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  Quantifying sustainable intensification of agriculture: the contribution of metrics and modelling

Mouratiadou, I., Latka, C., van der Hilst, F., Müller, C., Berges, R., Bodirsky, B. L., Ewert, F., Faye, B., Heckelei, T., Hoffmann, M., Lehtonen, H., Lorite, I. J., Nendel, C., Palosuo, T., Rodríguez, A., Rötter, R. P., Ruiz-Ramos, M., Stella, T., Webber, H., Wicke, B. (2021): Quantifying sustainable intensification of agriculture: the contribution of metrics and modelling. - Ecological Indicators, 129, 107870.
https://doi.org/10.1016/j.ecolind.2021.107870

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_25692 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_25692_2
Genre: Journal Article

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 Creators:
Mouratiadou, Ioanna1, Author
Latka, Catharina1, Author
van der Hilst, Floor1, Author
Müller, Christoph2, Author              
Berges, Regine1, Author
Bodirsky, Benjamin Leon2, Author              
Ewert, Frank1, Author
Faye, Babacar1, Author
Heckelei, Thomas1, Author
Hoffmann, Munir1, Author
Lehtonen, Heikki1, Author
Lorite, Ignacio Jesus1, Author
Nendel, Claas1, Author
Palosuo, Taru1, Author
Rodríguez, Alfredo1, Author
Rötter, Reimund Paul1, Author
Ruiz-Ramos, Margarita1, Author
Stella, Tommaso1, Author
Webber, Heidi1, Author
Wicke, Birka1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Sustainable intensification (SI) of agriculture is a promising strategy for boosting the capacity of the agricultural sector to meet the growing demands for food and non-food products and services in a sustainable manner. Assessing and quantifying the options for SI remains a challenge due to its multiple dimensions and potential associated trade-offs. We contribute to overcoming this challenge by proposing an approach for the ex-ante evaluation of SI options and trade-offs to facilitate decision making in relation to SI. This approach is based on the utilization of a newly developed SI metrics framework (SIMeF) combined with agricultural systems modelling. We present SIMeF and its operationalization approach with modelling and evaluate the approach’s feasibility by assessing to what extent the SIMeF metrics can be quantified by representative agricultural systems models. SIMeF is based on the integration of academic and policy indicator frameworks, expert opinions, as well as the Sustainable Development Goals. Structured along seven SI domains and consisting of 37 themes, 142 sub-themes and 1128 metrics, it offers a holistic, generic, and policy-relevant dashboard for selecting the SI metrics to be quantified for the assessment of SI options in diverse contexts. The use of SIMeF with agricultural systems modelling allows the ex-ante assessment of SI options with respect to their productivity, resource use efficiency, environmental sustainability and, to a large extent, economic sustainability. However, we identify limitations to the use of modelling to represent several SI aspects related to social sustainability, certain ecological functions, the multi-functionality of agriculture, the management of losses and waste, and security and resilience. We suggest advancements in agricultural systems models and greater interdisciplinary and transdisciplinary integration to improve the ability to quantify SI metrics and to assess trade-offs across the various dimensions of SI.

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Language(s): eng - English
 Dates: 2021-06-162021-06-192021-10
 Publication Status: Finally published
 Pages: 16
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: MDB-ID: No data to archive
PIKDOMAIN: RD2 - Climate Resilience
Organisational keyword: RD2 - Climate Resilience
Working Group: Land Use and Resilience
Research topic keyword: Food & Agriculture
Research topic keyword: Sustainable Development
Research topic keyword: Land use
Regional keyword: Global
Model / method: LPJmL
Model / method: MAgPIE
OATYPE: Gold Open Access
DOI: 10.1016/j.ecolind.2021.107870
 Degree: -

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Title: Ecological Indicators
Source Genre: Journal, SCI, Scopus, p3
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Publ. Info: -
Pages: - Volume / Issue: 129 Sequence Number: 107870 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/140811
Publisher: Elsevier