Journal
BIOENERGY RESEARCH
Volume 3, Issue 2, Pages 159-171Publisher
SPRINGER
DOI: 10.1007/s12155-009-9045-y
Keywords
Switchgrass; Great Plains; Bioenergy; On-farm trials; Spatial variation; Temporal variation
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Switchgrass (Panicum virgatum L.) is being developed into a perennial, herbaceous, cellulosic feedstock crop for use in temperate regions of the USA. Information on spatial and temporal variation for stands and biomass yield among and within fields in large agroecoregions is not available. Spatial and temporal variation information is needed to model feedstock availability for biorefineries. In this 5-yr study, the spatial and temporal variation for biomass yield and stands was determined among and within 10 fields located in North Dakota, South Dakota, and Nebraska. Switchgrass fields were managed for bioenergy from 2000 to 2004 for the Nebraska locations and 2001 to 2005 for the South Dakota and North Dakota locations. A global positioning system (GPS) receiver was used to repeatedly measure within field quadrat sites for switchgrass stands using frequency grid (2.25 m(2)) measurements in June for five growing seasons. Sixteen quadrat (>= 1 m(2)) yield samples were taken post-killing frost in the establishment year and in August in subsequent years at each location. Topographic within field effects on switchgrass stand frequency and biomass yields were largely insignificant. Stands tended to increase from establishment year to year 3 and then begin to plateau. Weather factors, which were the principal source of temporal variation, were more important in switchgrass yield variation than on switchgrass stand frequencies. Temporal standard deviations for yield were higher on quadrat sites with higher than average field means while temporal standard deviations were smaller in quadrat sites that had lower than average field means at six locations. In the Northern Great Plains agroecoregion, there is greater temporal and spatial variation for switchgrass biomass yields among fields than within fields. Results indicate that modeling feedstock availability for a biorefinery can be based on field scale yields.
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