Multi-scale hybrid modeling of plant growth in response to environmental conditions and soil nutrients availability

Crop modeling plays a crucial role in agriculture, aiding our understanding and prediction of crop growth and yield in diverse environmental conditions. This study aims to develop a comprehensive mathematical model describing plant growth in response to environmental conditions and soil nutrient availability. To achieve this, we relied on a field experiment with lettuce plants under varying environmental conditions. Employing growth models such as logistic, Gompertz, Aikman & Scaife, and Scaife, Cox & Morris, we assessed the influence of time, day-degrees, and effective day-degrees across different plant densities and during distinct periods throughout the year. In general, describing plant growth in terms of day-degrees or effective day-degrees yielded an improved model fit and more precise estimations of growth parameters. As a result, we described the growth of plant length in terms of effective day-degrees instead of time in the equations of the Bessonov–Volpert system. Additionally, we modified the equation describing plant length growth using previously fitted functions. By incorporating these adjustments, we characterized the one-dimensional growth of plant weight under varying environmental conditions without branching, using the Bessonov–Volpert model. This study contributes valuable insights into crop modeling techniques, refining our understanding of optimizing plant growth under different environmental conditions.