Subsoil organic carbon (OC) is generally lower in content and more heterogeneous than topsoil OC, rendering it difficult to detect significant differences in subsoil OC storage. We tested the application of laboratory hyperspectral imaging with a variety of machine learning approaches to predict OC distribution in undisturbed soil cores. Using a bias-corrected random forest we were able to reproduce the OC distribution in the soil cores with very good to excellent model goodness-of-fit, enabling us to map the spatial distribution of OC in the soil cores at very high resolution (~53 × 53 µm). Despite a large increase in variance and reduction in OC content with increasing depth, the high resolution of the images enabled statistically powerful analysis in spatial distribution of OC in the soil cores. In contrast to the relatively homogeneous distribution of OC in the plough horizon, the subsoil was characterized by distinct regions of OC enrichment and depletion, including biopores which contained ~2–10 times higher SOC contents than the soil matrix in close proximity. Laboratory hyperspectral imaging enables powerful, fine-scale investigations of the vertical distribution of soil OC as well as hotspots of OC storage in undisturbed samples, overcoming limitations of traditional soil sampling campaigns.

Summary Arable subsoils store large amounts of phosphorus (P); however, it is unclear to what extent, and under which conditions, subsoil resources might supplement crop P acquisition. Here, we hypothesized that (i) insufficient supply of P in topsoil promotes P acquisition from subsoil and (ii) subsoil P cycling is regulated by nitrogen (N) supply. We sampled two German long‐term fertilizer trials in Thyrow (sandy soil) and Gießen (loamy‐clayey soil) to 100‐cm depth. Treatments received either NPK, NK or PK fertilizer for > 60 years. We assessed soil inorganic (Pi) and organic (Po) P pools following Hedley sequential extraction, and the oxygen isotopic composition of HCl‐extractable phosphate (δ18OHCl‐P), which differentiates P from primary and secondary (previously biologically cycled) minerals. We found that in the Hedley sequential extraction subsoil resin‐P stocks (30–100 cm) in NK plots were 60% (Thyrow) and 8% (Gießen) less than those in NPK plots. Subsoil HCl Pi stocks in NK exceeded those of NPK plots by 70% in Thyrow, but not in Gießen. The NK treatments showed significantly smaller subsoil δ18OHCl‐P values than NPK treatments, indicating a predominance of primary (not biologically cycled) minerals and refuting our hypothesis that P deficiency promotes P acquisition from primary minerals. Under N‐limiting conditions, subsoil resin‐P stocks exceeded those under NPK fertilizer by 117% (Thyrow) and 22% (Gießen), supporting our second hypothesis. We conclude that an efficient use of subsoil P resources is achieved only when nutrient supply in arable topsoils is sufficient. Highlights Long‐term N and P fertilization promotes use of P from subsoil (> 30-cm depth) Subsoil stocks of resin P were less in NK than NPK fertilized plots Elevated δ18OHCl‐P in the subsoil of NPK plots indicates effects by enzymatic activity Small δ18OHCl‐P values in the subsoil of NK plots indicate a predominance of primary minerals

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Background and aims The main difficulty in the use of 3D root architecture models is correct parameterization. We evaluated distributions of the root traits inter-branch distance, branching angle and axial root trajectories from contrasting experimental systems to improve model parameterization. Methods We analyzed 2D root images of different wheat varieties (Triticum Aestivum) from three different sources using automatic root tracking. Model input parameters and common parameter patterns were identified from extracted root system coordinates. Simulation studies were used to (1) link observed axial root trajectories with model input parameters (2) evaluate errors due to the 2D (versus 3D) nature of image sources and (3) investigate the effect of model parameter distributions on root foraging performance. Results Distributions of inter-branch distances were approximated with lognormal functions. Branching angles showed mean values <90°. Gravitropism and tortuosity parameters were quantified in relation to downwards reorientation and segment angles of root axes. Root system projection in 2D increased the variance of branching angles. Root foraging performance was very sensitive to parameter distribution and variance. Conclusions 2D image analysis can systematically and efficiently analyze root system architectures and parameterize 3D root architecture models. Effects of root system projection (2D from 3D) and deflection (at rhizotron face) on size and distribution of particular parameters are potentially significant. Abbreviations β, root segment angle to the horizontal ∆β, reorientation angle of an individual root segment D e , diffusion coefficient of a solute in soil ibd, inter-branch distance IRC, inter-root competition μ, mean value σ, standard deviation of the random deflection angle (tortuosity) sg, sensitivity to gravitropism std, standard deviation θ, branching angle in the vertical plane

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