optimum nutrient concentrations and cnd scores of mature white spruce determined using a...

This article was downloaded by: [University of California Santa Cruz]On: 07 November 2014, At: 22:56Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,UK

Journal of Plant NutritionPublication details, including instructions forauthors and subscription information:http://www.tandfonline.com/loi/lpla20

Optimum NutrientConcentrations and CNDScores of Mature White SpruceDetermined Using a Boundary-Line Approach and SpatialVariation of Tree Growth andNutritionPierre-Olivier Quesnel a , Benoît Côté a , James W.Fyles a & Alison D. Munson ba Department of Natural Resource Sciences ,Macdonald Campus of McGill University , Ste-Anne-de-Bellevue, Canadab Département des sciences du bois et de la forêt ,Laval University , Quebec City, CanadaPublished online: 23 Nov 2006.

To cite this article: Pierre-Olivier Quesnel , Benoît Côté , James W. Fyles & AlisonD. Munson (2006) Optimum Nutrient Concentrations and CND Scores of MatureWhite Spruce Determined Using a Boundary-Line Approach and Spatial Variationof Tree Growth and Nutrition, Journal of Plant Nutrition, 29:11, 1999-2018, DOI:10.1080/01904160600928177

To link to this article: http://dx.doi.org/10.1080/01904160600928177

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all theinformation (the “Content”) contained in the publications on our platform.However, Taylor & Francis, our agents, and our licensors make no

http://www.tandfonline.com/loi/lpla20

http://www.tandfonline.com/action/showCitFormats?doi=10.1080/01904160600928177

http://dx.doi.org/10.1080/01904160600928177

representations or warranties whatsoever as to the accuracy, completeness,or suitability for any purpose of the Content. Any opinions and viewsexpressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of theContent should not be relied upon and should be independently verified withprimary sources of information. Taylor and Francis shall not be liable for anylosses, actions, claims, proceedings, demands, costs, expenses, damages,and other liabilities whatsoever or howsoever caused arising directly orindirectly in connection with, in relation to or arising out of the use of theContent.

This article may be used for research, teaching, and private study purposes.Any substantial or systematic reproduction, redistribution, reselling, loan,sub-licensing, systematic supply, or distribution in any form to anyone isexpressly forbidden. Terms & Conditions of access and use can be found athttp://www.tandfonline.com/page/terms-and-conditions

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014

http://www.tandfonline.com/page/terms-and-conditions

Journal of Plant Nutrition, 29: 1999–2018, 2006

Copyright © Taylor & Francis Group, LLC

ISSN: 0190-4167 print / 1532-4087 online

DOI: 10.1080/01904160600928177

Optimum Nutrient Concentrations and CND Scoresof Mature White Spruce Determined Using a

Boundary-Line Approach and Spatial Variationof Tree Growth and Nutrition

Pierre-Olivier Quesnel,1 Benoıt Cote,1 James W. Fyles,1

and Alison D. Munson2

1Department of Natural Resource Sciences, Macdonald Campus of McGill University,Ste-Anne-de-Bellevue, Canada

2Departement des sciences du bois et de la foret, Laval University,Quebec City, Canada

ABSTRACT

Standards of optimum nutrition are not readily available for mature trees of the Canadianboreal forest. The objective of this study was to determine foliar nutritional standardsfor white spruce for all major nutrients [nitrogen (N), phosphorus (P), potassium (K),calcium (Ca), magnesium (Mg), and manganese (Mn)] using critical values (CVA) andcompositional nutrient diagnosis (CND). Trees were sampled at two locations in Ontarioand Quebec to cover a gradient of soil fertility levels. A boundary-line approach was usedin combination with quadratic regression models to estimate the relationship betweengrowth and foliar-nutrient concentrations or CND scores when free of the effects of in-teracting environmental factors. White spruce optimum nutrition ranges were computedfrom significant relationships (P ≤ 0.10) for N, P, K, Ca, and Mn concentrations and forN, P, and K CND scores. Optimum concentrations for first-year needles were 12.3, 1.9,7.3, 6.5, and 0.39 mg g−1 for N, P, K Ca, and Mn, respectively, whereas optimum CNDscores were 0.17, −1.65, −0.40, and −0.30 for N, P, K, and Ca, respectively. Samplesfrom a broader range of environmental conditions will be required in order to establishstandards for all major nutrients and to ascertain toxicity levels of most nutrients.

Keywords: white spruce, nutritional standards, compositional nutrient diagnosis(CND), boundary-line approach

Received 11 July 2005; accepted 5 January 2006.Address correspondence to Benoıt Cote, Department of Natural Resource Sciences,

Macdonald Campus of McGill University, 21111 Lakeshore Rd., Ste-Anne-de-Bellevue,Quebec, Canada, H9X 3V9. E-mail [email protected]

1999

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014

2000 P.-O. Quesnel et al.

INTRODUCTION

Environmental concerns about the intensification of forest-management activ-ities in Canada have led to the elaboration of rules and objectives to protectforest ecosystems and to sustain the supply of forest goods and services forfuture generations (Canadian Council of Forest Ministers, 1997). Sustainableforest management of Canadian forests depends on maintaining or improvingforest nutrition to optimum levels. This goal can be achieved only through foresthealth monitoring that will allow for the early detection of suboptimum levelsof nutrition and subsequent correction of nutritional problems.

In Canada, the coniferous forest is generally assumed to be nitrogen defi-cient, based on numerous studies that showed a positive response to nitrogen (N)fertilization (Foster and Morrison, 1983, 1987; Weetman et al., 1987; Timmerand Ray, 1988; Morrison and Foster, 1995; Paquin et al., 1998). However, defi-ciencies of nutrients other than N have also been reported. Phosphorous (P) de-ficiency was observed in black spruce [Picea mariana (Mill.) BSP] growing onwet sites with thick organic soils (Wells, 1994; Teng et al., 2003). Potassium (K)limited white spruce [Picea glauca (Moench) Voss] growth in plantations estab-lished on sandy soils formerly used for agriculture (Heiberg and White, 1951;Truong and Gagnon, 1975). Plantations of red pine (Pinus resinosa Ait.) andtamarack (Larix laricina Du Roy, Kock) established on sandy soils in southernQuebec were also found to be K deficient (Truong, 1975a, 1975b). Magnesium(Mg) deficiencies were reported in red pine and in white pine (Pinus strobus L.)plantations grown on sandy soils (Stone, 1953; Lafond, 1958). Recently, Hamelet al. (2004) showed that Mg availability in the forest floor could explain someof the variation of forest productivity in several upland black spruce stands OFnorthern Quebec. These results suggested that K and Mg deficiencies might becommon among conifers growing on sandy soils in eastern Canada.

Apart from intrinsically nutrient-poor sites, nutrient deficiencies can de-velop following forest harvesting through nutrient removal (Morris, 1997). Re-search on the effect of forest harvesting on soil fertility has focused on basecations rather than on N. Significant reductions of available base cations oc-curred in organic and mineral horizons of imperfectly drained clay soils follow-ing harvesting (Brais et al., 1995). In a recent study, Belanger et al. (2003) foundthat full-tree harvesting decreased the base status of upland black spruce standsthree years after logging in comparison with stem-only harvesting. Severalstudies have suggested that dry and mesic sites may be less prone to significantshort-term nutrient losses from harvesting than wet sites (Weetman and Webber,1972; Foster and Morrison, 1987; Brais et al., 1995).

Foliar nutrient standards for tree species have frequently been determined,at least initially, with seedlings grown in greenhouses (Ingestad, 1959, 1960;Swan, 1970, 1971). These standards were useful for nursery managers and canprovide information about relative species nutrient requirements. Seedlings andtrees, however, have different life strategies and nutrient-allocation patterns that

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014

Foliar Nutritional Standards for White Spruce 2001

dictate the need for the determination of nutritional standards for mature trees.For example, Weetman and Fournier (1984) determined an optimum of 14mg N g−1 for mature jack pine (Pinus banksiana L.) trees using a long-termfertilizer trial, compared with an optimum for seedlings of 29.6 mg N g−1 (Swan,1970). Recognizing the need to develop nutritional standards for trees, Ballardand Carter (1986) modified the nutritional standards of Swan (1971) for whitespruce seedlings based on observations of natural stands. More recently, Wangand Klinka (1997) concluded that both sets of standards (Swan, 1971; Ballardand Carter, 1986) were not valid, and therefore determined new optimum valuesfrom regression analysis of relationships between site index and foliar-nutrientconcentrations from several stands in British Columbia.

The diagnosis of plant nutritional status was first based on the interpretationof single-nutrient concentrations (Bates, 1971; Leaf, 1973; van den Driessche,1974) and on bivariate ratios. Then, Parent and Dafir (1992) introduced com-positional nutrient diagnosis (CND) to alleviate the effects of the multivariatenature of nutrient interactions. Such an approach ensures that the variationin one element in plant tissue inevitably changes the proportion of the otherelements. This method enables the computation of multivariate nutrient ra-tios that are more representative of the compositional nature of plant tissue(Aitchison, 1986). Although CND has been proven successful with agricul-tural crops (Khiari et al., 2001a, 2001b; Parent et al., 1993, 1994), few studieshave used CND on tree species. Parent et al. (1995) investigated N nutritionof coniferous seedlings grown in a greenhouse, whereas Schleppi et al. (2000)investigated nutrient interactions in foliage of adult Norway spruce.

Foliar nutrient concentrations vary greatly under the action of numerousenvironmental factors (van den Driessche, 1974). Light, temperature, waterstress, and disease can induce variations in nutrient concentrations of tree foliage(Bates, 1971). As multiple environmental variables interact on trees, bivariaterelationships between growth and foliar chemistry can go undetected due tothe large natural variation in these variables. (Walworth and Sumner, 1988).To circumvent this problem, a boundary-line approach (Webb, 1972) can beused to estimate the growth-nutrition relationship when exempted of the effectof environmental factors. For example, using sugar maple (Acer saccharumMarsh.) as a reference species, Vizcayno-Soto and Cote (2004) found that thisapproach yielded nutritional standards comparable to those already published,and therefore demonstrated that the need to maintain environmental factorsartificially at optimum levels or to control the nutrient supply by using time-consuming and expensive fertilizer trials could be circumvented.

The primary objective of this research was to determine foliar-nutrientstandards for white spruce, a commercially important tree species in Canada,for which a complete set of nutritional standards has yet to be determined.Standards were to be developed for both foliar nutrient concentrations andCND scores of N, P, K, calcium (Ca), Mg, and Mn. Access to a large dataset also allowed further development of the boundary-line approach, introduced

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


by Vizcayno-Soto and Cote (2004), to facilitate its application and improve itsreliability.

MATERIALS AND METHODS

Study Sites

Two sites were selected for this research. The first was the Lac Duparquet Teach-ing and Research Forest, located north of Rouyn-Noranda, Quebec (lat 48◦30′

N, long 79◦20′ W) (Figure 1). Data from this site were from four unmanaged

Figure 1. The different steps of the boundary-line approach as applied to nitrogen (N)concentrations of white spruce current-year needles.

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


white spruce stands of fire origin ranging from 51 to 125 years old (Doucet,1997). The study area was located in the Missinaibi-Cabonga forest region(Row, 1972) of the southern boreal forest. The mean annual temperature was0.8◦C and average precipitation was 857 mm (Environment Canada, 1993). Themean frost-free period was 64 d (Environment Canada, 1993). Lacustrine claydeposits and glacial tills were common in the sampling area (Doucet, 1997).

The second study area was located at Cartier Lake in the PetawawaResearch Forest (lat 45◦57′ N, long 77◦34′ W) in Ontario. Data and sampleswere collected from a 17-year-old white spruce plantation. The plantation waslocated in the Middle Ottawa Section of the Great Lakes-St-Lawrence For-est region (Rowe, 1972). The mean annual temperature was 4.3◦C and meanannual precipitation was 814 mm (Environment Canada, 1993). The soil wasa deep, well-drained loam to sandy-loam (Brand and Janas, 1988; Perie andMunson, 2000). The plantation (2 × 2 m spacing) was established as a repli-cated 2 × 2 × 2 factorial experiment design with scarification, herbicide, andfertilizer treatments (Brand and Janas, 1988). Fertilizer (17:16:10 NPK plusmicronutrients) was applied annually for six years with increasing amountsranging from 30 to 200 g per seedling.

Foliage Sampling and Analysis

The number of trees selected for foliage sampling and growth measurements atthe two sites differed. At Petawawa, five white spruce trees representative of plotaverage height and diameter were sampled from each of the 32 experimentalplots (n = 160), thus comprising all treatment combinations. At Lac Duparquet,52 white spruce trees were sampled from 18 10 × 10 m plots spread across thefour selected stands, with 85% percent of the trees sampled in the two oldeststands (72 and 125 years old) and with about 50% of the trees sampled eitheron lacustrine clay deposits or glacial tills. Some of the trees sampled at thesesites were suppressed.

Needles were collected on three to five shoots from the upper third ofthe crown of trees with a telescopic pruner at Petawawa and a shotgun at LacDuparquet. Sampling of first and second-year needles took place at the endof August 1995 at Lac Duparquet and on October 2–3, 2002 at Petawawa.These periods corresponded approximately to the first frost of the winter at re-spective locations and, therefore, were representative of a similar physiologicalstatus relative to dormancy. After harvest, twigs and needles were immedi-ately stored in paper bags. Samples were oven-dried at 65◦C for 48 h. Afterdrying, needles were detached from twigs, sieved, and sorted to remove dust,debris, and dead (necrotic) or damaged needles. Needles were ground to finepowder with a Tecator Cylotec mill when samples had a volume exceeding30 mL. Smaller samples were ground with a Wiley mill using a 1 mm meshscreen. Samples were digested in H2O2-H2SO4, according to Allen (1989).

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


Nitrogen and P concentrations were determined colorimetrically on a LachatQuick Chem Autoanalyzer (Norwalle, CT). An atomic absorption spectropho-tometer (Perkin-Elmer model 2380, Lachat Instruments, Milwaukee, WI) wasused to determine K, Ca, Mg, and Mn concentrations. Manganese (Mn) wasnot measured on Lac Duparquet samples. Therefore, analysis of Mn nutritionin white spruce was performed on data from Petawawa only.

Growth Measurements

Tree cores were taken at Lac Duparquet and were analyzed with MacDen-dro V6.1D (Regent Instruments, 1996). For each tree, basal area growth wascalculated for both the current year (year of leaf sampling) and the previ-ous year. Growth was averaged to obtain the mean annual basal area incre-ment. Due to the small diameter of some trees at the Petawawa site, terminalshoot length measurements were used instead of increment cores to assess treegrowth.

A preliminary analysis revealed that the two sites had nutrient concentrationranges that were complementary. In order to increase the sample size and therange of nutrient concentrations and CND scores to be used for the developmentof boundary-line regressions, the data were pooled after the growth data fromthe two sites had been standardized to a mean of zero and a standard deviationof one (standardized growth).

Computation of Multivariate Nutrient Ratios

Multivariate nutrient ratios or CND scores were calculated from nutrient con-centrations following Parent and Dafir (1992). The filling value (R) was calcu-lated as follows, with concentrations measured in milligrams per gram:

R = 1000 − (N + P + K + Ca + Mg) (1)

Log-centered ratios (V scores) were then calculated using the geometric mean(in grams):

g = (N × P × K × Ca × Mg × R)1/6 (2)

and

VN = ln(N/g) (3)

where VN was the log-centered ratio for N.

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


Data Analysis

The boundary-line approach used in this study was based on the method de-veloped by Vizcayno-Soto and Cote (2004). Foliar nutrient concentrations andCND scores were used as independent variables and standardized tree growth asthe dependent variable. First, scatter plots of standardized growth against eachnutrient concentration of CND score were drawn for preliminary examination(Figure 1a). Data were then screened in order to identify outliers. Observationsthat met the following criterion were rejected:

If Yi > Y + 3.29 ∗ SDY (4)

where Yi was the standardized tree growth of the ith observation, and Y andSDY were the mean and standard deviation of standardized tree growth ofsampled trees. This criterion was included to remove the most extreme 0.05%of the observations (one-tail t-test), thus excluding trees with high growth ratessuspected to be the result of manipulation errors.

Trees with maximum growth were selected over the entire range of theindependent variables. To this end, the range was divided into 11 intervals,with the first interval centered on the smallest observed nutrient concentrationor CND score. The tree with the highest growth in each interval was selectedfor regression analysis (Figure 1b). The latter operation was performed witha computerized routine written in Q-Basic and operating in MS-DOS. Thenumber of selected boundary points represented a compromise between theneed to have enough observations to obtain significant regression models andthe need to minimize the probability of selecting trees that were growing atsuboptimum rates, which would reduce the accuracy of the models. Boundarypoints that were located outside the interval bounded by the mean nutrientconcentration or CND score plus or minus two times its standard deviationwere excluded from further analysis (Figure 1c). This step was performed toavoid the selection of boundary points from the lowest and highest part of therange of nutrient concentrations or ratios where the number of observationswas likely to be low. This operation excluded on average two to three boundarypoints.

A preliminary quadratic regression (equation 5) was then computed withthe boundary points (Figure 1d) to fit the growth-nutrition relationships whenexempted from the negative effect of environmental factors other than nutrientsupply:

Y = ax2 + bx + c (5)

where Y was the standardized tree growth and x was the nutrient concentra-tion or CND score. In some cases, the selected trees fit poorly the theoreticalgrowth/nutrition model and were suspected to be growing at suboptimum rates

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


or to have a growth rate that was unusually high in comparison with their neigh-boring boundary points. The occurrence of trees with suboptimum growth wasa result of the limited sample size, as optimum growth was rarely observed. Inorder to eliminate such trees from further analysis, the criteria developed byVizcayno-Soto and Cote (2004) were applied. Trees with a high growth rateand for which equation 6 was true were removed from further analysis:

Y > Y−1 and Y+1 and Y > Y−1 + Y+1 (6)

where Y, Y−1, and Y+1 were the growth rates of the fast-growing tree andadjacent points, respectively. Similarly, trees with suboptimum growth and forwhich equation 7 was true were rejected (Figure 1d):

Y < Y−1 and Y+1 and Y/[(Y−1 + Y+1)/2] < 90% (7)

However, two to three consecutive suboptimum trees were frequently observedand criterion 7 was modified to account for these cases:

Y < Y−n and Y+n and Y/[(Y−n + Y+n)/2] < 90% (8)

where n = 1, 2. Thus, Y−n and Y+n were the non-immediate adjacent points oftrees with suboptimum growth. This formula allowed for the rejection of consec-utive trees showing suboptimum growth rates. Finally, a 2nd-degree polynomialregression was fitted on the remaining boundary points (Figure 1e). Statisticalanalyses were performed with STATISTICA version 4.1 (StatSoft 1994). Re-gressions were considered to be statistically significant if both the model andthe quadratic coefficient “a” had a probability level below 0.1. Statisticallysignificant regression models were kept for further analysis.

Determination of Nutritional Standards

Optimum nutrient concentrations or CND scores were computed by solving thefirst derivative of the quadratic regression when it was set to zero:

Optimum = −b/2a (9)

Optimum nutritional ranges correspond to the interval delimited by the twopoints corresponding to 90% of maximum growth (slope = 0). Both valueswere determined by solving the regression equation for:

Y = 0.9∗ maximum growth (10)

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


and finding the two nutrient concentrations or CND score values that couldsolve this equation using:

−b ±√

(b2 − 4ac)/2a (11)

for which a, b, and c were the coefficients of the quadratic equation.The determination of nutritional standards was not performed when mod-

els had to be extrapolated to determine an optimum of nutrition (slope = 0).In addition, critical and toxicity levels were rejected if the lowest predictedgrowth was equal to or higher than 80% of the predicted maximum growth rate(Figure 1f). Failure to observe a 20% decrease in growth because of nutrientdeficiency also prevented the calculation of a nutritional optimum. However,failure to observe a growth decrease of 20% because of toxicity did not preventthe calculation of critical and optimum nutrient concentrations or CND scores.

RESULTS

Current-Year Needles

A wide range of nutrient concentrations and CND scores was observed forcurrent year needles (Table 1). Nitrogen, P, K, and Mg concentration values

Table 1Statistics of needle nutrient concentrations and CNDscores of white spruce (n = 212)

Current-year needles Second-year needles

Range Mean Range Mean

Nutrient concentrations (mg g−1)N 6.7–16.8 11.8 7.0–13.8 10.1P 1.0–2.7 2.0 0.9–2.2 1.5K 3.1–13.5 7.3 2.9–7.9 4.7Ca 2.2–13.9 6.2 4.8–18.4 9.5Mg 0.5–1.8 1.1 0.4–1.7 1.0Mn∗ 0.1–0.9 0.4 0.2–1.4 0.5

CND scoresN −0.3–0.5 0.2 −0.3–0.5 0.1P −2.1–1.4 −1.6 −2.3–1.5 −1.8K −1.1–0.3 −0.4 −1.1–0.3 −0.7Ca −1.3–0.4 −0.5 −0.6–0.7 0.0Mg −2.8–1.9 −2.2 −2.9–1.8 −2.3

∗Mn data from Petawawa only (n = 160).

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


Table 2Statistics of boundary-line models (ax2 + bx + c) for nutrient concentrations (mgg−1) and CND scores of current year needles of white spruce

Regressioncoefficients

Predictedgrowth∇ (%)

Nutrient N r2 Prob. a b c

Curve-fittingrange

(mg g−1) X-min X-max

Nutrientconcentrations

N 7 0.89 0.011 −0.2 3.9 −20.8 8.1–15.0 35.9 72.9P 7 0.87 0.016 −3.9 15.2 −11.9 1.3–2.5 58.3 64.8K 7 0.88 0.014 −0.1 1.8 −3.7 3.5–10.4 57.8 73.6Ca 6 0.84 0.062 −0.1 1.2 −0.9 2.2–10.3 59.5 68.1Mg 6 0.86 0.050 −9.3 19.3 −7.1 0.8–1.5 87.2+ 60.1Mn∗ 7 0.80 0.040 −1017.0 794.8 −42.8 0.1 −0.6 43.8 48.8

CND scoresN 5 0.98 0.017 −21.7 7.2 2.5 −0.12–0.38 59.1 77.7P 7 0.91 0.009 −30.5 −100.4 −79.8 −1.87–1.40 62.8 54.6K 7 0.91 0.003 −8.1 −6.5 1.7 −0.86–0.01 60.1 56.1Ca 5 0.94 0.0055 −4.1 2.4 2.9 −1.14–0.06 37.0 88.7Mg 7 0.96 0.002 −11.4 −53.9 −60.4 −2.53–1.93 92.5 52.9

∗ Analysis based on Petawawa data only.∇ Model-predicted tree growth, expressed as a percentage of the model’s max-

imum growth, at the limits of the nutritional range.+ Calculation of nutritional threshold is prohibited for underlined values.

ranged from deficiency to sufficiency levels according to proposed standardsfor white spruce, whereas Ca was above the optimum level (Swan, 1970,1971; Ballard and Carter, 1986). Significant regression models (P ≤ 0.1) basedon concentrations and CND scores were obtained for all nutrients (Table 2).Predicted tree-growth rates at the lowest and highest nutrient concentrationsand CND scores were below 80% of the predicted maximum rate in all but threecases (Table 2); deficiency levels were not computed for Mg concentrationand CND-Mg, nor were toxicity levels calculated for CND-Ca (Table 2).Optimum ranges were therefore computed in eight of 11 cases (Table 3). Theoptimum nutrient concentrations calculated with the boundary-line modelswere 12.3, 1.9, 7.3, 6.5, and 0.39 mg.g−1 for N, P, K, Ca, and Mn, respectively;optimum CND scores were 0.17, −1.65, −0.40, and −0.30 for N, P, K, and Ca,respectively.

Optimum ranges for N, P, K, Ca, and Mn nutrient concentrations wereequivalent to ±13.6%, 16.5%, 25.2%, 33.1%, and 28.2% of the optimum con-centrations, respectively. Optimum ranges were generally more variable forCND scores with thresholds equivalent to ±82.4, 7.8, 57.5, and 110% of theoptimum scores for N, P, K, and Ca, respectively.

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014

Tabl

e3

Whi

tesp

ruce

optim

um-n

utri

ent

conc

entr

atio

nsan

dC

ND

scor

esof

curr

ent-

year

need

les,

mea

sure

dfr

ombo

unda

ry-l

ine

mod

els

and

publ

ishe

dnu

triti

onal

stan

dard

sfo

rse

edlin

gsan

dtr

ees

Ref

eren

ceN

PK

Ca

Mg

Mn∗

Cal

cula

ted

12.3

1.9

7.3

6.5

N.d

.0.

39op

timum

Con

c.(m

gg−1

)10

.6–1

4.0

1.6–

2.3

5.5–

9.2

4.3–

8.6

N.d

.−1.

30.

3–0.

5an

dT

his

stud

yop

timum

CN

D0.

17−1

.65

−0.4

0−0

.30

N.d

.ra

nge

0.02

–0.3

1−1

.77–

1.53

−0.6

3–0.

17−0

.63–

N.d

.N

.d.–

2.16

N.d

.O

ptim

umSw

an(1

971)

Con

c.(m

gg−1

)21

.23.

15.

01.

70.

9N

.d.

for

seed

lings

CN

D0.

87−1

.05

−0.5

7−1

.65

−2.2

9

Opt

imum

Swan

(197

1)C

onc.

(mg

g−1)

15.0

1.8

4.5

1.5

1.0

N.d

.fo

rtr

ees

CN

D0.

69−1

.43

−0.5

1−1

.61

−2.0

1

Cri

tical

Bal

lard

and

Con

c.(m

gg−1

)15

.51.

65.

02.

01.

20.

025

for

tree

sC

ritic

al(1

986)

CN

D0.

64−1

.63

−0.4

9−1

.40

−1.9

1N

.d.

Cri

tical

Wan

gan

dC

onc.

(mg

g−1)

12.0

2.5

6.3

4.9

1.3

N.d

.fo

rtr

ees

Klin

ka(1

997)

CN

D0.

16−1

.41

−0.4

9−0

.74

j2.0

7

∗A

naly

sis

base

don

Peta

waw

ada

taon

ly.

N.d

.:N

otde

term

ined

.

2009

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


Table 4Statistics of boundary-line models (ax2 + bx + c) for nutrient concentrations (mgg−1) and CND scores of second-year needles of white spruce

Regression Predictedcoefficients# growth∇ (%)

Nutrient N r2 Prob. a b c

Curve-fittingrange

(mg g−1) X-min X-max

Nutrient concentrationsN 6 0.75 0.122 −0.3 5.8 −26.5 7.8–12.9 60.0 63.7P 7 0.92 0.006 −9.9 29.9 −19.3 2.–1.9 54.3 64.0K 7 0.79 0.046 −0.4 3.9 −6.8 2.8–6.2 59.8 80.3+

CND scoresN 4 0.673 0.572 N.d. N.d. N.d. −0.18–0.28 N.d. N.d.P 6 0.882 0.041 −20.7 −76.1 −66.9 −2.17–1.57 50.9 66.3K 7 0.881 0.014 −11.5 −14.1 −1.4 −0.97–0.33 64.6 78.2

#Regression coefficients are presented only if the quadratic coefficient “a” is sta-tistically significant (p < 0.1).

∇ Model-predicted tree growth, expressed as a percentage of the model’s maximumgrowth, at the limits of the nutritional range.

+ Calculation of nutritional threshold is prohibited for underlined values.N.d. Not determined.

Second-Year Needles

Significant regression models (P≤ 0.1) were obtained for P and K for bothconcentrations and CND scores (Table 4). The regression for K concentrationdid not, however, meet the 20% growth decrease criterion for toxicity (Table 4).Optimum ranges were computed for P concentration and P and K CND scores(Table 5). Optimum P and K concentrations were 1.5 and 4.8 mg g−1, respec-tively. Optimum CND scores were −1.84 and −0.61 for P and K, respectively.Optimum concentrations of P and K in second year foliage were 21.1% and34.2% lower than for current-year needles. Optimum CNDscores for P and Kwere 11.5% and 52.5% lower, respectively, in second-year needles.

DISCUSSION

The Boundary-Line Approach

The use of a modified boundary-line approach developed by Vizcayno-Sotoand Cote (2004) enabled the computation of optimum concentrations andCND scores for most nutrients in first-year needles of white spruce. With the

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


Table 5White spruce optimum-nutrient concentrations and CND scores forsecond-year needles measured from boundary-line models

N P K

Calculated optimum and 1.5 4.8optimum range Conc. (mg g−1) N.d.

1.3–1.7 3.8–N.d.−1.84 −0.61

CND N.d.−1.99–1.69 −0.80–0.42

∗Analysis based on Petawawa data only.N.d. Not determined.

underlying principles/criteria used in this approach, such a high yield of nu-tritional optima was clearly indicative of a sample of trees used in this studythat were predominantly growing at close to optimum levels for most nutrients,while allowing for a sufficiently large number of trees to grow at suboptimumnutrition levels. Hence, the sampling of current-year foliage of a relativelysmall number of trees over two limited geographical areas can encompass alarge range/gradient of tree nutrition, and thus can allow for the successfulapplication of a boundary-line approach to assess nutritional standards.

Second-year needles produced fewer significant models than first-year nee-dles. Black spruce growth was found to be most closely associated with thenutrient composition of new foliage (Lowry, 1970). The fact that growth wascomputed as an average of the last two years for the Duparquet site and thattranslocation from second- to first-year needles likely occurred at different ratesdepending on the N-sink strength of current-year needles (Munson et al., 1995)may have blurred the relationships between second-year needles and growth.With 61% of the sampled trees below the critical N concentration, many treeswere likely severely N deficient. A possible foliar-N concentration thresholdfor second-year needles, below which little translocation occurs (Cote et al.,2002), could have contributed to the non-significant quadratic model for Nconcentration. A larger sample or a different model that would better fit a veryshort/steep critical zone would likely yield a significant model. Such an effect oftranslocation and distribution of boundary points would not apply to P and K, assignificant models were obtained for these nutrients. Both are mobile nutrients,but growth response for these two nutrients has been generally less than thatof N (Weetman et al., 1987), and fewer trees may have been deficient in thesenutrients. Whether the use of second-year needles to detect early deficienciesof mobile nutrients in conifers was superior to the use of first-year needles, asgenerally accepted (Morrow and Timmer, 1981), could be tested with this study.

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


However, these results suggest that the determination of a critical/optimum nu-trient concentration using a boundary-line approach may be more difficult forsecond-year needles than for first-year needles.

Comparison with Published Values

In comparison with nutritional standards developed for seedlings grown inhydroponic culture (Swan, 1971), white spruce optimum nutrient-concentrationranges derived from the boundary-line models were low for N and P, similarfor K, and high for Ca. Despite the differences in optimum N and P nutrientconcentrations computed for trees and for seedlings, the N:P ratio was nearlythe same for trees (100:16) as for seedlings (100:15). Both values correspondedclosely to the optimum 100:15 ratio for conifers proposed by Ericsson (1994).These similarities suggested that the optimum values for N and P were accurateand that a precise chemical balance was maintained between these nutrientsthrough the different life stages of the trees.

Comparisons of the optimum CND scores with CND scores derived fromthe nutrient standards of Swan (1971) for seedlings showed that CND-N wasalso lower in trees than in seedlings, whereas the reverse was observed forCa scores. Knecht and Goransson (2004) found a similar trend with bivariatenutrient ratios in many tree species growing in diverse ecosystems, suggestingthat differences in N and Ca nutritional balance of trees and seedlings wereconstant for a broad range of tree species. In contrast to N and Ca, the optimumCND scores for P, K, and Mg were quite similar to scores derived from Swan(1971), which suggests that seedlings and trees of white spruce did not differsignificantly in terms of optimal balance of these nutrients.

Nutritional standards for mature white spruce determined from field exper-iments are few. Wang and Klinka (1997) developed critical nutrient levels fromthe relationships between site index and foliar nutrient concentrations of 102stands in British Columbia. The critical nutrient concentrations for N, K, Ca,and Mg developed by Wang and Klinka (1997) were within the optimum con-centration ranges, while their P critical level was higher than the upper limits ofthe optimum range (Table 3). The fact that Wang and Klinka’s critical P concen-tration was also higher than the optimum P range computed by Bonneau (1995)for saplings of conifers with short needles, such as Norway spruce, suggeststhat it may have been overestimated. As for Mg, no critical concentration wasprovided in this study because of the 20% reduction in predicted growth criteria.Using a less restrictive criterion of 10% would yield critical and optimum Mgconcentrations of 0.83 and 1.04 mg g−1, respectively. Such an optimum fallswithin the range of 1.0–1.4 mg g−1 suggested by Bonneau (1995) for coniferswith short needles, and hence could be considered a good approximation ofthe optimum concentration for this nutrient. The optimum range for K was in

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


agreement with that proposed by Truong and Gagnon (1975), who set a criticalK level of 6.0 mg g−1 for white spruce.

Comparisons of the optimum CND scores with those derived from Wangand Klinka’s study (1997) were somewhat consistent with those of nutrient con-centrations. Their optimum CND scores for N and K were within the optimumrange for these elements, while their CND scores for P and Mg were slightlyhigher than the upper limit of the optimum ranges. However, their optimumscore for Ca was 17.4% lower than the critical score (Table 4).

Overall, optimum nutrient concentrations and CND scores from this studyand those of Wang and Klinka (1997) and Bonneau (1995) were very similar,given the large distance separating the sites of study, the spatial and tempo-ral variation in foliar chemistry that can be expected in natural forests andplantations that are managed extensively, and the differences in developmentalstages and species. This general agreement suggests that the optimum rangesfor nutrient concentrations and CND scores of white spruce derived from theboundary-line approach were sound. More robust estimates could be obtainedby averaging critical nutrient concentrations from this study with those of Wangand Klinka (1997). This technique would appear to be particularly appropriatefor N, K, and Ca.

Few studies have reported standards of Mn nutrition for white spruce.Ballard and Carter (1986) proposed a critical Mn concentration for many conif-erous species of western Canada that was below the optimum concentrationby an order of magnitude. Bonneau (1995), on the other hand, suggested anoptimum range for conifers with short needles that had a low of 0.1 mg g−1

and an unknown threshold for toxicity. Manganese concentrations from severalwhite spruce plantations across Quebec were available and averaged 0.44 mgg−1 (Sheedy and Thomassin, 1994), a value that was similar to the optimum.The fact that Mn toxicity or deficiency has not been reported for white sprucein the Canadian boreal forest suggests that the computed optimum Mn concen-tration for white spruce was at least reasonable, but also that further research isneeded to confirm both critical and toxic levels.

The fact that the computed toxicity threshold for N was lower than somepublished optimum values for seedlings or trees (Swan, 1971; Ballard andCarter, 1986) suggests that it could have been underestimated. The toxic-ity threshold for Ca, although higher than published optimum values (Swan,1971; Ballard and Carter, 1986; Wang and Klinka, 1997), was also ques-tionable. Conifers have been shown to have the capacity to accumulate ex-cess Ca in the form of non-toxic Ca oxalate at levels that were far abovetheir optimum concentrations (Fink, 1991; Borer et al. 2004). Trees grow-ing in boreal ecosystems are often nutrient limited and trees with excessiveor toxic foliar-nutrient levels will be uncommon. The sampling of a smallnumber of trees may not have resulted in the selection of trees with op-timum rates of growth within the luxury consumption and toxicity range.If the selected boundary points in high nutrient-concentration intervals had

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


suboptimum growth, they would have induced an artificial decrease in thequadratic models. The additional precautionary steps developed in this studyto reduce the risk of including trees with less-than-optimum growth for a spe-cific interval of nutrition in the boundary-line models may not have been suf-ficient to prevent a possible underestimation of toxicity levels for N, P, andK, nutrients that are often considered growth limiting in the boreal forest.Such underestimations or overestimations do not apply to deficiency levels,because the generally low availability of these nutrients would increase theprobability of sampling trees growing at optimum levels within the intervalsof nutrition in the deficiency range, thus ensuring the accuracy of the com-puted critical and optimum levels for these nutrients using the boundary-lineapproach.

CONCLUSION

The results of this research show that the use of a statistically conservativebut conceptually robust boundary-line approach can be suitable to determineoptimum nutritional ranges of a boreal tree species given that sampling wasspread over more than one year and one site. The successful development ofoptimum ranges both in terms of foliar-nutrient concentrations and CND scoresfor white spruce will allow the determination of the nature and the frequencyof nutritional disorders in this species, and lead to the application of correctivetreatments and development of appropriate silvicultural methods for sustainingforest health and timber yield.

ACKNOWLEDGEMENTS

The research was supported in part by the Fonds Quebecois de la Recherchesur la Nature et les Technologies (FQRNT), the Natural Sciences and Engi-neering Research Council of Canada (NSERC) and the Centre of Excellenceon Sustainable Forest Management Network (SFMN). We are also thankful tothe Petawawa Research Forest and Foret d’Enseignement et de Recherche duLac Duparquet, which provided access to their facilities.

REFERENCES

Aitchison, J. 1986. Statistical analysis of compositional data. New York: Chap-man and Hall.

Allen, S. E. 1989. Analysis of vegetation and other organic constituents. InChemical analysis of ecological materials, 2nd edition. S. E. Allen, 160–200. Oxford: Blackwell Scientific.

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


Ballard, T. M., and R. E. Carter. 1986. Evaluating forest stand nutrient status.British Columbia Ministry of Forests Land Management Report No. 20.Victoria, Canada: British Columbia. Minister of Forests.

Bates, T. E. 1971. Factors affecting critical nutrient concentrations in plants andtheir evaluation: a review. Soil Science 112: 116–130.

Belanger, N., D. Pare, and S. H. Yamasaki. 2003. The soil acid-base statusof boreal black spruce stands after whole-tree and stem only harvesting.Canadian Journal of Forest Research 33: 1874–1879.

Bonneau, M. 1995. Fertilisation des forets dans les pays temperes: theorie,bases du diagnostic, conseils pratiques, realisations experimentales. [For-est fertilization in temperate countries: Theory, diagnostic, practical ad-vices, experimental results] Nancy, France: ENGREF.

Borer, C. H., P. G. Schaberg, D. H. deHayes, and G. J. Hawley. 2004. Accre-tion, partitioning and sequestration of calcium and aluminum in red sprucefoliage: Implication for tree health. Tree Physiology 24: 929–939.

Brais, S., C. Camire, and D. Pare. 1995. Impacts of whole-tree harvesting andwinter windrowing on soil pH and base status of clayey sites of northwest-ern Quebec. Canadian Journal of Forest Research. 25: 997–1007.

Brand, D. G., and P. S. Janas. 1988. Growth and acclimation of planted whitepine and white spruce seedlings in response to environmental conditions.Canadian Journal of Forest Research 18: 320–329.

Canadian Council of Forest Ministers. 1997. Criteria and indicators of sustain-able forest management in Canada, Technical Report. Ottawa: CanadianForest Service.

Cote, B., J. W. Fyles, and H. Djalilvand. 2002. Increasing N and P resorption ef-ficiency and proficiency in northern deciduous hardwoods with decreasingfoliar N and P concentrations. Annals of Forest Science 59: 275–281.

Doucet, K. 1997. Compositional nutrient diagnosis of forest trees in northwestern Quebec, master’s thesis, McGill University.

Environment Canada. 1993. Canadian climate normals, 1961–1990. Ottawa:Canadian Atmospheric Environment Service.

Ericsson, T. 1994. Nutrient dynamics and requirement of forest crops. NewZealand Journal of Forestry Science 24: 133–168.

Fink, S. 1991. The micromorphological distribution of bound calcium in needlesof Norway spruce (Picea Abies (L.) Karst.). New Phytologist 119: 33–40.

Foster, N. W., and I. K. Morrison. 1983. Soil fertility, fertilization and growth ofCandian forests. Information Report O-X-353. Sault Sainte Marie, Canada:Canadian Forestry Service.

Foster, N. W., and I. K. Morrison. 1987. Alternate strip clear cutting in uplandblack spruce. IV. Projected nutrient removals associated with harvesting.Forestry Chronicle 63: 451–456.

Hamel, B., N. Belanger, and D. Pare. 2004. Productivity of black spruce andjack pine stands in Quebec as related to climate, site biological featuresand soil properties. Forest Ecology and Management 191: 239–251.

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


Heiberg, S. O., and D. P. White. 1951. Potassium deficiency of reforested pineand spruce stands in northern New York. Soil Science Society of AmericaProceedings 15: 369–376.

Ingestad, T. 1959. Studies on the mineral nutrition of forest tree seed-lings I. Mineral nutrition of spruce. Physiologica Plantarum 12: 568–593.

Ingestad, T. 1960. Studies on the mineral nutrition of forest tree seedlings. II.Mineral nutrition of pine. Physiologica Plantarum 13: 513–533.

Khiari, L., L. E. Parent, and N. Tremblay. 2001a. Critical compositional nutrientindexes for sweet corn at early growth stage. Agronomy Journal 93: 809–814.

Khiari, L., L. E. Parent, and N. Tremblay. 2001b. The phosphorus compositionalnutrient diagnosis range for potato. Agronomy Journal 93: 815–819.

Knecht, M. F., and A. Goransson. 2004. Terrestrial plants require nutrients insimilar proportions. Tree Physiology. 24: 447–460.

Lafond, A. 1958. Les deficiences en potassium et magnesium de quelques plan-tations de Pinus strobus, Pinus resinosa et Picea glauca dans la provincede Quebec. [Potassium and magnesium deficiencies of some plantations ofPinus strobus, Pinus resinosa and Picea glauca in the province of Quebec].Fonds Recherches Forestieres Univ. Laval. Contribution No. 1. Quebec:Laval University.

Leaf, A. L. 1973. Plant analysis as an aid in fertilizing forests. In Soil testingand plant analysis, eds. L.M. Walsh and J.D. Beaton, 427–454. Madison,WI: Soil Science Society of America.

Lowry, G. L. 1970. Variations in nutrients of black spruce needles. In Treegrowth and forest soils, eds. C.T. Youngberg and C.B. Davey, 235–259.Corvallis, OR: Oregon State University Press.

Morris, D. M. 1997. The role of long-term site productivity in maintaininghealthy ecosystems: A prerequisite of ecosystem management. ForestryChronicle 73:731–740.

Morrison, I. K., and N. W. Foster. 1995. Effect of nitrogen, phosphorus, andmagnesium fertilizers on growth of a semi-mature jack pine forest, north-western Ontario. Forestry Chronicle 71: 422–425.

Morrow, L. D., and V. R. Timmer. 1981. Intra seasonal growth and nutrientcomposition of jack pine Pinus-banksiana needles following fertilization.Canadian Journal of Forest Research 11: 696–702.

Munson, A. D., H. A. Margolis, and D. G. Brand. 1995. Seasonal nutrientdynamics in white pine and white spruce in response to environmentalmanipulation. Tree Physiology 15:141–149.

Paquin, R., H. A. Margolis, and R. Doucet. 1998. Nutrient status and growth ofblack spruce layers and planted seedlings in response to nutrient additionin the boreal forest of Quebec. Canadian Journal of Forest Research 28:729–736.

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


Parent, L. E., and M. Dafir. 1992. A theoretical concept of compositional nutrientdiagnosis. Journal of the American Society of Horticultural Science 117:239–242.

Parent, L. E., D. Isfan, N. Tremblay, and A. Karam. 1994. Multivariate nutrientdiagnosis of the carrot crop. Journal of the American Society of Horticul-tural Science 119: 420–426.

Parent, L. E., A. Karam, and S. A. Visser. 1993. Compositional nutrient diag-nosis of the greenhouse tomato. HortScience 28: 1041–1042.

Parent, L. E., M. Poirier, and M. Asselin. 1995. Multi-nutrient diagno-sis of nitrogen status in plants. Journal of Plant Nutrition 18: 1013–1025.

Perie, C., and A. D. Munson. 2000. Ten-year responses of soil quality andconifer growth to silvicultural treatments. Soil Science Society of AmericaJournal 64: 1815–1826.

Regent Instruments. 1996. WindDendro V6.1D. Chicoutimi, Canada: RegentInstruments, Quebec University at Chicoutimi.

Rowe, J. S. 1972. Forest regions of Canada. Canadian Forestry Service Publi-cation No. 1300. Ottawa: Canadian Forestry Service.

Schleppi, P., L. Tobler, J. B. Bucher, and A. Wyttenbach. 2000. Multivariateinterpretation of the foliar chemical composition of Norway spruce (Piceaabies). Plant and Soil 219: 251–262.

Sheedy, G., and C. Thomassin. 1994. Concentrations moyennes en elementsdans les aiguilles des plantations resineuses du Quebec [Average elementconcentrations in needles of coniferous plantations of Quebec]. QuebecMinistry of Natural Resources Internal Report no. 386. Quebec: QuebecMinistry of Natural Resources.

StatSoft. 1994. Statistica for the Macintosh, V. 4.1. Tulsa: StatSoft.Stone, E. L. 1953. Magnesium deficiency in some north-eastern pines. Soil

Science Society of America Proceedings 17: 297–300.Swan, H. D. S. 1970. Relationships between nutrient supply, growth and nutrient

concentrations in the foliage of black spruce and jack pine. Pulp and Pa-per Research Institute of Canada Woodland Report No. 19. Pointe-Claire,Canada: Pulp and Paper Research Institute of Canada.

Swan, H. D. S. 1971. Relationships between nutrient supply, growth and nu-trient concentrations in the foliage of white spruce and red spruce. Pulpand Paper Research Institute of Canada Woodland Report No. 34. Pointe-Claire, Canada: Pulp and Paper Research Institute of Canada.

Teng, Y., S. E. Bailey, N. W. Foster, and P. W. Hazlett. 2003. Response ofadvance regeneration to intensity of harvest and fertilization on borealwetlands. Forestry Chronicle 79: 119–126.

Timmer, V. R., and P. N. Ray. 1988. Evaluating soil nutrient regime for blackspruce in the Ontario claybelt by fertilization. Forestry Chronicle 64: 40–46.

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014


Truong, D. P. 1975a. Potassium and magnesium: Two elements limiting heightgrowth of red pine. Canadian Journal of Forest Research 5: 73–79.

Truong, D. P. 1975b. Relations entre la croissance en hauteur du meleze laricinet les teneurs en elements mineraux du sol et des aiguilles [Relationshipsbetween height growth of tamarack and concentrations in mineral elementsof the soil and needles]. Canadian Naturalist 102: 99–108.

Truong, D. P., and J. D. Gagnon. 1975. Nutrient–growth relationships in theGrand-Mere white spruce plantations before and after fertilization. Cana-dian Journal of Forest Research 5: 640–648.

van den Driessche, R. 1974. Prediction of mineral nutrient status of trees byfoliar analysis. Botanical Review. 40: 347–394.

Vizcayno-Soto, G., and B. Cote. 2004. Boundary line approach to determinestandards of nutrition for mature trees from spatial variation of growth andfoliar nutrient concentrations in natural environments. Communications inSoil Science and Plant Analysis 35: 2965–2985.

Walworth, J. L., and M. E. Sumner. 1988. Foliar diagnosis: a review. In Advancesin plant nutrition, Vol. 3, eds. B. Tinker and A. Lauchli, 173–189. London:Academic Press.

Wang, G. G., and K. Klinka. 1997. White spruce foliar nutrient concentrationsin relation to tree growth and soil nutrient amounts. Forest Ecology andManagement 98: 89–99.

Webb, R. A. 1972. Use of the boundary line in the analysis of biological data.Journal of Horticultural Science 47: 309–319.

Weetman, G. F., and R. M. Fournier. 1984. 10-year growth and nutrition effectsof a straw treatment and of repeated fertilization on jack pine. CanadianJournal of Forest Research 14: 416–423.

Weetman, G. F., H. H. Krause, E. Koller, and J.-M. Veilleux. 1987. Interprovin-cial forest fertilization trials: 5- and 10-year results. Forestry Chronicle 63:184–192.

Weetman, G. F., and B. Webber. 1972. The influence of wood harvesting on thenutrient status of two spruce stands. Canadian Journal of Forest Research2: 351–369.

Wells, E. D. 1994. Effects of planting spacing and refertilization on growthand nutrition of black spruce (Picea mariana (Mill) B.S.P.) planted on aminerotrophic peatland in Newfoundland, Canada. Canadian Journal ofForest Research 24: 1302–1311.

Dow

nloa

ded

by [

Uni

vers

ity o

f C

alif

orni

a Sa

nta

Cru

z] a

t 22:

56 0

7 N

ovem

ber

2014

optimum nutrient concentrations and cnd scores of mature white spruce determined using a...

Documents