Integrating carbon utilization and transport processes into a crop growth model enables the prediction of emergent soybean carbon allocation behavior

Abstract
How a plant allocates fixed carbon (C) among its organs governs growth, resource acquisition, and yield, yet it remains one of the least mechanistic components of crop models. Most crop models allocate C empirically, through partitioning tables or harvest indices, limiting their utility as a tool for understanding how allocation is impacted when their source and/or sink strengths are altered by changes in environment, management, or engineering efforts. In this study, we present the first application of a utilization–transport–resistance (UTR) framework to simulate a field-grown annual crop over its entire life cycle. Integrated into the Soybean-BioCro crop growth model, this UTR framework allows C allocation to emerge from three local processes: organ-level substrate utilization governed by Hill equations, C gradient-driven transport between organs, and senescence governed by development stage. We calibrated the model with two years of biomass data from soybean grown at ambient CO2, then validated it against organ biomass from two cultivars grown across two CO2 levels over eight growing seasons and against measured leaf and stem nonstructural carbohydrate concentrations. With the same set of calibrated parameters, the model not only reproduces the organ biomass and carbohydrate dynamics but also predicts behaviors that empirical models must be recalibrated to capture, including yield responses to shading, pod removal, defoliation, hail, and a rise in root-to-shoot ratio under elevated CO2. Vegetative-stage carbohydrate storage and reproductive-stage mobilization likewise emerge from the framework rather than being empirically prescribed in the model. A local sensitivity analysis of the model parameters indicated that the onset of reproductive growth influenced yield more strongly than utilization or transport parameters suggesting the timing of this transition as a more promising target for crop improvement. By grounding C allocation in physiological mechanisms, this framework expands the current capabilities of crop growth models, improving their utility as tools for generating testable hypotheses that span across biological scales and for exploring field-level impacts of plant engineering strategies.

Bio
Ximin Piao is a Ph.D. candidate in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign, working under the advisorship of Dr. Megan Matthews. Her research focuses on incorporating a dynamic carbon allocation model into BioCro, a crop growth simulation model.