2013
DOI: 10.1016/j.soilbio.2013.05.005
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An experimental study of charcoal degradation in a boreal forest

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Cited by 31 publications
(15 citation statements)
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“…The much higher variability in pyOM-associated carbon, nitrogen and pH accounted for by time (compared to location or interactive effects) pointed to temporally variable environmental, rather than location-based, factors being the primary drivers of pyOM dynamics in this study. This also indicated that in contrast to other studies (Kane et al, 2010;Kasin and Ohlson, 2013), variations in early-stage pyOM dynamics due to location-based parameters such as elevation, soil type and position in landscape would not be important in our study. Of such, properties associated with a given pyOM source and within a given fire zone could be considered as replicates (i.e., properties at U1 and U2 are replicates for unburnt treatment while B1, B2, B3, and B4 are replicates for burnt treatments) and can be spatially-aggregated based on pyOM feedstock and fire history without any significant loss in the amount of variability captured in the study.…”
Section: Overall Effects Of Time and Space On Pyom Dynamicscontrasting
confidence: 66%
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“…The much higher variability in pyOM-associated carbon, nitrogen and pH accounted for by time (compared to location or interactive effects) pointed to temporally variable environmental, rather than location-based, factors being the primary drivers of pyOM dynamics in this study. This also indicated that in contrast to other studies (Kane et al, 2010;Kasin and Ohlson, 2013), variations in early-stage pyOM dynamics due to location-based parameters such as elevation, soil type and position in landscape would not be important in our study. Of such, properties associated with a given pyOM source and within a given fire zone could be considered as replicates (i.e., properties at U1 and U2 are replicates for unburnt treatment while B1, B2, B3, and B4 are replicates for burnt treatments) and can be spatially-aggregated based on pyOM feedstock and fire history without any significant loss in the amount of variability captured in the study.…”
Section: Overall Effects Of Time and Space On Pyom Dynamicscontrasting
confidence: 66%
“…Particularly, since like the spodosol and entisol of Mukherjee et al (2014) and the ultisol at our site, the surface soils of the region are predominantly coarse (sandy) textured, with low pH, low organic matter and low retention capacities (Shaw et al, 2004;Novak et al, 2009). Other studies have also reported either the accumulation of carbon (Kasin and Ohlson, 2013;Dong et al, 2017) or nitrogen (Sorrenti et al, 2016;de la Rosa et al, 2018), but not both, after the field aging of pyOM for up to 5 years. It is also interesting to note that for studies having similar trajectories in pyOM carbon and nitrogen dynamics, there were suggestions of similar climate-either similar Koppen-Geiger climate classification (continental climate; Kasin and Ohlson, 2013;Dong et al, 2017) or similar annual precipitations and temperatures (Sorrenti et al, 2016;de la Rosa et al, 2018).…”
Section: Dynamics Of Pyom-associated Carbon and Nitrogenmentioning
confidence: 92%
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“…Similarly, Lynch and Clark (2004) found that less than 1 % of biochar was moved further than 20 m from the fire edge in the field by, for example, water or wind. Biochar contents in soils also vary significantly across different types of forest landscapes and regional climates (Ohlson et al 2009;Kasin and Ohlson 2013). A geo-statistical approach to present a detailed analysis of biochar contents in a boreal forest estimated a mean value of 179 g C m −2 .…”
Section: Spatial Distributionmentioning
confidence: 99%
“…These factors complicate estimates of biochar decomposition time (Kane et al 2010), designed to better understand the link between fire, biochar, and soil in terms of their potential for carbon sequestration, both in incubation experiments under controlled laboratory conditions (Liang et al 2010;Luo et al 2011) without or with substrates (Nocentini et al 2010). An initiation of long-term field experiments (Wardle et al 2008b;Kasin and Ohlson 2013) is recommended in different forest systems under different climates, by adoption of techniques such as radiocarbon ( 14 C) and stable isotope ( 13 C) analysis and 13 C NMR to address the decomposition dynamics of biochar.…”
Section: Difficulty In Estimating Biochar Mineralization Ratesmentioning
confidence: 99%