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

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.

A Precision Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data and Yield Mapping

“…Due to the strong correlation between leaf area index (LAI) and aboveground dry biomass [45], because aboveground dry biomass of plants generally determines LAI, the Delphi model was implemented to calculate LAI for each crop season. The model also predicted the heading, anthesis, maturity dates and length of time between these phases.…”

Agronomic Management for Enhancing Plant Tolerance to Abiotic Stresses—Drought, Salinity, Hypoxia, and Lodging

“…Granular structure can be maintained or enhanced by amendments with organic matter like manure, slurry, compost, crop residues [24,25], or macromolecule polymers acting as soil conditioners [26,27]; or by mixing two or more soil layers in order to reach a more equilibrated texture [28]. Amendments improve soil physical properties, including increasing the content of water-stable aggregates, improving soil porosity and soil penetrability, improving water retention, decreasing soil bulk density and evaporation, and decreasing runoff amount and velocity.…”

“…Self-healing capacity of soils [20] Organic or inorganic soil mulching [22] Minimum tillage and no tillage [19,21] Tillage at moisture content at which the largest number of small aggregates is produced [23] Amendments: -organic matter (e.g., manure, slurry, crop residues) [24,25] -soil conditioners (polymers) [26,27] -mixing of two or more soil layers in order to reach a more equilibrated texture [28] Impact of rain and irrigation drops on bare soil surface [29] Runoff effect of rain and irrigation [29] Heavy traffic of agricultural machinery with high-volume pneumatics [28,32] Clods' exposure to freezing-thawing cycles [33] 4. The Impacts of Individual Stress Factors on Crops…”

Spatial-Temporal Simulation of LAI on Basis of Rainfall and Growing Degree Days

“…In cases that there is no relationship between the simulated and remotely sensed LAI values then the NS coefficient is close to or less than 0. R 2 ranges from 0 to 1 with higher values indicating better agreement [23,48]. An R 2 of 1 indicates that the estimated LAI perfectly correlates with the LAI data.…”