke, P., 2019. Modelling the environmental impacts of pesticides in agriculture. in: Weidema, B.P. (Ed.). Assessing the Environmental Impact of Agriculture. Burleigh Dodds, Cambridge, UK, pp. 177-228.

http://doi.org/10.19103/AS.2018.0044.08

Pesticides in LCA: Method Summary

http://doi.org/10.19103/AS.2018.0044.08

11

Project output: LCI-LCIA Model Coupling

Gentil et al. 2020 http://doi.org/10.1007/s11367-019-01685-9

http://doi.org/10.1007/s11367-019-01685-9

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Project output: LCI Consensus Modelhttps://pestlciweb.man.dtu.dk

- Single pesticide- Batch runs- Adapted drift- Initial and secondary emissions

https://pestlciweb.man.dtu.dk/

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Example: Pesticide physicochemical properties

Complete datasets for ~800 pesticidesAdditional (incomplete) datasets for >1500 pesticides

Project output: New Pesticide Data

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Project output: LCI Implementation

1. Default pesticide emission fractions (primary distribution)

2. Procedure for model linking pesticide emission fractions to environmental compartments of LCIA models

3. Proposal how to consistently implement the Glasgow consensus in LCI databases and LCA software:

a) Introduce a new emission compartment "crop" with 13 subcompartments

b) Calculate characterisation factors for impacts of pesticide residues in food on human toxicity

c) Add metadata to the crop datasets

15

1. Ryberg, M.W., Rosenbaum, R.K., Mosqueron, L., Fantke, P., 2018. Addressing bystander exposure to agricultural pesticides in life

cycle impact assessment. Chemosphere 197, 541-549. http://doi.org/10.1016/j.chemosphere.2018.01.088

2. Steingrimsdottir, M.M., Petersen, A., Fantke, P., 2018. A screening framework for pesticide substitution in agriculture. Journal of

Cleaner Production 192, 306-315. http://doi.org/10.1016/j.jclepro.2018.04.266

3. Peña, N., Antón, A., Kamilaris, A., Fantke, P., 2018. Modelling ecotoxicity impacts in vineyard production: Addressing spatial

differentiation for copper fungicides. Science of the Total Environment 616-617, 796-

804. http://doi.org/10.1016/j.scitotenv.2017.10.243

4. Fantke, P., 2019. Modelling the environmental impacts of pesticides in agriculture. in: Weidema, B.P. (Ed.). Assessing the

Environmental Impact of Agriculture. Burleigh Dodds Science Publishing, Cambridge, United

Kingdom. http://doi.org/10.19103/AS.2018.0044.08

5. Peña, N., Knudsen, M.T., Fantke, P., Antón, A., Hermansen, J.E., 2019. Freshwater ecotoxicity assessment of pesticide use in crop

production: Testing the influence of modeling choices. Journal of Cleaner Production 209, 1332-

1341. http://doi.org/10.1016/j.jclepro.2018.10.257

6. Gentil, C., Fantke, P., Mottes, C., Basset-Mens, C., 2020. Challenges and ways forward in pesticide emission and toxicity

characterization modeling for tropical conditions. The International Journal of Life Cycle Assessment.

http://doi.org/10.1007/s11367-019-01685-9

7. Crenna, E., Jolliet, O., Collina, E., Sala, S., Fantke, P., 2020. Characterizing honey bee exposure and effects from pesticides for

chemical prioritization and life cycle assessment. Environment International 138, 105642.

http://doi.org/10.1016/j.envint.2020.105642

8. Gentil, C., Basset-Mens, C., Manteaux, S., Mottes, C., Maillard, E., Biard, Y., Fantke, P., 2020. Coupling pesticide emission and

toxicity characterization models for LCA: Application to open-field tomato production in Martinique. Journal of Cleaner

Production 227, 124099. http://doi.org/10.1016/j.jclepro.2020.124099

Several additional journal articles currenly in preparation (e.g. Maillard et al., Gentil et al., Peña et al., Fantke et al.)

Project output: Scientific Advances

THANK YOU!

1


OLCA-Pest 2020 Stakeholder WorkshopModel linking, default emission fractions,

recommendations for LCI databases and LCA software

Thomas Nemecek, AgroscopeCéline Gentil, CIRAD

2

Challenges in the emission modelling and impact assessment for pesticides

• Quantify pesticide emissions to the different emission compartments

• Conserve the mass balance

• Ensure consistent linking between pesticide emission modelling and impact assessment for ecotoxicity and human toxicity

• Model pesticide residues on food products and subsequent human toxicity impacts

• Find a generic solution working with a minimum of data for wide application

• Implement the proposal in LCI databases and LCA software so that it can be used by mainstream LCA applications

3

The challenge to produce useful data and models for the two main types of agricultural LCA practitioners

Pesticides

LCA goal = eco-labelling, eco-design of processed

products, benchmarking, lifestyle LCA, etc.

Agriculture = background activity among logistics,

packaging and cooking

Common LCA practitioners of food

products

LCA goal = diagnosis and eco-

design of new agricultural practices

Agronomists

Need to differentiate archetypes of food productions:

• Average pesticide application practices

• Average pedoclimatic conditions (site generic/dependent)

Need to differentiate in detail agricultural practices related

to pesticide application for specific pedoclimatic

conditions (site specific)

probably more than 90% of

users of agricultural LCAsA B

4

PestLCI Consensus – dynamiCROP coupling

(Gentil et al 2020)

• Coupling of PestLCI Consensus initial distribution to dynamiCROP

• For organic substances

• Update of harvested fraction calculation according to PestLCI outputs

LAI: Leaf Area Index, FAI: Fruit Area Index

5

Calculation

𝐼𝑆 =𝑝,𝑐

𝑚emi,𝑝,𝑐 × 𝐶𝐹𝑝,𝑐

𝐶𝐹𝑝,𝑐 = ℎ𝐹𝑝,𝑐 𝑡 × 𝑃𝐹𝑓 × 𝐸𝐹𝑝

ℎ𝐹𝑝,𝑐(𝑡) =σℎ ൯𝑚res,𝑝,ℎ(𝑡

𝑚emi,𝑝,𝑐

Impact Score p: pesticide

c: environmental

compartment

𝑚emi,𝑝,𝑐 = 𝑚app,𝑝 ×𝑚𝑓𝑝,𝑐kgapplied /FUkgemitted /FU kgemitted/kgapplied

impact /FU

From LCI –

PestLCI

impact

/kgintake of processed food

impact/kgemitted

kgin crop harvest/kgemitted

Pesticide residual mass in crop

components (h) harvested at time tMatrix algebra

of dynamiCROP

kgin processed food

/kgin crop harvest

t = 0

harvest fraction (ℎ𝐹) x food

processing factor (𝑃𝐹) =

intake fraction (USEtox)

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dynamiCROP user interface• Further development required for coupling with secondary emissions fractions of PestLCI

• Development of dynamiCROP [3.12] for LCA (http://dynamicrop.org/)

For further reading: Gentil et al (2020) Coupling pesticide emission and toxicity characterization

models for LCA: Application to open-field tomato production in Martinique. J. Clean. Prod.

http://dynamicrop.org/

http://dynamicrop.org/

https://authors.elsevier.com/a/1bl~J3QCo9Yn35


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Application example

8

Linking LCI to LCIA for background LCA applications

Amount of pesticide applied

Air

100%

Field soil

surface

Field crop surface Off-field

surfaces

Current LCI DB

PestLCI consensus

primary (initial)

distribution

Agricultural soil

Air Agricultural soilCrop

food

Crop

non-food

Wa-

ter

Nat.

soilAdapted LCI DB

Aquat. ecotox.

Human tox. CFair CFagr.soilCfcrop (dynamiCROP) CfwaterCFnat.soil

LC

IL

CIA

CFair CFagr.soil CfwaterCFnat.soilCFagr.soil (preliminary)

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Proposal to adapt LCI databases

1. Include a new emission compartment "crop" with 13 subcompartments:

Subcategory of "Crop"

for food uses

Subcategory of "Crop"

for non-food (feed, fuel) uses

Grain crops, food Grain crops, non-food

Flooded crops, food Flooded crops, non-food

Herbaceous fruits and vegetables,

food

Herbaceous fruits and vegetables, non-food

Fruit trees, food Fruit trees, non-food

Leafy vegetable crops, food Leafy vegetable crops, non-food

Roots and tuber crops, food Roots and tuber crops, non-food

Forage crops, non-food

6 s

pec

ific

CF

s fo

rh

um

an

to

xic

ity

Record the emission fraction of pesticides going to the crop in the crop datasets.

This is usually higher than the pesticide residues at harvest.

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Modelling of pesticide residues in LCI DBalong the food supply chain: examples

Food Non-Food

Food Non-Food

Food Non-Food

Food Non-Food

Food Non-Food

Food Non-Food

Food

Degradation:

negative emission to food

Non-

Food

Food-

=

-+

Water

Food

Water+-

Food processing

by-products to feed:


positive emission to non-food

Washing:


positive emission to water

Crop

level

Food

processing

Whole food supply chain

Pesticide residues to:

CF for human toxicity

Human toxicity potential

LCIA

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Proposal to adapt LCI databases2. Split the total pesticide emissions into emission fractions:

a) Tier 1A: Use default emission fractions proposed by OLCA-Pest, based on the primary (initial) distribution. The emission (including the crop compartment) will sum up to 100%. or

b) Tier 1B: Use the web tool to calculate customized emission fractions

3. Add metadata to the crop datasets.

This approach requires the following data, which are available in LCI databases:

– 18 crop classes– 11 target classes (herbicides, …)

Default application methodDefault crop stage and soil coverDefault emission fractions

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Illustrative examples of default emission fractions

Emission compartment

Crop class Target class

air, low

population

density

soil,

agricultural

soil, forest

(natural soil)

water,

river Crop Total

Cereals

(Pooideae)

Herbicide (post-

emergence) 10% 67% 0.6% 0.02% 22% 100%

Root and

tuber crops Fungicide 10% 18% 0.7% 0.02% 71% 100%

Fruit trees

temperate Insecticide 8% 19% 1.9% 0.07% 71% 100%

94 scenarios defined

46 groups of non-redundant scenarios

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Unit process: Subcompartment Name Value Potato {US}| production | Cut-off, U

General dataset information GeneralComment

Pesticide application paramter:

Field width=100m

Field length=200m

Slope=4%

Buffer zone width=6m, in technosphere

Fraction deposited on leaves in bufferzone=0.5

Inputs from technosphere

[sulfonyl]urea-compound {GLO}| market for | Cut-

off, U 1.5 Source: USDA statistics

[thio]carbamate-compound {GLO}| market for |

Cut-off, U 0.7 Source: USDA statistics

Application of plant protection product, by field

sprayer {GLO}| market for | Cut-off, U 3

Application method: Potato, conventional boom

sprayer (NL, Holterman&Van de Zande), with drift

reduction

Emissions to air low. pop. Dimefuron 0.0750 Default pesticide emission fraction (OLCA-Pest)

Emissions to soil agricultural Dimefuron 1.0800 Default pesticide emission fraction (OLCA-Pest)

Emissions to soil forest Dimefuron 0.0150 Default pesticide emission fraction (OLCA-Pest)

Emissions to water river Dimefuron 0.0300 Default pesticide emission fraction (OLCA-Pest)

Emissions to crop Roots, tubers and bulbs, food Dimefuron 0.2250 Default pesticide emission fraction (OLCA-Pest)

Emissions to crop Roots, tubers and bulbs, non-food Dimefuron 0.0750 Default pesticide emission fraction (OLCA-Pest)

Emissions to air low. pop. Cymoxanil 0.0350 Default pesticide emission fraction (OLCA-Pest)

Emissions to soil agricultural Cymoxanil 0.2800 Default pesticide emission fraction (OLCA-Pest)

Emissions to soil forest Cymoxanil 0.0140 Default pesticide emission fraction (OLCA-Pest)

Emissions to water river Cymoxanil 0.0210 Default pesticide emission fraction (OLCA-Pest)

Emissions to crop Roots, tubers and bulbs, food Cymoxanil 0.2625 Default pesticide emission fraction (OLCA-Pest)

Emissions to crop Roots, tubers and bulbs, non-food Cymoxanil 0.0875 Default pesticide emission fraction (OLCA-Pest)

{{

100% of applied mass

100% of applied mass

Proposal to adapt LCI databases

Example of the presentation of pesticide applications in a LCI unit process dataset:

General

information

Pesticide

manufacturing

Application

technique

Pesticide

emissions

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Proposal to adapt LCIA methods

Characterisation factors (CF) for human toxicity for the emissions to the crop compartment (only food) will be calculated by dynamiCROP:

up to 6 CFs for each pesticide

Impact calculated per kg emission to the cropFor pre-emergency herbicides we will assume crop stage I (in the final calculation the fraction to crop for pre-emergence herbicides will be 0 no impact)

• Human toxicity CFs=0 for the "non-food" subcompartments

• Additional CFs as add-ons to USEtox

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Survey of database and software developers

Feedbacks of 8 database and 3 software/tools developers received

Generally positive responses

Several concerns were raised:

1. Technical feasibility, resource needs

– Implementation is technically demanding

– Implementation in databases dependent on adaptation of LCA software

2. Modelling of pesticide residues at crop level

– Possible bias by allocation to co-products

– Reduction of residues through later processing

– Distinction food/non-food not easy a crop level, can change later

3. Acceptance

– Solutions should officially/internationally accepted (UNEP, PEF, …).

– Modelling of human toxicity of pesticide residues is a highly sensitive issue

4. Others:

– Pesticide data (amounts applied) are often very low quality or missing.

16

Questions / Discussion

• Do you agree with this proposal?

• What do you think about

– Using the default emission fractions?

– Linking between models and emission compartments?

– Introducing an emission compartment "crop"?

– Distinguishing "food" from "non-food" / Introducing the 13 subcompartments?

– Modelling of pesticide residues along the food chain?

• What can be done to support implementation in LCI databases and LCA software?

1



- PestLCI Consensus web tool:

Potentials, limitations and practical usage -

Carlos M. Moraleda Melero, DTU

2

TABLEOFCONTENTS

01 - PROJECT CONTEXT

02 - FUNCTIONALITIES- Data Requirements, outputs

03 – WEB-BASED EMISSION MODEL- MVC software architecture

- Live demonstration

04 – QUESTIONS/COMMENTS

3






4

- Develop and implement a flexible and operational web-based systems for data for organic and inorganic pesticides used in LCI and LCIA models and based on that develop spatial or archetypal emissions.

Starting point: PestLCI 2.0 => various consensual modifications


Dijkman, T.J., Birkved, M., Hauschild, M.Z. (2012). PestLCI 2.0: A second generation model for estimating emissions of pesticides from arable land in LCA. International Journal of Life Cycle Assessment 17(8): 973-986.

5

Starting point: PestLCI 2.0 => various consensual modifications


PestLCI 2.0 in analytica (required license)

Only possible to run one scenario

Drift curves

Different secondary processes needed adaptation

Volatilization, Degradation, Uptake

6


02 - FUNCTIONALITIES- Data requirements, outputs




7

FUNCTIONALITIES NEEDED

1. User Interface (UI)

Self-explanatory, efficient and user-friendly

2. File Download/Upload system

A FTP (File Transfer Protocol) is needed

3. Database Management

MySQL

4. Online calculation PestLCI Consensus model (including batch)

5. User management and roles

Log in, different levels - Registered user (single calculation) - Batch user (requires additional rights) - Administrator

6. Dashboard Save and manage the results

7. Automatic report Auto-generated .csv file containing the scenarios that have been calculated

02 – FUNCTIONALITIES I

8

02 – FUNCTIONALITIES II

Single or batch processing scenarios

Default data available for the users to run the model

Initial distribution with fewer mandatory user inputs

Secondary distribution with more detailed level of user inputs.

Mandatory user inputs

Optional user inputs

Model default parameters

9

02 – FUNCTIONALITIES III

Possible to include your own data (climate and soils)

Publicly available for everyone vs only for you

10






11

Environment

03 – WEB EMISSION MODEL

Windows. Operating system

Apache. Web server software

MySQL. Database structure implemented

PHP (programming language) is an acronym for "PHP: Hypertext Preprocessor"

a widely-used, open source scripting language

scripts are executed on the server

is free to download and use

12

Software Architecture. MVC model

1.Model is responsible for maintainingapplication data and logic.

2.View is a user interface of theapplication, which displays the data.

3.Controller handles user's requests andrenders appropriate View with Modeldata.


13

Type

Use

rin

pu

t

Crop crop

Substance active ingredient

Dose Applied exact value

Drift reduction exact type (0-100%)

Application method exact type

Buffer zone y/n + dimensions

Leaf area interception exact value (0-100%)

Soil type soil parameters

Climate climate parameters

Application time exact value

Field Area Width/Length field

Tool PestLCI Consensus

Model input data


14

Initial (primary)

pesticide distributionSecondary pesticide emission

Mo

de

l O

utp

ut

Air Air

Field soil surface Field soil surface

Field crop leaf surfaces Field crop leaf surfaces

Off-field surfaces Field crop leaf (via leaf uptake)

Groundwater

Off-field surfaces

Field soil and crop (via degradation)

Model output data


15


pestlciweb.man.dtu.dk

https://pestlciweb.man.dtu.dk/

16


Potentials

Possible to calculate with few inputs

Batch (input file .csv)

Possible for collaboration in GIThub (upon registration)

Drift reduction & spray buffer optional

Limitations

Currently not possible to edit all databases

Limitation in the number of batch scenarios ~ 1000 per run (on the

other hand it is possible to make as many runs as you want)

17


02 - FUNCTIONALITIES- Requirements, inspiration/ideas

03 – WEB EMISSION MODEL- MVC software architecture



1



- case studies: Wine grape production -

2

Case Study: D.O. Penedes, CataloniaGoal:

– Identify most relevant aspects in ecotoxicity assessment for pesticide use in viticulture

– make recommendations on the models and methodological characteristics for ecotoxicity assessment in LCA context

Scope:

– At farm gate, excluding logistics and further processing of grapes

– Crop field as part of the ecosphere

– Two scenarios: Organic: A, B, C ; Conventional: D, E, F

FU: - 1 ha

Emissions modelling:

– PEF (nutrients),

– Consensus (pesticides)

Impact categories:

– Human toxicity and freshwater ecotoxicity (USEtox 2.02 (recommended + interim)

3

Case Study: D.O. Penedes, CataloniaInventory

Pesticides (kg/ha) CAS RN A B C D E F

Bordeaux mixture 8011-63-0

0,25 BBCH 55

0,48 BBCH 60

0,34 BBCH 75

0,36 BBCH 79

1,4 BBCH 79

1,4 BBCH 81

0,40 BBCH 13

0,44 BBCH 53

0,54 BBCH 57

1,00 BBCH 79

0,34 BBCH 54

Copper oxychloride 1332-40-7

0,53 BBCH 54

0,68 BBCH 57

0,85 BBCH 76

0,95 BBCH 13

1,9 BBCH 540,41 BBCH 79 0,55 BBCH 73

Copper hydroxide 20427-59-2 0,41 BBCH 79

Tribasic copper sulphate 12527-76-3 0,07 BBCH 89

Cymoxanil 57966-95-70,05 BBCH 54

0,12 BBCH 55

Folpet 133-07-31 BBCH 54

1 BBCH 57

Fosetyl-Al 39148-24-8 1,50 BBCH 55

Mancozeb 8018-01-7 0,22 BBCH 570,75 BBCH 55

0,44 BBCH 73

Metalaxil 57837-19-10,15 BBCH 54

0,25 BBCH 57

Metrafenon 220899-03-6 0,11 BBCH 680,05 BBCH13

0,10 BBCH 53

Penconazole 66246-88-6 0,04 BBCH 57 0,02 BBCH 13

Sulphur 7704-34-9

31,5 BBCH 13

3,80 BBCH 55

4,20 BBCH 60

34,6 BBCH 75

42,4 BBCH 77

3,84 BBCH 79

0,80 BBCH 89

42,6 BBCH 54

4,6 BBCH 57

69,0 BBCH 65

29,6 BBCH 68

5,6 BBCH 74

69,0 BBCH 79

2,20 BBCH 13

3,60 BBCH 54

39,4 BBCH 55

39,4 BBCH 68

39,4 BBCH 73

4,8 BBCH 68

9,6 BBCH 81

29,6 BBCH 55

3,20 BBCH 57

24,6 BBCH 68

27,0 BBCH 79

2,40 BBCH 54

29,6 BBCH 68

34,5 BBCH 79

4

Case Study: D.O. Penedes, Catalonia

• Sensitivity analysis

⁻ Field size and wind direction

⁻ Dimension of buffer zones being part of crop field or not

⁻ The no inclusion of secondary distribution emissions for Copper wouldjustify to deal with primary distribution for the comparison of organicand conventional production systems. (However, we have used thewebtool to deal with secondary emissions and check differences)

⁻ The presence and grade of drift reduction equipment, and the effect oftheir drift curves

⁻ Other causes of toxicity: synthetic fertilisers, NPK, 8-12-24 with a doseof 300 kg/ha.

5

• Results I : emissions


Influence of crop stage at the moment of application

for crop and soil emission fractions.

Scenario C

a.i.

kg/haMonth

Growth

stageAir (%)

Off field

(%)

Crop

(%)

Soil

(%)

Copper oxychloride0,95 April I 12,50 2,14 25,61 59,75

1,9 May II 12,50 2,14 42,68 42,68

6

• Results II Impacts: Variability between scenarios > between practices

Organic: A, B, C

Conventional: D, E, F

Organic: A, B, C

Conventional: D, E, F


Influenceof other

processes in toxicity

7

Case study : Champagne viticultureGoals:

– Perform the toxicity assessment of 3 different vineyard pest management archetypes in the Champagne region

– Ecodesign perspective: ensure that actions known to mitigate dispersion have a significant influence on the model results.

Scope:– Assessment of plant protection archetypes in Champagne

Functional unit: Protecting 1 ha of vineyard over 1 year

Emissions modelling: PestLCI Consensus

Impact categories:– Human toxicity non cancer+ freshwater ecotoxicity impact

categories (USEtox 2.02)

Average of practices in champagne

Certified Viticulture Durable en Champagne (Sustainable

or integrated viticulture)

Certified organicviticulture

Average VDC Organic

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Ecodesign parameters for pesticides application

• Vineyard management:– Covercrop density

– Tilling/ploughing

– Leaves height and volume

– Buffer zones, hedges

• Weather Conditions :– Wind

– Temperature

– Rainfall

– Humidity

• Pesticide : (must be updatable)

– Dose

– Substance

• Sprayer : (must be updatable)

– Type of sprayer (drift), confined spraying

[Soil, slope : no action possible for us here, so not really parameters]

Case study : Champagne viticulture

9

• Sensitivity analysis

Case Study: Champagne

The assessed sprayers are the most commonly used in Champagne

Air-assisted sprayer side by side - flat fan nozzles

Non air-assisted sprayer side by side - Air Induction Hollow Cone Spray Tip

Buffer zone : in field Primary distribution Secondary distribution

Active ingredient Month BBCH Air (%) Soil (%)Crop (%)

Off field(%)

Air (%) Soil (%)Leaves

(%)Uptake

(%)Gw (%)

Off-field(%)

Degradation (%)

Potassium bicarbonate June 60 - 69 7,5% 44,7% 47,4% 0,4% 7,5% 44,0% 46,5% 0,0% 0 0,4% 1,6%

Rosemary oil June 60 - 69 7,5% 44,7% 47,4% 0,4% 46,7% 43,9% 0 8,7% 0 0,4% 0,3%

Ametoctradin May 11 - 19 7,5% 63,1% 29,0% 0,4% 7,5% 61,8% 0 29,0% 0 0,4% 1,3%

Cyazofamid June 60 - 69 7,5% 44,7% 47,4% 0,4% 7,5% 40,3% 2,3% 44,4% 0 0,4% 5,1%

Buffer zone : in field Primary distribution Secondary distribution

Active ingredient Month BBCH Air (%) Soil (%)Crop (%)

Off field (%)

Air (%) Soil (%)Leaves

(%)Uptake

(%)Gw (%)

Off-field(%)

Degradation (%)

Potassium bicarbonate June 60 - 69 0,6% 48,4% 50,9% 0,1% 0,6% 47,7% 50,0% 0,0% 0 0,1% 1,7%

Rosemary oil June 60 - 69 0,6% 48,4% 50,9% 0,1% 42,7% 47,6% 0 9,3% 0 0,1% 0,3%

Ametoctradin May 11 - 19 0,6% 68,3% 31,1% 0,1% 0,6% 66,9% 0 31,1% 0 0,1% 1,4%

Cyazofamid June 60 - 69 0,6% 48,4% 50,9% 0,1% 0,6% 43,7% 2,5% 47,7% 0 0,1% 5,5%

10

• Impact assessment of the 3 viticulture archetypes

Case Study: Champagne

1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+00

Non metals Metals Non metals Metals Non metals Metals

Average Champagne vine(CHP)

Organic Champagne vine(Organic)

Sustainable Champagne vine(VDC)

Log

10 (

case

s/h

a)

Midpoint human health (non-canc)

1.00E+00

1.00E+01

1.00E+02

1.00E+03

1.00E+04

1.00E+05

Non metals Metals Non metals Metals Non metals Metals

Average Champagne vine(CHP)

Organic Champagne vine(Organic)

Sustainable Champagnevine (VDC)

Log1

0

Midpoint ecotox (PAF.m3.day/ha)

11

Case study : Loire Valley Vineyards

Goal:

– test the pesticide toxicity assessment chain for ecodesign of vineyard management,

– compare with former PestLCI2.0 viticulture (Renaud-Gentié et al, 2012)

– make recommendations on the models and methods;

Scope:

– Impacts of pesticide emissions assessed to farm gate

Functional unit :

– 1ha of vineyard cultivated during one year

Emissions modelling :

– PestLCI Consensus + PestLCI 2.0 viticulture

Impact categories:– Human toxicity non cancer+ freshwater ecotoxicity impact categories

(USEtox 2.02)

12

TMR Main characteristics

TMR1 biodynamic production,

mechanical and manual weeding on rows and inter-rows

pneumatic sprayer

TMR1_ED Ecodesign :

+ Tunnel spraying, Vario technology, cover crop between vine rows

TMR2 Conventionnal production

50% mechanical weeding / 50% grass cover, herbicide on the vine

row

pneumatic sprayer for fungicides and insecticide + boomsprayer

for herbicides

TMR2_ED

XEcodesign :

+ Drift reduction system for treatments 1 and 2; CMR* products

removed; coupling weeding operations

Scenarios : contrasted real cases+ their ecodesigned version

Pneumatic (source IFV)

Tunnel sprayersource CA 17

Cette photo par Auteur inconnu est soumis à

la licence CC BY-SA

Anjou

Touraine


CMR = Carcinogenic, Mutagenic, Reprotoxic

Copper

sulphateSulfur

Glypho-

sate

Metiram Difeno-

conazole

Benalaxyl-

MFolpet

Dimethomorp

h

Dithianon

e

Indoxacar

bCymoxanil

Cyazofami

d

Potassium

phospho-

nate

Disodium

phospho-

nate

Zoxamide

nb Kg nb Kg nb Kg nb Kg nb Kg nb Kg nb Kg nb Kg nb Kg nb Kg nb Kg nb Kg nb Kg nb Kg nb Kg

TMR1 initial 7 2,6 4 11,2

TMR1 ED 8 1,8 4 6,7

TMR2 iniial 1 0,5 2 9,6 2 1,1 2 2,7 2 0,1 1 0,2 1 1,0 1 0,2 1 0,5 1 0,1 1 0,1

TMR2 ED 3 1,6 1 1,0 1 0,3 1 0,1 1 0,4 1 1,1 1 0,1

Inventory

https://commons.wikimedia.org/wiki/File:Cabernet_sauvignon_Viala_Vermorel.jpg

https://creativecommons.org/licenses/by-sa/3.0/

13

Sensitivity analysis for TMR2

Initial distibution secondary distribution PestLCI 2.0 viticulture

• Intial : effect of crop stage, sprayer

• Secondary : Degradation and uptake at 1 day far lower than PestLCI2.0

• Ecodesign : sprayer choice effect only, missing impact of cover crop


Ecodesigned

14

Impact assessment sensitivity analysis TMR2: accounting (field ecosphere) or not (field as technosphere) emissions to field soil

• High effect of ignoring emissions in the field soil => consider it

• No rain event in 1 day (↘≠ secondary distrib. with Initial distrib.)

• Variable differences with PestLCI2.0

• Substances without USEtox Characterization factors (sulphur, phosphonates),

• Effect of ecodesign by change of application method, dose, and active ingredient


0.0E+00

5.0E+00

1.0E+01

1.5E+01

2.0E+01

2.5E+01

3.0E+01

3.5E+01

Fres

hw

ater

eco

toxi

city

(C

TUe)

Non-metal Pesticide active ingredient

initial fieldtechnosphere

initial fieldecosphere

II distr. 1 day fieldtechnosphere

II distr. 1 day fieldecosphere

PestLCI2.0 fieldtechnosphere

0.0E+00

1.0E+06

2.0E+06

3.0E+06

4.0E+06

5.0E+06

6.0E+06

Fres

hw

ater

eco

toxi

city

(C

TUe)

Metal Pesticide active ingredient

Missing substances

Midpoint ecotoxicity

Primary /secondary distributionPestLCI2.0

15

Discussion/conclusions

Emissions:

• Sensistivity assessment : results concur with experimental data tendencies (drift reduction from different sprayers, presence of buffer zone, leaves interception at different vine stages)

• Important differences in emission fractions between PestLCI2.0 viticulture and PestLCI consensus, variable according to the pesticide active ingredient (P.A.I.).

– lower degradation, leaf uptake and lixiviation to ground water in PestLCIconsensus

• Air and off-field emission fractions are not affected by secondary emission processes included in the model.

• Importance of presence of buffer zone of at least 5 meters

• The relative low importance of water emissions in dry region, if buffer zones exist

• Primary distribution for organic viticulture or comparisons conventional/organic

Case Study: Wine grapes

16


Impact assessment:

• For human toxicity: not every substance is characterized, hence some substances can appear relatively high (here essential oil for organic viticulture)

• Not only P.A.I.s are contributing to toxicity (nutrients)

• Ignoring impacts of emissions to field soil (field as ecosphere vs technosphere) led to an average division by 3.5 of Human toxicity impacts and by 1.5 of freshwater ecotoxicity impacts. (variable according to the P.A.I.)

=> consider them


17


• Relevance of this assessment chain for assessment and ecodesign of viticulture practices:

– Initial distribution: effect of sprayer, buffer zone, crop stage or P.A.I. dose on air and/or off field emissions,

– secondary emissions: effect of environment (soil, climate), tillage and P.A.I. on soil, groundwater emissions or degradation, but impossible for Inorganic P.A.I.s.

– This is a significant improvement from the “100% to soil” approach.– final impact combines all effects of ecodesign decisions, incl. P.A.I.’s toxicity.

• The new model permitted to well define application methods and pesticides used in viticulture case studies thanks to an extensive database

• It allows the user to enter user defined climate and soil data and calculate in batches of data, very useful to quickly assess an entire sequence of pesticide applications.


18


• Future research needs for viticulture assessment and ecodesign:

– Vineyards soils grass or legume covers intercept pesticides in intial distribution and affect secondary distribution processes. The work of Gentil et al. (in prep.) should help for future inclusion in the model.

– Characterisation factors for sulphur,biosubstances or phosphonates

– More elements for quantification of known metabolites of degradation for inclusion in the assessment


=>if not: unbalanced comparisons between different vineyard managements either in conventional or, even more, in organic viticulture

19



- case studies: Wine grape production -

Thank you for your [email protected]

[email protected]@groupe-esa.com

mailto:[email protected]



1



- Tropical Case Study-

Céline Gentil-Sergent, Claudine Basset-Mens, Peter Fantke

2

Case study – Tomato – Martinique

• Martinique, volcanic island, tropical maritime climate

• Cradle-to-farm-gate LCA on tomato production in (green-house and) open-field system

• Functional unit = 1 kg of tomatoes harvested

• Production cycle = 3 months

• Sample: 6 farms

3


• Application method: knapsack sprayer

• Pesticide applications per production cycle

– 6 to 17 insecticides

– 2 to 9 fungicides

– 1 to 2 herbicides

• Generally compliance with

– approved pesticide doses

– pre-harvest interval

4

Model coupling

• Parametrization of PestLCI and dynamiCROP with local climatic and soil conditions

(Gentil et al 2020)

5

Emission results – initial distribution

• According to the pesticide target: pesticide emissions to field soil and field crop

(Gentil et al 2020)

6

Freshwater ecotoxicity with initial distribution

(Gentil et al 2020)

• Distinction of organic and metal-based substances, ≠ order of magnitude

• Pesticide field emissions higher contributor to freshwate ecotoxicity for organic substances

7

Human toxicity with initial distribution

• Human non-cancer toxicitymainly from residue-based exposure

(Gentil et al 2020)

8

Case study conclusions

• Major impact of pesticide residues in crops on human non-cancer toxicity

• Freshwater ecotoxicity impacts dominated by pesticides ( organic substances)

• Copper sulfate dominated freshwater ecotoxicity from pesticide field emissions

• Respect of homologated doses, pre-harvest interval and consequently the RMLs

• Overall, low (eco-)toxicity impacts compared to other tomato production systems (from

Morocco, Rwanda, Benin)

For further reading: Gentil et al (2020) Coupling pesticide emission and toxicity characterization models for LCA: Application to open-field tomato production in Martinique. J. Clean. Prod.



9

Methodological conclusions

• Consistent coupling of LCI/LCIA models with initial distribution

• Inclusion in LCA of human toxicity from pesticide residues in crops ingestion

• Distinction of organic and metal-based substances

• Underestimation of freshwater ecotoxicity when assuming 100% of applied pesticide

emitted to agricultural soil

• Initial distribution for background system (and foreground with the consideration of

application method and its drift, and crop characteristics)

• Secondary emission fractions farm stage part of the foreground system and purpose to

discriminate pest management practices, climate and soil characteristics

• Future required development LCI/LCIA model coupling with secondary emission

fractions

10

Tropical Case Study

1



- Case study on arable crops -

Thomas Nemecek, Sebastian Röthlin, Agroscope

2

Crops and scenarios investigated• 5 crops:

– Winter oilseed rape (OSR)

– Winter wheat (WW)

– Carrots (CAR)

– Potatoes (POT)

– Sugar beets (SB)• Treatments scenarios (for each crop):

• MEAN: ‘Typical’ (i.e. common) spraying sequence based on PEP (Proof of Environmental Performance = standard in Switzerland). Using average number of interventions per pesticide group (e.g. herbicides) and the most commonly used active ingredients from the Central Evaluation of Agri-Environmental Indicators (AEI data) for 2009-2014.

• HIGH: Reflects PEP farming under high pest, disease or weed pressure (75th

percentile from AEI data).

• LOW: Derived from MEAN, adapted for the crop in question according to the IPS guidelines by implementing bans and restrictions.

3

Case study for Swiss cropsFreshwater ecotoxicity impacts

4

Case study for Swiss cropsFreshwater ecotoxicity: impact pathways

5

Case study for Swiss cropsHuman toxicity impacts

6

Case study for Swiss cropsHuman toxicity: impact pathways

7

Sensitivity analysis:Secondary emissions vs. primary distribution

• Freshwater ecotoxicity: deviations of max. 1/3, mostly increasing due to emissions to the air

• Human toxicity: decreases of up to -11%, due to degradation

-20% -10% 0% 10% 20% 30% 40%

Oilseed rape-LOWWinter wheat-LOW

Carrots-LOWPotatoes-LOW

Sugar beet-LOW

Oilseed rape-MEANWinter wheat-MEAN

Carrots-MEANPotatoes-MEAN

Sugar beet-MEAN

Oilseed rape-HIGHWinter wheat-HIGH

Carrots-HIGHPotatoes-HIGH

Sugar beet-HIGH

Human tox. Aq. ecotox.

8

Sensitivity analysis: soil and climate

-15% -10% -5% 0% 5% 10%

Oilseed rape-LOW

Winter wheat-LOW

Carrots-LOW

Potatoes-LOW

Sugar beet-LOW

Oilseed rape-MEAN

Winter wheat-MEAN

Carrots-MEAN

Potatoes-MEAN

Sugar beet-MEAN

Oilseed rape-HIGH

Winter wheat-HIGH

Carrots-HIGH

Potatoes-HIGH

Sugar beet-HIGH

Heavy vs. medium soil Light vs. medium soil

-6% -4% -2% 0% 2%

Climate 3 vs. 2 Climate 1 vs. 2

Freshwater ecotoxicity: deviations due to climate and soil are small

9

Case study on Swiss arable crops:Lessons learnt

• Few active ingredients dominate the impacts

• Impacts pathways:

– Freshwater ecotoxicity: soil > air > water

– Human toxicity: soil > air. Pesticide residues on the harvested products important for grain crops (wheat and rape seed), not for roots and tubers

• Primary distribution and secondary emissions lead to similar results

• Soil and climates had relatively small effects

1


Learnings from the case studies,

interpretation of toxicity results, potentials,

limits and sensitivity of the models

Claudine Basset-Mens, Assumpcio Anton, Céline Gentil, Thomas Nemecek, Pierre Naviaux, Christel Renaud-Gentié, Peter Fantke

2

What have the case studies taught us: LCA practitioners, stakeholders, decision-makers?

1. What systems could we compare? main conclusions for studied systems?

2. To which parameters the models and impact results are most sensitive?

3. What are the main remaining challenges for a better use of models in LCA studies?

4. How easy is the use of the models?

3

Can we compare organic versus conventional production systems?

Org / conv.: 3 case studies on vineyards finalised a comparative assessment• Inclusion of non-pest management inputs (fertilizers, irrigation,

field operations…)• Copper-based fungicides included at primary distribution level in

PestLCI Consensus and characterised in USEtox greatcontribution to impacts

• No accounting of sulphur although used in great amounts norphosphonates no toxicity? Not organic pesticides metabolites..

• Accounting of certain essential oils but not of bacteria

Comparison of organic versus conventional systems has improved but some margins for further improvement remain

4

Can we use models for eco-design, and comparison of different pest manag. strategies?Eco-design: Swiss, Champagne and Loire Valley case studies

• Finalised assessments of different pest management strategies, including key ecodesignparameters like choice of different active ingredients, doses, application methods and drift reduction technologies, mechanical weeding…

• The models allowed us to consider those parameters, it was then necessary to assess theirinfluence on results.

Reached realistic conclusions.

• Pesticide management intensity in Swiss crops: low < mean < high

• Archetypes for Champagne vineyards: average conventional > « sustainable » label > organic

• Loire Valley vineyard scenarios: clear benefits of eco-designed scenarios compared to baseline scenarios

Need to finalise inclusion of ground cover management (GCM) module in webtool for all users

5

Can we use available models in tropical conditions?

Validity of models in tropical conditions: tomato case study in FWI

• Great advances (InnovACV project)• First identified important margins of progress Gentil et al. (2020a)• Correction of field capacity equation, • Inclusion of tropical soil and climate conditions• Development of specific crop interception and drift curves emission fractions for a

panel of tropical crops• Development of GCM module also important in temperate cropping systems

• Allowed to reach interesting conclusions on moderate (eco)-tox impacts of open-field tomato in Martinique (Gentil et al., 2020b)

Today the model is more applicable to tropical conditions

Need more research and measurements on drift in tropical conditions

Need to finalise inclusion of ground cover module and rain-related processes in PestLCI webtool

6

Can we account for the impacts frompesticide residues in the crop?

Modelling of exposure route from pesticide residues in crop/food

• Was done successfully in tomato in FWI and Swiss crop case studies

• Highly variable results:

• Tomato: main contributor for human toxicity non-cancer

• Swiss crops: important only for above-ground plant organs: wheat and

rape seed

Yes we can!

Need to develop more crop archetypes (e.g. banana, grape)

Pathway from crop to human (cooking, transforming, feed, winemaking...)

7

SENSITIVITY ASSESSMENT: Hierarchy of parametersand methodological choices influencing emissionfractions and impacts results

Methodological choices

• Field status (techno / ecosphere)?

• Primary versus secondarydistribution?

• Simulation time for PestLCI for secondary distribution?

Field environment and practices

• Crop and crop growth stage

• Active ingredient

• Application methods and drift reduction techniques

• Field size, wind direction, buffer zone

• Soil and climate conditions

8

Hierarchy of parameters and methodological choicesinfluencing emission fractions and impacts results

Methodological choices

• Emissions to soil

• Primary versus secondarydistribution?

• Simulation time for PestLCI

Great influence on results

should be considered for all impact pathways and all

toxicity cat. (freshwater & terrestrial ecotox, human tox)

At impact level: differences are moderate

We recommend using primary distribution by default

and test sensitivity to the use of secondary distribution

Influence on degradation: depends on DT50 of a.i.;

Volatilisation: less important in Consensus version than

in previous ones

Rain-related processes: washoff, leaching, runoff not

properly accounted for

9

Hierarchy of parameters and methodological choicesinfluencing emission fractions and impacts results

Effect of… on… Primarydistrib°

Secondarydistrib°

Impacts (primary

distr°)

Impacts (secondary

distr°)

Crop and crop growthstage

+++ +++ + +

Active ingredient 0 + +++ +++

Dose + +

Application methods and drift reduction techniques

+++ +++ + +

Field size, buffer zone + + + +

Soil and climate conditions 0 + 0 +

Field environment and practices

10

Main remaining challenges for a better use of models in LCA studies

• Stabilise clear recommendations on methodological choicesfor users in PestLCI Consensus model

• Finalising the Ground Cover Management module up to the secondary distribution and then inclusion of cropsassociations

• Better inclusion of metal-based pesticides, bio-pesticides, metabolites, and sulphur

11

How easy is (was) the use of the models?

• It proved possible but still resource intensive.

• Great advances during the project for estimating pesticide emissionsat field level creation of PestLCI Consensus Webtool for universalaccess and updates; batch version of Webtool for PestLCI Consensus; addition of missing CFs for many pesticide active ingredients

• Table of emission fractions for a panel of typical crops

• Clear coupling of all 3 models: PestLCI / dynamiCROP / USEtox (Gentil et al., 2020b)

12

Thank you for your attention!


Outlook and need for future research

I. Synthesis and state of the art

II. Prospects as a direct extension of OLCA-Pest project

III. Unresolved remaining issues for future research


2

LCA practitioners of agrifood systems and food products

probably more than 90% of users of agricultural LCAs

As already said,

there are 2 main types of agricultural LCAs practitioners

3

A

Agronomists

B

For them, pesticide is only 1 component of the first step of food life cycle!

LCA goal = eco-labelling, eco-design of processed products, benchmarking, lifestyle LCA, etc.

Agriculture = background activity incl. processing, logistics, packaging and cooking

They only need to differentiate archetypes of food productions:• Average pesticide application practices• Average pedoclimatic conditions (site generic or site dependant is enough for them)

4

ALCA practitioners of agrifood systems & food products

LCA goal = diagnosis and eco-design of new agricultural practices

Agronomists using LCA

They need to differentiate in detail agricultural practices related to pesticide application for specific pedoclimatic conditions (site specific)

5

B

OLCA-Pest challenge was to provide useful models and data that fulfil these two users needs


Dose (kg)/hectare

LCIALCI

Distribution within the environment (Emission fractions)

Resulting potential impacts (Characterisation factors)

Pesticide in LCA: Current situation

Human toxicity


Human toxicity

• Fate

• Exposure• Effects

Aquatic ecotoxicity

WaterSoil

Air

Glasgow Consensus*provides general recommendations

but no operational guidance and tools

*Rosenbaum et al., 2015. The Glasgow consensus on the delineation between pesticide emission inventory and impact assessment for LCA. Int. J. Life Cycle Assess. 20, 765–776. https://doi.org/10.1007/s11367-015-0871-1

Inventory (LCI)

6

Embedded in LCIA models & LCA software as characterisation factors

??? No consensual emission

models available

100 % Soil

In most of the cases (Ecoinvent, etc.)*

?

*Agri-Footprint database proposed arbitrary emission factors: 90% Soil, agriculture, 1% water, lake river (surface), 9% air

https://doi.org/10.1007/s11367-015-0871-1


Dose (kg)/hectare

PestLCI Consensus

% Water

% Soil

% Air

% Crop

LCIALCI

Pedo-climatic conditions

Technologies and practices

Distribution within the environment

(Emission fractions)

Resulting potential impacts (Characterisation factors)

New pesticides characterised in USEtox (input data and CFs)

Consistent coupling between dynamiCROP and PestLCI Consensus for crop residue-related

human toxicity

Guidance for different levels of expertise (model coupling, interpretation, etc.)

Operationalisation of Glasgow Consensus

New emission model with adjustable parameters (practices, pedoclimatic, etc.) for

advanced practitioners

Averaged emissions fractions for most common situations needed by LCA

practitioners and LCI databases

Improvements from OLCA-Pest

Human toxicity• Fate

• Exposure• Effects

Aquatic ecotoxicity

Human toxicity


WaterSoil

Air

7





8

Remaining OLCA-Pest tasks

– Implementation by database developers of default emissions fractions in LCI (Ecoinvent, Agribalyse, etc.)

– When using secondary emissions: identify and quantify any remaining inconsistencies in emission processes and alignment with LCIA models

Prospects as a direct extension of OLCA-Pest project Improving or updating crop protection technologies and/or practices

• Taking into account any new generation of sprayers with lower emissions such as "adaptive sprayers"

• Implementing new models or parameters to better discriminate good practices from bad ones worldwide.

• Better integrate the pre- and post-treatment phases

• Effect of various parameters on pesticide diffusion: Soil compaction, cover crops, Others ?

• Pre-post harvest treatments, seeds treatments ?

9





10

1. Integration of the effect on health of pesticide residues on food in common LCAs (today dynamiCROP is available for expert practitioners and not included in LCA software)

2. Emissions and toxicity of inorganic compounds (metals, sulphur, etc.)

3. The issue of formulation (adjuvants and surfactants, nanoparticles, etc.) in link with data availability

4. The issue of metabolites in link with data availability

5. Accounting for biological pest control technologies (all methods of plant protection using natural mechanism ): Organic "natural" compounds, new upcoming application technologies, dissemination and effects of "natural enemies" …

6. Improving the integration of human exposure to pesticides: exposure of bystanders and agricultural workers during application and also during pre-post treatment phases.

7. Integration of latest advances on:

• Terrestrial ecotoxicity

• the effects of pesticides on pollinating insects …

8. Veterinary chemicals? Transgenic crops producing "pesticides" ?, other issues ?

11

OLCA-Pest provides a great step forward for pesticide

consideration in LCAs

These results, based on Glasgow consensus, should be useful for the

two types of users : LCA of agrifood and agronomists.

There are still research questions to be resolved to further improve

this consideration, but many limitations are not related to the

development of LCA, but to the limits of knowledge altogether (ex.

Fate & effects of metabolites, pesticides adjuvants composition,

cocktail effects, data availability for terrestrial ecotoxicity, etc.).

12

Open discussion/questions

Thank you for your attention

13

OLCA-Pest Workshop Slides - DTU

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