• Authors:
    • Chen, D.
    • Kelly, K.
    • Li, Y.
  • Source: ASA-CSSA-SSSA International Annual Meetings (November 4-8, 2007)
  • Year: 2007
  • Authors:
    • Denmead, O. T.
    • Kelly, K. B.
    • Baigent, R.
    • Leuning, R.
    • Phillips, F. A.
  • Source: Agricultural and Forest Meteorology
  • Volume: 143
  • Issue: 1-2
  • Year: 2007
  • Authors:
    • White, R. E.
    • Chapman, D. F.
    • Eckard, R. J.
  • Source: Australian Journal of Agricultural Research
  • Volume: 58
  • Issue: 12
  • Year: 2007
  • Authors:
    • Apan, A.
    • Maraseni, T. N.
    • Cockfield, G.
  • Source: Journal of Environmental Science and Health, Part A: Toxic/Hazardous Substances and Environmental Engineering
  • Volume: 42
  • Issue: 1
  • Year: 2007
  • Authors:
    • McGregor, A.
    • Slattery, B.
    • Ugalde, D.
    • Brungs, A.
    • Kaebernick, M.
  • Source: Soil & Tillage Research
  • Volume: 97
  • Issue: 2
  • Year: 2007
  • Authors:
    • Barlow, E. W. R.
    • Whetton, P. H.
    • Webb, L. B.
  • Source: Australian Journal of Grape and Wine Research
  • Volume: 13
  • Issue: 3
  • Year: 2007
  • Authors:
    • North Carolina Department of Revenue
  • Year: 2007
  • Authors:
    • Paustian, K.
    • Williams, S.
    • Easter, M.
    • Breidt, F. J.
    • Ogle, S. M.
  • Source: Ecological Modelling
  • Volume: 205
  • Issue: 3-4
  • Year: 2007
  • Summary: Simulation modelling is used to estimate C sequestration associated with agricultural management for purposes of greenhouse gas mitigation. Models are not completely accurate or precise estimators of C pools, however, due to insufficient knowledge and imperfect conceptualizations about ecosystem processes, leading to uncertainty in the results. It can be difficult to quantify the uncertainty using traditional error propagation techniques, such as Monte Carlo Analyses, because of the structural complexity of simulation models. Empirically based methods provide an alternative to the error propagation techniques, and our objective was to apply this alternative approach. Specifically, we developed a linear mixed-effect model to quantify both bias and variance in modeled soil C stocks that were estimated using the Century ecosystem simulation model. The statistical analysis was based on measurements from 47 agricultural experiments. A significant relationship was found between model results and measurements although there were biases and imprecision in the modeled estimates. Century under-estimated soil C stocks for several management practices, including organic amendments, no-till adoption, and inclusion of hay or pasture in rotation with annual crops. Century also over-estimated the impact of N fertilization on soil C stocks. For lands set-aside from agricultural production, Century under-estimated soil C stocks on low carbon soils and over-estimated the stocks on high carbon soils. Using an empirically based approach allows for simulation model results to be adjusted for biases as well as quantify the variance associated with modeled estimates, according to the measured "reality" of management impacts from a network of experimental sites.
  • Authors:
    • Nicolardot, B.
    • Labreuche, J.
    • Grehan, E.
    • Merckx, R.
    • Oorts, K.
  • Source: Soil & Tillage Research
  • Volume: 95
  • Issue: 1-2
  • Year: 2007
  • Summary: The greenhouse gases CO2 and N2O emissions were quantified in a long-term experiment in northern France, in which no-till (NT) and conventional tillage (CT) had been differentiated during 32 years in plots under a maize-wheat rotation. Continuous CO2 and periodical N2O soil emission measurements were performed during two periods: under maize cultivation (April 2003-July 2003) and during the fallow period after wheat harvest (August 2003-March 2004). In order to document the dynamics and importance of these emissions, soil organic C and mineral N, residue decomposition, soil potential for CO2 emission and climatic data were measured. CO2 emissions were significantly larger in NT on 53% and in CT on 6% of the days. From April to July 2003 and from November 2003 to March 2004, the cumulated CO2 emissions did not differ significantly between CT and NT. However, the cumulated CO2 emissions from August to November 2003 were considerably larger for NT than for CT. Over the entire 331 days of measurement, CT and NT emitted 3160 +/- 269 and 4064 +/- 138 kg CO2-C ha(-1) respectively. The differences in CO2 emissions in the two tillage systems resulted from the soil climatic conditions and the amounts and location of crop residues and SOM. A large proportion of the CO2 emissions in NTover the entire measurement period was probably due to the decomposition of old weathered residues. NT tended to emit more N2O than CTover the entire measurement period. However differences were statistically significant in only half of the cases due to important variability. N2O emissions were generally less than 5 g N ha(-1) day(-1), except for a few dates where emission increased up to 21 g N ha(-1) day(-1). These N2O fluxes represented 0.80 +/- 0.15 and 1.32 +/- 0.52 kg N2O-N ba(-1) year(-1) for CT and NT, respectively. Depending on the periods, a large part of the N2O emissions occurred was probably induced by nitrification, since soil conditions were not favorable for denitrification. Finally, for the period of measurement after 32 years of tillage treatments, the NT system emitted more greenhouses gases (CO2 and N2O) to the atmosphere on an annual basis than the CT system. (C) 2006 Elsevier B.V. All rights reserved.
  • Authors:
    • Olofsson,J.
    • Hickler,T.
    • Orloff,Steve B.
    • Klonsky,Karen M.
    • Livingston,Pete
  • Source: University of California Cooperative Extension Publication
  • Year: 2007