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Research ArticleModelling the Effects of Soil Conditions on Olive Productivity inMediterranean Hilly Areas

Ashraf Tubeileh,1,2 Francis Turkelboom,1,3 Anwar Al-Ibrahem,4

Richard Thomas,1 and Kholoud Sultan-Tubeileh2

1 International Center for Agricultural Research in the Dry Areas, P.O. Box 5466, Aleppo, Syria2 University of Guelph, Kemptville Campus, P.O. Box 2003, Kemptville, ON, Canada K0G 1J03 Research Group of Nature and Society, Research Institute for Nature and Forest, 1070 Brussels, Belgium4General Commission for Scientific Agricultural Research, Department of Olive Research, Idleb, Syria

Correspondence should be addressed to Ashraf Tubeileh; [email protected]

Received 18 July 2014; Accepted 18 September 2014; Published 19 October 2014

Academic Editor: David Clay

Copyright © 2014 Ashraf Tubeileh et al.This is an open access article distributed under the Creative CommonsAttribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The majority of olive (Olea europaea L.) production in Mediterranean environments is characterized by low external inputs and ispracticed in hilly areas with shallow soils. This study aimed to study the yield and nutritional status for olive (cv. “Zeiti”) trees innorthwestern Syria and establish correlations between yield, on the one hand, and soil/land factors and tree nutrition, on the otherhand, to determine themost yield-affecting factors. Land and soil fertility parameters (field slope, soil depth, and soil nutrients) andconcentrations of leaf minerals were determined. As olive roots can go deep in the soil profile to extract nutrients, the total availablenutrients per tree (over the whole profile) were estimated. Multiple regression analyses were performed to determine the modelthat best accounts for yield variability. Total available soil potassium amount (𝑅2 = 0.68), soil total N amount (𝑅2 = 0.59), and soildepth (𝑅2 = 0.56) had the highest correlations with olive fruit yields. Available soil potassium amount and soil depth explainedtogether 77% of the yield variability observed. In addition to these two factors, adding leaf B and Fe concentrations to the modelincreased the variability explained to 83%.

This paper is dedicated to the good memory of Mr. Malek Abdeen.

1. Introduction

Olive trees have played an important role in the ruraldevelopment of the Mediterranean’s relatively poor rainfedareas over the centuries [1]. The maintenance of traditionalolive groves is crucial for the environment, as these systemsshow high levels of biodiversity and soil conservation [2, 3],especially given thatmost olive groves are established on slop-ing lands. For example, 84% of rainfed olive trees in Andalu-sia are grown on slopes greater than 5% [4].

This study was carried out in Aleppo Governorate innorthern Syria. Olive productivity in this part of Syria lagsbehind other producing areas of similar climatic conditionsand some fields are being deserted. External inputs are lowand consist, when practiced, of sheep manure every second

or third year [5, 6], due to farmers’ perception that olive treesdo not need to be fertilized. Moreover, the export of pruningmaterial for animal feed or combustion results in nutrientmining and nutrient export from the area, which is typical forrainfed dry areas [7]. The combination of these managementfactors with the high erosion rates results in shallow soildepths that can be as low as 10 cm in severe cases.

According to Klewinghaus et al. [8], erosion in Afrin areais function of relief parameters (i.e., slope gradient, aspect,curvature, landform elements, and upslope area), agriculturalpractices, land use and vegetation cover, and roads and build-ings. In the same area, Hoorelbeke [9] found that soils of olivegroves established on forest lands cleared a few decades agowere shallower by 31% and less fertile by 50% than neighbour-ing conifer forest land. The main causes for this degradation

Hindawi Publishing CorporationInternational Journal of AgronomyVolume 2014, Article ID 672123, 12 pageshttp://dx.doi.org/10.1155/2014/672123

2 International Journal of Agronomy

were the lack of vegetation cover, the intensive subsequenttillage operations, and the lack of sufficient manure or fertil-izer application.This researcher also reported that two-thirdsof the interviewed farmers had observed a declining yieldtrend due to soil erosion. Barneveld et al. [10] estimated theactual annual soil loss inAfrin area to reach 8 kgm−2 with fivecultivator passes per year on an olive grove with an averageslope of 25%. To compensate for this loss, some farmerspurchase soil and add it under the tree canopy to increase soildepth and replenish soil fertility [11].

In spite of these studies, there is almost no informationon the overall effect of soil status on olive yield. Furthermore,literature information on the relation between soil and leafnutrient status and yield is not straight forward and is mostlyobtained through fertilizer trials. While most literature stud-ies (e.g., [12–14]) indicated that adding N resulted in higherleaf N concentrations, some authors (e.g., [15]) reported thatN fertilization did not translate into higher yields. Similarly,there is no agreement among researchers with regard to therelationship between olive yield and leaf K and P concentra-tions. For instance, Jasrotia et al. [16] noticed a strong negativecorrelation between olive yield and leaf minerals P, K, Ca,and Mg, whilst Sharma et al. [17] reported that maximumleaf concentration values of these minerals (in addition to N)ensured regular olive bearing behaviour. Working on carob,another evergreen Mediterranean tree, Correia et al. [18]found that 92% of carob yield variabilitymay be related to leafconcentration of N, P, K,Mn, and Fe.Marcelo and Jordão [15]did not find an effect of K

2O addition on olive yields. The

above studies were designed as fertilizer rate studies and didnot include a wide range of nutrient concentrations or othervariables that could interact with the element in question(e.g., soil volume explored by the tree). It is therefore fair toconclude that the occurrence of fertilization effects dependson soil nutrient levels, balanced fertilization, and the durationof the study.The assessment of these effects can contribute toa better identification of bottlenecks for olive productivity inthe area and therefore formulate targeted fertilizer and landmanagement recommendations for olive growers. The objec-tives of the present study were to

(i) present the characteristics of a typical low input olivesystem in the eastern Mediterranean,

(ii) identify key nutrient deficiencies in marginal-landolive groves,

(iii) present strategies for orchard and nutrient manage-ment,

(iv) present recommendations for further research.

2. Methodology

This study was carried out in 2005 in Afrin district, AleppoGovernorate, northwestern Syria. Tomodel the effects of landand soil parameters on olive yields, parameters such as landslope, soil depth, soil bulk density, organic matter content,and chemical soil fertility were measured as detailed below.It is assumed that soil moisture will be correlated to soildepth. In addition, concentrations of leaf minerals were also

determined. Our methodology relied on establishing simpleandmultiple regressions to find the model that best describesthe variability observed.

2.1. Study Area. Afrin district is known to be the first pro-ducer of olive oil in Syria with its 10+ million trees [19]. Thelocally predominant “Zeiti” cultivar ranks first in Syria interms of number of trees and oil content, which exceeds 30%(on dry weight basis) in its natural habitat [20, 21]. Lands inAfrin area are sloping and are prone to erosion due to widetree planting distance (∼10m × 10m), up-down tillage, min-imal canopy size induced by severe pruning every second orthird year, and absence of cover crops. Afrin area features lowmountains (up to ∼700m asl) and gently undulating roundedlimestone hills. According to Cools et al. [22], the deeper soilsin the calcareous hills are mainly Calcaric Cambisols, whilethe shallower soils are classified as Hypersceletic Leptosols,and Leptic Regosols [23]. Soils are slightly alkaline with pHranging between 7.2 and 8.4. Calcium carbonate levels rangedbetween 20 and 480 g kg−1 [22]. The climate in the area is atypical Mediterranean with rainy cold winters and dry hotsummers. Average annual rainfall in the area ranges between440 and 470mm in the valleys and 600 and 700mm onhilltops.The amount of rainfall recorded in Afrin in 2005 was461mm [19].

The sites where the trees were selected are locatedbetween longitudes 36∘5694 E and 37∘1256 E and latitudes36∘2245N and 36∘5688N (Figure 1). The elevation of theselected groves ranged between 282 and 654m asl, and themean value was 464m asl.

2.2. Criteria for Tree Selection. In order to clearly identifyyield-controlling factors, the fields were selected to accountformaximumdiversity of soil and land factors available in thearea. For example, whenever possible, trees with different soildepths were sampled on the same site. However, only healthylooking trees were selected and any disease-affected trees orthose with visible disorders were avoided as those disordersmight distort normal yield relations. It is assumed that large-size trees selected in flat areas with deep soils would representoptimum rainfed, low-input growing conditions and yield forthe area.

In spring (March-April) 2005, fifty trees were selected andmarked from 25 orchards across Afrin area. The number oftrees per orchard varied from one to three according to theuniformity of the sites. In sites with variable tree size and/orsoil conditions (depth, land slope, etc.), two or three treeswere selected to better represent that variability. The follow-ing criteria were considered in tree selection.

(i) Trees belong to the “Zeiti” cultivar, being the domi-nant cultivar in the area.

(ii) Trees are in their bearing (on) year.

(iii) Trees are in stable commercial production stage(older than 15 years, less than 80 years).

(iv) Trees are representative for the grove or a significantpart of it.

International Journal of Agronomy 3

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Figure 1: A topography map of northwestern Aleppo Governorate showing Afrin area with the orchard locations (red dots).

(v) Trees were pruned after 2003 harvest in a similar way(heavy pruning every second year after a good crop,resulting in a full crop every second year with no cropin the other year).

(vi) Trees have only one main trunk.(vii) Trees are healthy and do not show pest infection or

disorder symptoms.(viii) No trees were selected on north-facing slopes, which

can have particular microclimatic conditions.(ix) Trees are rainfed (i.e., no irrigation is applied).(x) No fertilizer or manure was added this year or during

the past 2 years.(xi) Soil is traditionally ploughed 4–6 times per year to

increase rainwater infiltration, control weeds, anddestroy capillary evaporation.

Most of the selected grove owners were already known toAgricultural Extension agents in Afrin, which facilitated theselection of trees, and ensured the collection of good-qualitydata. Age of the selected trees varied between 18 and 80 years,

with a mean value of 50 years. Field slope ranged between 0%and 38% with a mean value of 7.4%.

2.3. Soil and Plant Characteristics. Field slope was estimatedusing an inclinometer. Since visual soil colour is one of thefavourite indicators local farmers use to describe a soil [9], asimple characterization of soil colour (i.e., white, brown, red,and black and their gradients) was visually performed inMayand June 2005 after the end of the rainy season when the top-soil was dry. Based on field observations and literature datashowing that olive roots can extend deeply in the soil, thecomplete soil depth was measured by augering soil profile attwo opposite points one meter away from the tree trunk. Soilaugering was then divided into 20 cm increments, and onecomposite sample was taken for each depth increment andanalyzed separately to account for soil fertility in the completeprofile, rather than simply using nutrient concentrationsfor the topsoil (which is the common approach). Stoneswere separated from soil particles by wet sieving through a2mmmesh, and the stone volumetric fraction in eachincrement was computed. Undisturbed soil clods were also


Table 1: Analytical methods used to determine soil physical and chemical properties.

Element Method and analysis equipment ReferenceSoil bulk density Undisturbed clod method Blake (1965) [24]Soil organic matter Walkley-Black method Walkley and Black (1934) [25]Nitrogen Kjeldahl wet digestion Bremner and Mulvaney (1982) [26]Available phosphorus Olsen colorimetry Olsen and Sommers (1982) [27]Extractable potassium Extractable potassium, Flame Photometer 409 Richards (1954) [28]Extractable boron Spectrocolorimetry Bingham (1982) [29]Iron, copper DTPA test. Atomic Absorption Spectrometry Lindsay and Norvell (1978) [30]

collected for bulk density determination. Soil bulk density,organic matter, total nitrogen, mineral N, and availableconcentrations of P, K, and B were determined using themethods described in Table 1.

The rootzonewas assumed to have a cylindrical shape andto compute its volume, the soil depth (to a maximum depthof 1.7m) was measured for each tree, and the root radius wasestimated to be 3m based on planting density.The amount ofeach element (𝐴, in kg) in the tree rootzone was computedby summing up the amounts for each increment, accordingto the following formula:

𝐴 = ∑𝐶𝑖(𝑉tot − 𝑉stone) × BD, (1)

where 𝐶 is nutrient concentration (kg kg−1) in each soilincrement 𝑖,𝑉tot is soil volumewith stones (L),𝑉stone is volumeof stones in the increment, and BD is bulk density (kg L−1).

These mineral elements will be referred to as totalNrootzone, mineral Nrootzone, Prootzone, Krootzone, and Brootzone,respectively. In addition to the amounts of mineral elementsin the soil, the concentration of those elements in the top20 cm soil layer will be presented to give an idea about soilfertility levels independently from variable soil depths.

Approximate tree age was determined through discus-sions with grove owners and/or by expert visual assessments.Yield per tree (in terms of fresh fruit weight) was assessed inNovember 2005.

To study tree nutritional status and its relationship withyield, leaf samples were collected in late April 2005 beforeflowering to determine concentrations of N, P, K, Fe, Cu, Mn,Zn, and B on a dry weight basis. From each tree, 50–60fully expanded leaves, initiated during 2004, were selectedbetween the fourth and sixth pairs of leaves below the shoottip from different parts of the tree, as suggested by Bouat[31]. Leaves were dried in a fan-assisted oven at 85∘C forfour days. Leaf N concentration was determined using theKjeldahl wet digestion method. For leaf P concentration, thesample was digested by Kjeldahl wet digestion method (inconcentrated H

2SO4at 490∘C for 3 hours) and P concen-

trationwas determined by spectrometry. For K andmicroele-ments (Fe, Cu, and B), the stored samples were ashed in amuffle furnace at 525∘C for 6 hours and dissolved in 2MHCl. K concentrationwas determined using Flame Photome-ter 410 (Sherwood Scientific Ltd., Cambridge, UK) whileFe and Cu were determined using Atomic Absorption Spec-trometer 374 (PerkinElmer,Wellesley,MA,USA). Leaf B con-centration was determined by spectrometry. For leaf Mn

and Zn concentrations, the samples were wet-digested usingHNO

3-HClO

4[32] and the concentrations were determined

by Atomic Absorption Spectrophotometry.

2.4. Statistical Analysis. The statistical analysis was per-formed on 47 trees after dropping three outliers due totree health reasons (e.g., disease on tree or fruit, physi-cal/mechanical damage) developing after the tree selection.To evaluate the influence of external factors (i.e., soil depth,soil B amount, soil K amount, soil mineral N amount, and soilexchangeable P amount) on final tree yields, simple regres-sions were made between yield and each factor. A multipleregression analysis was then performed to determine themodel that best accounts for the variance. Although multipleregression analysis is the standard approach in biological andecological system modelling, the inherent collinearity (mul-ticollinearity) of confounded explanatory variables couldencumber analyses and threaten their statistical and infer-ential interpretation [33]. However, since our goal of con-ducting multiple regressions is to develop a model that bestpredicts variability in the response, then the problems due tomulticollinearity can be effectively ignored.

Correlations between fruit production and leaf mineralconcentrations were also examined to understand the contri-bution of these minerals to fruit yield. Analysis of variancewas performed for all the simple and multiple regressionsestablished. Regressions were considered statistically signifi-cant at the 𝑃 < 0.05 level. Statistical analysis and correlationswere performed using SPSS 14.0 software [34].

3. Results

3.1. Soil Fertility. Soil colour in the selected groves differedfrom white (originated from chalky limestone) to basaltic-origin black soil (Table 2). For a given site, soil fertility levelsdecreased in the lower soil layers compared to the topsoil.Thetopsoil was considered as a basis for comparison given thedifferences in soil depth. A huge variation among the selec-ted sites was discovered with regard to the concentration oforganic matter and nutrients in the topsoil, especially withregard to K concentration, which varied from 14 to386mg kg−1 (Figure 2). Red soils, mainly on limestone parentmaterial, generally had the highest organic matter and nutri-ent concentrations followed by dark brown and black soils,respectively. White and grey soils had the lowest organicmatter and K concentrations. P concentrations in the topsoil


Table 2: Characteristics and fertility levels of the topsoil (0–20 cm)for the selected trees.

Tree number Visual soil colour Soil depth (m)1 Light black 0.302 Brown 1.203 Light black 0.204 Light black 0.205 Light black 0.206 Light black 0.207 Red 0.608 Light brown 0.709 Brown 0.9010 Brown-white 0.2011 Brown-white 0.4012 White-grey 0.5013 White-grey 0.3014 White 1.2015 White 0.7016 White 0.8017 Brown 0.2018 Brown 0.2019 Brown-red 0.2520 Brown-red 0.2021 Brown-red 0.2522 Grey 0.1523 Grey 0.1524 Grey 0.3025 Red 0.3026 Red 0.1527 Dark brown 0.6028 Dark brown 0.5029 Grey 0.2030 Grey 0.4031 Grey 0.2532 Discarded 0.6033 Light brown 0.9034 Light brown 0.7035 Black 1.1036 Black 1.2037 Dark brown 1.6038 Dark brown 1.7039 Pale brown 0.6040 Light brown 0.6041 Red 0.5042 Red 0.3043 Red 0.3044 Discarded 0.6045 White 0.2046 Discarded 0.6047 Grey 0.4048 Grey 1.0049 Grey 0.4050 Brown 0.80

Table 2: Continued.

Tree number Visual soil colour Soil depth (m)Minimum value 0.15Maximum value 1.70Median value 0.40

Mean 0.536Standard error 0.055

of the selected fields ranged between 2.4 and 23.2mg kg−1 andwere within the same ranges for all soil colours. Extractableboron concentrations were highest in black (and grey) soils(2.86mg kg−1), while the maximum concentration recordedfor any other soil colour was 1.27mg kg−1 (brown soil). Therewas no correlation between topsoil fertility and soil depth,and the highest soil organic matter level was detected in a soilthat was 30 cm deep.

3.2. Tree Nutrient Status and Fruit Yield. Most of the treesmonitored had leaf nutrient concentration values belowdeficiency thresholds reported in the literature (Table 3).Still, no visible nutrient deficiency symptoms were detectedon any of those trees. Almost three of every four trees hadleaf N concentrations lower than the deficiency thresholdlevel of 14 g kg−1 (in July) reported by Freeman et al. [35] inCalifornia, and 12 trees were below Bouat’s critical value of12 g kg−1 in France [31]. It is interesting to highlight that leafN concentration was not correlated with soil depth.

The mean leaf P concentration was exactly at the defi-ciency threshold level, and 29 trees were located below thatthreshold. Tree K nutrition was better than the other primarymacroelements as half the trees did not have deficient leafK concentration values and the mean leaf K concentrationwas slightly higher than the deficiency threshold. All the treeswith higher leaf K concentrations were located on red, darkbrown, or black soils.

Wide ranges were also observed for leaf micronutrientconcentrations. For 26 trees, leaf Cu concentration valueswere right at the threshold level and only four trees werebelow that level. For leaf Fe concentration, there is noinformation available on deficiency threshold in olive. Boronnutrition did not seem to be a concern, as only five treeshad leaf B concentration levels slightly below deficiencythreshold.

Olive fruit yield varied considerably among the trees.Four trees produced less than 11 kg tree−1, while ten trees pro-duced more than 40 kg tree−1. In general, trees grown on redsoils tended to have the highest yields and those on white andgrey soils had the lowest yields.

3.3. Simple Regressions between Soil and Land Factors andYield. To identify the most important soil and land factorsin determining yield and to separate the individual effectsof each of those factors, simple regression analyses werecarried out between each soil and land factor and tree yield(Table 4).Themost important factor was Krootzone, explaining


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Figure 2: Fertility levels in the topsoil (0–20 cm): (a) organic matter, (b) total nitrogen, (c) mineral nitrogen, (d) Olsen phosphorus, (e)extractable potassium, and (f) extractable boron.

two-thirds of the yield variability according to the followingrelationship:

𝑌 = 21.4 (±2.16) Log (K) + 30.7 (±1.28)

𝑅2= 68.4%, 𝑃 < 0.001,

(2)

where 𝑌 is yield and K is the total amount of available Kin the rootzone soil per tree. The numbers in parenthesesrepresent the standard error. A logarithmic curve explainedmore variability than a linear relation (Figure 3).

Total Nrootzone ranked second with 58% of the variabilityexplained, while soil depth was the third most importantfactor affecting tree yield, explaining 56% of the variability

(Figure 4). The logarithmic relation for soil depth proved toaccount formore variability than the linear one. Interestingly,leaf K concentrationwas significantly correlated to soil depth,unlike leaf N or leaf P concentrations (Table 5).

Other soil factors related to nutrientswere also important.Mineral Nrootzone and Brootzone accounted, respectively, for44% and 41% of the total variability (Table 4) and thecorrelations were statistically significant. On the other hand,olive fruit yield was not significantly correlated to soil organicmatter content.

3.4. Multiple Regressions between Soil/Land Factors andYield. Multiple regression analysis data are presented in


Table 3: Leaf mineral composition of main macro- and micronutrients and olive fruit yield.

Parameter Deficiencythresholds∗Number of treesbelow deficiency

thresholdMinimum Mean (±standarderror) Maximum

Leaf N concentration(g kg−1) 14.0 34 9.5 12.9 (±0.2) 15.6

Leaf P concentration(g kg−1) 1.0 29 0.7 1.0 (±0.2) 1.4

Leaf K concentration(g kg−1) 4.0 24 1.2 4.4 (±0.3) 10.7

Leaf Cu concentration(mg kg−1) 5.0 4 2.50 5.80 (±0.28) 10.0

Leaf Fe concentration(mg kg−1) Unknown N/A 25.0 56.8 (±2.68) 110

Leaf B concentration(mg kg−1) 14 5 12.8 18.2 (±0.57) 29.4

Leaf Mn concentration(mg kg−1) 20 6 15.0 19.0 (±0.56) 25.0

Leaf Zn concentration(mg kg−1) Unknown N/A 9.0 12.7 (±0.31) 15.5

Fresh fruit yield (kg tree−1) N/A N/A 6 30.2 (±2.26) 65∗Deficiency thresholds as suggested by Freeman et al. (2005) [35].

Table 4: Regressions between olive fruit yield (𝑌) and external factors (𝑥). Coefficients of determination (𝑅2) and statistical significance (𝑃)are also presented. Models are sorted in descending order of 𝑅2.

Yield vs. parameters Model 𝑅2 𝑃<Krootzone 𝑌 = 21.4 log(𝑥) + 30.7 0.68 0.001Total Nrootzone 𝑌 = 13.4 log(𝑥) − 1.47 0.58 0.000Soil depth 𝑌 = 38.3 log(𝑥) + 44.7 0.56 0.000Ktopsoil 𝑌 = 0.0953𝑥 + 17.0 0.47 0.000Mineral Nrootzone 𝑌 = 10.4 log(𝑥) + 54.2 0.44 0.001Brootzone 𝑌 = −7784.9𝑥

2+ 1221.4𝑥 + 19.0 0.41 0.001

Prootzone 𝑌 = 9.90 log(𝑥) + 55.7 0.27 0.023Slope 𝑌 = −1.0133𝑥 + 37.8 0.21 0.009Altitude 𝑌 = 0.0006𝑥2 − 0.5474𝑥 + 157 0.19 0.01Soil organic matter in topsoil 𝑌 = −3.86𝑥 + 38.1 0.05 0.14

Table 6. Combining “LogKrootzone” with “LogNrootzone”explained 71% of the total variability, while combining“LogKrootzone” with “Log soil depth” explained 77% of thetotal variability as (3) shows:

𝑌 = 15.3 (±2.44) Log (K) + 19.0 (±4.85) Log (𝐷)

+ 37.8 (±2.12)

𝑅2= 76.6%, 𝑃 < 0.001.

(3)

However, a model that combines these three factors didnot improve the model (𝑅2 = 0.77) as compared to (3), andthe effect of “LogNrootzone” was not statistically significant.Consequently, “LogNrootzone” was dropped from the finalmodel.

The analysis of best subsets showed that other soil factorshad no or very little addition to the final model, so theywere excluded.On the other hand, the addition of tree-related

parameters significantly improved the model. Leaf macronu-trients did not add much to the variability explained, butmicronutrients were more important. The biggest improve-ment in the variability explained by themodel was brought inby adding “Leaf Fe” and “Leaf B” concentrations to the firsttwo components as shown in

𝑌 = 12.4 (±2.44) Log (K) + 17.6 (±4.26) Log (𝐷)

+ 0.79 (±0.31) Leaf B + 0.17 (±0.05) Leaf Fe

+ 13.1 (±6.88)

𝑅2= 83.0%, 𝑃 < 0.000.

(4)

3.5. Correlations between Leaf Minerals and Yield. Interest-ingly, fruit yield did not correlate well with leaf N or Pconcentrations but correlated significantly with leaf K with26%of the variability explained (Table 7). Formicronutrients,


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fruit yield was significantly correlated with leaf B and Feconcentrations, which, respectively, explained 35% and 10%of the total variability. On the other hand, leaf Mn, Zn, andCu concentrations were not correlated to fruit yield.

4. Discussion

4.1. Characteristics of a Typical Low Input Olive Systemin the Eastern Mediterranean. Soil fertility in our study isconsidered on the low end when compared to results fromrainfed Mediterranean basin olive groves, although theselevels of fertility are not uncommon. In a soil fertility surveyacross olive groves in Sidi Bouzid area (northwesternTunisia), which has rainfall and topography characteristicscomparable to Afrin area, Gargouri and Mhiri [36] reportedvalues between 6 and 19.9mg kg−1 for P (average of11.8mg kg−1) and between 90 and 270mg kg−1 for K (average

of 133mg kg−1) in the top soil layer (0–30 cm). Similarly, oursoil K concentrations were within the same range of recentresults from one Andalusian farm reported by Restrepo-Diaz et al. [37] showing 61–171mgK kg−1 for the topsoil(30 cm). However, our P and K levels were lower than surveyresults from Andalusian olive groves showing extractable Kconcentrations in the top 30 cm to range between 105 and565mg kg−1 [38]. Extractable P levels in the same studyranged between 60 and 162mg kg−1. In Portugal,Marcelo andJordão [15] reported 95 and 191mg kg−1 for available P and K,respectively, in the topsoil (0–20 cm) of a calcareous olivegrove. The lower values we observed compared to Spanishand Portuguese levels could be attributed to the lower inputsin our study area.

4.2. Nutrient Deficiencies. Mineral composition of leaves inour study was often below sufficiency threshold values, espe-cially for macronutrients. However, no nutrient deficiencysymptoms could be detected in any of the orchards studied.This might be explained by the fact that nutrient concen-trations below the critical level can occur long before anydeficiency symptoms appear on the leaves [35].

Comparing our leaf nutrition results with other Mediter-ranean areas shows that our trees have consistently lower val-ues. Leaf N concentrations in our studywere lower than thoserecorded by Tubeileh et al. [39] in April for young “Qaisi”olive trees in an arid area (220mm annual rainfall) in AleppoGovernorate, Syria, which ranged between 15 and 17 g kg−1 forrainfed and deficit-irrigated trees, respectively.Moreover, ourvalues for leaf N, P, and K concentrations were lower than thevalues obtained by Dimassi et al. [40] on 17 European culti-vars grown inGreece. Similarly, in a study conducted on rain-fed olive trees in Cordoba, Spain, Fernández-Escobar et al.[41] observed leafN concentration values around 16 g kg−1 forone-year-old leaves sampled in April (bearing year). Resultsfrom Andalusia, Spain, compiled by Ferreira Llamas [42],showed higher leaf nutrient concentrations than ours. Ingeneral, low leaf K concentration values are common inrainfed olive trees [43] and were observed in some foothillorchards in California [35]. However, leaf K values in ourstudy were within the same range of those observed byFernández-Escobar et al. [41], which were around 5 g kg−1 inApril and slightly above 4 g kg−1 in May for one-year-oldleaves sampled in an “on” year. Survey results from Tunisia[36] showed similar leaf P (0.58–1.4 g kg−1) and generallyhigher K (4.3–9.3 g kg−1) concentrations than our trees.

Leaf micronutrient concentrations in our study weregenerally above the deficiency threshold. Our mean valuefor leaf B concentration was comparable to the levels of16.7mg kg−1 for rainfed trees and 17-18mg kg−1 for irrigatedtrees found by Androulakis et al. [44] in Crete but lower thanAndalusian values (29mg kg−1) reported by Fernández-Escobar et al. [41]. Our leaf Fe concentrations were generallyhigher than those reported by Fernández-Escobar et al. [41].Nevertheless, leaf Fe analysis is generally not useful fordiagnosing Fe deficiency because the inconsistency of leaf Felevels in separating chlorotic from nonchlorotic leaves [45],relying on visual leaf assessment, remains the most reliable


Table 5: Regressions between olive leaf primary macronutrient concentrations (𝑌) and soil depth (𝑥). Coefficients of determination (𝑅2) andstatistical significance (𝑃) are also presented. Models are sorted in descending order of 𝑅2.

Leaf minerals vs. soil depth Model 𝑅2 𝑃<Leaf K concentration 𝑌 = 0.11 log(𝑥) + 0.54 0.12 0.019Leaf P concentration 𝑌 = −0.07 log(𝑥) + 0.10 0.03 0.21Leaf N concentration 𝑌 = 1.31𝑒−0.032𝑥 0.01 0.50

Table 6: Multiple regressions between olive fruit yield (𝑌) and soil/land factors (𝑥). Coefficients of determination (𝑅2) and statisticalsignificance (𝑃) are also presented.

Yield vs. parameters Model 𝑅2 𝑃<Krootzone + Nrootzone 𝑌 = 15.4 log(K) + 12.8 log(Nrootzone) + 16.7 0.71 0.000Krootzone + depth 𝑌 = 15.3 log(K) + 19.0 log(𝐷) + 37.8 0.77 0.0001Krootzone + depth + Tot Nrootzone 𝑌 = 15.9 log(K) + 20.3 log(𝐷) − 2.21 log(Nrootzone) + 40.7 0.77 0.000Krootzone + depth + leaf B 𝑌 = 12.5 log(K) + 19.2 log(𝐷) + 0.73leaf B + 24.5 0.79 0.000Krootzone + depth + leaf Fe 𝑌 = 15.4 log(K) + 17.3 log(𝐷) + 0.165Leaf Fe + 27.8 0.79 0.000Krootzone + depth + leaf B + Leaf Cu 𝑌 = 13.6 log(K) + 17.5 log(𝐷) + 0.59Leaf B + 1.46Leaf Cu + 18.0 0.82 0.000Krootzone + depth + leaf B + Leaf Fe 𝑌 = 12.4 log(K) + 17.6 log(𝐷) + 0.79Leaf B + 0.17Leaf Fe + 13.1 0.83 0.000

Table 7: Correlations between olive fruit yield (𝑌) and leaf nutrients (𝑥) sorted in descending order of 𝑅2. Coefficients of determination (𝑅2)and statistical significance (𝑃) are also presented.

Yield vs. parameters Model 𝑅2 𝑃<Leaf B concentration 𝑌 = 44.4 log(𝑥) − 97.7 0.35 0.000Leaf K concentration 𝑌 = 14.9 log(𝑥) + 44.5 0.26 0.000Leaf Fe concentration 𝑌 = 14.5 log(𝑥) − 27.8 0.10 0.032Leaf Cu concentration 𝑌 = 12.2 log(𝑥) + 9.45 0.07 0.073Leaf Mn concentration 𝑌 = 11.4 log(𝑥) − 49.8 0.037 0.491Leaf Zn concentration 𝑌 = 10.8𝑒0.028𝑥 0.038 0.486Leaf P concentration 𝑌 = 20.6𝑒2.52𝑥 0.00 0.657Leaf N concentration 𝑌 = 20.9𝑒0.1748𝑥 0.00 0.75

method for diagnosing Fe deficiency. On the other hand, leafCu, Zn, and Mn concentrations in our trees were substan-tially lower than the concentration observed by Fernández-Escobar et al. [41], who reported values of ∼27, 16 and30mg kg−1, for the three elements, respectively, for leavessampled in April.

Our generally low soil and leaf macronutrient values canbe attributed to the fact that olive trees in this part of Syria,like most Mediterranean olive growing regions, are usuallygrown on poor, hilly, and shallow soils. In addition, tradi-tional no input or low input production systems do not com-pensate for crop nutrient uptake. Olive growers add organicamendments (mostly manure) once every two or threeyears while mineral fertilizer application is almost nonexis-tent in dryland olive groves. Most pruning residues are alsoremoved from the grove to be used for combustion or animalfeed, which minimizes nutrient recycling within the treeand in the tree-soil system. Moreover, to avoid any grazingdamage to their trees, most olive growers do not allow sheepto graze weeds within their groves, which excludes anyindirect manure input in those groves.

Our study revealed that variations in soil potassium andsoil depth were the major factors affecting olive yield under

our typical olive growing conditions. The importance of soildepth for olive production has already been shown by otherstudies in countries south of theMediterranean (e.g., [46]), asdeeper soils allow more water and nutrients to be stored andexplored by tree roots. However, the effect of soil potassium ismore controversial.The good correlations between fruit yieldon the one hand and Krootzone, Ktopsoil, and, to a lesserextent, leaf K, on the other hand, provide evidence that theavailability of this element can be critical for olive production.This finding enables us to confirm the observations ofHartmann et al. [47] in California, who compiled literaturedata showing a close relationship between productivity andleaf K. More recently, Jasrotia et al. [19] found a strongcorrelation between soil K and each of fruit set (𝑅2 = 0.8) andolive yields (𝑅2 = 0.81) in sub-Himalayan India. Jordão et al.[12] and Fernández-Escobar et al. [41] suggested that a large Kdemand by the reproductive structures of olive is responsiblefor a buildup of K in leaves during “off” years and a decline ofthat K during “on” years. Similarly, Kramer and Kozlowsky[48] indicated that fruits represent a strong sink for K.Under variable Mediterranean conditions, foliar applicationof potassium significantly increased shoot growth [49] andolive yields [50]. This is probably because K increases plant


resistance to drought and cold [51]. In addition, K deficiencymakes olive leaves lose ability to regulate stomatal closurein irrigated conditions and even more under water-limitedconditions [52], which makes plants more vulnerable todrought. It is also important to highlight that the authors whoshowed no effect of potassium addition attributed that eitherto the high level of potassium (inherent or due to previousfertilizations) in the soil [15, 38, 53] or to the short durationof their studies [54].

The importance of soil K ahead of soil N is noteworthy.Soil N in our study ranked second in explaining the totalvariability observed and was even not significant when soildepth was included in the model. Although several papersemphasize the importance of N for olive production (e.g.,[38, 55]), some other authors (e.g., [14, 15]) did not observean effect of N fertilization on olive yields because leaf N wasabove sufficiency threshold.

The absence of correlations between yield on the onehand and leaves N and P on the other hand, contrarily tosome literature reports, is probably due to mobilization ofthese minerals out of the leaves for active vegetative growthreproductive purposes. Loupassaki et al. [56] suggested thepresence of sink competition between the fruits and the leavesfor minerals and carbohydrates. The strongly negative corre-lations between yield and leaf nutrients P and K observed byJasrotia et al. [16] provide further confirmation of thiscompetition.

The low nutrient levels in the soil and leaves in our studystem from different factors. Erosion is a problem throughoutAfrin area, and its negative effects on soil depth and fertilityhave been shown by other researchers (e.g., [8, 9]).The lack ofsoil inputs to compensate for crop uptake and erosion lossesis also contributing to this degradation problem.

Our results show that themodel is substantially improvedand the variability explained is increased up to 83% ifleaf micronutrient concentrations are taken into consider-ation according to (4) above. The importance of includingmicronutrients in yield predicting models was also shownby Correia et al. [18] on carob tree (Ceratonia siliqua L.).However, it should be taken into account that leaf nutrientconcentrations have seasonal variations and they changeaccording to the bearing pattern (lower values in “on” years).Our model is therefore a good tool to estimate yield if leafnutrient status is determined just before flowering. However,validation and calibration of this model under differentconditions are needed.

Conducting this type of studies involves challengesrelated to the design and technical, statistical, and logisticlevels. The statistical design and selection of trees that wouldrepresent a large area could be tricky tasks.

4.3. Strategies for Orchard and Nutrient Management. Theresults of this study show the importance of sound landman-agement and soil conservation. Reducing up-down tillageand adopting minimum tillage options would decrease soilerosion rates. Our findings show the importance of replenish-ing the amounts of nutrients removed by the crop to improveolive productivity in this area. Alternative soil amendments

(e.g., olive vegetation water) are being explored to improvesoil fertility and olive yields in this low input system.

5. Conclusion

This field-scale study aimed to relate olive yields to soil andland parameters and tree nutritional status. Olive tree yield,soil fertility, and plant nutrition in our study area were lowerthan those in other Mediterranean areas of similar climaticconditions, mainly due to the prevailing low input oliveproduction system and high erosion rates.

Our results showed that potassium is an element ofprime importance for olive production, which helps explainthe contradicting literature information with regard to theimportance of this element for olive yields. Soil depthwas another important factor and together with soil potas-sium explained 77% of the total variability. The variabilityexplained by our model increased to 83% by incorporatingleaf B andFe concentrations.The information and regressionspresented here contribute towards achieving a model thatcan predict and optimize olive production in areas of similarconditions. This model will need more data from other areasfor calibration and validation, especially from areas of higherand lower rainfall as well as different soil types.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

Acknowledgments

The guidance and contributions of late Mr. Malek Abdeenare gratefully acknowledged. Thanks are due to the farmersof Afrin area for their collaboration and help throughout thiswork. The authors would also like to acknowledge the excel-lent collaboration and facilitation provided by the Depart-ment of Agriculture and Agrarian Reform in Afrin andthe Department of Olive Research in Idleb. Thanks are dueto Piero Daltan for developing the GIS map of the area. Theyare thankful toGeorge Estefan andhis lab staff at ICARDA forthe analyses of soil and plant material. They would also liketo express their gratitude to the Soil Conservation and LandManagement project at ICARDA for funding this work.

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