Agriculture and climate change in global scenarios: why don't the models agree. (19th December 2013)

• Record Type: Journal Article
• Authors: Nelson, Gerald C.; van der, Dominique; Ahammad, Helal; Blanc, Elodie; Calvin, Katherine; Hasegawa, Tomoko; Havlik, Petr; Heyhoe, Edwina; Kyle, Page; Lotze‐Campen, Hermann; von, Martin; Mason d'Croz, Daniel; van, Hans; Müller, Christoph; Reilly, John; Robertson, Richard; Sands, Ronald D.; Schmitz, Christoph; Tabeau, Andrzej; Takahashi, Kiyoshi; Valin, Hugo; Willenbockel, Dirk
• Is Part Of: Agricultural economics. Volume 45:Number 1(2014:Jan.)
• Page Start: 85
• Page End: 101
• Publication Date: 2013-12-19
• Subjects: Agriculture -- Economic aspects -- Periodicals; 338.105
• DOI: 10.1111/agec.12091
• Languages: English
• ISSNs: 0169-5150

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs involves direct use of weather inputs (temperature, solar radiation available to the plant, and precipitation). Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes such as prices, production, and trade arising from differences in model inputs and model specification. This article presents climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By harmonizing key drivers that include climate change effects, differences in model outcomes were reduced. The particular choice of climate change drivers for this comparison activity results in large and negative productivity effects. All models respond with higher prices. Producer behavior differs by model with some emphasizing area response and others yield response. Demand response is least important. The differences reflect both differences in model specification and perspectives on the future. The results from this study highlight the need to more fully compare the deep model parameters, to generate a call for a combination of econometric and validation studies to narrow the degree of uncertainty and variability in these parameters and to move to Monte Carlo type simulations to better map the contours of economic uncertainty.</p> </abstract>