Modeling Soil Responses to Nitrogen and Phosphorus Fertilization Along a Soil Phosphorus Stock Gradient

Abstract

In this study, we investigate the responses of soil organic carbon (C) to nitrogen (N) and phosphorus (P) additions along a soil P stock gradient of five beech forest stands in Germany, using a modeling approach. Two different soil models with coupled C, N, and P cycles are used to simulate fertilization experiments conducted at the study sites. The first model, the stand-alone soil module of QUINCY (QUINCY-soil, Thum et al., 2019), is a conventional soil model that uses first-order kinetics to describe soil organic matter (SOM) turnover and represents microbial biomass only implicitly. The second model, the Jena Soil Model (JSM) (Yu et al., 2020), is a novel microbial soil model, which explicitly simulates microbial dynamics and describes the turnover of SOM as the consequence of several interactive processes, such as microbially mediated depolymerization of litter and SOM, organo-mineral association, and vertical transport. We applied both site-level models to five study sites and compared the modeled soil profile with observations. In addition, model scenarios were conducted to simulate the fertilization of N and P, and we further evaluate the effect of soil P stock, plant litter quality, and SOM CNP stoichiometry, on the responses of soil (heterotrophic) respiration (R s) to nutrient addition. We found that the fitness between simulated and observed SOM profiles (defined as normalized root mean square ratios, K nrmsr) were generally better in JSM than in QUINCY-soil (K nrmsr larger by 0.03 ± 0.10 to 0.16 ± 0.06 for various soil measurements at all sites); The general pattern of observed R s responses to nutrient fertilization, that N addition decreases R s whereas P addition increases it, can be reproduced by JSM but not by QUINCY-soil. Our results indicated that a detailed explicit description of microbial processes and organo-mineral association is required to model plant-soilmicrobial interactions, thus to better reproduce SOM profiles and their responses to nutrient additions. It highlights the need to better represent these processes in future model developments.

Improved representation of phosphorus exchange on soil mineral surfaces reduces estimates of phosphorus limitation in temperate forest ecosystems

“…3). The model performance in simulating the relationship between soil labile and sorbed Pi pools (Lab-to-Exchangeable P) has been improved by dbLang at our study sites of varying soil P stocks, soil texture, and stoichiometry (Lang et al, 2017;Yu et al, 2020b). Such an improvement of dbLang with respect to reproducing the Lab-to-Exchangeable P ratio much better captures the vertical patterns of soil inorganic pools (Figs.…”

“…In the Mann-Kendall test, the tau (τ ) value varies between −1 and 1, where −1 represents a decreasing trend and 1 repre-sents an increasing trend. The modeled soil profile against the measured soil profile was evaluated with a normalized rootmean-square ratio term, K nrmsr , which is modified to represent the average proportions between modeled and measured values (Yu et al, 2020b).…”

Upscaling microbial stoichiometric adaptability in SOM turnover: The SESAM Soil Enzyme Steady Allocation Model (v3.0)

“…Another avenue forward is studying the effect of stoichiometric constraints by other elements than C and N. The next step is to include phosphorus (P) constraints. A first implementation in the JSM model (Yu et al, 2020b) revealed several challenges.…”

Development of a plant carbon-nitrogen interface coupling framework in a coupled biophysical-ecosystem-biogeochemical model (SSiB5/Triffid/DayCent-SOM v1.0): Its parameterization, implementation, and evaluation

“…There are several different ways to represent N limitation effects in land models, including using N to scale down photosynthesis (Ghimire et al, 2016;Goll et al, 2017;Thum et al, 2019;Yu et al, 2020;Zaehle et al, 2015;Zhu et al, 2019), scaling down potential GPP based on N availability (Gerber et al, 2010;Oleson et al, 2013;Wang et al, 2010), or defining an NPP cost of nitrogen uptake (Fisher et al, 2010). We choose the most physiological way by adjusting maximum Rubisco carboxylation rate (𝑉 !,789 ) during the photosynthesis process, rather than adjusting NPP at the end of the photosynthesis process.…”