Development of the global maize production model MATCRO-Maize version 1.0

Abstract. Process-based crop models combined with land surface models are useful tools for accurately quantifying the impacts of climate change on crops while considering the interactions between agricultural land and climate. We developed a new process-based crop model for maize, named MATCRO-Maize, by incorporating leaf-level photosynthesis of C4 plants and adjusting crop-specific parameters into the original MATCRO model, which is a process-based crop model initially developed for paddy rice combined with a land surface model. The model was validated at both a point scale and a global scale through comparisons with observational values. The validation at the point scale was conducted at four globally distributed sites. It showed statistically significant correlation for three variables (leaf area index: correlation coefficient (COR) of 0.76 with a p value < 0.01; total aboveground biomass: COR of 0.89 with a p value < 0.001; final yield: COR of 0.34 with a p value < 0.01). For the global scale validation, the simulated yield was statistically compared with the FAOSTAT data at the country level and total global level. Although the absolute value of the simulated yield tended to be overestimated, MATCRO-Maize could capture spatial variability, as indicated by a COR of 0.58 (p value < 0.01) for the 30-year average yield comparison of the top 20 maize-producing countries. In addition, the comparisons of the interannual variability derived from detrended deviation were statistically significant for the total global yield (COR of 0.54 with p value < 0.01) and for half of the top 20 countries (COR of 0.64–0.90 with p value < 0.001 for 6 countries; COR of 0.50–0.51 with p value < 0.01 for 2 countries; COR of 0.48–0.55 with p value < 0.05 for 2 countries), which are comparable with those of other global crop models. One of the reasons for this overestimation could be related to the strong nitrogen fertilization effect observed in MATCRO-Maize. With experimental field data under more comprehensive conditions, improvements in the functions of nitrogen fertilizer in the model would be needed to simulate the maize yield more accurately.