A possible explanation for higher prediction when applying the
model of Brown (1997) and Chave et al. (2005) to the current data
is the deference in wood density and tree architecture. Although,
some of Brown’s and Chave’s data were collected in Kalimantan, it
does not imply that the characteristics of the trees from Kalimantan, as used by Brown (1997) and Chave et al. (2005),
are the same as the trees used in this study. As can be seen from
Brown’s data, one of the trees from Kalimantan with the diameter
of 130 cm had dry weight of 42.8 tons (Brown, 1997), whereas
from the current research, Shorea sp. with a diameter of 200 cm has
dry weight of 36 tons. However, the prediction line of Ketterings’
equation lies below the observed values and the prediction line of
model 1 (Fig. 5). This may be caused by the marked difference
between the sampled trees in this study and Ketterings’ data. The
only species which are found both in Ketterings’ data and this
study are Shorea and Alseodaphne, sp. The lower prediction of
Ketterings’ equation is because the trees used to construct
Ketterings’ equation were much smaller than those from the
current study, as elaborated in the previous section. In addition to
the explanations above, the inclusion of wood density in assessing
biomass carbon will reduce uncertainty due to the variation among
differences of sites (Chave et al., 2006; Baker et al., 2004).
A possible explanation for higher prediction when applying the
model of Brown (1997) and Chave et al. (2005) to the current data
is the deference in wood density and tree architecture. Although,
some of Brown’s and Chave’s data were collected in Kalimantan, it
does not imply that the characteristics of the trees from Kalimantan, as used by Brown (1997) and Chave et al. (2005),
are the same as the trees used in this study. As can be seen from
Brown’s data, one of the trees from Kalimantan with the diameter
of 130 cm had dry weight of 42.8 tons (Brown, 1997), whereas
from the current research, Shorea sp. with a diameter of 200 cm has
dry weight of 36 tons. However, the prediction line of Ketterings’
equation lies below the observed values and the prediction line of
model 1 (Fig. 5). This may be caused by the marked difference
between the sampled trees in this study and Ketterings’ data. The
only species which are found both in Ketterings’ data and this
study are Shorea and Alseodaphne, sp. The lower prediction of
Ketterings’ equation is because the trees used to construct
Ketterings’ equation were much smaller than those from the
current study, as elaborated in the previous section. In addition to
the explanations above, the inclusion of wood density in assessing
biomass carbon will reduce uncertainty due to the variation among
differences of sites (Chave et al., 2006; Baker et al., 2004).
การแปล กรุณารอสักครู่..
A possible explanation for higher prediction when applying the
model of Brown (1997) and Chave et al. (2005) to the current data
is the deference in wood density and tree architecture. Although,
some of Brown’s and Chave’s data were collected in Kalimantan, it
does not imply that the characteristics of the trees from Kalimantan, as used by Brown (1997) and Chave et al. (2005),
are the same as the trees used in this study. As can be seen from
Brown’s data, one of the trees from Kalimantan with the diameter
of 130 cm had dry weight of 42.8 tons (Brown, 1997), whereas
from the current research, Shorea sp. with a diameter of 200 cm has
dry weight of 36 tons. However, the prediction line of Ketterings’
equation lies below the observed values and the prediction line of
model 1 (Fig. 5). This may be caused by the marked difference
between the sampled trees in this study and Ketterings’ data. The
only species which are found both in Ketterings’ data and this
study are Shorea and Alseodaphne, sp. The lower prediction of
Ketterings’ equation is because the trees used to construct
Ketterings’ equation were much smaller than those from the
current study, as elaborated in the previous section. In addition to
the explanations above, the inclusion of wood density in assessing
biomass carbon will reduce uncertainty due to the variation among
differences of sites (Chave et al., 2006; Baker et al., 2004).
การแปล กรุณารอสักครู่..