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CFFERENTIATE 
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ness at the 
led. 
three different 
are field which 
e of organic 
rface soil 
analysis and 
highly 
variable in organic carbon content. The second site 
was an agricultural field in the driest part of 
Canada located on a recently cleared glacio-lacus- 
trine deposit. 58 soil samples were collected at 
that site and the samples had very low organic matter 
content (0.5-2.0%), while the soil texture was ex 
tremely variable. The third set, consisting of 30 
samples collected from ten different metal mine tail 
ings in British Columbia and represented a sample set 
with virtually no organic matter content but highly 
variable iron content. 
The following soil properties were analysed in all 
samples: Organic carbon content (Walkley-Black 
method), total carbon (Leco-Method), extractable iron 
(citrate-dithionite-bicarbonate method), total iron 
(HF-teflone bomb method), total nitrogen (autoanalys 
er method), available P (Bray P-1 method), extracta 
ble K and Ca (neutral ammonium acetate method), and 
particle size (using the hydrometer method) and soil 
color (Munsell Color System). All methods are des 
cribed in Black et al (1965). 
The spectral reflection measurements of the soil 
samples were carried out with a Barringer Multi 
channel spectrometer using various spectral bands 
covering the 500-2350 nm wavelength range. The 
measurements were carried out in the laboratory 
under artificial light and all samples were air-dried 
and the greater than 2mm fraction was removed. 
The data were analysed using single and multiple 
correlation and regression techniques so as to deter 
mine the best relationships between soil fertility 
properties and spectral reflection measurements at 
different wavelength bands. 
3.2 Results 
The three soil data sets were analysed separately and 
a plot of the reflection curves over the 500-2350 nm 
range showed that set one and set two had character 
istic curves which were unique and significantly 
different from one another. Sets one and three could 
also be separated on the basis of the shape of the 
spectral curve but there was considerable overlap 
between set two (low organic content and high 
particle size variability) and set three (no organic 
carbon, little particle size variation and high iron 
variability). The results suggest that sample prov 
enance or overall composition has an important in 
fluence on reflection. 
When individual properties were related to the 
reflection at specific wavelengths the following 
picture emerged: Significant correlations were 
found between spectral reflection vs organic carbon 
content, spectral reflection vs iron content, and 
spectral reflection vs % sand content in the samples. 
In each case the correlation was only significant 
within a restricted concentration range and when the 
data sets were combined the significance of the re 
lationships were dramatically reduced. A regression 
analysis was carried out for the best relationships 
and the results are provided in Figures 3-5. Organic 
carbon could best be predicted from reflection in 
the 550 nm wavelength range using the organic rich 
samples of sample set 1. In this case all samples 
had organic carbon values which were greater than 
2.0% and the regression in Figure 3 explains 86% of 
the total variance in carbon. When the data from 
samples with less than 2% carbon were introduced to 
the equation the relationship could no longer be 
extended. This clearly suggests that above a certain 
threshold value organic carbon has a dominant in 
fluence on reflection and can thus be predicted with 
fairly good accuracy. Below 2% other properties 
such as texture and iron appear to influence reflec 
tion significantly. Figure 4 shows the regression 
between total iron content and spectral reflection 
at 630 nm wavelength for the mine tailing samples 
which were characterized as having no organic carbon 
content and very little textural variability but 
great total iron variability. In this regression 
the reflection values explained 87% of the variation 
in iron content. Finally the soil reflection data 
from the second data were found to correlate signifi 
cantly with soil texture (% sand content) at 1600 nm 
wavelength. 
Figure 3. Relationship Figure 4. Relationship 
between reflection (550 nm) between reflection (630 
and organic carbon content nm) and total iron con 
fali samples 2% carbon). tent (all samples 0.01% 
carbon) «. 
As shown in Figure 5 this relationship explained 
61% of the variance in % sand content. Curvilinear 
relationships with reflection and appropriate log- 
transformations were made prior to the analysis. 
% Sand Content 
Figure 5. Relationship between reflection (1600 nm) 
and % sand content (all samples 2% carbon). 
In addition to these relationships two other correla 
tions were significant. Total nitrogen was related 
to reflection at 550 nm (r = -0.78) for the set rich 
in organic carbon, and exchangeable Ca was signifi 
cantly related to reflection at 1600 nm (r - 0.56) 
in the low carbon content soils. The former is of 
interest because it is one of the main and most var 
iable fertility components in the soil. It is 
obvious that nitrogen has no direct effect on reflec 
tion but it is most often highly correlated with 
organic carbon content thus giving us a significant 
relationship. Because of this secondary relationship 
it might be possible to also get a crude assessment 
of the nitrogen status in the field using the reflec 
tion data and this has important implications for 
improving the efficiency of fertilizer use since con 
ventional nitrogen tests are invariably inaccurate 
and unreliable. The relationship between Ca and 
reflection is also suspect but a possible explanation 
for it is the fact that Ca is easily absorbed by clay 
minerals and this results in clay flocculation 
(Hosterman and Whitlow 1980). This might prevent 
organic matter from replacing Ca in the clay structure