Crop Growth Information Dynamics: A Theoretical Framework for Information-Driven Phenotypic Evolution and Biomass Allocation
Traditional crop growth models predominantly rely on mass conservation and energy flow to simulate biomass accumulation and yield formation, often struggling to capture non-linear responses, critical phase transitions, and phenotypic plasticity under fluctuating environmental stresses. To address these limitations, this paper proposes a novel conceptual and mathematical framework termed Crop Growth Information Dynamics (CGID), which introduces "information" as a fundamental third element alongside mass and energy. By treating the crop as a non-linear information-dynamic system, we define the spatial state vector of crop growth and quantify phenotypic uncertainty and environmental sensing capabilities using state-space Information Entropy and Mutual Information. A continuous partial differential field equation and a spatial-temporal memory convolution kernel are established to model signal generation, long-distance transmission, signal attenuation, and long-range temporal memory within plant tissues. Furthermore, we construct a fully coupled system of differential equations linking mass, energy, and information, where biomass allocation ratios dynamically evolve as a non-linear function driven by accumulated historical and real-time environmental information. Theoretical deductions demonstrate that CGID effectively explains the priming effect (stress memory) and shadow avoidance behaviors in dense crop canopies, offering a refined mathematical mechanism for stress tolerance and phenotypic plasticity. This work provides a transformative theoretical foundation for next-generation digital twin agriculture, precision crop management, and the design of information-smart crop cultivars.