Abstract
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 LAIs 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.
Highlights
Soil salinisation and water shortages are important problems that restrict the efficiency of agricultural production in the south of Xinjiang, China [1, 2]
The impact of the different soil conditioners and their application rates on changes in leaf area index (LAI) can be ignored, and the unified, normalised growth models for LAI should use the mean values of relative leaf area index (RLAI) based on Eq 9
Normalising the measured LAI allowed us to disregard the impacts of the different soil conditioners and application rates on the dynamic changes in the LAI of cotton
Summary
Soil salinisation and water shortages are important problems that restrict the efficiency of agricultural production in the south of Xinjiang, China [1, 2]. The use of soil conditioners is necessary to improve saline and alkaline land and to conserve irrigation water in this special arid climate. Cotton is second only to grain as the most important economic crop, especially in Xinjiang province, and its yield has reached 50 percent of the total yield for the entire country. Research on simulation models of cotton leaf area index (LAI) and yield under soil conditioners can provide a comprehensive treatment theory to improve production and to reform the saline-alkaline soil.
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