e-ISSN: 1614-7502

Project code: Grant agreement No. 713679

Keywords: Unfccc Salca ipcc tiers Pef Nitrous oxide Nitrate leaching Ipcc tiers Daisy Animo Ammonia volatilization

Record's date: 2023-02-19

Papper version: info:eu-repo/semantics/publishedVersion

Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/

Papper original source: International Journal Of Life Cycle Assessment. 26 (2): 371-387

APA: Andrade EP; Bonmati A; Esteller LJ; Montemayor E; Vallejo AA (2021). Performance and environmental accounting of nutrient cycling models to estimate nitrogen emissions in agriculture and their sensitivity in life cycle assessment. International Journal Of Life Cycle Assessment, 26(2), 371-387. DOI: 10.1007/s11367-021-01867-4

Acronym: MFP

Publication Type: Journal Publications

Author, as appears in the article.: Andrade EP; Bonmati A; Esteller LJ; Montemayor E; Vallejo AA

Department: Enginyeria Química

URV's Author/s: Jiménez Esteller, Laureano / PEREIRA ANDRADE, EDILENE

Abstract: © 2021, The Author(s). Purpose: Several models are available in the literature to estimate agricultural emissions. From life cycle assessment (LCA) perspective, there is no standardized procedure for estimating emissions of nitrogen or other nutrients. This article aims to compare four agricultural models (PEF, SALCA, Daisy and Animo) with different complexity levels and test their suitability and sensitivity in LCA. Methods: Required input data, obtained outputs, and main characteristics of the models are presented. Then, the performance of the models was evaluated according to their potential feasibility to be used in estimating nitrogen emissions in LCA using an adapted version of the criteria proposed by the United Nations Framework Convention on Climate Change (UNFCCC), and other relevant studies, to judge their suitability in LCA. Finally, nitrogen emissions from a case study of irrigated maize in Spain were estimated using the selected models and were tested in a full LCA to characterize the impacts. Results and discussion: According to the set of criteria, the models scored, from best to worst: Daisy (77%), SALCA (74%), Animo (72%) and PEF (70%), being Daisy the most suitable model to LCA framework. Regarding the case study, the estimated emissions agreed to literature data for the irrigated corn crop in Spain and the Mediterranean, except N2O emissions. The impact characterization showed differences of up to 56% for the most relevant impact categories when considering nitrogen emissions. Additionally, an overview of the models used to estimate nitrogen emissions in LCA studies showed that many models have been used, but not always in a suitable or justified manner. Conclusions: Although mechanistic models are more laborious, mainly due to the amount of input data required, this study shows that Daisy could be a suitable model to estimate emissions when fertilizer application is relevant for the environmental study. In addition, and due to LCA urgently needing a solid methodology to estimate nitrogen emissions, mechanistic models such as Daisy could be used to estimate default values for different archetype scenarios.

Thematic Areas: Materiais Interdisciplinar General environmental science Farmacia Environmental sciences Environmental science (miscellaneous) Environmental science (all) Ensino Engineering, environmental Engenharias iii Engenharias ii Engenharias i Economia Ciências ambientais Ciências agrárias i Ciência de alimentos Biotecnología Administração pública e de empresas, ciências contábeis e turismo

licence for use: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 0948-3349

Author's mail: laureano.jimenez@urv.cat edilene.pereira@urv.cat

Author identifier: 0000-0002-3186-7235 0000-0001-8910-4911

Link to the original source: https://link.springer.com/article/10.1007%2Fs11367-021-01867-4

Funding program: Marie Skłodowska-Curie Actions - European Union's Horizon 2020 research and innovation programme

Article's DOI: 10.1007/s11367-021-01867-4

Entity: Universitat Rovira i Virgili

Journal publication year: 2021

Funding program action: Martí i Franquès COFUND Doctoral Programme