A General Model for Estimating Leaf Area Index of Maize

doi:10.3969/j.issn.1000-6362.2020.08.004

Abstract

Abstract: In order to build a more general model for estimating leaf area index of maize, in this paper, we used the staged seeding test data (based on the local actual field sowing date, the first sowing date was 10 days earlier than normal, the second sowing date was normal, the third sowing date was 10 days later, and the fourth sowing date was 20 days later) with different varieties of maize in six agro-meteorological experiment stations in 2018, including Harbin of Heilongjiang province, Yushu of Jilin province, Jinzhou of Liaoning province, Wulanwusu of Xinjiang province, Xifeng of Gansu province and Gucheng of Hebei province. Taking the relative integrated temperature of the first, third and fourth sowing period as independent variables and the relative value of leaf area index (RLAI) as the dependent variable, the modified Logistic equation was used to construct the estimation model of maize leaf area index. The fitting curve of the model showed that the simulated RLAI in the three-leaf stage and the seven-leaf stage of maize were higher than the measured RLAI, and a significant linear correlation between the residual of the model in these two stages and the relative values of the effective integrated temperature. Therefore, the relative values of the effective integrated temperature were used to fix the residuals of the model in these two stages. The model was tested using the data of 8 different varieties in six agro-meteorological experiment stations in 2018 and 3 agro-meteorological experiment stations (Yushu of Jilin province, Xifeng of Gansu province and Tai’an of Shandong province) in 2019. The results showed that the RLAI fitting curve with the relative integrated temperature of the multi-attribute varieties of maize as independent variables was completely in line with the modified Logistic equation. The model fitting coefficient of determination (R2) reached 0.93, and passed the significance test of 0.01 level with high accuracy. The test results showed that the simulated RLAI of different varieties of maize had a high correlation with the measured RLAI. The correlation coefficient exceeded 0.9 and passed the significance test of 0.01 level. The average relative error of different varieties ranged from 13.8% to 27.6%. The average relative error between simulated RLAI and measured RLAI at different growth stages was between 9.4% and 30.7%, with the highest in the seven-leaf stage and the lowest in the milk-ripe stage. In general, the estimation model constructed based on relative values, eliminated the differences in maize variety attributes, soil properties, management measures, planting density, etc. It has a wider applicability than the previous LAI estimation model based on single station, single variety, single sowing period, or multiple varieties in single station, is suitable for most varieties LAI simulation of maize.

Key words: Maize, Leaf area index, Logistic curve fitting, Estimation model

CLC Number:

LUAN Qing, GUO Jian-ping, MA Ya-li, ZHANG Li-min, WANG Jing-xuan. A General Model for Estimating Leaf Area Index of Maize[J]. Chinese Journal of Agrometeorology, 2020, 41(08): 506-519.

References

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