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Improvement of Agricultural Life Cycle Assessment Studies through Spatial Differentiation and New Impact Categories: Case Study on Greenhouse Tomato Production

Author:
Antón, Assumpció, Torrellas, Marta, Núñez, Montserrat, Sevigné, Eva, Amores, Maria José, Muñoz, Pere, Montero, Juan I.
Source:
Environmental Science & Technology 2014 v.48 no.16 pp. 9454-9462
ISSN:
1520-5851
Subject:
biodiversity, case studies, crop models, crop production, emissions, environmental assessment, fertilizers, greenhouses, humans, inventories, land use, life cycle assessment, pesticide application, pesticide residues, pesticides, toxicity, wetlands
Abstract:
This paper presents the inclusion of new, relevant impact categories for agriculture life cycle assessments. We performed a specific case study with a focus on the applicability of spatially explicit characterization factors. The main goals were to provide a detailed evaluation of these new impact category methods, compare the results with commonly used methods (ReCiPe and USEtox) and demonstrate how these new methods can help improve environmental assessment in agriculture. As an overall conclusion, the newly developed impact categories helped fill the most important gaps related to land use, water consumption, pesticide toxicity, and nontoxic emissions linked to fertilizer use. We also found that including biodiversity damage due to land use and the effect of water consumption on wetlands represented a scientific advance toward more realistic environmental assessment of agricultural practices. Likewise, the dynamic crop model for assessing human toxicity from pesticide residue in food can lead to better practice in pesticide application. In further life cycle assessment (LCA) method developments, common end point units and normalization units should be agreed upon to make it possible to compare different impacts and methods. In addition, the application of site-specific characterization factors allowed us to be more accurate regarding inventory data and to identify precisely where background flows acquire high relevance.
Agid:
5349140