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1. There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and netecosystem exchange varied significantly according to the length of the modeler’s experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in 'trial-and-error' calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler’s assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details

Author

Fabrizio Albanito, David McBey, Matthew Harrison, Pete Smith, Fiona Ehrhardt, Arti Bhatia, Gianni Bellocchi, Lorenzo Brilli, Marco Carozzi, Karen Christie, Jordi Doltra, Christopher Dorich, Luca Doro, Peter Grace, Brian Grant, Joël Léonard, Mark Liebig, Cameron Ludemann, Raphael Martin, Elizabeth Meier, Rachelle Meyer, Massimiliano De Antoni Migliorati, Vasileios Myrgiotis, Sylvie Recous, Renáta Sándor, Val Snow, Jean-François Soussana, Ward N. Smith, Nuala Fitton, Producció Vegetal, Cultius Extensius Sostenibles, University of Aberdeen, School of Biological Sciences, University of Aberdeen, Aberdeen, UK, Tasmanian Institute of Agriculture, University of Tasmania [Hobart, Australia] (UTAS), RITTMO Agroenvironnement (RITTMO), Collège de Direction (CODIR), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Indian Council of Agricultural Research (ICAR), Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institute for BioEconomy [Sesto Fiorentino] (IBE | CNR), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Recerca i Tecnologia Agroalimentàries = Institute of Agrifood Research and Technology (IRTA), Natural Resource Ecology Laboratory [Fort Collins] (NREL), Colorado State University [Fort Collins] (CSU), Texas A and M AgriLife Research, Texas A&M University System, Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS), Queensland University of Technology [Brisbane] (QUT), Ottawa Research and Development Center, Agriculture and Agri-Food (AAFC), Transfrontalière BioEcoAgro - UMR 1158 (BioEcoAgro), Université d'Artois (UA)-Université de Liège-Université de Picardie Jules Verne (UPJV)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), USDA-ARS, Northern Plains Agricultural Research Laboratory, Sidney, Montana, Cameron Ludemann Consulting, CSIRO Agriculture and Food (CSIRO), Faculty of Veterinary & Agricultural Sciences, University of Melbourne, Parkville, Victoria, School of Geosciences, University of Edimburgh, Fractionnement des AgroRessources et Environnement (FARE), Université de Reims Champagne-Ardenne (URCA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre for Agricultural Research [Budapest] (ATK), Hungarian Academy of Sciences (MTA), AgResearch Ltd, and Institute of Biological and Environmental Sciences, University of Aberdeen

Subjects
model ensembles, biogeochemical models, model calibration, Nitrogen, [SDE.MCG]Environmental Sciences/Global Changes, Uncertainty, General Chemistry, Carbon, Soil, model intercomparison, climate change, greenhouse gases, AgMIP, Environmental Chemistry, multi-criteria decision-making, Humans, soil carbon, Ecosystem
Abstract

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler’s experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in “trial-and-error” calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler’s assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details. info:eu-repo/semantics/acceptedVersion

Published
2022

3. Nudging, formulating new products, and the lifecourse: A qualitative assessment of the viability of three methods for reducing Scottish meat consumption for health, ethical, and environmental reasons

Author

Alexandra M. Johnstone, David McBey, and David Watts

Subjects
0301 basic medicine, Adult, Male, Health Knowledge, Attitudes, Practice, Meat, Adolescent, Consumer choice, 030209 endocrinology & metabolism, Health Promotion, Environment, Choice Behavior, 03 medical and health sciences, Food Preferences, Young Adult, 0302 clinical medicine, Sustainable consumption, Humans, General Psychology, Consumer behaviour, Aged, Consumption (economics), Aged, 80 and over, Government, 030109 nutrition & dietetics, Nutrition and Dietetics, Public economics, Plant based, Consumer Behavior, Focus Groups, Middle Aged, Diet, Scotland, Female, Plants, Edible
Abstract

Most governmental initiatives designed to improve dietary and planetary health have adopted a light-touch informing approach. However, it may be necessary to consider more direct measures that go beyond simply informing the public if the current high levels of meat consumption in Scotland are to be addressed. This paper considers three possible avenues through which more sustainable meat consumption patterns may be promoted: 'nudging', the formulation of new meat-alternative products, and targeting those in particular stages of the lifecourse. Through focus groups held in various locations in Scotland, the perceived viability of these measures was explored. While each measure shows some promise for reducing Scottish meat intake, the complex nature of food choice means that more qualitative research into meat consumption in Scotland is required.

Published
2019

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