Global yields of the world-s six most widely grown crops--wheat, rice, maize, soybeans, barley, sorghum--have increased since 1961. Year-to-year variations in growing season minimum temperature, maximum temperature, and precipitation explain 30% or more of the variations in yield. Since 1991, climate trends have significantly decreased yield trends in all crops but rice, leading to foregone production since 1981 of about 12 million tons per year of wheat or maize, representing an annual economic loss of 1.2to1.7 billion. At the global scale, negative impacts of climate trends on crop yields are already apparent. Annual global temperatures have increased by {approx}0.4 C since 1980, with even larger changes observed in several regions (1). While many studies have considered the impacts of future climate changes on food production (2-5), the effects of these past changes on agriculture remain unclear. It is likely that warming has improved yields in some areas, reduced them in others, and had negligible impacts in still others; the relative balance of these effects at the global scale is unknown. An understanding of this balance would help to anticipate impacts of future climate changes, as well as to more accurately assess recent (and thereby project future) technologically driven yield progress. Separating the contribution of climate from concurrent changes in other factors--such as crop cultivars, management practices, soil quality, and atmospheric carbon dioxide (CO{sub 2}) levels--requires models that describe the response of yields to climate. Studies of future global impacts of climate change have typically relied on a bottom-up approach, whereby field scale, process-based models are applied to hundreds of representative sites and then averaged (e.g., ref 2). Such approaches require input data on soil and management conditions, which are often difficult to obtain. Limitations on data quality or quantity can thus limit the utility of this approach, especially at the local scale (6-8). At the global scale, however, many of the processes and impacts captured by field scale models will tend to cancel out, and therefore simpler empirical/statistical models with fewer input requirements may be as accurate (8, 9). Empirical/statistical models also allow the effects of poorly modeled processes (e.g., pest dynamics) to be captured and uncertainties to be readily quantified (10). Here we develop new, empirical/statistical models of global yield responses to climate using datasets on broad-scale yields, crop locations, and climate variability. We focus on global average yields for the six most widely grown crops in the world: wheat, rice, maize, soybeans, barley, and sorghum. Production of these crops accounts for over 40% of global cropland area (11). 55% of non-meat calories, and over 70% of animal feed (12)
