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Title: Assessment of microbial biomass carbon nitrogen of native and non native perennial pasture soil using hyperspectral data

item AULD, KEIRA - Nasa Goddard Institute For Space Studies
item PETERSON, BREKKE - Orise Fellow
item Starks, Patrick

Submitted to: Grazinglands Research Laboratory Miscellaneous Publication
Publication Type: Abstract Only
Publication Acceptance Date: 4/8/2016
Publication Date: N/A
Citation: N/A

Interpretive Summary: Abstract only.

Technical Abstract: Soil microbial biomass carbon and nitrogen (MBC/MBN) are integral parts of soil organic matter, and if left out of nutrient calculations may suggest increased need of fertilizer resulting in increased production costs and chemical runoff. Timely and cost-effective methods are needed to assess MBC and MBN, and remote sensing techniques may provide such a solution. A study was conducted at the USDA-ARS Grazinglands Research Laboratory Old World Bluestem (Bothriochloa sp.) and native, tallgrass pastures to: 1) determine MBC/MBN content of native and non-native perennial pasture soils, and 2) correlate MBC/MBN values with hyperspectral reflectance data of dried, ground soils to determine if reflectance data can be used to determine MBC/MBN content of soil. Bi-weekly soil sampling (0-15cm) occurred at toe-, mid- and upper-slope positions along four parallel and widely-spaced transects. Each soil sample was processed for MBC/MBN using standard techniques. Bulk unfumigated and fumigated soil was dried at 65 ° for 24 hrs, ground to pass through a 2mm sieve and hyperspectral reflectance data were obtained using an ASD FieldSpec FR radiometer. Soil reflectance and MBC/MBN concentrations were statistically analyzed to determine if soil reflectance could be used for predicting MBC/MBN. Initial results indicate that utilizing hyperspectral soil data to determine soil MBC and MBN concentration is favorable (R2=0.65). Further study is needed to determine if this will be a suitable tool to determine MBC/MBN. Implications of this research could lead to real time soil fertility decision making reducing input cost and loss of C and N to the environment.