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The International Journal of Life Cycle Assessment

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22.01.2016 | LCA FOR AGRICULTURE

Data strategy for environmental assessment of agricultural regions via LCA: case study of a French catchment

verfasst von: Angel Avadí, Laure Nitschelm, Michael Corson, Françoise Vertès

Erschienen in: The International Journal of Life Cycle Assessment | Ausgabe 4/2016

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Abstract

Purpose

Various approaches have been carried out to extrapolate environmental assessments of farms to the regional level, some of them oversimplified and thus leading to high uncertainty. Key challenges include selection of a representative sample, construction of a farm/land use typology, the extrapolation strategy and dealing with data limitations. This work proposes a method for addressing these issues by means of statistically supported approaches.

Methods

We applied a novel approach combining a sampling strategy, estimation of farm-level environmental impacts via life cycle assessment (LCA), a farm typology based on principal component analysis, a statistical method for extending the farm sample given data constraints and finally linear extrapolation based on regional production and land use, taking into account the regional import–export balance. The approach was applied to a French case study, the Lieue de Grève catchment in the dairy-intensive Brittany region. A decision flowchart was developed to generalise the approach for similar applications dealing with farm and LCA data constraints. Additionally, innovative farm practices were modelled and their impacts propagated to the regional level.

Results and discussion

The typology developed identified “dairy”, “beef”, “dairy + beef” and “swine” farms as the dominant farm types in the region. While swine farms had the highest mean impacts per hectare, dairy and dairy + beef farms had impacts two to five times as high as those of beef and swine farms, when extrapolated to the entire catchment. Multiple linear regressions based on an extended farm and LCA dataset were used to predict environmental impacts of dairy farms lacking LCA results, thus increasing their sample size before extrapolation. The inclusion of farm and LCA data from a neighbouring region did not contribute to the accuracy of predicted impacts, as determined by comparing them to those of the farm closest to the dairy cluster’s centre, but rather produced significantly larger coefficients of variation. Results of tests of including two extra-regional farm and LCA datasets helped determine decision rules for the decision flowchart. Modelling of innovative agricultural practices yielded regional impacts consistent with previous estimates.

Conclusions

This approach provides a generalisable approach for farm typologies, data handling and regional extrapolation of farm-level LCAs, applicable to estimate environmental impacts of any agricultural area if requirements of a representative farm sample are met. We demonstrate the utility of the method for estimating effects of innovative agricultural practices on a region’s impacts by modelling practices on virtual farms and extrapolating their results.

Vorheriger Artikel An environmental comparison of biocomposite film based on orange peel-derived pectin jelly-corn starch and LDPE film: LCA and biodegradability

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Minimum sample size = (Z-score)² × standard deviation × (1 − standard deviation) / (margin of error)² = (2.326)² × 0.5 × (1 − 0.5) / (0.25)² = 21.6

Previous typologies developed for the Lieue de Grève (e.g. E. Doussal, INRA UMR SAS, pers. comm.; Gascuel et al. (2015); Mabon (2008)) were not used because they were too detailed for extrapolating farm results (and would have reduced sample size per cluster) or were not reproducible due to data-access constraints.

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Titel: Data strategy for environmental assessment of agricultural regions via LCA: case study of a French catchment
verfasst von: Angel Avadí
Laure Nitschelm
Michael Corson
Françoise Vertès
Publikationsdatum: 22.01.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: The International Journal of Life Cycle Assessment / Ausgabe 4/2016
Print ISSN: 0948-3349
Elektronische ISSN: 1614-7502
DOI: https://doi.org/10.1007/s11367-016-1036-6

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Abstract

Purpose

Methods

Results and discussion

Conclusions

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