Optimizing radiation capture in machine-harvested cotton: A functional-structural plant modelling approach to chemical vs. manual topping strategies

Abstract

Topping and planting density are key agronomic management practices to optimize cotton plant structure for machine harvesting and light capture. However, modelling the effects of these practices on canopy light utilization in the field, in order to improve cotton management, remains challenging. Functional-structural plant modelling is a computational approach to explore the effects of agronomic practices on shaping plant architecture and thus light interception. This study, for the first time, utilizes the CottonXL model to quantify the significant impact of chemical topping compared to manual topping on radiation interception on machine-harvested cotton in China at different planting densities, providing new management strategies for cotton production. A more compact plant structure is shaped by chemical topping through inhibiting leaf expansion and shortening internodes of both the main stem and fruiting branches, thereby allowing more PAR interception by middle and lower leaves. Simulation results showed that the total PAR intercepted by the canopy over entire growth season was increased by 11.3 % under chemical topping compared to manual topping. This positive effect became even more pronounced with increasing plant density. These results indicate that chemical topping could be beneficial for optimizing canopy structure, enhancing light interception and lint yield, especially at higher plant densities. The results illustrate the importance of shaping plant structure for improving radiation resource capture and exemplify the potential of optimizing topping strategy and plant density to enhance crop performance. They also demonstrate the utility of a functional-structural plant model for guiding crop management.