Abstract
Full assessment of soil carbon (C) and nitrogen (N) pools is necessary for long-term sustainability of agricultural production and provides information on plant health and nutrient cycling. A major component of nutrient cycling is plant root C and N. Although root C and N contribute to nutrient cycling, determination of these quantities is laborious and tedious and is, therefore, not commonly done. In this study we attempt to determine the feasibility of using remotely sensed canopy reflectance as a proxy to determine root C and N data of live, standing forages. The study site was the United States Department of Agriculture-Grazinglands Research Laboratory located in El Reno, Oklahoma. Twelve plots in each of two sites (a native, tallgrass prairie and an improved, Old World Bluestem pasture) were used for collection and measurement of root C and root N and measurement of canopy reflectance using a field portable hyperspectral spectroradiometer. Root and soil samples were then taken from under the remote sensed area for total C and N analysis using the combustion method. The results of this study indicated that it is feasible to predict root C and N, but further study is required to improve model accuracy.
Highlights
Van Ginkle et al (1996) indicated that forage tissue nutrients in grassland aboveground biomass is shuttled to belowground biomass storage in the roots
The systems we included in our study represented unmanaged and managed forages, our results showed that forage system was not a factor in inducing variation in root biomass C or N
Both the recursive partitioning (RP) and artificial neural networks (ANN) indicated that, during cross-validation, from 56% to 80% of the variation in root C and N could be explained by the hyperspectral canopy reflectance data
Summary
Van Ginkle et al (1996) indicated that forage tissue nutrients in grassland aboveground biomass is shuttled to belowground biomass storage in the roots. Cheng et al (2015) investigated the allometric partitioning theory on the above- and below-ground biomass in understory tropical plants (n = 1586), and noted a strong statistical relationship between the two variables. Kerkhoff et al (2006) conducted an analysis on a large data set (n = 1287 plant species) containing N concentration of leaves, stems, roots, and reproductive structures of both woody and herbaceous species. They showed that a statistically significant relationship exists between the root N and that in the plant’s leaves
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