The majority of olive (
Olive trees have played an important role in the rural development of the Mediterranean’s relatively poor rainfed areas over the centuries [
This study was carried out in Aleppo Governorate in northern Syria. Olive productivity in this part of Syria lags behind other producing areas of similar climatic conditions and some fields are being deserted. External inputs are low and consist, when practiced, of sheep manure every second or third year [
According to Klewinghaus et al. [
In spite of these studies, there is almost no information on the overall effect of soil status on olive yield. Furthermore, literature information on the relation between soil and leaf nutrient status and yield is not straight forward and is mostly obtained through fertilizer trials. While most literature studies (e.g., [ present the characteristics of a typical low input olive system in the eastern Mediterranean, identify key nutrient deficiencies in marginal-land olive groves, present strategies for orchard and nutrient management, present recommendations for further research.
This study was carried out in 2005 in Afrin district, Aleppo Governorate, northwestern Syria. To model the effects of land and soil parameters on olive yields, parameters such as land slope, 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 soil depth. In addition, concentrations of leaf minerals were also determined. Our methodology relied on establishing simple and multiple regressions to find the model that best describes the variability observed.
Afrin district is known to be the first producer of olive oil in Syria with its 10+ million trees [
The sites where the trees were selected are located between longitudes 36°5694 E and 37°1256 E and latitudes 36°2245 N and 36°5688 N (Figure
A topography map of northwestern Aleppo Governorate showing Afrin area with the orchard locations (red dots).
In order to clearly identify yield-controlling factors, the fields were selected to account for maximum diversity of soil and land factors available in the area. For example, whenever possible, trees with different soil depths were sampled on the same site. However, only healthy looking trees were selected and any disease-affected trees or those with visible disorders were avoided as those disorders might distort normal yield relations. It is assumed that large-size trees selected in flat areas with deep soils would represent optimum rainfed, low-input growing conditions and yield for the area.
In spring (March-April) 2005, fifty trees were selected and marked from 25 orchards across Afrin area. The number of trees per orchard varied from one to three according to the uniformity of the sites. In sites with variable tree size and/or soil conditions (depth, land slope, etc.), two or three trees were selected to better represent that variability. The following criteria were considered in tree selection. Trees belong to the “Zeiti” cultivar, being the dominant cultivar in the area. Trees are in their bearing (on) year. Trees are in stable commercial production stage (older than 15 years, less than 80 years). Trees are representative for the grove or a significant part of it. 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 crop in the other year). Trees have only one main trunk. Trees are healthy and do not show pest infection or disorder symptoms. No trees were selected on north-facing slopes, which can have particular microclimatic conditions. Trees are rainfed (i.e., no irrigation is applied). No fertilizer or manure was added this year or during the past 2 years. Soil is traditionally ploughed 4–6 times per year to increase rainwater infiltration, control weeds, and destroy capillary evaporation.
Most of the selected grove owners were already known to Agricultural Extension agents in Afrin, which facilitated the selection of trees, and ensured the collection of good-quality data. 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%.
Field slope was estimated using an inclinometer. Since visual soil colour is one of the favourite indicators local farmers use to describe a soil [
Analytical methods used to determine soil physical and chemical properties.
Element | Method and analysis equipment | Reference |
---|---|---|
Soil bulk density | Undisturbed clod method | Blake (1965) [ |
Soil organic matter | Walkley-Black method | Walkley and Black (1934) [ |
Nitrogen | Kjeldahl wet digestion | Bremner and Mulvaney (1982) [ |
Available phosphorus | Olsen colorimetry | Olsen and Sommers (1982) [ |
Extractable potassium | Extractable potassium, Flame Photometer 409 | Richards (1954) [ |
Extractable boron | Spectrocolorimetry | Bingham (1982) [ |
Iron, copper | DTPA test. Atomic Absorption Spectrometry | Lindsay and Norvell (1978) [ |
The rootzone was assumed to have a cylindrical shape and to compute its volume, the soil depth (to a maximum depth of 1.7 m) was measured for each tree, and the root radius was estimated to be 3 m based on planting density. The amount of each element (
These mineral elements will be referred to as total Nrootzone, mineral Nrootzone, Prootzone, Krootzone, and Brootzone, respectively. In addition to the amounts of mineral elements in the soil, the concentration of those elements in the top 20 cm soil layer will be presented to give an idea about soil fertility levels independently from variable soil depths.
Approximate tree age was determined through discussions with grove owners and/or by expert visual assessments. Yield per tree (in terms of fresh fruit weight) was assessed in November 2005.
To study tree nutritional status and its relationship with yield, leaf samples were collected in late April 2005 before flowering to determine concentrations of N, P, K, Fe, Cu, Mn, Zn, and B on a dry weight basis. From each tree, 50–60 fully expanded leaves, initiated during 2004, were selected between the fourth and sixth pairs of leaves below the shoot tip from different parts of the tree, as suggested by Bouat [
The statistical analysis was performed on 47 trees after dropping three outliers due to tree health reasons (e.g., disease on tree or fruit, physical/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 soil exchangeable P amount) on final tree yields, simple regressions were made between yield and each factor. A multiple regression analysis was then performed to determine the model that best accounts for the variance. Although multiple regression analysis is the standard approach in biological and ecological system modelling, the inherent collinearity (multicollinearity) of confounded explanatory variables could encumber analyses and threaten their statistical and inferential interpretation [
Correlations between fruit production and leaf mineral concentrations were also examined to understand the contribution of these minerals to fruit yield. Analysis of variance was performed for all the simple and multiple regressions established. Regressions were considered statistically significant at the
Soil colour in the selected groves differed from white (originated from chalky limestone) to basaltic-origin black soil (Table
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 | 0.30 |
2 | Brown | 1.20 |
3 | Light black | 0.20 |
4 | Light black | 0.20 |
5 | Light black | 0.20 |
6 | Light black | 0.20 |
7 | Red | 0.60 |
8 | Light brown | 0.70 |
9 | Brown | 0.90 |
10 | Brown-white | 0.20 |
11 | Brown-white | 0.40 |
12 | White-grey | 0.50 |
13 | White-grey | 0.30 |
14 | White | 1.20 |
15 | White | 0.70 |
16 | White | 0.80 |
17 | Brown | 0.20 |
18 | Brown | 0.20 |
19 | Brown-red | 0.25 |
20 | Brown-red | 0.20 |
21 | Brown-red | 0.25 |
22 | Grey | 0.15 |
23 | Grey | 0.15 |
24 | Grey | 0.30 |
25 | Red | 0.30 |
26 | Red | 0.15 |
27 | Dark brown | 0.60 |
28 | Dark brown | 0.50 |
29 | Grey | 0.20 |
30 | Grey | 0.40 |
31 | Grey | 0.25 |
|
|
0.60 |
33 | Light brown | 0.90 |
34 | Light brown | 0.70 |
35 | Black | 1.10 |
36 | Black | 1.20 |
37 | Dark brown | 1.60 |
38 | Dark brown | 1.70 |
39 | Pale brown | 0.60 |
40 | Light brown | 0.60 |
41 | Red | 0.50 |
42 | Red | 0.30 |
43 | Red | 0.30 |
|
|
0.60 |
45 | White | 0.20 |
|
|
0.60 |
47 | Grey | 0.40 |
48 | Grey | 1.00 |
49 | Grey | 0.40 |
50 | Brown | 0.80 |
|
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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.
Most of the trees monitored had leaf nutrient concentration values below deficiency thresholds reported in the literature (Table
Leaf mineral composition of main macro- and micronutrients and olive fruit yield.
Parameter | Deficiency thresholds |
Number of trees below deficiency threshold | Minimum | Mean (±standard error) | 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 |
|
N/A | N/A |
|
30.2 (±2.26) | 65 |
The mean leaf P concentration was exactly at the deficiency threshold level, and 29 trees were located below that threshold. Tree K nutrition was better than the other primary macroelements as half the trees did not have deficient leaf K concentration values and the mean leaf K concentration was slightly higher than the deficiency threshold. All the trees with higher leaf K concentrations were located on red, dark brown, or black soils.
Wide ranges were also observed for leaf micronutrient concentrations. For 26 trees, leaf Cu concentration values were right at the threshold level and only four trees were below that level. For leaf Fe concentration, there is no information available on deficiency threshold in olive. Boron nutrition did not seem to be a concern, as only five trees had leaf B concentration levels slightly below deficiency threshold.
Olive fruit yield varied considerably among the trees. Four trees produced less than 11 kg tree−1, while ten trees produced more than 40 kg tree−1. In general, trees grown on red soils tended to have the highest yields and those on white and grey soils had the lowest yields.
To identify the most important soil and land factors in determining yield and to separate the individual effects of each of those factors, simple regression analyses were carried out between each soil and land factor and tree yield (Table
Regressions between olive fruit yield
Yield vs. parameters | Model |
|
|
---|---|---|---|
Krootzone |
|
0.68 | 0.001 |
Total Nrootzone |
|
0.58 | 0.000 |
Soil depth |
|
0.56 | 0.000 |
Ktopsoil |
|
0.47 | 0.000 |
Mineral Nrootzone |
|
0.44 | 0.001 |
Brootzone |
|
0.41 | 0.001 |
Prootzone |
|
0.27 | 0.023 |
Slope |
|
0.21 | 0.009 |
Altitude |
|
0.19 | 0.01 |
Soil organic matter in topsoil |
|
0.05 | 0.14 |
Regression between individual olive tree fruit yield (
Total Nrootzone ranked second with 58% of the variability explained, while soil depth was the third most important factor affecting tree yield, explaining 56% of the variability (Figure
Regressions between olive leaf primary macronutrient concentrations
Leaf minerals vs. soil depth | Model |
|
|
---|---|---|---|
Leaf K concentration |
|
0.12 | 0.019 |
Leaf P concentration |
|
0.03 | 0.21 |
Leaf N concentration |
|
0.01 | 0.50 |
Regression between individual olive tree fruit yield (
Other soil factors related to nutrients were also important. Mineral Nrootzone and Brootzone accounted, respectively, for 44% and 41% of the total variability (Table
Multiple regression analysis data are presented in Table
Multiple regressions between olive fruit yield
Yield vs. parameters | Model |
|
|
---|---|---|---|
Krootzone + Nrootzone |
|
0.71 | 0.000 |
Krootzone + depth |
|
0.77 | 0.0001 |
Krootzone + depth + |
|
0.77 | 0.000 |
Krootzone + depth + leaf B |
|
0.79 | 0.000 |
Krootzone + depth + leaf Fe |
|
0.79 | 0.000 |
Krootzone + depth + leaf B + Leaf Cu |
|
0.82 | 0.000 |
Krootzone + depth + leaf B + Leaf Fe |
|
0.83 | 0.000 |
However, a model that combines these three factors did not improve the model (
The analysis of best subsets showed that other soil factors had no or very little addition to the final model, so they were excluded. On the other hand, the addition of tree-related parameters significantly improved the model. Leaf macronutrients did not add much to the variability explained, but micronutrients were more important. The biggest improvement in the variability explained by the model was brought in by adding “Leaf Fe” and “Leaf B” concentrations to the first two components as shown in
Interestingly, fruit yield did not correlate well with leaf N or P concentrations but correlated significantly with leaf K with 26% of the variability explained (Table
Correlations between olive fruit yield
Yield vs. parameters | Model |
|
|
---|---|---|---|
Leaf B concentration |
|
0.35 | 0.000 |
Leaf K concentration |
|
0.26 | 0.000 |
Leaf Fe concentration |
|
0.10 | 0.032 |
Leaf Cu concentration |
|
0.07 | 0.073 |
Leaf Mn concentration |
|
0.037 | 0.491 |
Leaf Zn concentration |
|
0.038 | 0.486 |
Leaf P concentration |
|
0.00 | 0.657 |
Leaf N concentration |
|
0.00 | 0.75 |
Soil fertility in our study is considered on the low end when compared to results from rainfed Mediterranean basin olive groves, although these levels of fertility are not uncommon. In a soil fertility survey across olive groves in Sidi Bouzid area (northwestern Tunisia), which has rainfall and topography characteristics comparable to Afrin area, Gargouri and Mhiri [
Mineral composition of leaves in our study was often below sufficiency threshold values, especially for macronutrients. However, no nutrient deficiency symptoms could be detected in any of the orchards studied. This might be explained by the fact that nutrient concentrations below the critical level can occur long before any deficiency symptoms appear on the leaves [
Comparing our leaf nutrition results with other Mediterranean areas shows that our trees have consistently lower values. Leaf N concentrations in our study were lower than those recorded by Tubeileh et al. [
Leaf micronutrient concentrations in our study were generally above the deficiency threshold. Our mean value for leaf B concentration was comparable to the levels of 16.7 mg kg−1 for rainfed trees and 17-18 mg kg−1 for irrigated trees found by Androulakis et al. [
Our generally low soil and leaf macronutrient values can be attributed to the fact that olive trees in this part of Syria, like most Mediterranean olive growing regions, are usually grown on poor, hilly, and shallow soils. In addition, traditional no input or low input production systems do not compensate for crop nutrient uptake. Olive growers add organic amendments (mostly manure) once every two or three years while mineral fertilizer application is almost nonexistent in dryland olive groves. Most pruning residues are also removed from the grove to be used for combustion or animal feed, which minimizes nutrient recycling within the tree and in the tree-soil system. Moreover, to avoid any grazing damage to their trees, most olive growers do not allow sheep to graze weeds within their groves, which excludes any indirect manure input in those groves.
Our study revealed that variations in soil potassium and soil depth were the major factors affecting olive yield under our typical olive growing conditions. The importance of soil depth for olive production has already been shown by other studies in countries south of the Mediterranean (e.g., [
The importance of soil K ahead of soil N is noteworthy. Soil N in our study ranked second in explaining the total variability observed and was even not significant when soil depth was included in the model. Although several papers emphasize the importance of N for olive production (e.g., [
The absence of correlations between yield on the one hand and leaves N and P on the other hand, contrarily to some literature reports, is probably due to mobilization of these minerals out of the leaves for active vegetative growth reproductive purposes. Loupassaki et al. [
The low nutrient levels in the soil and leaves in our study stem from different factors. Erosion is a problem throughout Afrin area, and its negative effects on soil depth and fertility have been shown by other researchers (e.g., [
Our results show that the model is substantially improved and the variability explained is increased up to 83% if leaf micronutrient concentrations are taken into consideration according to (
Conducting this type of studies involves challenges related to the design and technical, statistical, and logistic levels. The statistical design and selection of trees that would represent a large area could be tricky tasks.
The results of this study show the importance of sound land management and soil conservation. Reducing up-down tillage and adopting minimum tillage options would decrease soil erosion rates. Our findings show the importance of replenishing the amounts of nutrients removed by the crop to improve olive productivity in this area. Alternative soil amendments (e.g., olive vegetation water) are being explored to improve soil fertility and olive yields in this low input system.
This field-scale study aimed to relate olive yields to soil and land parameters and tree nutritional status. Olive tree yield, soil fertility, and plant nutrition in our study area were lower than those in other Mediterranean areas of similar climatic conditions, mainly due to the prevailing low input olive production system and high erosion rates.
Our results showed that potassium is an element of prime importance for olive production, which helps explain the contradicting literature information with regard to the importance of this element for olive yields. Soil depth was another important factor and together with soil potassium explained 77% of the total variability. The variability explained by our model increased to 83% by incorporating leaf B and Fe concentrations. The information and regressions presented here contribute towards achieving a model that can predict and optimize olive production in areas of similar conditions. This model will need more data from other areas for calibration and validation, especially from areas of higher and lower rainfall as well as different soil types.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The guidance and contributions of late Mr. Malek Abdeen are gratefully acknowledged. Thanks are due to the farmers of Afrin area for their collaboration and help throughout this work. The authors would also like to acknowledge the excellent collaboration and facilitation provided by the Department of Agriculture and Agrarian Reform in Afrin and the Department of Olive Research in Idleb. Thanks are due to Piero Daltan for developing the GIS map of the area. They are thankful to George Estefan and his lab staff at ICARDA for the analyses of soil and plant material. They would also like to express their gratitude to the Soil Conservation and Land Management project at ICARDA for funding this work.