Location: Application Technology ResearchTitle: Relationship between particle size summation curves and the moisture characteristic curve for soilless substrates
|ZAZIRSKA, MAGDA - Oregon State University
|Owen Jr, James - Jim
|FIELD, JEB - Louisiana State University Agcenter
Submitted to: Acta Horticulturae
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/20/2021
Publication Date: 3/18/2021
Citation: Zazirska, M., Owen Jr, J.S., Altland, J.E., Field, J.S. 2021. Relationship between particle size summation curves and the moisture characteristic curve for soilless substrates. Acta Horticulturae. 1305:209-218. https://doi.org/10.17660/actahortic.2021.1305.29.
Interpretive Summary: The individual components of soilless substrates or growing media generally have differing physical and chemical properties, and when blended together create unique physical, hydrological, and chemical properties with the goal to optimize crop growth and development. Particle size distributions (PSD) are used as one method to describe and characterize resulting substrates. The shape and particle density of the organic (e.g. bark) and mineral (e.g. pumice) components used to manufacture soilless substrates can vary greatly. As a result of this, measuring PSD volumetrically or gravimetrically may result in differing or incomplete measurements in substrates composed of components with different shapes and densities. A moisture characteristic curve (MCC) is another useful, but time consuming, tool to assess a substrate’s water dynamics. Much research has been conducted to model particle size distribution curves to create MCCs in mineral soils, but not soilless substrates. This research aimed to look at the differing methods, volumetric or gravimetric, to measure PSD and determine if either could be used to predict MCC of given substrate. The volumetric PSD created a more discernible shift in the summation curve when amended with components that dramatically alter physical properties such as air space; however, both methods are inherently flawed. The gravimetric PSD cannot be easily corrected for the large variation in particle density of components used in soilless substrates. Bulk density varied across particles size when using the volumetric method, overestimating distribution of large pores that was filled with small particles. Neither method created PSD that could be used to model or predict MCC for the like substrate, making it impossible to predict the hydrology of the substrate.
Technical Abstract: Soilless substrates are commonly composed from multiple components with each component varying in particle density, which can affect the meaningfulness or accuracy of gravimetric particle size distribution. The objective of this study was to compare volume or weight-based methods to determine particle size distribution of single [Douglas-fir bark (DFB) only], dual (DFB plus peat or pumice), or multiple component (DFB plus peat and pumice) soilless substrates. A secondary objective was to determine if existing model of Haverkamp and Parlange can be used to predict moisture characteristic curve of single, dual or multiple component substrates with known particle size distribution. Treatment design was a 3×3 factorial with three rates each of sphagnum peat moss and pumice (0, 15, and 30% by vol) added to DFB. Particle size distribution of the nine substrates was determined using volumetric (cm-3) and gravimetric methods (g). The particle size distribution of each substrate was used to determine if an existing model could be used to accurately estimate a moisture characteristic curve for each substrate. There were statistical differences in particle size distribution between volumetric and gravimetric method. This resulted in a shift in the particle size summation curve (weight or volume based), however both methods remained strongly correlated providing equivalent information. Regardless of method used for measuring particle size distribution, we were unable to develop models to predict moisture characteristic curves from particle size data.