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Articles by Ronald D. Sands
Total Records ( 3 ) for Ronald D. Sands
  Martin von Lampe , Dirk Willenbockel , Helal Ahammad , Elodie Blanc , Yongxia Cai , Katherine Calvin , Shinichiro Fujimori , Tomoko Hasegawa , Petr Havlik , Edwina Heyhoe , Page Kyle , Hermann Lotze-Campen , Daniel Mason d`Croz , Gerald C. Nelson , Ronald D. Sands , Christoph Schmitz , Andrzej Tabeau , Hugo Valin , Dominique van der Mensbrugghe and Hans van Meijl
  Recent studies assessing plausible futures for agricultural markets and global food security have had contradictory outcomes. To advance our understanding of the sources of the differences, 10 global economic models that produce long-term scenarios were asked to compare a reference scenario with alternate socioeconomic, climate change, and bioenergy scenarios using a common set of key drivers. Several key conclusions emerge from this exercise: First, for a comparison of scenario results to be meaningful, a careful analysis of the interpretation of the relevant model variables is essential. For instance, the use of “real world commodity prices” differs widely across models, and comparing the prices without accounting for their different meanings can lead to misleading results. Second, results suggest that, once some key assumptions are harmonized, the variability in general trends across models declines but remains important. For example, given the common assumptions of the reference scenario, models show average annual rates of changes of real global producer prices for agricultural products on average ranging between –0.4% and +0.7% between the 2005 base year and 2050. This compares to an average decline of real agricultural prices of 4% p.a. between the 1960s and the 2000s. Several other common trends are shown, for example, relating to key global growth areas for agricultural production and consumption. Third, differences in basic model parameters such as income and price elasticities, sometimes hidden in the way market behavior is modeled, result in significant differences in the details. Fourth, the analysis shows that agro-economic modelers aiming to inform the agricultural and development policy debate require better data and analysis on both economic behavior and biophysical drivers. More interdisciplinary modeling efforts are required to cross-fertilize analyses at different scales.
  Hugo Valin , Ronald D. Sands , Dominique van der Mensbrugghe , Gerald C. Nelson , Helal Ahammad , Elodie Blanc , Benjamin Bodirsky , Shinichiro Fujimori , Tomoko Hasegawa , Petr Havlik , Edwina Heyhoe , Page Kyle , Daniel Mason-D`Croz , Sergey Paltsev , Susanne Rolinski , Andrzej Tabeau , Hans van Meijl , Martin von Lampe and Dirk Willenbockel
  Understanding the capacity of agricultural systems to feed the world population under climate change requires projecting future food demand. This article reviews demand modeling approaches from 10 global economic models participating in the Agricultural Model Intercomparison and Improvement Project (AgMIP). We compare food demand projections in 2050 for various regions and agricultural products under harmonized scenarios of socioeconomic development, climate change, and bioenergy expansion. In the reference scenario (SSP2), food demand increases by 59–98% between 2005 and 2050, slightly higher than the most recent FAO projection of 54% from 2005/2007. The range of results is large, in particular for animal calories (between 61% and 144%), caused by differences in demand systems specifications, and in income and price elasticities. The results are more sensitive to socioeconomic assumptions than to climate change or bioenergy scenarios. When considering a world with higher population and lower economic growth (SSP3), consumption per capita drops on average by 9% for crops and 18% for livestock. The maximum effect of climate change on calorie availability is –6% at the global level, and the effect of biofuel production on calorie availability is even smaller.
  Gerald C. Nelson , Dominique van der Mensbrugghe , Helal Ahammad , Elodie Blanc , Katherine Calvin , Tomoko Hasegawa , Petr Havlik , Edwina Heyhoe , Page Kyle , Hermann Lotze-Campen , Martin von Lampe , Daniel Mason d`Croz , Hans van Meijl , Christoph Muller , John Reilly , Richard Robertson , Ronald D. Sands , Christoph Schmitz , Andrzej Tabeau , Kiyoshi Takahashi , Hugo Valin and Dirk Willenbockel
  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.
 
 
 
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