Simulation of Optimal Decision-Making Under the Impacts of Climate Change

Research output: Contribution to journalJournal article – Annual report year: 2017Researchpeer-review

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Simulation of Optimal Decision-Making Under the Impacts of Climate Change. / Møller, Lea Ravnkilde; Drews, Martin; Larsen, Morten Andreas Dahl.

In: Environmental Management, Vol. 60, No. 1, 2017, p. 104-117.

Research output: Contribution to journalJournal article – Annual report year: 2017Researchpeer-review

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@article{d6c66a7a23c74c858306aa07f9868cbf,
title = "Simulation of Optimal Decision-Making Under the Impacts of Climate Change",
abstract = "Climate change causes transformations to the conditions of existing agricultural practices appointing farmers to continuously evaluate their agricultural strategies, e.g., towards optimising revenue. In this light, this paper presents a framework for applying Bayesian updating to simulate decision-making, reaction patterns and updating of beliefs among farmers in a developing country, when faced with the complexity of adapting agricultural systems to climate change. We apply the approach to a case study from Ghana, where farmers seek to decide on the most profitable of three agricultural systems (dryland crops, irrigated crops and livestock) by a continuous updating of beliefs relative to realised trajectories of climate (change), represented by projections of temperature and precipitation. The climate data is based on combinations of output from three global/regional climate model combinations and two future scenarios (RCP4.5 and RCP8.5) representing moderate and unsubstantial greenhouse gas reduction policies, respectively. The results indicate that the climate scenario (input) holds a significant influence on the development of beliefs, net revenues and thereby optimal farming practices. Further, despite uncertainties in the underlying net revenue functions, the study shows that when the beliefs of the farmer (decision-maker) opposes the development of the realised climate, the Bayesian methodology allows for simulating an adjustment of such beliefs, when improved information becomes available. The framework can, therefore, help facilitating the optimal choice between agricultural systems considering the influence of climate change.",
keywords = "Adaptation, Agriculture, Bayesian updating, Climate change, Monte Carlo simulation, Uncertainty",
author = "M{\o}ller, {Lea Ravnkilde} and Martin Drews and Larsen, {Morten Andreas Dahl}",
year = "2017",
doi = "10.1007/s00267-017-0852-1",
language = "English",
volume = "60",
pages = "104--117",
journal = "Environmental Management",
issn = "0364-152X",
publisher = "Springer New York",
number = "1",

}

RIS

TY - JOUR

T1 - Simulation of Optimal Decision-Making Under the Impacts of Climate Change

AU - Møller, Lea Ravnkilde

AU - Drews, Martin

AU - Larsen, Morten Andreas Dahl

PY - 2017

Y1 - 2017

N2 - Climate change causes transformations to the conditions of existing agricultural practices appointing farmers to continuously evaluate their agricultural strategies, e.g., towards optimising revenue. In this light, this paper presents a framework for applying Bayesian updating to simulate decision-making, reaction patterns and updating of beliefs among farmers in a developing country, when faced with the complexity of adapting agricultural systems to climate change. We apply the approach to a case study from Ghana, where farmers seek to decide on the most profitable of three agricultural systems (dryland crops, irrigated crops and livestock) by a continuous updating of beliefs relative to realised trajectories of climate (change), represented by projections of temperature and precipitation. The climate data is based on combinations of output from three global/regional climate model combinations and two future scenarios (RCP4.5 and RCP8.5) representing moderate and unsubstantial greenhouse gas reduction policies, respectively. The results indicate that the climate scenario (input) holds a significant influence on the development of beliefs, net revenues and thereby optimal farming practices. Further, despite uncertainties in the underlying net revenue functions, the study shows that when the beliefs of the farmer (decision-maker) opposes the development of the realised climate, the Bayesian methodology allows for simulating an adjustment of such beliefs, when improved information becomes available. The framework can, therefore, help facilitating the optimal choice between agricultural systems considering the influence of climate change.

AB - Climate change causes transformations to the conditions of existing agricultural practices appointing farmers to continuously evaluate their agricultural strategies, e.g., towards optimising revenue. In this light, this paper presents a framework for applying Bayesian updating to simulate decision-making, reaction patterns and updating of beliefs among farmers in a developing country, when faced with the complexity of adapting agricultural systems to climate change. We apply the approach to a case study from Ghana, where farmers seek to decide on the most profitable of three agricultural systems (dryland crops, irrigated crops and livestock) by a continuous updating of beliefs relative to realised trajectories of climate (change), represented by projections of temperature and precipitation. The climate data is based on combinations of output from three global/regional climate model combinations and two future scenarios (RCP4.5 and RCP8.5) representing moderate and unsubstantial greenhouse gas reduction policies, respectively. The results indicate that the climate scenario (input) holds a significant influence on the development of beliefs, net revenues and thereby optimal farming practices. Further, despite uncertainties in the underlying net revenue functions, the study shows that when the beliefs of the farmer (decision-maker) opposes the development of the realised climate, the Bayesian methodology allows for simulating an adjustment of such beliefs, when improved information becomes available. The framework can, therefore, help facilitating the optimal choice between agricultural systems considering the influence of climate change.

KW - Adaptation

KW - Agriculture

KW - Bayesian updating

KW - Climate change

KW - Monte Carlo simulation

KW - Uncertainty

U2 - 10.1007/s00267-017-0852-1

DO - 10.1007/s00267-017-0852-1

M3 - Journal article

VL - 60

SP - 104

EP - 117

JO - Environmental Management

JF - Environmental Management

SN - 0364-152X

IS - 1

ER -