Agriculture in silico: Perspectives on radiative transfer optimization using vegetation modeling

Abstract

Advancing crop yield within limited agricultural land use is crucial to alleviate potential food shortages from the increasing world population. While genetic breeding holds great potential in improving crop yield, real-world practices are often constrained by the limitations of scaling the laboratory findings with respect to coupled environmental feedback and limited tools to project the optimal strategies based on environment and crop traits such as crop density management. Aided by a process- and trait-based vegetation model, we review and theoretically evaluate approaches that aim to improve crop yield through canopy radiative transfer optimization. The evaluated approaches include trait breeding (e.g. leaf color and chlorophyll action spectrum), canopy structure (e.g. canopy density and spacing), and environment manipulation (e.g. supplemental radiation intensity and source). We prototype vegetation modeling applications that can theoretically explore the potentials of a number of approaches at various setups that otherwise require tremendous effort in the real world, and propose to use vegetation modeling to guide more efficient agricultural practices. Future elaborations in vegetation modeling with respect to more physiological representations of vegetation processes, quantification of maintenance costs, and utilization of remote sensing data would further advance the utilization of modeling in agriculture.