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Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes

Agricultural landscapes provide many functions simultaneously including food production, regulation of water and regulation of greenhouse gases. Thus, it is challenging to make land management decisions, particularly transformative changes, that improve on one function without unintended consequence...

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Autores principales: Todman, Lindsay C., Coleman, Kevin, Milne, Alice E., Gil, Juliana D.B., Reidsma, Pytrik, Schwoob, Marie-Hélène, Treyer, Sébastien, Whitmore, Andrew P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6692559/
https://www.ncbi.nlm.nih.gov/pubmed/31212161
http://dx.doi.org/10.1016/j.scitotenv.2019.06.070
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author Todman, Lindsay C.
Coleman, Kevin
Milne, Alice E.
Gil, Juliana D.B.
Reidsma, Pytrik
Schwoob, Marie-Hélène
Treyer, Sébastien
Whitmore, Andrew P.
author_facet Todman, Lindsay C.
Coleman, Kevin
Milne, Alice E.
Gil, Juliana D.B.
Reidsma, Pytrik
Schwoob, Marie-Hélène
Treyer, Sébastien
Whitmore, Andrew P.
author_sort Todman, Lindsay C.
collection PubMed
description Agricultural landscapes provide many functions simultaneously including food production, regulation of water and regulation of greenhouse gases. Thus, it is challenging to make land management decisions, particularly transformative changes, that improve on one function without unintended consequences for other functions. To make informed decisions the trade-offs between different landscape functions must be considered. Here, we use a multi-objective optimization algorithm with a model of crop production that also simulates environmental effects such as nitrous oxide emissions to identify trade-off frontiers and associated possibilities for agricultural management. Trade-offs are identified in three soil types, using wheat production in the UK as an example, then the trade-off for combined management of the three soils is considered. The optimization algorithm identifies trade-offs between different objectives and allows them to be visualised. For example, we observed a highly non-linear trade-off between wheat yield and nitrous oxide emissions, illustrating where small changes might have a large impact. We used a cluster analysis to identify distinct management strategies with similar management actions and use these clusters to link the trade-off curves to possibilities for management. There were more possible strategies for achieving desirable environmental outcomes and remaining profitable when the management of different soil types was considered together. Interestingly, it was on the soil capable of the highest potential profit that lower profit strategies were identified as useful for combined management. Meanwhile, to maintain average profitability across the soils, it was necessary to maximise the profit from the soil with the lowest potential profit. These results are somewhat counterintuitive and so the range of strategies supplied by the model could be used to stimulate discussion amongst stakeholders. In particular, as some key objectives can be met in different ways, stakeholders could discuss the impact of these management strategies on other objectives not quantified by the model.
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spelling pubmed-66925592019-10-15 Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes Todman, Lindsay C. Coleman, Kevin Milne, Alice E. Gil, Juliana D.B. Reidsma, Pytrik Schwoob, Marie-Hélène Treyer, Sébastien Whitmore, Andrew P. Sci Total Environ Article Agricultural landscapes provide many functions simultaneously including food production, regulation of water and regulation of greenhouse gases. Thus, it is challenging to make land management decisions, particularly transformative changes, that improve on one function without unintended consequences for other functions. To make informed decisions the trade-offs between different landscape functions must be considered. Here, we use a multi-objective optimization algorithm with a model of crop production that also simulates environmental effects such as nitrous oxide emissions to identify trade-off frontiers and associated possibilities for agricultural management. Trade-offs are identified in three soil types, using wheat production in the UK as an example, then the trade-off for combined management of the three soils is considered. The optimization algorithm identifies trade-offs between different objectives and allows them to be visualised. For example, we observed a highly non-linear trade-off between wheat yield and nitrous oxide emissions, illustrating where small changes might have a large impact. We used a cluster analysis to identify distinct management strategies with similar management actions and use these clusters to link the trade-off curves to possibilities for management. There were more possible strategies for achieving desirable environmental outcomes and remaining profitable when the management of different soil types was considered together. Interestingly, it was on the soil capable of the highest potential profit that lower profit strategies were identified as useful for combined management. Meanwhile, to maintain average profitability across the soils, it was necessary to maximise the profit from the soil with the lowest potential profit. These results are somewhat counterintuitive and so the range of strategies supplied by the model could be used to stimulate discussion amongst stakeholders. In particular, as some key objectives can be met in different ways, stakeholders could discuss the impact of these management strategies on other objectives not quantified by the model. Elsevier 2019-10-15 /pmc/articles/PMC6692559/ /pubmed/31212161 http://dx.doi.org/10.1016/j.scitotenv.2019.06.070 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Todman, Lindsay C.
Coleman, Kevin
Milne, Alice E.
Gil, Juliana D.B.
Reidsma, Pytrik
Schwoob, Marie-Hélène
Treyer, Sébastien
Whitmore, Andrew P.
Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes
title Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes
title_full Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes
title_fullStr Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes
title_full_unstemmed Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes
title_short Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes
title_sort multi-objective optimization as a tool to identify possibilities for future agricultural landscapes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6692559/
https://www.ncbi.nlm.nih.gov/pubmed/31212161
http://dx.doi.org/10.1016/j.scitotenv.2019.06.070
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