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Forecasting future global food demand: A systematic review and meta-analysis of model complexity

Flies, EJ ORCID: 0000-0002-1013-0330, Brook, BW ORCID: 0000-0002-2491-1517, Blomqvist, L and Buettel, JC ORCID: 0000-0001-6737-7468 2018 , 'Forecasting future global food demand: A systematic review and meta-analysis of model complexity' , Environment International, vol. 120 , pp. 93-103 , doi: 10.1016/j.envint.2018.07.019.

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Predicting future food demand is a critical step for formulating the agricultural, economic and conservation policies required to feed over 9 billion people by 2050 while doing minimal harm to the environment. However, published future food demand estimates range substantially, making it difficult to determine optimal policies. Here we present a systematic review of the food demand literature - including a meta-analysis of papers reporting average global food demand predictions - and test the effect of model complexity on predictions. We show that while estimates of future global kilocalorie demand have a broad range, they are not consistently dependent on model complexity or form. Indeed, time-series and simple income-based models often make similar predictions to integrated assessments (e.g., with expert opinions, future prices or climate influencing forecasts), despite having different underlying assumptions and mechanisms. However, reporting of model accuracy and uncertainty was uncommon, leading to difficulties in making evidence-based decisions about which forecasts to trust. We argue for improved model reporting and transparency to reduce this problem and improve the pace of development in this field.

Item Type: Article
Authors/Creators:Flies, EJ and Brook, BW and Blomqvist, L and Buettel, JC
Keywords: food demand, prediction, model complexity, global, aggregation, gross domestic product, modelling, meta-analysis
Journal or Publication Title: Environment International
Publisher: Pergamon-Elsevier Science Ltd
ISSN: 0160-4120
DOI / ID Number: 10.1016/j.envint.2018.07.019
Copyright Information:

Copyright 2018 Elsevier Ltd.

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