nutrient dynamics in orange trees: the effect of soil fertility

11
This article was downloaded by: [University of California, San Francisco] On: 05 December 2014, At: 11:08 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Communications in Soil Science and Plant Analysis Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/lcss20 Nutrient Dynamics in Orange Trees: The Effect of Soil Fertility Maribela Pestana a , Pedro José Correia a , Hugo Marques a , Irina Domingos a & Amarilis de Varennes b a Universidade do Algarve , Faro, Portugal b CEER , Lisboa, Portugal Published online: 30 Sep 2011. To cite this article: Maribela Pestana , Pedro José Correia , Hugo Marques , Irina Domingos & Amarilis de Varennes (2011) Nutrient Dynamics in Orange Trees: The Effect of Soil Fertility, Communications in Soil Science and Plant Analysis, 42:19, 2351-2360, DOI: 10.1080/00103624.2011.605493 To link to this article: http://dx.doi.org/10.1080/00103624.2011.605493 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. 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 is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms- and-conditions

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Page 1: Nutrient Dynamics in Orange Trees: The Effect of Soil Fertility

This article was downloaded by: [University of California, San Francisco]On: 05 December 2014, At: 11:08Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Communications in Soil Science andPlant AnalysisPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/lcss20

Nutrient Dynamics in Orange Trees: TheEffect of Soil FertilityMaribela Pestana a , Pedro José Correia a , Hugo Marques a , IrinaDomingos a & Amarilis de Varennes ba Universidade do Algarve , Faro, Portugalb CEER , Lisboa, PortugalPublished online: 30 Sep 2011.

To cite this article: Maribela Pestana , Pedro José Correia , Hugo Marques , Irina Domingos & Amarilisde Varennes (2011) Nutrient Dynamics in Orange Trees: The Effect of Soil Fertility, Communications inSoil Science and Plant Analysis, 42:19, 2351-2360, DOI: 10.1080/00103624.2011.605493

To link to this article: http://dx.doi.org/10.1080/00103624.2011.605493

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

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

Page 2: Nutrient Dynamics in Orange Trees: The Effect of Soil Fertility

Communications in Soil Science and Plant Analysis, 42:2351–2360, 2011Copyright © Taylor & Francis Group, LLCISSN: 0010-3624 print / 1532-2416 onlineDOI: 10.1080/00103624.2011.605493

Nutrient Dynamics in Orange Trees: The Effectof Soil Fertility

MARIBELA PESTANA,1 PEDRO JOSÉ CORREIA,1

HUGO MARQUES,1 IRINA DOMINGOS,1 ANDAMARILIS DE VARENNES2

1Universidade do Algarve, Faro, Portugal2CEER, Lisboa, Portugal

Lime-induced iron (Fe) chlorosis is a nutritional disorder common in calcareous soils,which may result from a low level of Fe available or adverse factors that inhibit Femobilization and uptake by plants. Organic-matter amendments can prevent or cor-rect Fe chlorosis in plants but the effect of endogenous soil organic matter (SOM) onthis disorder is not known. The main subject of this work was to investigate the conse-quence of two contrasting levels of soil fertility on the nutritional status of an orangegrove [Citrus sinensis (L.) Osb. cv. Valencia Late]. The field experiment was conductedin a commercial citrus grove using mature trees distributed in two plots with differentvalues of SOM, phosphorus (P), and potassium (K), but with the same level of activelime. The concentration of nitrogen (N), P, K, magnesium (Mg), calcium (Ca), Fe, cop-per (Cu), zinc (Zn), and manganese (Mn) in young and mature leaves and flowers wasevaluated. The level of Mg and the Mg/Zn ratio in flowers from both plots, althoughsignificantly different, only indicated moderate Fe chlorosis, as predicted by a previ-ously developed model, and was consistent with the amount of chlorophyll present inthe leaves. However, nutrient partitioning between leaves of contrasting age was verydifferent. Mature leaves from trees grown in the high-fertility plot (HF) had larger con-centrations of N, P, and K but lower concentrations of Ca, Fe, and Mn than did thosefrom the low-fertility plot (LF). Young leaves from the LF had more N, P, Mg, Cu, andMn and less Ca and Fe than did those from the HF. Flower analysis, although useful topredict Fe chlorosis, failed to detect differences in the nutritional status of plants result-ing from contrasting levels of soil fertility. Furthermore, endogenous SOM had only amarginal effect on Fe chlorosis.

Keywords Citrus, flowers, iron chlorosis, leaves, nutrient partition, soil organicmatter

Introduction

The evaluation of nutrient concentrations in plants is important in modern agriculture notonly to prevent deficiencies, but also as a management tool to monitor the nutritional statusof healthy crops. Excess or deficient nutrients are a special concern in fruit trees, because inthese perennial plants nutritional imbalances can affect yield for more than a single season.

Calcareous soils, common in Mediterranean regions, are rich in calcium and magne-sium carbonates and have an alkaline pH (Loeppert 1986). The concentration of several

Received 10 September 2010; accepted 11 February 2011.Address correspondence to Maribela Pestana, Universidade do Algarve, ICAAM, Pólo Algarve,

FCT-DCBB, Edifício 8, Campus de Gambelas, 8005-139 Faro, Portugal. E-mail: [email protected]

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nutrients in these soils may not provide the best indication of their bioavailability, becausecarbonates interfere with their presence in solution (Franzen and Richardson 2000; Rashidand Ryan 2004). For example, in the Mediterranean basin it is estimated that from 20to 50% of fruit trees suffer from iron (Fe) deficiency, known as lime-induced Fe chlorosis(Jaegger, Goldbach, and Sommer 2000). In many instances, there is no correlation betweenleaf Fe concentration and the degree of chlorosis based on chlorophyll content (Abadía1992; Pestana et al. 2001). This is the “chlorosis paradox” (Römheld 2000) and couldresult from the inactivation of Fe in leaves or from inhibition of leaf growth due to Fechlorosis (Morales et al. 1998).

It has often been claimed that leaf as well as flower analysis can be used to diagnose Fechlorosis (for a review, see Pestana, de Varennes, and Faria 2004). For example, the levelsof nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), manganese (Mn), andzinc (Zn) were increased and that of calcium (Ca) decreased in severely chlorotic peachleaves compared to green leaves (Belkhodja et al. 1998). The use of mineral compositionof flowers as a novel approach to indicate onset of Fe deficiency in citrus trees has beenproposed by Pestana et al. (2001, 2004, 2005). The Mg/Zn ratio could be used to predictthe degree of Fe chlorosis developed later in the season (Pestana et al. 2004).

Iron chlorosis is usually corrected by the application of Fe-chelates to the soil orplant, but organic-matter amendments have also been used to reduce the adverse affectsof carbonates in soils, favoring complexation and solubilisation of Fe (Horesh, Levy, andGoldschmidt 1986; Tagliavini et al. 2000; Tagliavini and Rombolà 2001; Wallace 1991).Efficacy of this approach depends on the composition of the organic amendment, its capac-ity to complex Fe, and the stability of the Fe chelates formed (Hagstrom 1984; Moralet al. 2002).

Information relating endogenous soil organic matter (SOM) and Fe chlorosis is scarce.In sorghum and soybean, Loeppert et al. (1988) reported that Fe chlorosis was usually moreintense when SOM content was low, and they postulated that the soil microbial populationcould enhance the mobilization of Fe.

In the present work, two levels of soil fertility were evaluated to determine the influ-ence on the content of nutrients in young and mature leaves and in flowers and how thisrelates to Fe chlorosis in orange trees grown in a calcareous soil.

Material and Methods

The experimental site was a commercial grove of orange trees [Citrus sinensis L. (Osb.)cv. Valencia Late, grafted on Citrus aurantium L.] established on a calcareous soil locatedin southern Portugal (37◦ 05´ N, 8◦ 28´ W).

The climate of the region is typically Mediterranean. Total precipitation from Januaryto December 2002 was about 450 mm compared with the mean value (514 mm reportedbetween 1964 and 1980), thus being close to a typical year (INMG 1991). Seasonalvariations of temperature were also similar to expected values. Maximum and minimumtemperatures during April 2002 were 17.2 ± 3.4◦C and 11.3 ± 3.1◦C, respectively.

Soil Characterization

The soil was a Calcic Cambisol (FAO-Unesco 1985) with 48% clay, 20% silt, and32% sand, a pH in water of 8.4–8.5, and 15–16% of active lime at 0–30 cm deep(Table 1). Preliminary results indicated that two distinct levels of soil fertility were present.Therefore, to assess soil fertility, two composite soil samples were taken in April 2002 to

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Nutrient Dynamics in Orange Trees 2353

Table 1Chemical characteristics of the soil in April 2002

Values

Parameters LF plot HF plot

K (mg kg−1) 166 b 332 aP (mg kg−1) 20 b 41 aOrganic matter (%) 0.42 b 2.34 aActive lime (%) 15 a 16 apH (H2O) 8.4 a 8.5 a

Notes. LF, low-fertility plot; HF, high-fertility plot. Means ina row followed by the same letter are not significantly different(P ≤ 0.05).

a depth of 30 cm around the canopy of each tree (each composite sample comprising fivesubsamples of soil) that were oven dried for 48 h at 30 oC and passed through a 2-mmsieve. Potassium was analyzed by flame photometry on an ammonium acetate extract ofsoil (Riehm 1958), and P was determined colorimetrically on a sodium bicarbonate extract(Olsen and Sommers 1982). Soil pH was determined in a soil–water suspensions (1:2.5)and organic carbon was measured by oxidation using dichromate (Walkley and Black1934). Active lime was determined by the Drouineau method (Drouineau 1942). Basedon the results, two separate plots, with approximately 700 m2 each and different SOM con-tents, were identified. The plot designated as low fertility (LF) had 0.42% organic matter,20 mg P kg−1 and 166 mg K kg−1. The plot identified as having high fertility (HF) had2.34% organic matter, 41 mg P kg−1, and 332 mg K kg−1. Six 12-year old orange trees ata 4 × 3 m spacing (833 trees ha−1) were selected in each plot.

The plots were irrigated and fertilized with NPK, in same rates to all trees. Amicrosprinkler system (two sprinklers per tree close to tree trunks) delivered 10 L h−1.Irrigation scheduling was based on potential evapotranspiration calculated using the modi-fied Penman method and the computer program CROPWAT (FAO, Rome Italy) and did notconstitute a limiting factor. A total of 100 g tree−1 of N (with N in the ammonium form),31 g tree−1 of P, and 55 g tree−1 of K were distributed in November 2001 and January2002. No foliar fertilization or Fe chelates were applied during the growing season. Weedswere manually removed.

Fruits were harvested in June 2002 at physiological maturity. No preharvest fruitthinning was applied.

Because cultural and fertigation practices were the same in both plots since tree estab-lishment, the differences in soil fertility were only due to endogenous organic matter,presumably due to differential soil use prior to planting the grove.

Mineral Composition of Flowers and Leaves

At full bloom (April 2002), at least 40 flowers were randomly collected per trees. Flowerswere taken from the distal part of the branches. On the same day, two types of leaveswere also collected: most recently expanded leaves (young leaves in the upper third of thebranch) and expanded leaves from the previous year (mature leaves in the lower third).Thirty leaves of each type were collected per tree from all canopy orientations.

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2354 M. Pestana et al.

The samples were transported to the laboratory in refrigerated boxes (±10 ◦C).Flowers, including petals, sepals, reproductive parts, bracts, and peduncles, were washedwith distilled water. Leaves were washed with tap water, followed by distilled water con-taining a nonionic detergent and then 10 mM hydrochloric acid (HCl), and finally rinsedthree times with distilled water.

Young and mature leaves and flowers were dried at 60 ◦C for 48 h, then ground, ashedat 450 ◦C, and digested in 10 mL HCl (1 mM). Standardized procedures (AOAC 1990)were used to measure nutrient concentrations. Nitrogen was analyzed by the Kjeldahlmethod, P was determined colorimetrically by the molybdo-vanadate method, K was mea-sured by flame photometry, and Mg, Ca, Fe, copper (Cu), Mn, and Zn were measured byatomic absorption spectrometry.

Chlorophyll Concentration

Leaf chlorophyll concentration was estimated with a SPAD (Soil and Plant AnalyzerDevelopment) 502 meter (Minolta Co., Osaka, Japan) in all leaves sampled. SPAD val-ues (S) were converted into chlorophyll concentration (Chl, µmol m−2) using a calibrationequation (Chl = 0.0125 × S2 + 0.0098 × S + 64.29; r2 = 0.97; n = 24; P < 0.001;minimum and maximum chlorophyll values were 59 µmol m−2 and 863 µmol m−2, respec-tively) obtained by Pestana et al. (2001) for the same month and using leaves from the sameorchard.

Statistical Analysis

The fertigation lines were considered as main plots and the two levels of fertility as sub-plots, with six replications (trees) per treatment. The means were compared using the t-testat P ≤ 0.05.

To assess the effect of SOM level, the main patterns of covariation in the variablesthat described the nutritional composition of young and mature leaves were evaluated byprincipal component analysis (PCA) with a varimax (normalized) rotation.

Using this exploratory multivariate statistical method, it is possible to reveal associa-tions in the data that cannot be found by analyzing each variable separately. Each extractedcomponent or factor accounts for part of the variation of all data sets and is associatedwith an eigenvalue. The eigenvalue associated with each eigenvector is a measure of thevariance within variables of the corresponding principal component. The eigenvectors canbe used to calculate new values, called scores, for each observation on each principal com-ponent. The scores can be positioned on a plot to identify the cases that contributed moretoward the formation of the axis (Legendre and Legendre 1998).

Statistical analyses were carried out using SPSS (v. 14.0; SPSS, Chicago, Ill.) andStatistica software (StatSoft, Tulsa, Okla.).

Results

The yield in 2002 was about 18 T ha−1 year−1. As in previous years, the yield was similarin both plots. Fruits had a mean diameter of 72 mm and a mean maturation index of 9.8;these values are similar to those reported for this cultivar by Davies and Albrigo (1998).

There were no significant differences in Chl between plots, and all the trees at thisdate exhibited moderate symptoms of Fe chlorosis. The Chl levels of young leaves were

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Tabl

e2

Mac

ro-

and

mic

ronu

trie

nt(d

rym

ass)

com

posi

tion

ofle

aves

and

flow

ers

colle

cted

atfu

llbl

oom

NP

KC

aM

gFe

Cu

Zn

Mn

Plan

tpar

tPl

ots

(gkg

−1)

(gkg

−1)

(gkg

−1)

(gkg

−1)

(gkg

−1)

(mg

kg−1

)(m

gkg

−1)

(mg

kg−1

)(m

gkg

−1)

Lea

ves

You

ngL

F22

.7a

2.2

a15

.8a

22.8

b2.

7a

53b

11a

18a

9a

HF

18.3

b0.

8b

13.3

a54

.3a

1.7

b13

0a

7b

19a

6b

Mat

ure

LF

17.0

b1.

1b

7.5

b65

.4a

5.0

a16

2a

10a

17a

12a

HF

20.7

a1.

8a

11.1

a28

.8b

4.9

a84

b9

a20

a6

b

Flow

ers

LF

30.0

a3.

2a

19.9

a11

.9a

3.7

b36

a11

a22

a11

aH

F30

.3a

3.4

a20

.8a

12.1

a4.

2a

31a

12a

19a

9a

Not

es.F

orea

chty

peof

mat

eria

l,m

eans

ina

colu

mn

with

sam

ele

ttera

reno

tsig

nific

antly

diff

eren

t(P

≤0.

05).

LF,

low

-fer

tility

plot

;HF,

high

-fer

tility

plot

.

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2356 M. Pestana et al.

249 ± 13 µmol m−2 in the LF plot and 244 ± 8 µmol m−2 in the HF plot. Mature leaveshad Chl levels of 383 ± 29 µmol m−2 and 363 ± 34 µmol m−2, respectively, in the LFand HF plots.

In trees established in the HF plot, mature leaves had larger concentrations of N, P,and K and smaller concentrations of Ca, Fe, and Mn compared with those from the LF plot(Table 2). On the other hand, in young leaves in the HF plot the concentrations of N, P,Mg, Cu, and Mn were smaller while those of Ca and Fe were greater than values for the LFplots. Nutrient concentrations in flowers were similar in both plots, except Mg, which wasgreater in the HF plot (Table 2). The Mg/Zn ratio, which is an indicator of Fe chlorosis,was significantly larger (P = 0.002; t-test) in flowers collected in the HF plot (220 ± 24)than in the LF plot (170 ± 7).

The PCA of nutrients in leaves indicated that data could be summarized in twodimensions. In young leaves, the first (PC1) and the second (PC2) principal componentsexplained 61% and 16% of the total variance, respectively (Figure 1). Increases in N, P,Mg, Mn, and Cu concentrations had opposite vectors than Ca and Fe along PC1. In matureleaves, the two principal components explained 71% (49% by PC1 and 22% by PC2) of thetotal variation (Figure 2). The Fe concentration was positively associated with increases inMn and Ca and decreases in N, P, and K along PC1 in LF plots (Table 2).

Fifty-four percent of the total variance of nutrients in flowers could be explained bytwo principal components (33% by PC1 and 24% by PC2). Along PC1, the P, Cu, K, and

N

P

Ca

Mg

Fe

MnZn

Cu

−1.0 −0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 0.8 1.0

PC1 (61 %)

−1.0

−0.8

−0.6

−0.4

−0.2

0.0

0.2

0.4

0.6

0.8

1.0

PC

2 (1

6 %

)

K

−2.0−1.5

−1.0−0.5

0.00.5

1.01.5

2.0

LF

HF

Young leaves

Figure 1. Principal component analysis (PCA) of nutrient concentrations (N, P, K, Ca, Mg, Fe, Cu,Zn, and Mn) in young leaves of orange trees. Each vector represents the loading of a variable (nutri-ent) in each principal component (PC1, first principal component; PC2, second principal component).The graph within the figure shows the scores obtained for each tree (1 to 12) along PC1. Each barrepresents one tree. LF, low-fertility plot—trees 1 to 6; HF, high-fertility plot—trees 7 to 12.

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Nutrient Dynamics in Orange Trees 2357

N

P

K

Ca

FeZn

−1.0 −0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 0.8 1.0PC1 (49 %)

−1.0

−0.8

−0.6

−0.4

−0.2

0.0

0.2

0.4

0.6

0.8

1.0P

C2

(22

%)

Mg

Cu

Mn

−2.5

−2.0−1.5

−1.0−0.5

0.0

0.51.0

1.5

LF

HF

Mature leaves

Figure 2. Principal component analysis (PCA) of nutrient concentrations (N, P, K, Ca, Mg, Fe, Cu,Zn, and Mn) in mature leaves of orange trees. Each vector represents the loading of a variable (nutri-ent) in each principal component (PC1, first principal component; PC2, second principal component).The graph within the figure shows the scores obtained for each tree (1 to 12) along PC1. Each barrepresents one tree. LF, low-fertility plot—trees 1 to 6; HF, high-fertility plot—trees 7 to 12.

Mg concentrations were positively associated, while the same was true for Mn, Fe, andZn along PC2 (Figure 3). Importantly, Fe and Zn were on the same side of one principalcomponent, and Mg was represented on the other, therefore containing information thatwas not redundant with that of Fe and Zn. These are the nutrients in flowers that can beused to predict the degree of Fe chlorosis, as reported previously (Pestana et al. 2004).

The scores for each tree along PC1 and PC2 were also analyzed (shown within graphsin Figures 1–3). For leaves, it is clear that the main patterns identified along PC1 and PC2were related mainly to differences between plots rather than differences between trees.Along PC1, the scores obtained for the LF plot were opposite to those obtained for the HFplot (Figures 1 and 2). This pattern was not observed in flowers (Figure 3), meaning that thepatterns identified derived from differences between individual trees and not between plots.

Discussion

The positive effects of applying organic amendments to orchards on tree growth and yieldare well documented in several crops, including citrus, particularly those under organicmanagement (Sorrenti et al. 2004). In calcareous soils, the efficacy of organic amendmentsdepends on several factors, but the effects on the nutritional balance in trees are not welldocumented (Srivastava and Singh 2006). There are few studies that evaluate the role ofSOM on the incidence of Fe chlorosis in trees. Loeppert et al. (1988) reported a negativecorrelation between visual chlorosis scores and decomposable organic-matter fractions,

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2358 M. Pestana et al.

−1.0 −0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 0.8 1.0

PC1 (33 %)

−1.0

−0.8

−0.6

−0.4

−0.2

0.0

0.2

0.4

0.6

0.8

1.0P

C2

(24

%)

Flowers

Zn

FeMn

P

CuK

Ca

N Mg

−2.0

−1.0

0.0

1.0

2.0

3.0

LF

HF

Figure 3. Principal component analysis (PCA) of nutrient concentrations (N, P, K, Ca, Mg, Fe, Cu,Zn, and Mn) in flowers of orange trees. Each vector represents the loading of a variable (nutrient)in each principal component (PC1, first principal component; PC2, second principal component).The graph within the figure shows the scores obtained for each tree (1 to 12) along PC1. Each barrepresents one tree. LF, low-fertility plot—trees 1 to 6; HF, high-fertility plot—trees 7 to 12.

and they postulated that the soil microbial population could enhance the mobilization ofFe. In our conditions, SOM had no apparent effect on Fe chlorosis.

The nutrient ranges obtained in the flowers in the present study are similar to thosereported by Pestana et al. (2004; 2005). No significant differences between nutrients inflowers from trees grown in the HF or LF plots were detected, with the exception of Mg.According to Pestana et al. (2004), Mg was the flower nutrient that had the greater weight inthe prediction of Fe chlorosis and had a positive relationship (i.e., the more Mg in flowers,the greater the Chl later in the season) (Pestana et al. 2004, 2005).

The Mg/Zn ratio in flowers is an important indicator of Fe chlorosis later in the season(Pestana et al. 2004). In this study, we found a significant difference between the Mg/Znratios in flowers between the two plots, but the values were both borderline to the criticalvalue of 200 where leaves should remain green throughout the season. This is compati-ble with the assessment of moderate Fe chlorosis made for both plots. As a consequence,flower analysis, although useful to predict Fe chlorosis, failed to detect differences in thenutritional status of plants resulting from contrasting levels of soil fertility. However, thePCA for leaves of different ages clearly demonstrated the effect of soil fertility on theirnutritional status. In the HF plot, the concentrations of mobile nutrients such as N, P, and Kwere larger in mature leaves. Castel and Ginestar (1996) found that N application increasedN content in 6- to 8-month-old clementine leaves. Therefore, a greater SOM level appar-ently resulted in an increase in the pools of nutrients in mature leaves, whereas in the LF

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Nutrient Dynamics in Orange Trees 2359

plot, these nutrients probably moved toward active sinks such as young leaves. It is likelythat greater levels of SOM led to the differences detected in P and K in the soil, becausemineral fertilization was the same in both plots.

The foliar concentration of Mg, a mobile nutrient in the phloem (Marschner 1995), wasnot affected in mature leaves by SOM. The different allocation pattern of mobile nutrientscould be a consequence of their origin. While N, P, and K were mainly supplied by fer-tigation, the Mg in leaves came mainly from the endogenous soil pools. In contrast, lessmobile nutrients (such as Ca, Fe, Cu, and Mn) in young leaves had larger concentrationsin the HF plot than in the LF plot.

Changes in nutrient patterns related to Fe chlorosis were reported by Belkhodja et al.(1998) in peach. Levels of N, P, K, Mg, Mn, and Zn increased and that of Ca decreasedin severely chlorotic leaves by comparison with green leaves. These results show that Fechlorosis and soil fertility can both influence nutrient allocation in fruit trees.

In conclusion, trees grown in a less fertile site had a different pattern of nutrient allo-cation between leaves of contrasting age. The net effect was to maintain the reproductivepotential, at least in terms of the nutritional status of the flowers, because this was similarirrespective of the nutrient status of leaves. This type of conservative strategy, which relieson labile nutrient pools within leaves, is typical of evergreen species (Cohen and Pastor1996). Our results show that leaf analysis is very sensitive to differences in soil fertility,such as those derived from different levels of SOM. Importantly, the observation that theuse of leaf analysis to evaluate Fe deficiency can be confounded by general soil fertil-ity reaffirms the importance of using floral analysis in the prediction of lime-induced Fechlorosis in citrus groves.

Acknowledgments

We thank Nuno Correia for the use of his orchard. We thank Michael Goss for reading themanuscript and providing helpful comments.

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