the gridded global dataset by combining two data  products47. Second, genetic parameters of specific cultivars of 
maize from previous works were adopted for the initial parameters; model parameters related to crop genotype 
characteristics were calibrated and tuned following the method in Xiong et al. 52, in which the simulated yields 
from 1986–2005 were comparable to the statistical data. Third, maize yields across the world were simulated 
under global warming by 1.5 °C and 2.0 °C. Finally, global and national maize yields were aggregated from grid-
ded values; changes in national and global yields under global warming by 1.5 °C and 2.0 °C were calculated, 
comparing maize yield average for 1986–2005.
Simulation of market price using GTAP . The yield changes for maize from the DSSAT models under 
1.5 °C and 2.0 °C temperature increase are used to carry out simulations using competitive market for changes 
in production, market price, and self-sufficiency ratio of maize at national and global  levels53,54. For this study, we 
use a comparative static analysis approach to simulate the impact of climate changes on the prices and trade of 
the major food crops under current economic conditions. Utilizing current economic conditions has the advan-
tage of minimizing assumptions and model uncertainties related to future economic  conditions55,56.
The original GTAP database doesn’t include maize as a separate sector, rather it is combined with other coarse 
grains to form an “other coarse grain” sector. For this study, we updated the GTAP database by splitting maize 
from the original sector in the database, design an appropriate sectoral and regional aggregation scheme to the 
original database. The detailed method is given as follows:
First, we improved the database by splitting maize from the existing sector “other coarse grain” , following 
similar work using  GTAP57–59 based on the routines from the Splitcom  method60. In this procedure, the old flows 
of data both at national and trade levels are allocated between the new flows using weights. The national weights 
include the division of each unsplit user’s use of the original split commodity among the new commodities; the 
division of unsplit inputs to the original industry between the new industries; the splitting of new industry’s use 
of each new commodity. Maize use is mainly shared between feed, food, processing and others (seed, waste, etc.).
Trade shares allocate the original slice of the split commodity into the new commodity for all elements of 
basic price value, tax, and margin. Finally, we used the RAS method for balancing the newly created database. 
The values for the national shares matrix were obtained from FAOSTAT. The trade shares matrix was calculated 
based on the data from UN Comtrade Database.
Second, our sectoral aggregation scheme for GTAP ensures that all the competing and complimenting sectors 
for maize are present in the most disaggregated form. For example, for maize, other crops compete for inputs of 
production and both livestock and households are major users of maize. For regional aggregation, we kept the 
details for all the main producing, consuming, and trading regions, for maize.