Modeling plant-soil interactions to predict the vertical distributions of plant fine root systems
Creators: name: Amato, Matthew Thomas
Publisher: name: No Publisher Supplied
Year: 2023

description: Fine roots (≤ 2 mm in diameter) facilitate the exchange of mass and energy between plants and soil that take place along a soil profile. These exchanges can be quantified with knowledge of vertical fine root distributions (VRDs), but the direct observation of VRDs demands invasive and/or costly sampling techniques. This dissertation aims to address this limitation by developing a model to predict VRDs from knowledge of local soil and climate conditions. In the model developed in this dissertation, temporal fluctuations in fine root biomass (r) arise from the interplay between growth, respiration and decomposition processes, such that net changes in r over time follow first order kinetics. Furthermore, the central hypothesis of the model posits that r is greater in soil layers with the smallest hydraulic resistance to root water uptake and the least exposure to abiotic stress, such as from drought, low concentrations of oxygen, suboptimal temperatures and/or limiting penetration resistances to roots. Several assumptions and concepts were tested, adapted or augmented for inclusion in the newly developed model. The most significant of these advancements are presented in chapters 2-4, in which extensive datasets with global coverage were leveraged to evaluate their utility in a diversity of environmental conditions. In chapter 2, the assumption of temporal r dynamics was validated using Bayesian methods and a historical dataset consisting of 111 growing season records from 15 grasslands worldwide. An important result of this investigation is that predicted fine root turnover rates were 3 to 7 times faster than pre-existing estimates that neglect fine root decomposition during the growing season.Chapter 3 presents a conceptual framework for linking plant water stress to the shape of the soil water retention relationship (swr), expressed between the pressure potential of soil water (ψ) and volumetric water content. A novel indicator of stress (ψ_wdp) was derived at the ψ value corresponding to a relative soil water content of about 10-15%. The value of ψ_wdp was correlated with a reduction in potential transpiration of approximately 50% in 10 different plant species and 19 soil textures, suggesting it is a promising indicator of plant drought stress.In chapter 4, a relationship between near-surface soil temperature (Tsoil), air temperature (Tair) and leaf area index (LAI) was calibrated to improve predictions of belowground temperatures that fine roots experience. A piece-wise function was fit to monthly Tair and Tsoil measurements (n = 2001) from 169 eddy covariance towers in the FLUXNET2015 dataset accompanied by LAI observations from the MODIS satellite. When compared to global predictions from a machine learning algorithm, the fitted model exhibited a mean bias error of -0.73° C and a root mean square error of 1.76° C, which almost doubled if Tsoil = Tair were assumed.The model developed in chapter 5 predicts VRDs in vertically discretized soil profiles subjected to stochastic representations of the mean seasonal climate conditions. The model consists of four interacting modules that simulate daily water, temperature, root biomass and oxygen dynamics and incorporates the developments presented in chapters 2-4. To validate the model’s performance, simulations were setup at 40 sites in the United States’ National Ecological Observatory Network (NEON), covering 17 different eco-climatic zones. Predicted VRDs had less than 5% error at most sites and reproduced VRD shapes that decreased non-monotonically with depth. Soil hydraulic and penetration resistances emerged as the most influential environmental variables impacting fine root abundances, highlighting the control of soil structure on VRDs. The model opens opportunities to improve Earth system models by predicting VRDs from global soil and climate information.