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ARS Home » Midwest Area » Ames, Iowa » National Laboratory for Agriculture and The Environment » Soil, Water & Air Resources Research » Research » Research Project #431787

Research Project: Woody Bioenergy Feedstocks from Marginal Agricultural Lands: Red Cedar Feedstock and Environmental Sustainability CFDA# 10.320

Location: Soil, Water & Air Resources Research

Project Number: 5030-11610-005-69-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Dec 1, 2016
End Date: Jun 30, 2019

Measure soil organic carbon content and related soil quality indicators beneath red cedar plantings that can be used as sources of bioenergy feedstock. Soil analyses will include pH, particle size, bulk density, organic and inorganic carbon, and total nitrogen.

A transect for soil sample collection will be established within each of the tree plantings used for tree biomass sampling and at an adjacent agricultural field. Transect length and sample spacing will be adjusted based on the tree planting dimensions and tree spacing. All samples will be taken within a single soil map unit and coordinates logged using a global positioning system (GPS). Each tree planting and adjacent crop field or pasture will be represented by 9 sample points. Composite samples (n= 4) will be collected at each sample point with 0-10, 10-20, and 20-30 cm depth increments using a 3.2-cm i.d., split-tube probe. Soil samples will be sieved, air dried, and placed on a roller mill to create a fine powder. Any identifiable plant material in the soil sample will be removed prior to grinding. Bulk density will be determined from oven dry sample mass and used to calculate soil organic carbon stocks. Soil particle size distribution will be quantified using the hydrometer method. Additional measures of soil quality will include pH (1:1 in water and KCl) and wet aggregate stability (Nimmo and Perkins, 2002). Analyses of variance (ANOVA) models and Tukey’s Honestly Significant Distance tests at a critical value of 0.05 will be run to examine land-use effects (e.g., tree vs. crop or grassland). Inferences for non-normal distributed data will be completed using Kruskal-Wallis one way ANOVA on ranks followed by Dunn's pairwise multiple comparison procedure.