Evaluation and diagnosis of tree nutritional status in Chinese-fir (Cunninghamia lanceolata (Lamb) Hook) plantations, Jiangxi, China

Forest Ecology and Management, 62 ( 1993 ) 245-270

245

Elsevier Science Publishers B.V., Amsterdam

Evaluation and diagnosis of tree nutritional status in Chinese-fir (Cunninghamia lanceolata (Lamb) Hook) plantations, Jiangxi, China A n - L i a n g Z h o n g *,a, W e n - Y u e H s i u n g b

aFaeulty of Forestry, Universityof British Columbia, 270-2357Main Mall, Vancouver,B.C. V6T 1Z4, Canada bDepartment of Forestry, Nanjing Forestry University, Nanjing, Jiangsu, 210037, People's Republic of China (Accepted 7 June 1993)

Abstract

During the period 1987-1989, 12 plots representing different stand age and site quality were established in the Chinese-fir plantations of Fudong Branch, Fengshushan Forest Station, Jiangxi province. Soil physical and chemical properties, and nutrient (N, P, K, Ca, Mg and S) concentrations in needles and twigs from dominant, average and depressed trees were analyzed in different growing seasons. Principal component analysis (PCA), discriminant analysis (DA), parabola modeling, and diagnosis and recommendation of integrated system (DRIS) were used to evaluate the nutritional quality and nutrient balance of the stands, and to develop nutrient criteria for the soils and trees. Stands CL1, CH1 and TK1 were of high nutritional quality, XW1, CL2 and XW2 showed a moderate nutritional condition, and CH2 and TK2 were poor in nutrition. The discriminant functions YI= - 10.94 + 22.97 XN and YII ~-- - - 6 . 4 2 + 17.59 XN (XN is the N concentration in needles) were developed with correctness of 80% for judging tree nutritional quality. Gravel ( > 2 mm) content, pH, total N, hydrolyzable N and extractable P were the main factors affecting tree nutrition and growth. Tree N nutrition closely interacted with P nutrition in the stands. Optimum soil total N, hydrolyzable N and extractable P were 540, 65 and 8 ppm, respectively for 5-year-old stands, 650, 83 and 5 ppm for 10-year-old stands, and 560, 88 and 4 ppm for 20-year-old stands. A uni-variable quadratic model ( Y=a+bX+cX 2) was most suitable for determining the criteria of N and P concentrations in needles; basically good for K and S criteria establishment, and not fit for that of Ca and Mg. Log ( (DBH)2 ×height) was the most appropriate growth index for developing such diagnostic equations. Criteria of nutrient concentrations varied between different growing seasons and stand age. Critical nutrient concentrations of N and P during the first fast-growing season, a sensitive period for nutrient deficiency, were 2.19% and 0.13%, 0.83% and 0.13%, and 0.76% and 0.13% for 5-, 10- and 20-year-old stands, respectively. The nutrient indices of N, P and K derived from DAIS demonstrated that most stands needed more K and P than N during the early growing season, N was a key nutrient and most needed, then P in the fastgrowing seasons. Optimum ratios ofN:P, P:K and K'N were 7.68, 0.12 and 1.09, respectively, for the Chinese-fir stands. *Corresponding author.

© 1993 Elsevier Science Publishers B.V. All rights reserved 0378-1127/93/$06.00

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Introduction Chinese-fir (Cunninghamia lanceolata) plantations have been widely established in China since the 1950s. The total area has reached 6 million ha (Yu, 1988 ). Site quality in some Chinese-fir plantations has declined sharply owing to the rapid depletion of nutrients to support fast-growing trees and the deterioration of soil physical and biological conditions caused by poor litterfall accumulation and decomposition in the soil, especially during the second rotation (Fang, 1987; Yu and Zhang, 1989 ). As a result of the trend to shorten rotations and whole-tree harvest, site fertility and productivity will further deteriorate, even in some newly established Chinese-fir plantations (Zhong, 1988b). Amelioration of site quality by means of fertilization, introduction of N-fixing species, and establishment of mixed plantations with broadleaf tree species have become very important for the future management of Chinese-fir plantations (Zhong, 1988b). Nutritional conditions in the soil are either directly or indirectly influenced by various ecological and biological factors, including soil moisture, even though it mainly affects physiological activities such as photosynthesis and transpiration. Nutrient content and availability should be considered as the decisive or essential factors for tree growth and productivity of the Chinese-fir plantations in southern China, where precipitation and soil moisture are favorable for tree growth in most seasons (Zhong, 1988a). A good understanding of nutrient dynamics and nutritional diagnosis in soils and trees is necessary for the nutritional management of widely distributed Chinese-fir plantations. Nutritional quality in soils and trees should be evaluated and diagnosed on the basis of the site unit, and monitored during the growing seasons. This study was conducted to evaluate the nutritional quality of different Chinese-fir stands and to develop nutrient criteria for soils and trees at a regional level. Study site and methods

Study site The research area lies in northern Jiangxi (29°09'-29°35'N, 11703 ' 117 ° 37' E), which belongs to the hill-around-lake subzone of East Zone, Central Region of Chinese-fir distribution (Wu, 1984). Hills in this area are at an altitude of 150-200 m and the slope gradient is 15-45 °. Common parent rocks are phyllite, slate and sandstone. Typical soils belong to the eluvialpodzolic family with podzolized krasnozems (red earth) (Wu, 1984). Mean annual temperature in this area is 17.1 ° C. Mean annual precipitation is 1757 mm and mostly falls between April and June (Zhong, 1990). Common plant species forming the minor vegetation in the Chinese-fir plantations were investigated and are listed in Table 1. The research sites are located in Chun-

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Table 1 Common plant species of minor vegetation in the Chinese-fir plantations at Fudong Branch of Fengshushan Forest station Common name of species

Latin name of species

Roxburgh engelhardtia Oriental blueberry Chinese adinandra Starshape sweetleaf Muricate eurya Lobed kudzuvine Boxleaf syzygium Greensulphur bamboo Ciliate eurya Maries azalea Chinese wisteria Lalang grass Manyflower silvergrass Linear fern

Engelhardtia roxburghiana Wall. Vaccinium bracteatum Thunb. Adinandra chinensis Merr. Symplocos stellaris Brand Eurya muricata Dunn. Pueraria lobata (Willd.) Ohwi. Syzygium buxifolium Hook. et Arn. Phyllostacyys viridis (Young) McClure Eurya ciliata Merr. Rhododendron mariesii Hemsl. et Wils. Wisteria sinensis Sweet Imperata cylindrica var major (Nees.) C.E. Hubb. Miscanthus floridulus ( Labill ) Warb. Dicranopteris linearis Thunb.

buling, Xiachaowu, Chezhanhou and Tanken, which represent different sites for quality, tree growth and stand productivity of Chinese-fir plantations at the Fudong Branch of the Fengshushan Forest station. Twelve plots selected from 5-, 10- and 20-year-old stands with various site quality were laid out in the four locations in December 1987. Most plots were 20 m X 20 m and a few were 15 m X 20 m owing to the limitations of the topographical conditions. The basic characteristics of the plots are shown in Table 2.

Methods Tree measurement and selection of sample trees The diameter at breast height (DBH) of all trees in 12 plots was measured in December 1987. In each plot, the five trees with the largest DBH and healthy crowns were selected as dominant sample trees, the five trees with DBH nearest to stand mean and healthy crowns as average trees, and the five trees with the smallest DBH as depressed trees, which gave 15 sample trees in total for each plot. The DBH of all trees and total height of dominant, average and depressed sample trees were then determined in mid-April (bud flush), midJune (the first fast-growing season), mid-September (the second fast-growing season) and mid-December (pre-dormancy) 1988 (Yu, 1988).

Tree sampling and chemical analysis One sample branch was taken from the south upper one-third crown of each sample tree, giving five sample branches for each class of dominant, average and depressed sample trees, and 15 sample branches in total for each plot in

5 2235 9.35 6.08 8.22 Down 18 18

5 2700 6.99 5.43 6.97 Upper 36 16

TK2 10 2700 11.68 9.28 10.99 Down 39 16

CH1 10 2805 9.79 6.86 9.79 Upper 35 14

CH2 20 900 23.05 16.96 21.45 Down 11 18

CL1 20 1230 16.99 14.50 15.81 Upper 23 14

CL2 5 2670 8.90 5.97 7.87 Down 28 18

TK3 5 3120 8.45 5.85 7.51 Middle 28 18

TK4

aTK, CH, CL and XW were plot locations in Tanken, Chezhanhe, Chunbuling and Xiachaowu, respectively. bMean height of five dominant trees. Cm at 20 years.

Age (year) Density ( s h a -~ ) DBH (cm) Mean Ht (m) Hd b (m) Slope position Slope gradient Site index c

TK1

Table 2 Basic characteristics of Chinese-fir stands in Fudong Branch of Fengshushan Forest Station"

10 3465 9.66 7.32 8.62 Middle 35 14

CH3

10 3705 8.96 6.66 7.83 Upper 43 12

CH4

20 1245 21.62 16.06 20.31 Down 39 18

XW1

20 1080 20.19 15.16 19.17 Middle 35 16

XW2

,.

.~

.~

N

oo

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249

a single sampling time. Tree samples were taken during bud flush, the first fast-growing season, the second fast-growing season and pre-dormancy, respectively. Current and/or 1-year-old needles and twigs, depending on tree growing season, at the terminus of each sample branch were taken, then composted by tree class to produce three composite needle and twig samples for each age class of needle and twig in each plot. From each composite sample, about 200 g (fresh weight) of needles or twigs were subsampled. Foliar and twig samples were oven-dried at 75°C for 24 h and ground to pass a 40-mesh screen. Samples were digested in sulphuric acid and measured for N concentration by the semi-micro Kjeldahl method, P concentration by the molybdate-blue method, and nutrient concentrations of K, Ca and Mg by atomic absorption spectrophotometry. Sulphur concentration in needles and twigs was determined by HNO3-H2SO4 digestion and the BaSO4 turbidimetric method (State Bureau of Standards of PRC, 1988 ).

Soil sampling and analysis The soil survey was conducted in mid-December 1988. The soil profile was heavily disturbed due to weeding and the soil layers were too obscure to be distinguished. The Chinese-fir root system is largely distributed within 0-60 cm depth and organic matter is mostly distributed in topsoil of 20 cm depth (Yu, 1983). For the above reasons, the soil profile was divided into three layers: 0-20, 21-40, 41-60 cm. Five sampling spots were evenly located along two diagonal lines of a plot. The crosspoint of the two lines was used as both a sampling point and a soil profile. One soil core (5 cm diameter) was taken from each soil layer in each spot, giving 15 soil sample cores in each plot. Soil sample cores were composted by soil layer, which produced three composite samples, each layer having one sample for analysis of soil physical and chemical properties. The mean values of soil properties of the three soil layers were used to diagnose soil nutritional status and develop soil diagnostic criteria in Chinesefir plantations. Soil porosity was calculated based on bulk density and particle density, which were determined by using the core and pycnometer methods, respectively. A glass electrode pH meter and Schollenberger method were employed to determine soil pH and organic carbon content separately (State Bureau of Standards of PRC, 1988 ). Total N in the soil was determined using a semimicro Kjeldahl procedure. Hydrolyzable N was determined using a diffusion absorption method. The soil P available for tree growth was extracted using a mixed solution of 0.03 N NH4F-0.025 N HC1 and determined by the molybdate-blue method. Soluble K, exchangeable Ca and Mg were determined by atomic absorption spectrophotometry (State Bureau of Standards of PRC, 1988).

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Calculations and statistics Principal component analysis (PCA) Nutrient concentrations of N, P, K, Ca and Mg in current and/or 1-yearold needles of dominant sample trees in each plot during different growing seasons were used to perform PCA. Only eight plots out of 12 were involved in the statistical analysis because they typically represented different site quality and tree growth. Following data standardization and calculation, coefficients for each initial nutrient variable and the overall contribution rate for each principal component were determined. This was done to reflect the relative importance of each nutrient in distinguishing various stands, in terms of tree nutritional quality, and to indicate the degree that nutritional differences between stands can be explained by a new integrative variable (principal component). When the cumulative contribution rate was not less than 80%, the principal components rather than the initial nutrient variables were used to make a multidimensional (usually two-dimensional) ordinal graph in which the first and second principal components were treated as the X axis and Y axis, respectively. Accordingly, foliar samples from various stands can be plotted in this graph, therefore the stands were grouped based on the distance between samples. The groups were nutritionally defined by tree nutritional status and corresponding tree growth.

Discriminant analysis (DA) Based on the nutrient concentrations of N, P and K in current and/or 1year-old needles of dominant sample trees and corresponding tree growth in each plot during different growing seasons, 12 plots were divided into two groups: appropriate nutrition (I); and inappropriate nutrition (II) (see Table 5 ). Bayes stepwise discriminant analysis was employed to identify key nutrients, and to develop discriminant functions of the key nutrients for judging nutritional quality of Chinese-fir stands.

Correlation and regression Organic carbon content, total N, hydrolyzable N, extractable P and soluble K (as independent variables) in soil were correlated with the mean annual nutrient concentrations of N, P and K in needles of dominant sample trees, and tree growth rates of DBH and height measured during pre-dormancy (as dependent variables). Coefficients of correlation (R) derived from correlation analysis were used to identify the nature of the correlation between independent and dependent variables, the larger the R value is, the more important an independent variable to a dependent variable. Twelve pairs of data (dependent variable versus independent variable) from 12 plots were used in the correlation analysis.

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251

Double-sieving stepwise multiple regression was employed to examine, in a more accurate way, the relationships between independent variables and dependent variables. Through sieving independent variables, those selected in the final regression equations were considered important for dependent variables (Tang, 1986). This regression technique was used to identify the major soil physical and chemical factors affecting tree nutritional quality and growth, based on the averaged nutrient concentrations in needles and twigs during different growing seasons, measurements of soil properties, such as depth of forest floor, gravel ( > 2 m m ) content, pH, total N, hydrolyzable N, extractable P and soluble K, and tree growth in Chinese-fir stands.

Determination of nutrient critical levels Based on some previous studies (Everard, 1973; Binkley, 1987; Zhong and Yu, 1988 ), the uni-variable quadratic equation ( Y= a + bX+ cX ~) could be satisfactorily used to express the relationships between foliar nutrient concentrations and tree growth, biomass and some physiological activities such as net photosynthetic capacity. Since most climatic conditions were similar (especially soil moisture availability in different plots which were located in a small area), soil nutrient availability became a dominant factor influencing tree nutrition and growth, and it could be reasoned that there would be close relationships between foliar nutrient concentrations and tree growth for stands of a certain age range. For this, the model was adopted to determine quantitatively the criteria of nutrient concentrations in needles and twigs of Chinesefir trees. The paired data on nutrient concentrations (as independent variables) in current and/or 1-year-old needles and twigs of dominant, average and depressed sample trees in the 5-, 10- and 20-year-old stands, respectively, and corresponding DBH, height and log ((dbh) 2 × height) (as dependent variables) of the sample trees in each growing season were used to establish regression equations. The F-test was employed to test the statistical significance of the equations by comparing the difference between predicted values and actual values. If an equation was significant, an optimum nutrient concentration (Xo) could be calculated as Xo = - b ~ (2 X c). Based on the generalized concepts, a critical nutrient level (Xc) is usually defined as 90% of an optimum value, i.e. Xc=0.9 ×Xo (Binkley, 1987; Zhong and Yu, 1988 ). Determination of optimum nutrient ratios Diagnosis and recommendation of integrated system (DRIS) was applied as a diagnosing technique in which the balances between nutrients in leaves and other factors affecting nutrient status and tree growth were all involved to determine appropriate ratios of nutrients and sequences of fertilizers to be applied (Beaufils, 1973; Leech and Kim, 1981; Zhong and Yu, 1988; A1koshab et al., 1988).

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Various Chinese-fir stands were divided on the basis of tree growth into a high-yield group (stands CL1, CH1, XW1, TK1, TK3 and TK4) and a lowyield group (stands CL2, XW2, TK2, CH2, CH3 and CH4). The ratios of N, P and K concentrations in current and/or 1-year-old needles of all sample trees of the two groups in different growing seasons and the means of standard error, coefficients of variation and variance in each group were computed. The ratios of variance in the high-yield group compared with those in the lowyield group were tested for their statistical significance. Ratios at significant levels were selected as diagnosing parameters. In most cases, N, P and K were the most important nutrients affecting Chinese-fir growth (Yu, 1988; Zhong, 1989 ). The index values of N, P and K were calculated, based on the means and coefficients of variation. The relative need for fertilizer application could be estimated according to the index values. Nutrients with the lowest index values should be the first added to the Chinese-fir plantations. Results

Evaluation of tree nutritional quality Principal component analysis As indicated in Table 3, N and P were the key factors of the first principal component group, with a greater contribution in all growing seasons except the pre-dormancy season. This demonstrates the crucial role of N and P in improving the nutritional quality and growth of Chinese-fir trees, especially during fast-growing seasons. Potassium became the main nutrient factor in the first principal component with a contribution rate of 0.51 during pre-dormancy, showing the importance of K nutrition in tree growth after the fastgrowing seasons. Like N coefficients in the first and second principal component, P coefficients in all seasons except pre-dormancy were higher, suggesting positive interactions between N and P nutrition. Calcium and Mg coefficients in the first principal component equations were negatively high during the fast-growing and pre-dormancy seasons (Table 3 ), indicating their negative effects on tree nutritional quality and growth if Ca and Mg are oversupplied to the stands in these seasons. Based on PCA bi-dimensional coordination, eight different stands were plotted and classified, by using the first and second principal component equations, into three or four types with varying nutritional quality from good to poor nutrition (Table 4). Trees in stands CL1, CH1 and TH1 were of high nutritional quality, especially with N and P. Trees in stands XW 1, CL2 and XW2 were under moderate nutritional conditions. Those in stands CH2 and TK2 were poor in nutrition.

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Table 3 Coefficients and contribution rates of pricipal component functions in different growing seasons Time

An a

PC b

N

Bud flush

1

Y1 Y2 Y3

0.67 0.72 -0.04

First fastgrowing season

0

Y1 Y2 Y1 Y2

1

P

K

Ca

Mg

CR c

CCR a

0.77 0.61 0.02

-0.69 0.66 0.14

0.33 -0.24 0.92

0.75 -0.56 -0.25

0.44 0.34 0.18

0.44 0.77 0.96

0.87 0.00 0.86 -0.47

0.91 -0.36 0.38 -0.30

0.53 0.85 0.81 0.52

-0.82 -0.02 -0.14 -0.82

-0.84 0.16 -0.96 -0.18

0.65 0.18 0.50 0.37

0.65 0.83 0.50 0.86

Second fast growing season

0

Y1 Y2

0.97 -0.13

0.96 0.17

-0.07 0.99

-0.80 -0.53

0.27 -0.81

0.49 0.34

0.49 0.83

Pre-dormancy

1

Y1 Y2 Y3

0.45 0.60 -0.62

-0.06 0.80 0.58

0.95 0.01 0.14

-0.72 0.48 -0.20

-0.96 -0.11 -0.04

0.51 0.25 0.16

0.51 0.76 0.92

Whole year

1

Y1 Y2

0.78 -0.49

0.76 -0.60

-0.78 -0.51

0.05 0.78

0.83 0.48

0.50 0.34

0.50 0.83

aNeedle age, current (0) and 1-year-old ( 1 ) needles. bprincipal component. cContribution rate. dCumulative contribution rate. Table 4 Nutritional classification of Chinese-fir stands based on the results from PCA a Nutritional class c

Growing season b I

A B C

II

III

IV

NId

N2 d

No a

N1 a

NOd

N1 a

CL1 CH1 XWI TK1 CL2 XW2 CH2 TK2

CL1 CH1 WW1 TK1 CL2 XW2 CH2 TK2

TK1 CL1 CL2 XW1 XW2 CH1 CH2 TK2

CL1 CH1 XWl TK1 TK2 CL2 CH2 XW2

CL1 CH1 TK1 CL2 CH2

CL1 TK1 TK2 XW1 CH1 CL2 XW2 CH2

D

TK2

"Plot code was shown in this table. See Table 2 for an explanation of each code. hi, bud flush; II, the first fast-growing season; III, the second fast-growing season; and IV, predormancy. CA, good nutrition; B, intermediate nutrition; C, nutrient insufficient; and D: poor nutrition. dNeedle age: NO, N 1 and N2 were current, 1-year-old and 2-year-old needles, respectively.

Discriminant analysis On the basis of the nutrient concentrations of N, P and K in the needles of dominant sample trees, it was found from sieving independent variables that

5-20 12.5

Range Mean

Range Mean

Range Mean

I

II

All

0.459-1.481 0.841

0.459-1.186 0.730

0.618-1.481 0.953

N

0.072-0.149 0.113

0.072-0.134 0.106

0.081-0.149 0.121

P

Nutrient concentration (% dry weight)

0.356-1.675 0.908

0.356-1.498 0.854

0.535-1.675 0.963

K

6.99-16.99 12.22 6.99-23.05 13.79

5.43-14.50 9.85 5.43-16.96

4.55-11.52 6.85 4.55-11.52 7.42

11.40

8.90-23.05 15.98 5.97-16.96

(cm)

DBH

11.47

Height (m)

5.44-9.96 7.87

N/P

~Based on nutrient concentrations in current and 1-year-old needles (N= 15 for both stand types). hi, good nutrition; II, medium or poor nutrition; and All, average nutrition in all stands.

5-20 12.5

5-20 12.5

Age (year)

Stand type b

Table 5 Parameters for discriminant analysis on needle nutritional quality of different Chinese-fir stands ~

t,o

o~

I,O

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the N concentration in needles was the most important factor involved in the discriminant functions (i.e. YI = - 10.94+22.97 XN, Y~t= - 6 . 4 2 + 17.59 XN where: Y~and Y~Iare discriminant indices; XN is the N concentration in needles (% dry weight). When the N concentration in needles from stands outside the 12 plots was substituted into the functions, if YI was greater than Yn the nutritional status was judged as 'appropriate nutrition'. The established functions were used to test the accuracy of the pre-classified nutritional quality of all plots. The correctness was 80% for Groups I and II (Table 5 ), showing the feasibility of the functions for evaluating nutritional quality of Chinese-fir plantations in the research area. On the basis of nutrient concentrations of N, P and K in twigs of dominant sample trees, it was found that the discriminant functions were: Y~= - 11.78+30.73 XN, Yn = - 8 . 6 9 + 2 6 . 3 9 XN where: XN is the N concentration in twigs (% dry weight). The accuracy was only 46.7% for Group I and 66.7% for Group II (Table 5 ), much lower than that from needle nutrient data, indicating that the total N in a twig was unsuitable as a discriminant factor.

Soil nutritional diagnosis Table 6 shows that only the K concentration in needles was positively correlated at a significant level with extractable P in soil, and tended to be negatively correlated with the total N in the soil. There were no close relationships Table 6 Coefficients of correlations between soil nutrient concentrations, nutrient concentrations in needles, and tree growth (R-value) Tree response

Soil parameter Total N (%)

Hydro-N (ppm)

Extractable P (ppm)

Soluble K (ppm)

Organic C (%)

Concentrations in needles (% dry weight) N P K

0.27 0.63 - 0.67

0.44 0.63 - 0.42

0.38 - 0.17 0.79*

0.23 0.42 0.38

0.56 0.65 0.17

0.11 - 0.19 0.35 - 0.10

0.35 0.43 0.00 0.37

Tree growth ~ Hd DBH MHa MHd

0.13 0.38 0.78* 0.72

0.58 - 0.40 -0.38 - 0.47

0.25 0.92** 0.55 0.96**

*P< 0.05; **P< 0.01. arid, mean height (m) of dominant trees; DBH, diameter at breast height (cm); MHa, mean annual height increment of average trees; and MHd, mean annual height increment of dominant trees.

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between N and P concentrations in the needles and corresponding concentrations of N and P in the soil. The negative correlation between total N in the soil and the K concentration in needles may have resulted from the competitive absorption ofNH4 + and K + by root systems. As shown in Table 6, there was a significant correlation between extractable P in the soil and the K concentration in needles, and between the total N in the soil and the mean annual height increment of average trees. There were very significant correlations between extractable P in the soil and the mean stand DBH and the mean annual height increment of dominant trees. The results indicated the importance of extractable P in promoting the growth of diameter and height of Chinese-fir, particularly the height of dominant trees. As indicated in Table 7, the N concentration in needles and twigs increased with the depth of the forest floor. There were positive relationships between total N in the soil and N concentration in twigs, between P concentration in needles and hydrolyzable N in soil, and between P concentration in needles and twigs and total N and soluble K in soil. Potassium concentration in needles and twigs increased with extractable P and soluble K in soil. The above results demonstrate that there is significantly positive interaction between N, P and K in soil and trees. Table 7 also shows that there were negative relationships between the gravel content and growth responses of Chinese-fir. The increase of hydrolyzable N and soil pH promoted both DBH and height growth, and the increase of soil extractable P was beneficial for stem volume growth. In conclusion, tree N nutrition was affected by soil total N, pH and forest floor depth, P nutrition by soil N and K status, and K nutrition by soil P and K status. Tree growth was mainly influenced by hydrolyzable N, gravel ( > 2 m m ) content, pH (H20), extractable P and total N, which are suitable as parameters for soil nutritional diagnosis in Chinese-fir plantations. Soil nutrient criteria vary with stand age and growing seasons because of changes in nutrient uptake by plants. For a stand of a certain age, under the condition that climate and soil physical properties are similar in a small area, tree growth may respond mainly to nutrient status in the soil. Nutrient status on a site where trees grow fast could be defined as a 'high' nutrient class, that on a site where trees grow slowly as 'low', and in between as 'medium' (Leaf, 1973; Van den Driessche, 1974). This approach was employed to establish the criteria of soil nutritional diagnosis and the corresponding mean annual increment of DBH and height of trees in different Chinese-fir stands of 5-, 10-, and 20-years-old, respectively. Optimum soil total N, hydrolyzable N and extractable P were 540, 65 and 8 ppm, respectively, for 5-year-old stands; 650, 83 and 5 p p m for 10-year-old stands; and 560, 88 and 4 p p m for 20-yearold stands (Table 8 ). The criteria for total N, hydrolyzable N and extractable P in soil changed between the stands of different ages, mainly because of the differences in the

Y3= 0.2702 + 0.0822Xlo + 0.0030X11

Y2= 5.0953 + 0.0010)(9 + 0.0002X1 Y3= - 2.8016 - 0.0017X2 + 0.6740X6 - 6.9107Xs + 0.0995Xao + 0.0052X~ l

0.996 0.998 0.998 0.970

0.978 0.884 0.994

0.998 0.998 0.954

R2

0.138 0.022 0.013 0.016

0.022 0.008 0.038

0.013 0.001 0.051

SEE b

pH (H20); X7, organic carbon content (%); )(8 total N ( % ); Xg, hydrolyzable N (ppm); X~o, extractable P ( p p m ) ; and X11, soluble K (ppm). bR 2 coefficient of determination; and SEE, standard error of estimate. cyl, DBH (cm); Y2, height of average trees (m); Y3, stem volume (m3); and Y4, height of dominant trees (m).

aXl, depth of forest floor (cm); X2, gravel ( > 2 m m ) content (% v / v ) ; )(3, soil bulk density (g cm-3), )(4, total porosity (%);)(5, pH(KC1); X6,

Ya=O.lO12-O.OO18Xz-O.O665X7 +O.OO31X9

Y~= - 11.8886- 0.1652)(2- 2.6665)(5 + 5.5171)(6 + 0.1456)(7 + 0.1532X9 + 1.3727X~ Yz = - 32.1465 - 0.1430)(2 + 5.9895Xs + 4.1904)(6 - 2.3689)(7 + 0.1626X9 - 0.0452X~o Y3= - 2 . 1 7 3 2 - 0.0576X2 + 3.6460X3 + 1.5066X6+O.O856X9+O.O585Xlo

Tree growth°

P K

Nutrient concentrations in twigs (% dry weight) N Y~=O.8244+O.O144Xl--O.1478Xg+3.9554Xs

K

Nutrient concentrations in needles(% dry weight) N Yt= -6.6081 +O.0332X~+O.OOO9X2+O.1846X3+ 1.8621X~- 0.1729X7 P Y2= -0.4191 +O.O002X2-O.O137X3+O.1415Xs+O.2415Xs+O.OOO3X9

Regression equation ~

Table 7 Stepwise multiple regression between soil properties and tree nutrient concentrations and growth of Chinese-fir

t-o Lit

~,

~

rn

~

< 370 <47 <7

1.78 1.57 1.19

370-540 47-65 7-8

1.87 1.65 1.22

> 540 >65 >8

1.18 1.10 0.54

< 500 <62 <4

1.20 1.11 0.67

500-650 62-83 4-5

M

1.21 1.12 0.80

> 650 >83 >5

H

1.01 0.96 0.76

< 500 <62 <3

L

20

1.08 1.02 0.80

500-60 62-88 3-4

M

1.15 1.07 0.85

> 560 >88 >4

H

~L, M and H were low, medium and high nutritional quality, respectively. bDBH, Hd and Ha were mean annual increments of diameter at breast height, height of dominant trees and height of average trees, respectively.

Mean annual increment of growthb DBH (cm) 1.69 Hd (m) 1.50 Ha (m) 1.17

Total N (ppm) Hydrolyzable N (ppm) Extractable P (ppm)

L

H

L"

M

10

5

Stand age (year)

Table 8 Criteria of soil nutritional diagnosis and growth responses in Chinese-fir plantations

.A

o~

A.L. Zhong, W.- Y. Hsiung / Forest Ecology and Management 62 (1993) 245-270

259

initial status of soil nutrient availability before planting, and of nutrient utilization associated with stand age and tree growth rate (Table 8 ). Owing to lower site quality (expressed by site index) in the 10-year-old stands (Table 2), the tree height growth rate evidently decreased from 5- to 10-year-old stands (Table 8).

Tree nutritional diagnosis Determination of nutrient criteria in needles Young stands (5-year-old). Table 9 shows that the uni-variable quadratic model was unsuitable for determining the criteria of Ca concentrations in needles and twigs, fairly appropriate for determining the N and P criteria, good for Mg and S, and poor for K. The regression between P concentration in twigs and tree diameter, height and log ( ( D B H ) 2 × height) was significant or very significant (Table 9), indicating that tree growth may be sensitive to P nutrition. As indicated in Table 10, during fast-growing seasons Chinese-fir required much more N than in early growing seasons especially during the first fastgrowing stage. Critical nutrient concentrations of N and P at this sensitive stage to nutrient deficiency were 2.19% and 0.13%, respectively. Twigs were unsuitable for the determination of other nutrient criteria except P (Table 10). Mid-age stands (lO-year-old). As shown in Table 9, the criteria of Ca concentration in needles and twigs could not be determined using regression equations. Poor regression of Mg and K occurred and as in 5-year-old stands, better regression was also produced for N and P concentrations in needles. The P concentration in twigs could also sensitively reflect Chinese-fir growth. As indicated in Table 10, during the first fast-growing season, critical nutrient concentrations of N and P were 0.83% and 0.13%, respectively. The demand of trees for N was higher than during the bud flush. Requirements for P and S increased with the advance of the growing season. During the second fast-growing season the demands for N and K increased sharply while demands for P and S decreased. It was also found that the requirement of trees for N decreased apparently during pre-dormancy while P seemed to accumulate in needles and twigs. Generally, the seasonal changes of nutrient criteria in mid-age Chinese-fir stands were opposite to those in young stands (Table 10). Relatively low N criteria during the first fast-growing season in I 0-year-old stands may be attributed to a larger biomass production rate than soil nutrient uptake rate, resulting in a nutrient dilution effect (Table 10).

Mature stands (20-year-old). There were significant parabolic relationships

260

A.L. Zhong, W..-K Hsiung / Forest Ecology and Management 62 (1993) 245-270

Table 9 Diagnostic equations for Chinese-fir stands (model: Y = a + b X + c X 2) Growing season

5-year-old stand Bud flush

Nutrient /Tissue a (x)

Growth indexb (Y)

N/N1

Ht G Ht DBH G Ht G G Ht DBI-I G Ht G G Ht DBH Ht DBH DBH Ht G

- 10.08 -1.19 -13.48 -26.91 -2.66 -7.97 1.09 - 1.96 -25.45 -25.05 -6.91 -31.44 2.87 25.62 - 18.33 -22.74 2.77 19.10 116.42 -33.23 2.12

43.98 9.56 39.87 67.91 10.46 392.81 2.19 66.71 482.02 453.91 8.17 36.72 - 1.30 -85.25 52.98 61.57 66.56 -43.11 -2338.03 766.61 7.97

-26.57 -4.96 -1856.03 -2855.12 -47.03 -2441.01 -0.45 -214.40 - 1715.14 - 1282.11 - 1.73 -8.40 6.28 95.85 -25.58 -25.54 -133.21 36.72 12447.21 -3498.12 -21.43

10.04" 10.18" 41.63"* 19.80" 23.71" 24.82** 7.66 + 39.69** 25.00** 25.45** 27.37** 22.41" 37.36** 19.39" 18.35" 13.69" 15.02" 19.64" 11.44" 14.42" 17.11"

G DBH Ht DBH G Ht DBH DBH G G Ht DBH

-4.41 -39.52 -9.71 -25.84 -2.55 2.56 1.92 -64.20 1.26 -1.27 -8.67 -26.11 - 14.82 -158.43 66.03 14.27 -61.26 -5.72 -9.60 0.26 -50.42 107.12 -3.63

16.38 109.81 277.42 641.12 98.92 - 16.32 -30.52 -283.31 32.63 10.26 34.48 92.45 243.81 2329.11 -559.15 158.04 606.91 21.09 28.76 5.00 869.32 -202.62 99.02

-8685.04 -56.17 -940.21 -2608.05 -423.11 668.31 1113.10 -257.51 - 128.81 -5.48 - 19.21 -5041.12 -817.32 -7826.04 1297.13 -349.31 - 1243.21 -6.60 -7.99 - 1.71 -3503.25 101.13 -356.41

34.45** 14.75" 11.17" 11.85" 24.33** 20.59** 7.39 + 10.55" 17.02" 22.61" 75.77** 20.88* 14.03" 11.15" 67.74** 29.36* 25.47* 49.51.* 554.41.* 45.22** 17.57" 10.31 + 5.65 +

P/T1

S/NI Firstfastgrowing N/N0 season P/N0

K/N0

Second fastgrowing season Pre-dormancy

10-year-old stand Bud flush

Mg/N0 S/N0 N/N0 S/N0 N/N1 P/N1 P/T1 Mg/N1

N/N1 P/N1

P/T1

First fastgrowing season

K/N1 S/N1 N/N0

P/N0 Mg/N0 S/N0 Second fastgrowing season

N/N0

P/N0 K/N0 S/N0

G

DBH Ht G Ht Ht DBH G G Ht G

Regression coefficients

a

b

F-testc

c

A.L. Zhong. I4L-Y. Hsiung / Forest Ecology and Management 62 (1993) 245-270

Growing season

1 O-year-old stand Predormancy

20-year-old stand Bud flush

F-test~

Nutrient /Tissue a (X)

Growth index b (Y)

P/N1 K/N1 S/N1

Ht Ht DBH

-67.77 1.26 5.55

1078.31 8.88 -80.72

-3606.24 - 1.73 1187.19

49.73** 19.32" 41.15"*

N/N1

G Ht Ht DBH G G Ht Ht DBH G Ht DBH Ht DBH G Ht Ht G DBH G Ht DBH G Ht DBH DBH G G

1.37 -2.78 -23.01 -63.63 - 1.94 24.53 - 1.91 1.91 - 188.23 -0.85 - 17.04 -34.74 - 15.05 - 144.97 0.17 12.11 6.10 -0.37 -28.86 -0.04 - 11.08 -20.97 -17.41 - 13.22 - 137.43 -33.70 1.52 0.81

6.47 47.47 800.05 1874.14 125.62 -677.34 39.07 39.07 2823.31 41.92 285.40 475.50 64.48 1624.45 8.62 55.55 35.32 38.95 428.87 9.02 59.74 75.34 321.09 2150.45 2103.87 784.72 7.28 52.45

-4.08 -28.12 -3952.87 - 10121.76 - 700.23 5288.98 -23.04 -23.04 -9344.43 -90.33 -572.45 -974.23 - 32.94 - 3904.83 -4.55 -20.09 - 12.07 -88.07 -899.34 -5.01 -29.61 -31.43 1198.43 -7552.54 -6570.41 -2690.65 -5.30 -208.40

16.98" 10.46" 36.00** 13.43" 26.33** 13.44" 8.50* 15.07" 7.38 + 60.85** 43.48** 43.48" 8.42 + 26.50** 21.78"* 16.14"* 6.34 + 11.94" 10.56" 16.61" 41.50"* 146.93"* 17.19" 39.71"* 13.02" 21.18" 20.09* 23.35*

P/N1

First fastgrowing season

261

S/N1 N/N0 P/N0 P/T0

K/N0 S/N0 N/N1 Second fastgrowing season

N/N0 P/N0 P/T0

Predormancy

K/N0 N/N1 P/N1 P/TI K/N1 S/N1

Regression coefficients

a

b

aN0, N 1 and TO, T 1 were current and 1-year-old needles and twigs, respectively. bAs dependent variables; Ht, tree height; G, Log (DBH2× Ht). c + , , and **, significant at P<0.10, 0.05 and 0.01, respectively. Ca a n d / o r Mg equations were not listed owing to their insignificance ( P > 0.10) in F-test.

between N and P concentrations in needles, especially the P concentration in twigs and various growth indices (Table 9). In comparison with 5-year-old and 10-year-old stands, the statistical significance of diagnosing equations of N concentration decreased while those of total P in needles and twigs increased, indicating that P nutrition was more important for 20-year-old stands. In addition, 1-year-old needles were more suitable for determining the criteria of most nutrients except P criteria, which were more precisely determined

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Table 10 Criteria of nutrient concentrations in Chinese-fir stands (% dry weight)" Growing season

Nutrient/Tissueb

Optimum value

Critical valuec

N/N1 P/T1 S/N1 N/N0 P/N0 K/N0 Mg/N0 S/N0 N/N0 S/N0

0.83-0.96 0.11-0.12 0.08 2.43 0.14-0.16 2.19-2.37 0.15 0.45 1.04 0.25

0.75-0.87 0.10-0.11 0.07 2.19 0.13-0.14 1.97-2.13 0.13 0.40 0.93 0.23

N/N1 P/N1 P/T1 Mg/N1

0.59 0.09 0.11 0.19

0.53 0.08 0.10 0.17

N/N1

0.94-0.98 0.12 0.12-0.14 0.55 0.13 0.92-0.94 0.15 0.22 0.23-0.24 1.46-1.56 0.12 1.00 0.14 0.15 2.57 0.04

0.82-0.88 0.11 0.11-0.12 0.50 0.11 0.82-0.88 0.13 0.19 0.20-0.22 1.32-1.44 0.11 0.90 0.12 0.14 2.32 0.03

0.79-0.84 0.09-0.10 0.06 0.85 0.15 0.23-0.25 0.98 0.21 0.95-I. 15

0.71-0.76 0.08-0.09 0.06 0.76 0.13 0.21-0.22 0.88 0.19 0.85-1.04

5-year-old stand

Bud flush

First fastgrowing season

Second fastgrowing season Pre-dormancy

1 O-year-old stand

Bud flush

First fastgrowing season

Second fastgrowing season Pre-dormancy

P/N1 P/TI K/N1 S/N1 N/N0 P/N0 Mg/N0 S/N0 N/N0 P/N0 K/N0 S/N0 P/NI K/N1 S/N1

20-year-old stand

Bud flush

First fastgrowing season

N/N1 P/N 1 S/N1 N/N0 P/N0 P/T0 K/N0 S/N0

N/N1

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A.L. Zhong, I,K-Y. Hsiung / Forest Ecology and Management 62 (1993) 245-270

Growing season

Nutrient/Tissue b

Optimum value

Critical value ¢

N/N0 P/N0 P/T0 K/N0 N/N1 P/N1 P/T1 K/N1 S/N1

1.46 0.16 0.22-0.24 1.01 1.15-1.20 0.13-0.14 0.15 0.88 0.13

1.32 0.14 0.20-0.22 0.91 1.03-1.08 0.12-0.13 0.13 0.79 0.11

20-year-old stand

Second fastgrowing season Pre-dormancy

"Some optimum and critical values resulted from different growth indices and are therefore expressed as a range. bN0, N 1 and TO, T 1 were current and 1-year-old needles and twigs, respectively. cCritical value = 0.9 × (optimum value ). Table 11 Selection of diagnostic parameters of DRIS in Chinese-fir stands Stand type

Item"

Nutrient ratio N:P

P:K

K:N

Ca:N

Ca:P

Ca:K

N:Mg

High-yield (A)

Mean SD CV (%)

7.68 1.05 13.63

0.12 0.03 23.09

1.09 0.28 25.74

0.98 0.29 29.89

6.72 1.90 28.30

0.83 0.21 25.16

2.64 0.49 18.47

Low-yield (B)

Mean SD CV (%)

6.91 1.54 22.31

0.14 0.05 33.15

1.27 0.53 41.69

1.12 0.54 47.90

7.58 3.52 46.40

0.99 0.54 54.31

2.00 0.83 41.57

Ratios of SB:SAb

2.17"

2.54*

3.51"*

3.38**

3.41"*

6.63"

2.91"

"SD, standard deviation; CV, coefficient of variation. bSB and SA, variances of low-yield and high-yield stands, respectively; *P< 0.05; **P< 0.01.

by using current needles or twigs. The parabolic model was basically suitable for establishing K criteria in different growing seasons, but seemed inappropriate for determining Ca and Mg criteria (Table 10). During the fast growing seasons the criteria for N and P were apparently higher than those in the early growing season and late growing stage, indicating the similar trends to 10-year-old stands (Table 10). Critical nutrient levels of N and P in the first fast-growing season were 0.76% and 0.13%, respectively (Table 10). In conclusion, model Y = a + b X + c X 2 was most suitable for determining the criteria for N and P, basically good for K and S, and not fit for Ca and Mg. Log ( ( D B H ) 2× height ) was the most appropriate growth index. Criteria

264

A.L. Zhong, V~- Y. Hsiung / Forest Ecology and Management 62 (1993) 245-270

Table 12 Indices of DRIS in the different Chinese-fir stands Timea/Tissueb

Index

High-yield stands CL1

I/N1

N P K

Fertilizing sequence II/N0

KPN N P K

Fertilizing sequence II/NI

-8.18 11.97 - 3.79 NKP

N P K

Fertilizing sequence III/NO c

8.53 0.59 -9.12

1.39 5.64 -7.03 KNP

XW1

CH1

25202 -5.05 -25197

I 1.42 10.49 -21.90

KPN

KPN

-21.07 8.62 12.45 NPK - 12.83 -11.18 24.01 NPK

-0.03 5.03 -5.00

0.34 -5.27 4.93 PNK -9.75 -5.62 15.37

TK3 - 7.74 -6.33 14.06

TK4 1.05 -10.93 9.88

NPK

PNK

-69.29 10.22 59.06

-59.80 3.47 56.33

KNP

NPK

NPK

NPK

-1.12 12.84 -11.71

-2.01 - 11.93 13.94

-56.36 13.08 43.28

-50.68 4,84 45,85

KNP

PNK

NPK

NPK

-

-

N

18.88

P K

-4.69 - 14.20

19.06 -2.48 -16.58

KPN

KPN

Fertilizing sequence

TK1

- 7.03 -1.63 8.65 NPK

R

IV/N1

N P K

Fertilizing sequence

3.64 4.10 -7.74 KNP

3.41 -0.23 -3.18 KPN

4.84 0.10 -4.94 KPN

-3.49 -2.21 5.70 NPK

- 19.67 5.73 13.94

-33.26 6.44 26.82

NPK

NPK

Low-yield stands CL2 I/N1

N P K

Fertilizing seuqnece II/N0

N P

K

8.61 7.71 -16.32 KPN -6.64 9.10 -2.46

XW2 7.30 -4.50 -2.80

CH2 6.47 8.90 -15.37

CH3 -2.89 -6.75 9.64

CH4 11.26 - 1.98 -9.28

TK2 7.19 -0.41 -6.78

PKN

KNP

PNK

KPN

KPN

-14.13 5.09 9.04

-11.97 3.76 8.21

-10.22 -3.80 14.02

-14.79 -3.14 17.93

-11.37 4.73 6.65

A.L. Zhong, W.- E Hsiung / Forest Ecology and Ma nagement 62 (1993) 245-270

265

Low-yield s t a n d s

Fertilizing sequence II/N1

N P K

Fertilizing sequence III/N0 ¢

XW2

CH2

CH3

CH4

TK2

NKP

NPK

NPK

NPK

NPK

NPK

5.01 7.86 - 13.87 KNP

N P K

Fertilizing sequence IV/NI

CL2

N P K

Fertilizing sequence

-7.07 -2.33 9.40 NPK

4.34 6.00 - 10.34

-

KNP

-

9.39 2.33 - 11.72 KPN

- 1.87 -0.77 2.63 NPK

0.44 2.64 -3.08 KNP

- 1.37 -13.27 14.64 PNK

NKP

-

-

-

-

-0.33 -0.34 0.67 PNK 12.50 -6.24 -6.26 KPN

-6.29 12.54 -6.25

17.55 -20.30 2.76 PKN

2.08 -2.14 0.06 PKN -8.14 -3.31 11.45 NPK

6.66 -2.60 -4.06

- 1.71 1.09 0.62

KPN

NKP

aI, b u d flush; II a n d IIl, the first a n d second fast-growing seasons, respectively; IV, p r e d o r m a n c y . bN0 a n d N 1 were current a n d 1-year-old needles, respectively. CFoliar s a m p l e s were only t a k e n in s o m e plots in the second fast-growing season.

Table 13 Criteria o f D R I S for nutritional diagnosis in Chinese-fir plantations N u t r i t i o n a l status

Ratio of nutrient concentration N:P

O p t i m u m (X) Balance ( X + S D ) Insufficient (X- 2XSD-X-SD) Slight o v e r s u p p l y (X+SD-X+2 XSD) Deficient (X-3XSD-X-2XSD) Oversupply (X+2XSD-X+ 3XSD) Severe deficiency (X+3×SD)

P:K

K:N

1.09

7.68 6.63-8.72 5.58-6.63

0.12 0.09-0.15 0.06-0.09

0.82-1.38 0.53-0.82

8.72-9.77

0.15-0.18

1.38-1.66

4.54-5.58

0.04-0.06

0.25-0.53

9.77-10.82

0.18-0.20

1.66-1.94

< 4.54

< 0.04

<0.25

> 10.82

> 0.20

> 1.94

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A.L. Zhong, W.- Y. Hsiung / ForestEcology and Management 62 (1993) 245-270

N/P

10.82

KIN

I 4.54

Fig. 1. Diagramof DAIS for nutritional diagnosisof Chinese-firplantations. were relatively higher when DBH was adopted for the establishment of such equations.

Diagnosis and recommendation of integrated system (DRIS). As indicated in Table 11, the variation of N:P, P:K, K:N, Ca:N, Ca:P, Ca:K and N:Mg between high and low yield groups of Chinese-fir stands were significant. The optimum ratios ofN:P, P:K and K:N were 7.68, 0.12 and 1.09, respectively, for the high-yield stands; those of Ca:N, Ca:P, Ca:K and N:Mg were 0.98, 6.72, 0.83 and 2.64, respectively. Because N, P and K are the major nutrient factors limiting tree growth and commonly added to stands, N:P, P:K and K:N were selected as the important parameters to evaluate the nutrient balance supply and predict the sequence of fertilizers to be applied. It may be concluded that during the early growing season most stands needed more K or P than N, while in the fast-growing seasons N was a key nutrient and most needed, then P except in CH 1 (Table 12). In addition, the fertilizing sequence was somehow affected by the age of the sample needles during the first fast-growing season. Younger needles tended to reflect tree nutritional quality more sensitively than the older ones and, therefore, should be selected as diagnosing material.

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267

Various ranges of ratios of the nutrient concentrations in needles of the trees were calculated based on means and standard errors of the selected parameters in which nutrient balance ranges of N:P, P:K and K:N were 6.638.72, 0.09-0.15 and 0.82-1.38, respectively (Table 13). The ranges of nutrient ratios were also expressed by a DRIS diagram (Fig. 1 ). Discussion

Criteria for nutrient concentrations and nutrient ratios are helpful for the nutritional management of tree plantations, especially fertilization (Everard, 1973; Leaf, 1973; Van den Driessche, 1974; Cepel, 1982). Quantified criteria from parabolic modeling and DRIS could be integrated into a diagnosing system and applied to judge the nutritional status of Chinese-fir seedlings (Zhong and Yu, 1988 ). The criteria and ratios established in this paper for tree nutritional diagnosis may be used as a guideline for the nutrient management of Chinese-fir plantations. They are useful in evaluating stand nutritional status and determining the sequence of fertilizers to be applied. Because of the seasonal changes of nutrient concentrations and tree growth, the optimum and critical values of nutrient concentrations should be built up on the basis of growing seasons (Van den Driessche, 1974; Zhong and Yu, 1988). Ratios of nutrient concentrations in needles were relatively less affected by stand age, site quality and growing season than criteria of nutrient concentrations (Beaufils, 1973; Leech and Kim, 1981 ). Optimum nutrient levels and ratios can be established on the basis of tree species and macroclimatic condition. When a regression technique for determining optimum and critical criteria of nutrient concentrations is combined with DRIS, tree nutritional status and nutrient balance can be objectively evaluated and the degree of requirement for various nutrients can be quantitatively predicted. However, tree nutritional diagnosis should be linked with soil nutritional diagnosis to give reasonable interpretations for some special cases, such as poor tree growth caused by heavy metal damage, waterlogged condition, and plant diseases (Leaf, 1973; Shrivastava, 1980; Hunger, 1986). Other ecological factors, especially soil moisture conditions in certain areas should also be considered in the process of nutritional diagnosis for trees and soils. Site quality is determined by many ecological and biological factors. Soil nutritional condition, as a product of interactions among the factors, can mostly reflect site quality and is, therefore, reflected in tree nutritional status and closely related to tree growth (Rehfuess, 1980; Chapin and Tryon, 1983; Radwan and Harrington, 1986 ). This and other studies suggested that total N, hydrolyzable N and extractable P in soil were suitable as soil indexes for nutritional diagnosis and evaluation of site quality (Rehfuess, 1980; Cepel, 1982; Hunger, 1986). Apart from the supply of soluble nutrients, nutrient stock and nutrient transformation and translocation in soils and trees should

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also be taken into account to evaluate the actual nutritional potentials of forest plantations of Chinese-fir and other tree species (Nambiar, 1987 ). Stepwise discriminant analysis was seldom used in the evaluation of site nutritional potentials and tree nutritional quality. The N concentration in needles was suitable as a discriminating factor with higher predicting accuracy, probably because N is a life element for tree survival and growth, and is closely related to the metabolism of P, K and S (Zhong, 1989). It is feasible to apply the discriminant functions to identify the nutritional quality of Chinese-fir stands. The N:P ratio can be used as a supplementary criterion, in view of the positive interaction between N and P assimilation. Principal component analysis is also applicable for stand classification of Chinese-fir plantations, in terms of nutritional quality in subzones of Chinese-fir distribution with homogeneous macroclimatic and geological conditions. Conclusions

This study was conducted to evaluate tree nutritional quality and develop nutrient criteria for the trees and soils in the various stands in the research area. The main conclusions drawn are listed here. ( 1 ) N nutrition in Chinese-fir stands closely interacted with P nutrition. (2) Stands CL l, CH 1 and TK1 were of high nutritional quality; XW l, CL2 and XW2 were of moderate nutritional quality; and CH2 and TK2 were poor in nutrition. (3) The discriminant functions Y I = - 1 0 . 9 4 + 2 2 . 9 7 XN and YII= - 6 . 4 2 + 17.59 XN where XN is the N concentration in needles were developed with correctness of 80% for judging tree nutritional quality. (4) Gravel ( > 2 mm ) content, pH, total N, hydrolyzable N and extractable P were the main factors affecting tree nutrition and growth. (5) Optimum soil total N, hydrolyzable N and extractable P were 540, 65 and 8 ppm, respectively, for 5-year-old stands, 650, 83 and 5 ppm for 10-yearold stands, and 560, 88 and 4 ppm for 20-year-old stands. (6) Uni-variable quadratic model N = a + b X + c X 2 ) was most suitable for determining the criteria of N and P concentrations in needles, basically good for K and S criteria establishment, and not fit for that of Ca and Mg. Log ( ( D B H ) 2 × height) was the most appropriate growth index for developing such diagnostic equations. (7) The criteria for nutrient concentrations varied between different growing seasons and stand ages. The critical nutrient concentrations of N and P during the first fast-growing season, a period sensitive to nutrient deficiency, were 2.19% and 0.13%, 0.83% and 0.13%, and 0.76% and 0.13% for 5-, 10and 20-year-old stands, respectively. (8) The nutrient index of N, P and K derived from DRIS demonstrated that most stands needed more K and P than N during the early growing sea-

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son, N was a key nutrient and most needed, then P in the fast-growing seasons. Optimum ratios of N:P, P:K and K:N were 7.68, 0.12 and 1.09, respectively. Acknowledgments We would like to thank Jiang XiaoJung, Li Shaoshen and Ceng Sheming, for their assistance in the field and laboratory work. Special thanks are due to Dr T.M. Ballard and Dr J.P. Kimmins for their helpful comments on the manuscript. Many thanks are also given to the anonymous referees for their critical and valuable comments.

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