New Tools for Interpreting Foliar Nutrient Status
Robert P. Brockley
Contract Research Report to Forest Practices Branch, Ministry of Forests, Lands
and Natural Resource Operations
Contract OT12FHQ299
March 2012
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TABLE OF CONTENTS
Introduction 1
Normalization of Foliar Nutrient Data 3
Inter-laboratory Comparisons 3
Laboratory Methods 3
Pacific Soil Analysis 3
Ministry of Environment 4
Data Analysis 4
Normalization Rationale 5
Total Nitrogen 5
Total Sulphur 6
Sulphate-sulphur 6
Phosphorus, Potassium, Calcium, Magnesium 7
Boron 8
Copper, Zinc, Iron, Manganese 8
Normalization Spreadsheet 9
Precautions 9
Revised Foliar Nutrient Interpretative Criteria 10
Data Sources 10
Macronutrients (N, P, K, Ca, Mg, S) 12
Sulphate-sulphur 12
Boron 13
Other Micronutrients 13
Nutrient Ratios 14
Use of Interpretative Tables 15
Precautions 16
Reference 17
TABLES
1 Interpretation of macronutrient concentrations in current year’s foliage of lodgepole pine,
interior spruce, and Douglas-fir 25
2 Interpretation of sulphate-sulphur concentrations in current year’s foliage of lodgepole pine,
interior spruce, and Douglas-fir 26
3 Interpretation of nutrient ratios in current year’s foliage of lodgepole pine, interior spruce, and
Douglas-fir 27
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FIGURES
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Relationship between foliar N (PSA) and foliar N (MoE) 28
Relationship between foliar S (combustion) (PSA) and foliar S (combustion) (MoE) 28
Relationship between foliar S (combustion) (MoE) and foliar S (ICP) (MoE) 29
Relationship between foliar S (combustion) (PSA) and foliar S (ICP) (MoE) 29
Relationship between foliar sulphate-S (PSA) and foliar sulphate-S (MoE) 30
Relationship between foliar K (MoE) and foliar K (PSA) 30
Relationship between foliar Ca (MoE) and foliar Ca (PSA) 31
Relationship between foliar Mg (MoE) and foliar Mg (PSA) 31
Relationship between foliar P (MoE) and foliar P (PSA) 32
Relationship between foliar B (PSA) and foliar B (MoE) 32
Relationship between foliar Cu (PSA) and foliar Cu (MoE) 33
Relationship between foliar Fe (PSA) and foliar Fe (MoE) 33
Relationship between foliar Zn (PSA) and foliar Zn (MoE) 34
Relationship between foliar Mn (PSA) and foliar Mn (MoE) 34
Foliar data “normalization” spreadsheet 35
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Introduction
Forests in British Columbia are commonly nutrient deficient, and foliar analysis is widely used by
forest practitioners to evaluate their nutrient status as part of the stand selection process for large-scale
fertilizer operations. Foliage is collected using standardized foliar sampling methodology (Ballard and
Carter 1986; Brockley 2001). Following laboratory nutrient analysis, foliar analytical results are
compared with published interpretative criteria to confirm nitrogen (N) deficiencies, and to infer
whether other nutrients will either limit growth response or become growth-limiting after N is added.
Analytical results are often used to develop appropriate fertilizer formulations to correct inferred
nutrient deficiencies.
The interpretation of foliar nutrient data is not straightforward. Foliar nutrient interpretations
are subject to serious shortcomings when foliage is collected using non-standardized methods, and
when foliar data are reviewed without knowledge or consideration of site ecological characteristics.
Also, to arrive at the correct diagnosis of stand nutrient status, appropriate weight must be assigned to
several different components of foliar nutrition: 1) absolute levels of individual foliar nutrients, 2)
balance of foliar levels of one nutrient to another, and 3) levels of inorganic fractions of specific
nutrients (e.g., SO4-S). Finally, foliar analytical results may differ depending on the methodology used for
laboratory extraction and determination. For example, recovery of N and S from plant tissue is typically
lower with wet (i.e., acid) digestion methods than with dry (i.e., combustion) methods (Randall and
Spencer 1980; Guthrie and Lowe 1984; Simonne et al. 1994; Horneck and Miller 1998). In some cases,
differences may be large enough to affect diagnoses of nutrient sufficiency or deficiency based on
available interpretative criteria. Published nutrient interpretative criteria do not typically account for
differences in laboratory analytical methodology. However, known differences in laboratory analytical
results for different nutrients can be used to “normalize” foliar nutrient data prior to interpretation. By
removing the effect of differences in laboratory analytical methodology on results, “normalization” can
improve both interpretative reliability and the development of appropriate fertilizer prescriptions. No
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inferences about the quality or integrity of results provided by any specific laboratory should be drawn
from the “normalization” requirement. It is simply a tool to facilitate reliable interpretation of stand
nutrient status.
Reliable interpretation of foliar nutrient data is also dependent on the availability of appropriate
foliar nutrient interpretative criteria for the tree species of interest. Ballard and Carter (1986) suggested
some interpretations of foliar nutrient levels in several conifer species occurring in British Columbia,
based largely on a review of earlier published research undertaken elsewhere. These interpretative
criteria were slightly revised by Carter (1992). A large amount of forest nutrition and fertilization
research with lodgepole pine (Pinus contorta Dougl. var. latifolia Engelm.), interior spruce (Picea glauca
(Moench) Voss and Picea engelmannii Parry, or naturally-occurring hybrids of these species), and
Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) was undertaken in the BC interior in the 1980’s and
1990’s by the B.C. Ministry of Forests. Published and unpublished results from this work were used by
Brockley (2001) to update lodgepole pine foliar nutrient interpretative criteria. These data, and
additional foliar data collected from repeated fertilization experiments with interior spruce and
lodgepole pine during the past decade, provide an excellent opportunity to develop revised
interpretative criteria for interior spruce and Douglas-fir and to further refine the lodgepole pine
criteria.
This report summarizes contract work undertaken in 2011/12 to develop a user-friendly tool to
“normalize” foliar nutrient data and to develop revised nutrient interpretative criteria for lodgepole
pine, interior spruce, and Douglas-fir. When used in conjunction with each other, the “normalization”
tool and the revised interpretative criteria should result in more reliable interpretation of foliar nutrient
data and improved fertilizer prescriptions.
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“Normalization” of Foliar Nutrient Data
Inter-laboratory Comparisons
The first step in the “normalization” process is to obtain a reliable set of comparative foliar data
from different analytical laboratories. In November 2011, 48 previously analyzed lodgepole pine foliage
samples were selected for use in an inter-laboratory comparative study between the Ministry of
Environment (MoE) laboratory (Victoria, BC) and the Pacific Soil Analysis Inc. (PSAI) laboratory
(Richmond, BC). These two laboratories undertake the vast majority of nutrient analyses on conifer
foliage samples collected by government and industrial forestry clients in British Columbia. The foliage
samples for the inter-laboratory study were specifically selected to cover a broad range of foliar levels
for most nutrients. All samples had been previously collected from several forest nutrition studies (EP
886.13, EP 1185) in the BC interior using standard foliar sampling protocol. Each foliage sample was
thoroughly mixed and split into two sub-samples, one of which was subsequently sent to each
laboratory. The following analyses were completed by each laboratory: N, phosphorus (P),potassium (K),
calcium (Ca), magnesium (Mg), total sulphur (S), inorganic sulphate-S (SO4), copper (Cu), zinc (Zn), iron
(Fe), manganese (Mn), and boron (B). Each laboratory used their standard procedures for extraction and
determination.
Laboratory Methods
Pacific Soil Analysis
Foliage was digested using a variation of the sulphuric acid – hydrogen peroxide procedure
described by Parkinson and Allen (1975). The digests were analyzed colorimetrically for N on a
Technicon Autoanalyzer using a phenol–hypochlorite reaction (Weatherburn 1967). A
spectrophotometer was used to measure P, using a procedure based on the reduction of the ammonium
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molybdiphosphate complex by ascorbic acid (Watanabe and Olson 1965). Total K, Ca, and Mg were
determined using a Perkin-Elmer atomic absorption spectrophotometer.
Separate subsamples were dry-ashed and Cu, Zn, Fe, and Mn were determined by atomic
absorption spectrophotometery. After dry-ashing, B was determined colorimetrically using the
azomethine-H method described by Gaines and Mitchell (1979).
Total S was determined by combustion using a LECO SC-132 sulphur analyzer.
Inorganic SO4 was extracted with dilute, boiling HCl and determined colorimetrically on a
hydriodic acid – bismuth reducible distillate (Johnson and Nishita 1952).
Ministry of Environment
Analysis of total N was by combustion using a Fisons NA-1500 elemental analyzer. All other
macro- and micro-nutrients were wet-ashed with concentrated nitric acid – HCl and hydrogen peroxide,
using a closed-vessel microwave digestion system (Kalra and Maynard 1991). The digest solutions were
diluted with HCl and individual nutrients were determined by inductively coupled plasma (ICP) optical
emission spectrophotometer.
Total S was determined using two different methodologies – by combustion using a Fisons NA1500 elemental analyzer and by wet-ashing (as above) followed by determination by ICP.
Sulphate-S was extracted with dilute HCl and determined by ion chromatography (Waters IC
system).
Data Analysis
For any given nutrient, the results provided from the PSAI laboratory are not functionally
dependent on the results from the MoE laboratory, or vice versa. Lacking a truly dependent relationship
between two variables, regression analysis is arguably an inappropriate statistical technique. However,
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simple regression offers a convenient way to quantify the relationship between the sample results from
each laboratory.
For each individual nutrient, both linear and polynomial models were tested. Across the data
ranges tested, both models gave similar fits. For simplicity, and to possibly provide more conservative
estimates when extrapolating outside the data range of the model, only linear models were used in the
subsequent normalization process. For each nutrient, the “normalization rationale” discussed below was
used to determine which laboratory would serve as the dependent variable in the regression analysis.
Regression lines were not forced through the origin.
Normalization Rationale
Total Nitrogen
Published foliar N interpretative criteria for most conifer species, both in BC and elsewhere,
have used analytical results obtained from wet digestion (i.e., modified Kjeldahl) procedures (Ballard
and Carter 1986). Because the MoE laboratory uses combustion methodology for N analysis (which, as
noted previously, typically produces higher N values), the MoE inter-laboratory results were
“normalized” (i.e., adjusted downward) to facilitate interpretation. The regression equation defining the
relationship between PSAI (dependent variable) and MoE (independent variable) foliar N results
(r2=0.78) was used to convert the “raw” N data provided by the MoE laboratory to a lower “normalized”
value (Figure 1). As stated previously, this does not mean than N analyses undertaken by the MoE are
inferior to PSAI data. The “normalization” of the MoE data simply recognizes known differences in
analytical results based on different methodology, so that the adjusted N values are consistent with wet
digestion N values from which N interpretative criteria are typically developed.
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Total Sulphur
Published foliar S interpretative criteria have typically used analytical results obtained from
combustion procedures (Ballard and Carter 1986). As noted previously, dry combustion typically yields
higher S values than wet digestion analytical methods. The PSAI laboratory uses dry combustion to
determine total S. The MoE laboratory reports total S from both wet (S-ICAP) and dry (S-comb) analytical
methodologies. The results from the inter-laboratory comparison indicated that the relationship
between the S-comb (MoE) method and the PSAI dry combustion method is relatively weak (r2=0.53)
(Figure 2), as is the relationship between the MoE wet (S-ICAP) and dry (S-comb) methods (r2=0.46)
(Figure 3). Conversely, the relationship between S-ICAP (MoE) wet digestion method and the PSAI dry
combustion method is much stronger (r2=0.76) (Figure 4). Therefore, the regression equation defining
the relationship between PSAI total S (dependent variable) and MoE (S-ICAP) wet digestion total S
(independent variable) was used to convert the “raw” S-ICAP (i.e., wet digestion) data provided by the
MoE laboratory to a higher “normalized” value for interpretative purposes (Figure 4). NOTE: The total Scomb (i.e., combustion) values reported by MoE should not be used in the “normalization” spreadsheet.
Sulphate-Sulphur
The use of foliar SO4 as a diagnostic tool for the evaluation of tree S status was pioneered by
research with radiata pine (Pinus radiata D. Don) and Douglas-fir (Kelly and Lambert 1972; Turner et al.
1977, 1979). Kelly and Lambert’s (1972) original procedure involved boiling acid extraction of SO4 and
precipitation with barium. The SO4 concentration was calculated indirectly after determination of the
barium concentration by atomic absorption (AA) spectrophotometry. The laborious and time-consuming
method was later modified so that SO4 was quantified in the extracting solution by ion chromatography
(IC) (M. Lambert, per. commun.). The modified IC method was later adopted by the MoE laboratory for
SO4 determination. The PSAI laboratory uses a similar method to extract SO4 from foliage, but
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determination is by hydriodic acid (HI) reduction of the extract and bismuth colorimetry using the
procedure of Johnson and Nishita (1952). In the inter-laboratory comparison, the IC and HI methods
gave similar results and the relationship between the two methods was very strong (r2=0.91). Both
methods have been used in British Columbia, but most research with SO4 as a diagnostic indicator of
foliar S status has been based on the PSAI method (Brockley 2000a,b). Therefore, the regression
equation defining the relationship between PSAI SO4 (dependent variable) and MoE SO4 (independent
variable) was used to convert the “raw” SO4 data provided by the MoE laboratory to a slightly lower
“normalized” value for interpretative purposes (Figure 5).
Phosphorus, Potassium, Calcium, Magnesium
The MoE and PSAI laboratories extract P, K, Ca, and Mg with different acid digestion methods.
At the MoE lab, determination is by ICP, whereas determination at PSAI is by AA spectrophotometry. In
the inter-laboratory comparison, the PSAI results for all four nutrients were typically higher than the
MoE results within the ranges tested. For K, Ca, and Mg, the inter-laboratory comparison indicated that
the relationships between the results from the two laboratories were relatively strong (r2=0.74–0.79)
(Figures 6–8). For P, the relationship was somewhat weaker (r2=0.61) (Figure 9).
Few fertilization research experiments have been undertaken to verify inferred deficiency levels
for P, K, Ca, and Mg in BC forests. For Douglas-fir, laboratory analysis has typically used methodology
consistent with that used by PSAI (Carter and Klinka 1988; Carter et al. 1998). Both analytical methods
have been used for lodgepole pine and interior spruce foliage samples collected from research trials
involving fertilization with these nutrients. However, development of revised interpretative criteria has
mostly been based on MoE methodology (Brockley 2007a, 2010a,b). In the “normalization” spreadsheet,
PSAI “raw” data for P, K, Ca, and Mg are converted to slightly lower “normalized” values for
interpretative purposes (Figures 6–9).
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Boron
A considerable amount of B foliar nutrition and fertilization research has been undertaken with
lodgepole pine and Douglas-fir in British Columbia (Carter et al. 1984; Brockley 1990, 2003; Carter and
Brockley 1990). In all cases, methods for extraction and determination of foliar B were consistent with
analytical methods used by PSAI. Boron research studies with radiata pine and black spruce (Picea
mariana (Mill.) B.S.P.) used similar methodology (Hopmans and Clerehan 1991; White and Krause 2001).
In the inter-laboratory comparison, the PSAI and MoE methods gave similar results and the relationship
between the two methods was very strong (r2=0.98) (Figure 10). However, to be consistent with
methods from which B foliar interpretative criteria have been developed, the MoE “raw” B data were
converted to slightly higher “normalized” values for interpretative purposes (Figure 10).
Copper, Zinc, Iron, and Manganese
The relationships between Cu and Fe analytical results from the two laboratories were very
weak (r2=0.08 and 0.16, respectively) (Figures 11 and 12). This was likely partially due to the small data
range for these two nutrients in the samples tested. However, quantitative analysis of these nutrients is
problematic with all routine analytical methods, especially at the low foliar levels typically encountered
in most BC conifers (C. Dawson, per. comm). Conversely, the relationships between the Zn and Mn
analytical results were strong in the inter-laboratory comparison (r2= 0.84 and 0.94, respectively), and
both labs reported similar results (Figures 13 and 14).
Given the weak inter-laboratory relationships for Cu and Fe, and the similarity of interlaboratory results for Zn and Mn, “normalization” was not undertaken for any of these micronutrients.
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Normalization Spreadsheet
An Excel spreadsheet was constructed to facilitate the “normalization” process (Figure 15). The
results for individual nutrient analyses obtained from a particular lab (either MoE or PSAI) are entered in
the appropriate column (labelled “raw data”). For each laboratory, the spreadsheet utilizes formulae
(i.e., regression equations developed from inter-laboratory comparisons) imbedded in specific cells to
automatically adjust “raw” (i.e., entered) values for some nutrients based on the “normalization
rationale” discussed above. The MoE “raw” data for N, total S(ICAP), SO4, and B are “normalized” (i.e.,
adjusted). Similarly, the spreadsheet adjusts P, K, Ca, and Mg “raw” foliar data provided by PSAI. For
reasons described above, “normalization” is not undertaken for Cu, Zn, Fe, and Mn.
The spreadsheet automatically calculates nutrient ratios (e.g., N:S, N:P, N:K, N:Ca, N:Mg.) for the
“normalized” data.
Precautions
Only lodgepole pine foliage was used in the inter-laboratory comparison and in the
development of “normalization” equations for each nutrient. As such, the reliability of the “normalized”
values may be lower for other species (e.g., spruce and Douglas-fir), especially for nutrients (e.g., P, K,
Ca, and SO4), whose foliar levels in those species are often outside of the range of those tested in this
study. Further inter-laboratory testing, using different species and a wider range of foliar nutrient levels,
is warranted.
The “normalization” formulae have been developed solely on inter-laboratory comparisons
between Pacific Soil Analysis and Ministry of Environment. Therefore, foliar nutrient results from other
laboratories should not be used in the “normalization” spreadsheet.
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For the MoE laboratory, only total S values obtained from wet ashing followed by ICP
determination (S-ICAP) should be entered as “raw” data. Foliar S results obtained by combustion (Scomb) should not be used in the “normalization” spreadsheet.
Revised Foliar Nutrient Interpretative Criteria
Data Sources
Ballard and Carter (1986) suggested some interpretations of foliar nutrient levels in several
conifer species in British Columbia. Many of the interpretations were consistent with those in the review
by Morrison (1974). Interpretations for N, P, and K for most species were based largely on the work of
Everard (1973), who dealt with young plantations in Britain. Lodgepole pine and white spruce
interpretations were based partly on nutritional research with conifer seedlings (Swan 1971, 1972).
Micronutrient interpretations were taken from several sources, most of which were from non-native BC
conifers and from locations elsewhere in the world (Stone 1968). However, B and Cu interpretations for
lodgepole pine and Douglas-fir were partially developed from work by Carter et al. (1984) and Majid
(1984) in British Columbia. Interpretations of S and SO4-S were based on data of Kelly and Lambert
(1972) for radiata pine and Turner et al. (1977, 1979) for Douglas-fir. The interpretative criteria
suggested by Ballard and Carter (1986) were modified slightly by Carter (1992).
Two decades of fertilization research in the interior of British Columbia resulted in the
development of revised interpretative criteria for lodgepole pine (Brockley 2001). The revised criteria for
B, S and SO4 suggested by Brockley (2001) were based on published fertilization research (Carter and
Brockley 1990; Brockley 1990, 1995, 1996, 2000a,b; Brockley and Sheran 1994). Unpublished results
from repeated fertilization experiments in the BC interior (EP 886.13) were useful in determining revised
deficiency thresholds for P, K, and Mg. Changes in foliar nutrient balance following repeated fertilization
of lodgepole pine, combined with growth response estimates, were useful in developing the
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interpretative criteria for foliar nutrient ratios suggested by Brockley (2001). Published nutrient
interpretative criteria for European conifers were also useful sources of information (Ingestad 1979;
Binns et al. 1980; Linder 1995; Rosengren-Brinck and Nihlgård 1995; Tamm et al. 1999).
Several sources were used in the development of the current revisions of interpretative criteria
for lodgepole pine and interior spruce. Published results from repeated fertilization research
experiments in the BC interior were major sources of information (Kishchuk et al. 2002; Brockley and
Simpson 2004, Amponsah et al. 2005; Brockley 2007a, 2010a,b). Foliar nutrient data from several other
pine and spruce fertilization and nutrition studies in the BC interior and eastern Canada were also very
useful (Truong Dinh Phu and Gagnon 1975; Brockley 1992, 2003, 2004, 2005, 2007b; Swift and Brockley
1994; Kishchuk and Brockley 2002; Brockley and Sanborn 2003). European foliar data and interpretative
criteria for lodgepole pine, white spruce, Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies
(L.) Karst.) were also consulted (Binns et al. 1980; van den Burg 1985, 1990; Cape et al. 1990; Sikström et
al. 1998; Braekke and Salih 2002). European literature on ‘forest decline’ associated with N saturation
provided useful information on foliar nutrient balance (Schulze et al. 1989; Cape et al. 1990; Ericsson et
al. 1993).
For Douglas-fir, results from fertilization studies in the BC interior were useful sources of
information (Brockley 2006). Published results from the Intermountain Forest Tree Nutrition
Cooperative, and results from other Douglas-fir fertilization studies in Washington and Oregon were also
used (Turner et al. 1977, 1979, 1988; Carter and Klinka 1988; Mika and Moore 1991; Velazquez-Martinez
et al. 1992; Hopmans and Chappell 1994; Mandzak and Moore 1994; Carter et al. 1998; Garrison et al.
2000). Other interpretative criteria for Douglas-fir were also consulted (Binns et al. 1980; Powers 1983;
van den Burg 1985, 1990; Ballard and Carter 1986; Walker and Gessel 1991; Webster and Dobkowski
1983; Carter 1992).
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Macronutrients (N, P, K, Ca, Mg, S)
Minor revisions were made to the lodgepole pine interpretative criteria suggested by Brockley
(2001). The “adequate” level for N (above which nutrient additions will likely not result in significant
growth gains) was been adjusted slightly downward from 1.35% to 1.30% (Table 1).
For interior spruce, several revisions were made to the interim interpretative criteria originally
suggested by Ballard and Carter (1986) and subsequently revised by Carter (1992). For N, P, Mg and S,
the “adequate” and deficiency thresholds were lowered. For K, the “adequate” level remains unchanged
but the moderate to severe deficiency thresholds were raised (Table 1).
Several changes were made to earlier interpretative criteria for Douglas-fir suggested by Ballard
and Carter (1986) and Carter (1992). For N, the “adequate” level was adjusted slightly downward from
1.35% to 1.30% (Table 1). The “adequate” level for P is unchanged, but the thresholds indicating
moderate to severe deficiencies were raised. For K, the “adequate” level was lowered from 0.65% to
0.60%, and the thresholds indicating slight to severe deficiencies were increased. For Mg, the
“adequate” level was lowered from 0.12% to 0.10%, and the thresholds indicating slight to severely
deficient were also lowered (Table 1).
Sulphate-Sulphur
For lodgepole pine, the interpretative criteria for SO4 remain unchanged from Brockley (2001)
(Table 1). For Douglas-fir, the use of pre-fertilization SO4 to predict responsiveness to N fertilization has
met with variable results (Turner et al. 1979; Carter et al. 1998). Turner et al. (1979) suggested a foliar
SO4 threshold of 400 ppm. However, based largely on fertilization research with Douglas-fir in the B.C.
interior (Brockley 2006) and in the Intermountain region (M. Coleman, per. comm.), the revised
threshold was lowered to 200 ppm (Table 2). Fertilization research with spruce in the BC interior
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indicates that 100 ppm likely indicates an adequate level of pre-fertilization foliar SO4 (Brockley, unpubl.
data). The revised interpretative criteria for interior spruce reflect these findings (Table 2).
Boron
Boron foliar interpretative criteria have been developed during the past two decades based on
extensive research with lodgepole pine, Douglas-fir, radiata pine, Scots pine, Norway spruce, and black
spruce (Aronsson 1983; Braekke 1983; Carter et al. 1984, 1986; Will 1985; Brockley 1990, 2003; Carter
and Brockley 1990; Stone 1990; Hopmans and Clerehan 1991; White and Krause 2001). Based on this
research, the B interpretative criteria for lodgepole pine that were suggested by Brockley (2001) were
revised slightly and were also applied for interior spruce and Douglas-fir (Table 2).
Other Micronutrients
Despite reports of low to perhaps deficient foliar levels of Fe, Cu, and Zn in coastal and interior
forests (Mahid 1984; Carter et al. 1986; Zasoski et al. 1990), inferred deficiencies have seldom been
confirmed in fertilizer trials. Also, there is no strong evidence that induced deficiencies of these
micronutrients have limited growth response following N fertilization of lodgepole pine, interior spruce,
or Douglas-fir. Interpretative criteria suggested by Ballard and Carter (1986) and Carter (1992) were
largely inferred from micronutrient research with other conifer species. Lacking compelling evidence
that revisions were warranted, no changes were made to the interpretative criteria for Cu, Zn, and Mn.
The same criteria were applied to all species (Table 2).
As discussed by Ballard and Carter (1986), total Fe may have limited diagnostic value, since only
a portion of foliar Fe may be physiologically active. Also, quantitative Fe analysis is problematic with
routine analytical methods (C. Dawson, per. comm). Brockley (2001) suggested revised interpretative
thresholds for total Fe based on favourable responses of lodgepole pine to N (and N+S) additions in
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stands with foliar Fe levels that were at, or slightly above, the revised “probable deficiency” threshold.
Similar results have been obtained in interior spruce and Douglas-fir fertilization trials (Brockley, unpubl.
data). Therefore, the foliar Fe thresholds suggested by Brockley (2001) were used for all three species in
the revised interpretative criteria (Table 2).
Nutrient Ratios
Based largely on results from lodgepole pine repeated fertilization experiments (Brockley and
Simpson 2004, Brockley 2007a, 2010a), the N:P threshold (above which P deficiency is indicated)
suggested by Brockley (2001) for lodgepole pine was raised from 9 to 10 (Table 3). Also, the N:S
threshold for lodgepole pine was raised from 14 to 15, with a notation that S deficiency is likely to be
induced by N fertilization if pre-fertilization foliar N:S is > 13 (Table 3). The N:Mg thresholds suggested
by Brockley (2001) for lodgepole pine were deemed reasonable following tested against foliar data from
lodgepole pine and interior spruce repeated fertilization experiments. The N:S and N:Mg thresholds are
also applied to interior spruce and Douglas-fir (Table 3).
The revised interpretative criteria suggest N:P and N:K thresholds for interior spruce and
Douglas-fir that are slightly lower than those for lodgepole pine (Table 3). These thresholds were set to
reflect the slightly higher demand for P and K for these two species, and were largely based on results
from fertilization research with interior spruce in the BC interior (Brockley 1992; Swift and Brockley
1994; Brockley and Simpson 2004; Brockley 2010b) and on K research with Douglas-fir in the
Intermountain region (Mika and Moore 1991).
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Use of Interpretative Tables
The revised interpretative criteria should be used in conjunction with the accompanying
“normalization” spreadsheet. All foliar data should be “normalized” before using the interpretative
tables.
The most important step in reviewing foliar nutrient data is to assess foliar N status. In virtually
all cases, there is no point fertilizing a stand unless a N deficiency is indicated. Assuming that all other
assessment factors are favourable, the best relative growth responses will likely occur in the most Ndeficient stands.
When interpreting foliar nutrient status, the relative proportions of nutrients in foliage are
typically more important than absolute amounts of individual nutrients. Most plants have the ability to
down-regulate uptake of most non-limiting nutrients so that proper foliar balance is maintained even
when growth is limited by N supply (Knecht and Göransson 2004). Therefore, low foliar levels of
macronutrients (P, K, Mg, S) in a N-deficient stand should not be interpreted as deficiencies unless
imbalances of N:P, N:K, N:Mg, or N:S are also indicated. In other words, the proportions of these
nutrients in relation to N are typically more important than the absolute foliar levels when interpreting
foliar data.
When interpreting S status, the interpretative criteria should be evaluated in the following
order: SO4 → N:S → total S.
A stand should likely not be diagnosed as S deficient (and S added to fertilizer prescriptions)
unless foliar SO4 levels are below “adequate” thresholds for that species. Some other commercial
laboratories determine foliar SO4 by acid digestion followed by ICP determination. However, the ICP
method also measures soluble organic S compounds in the acid digest (Kalra and Maynard 1991), so
reported results are generally considerably higher than those obtained from the HI and IC methods used
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by PSAI and MoE, respectively. The SO4 interpretative criteria are not applicable to SO4 foliar data
obtained from ICP analytical methodology.
An indication of slight deficiencies of nutrients other than N does not necessarily mean that
these other nutrients should be included in fertilizer prescriptions. In most cases, N-deficient stands with
slightly deficient levels of other nutrients will respond favourably, albeit at a slightly reduced level. In
most cases, the cost of adding a secondary nutrient will be an important consideration in cases of
“slight” (for macronutrient) and “possible” (for micronutrient) secondary deficiencies. However, special
precaution is recommended in cases where a “possible deficiency” of B is indicated. To minimize the risk
of inducing an acute B deficiency along with the associated severe symptoms (i.e., top dieback) that may
rapidly occur during periods of restricted B uptake, 10–12 ppm may be the lowest acceptable limit for
foliar B when evaluating candidate stands for operational fertilization. Where foliar B levels are below
this threshold, stands should either be eliminated as candidates for fertilization, or a small amount of B
(2–3 kg B/ha) should be added to the fertilizer prescription.
Precautions
These revised interpretative criteria are based largely on foliar nutrient data obtained from
young stands in the BC interior (i.e., 15–30 years old). Wang and Klinka (1997), and others, have found
that several foliar nutrients vary with stand age. Given the negative correlation between foliar N, P, and
K and stand age in their study, Wang and Klinka (1997) concluded that existing interpretative criteria for
these nutrients may be inappropriate for use in mature white spruce stands. Also, an apparent positive
correlation between foliar SO4 and stand age in lodgepole pine suggests that the threshold level for SO4
may be too low for diagnosing S deficiency in mature stands (Brockley, unpubl. data). Therefore, the
reliability of the foliar interpretative criteria may be lower for very young stands (e.g., < 5 years) and for
older stands (e.g., > 50 years).
17
The suggested threshold levels for several micronutrients (i.e., Cu, Zn, Fe) have not been fully
tested in fertilizer trials, nor have reliable methods for routine quantitative analysis been developed
(especially at the low levels typically found in conifer foliage). As a result, interpretative criteria for these
micronutrients remain rather poorly developed. Users of these interpretative criteria should not be
overly concerned if foliar levels of these micronutrients (especially Cu and Fe) fall within the “possible
deficiency” range. Several research experiments in the B.C. interior have reported favourable growth
responses following N fertilization despite having low pre-fertilization levels of Cu and Fe.
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18
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25
Table 1. Interpretation of macronutrient concentrations in current year’s foliage of lodgepole pine,
interior spruce, and Douglas-fir
Element
Nitrogen
b
Phosphorus
Potassiumb
Calcium
Magnesiumb
Sulphurc
a
Interpretation
Severely deficient
Moderately to severely deficient
Slightly to moderately deficient
Adequatea
Foliar concentration (% dry weight)
Lodgepole pine
Interior spruce
< 1.00
< 0.90
1.00 – 1.15
0.90 – 1.10
1.15 – 1.30
1.10 – 1.30
> 1.30
> 1.30
Douglas-fir
< 1.00
1.00 – 1.15
1.15 – 1.30
> 1.30
Severely deficient
Moderately to severely deficient
Slightly to moderately deficient
Adequatea
< 0.08
0.08 – 0.10
0.10 – 0.12
> 0.12
< 0.10
0.10 – 0.12
0.12 – 0.14
> 0.14
< 0.11
0.11 – 0.13
0.13 – 0.15
> 0.15
Severely deficient
Moderately to severely deficient
Slightly to moderately deficient
Adequatea
< 0.30
0.30 – 0.35
0.35 – 0.40
> 0.40
< 0.40
0.40 – 0.45
0.45 – 0.50
> 0.50
< 0.45
0.45 – 0.55
0.55 – 0.60
> 0.60
Severely deficient
Moderately to severely deficient
Slightly to moderately deficient
Adequatea
< 0.06
0.06 – 0.08
0.08 – 0.10
> 0.10
< 0.10
0.10 – 0.15
0.15 – 0.20
> 0.20
< 0.10
0.10 – 0.15
0.15 – 0.20
> 0.20
Severely deficient
Moderately to severely deficient
Slightly to moderately deficient
Adequatea
< 0.04
0.04 – 0.06
0.06 – 0.08
> 0.08
< 0.04
0.04 – 0.06
0.06 – 0.08
> 0.08
< 0.06
0.06 – 0.08
0.08 – 0.10
> 0.10
Severely deficient
Moderately to severely deficient
Slight to moderate deficiency
Adequatea
< 0.06
0.06 – 0.08
0.08 – 0.10
> 0.10
< 0.06
0.06 – 0.08
0.08 – 0.10
> 0.10
< 0.06
0.06 – 0.08
0.08 – 0.10
> 0.10
Level that is generally associated with an acceptable level of growth in young forest stands. At higher levels,
nutrient additions may not result in economically significant growth gains.
b
Foliar N:P, N:K, and N:Mg ratios typically have higher interpretative value than absolute levels of P, K, and Mg.
c
Foliar sulphate-S and N:S ratio typically have higher interpretative value than the absolute foliar level to total S.
26
Table 2. Interpretation of foliar sulphate-sulphur and micronutrient concentrations in current year’s
foliage of lodgepole pine, interior spruce, and Douglas-fir
Sulphate-S
Interpretation
Severely deficientb
Moderately to severely deficient
Slightly to moderately deficient
Adequate
Foliar concentration (ppm dry weight)
Lodgepole pine
Interior spruce
< 40
< 60
40 – 60
60 – 80
60 – 80
80 – 100
> 80
> 100
Douglas-fir
< 100
100 – 150
150 – 200
> 200
Copper
Probable deficiency
Possible deficiencyc
No deficiency
<1
1–3
>3
<1
1–3
>3
<1
1–3
>3
Zinc
Probable deficiency
Possible deficiencyc
No deficiency
< 10
10 – 15
> 15
< 10
10 – 15
> 15
< 10
10 – 15
> 15
Iron
Probable deficiency
Possible deficiencyc
No deficiency
< 20
20 – 30
> 30
< 20
20 – 30
> 30
< 20
20 – 30
> 30
Manganese
Probable deficiency
Possible deficiency
No deficiency
< 15
15 – 25
> 25
< 15
15 – 25
> 25
< 15
15 – 25
> 25
Severely deficientd
Probable deficiencye
Possible deficiencyf
No deficiency
<3
3–6
6 – 12
> 12
<3
3–6
6 – 12
> 12
<3
3–6
6 – 12
> 12
Element
a
Boron
a
Interpretations for sulphate-S apply only to unfertilized foliage and do not apply to sulphate-S analytical
procedures that use an inductively coupled plasma spectrophotometer (ICP).
b
Favourable growth response following N fertilization unlikely unless S is added in combination with N.
c
Favourable growth response following N fertilization will likely occur at these levels.
d
Visual symptoms of B deficiency (i.e., top die-back) likely present.
e
Sub-acute B deficiency, causing reduced height increment, likely exists in the absence of visual deficiency
symptoms.
f
A boron deficiency, causing reduced height increment and/or top die-back, may be induced by N fertilization.
27
Table 3. Interpretation of foliar nutrient ratios in current year’s foliage of lodgepole pine, interior
spruce, and Douglas-fir
Ratio
N:P
N:K
N:Mg
N:S
a
Interpretation
Moderate to severe P deficiency
Slight to moderate P deficiency
Possible slight P deficiency
No P deficiency
Threshold value
Lodgepole pine
> 13
11 – 13
10 – 11
< 10
Interior spruce
> 11
10 – 11
9 – 10
<9
Douglas-fir
> 11
10 – 11
9 – 10
<9
Moderate to severe K deficiency
Slight to moderate K deficiency
Possible slight K deficiency
No K deficiency
> 4.5
3.5 – 4.5
2.5 – 3.5
< 2.5
> 4.0
3.0 – 4.0
2.0 – 3.0
< 2.0
> 3.5
2.5 – 3.5
2.0 – 2.5
< 2.0
Moderate to severe Mg deficiency
Slight to moderate Mg deficiency
Possible slight Mg deficiency
No Mg deficiency
> 30
20 – 30
15 – 20
< 15
> 30
20 – 30
15 – 20
< 15
> 30
20 – 30
15 – 20
< 15
Severe S deficiency
> 25
20 – 25
15 – 20
< 15
> 25
20 – 25
15 – 20
< 15
> 25
20 – 25
15 – 20
< 15
Moderate to severe S deficiency
Slight to moderate S deficiency
No S deficiencya
Sulphur deficiency likely to be induced by N fertilization if pre-fertilization N:S > 13.
28
Figure 1. Relationship between foliar N (PSA) and foliar N (MoE).
1.40
1.30
y = 0.786x + 0.1336
R² = 0.775
%N (PSA)
1.20
1.10
1.00
0.90
0.80
0.90
1.00
1.10
1.20
1.30
1.40
1.50
%N (MoE)
Figure 2. Relationship between foliar S (combustion) (PSA) and foliar S (combustion) (MoE).
0.100
0.095
0.090
y = 0.7705x + 0.0135
R² = 0.529
%S (PSA)
0.085
0.080
0.075
0.070
0.065
0.060
0.055
0.065 0.070 0.075 0.080 0.085 0.090 0.095 0.100 0.105
%S-Comb (MoE)
29
Figure 3. Relationship between foliar S (combustion) (MoE) and foliar S (ICP) (MoE).
0.100
%S-comb (MoE)
0.090
0.080
0.070
0.060
0.050
y = 0.6324x + 0.0379
R² = 0.4574
0.060
0.070
0.080
0.090
%S-ICP (MoE)
Figure 4. Relationship between foliar S (combustion) (PSA) and foliar S (ICP) (MoE).
0.100
0.095
0.090
y = 1.0045x + 0.0064
R² = 0.7615
%S (PSA)
0.085
0.080
0.075
0.070
0.065
0.060
0.055
0.050
0.050 0.055 0.060 0.065 0.070 0.075 0.080 0.085 0.090
%S-ICP (MoE)
30
Figure 5. Relationship between foliar sulphate-S (PSA) and foliar sulphate-S (MoE).
160
ppm Sulphate-S (PSA)
140
y = 1.0575x - 8.0274
R² = 0.9091
120
100
80
60
40
20
0
0
20
40
60
80
100
120
140
ppm Sulphate-S (MoE)
Figure 6. Relationship between foliar K (MoE) and foliar K (PSA).
0.65
0.60
%K (MoE)
y = 0.8402x + 0.0365
R² = 0.7922
0.55
0.50
0.45
0.40
0.45
0.50
0.55
0.60
%K (PSA)
0.65
0.70
31
Figure 7. Relationship between foliar Ca (MoE) and foliar Ca (PSA).
0.20
%Ca (MoE)
0.18
y = 0.6774x + 0.0184
R² = 0.7404
0.16
0.14
0.12
0.10
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
%Ca (PSA)
Figure 8. Relationship between foliar Mg (MoE) and foliar Mg (PSA).
0.14
0.13
y = 0.7202x + 0.0255
R² = 0.777
%Mg (MoE)
0.12
0.11
0.10
0.09
0.08
0.07
0.07
0.08
0.09
0.10
0.11
%Mg (PSA)
0.12
0.13
0.14
0.15
32
Figure 9. Relationship between foliar P (MoE) and foliar P (PSA).
0.14
0.13
y = 0.7202x + 0.0255
R² = 0.777
%Mg (MoE)
0.12
0.11
0.10
0.09
0.08
0.07
0.07
0.08
0.09
0.10
0.11
0.12
0.13
0.14
0.15
%Mg (PSA)
Figure 10. Relationship between foliar B (PSA) and foliar B (MoE).
35
30
y = 0.9031x + 1.7344
R² = 0.9826
ppm B (PSA)
25
20
15
10
5
0
0
5
10
15
20
ppm B (MoE)
25
30
35
40
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Figure 11. Relationship between foliar Cu (PSA) and foliar Cu (MoE).
6.0
ppm Cu (PSA)
5.5
5.0
4.5
4.0
3.5
y = 0.4595x + 3.1794
R² = 0.0763
3.0
1.5
2.0
2.5
3.0
3.5
ppm Cu (MoE)
Figure 12. Relationship between foliar Fe (PSA) and foliar Fe (MoE).
50
45
ppm Fe (PSA)
40
35
30
25
y = 0.4044x + 20.686
R² = 0.1609
20
15
15
20
25
30
ppm Fe (MoE)
35
40
45
34
Figure 13. Relationship between foliar Zn (PSA) and foliar Zn (MoE).
55
ppm Zn (PSA)
50
y = 1.0268x - 0.4943
R² = 0.8352
45
40
35
30
30
35
40
45
50
ppm Zn (MoE)
Figure 14. Relationship between foliar Mn (PSA) and foliar Mn (MoE).
650
600
y = 1.1234x + 24.267
R² = 0.9404
ppm Mn (PSA)
550
500
450
400
350
300
250
200
150
200
250
300
350
400
ppm Mn (MoE)
450
500
550
35
Figure 15. Foliar data “normalization” spreadsheet
Pacific Soil Analysis Inc.
Element
Raw
data
Normalized
data
Ministry of Environment
Raw
data
N (%)
Normalized
data
#VALUE!
P (%)
#VALUE!
K (%)
#VALUE!
Ca (%)
#VALUE!
Mg (%)
#VALUE!
S (%)
#VALUE!
SO4 (ppm)
#VALUE!
B (ppm)
#VALUE!
Cu (ppm)
Zn (ppm)
Fe (ppm)
Mn (ppm)
Ratios
N:S
n/a
#VALUE!
n/a
#VALUE!
N:P
n/a
#VALUE!
n/a
#VALUE!
N:K
n/a
#VALUE!
n/a
#VALUE!
N:Ca
n/a
#VALUE!
n/a
#VALUE!
N:Mg
n/a
#VALUE!
n/a
#VALUE!
Instructions:
1. Input foliar data for individual nutrients received from the Ministry of Environment
(MoE)
or Pacific Soil Analysis Inc. (PSAI) in the appropriate "raw data" column.
2. Use "normalized" nutrient values and nutrient ratios for interpretative purposes.
Notes:
1. This "normalization" spreadsheet should only be used with foliar data from MoE or
PSAI.
2. For MoE foliar data, only S(ICAP) data (i.e., determined by Inductively Coupled Plasma
(ICP) optical emission spectrophotometry) should be used for total S "normalization".
36