GRANAR

GRANAR generates digital root anatomical networks to enable estimation and analysis of root radial hydraulic conductivity from measurable anatomical parameters.

Key Features:

• Digital anatomy generation: Constructs detailed digital networks of root anatomy from a set of anatomical parameters.
• Parameter-driven modeling: Builds anatomies from a concise set of root anatomical parameters derivable from existing image analysis tools.
• Hydraulic model integration: Integrates generated anatomies with hydraulic models such as the MECHA model to estimate hydraulic properties.
• Radial conductivity estimation: Estimates root radial hydraulic conductivity based on the generated anatomical networks.
• Virtual anatomy sampling: Generates virtual anatomies for re-analysis of maize (Zea mays) datasets and for extended ranges of anatomical parameters beyond experimental observations.
• Anatomical sensitivity analysis: Quantifies the influence of anatomical features such as stele width and cortex on radial conductivity.

Scientific Applications:

• Radial conductivity estimation: Deriving root radial hydraulic conductivity values from anatomical measurements and modeled anatomies.
• Re-analysis of published datasets: Reconstructing virtual anatomies to re-analyze maize (Zea mays) anatomical datasets from the literature.
• Anatomical impact assessment: Assessing how specific anatomical features (e.g., stele width, cortex) affect root hydraulic function.
• Linking anatomy and hydraulics: Combining anatomical models with hydraulic modeling (MECHA) to investigate drivers of plant water uptake.

Methodology:

Constructs digital root anatomical networks from a concise set of anatomical parameters obtainable via existing image analysis tools, integrates generated anatomies with hydraulic models such as MECHA to estimate radial hydraulic conductivity, and applies the approach to generate virtual anatomies for maize (Zea mays) datasets and extended parameter ranges.