Coupling pesticide emission and toxicity characterization models for LCA: Application to open-field tomato production in Martinique

Gentil, Céline; Basset-Mens, Claudine; Manteaux, Sarah; Mottes, Charles; Maillard, Emmanuel; Biard, Yannick; Fantke, Peter

doi:10.1016/j.jclepro.2020.124099

Cited by 34 publications

(26 citation statements)

References 45 publications

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“…In relation to the limits, also ecosystem services associated with intercropping, content of soil organic matter (SOM), and benefits of carbon sequestration are not implemented in the life cycle impact assessment method. There is also a need to overcome currently prevailing assumptions for pesticide emissions (leading to overestimation of freshwater ecotoxicity when considering field soil part of the ecosphere) and to consider pesticide residues in crops as a contributor to human toxicity, which is currently mostly missing in LCA studies (leading to underestimation of human toxicity impacts) [41].…”

Section: Discussionmentioning

confidence: 99%

Sustainability Estimation of Oat:Pea Intercrops from the Agricultural Life Cycle Assessment Perspective

et al. 2021

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Winter cereal:legume intercropping is considered a sustainable arable farming system not only in temperate regions but also in Mediterranean environments. Previous studies have shown that with suitable crop stand composition, high grain yield can be achieved. In this study, a life cycle assessment (LCA) of the influence of sowing ratio and nitrogen (N) fertilization on grain nitrogen yield of oat (Avena sativa L.) and pea (Pisum sativum L.) in intercrops was performed to find the optimal design to achieve low environmental impact. This study compared the environmental impact of oat:pea intercrops using agricultural LCA. Monocrops of oat and pea and substitutive intercrops, which were fertilized with different levels of N, were compared. The system boundaries included all the processes from cradle to farm gate. Mass-based (grain N yield) and area-based (land demand for generating the same grain N yield) functional units were used. The results covered the impact categories related to the agricultural LCAs. The ReCiPe 2016 Midpoint and Endpoint characterization model was used for the data expression. According to the results, an unfertilized combination of oat and pea (50%:50%) had the lowest environmental impact in comparison with the other 14 assessed variants and selected impact categories. In the assessed framework, pea monocrops or intensively fertilized oat monocrops can also be considered as alternatives with relatively low impact on the environment. However, an appropriate grain N yield must be reached to balance the environmental impact resulting from the fertilizer inputs. The production and use of fertilizers had the greatest impact on the environment within the impact categories climate change, eutrophication, and ecotoxicity. The results indicated that high fertilizer inputs did not necessarily cause the highest environmental impact. In this respect, the achieved grain N yield level, the choice of allocation approach, the functional unit, and the data expression approach played dominant roles.

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Section: Discussionmentioning

confidence: 99%

Sustainability Estimation of Oat:Pea Intercrops from the Agricultural Life Cycle Assessment Perspective

et al. 2021

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“…ELDAM has been used in various research projects ((Gentil et al, 2020), (Risch et al, 2020)), helping to improve and characterize LCI data quality. Its review procedure has proven very helpful for auditing the LCIs produced by LCA beginners such as interns.…”

Section: Statement Of Needmentioning

confidence: 99%

ELDAM: A Python software for Life Cycle Inventory data management

Coste¹,

Biard²,

Roux³

et al. 2021

JOSS

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Life Cycle Inventory (LCI) data are the backbone of Life Cycle Analysis (LCA). They reflect modelling choices made by the LCA practitioner that can strongly influence the results of environmental impact calculations. The high sensitivity of these data are the reason why they must be accurately documented and tend to the highest possible quality. High quality, well documented LCI data can also be shared between LCA practitioners, allowing productivity gains and better research reproducibility, in particular between practitioners who do not work on the same life cycle inventory database. Unfortunately, SimaPro (PRé Sustainability B.V., 2020), one of the most used LCA software solutions, has only limited features to document, review and exchange LCI data. ELDAM has been developed to fill this gap.

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“…dk). Combining it with the dynamiCROP plant uptake model for human exposure and toxicity characterisation with special focus on pesticide residues in food crops (Fantke et al 2011a, b;Gentil et al 2020a), and the USEtox scientific consensus model for human toxicity and ecotoxicity characterisation (Rosenbaum et al 2008;Fantke et al 2021), we propose a solution for the integration of pesticide emissions into LCI databases, in order to provide a consistent emission and impact modelling for pesticides in LCA as shown in the bottom part of Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Operationalising emission and toxicity modelling of pesticides in LCA: the OLCA-Pest project contribution

Nemecek

Antón

Basset-Mens

et al. 2022

Int J Life Cycle Assess

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Purpose Current field emission modelling and toxicity characterisation of pesticides suffer from several shortcomings like mismatches between LCI databases and LCIA methods, missing characterisation factors, missing environmental compartments, and environmental impact pathways. The OLCA-Pest project was implemented to address these aspects and to operationalise the assessment of pesticides in LCA. Based on this effort, we propose an approach to integrate pesticide emissions into LCI databases. Methods The PestLCI Consensus Model has been developed in order to estimate emission fractions to different environmental compartments. The initial distribution fractions should be linked to the compartments air, agricultural soil, natural soil, and freshwater. Emissions to off-field surfaces are hereby distributed between agricultural soil, natural soil, and freshwater by using surface cover data. Deposition on the crop surface should be recorded in an emission compartment crop with 13 sub-compartments for crop archetypes for both food and non-food uses. Default emission fractions are provided to calculate the emission fractions for different pesticide application scenarios. Results and discussion A sensitivity analysis shows the effects of the application technique, drift reduction, crop and development stage, field width, and buffer zone on the initial distribution fractions of field-applied pesticides. Recommendations are given for the implementation of a set of default initial distribution fractions into LCI databases, for the organisation of metadata, and for the modelling of pesticide residues in food along the supply chain (processing, storage). Priorities for further research are: improving the modelling of pesticide secondary emissions, further extending emission modeling (e.g. additional application techniques, including cover crops), considering metal-based pesticides in emission models, and systematically assessing human health impacts associated with pesticide residues in food crops. Conclusions The proposed approach allows to preserve the mass balance of the pesticide emitted after application, to make a consistent assessment of ecotoxicity and human toxicity, to define a clear and consistent interface between the LCI and LCIA phases, to estimate initial emission distribution fractions based on existing data, to document metadata transparently and efficiently within crop datasets, and to model the removal of pesticide residues in food during processing.

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Coupling pesticide emission and toxicity characterization models for LCA: Application to open-field tomato production in Martinique

Cited by 34 publications

References 45 publications

Sustainability Estimation of Oat:Pea Intercrops from the Agricultural Life Cycle Assessment Perspective

Sustainability Estimation of Oat:Pea Intercrops from the Agricultural Life Cycle Assessment Perspective

ELDAM: A Python software for Life Cycle Inventory data management

Operationalising emission and toxicity modelling of pesticides in LCA: the OLCA-Pest project contribution

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