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Simulation models of leaf area index ( LAI ) and yield for cotton can provide a theoretical foundation for predicting future variations in yield. This paper analyses the increase in  LAI  and the relationships between  LAI , dry matter, and yield for cotton under three soil conditioners near Korla, Xinjiang, China. Dynamic changes in cotton  LAI  were evaluated using modified logistic, Gaussian, modified Gaussian, log normal, and cubic polynomial models. Universal models for simulating the relative leaf area index ( RLAI ) were established in which the application rate of soil conditioner was used to estimate the maximum  LAI  ( LAI   m ). In addition, the relationships between  LAI   m  and dry matter mass, yield, and the harvest index were investigated, and a simulation model for yield is proposed. A feasibility analysis of the models indicated that the cubic polynomial and Gaussian models were less accurate than the other three models for simulating increases in  RLAI . Despite significant differences in  LAI s under the type and amount of soil conditioner applied,  LAI   m  could be described by aboveground dry matter using Michaelis-Menten kinetics. Moreover, the simulation model for cotton yield based on  LAI   m  and the harvest index presented in this work provided important theoretical insights for improving water use efficiency in cotton cultivation and for identifying optimal application rates of soil conditioners.

Simulation Models of Leaf Area Index and Yield for Cotton Grown with Different Soil Conditioners