Optimizing soil-coring strategies to quantify root-length-density distribution in field-grown maize: virtual coring...

Area of Science:

• Plant Science
• Agronomy
• Soil Science

Background:

• Root distribution significantly impacts plant water and nutrient uptake.
• Accurate estimation of root length density (RLD) is vital for understanding soil exploration.
• Optimizing soil coring strategies balances cost and accuracy in RLD estimation.

Purpose of the Study:

• To assess the accuracy of different soil-coring strategies for estimating maize root length density (RLD).
• To develop an improved, cost-efficient core-sampling strategy for RLD assessment.
• To leverage 3D root system architecture (RSA) models for virtual evaluation of sampling methods.

Main Methods:

• In situ 3D digitization of maize axile and lateral root trajectories.
• Construction of contrasting 3D RSA models based on measured root architecture.
• Virtual experiments using RSA models to evaluate various soil-core sampling strategies for RLD estimation.

Main Results:

• Single-core sampling strategies introduce substantial bias in RLD estimation.
• Two-core sampling can reduce bias, especially with weighting methods, but optimal methods are complex.
• A novel area-weighting algorithm-based strategy achieves low RLD estimation deviations, particularly when sampling between rows or using larger cores.

Conclusions:

• 3D RSA models provide an effective platform for evaluating soil-coring strategies.
• The proposed two-core sampling strategy, utilizing an area-weighting algorithm, is a promising, cost-efficient method for accurate RLD estimation in maize.