Abstract Rhizosphere pH is the consequence of numerous, complex processes and is a key factor of biogeochemistry at the soil-root interface. This work aimed at evaluating the impact of nutrient root absorption on rhizosphere pH using a mechanistic model formalizing major nutrient cation and anion transport, under the influence of concentration, electric potential gradients and of convection. The electrical neutrality of the system was maintained through a H+ root efflux or influx (or equivalent opposite OH− fluxes). The model outputs were compared to the rhizosphere pH of maize (Zea mays L.), ryegrass (Lolium perenne L.) and Alpine pennycress (Noccaea caerulescens (J. & C. Presl) F. K. Mey.) grown in three different soils, determined with planar optodes. A global sensitivity analysis was carried out in order to highlight the dominating factors. The composition of 37 soil solutions from the literature was used to predict rhizosphere pH. The model most of the time predicts the alkalinisation of the rhizosphere by up to +3.3 pH units, given that in aerobic soils, nitrate is the most absorbed nutrient and its internalization is equilibrated by H+ root absorption (or equivalent OH− excretion). This alkalinisation is close to that observed in the rhizosphere of ryegrass and Alpine pennycress. The rhizosphere pH predictions are very sensitive to the initial composition of the soil solution and quite insensitive to root absorption kinetics and soil buffer power for the various nutrients. Because it favours nitrate diffusion, water content is positively correlated with rhizosphere alkalinisation. Moreover, the lower the root density, the lower the effect of the root uptake on both ion concentrations and on rhizosphere pH. Convection has little influence on rhizosphere pH. This study confirms the prominent role of mineral nutrition in the determination of rhizosphere pH. The model therefore constitutes a tool for understanding and predicting rhizosphere pH.

中文翻译：

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根系养分吸收如何影响根际 pH：一项建模研究

摘要 根际 pH 值是众多复杂过程的结果，是土壤-根系界面生物地球化学的关键因素。这项工作旨在使用机械模型评估营养根吸收对根际 pH 值的影响，该模型在浓度、电势梯度和对流的影响下将主要养分阳离子和阴离子迁移形式化。系统的电中性通过 H+ 根流出或流入（或等效的相反 OH- 通量）来维持。将模型输出与在三种不同土壤中生长的玉米 (Zea mays L.)、黑麦草 (Lolium perenne L.) 和高山 pennycress (Noccaea caerulescens (J. & C. Presl) FK Mey.) 的根际 pH 值进行比较，确定与平面光极。进行了全局敏感性分析以突出主导因素。文献中 37 种土壤溶液的组成用于预测根际 pH 值。考虑到在好氧土壤中，硝酸盐是吸收最多的养分，其内在化通过 H+ 根吸收（或等效的 OH− 排泄）平衡，因此该模型大部分时间预测根际碱化高达 +3.3 pH 单位。这种碱化接近于在黑麦草和高山丁香菜的根际观察到的碱化。根际 pH 值预测对土壤溶液的初始组成非常敏感，对根系吸收动力学和土壤对各种养分的缓冲能力非常不敏感。因为它有利于硝酸盐扩散，所以含水量与根际碱化呈正相关。此外，根密度越低，根吸收对离子浓度和根际 pH 值的影响越小。对流对根际pH值影响不大。该研究证实了矿物质营养在测定根际 pH 值方面的突出作用。因此，该模型构成了理解和预测根际 pH 值的工具。