ABSTRACT
Background and Aims: Root architecture development determines the sites in soil where roots provide input of carbon and take up water and solutes. However, root architecture is difficult to determine experimentally when grown in opaque soil. Thus, root architecture models have been widely used and been further developed into functional-structural models that simulate the fate of water and solutes in the soil-root system. The root architecture model CRootBox presented here is a flexible framework to model root architecture and its interactions with static and dynamic soil environments. Methods: CRootBox is a C++-based root architecture model with Python binding, so that CRootBox can be included via a shared library into any Python code. Output formats include VTP, DGF, RSML and a plain text file containing coordinates of root nodes. Furthermore, a database of published root architecture parameters was created. The capabilities of CRootBox for the unconfined growth of single root systems, as well as the different parameter sets, are highlighted in a freely available web application. Key results: The capabilities of CRootBox are demonstrated through five different cases: (1) free growth of individual root systems; (2) growth of root systems in containers as a way to mimic experimental setups; (3) field-scale simulation; (4) root growth as affected by heterogeneous, static soil conditions; and (5) coupling CRootBox with code from the book Soil physics with Python to dynamically compute water flow in soil, root water uptake and water flow inside roots. Conclusions: CRootBox is a fast and flexible functional-structural root model that is based on state-of-the-art computational science methods. Its aim is to facilitate modelling of root responses to environmental conditions as well as the impact of roots on soil. In the future, this approach will be extended to the above-ground part of the plant.
Subject(s)
Models, Biological , Plant Roots/anatomy & histology , Software , Water/metabolism , Biological Transport , Computer Simulation , Phenotype , Plant Roots/growth & development , Plant Roots/physiology , Soil/chemistryABSTRACT
BACKGROUND: Earthworms are important for maintaining soil ecosystem functioning and serve as indicators of soil fertility. However, detection of earthworms is time-consuming, which hinders the assessment of earthworm abundances with high sampling density over entire fields. Recent developments of mobile terrestrial sensor platforms for proximal soil sensing (PSS) provided new tools for collecting dense spatial information of soils using various sensing principles. Yet, the potential of PSS for assessing earthworm habitats is largely unexplored. This study investigates whether PSS data contribute to the spatial prediction of earthworm abundances in species distribution models of agricultural soils. METHODOLOGY/PRINCIPAL FINDINGS: Proximal soil sensing data, e.g., soil electrical conductivity (EC), pH, and near infrared absorbance (NIR), were collected in real-time in a field with two management strategies (reduced tillage / conventional tillage) and sandy to loam soils. PSS was related to observations from a long-term (11 years) earthworm observation study conducted at 42 plots. Earthworms were sampled from 0.5 x 0.5 x 0.2 m³ soil blocks and identified to species level. Sensor data were highly correlated with earthworm abundances observed in reduced tillage but less correlated with earthworm abundances observed in conventional tillage. This may indicate that management influences the sensor-earthworm relationship. Generalized additive models and state-space models showed that modelling based on data fusion from EC, pH, and NIR sensors produced better results than modelling without sensor data or data from just a single sensor. Regarding the individual earthworm species, particular sensor combinations were more appropriate than others due to the different habitat requirements of the earthworms. Earthworm species with soil-specific habitat preferences were spatially predicted with higher accuracy by PSS than more ubiquitous species. CONCLUSIONS/SIGNIFICANCE: Our findings suggest that PSS contributes to the spatial modelling of earthworm abundances at field scale and that it will support species distribution modelling in the attempt to understand the soil-earthworm relationships in agroecosystems.
