Year: 2025
Type: article
Abstract: Leaf Area Index (LAI) is a key variable in modeling plant growth because it is the site of photosynthesis. However, there are significant differences in LAI between different models and between models and satellite-derived estimates. Empirical studies show that LAI is closely related to temperature. The theory provides an alternative method for predicting steady-state LAI. We have implemented this theory in a simple universal model for estimating the growth of the soybean leaf area index (LAI). This study presents a novel, pivotal parameter for assessing plant growth and productivity. We hypothesized that the maximum leaf area index for a specific variety is a constant value. In 2021, a field experiment was conducted at the Liaoning Jinzhou Agricultural Meteorological Experimental Station, where soybean cultivation was manipulated across seven distinct sowing dates, using the local traditional sowing date as a baseline. The model developed in this study demonstrated remarkable accuracy in LAI estimation across various growth stages and environmental conditions. Our findings reveal a robust correlation between the model’s predictions and actual LAI measurements. This model serves as a reliable tool for researchers and agronomists to monitor and predict soybean growth, thereby facilitating more informed decision-making in agricultural management.
Source: Agronomy
Authors: Qi Wang, Jianping Guo
Language: English
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,137
Awards: G4585406917, G4951829125
OpenAlex ID: W4414428285