4.5 Article

Spatial and Temporal Patterns of Carbon Dioxide Flux in Crop and Grass Land-Use Systems

Journal

VADOSE ZONE JOURNAL
Volume 12, Issue 4, Pages -

Publisher

WILEY
DOI: 10.2136/vzj2013.01.0005

Keywords

-

Funding

  1. Libyan Ministry of Higher Education and Scientific Research
  2. USDA Natural Resources Inventory [2008-35107-04649]
  3. USDA-ARS [6440-32630-001-035]
  4. FAPRU Hatch Project [KY006045-0210417]
  5. Kentucky Water Resources Research Institute [2010KY145B]
  6. Kentucky Agricultural Experiment Station [13-06-014]
  7. NIFA [2008-35107-04649, 582931] Funding Source: Federal RePORTER

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Understanding the field-scale variability of CO2 fluxes in space and time under different land-use conditions is important for designing efficient sampling schemes for monitoring C loss from soil. The objectives of this study were to characterize the variability structure of CO2 fluxes and its change throughout a year, evaluate the temporal stability of spatial CO2 flux patterns, and quantify relationships with biotic and abiotic factors in crop and grass systems. In an 80- by 60-m field divided into grass and crop systems, CO2 fluxes were measured with a photo-acoustic analyzer at 60 locations 22 times during a year. Soil respiration was high when it was warm and wet and low under cool and wet conditions. Measurements of CO2 flux were spatially and temporally dependent for a longer distance and time in the grass system than in the crop system. Spatial patterns of soil respiration were temporally stable, and the larger the average spatial CO2 flux, the more obvious was the temporal stability. The variation of soil respiration was more pronounced in time than in space during the study period. At each location, soil temperature was the major factor controlling the temporal variability of CO2 flux; however, soil temperature did not explain the spatial CO2 flux pattern. Land use impacted the spatial and temporal variability dynamics of CO2 flux. These dynamics should be taken into account for experimental design, the detection of spatial and temporal associations with other variables, and C loss prediction.

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