Synchronous crop failures and climate-forced production variability
https://advances.sciencemag.org/content/5/7/eaaw1976DOI: 10.1126/sciadv.aaw1976 .. Science Advances 03 Jul 2019:
Abstract
Large-scale modes of climate variability can force widespread crop yield anomalies and are therefore often presented as a risk to food security. We quantify how modes of climate variability contribute to crop production variance. We find that the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), tropical Atlantic variability (TAV), and the North Atlantic Oscillation (NAO) together account for 18, 7, and 6% of globally aggregated maize, soybean, and wheat production variability, respectively. The lower fractions of global-scale soybean and wheat production variability result from substantial but offsetting climate-forced production anomalies. All climate modes are important in at least one region studied. In 1983, ENSO, the only mode capable of forcing globally synchronous crop failures, was responsible for the largest synchronous crop failure in the modern historical record. Our results provide the basis for monitoring, and potentially predicting, simultaneous crop failures.
INTRODUCTION
"Rapid increases in agricultural trade have notably changed the character of the global food production system in recent decades. The fraction of food produced for human consumption that is traded internationally rose from 15% in 1986 to 23% in 2009 (1). While fewer people than ever before have inadequate access to a sufficient quantity of food, an increasing number of people are dependent on imported food to meet daily minimum caloric needs"
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RESULTS
Climate modes
To identify how climate modes influence global crop yields, we perform a maximum covariance analysis (MCA) of the coupled modes of variability between climate and crop yields (see Materials and Methods). The first two global modes correspond to an ENSO life cycle (fig. S1); the first (second) time expansion coefficient of the sea surface temperature (SST) mode is significantly correlated with September, October, and November [March, April, and May (MAM)] Niño 3.4 index at r = −0.98 (r = 0.90). Regional analyses for the North Atlantic, Indian Ocean, and tropical Atlantic reveal climate modes that are significantly correlated with the December, January, and February (DJF) station-based North Atlantic Oscillation (NAO) index, July, August, and September (JAS) Indian Ocean Dipole (IOD) mode index, April, May, and June (AMJ) tropical South Atlantic index, and AMJ tropical North Atlantic index (r = 0.89, −0.7, −0.75, and 0.81, respectively). The patterns of climate variability resulting from a partial regression using the climate time expansion coefficient (Figs. 1 and 2; Ak in Eq. 2) closely resemble the patterns obtained by creating positive minus negative phase composites for each variable (not shown), which confirms that the modes that we identify capture relevant large-scale climate teleconnections. To discuss the causal pathway between each climate mode and its crop yield teleconnections, we adopt a region-by-region approach.
Global analysis
When considering all growing regions, climate modes account for 23, 17, and 15% of local maize, wheat, and soybean production variability (Fig. 4, A, C, and E; see Eq. 4 in Materials and Methods). According to past estimates (
, climate-related stresses (i.e., both weather- and climate mode–related) account for 32 to 39% of global wheat, soybean, and maize yield variability. Climate modes and weather, therefore, contribute roughly equally (∼15 to 20%) to the overall climate-related crop yield variance.
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