Next Article in Journal
First Detection and Characterization of Cross- and Multiple Resistance to Acetyl-CoA Carboxylase (ACCase)- and Acetolactate Synthase (ALS)-Inhibiting Herbicides in Black-Grass (Alopecurus myosuroides) and Italian Ryegrass (Lolium multiflorum) Populations from Ireland
Next Article in Special Issue
Production of Pelleted Biochar and Its Application as an Amendment in Paddy Condition for Reducing Methane Fluxes
Previous Article in Journal
Diversity and Pathogenicity of Diaporthe Species Revealed from a Survey of Blueberry Orchards in Portugal
Previous Article in Special Issue
Characteristics of Agricultural Dust Emissions from Harvesting Operations: Case Study of a Whole-Feed Peanut Combine
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agriculture
Volume 11
Issue 12
10.3390/agriculture11121267
agriculture-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Short and Long-Term Effect of Land Use and Management on Soil Organic Carbon Stock in Semi-Desert Areas of North Africa-Tunisia

by
Fatma Baraket
1,
Manuel González-Rosado
2,
Nadhem Brahim
3,
Núria Roca
4,
Hadda Ben Mbarek
1,
Marcin Świtoniak
5,
Rayda Chaker
6,
Ángel Sánchez-Bellón
7,
Hafedh Rigane
1,
Kamel Gargouri
6 and
Luis Parras-Alcántara
2,*
1
Research Laboratory of Environmental Sciences and Sustainable Development (L18ES32), Earth Sciences Department, Sfax Faculty of Sciences, P.O. Box 1087, Sfax 3000, Tunisia
2
SUMAS Research Group, Department of Agricultural Chemistry, Soil Science and Microbiology, Faculty of Science, Agrifood Campus of International Excellence—ceiA3, University of Cordoba, 14071 Cordoba, Spain
3
Department of Geology, Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis 2092, Tunisia
4
Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain
5
Department of Soil Science and Landscape Management, Nicolaus Copernicus University in Toruń, 1 Lwowska Str., 87-100 Toruń, Poland
6
Olive Institute, University of Sfax, P.O. Box 1087, Sfax 3000, Tunisia
7
Department of Earth Sciences, Faculty of Sciences, Campus University of Puerto Real, Cadiz University, 11519 Puerto Real, Spain
*
Author to whom correspondence should be addressed.
Agriculture 2021, 11(12), 1267; https://doi.org/10.3390/agriculture11121267
Submission received: 30 September 2021 / Revised: 4 December 2021 / Accepted: 5 December 2021 / Published: 14 December 2021
(This article belongs to the Special Issue Agriculture: 10th Anniversary)
Browse Figures
Review Reports Versions Notes

Abstract

:
Soil organic carbon (SOC) plays an important role in the global C cycle, as well as in the maintenance and improvement of the soil quality. Over time, special attention has been paid to it in the study of the SOC reserves worldwide; however, reduced attention has been given to assessing the spatial patterns of SOC stock (SOCS) in semi-desert ecosystems. In this line, there are no conclusive studies in drylands of Africa affected by aeolian processes (semi-desert conditions) mainly due to the complexity of sample collection, and this is especially significant in some soil types such as Arenosols (AR) and Calcisols (CL). This study evaluated the spatial variability of SOC and SOCS in AR and CL with woody crops in relation to land use and management (old plantations > 100 years: centenary olive grove; new plantations < 12 years: young olive grove, almond, and pistachio) in semi-desert conditions. For this purpose, 16 soil profiles (for 0–40 and 40–100 cm depth) were selected and studied in an experimental area of Menzel Chaker-Sfax in southeastern Tunisia (North Africa). The main results indicated that the SOCS on average was higher in Old Cultivated AR (OC-AR) with 41.16 Mg ha−1 compared to Newly Cultivated AR (NC-AR) with 25.13 Mg ha−1. However, the SOCS decreased after a long period of cultivation in CL from 43.00 Mg ha−1 (Newly Cultivated CL: NC-CL) to 32.19 Mg ha−1 (Old Cultivated CL: OC-CL). This indicates that in the long term, CL has more capacity to store SOC than AR, and that in the short term, AR is more sensitive to land management than CL.

1. Introduction

In most terrestrial ecosystems, soil organic carbon (SOC) is the largest carbon (C) pool [1] containing approximately 2344 Gt of organic C globally [2]. In fact, SOC is an essential element of earthly life and constitutes the most fertilizing element of the soil, and SOC content is an important indicator of biological and microbiological activity [3,4]. Therefore, SOC and soil organic matter (SOM) are considered major factors that influence the soil quality, affecting agricultural productivity, soil structural stabilization, plant nutrients retention, and water-holding capacity, among other things [5,6]. However, in addition, SOC stock (SOCS) depends on factors such as wildlife, earthworms, ants, and microorganisms such as fungi and bacteria and organic waste [7], and this SOCS can be found at 0–2 m depth [8,9]. Other factors that can affect to SOCS are the climatic conditions (precipitation and temperature) and agriculture practices (land use and management) [10]. In this sense, high temperatures, low rainfall, and intensified tillage can accelerate SOM decomposition, conditioning agricultural production in many regions of the world [11]. Therefore, a decrease in the SOM content leads to reduction in soil fertility and productivity [12]. Therefore, SOCS should be estimated to evaluate the impact of land management practices on soil fertility and in the greenhouse gases generation [13,14].
All these considerations are especially important in drylands (semi-arid, arid, semi-desert, and desert areas) with low average annual rainfall (<500 mm) [15], with a cover more than one-third of the Earth’s land surface, and with more than 36% of the world’s SOCS [16,17]. In this line, [18] indicates that these ecosystems can be more responsive to elevated CO2 than others because net primary productivity is mostly limited by water availability. Additionally, other authors such as [19] have reported that desert soils exhibit higher atmospheric CO2 fixation capacity than other soils (e.g., meadow soils).
In this sense, researchers in [20] indicated that dryland areas in Tunisia is characterized by low SOCS (18.7 Mg ha−1) due to sandy texture, which is associated with low SOM content and reduced cation exchange capacity [21,22], highlighting that in sandy soils, the SOM decomposition has a higher rate than in soils with high clay content [23]. However, water scarcity is also considered as another crucial factor that causes soil degradation, and a decrease in SOC content in semi-arid and arid regions [24]. On average, every year, arid areas have long drought periods (4 to 6 months), so the generation of natural biomass as C source is limited by climatic conditions, but this problem is maximized due to management practices in these arid regions, since the highly intensified tillage causes a loss of spontaneous vegetation cover [25]. The main consequence of these processes acting synergistically is that these soils are highly vulnerable to degradation processes, due mainly to SOM depletion and wind erosion, which cause desertification, which is intensified by the current global warming crisis [26,27].
Recently, many researchers have shown great interest in the SOCS prediction (at regional scale), extrapolating the SOCS on a wide range of soil and/or vegetation types in arid areas of Africa [20]. In this line, [28] analyzed the SOC storage evolution in relation with tillage system in three soil types (Vertisols, Cambisols, and Luvisols) under Mediterranean climate, emphasizing that the SOC was significantly higher in no tillage compared to conventional tillage (10% more in Vertisols and 8% more in Cambisols), with no significant differences in the Luvisols case. More recently, [29] studied the SOCS distribution in Cambisols under different crops in semi-arid Tunisian climate, suggesting that the SOCS has exceeded the national and international standards in Cambisols. Additionally, in Tunisia, SOCS was assessed in different soil types including Lithosols, Solonochaks, Cambisols, and Regosols [20,29,30]; however, Calcisols (CL) and Arenosols (AR) have not been studied yet. In this line, is important to note that three quarters of Tunisia is under arid or semi-desert climate. Therefore, analyzing the SOCS in CL and AR in Tunisian is important to improve the soil quality and its productive capacity, in addition to being able to extrapolate these data to other semi-desert areas to intervene in the land use and management of these soil types. In this sense, several studies have established that soils in arid and semi-desert regions after cultivation suffer significant variations in their biological, physical, and chemical properties [31,32,33,34,35]. However, little research has focused on assessing the effects of woody crops (olive, almond, and pistachio) at different development stages in AR and CL during short- and long-term periods and their relationship with the soil properties and SOCS. Therefore, due to the role that these soils play in C sequestration and in the crop productivity, it is essential to understand the effectiveness of land use and managements under extreme climatic conditions not only in terms of physical properties but also in the C dynamic variation.
Given this scenario, the general objectives of this study are (i) to study the SOC in Calcisols and Arenosols of Tunisia (dryland rainfed area) and (ii) to analyze in the short and long terms the soil’s capacity for SOC stock under different land uses and management.

2. Materials and Methods

2.1. Study Area

The study area is in an experimental farm of Essalema, located at 50 km in the north-west in Sfax governorship, in the Menzel Chaker delegation—Tunisia (34°59′15″ N–10°20′03″ W) (Figure 1). This area covers a surface of 18,670 ha and is divided into seven sub-farms, with an average altitude of 161 m.a.s.l., ranging from 105 m.a.s.l. to 217 m.a.s.l. (meters above sea level), with slopes < 3% slightly undulating.
The climate is arid Mediterranean and hot arid steppe according to the Köppen–Geiger updated classification [36]. The annual average temperature was 19.5 °C, with a maximum air temperature of 33 °C in August and a minimum air temperature of 6 °C in January (temperature data provided by the National Institute of Metrology for the period 1966–2019). The annual average precipitation was 169 mm and monthly rainfall ranges from 6 mm (July) to 34 mm (October) (rainfall data provided by Salama farm station for the period 2008–2019).
The study area is characterized by outcrops of the Middle and Upper Continental Pleistocene, and the Lower Pleistocene, formed by limnic sabkhas (deposits of coastal flats subject to periodic flooding and evaporation) and recent alluvium. Most of the wadis are endorheic, leading to closed depressions of the Sebkhas and Garâas type. Depending on their morpho-structural conditions, these closed depressions take the form of synclinal basins (Menzel Chaker region) or the form of Sebkha sand Garâas (Bou Jmal, Karafita). In the dryland regions, the aeolian processes also play an important role, particularly where the precipitation is lows (<150 mm y−1) [37,38], so the aeolian materials accumulation leads to the poorly developed sandy soils formation [39], as it happens in the study area’s experimental field [40].

2.2. Soil Sampling and Analytical Methods

Samples from 16 soil profiles were collected: 10 in CL and 6 in AR (these soil types covered most of the studied area ~70%). All investigated soils were tilled to depths varying between 20 and 30 cm (can reach up to 40 cm deep) four to six times per year using a tractor with cultivator depending on rainfall. Soil profiles were dug with a mini excavator, and soil samples were collected at different soil control section for each soil profile (S1: 0–40 cm; S2: 40–100 cm), for a proper determination of physical and chemical soil properties [41,42].
The collected samples were labeled (A1–A5: samples of the Newly Cultivated Calcisols (tilled < 12 years)—NC-CL; M1–M5: samples of the Old Cultivated Calcisols (tilled > 100 years)—OC-CL; C1–C3: samples of the Newly Cultivated Arenosols (tilled < 12 years)—NC-AR; B1–B3: samples of the Old Cultivated Arenosols (tilled > 100 years)—OC-AR).
Soil samples were placed in polyethylene bags, which were labeled and transferred to the laboratory and air dried. Once dried, the samples were sieved at 2000 µm, separating the thick fragments and roots from the rest of the material. Three repetitions were carried out for each sample. The analytical methods, laboratory analysis, and other parameters calculated used in this study to determine different soil properties are reported in Table 1, according to handbook of plant and soil analysis for agricultural systems [43]. Soils were described and classified according to World Reference Base for Soil Resources [38].

2.3. Experimental Design

The research was carried out in an experimental farm in Essalema–Sfax–Menzel–Tunisia. For this, two soil types were selected (AR and CL), with different land uses (OG: olive grove, AT: almond and PT: pistachio) and managements. The soils’ evolution in the short (<12 years) and long terms (>100 years) was compared to analyze the effects of management on SOCS. Land use and management are characterized in Table 2 and Table 3 and Figure 2.
All soil samples were taken at the same time (synchronic approach) under different management practices at known durations from an initial reference state, and the SOCS was compared under this initial reference state [53].

2.4. Statistical Analyses

The effect of land management and soil depth on soil properties was analyzed using SPSS 20.0 for Windows. Data were tested for normality to verify the model assumptions using Duncan’s multiple range tests, and differences of p < 0.05 were considered statistically significant.

3. Results and Discussion

3.1. Soil Characterization

The soils studied are developed from loamy or sandy material often of aeolian origin, and all soils had simple morphology with two genetic horizons—Ap horizon and underlying horizons with calcium carbonates accumulation (Ck and Bk); according to [38], the studied soils were mainly Haplic CL (CLha), Calcaric AR (ARca) and Eutric AR (AReu) (Table 2, Table 3, Table 4 and Table 5).
In general, the surface horizon (topsoil) was weakly developed with low SOC content ranging from 0.12% (OC-AR: B1-Ap) to 0.56% (NC-CL: PT: A5-Ap). In this sense, the management type (strongly mechanized) caused the soil homogenization by mixing the surface horizons in the first 40 cm. Due to the low SOC, they are not diagnostic horizons [38], so the C content was below the 0.2% (Table 4 and Table 5). Similar results were obtained by [54,55] in CL in the Sfax region, near of the study area, stating that the natural SOC content in the Ap horizon of these soils was very low (<0.3%) due to climatic conditions, relief, and lithology. Calcium carbonate content in the deeper horizons (40–100 cm) was variable, with simultaneous presence of secondary precipitation (calcic horizons). According to [56], the soils developed from carbonate parent materials are common and belong to the main soil groups in North Tunisia.
In the remaining soils, the calcium carbonate content was not high, and due to texture (sandy) and the lack of diagnostic horizons, they were classified as AR. The presence of weakly developed AR derived from aeolian sediments has also been confirmed by [37]. Nevertheless, the carbonates’ presence in these soils was expressed using the Calcaric qualifier (C1, C2, B1, B2).

3.2. Soil Bulk Density and pH in AR and CL

One of the problems that the soils in the study area have is bulk density (BD) quantification, since they are sandy soils with strong wind erosion, together with the high carbonate concentration. In this sense, the BD determination is very important, since it affects SOCS analysis [50,51,52].
The BD study did not show a clear trend; although in most cases BD decreased in depth, this behavior was expected, since in most cases, the BD reduction prevented an increase in the SOC concentration (Table 4 and Table 5). In the superficial layer (0–40 cm), our results showed that BD on average were between 1.7 and 1.39 Mg m−3 for OC-AR and NC-CL, respectively; however, in sub-soil (40–100), the BD ranged between 1.69 and 1.35 Mg m−3 for NC-AR and NC-CL. It is important to note that BD was higher in AR compared to CL regardless of the study period (long-term and short-term) (Table 4 and Table 5). The studied soils have shown that BD decreased significantly in NC-CL and OC-CL; in this line, [57] confirmed that in temperate zones, a decrease in BD is related to an increase in calcium carbonate content. BD values tended to be higher in surface horizons than in deep layers specifically in CL. This increase can be explained by the effect of tillage, which reduces the SOM content, increasing soil compaction and increasing the soil DB. This effect has been shown by [58] in soils with different land uses, indicating a BD increase in cropland. In addition, this BD increase was more important in the soil that was most intensively tilled.
Our results show differences in BD between CL and AR, and these differences may be due to soil type, management practices, and climatic conditions, in addition due to several factors, such as volume and rainfall intensity, drying and wetting of soil, land position, and crop type, among others [59]. According to [60], the time factor could be the most important factor that explains the BD variations in surface and subsurface. In fact, these authors reported that under conventional tillage, the general trend in surface at 15 cm depth was a BD increase during the cropping cycle except in the surface rows where BD was already high at the first measurement time.
The soil pH values showed that pH decreased slightly with increasing depth in newly and previously cultivated CL and AR (Table 4 and Table 5). In the superficial layer, pH values were between 8.78 and 8.95 in NC-CL and between 8.94 and 9.17 in NC-AR. In the deep layer, pH values ranged from 8.74 to 8.93 in NC-CL and from 8.81 to 9.12 in NC-AR. In OC soil, pH values in superficial layers fluctuated between 8.78 and 9.29 in OC-CL and between 9.10 to 9.25 in OC-AR. In the deep layer, pH values varied between 8.74 and 9.19 in OC-CL and between 9.10 and 9.19 in OC-AR. These results were similar to those found by [61] in Sfax Tunisia in conventional tillage (cultivated soil) with alkaline pH (9.17 ± 0.76); since the SOC degradation and the CaCO3 solubilization may affect pH, in this line, we suggest that the alkaline condition can be caused by the high concentration of the calcium carbonate.

3.3. Soil Organic Carbon Stock Concentration

All SOCS values of newly cultivated soils (<12 years) and old cultivated soils (>100 years) are shown in Table 6 and Table 7. The results indicate important differences between SOCS in newly and previously cultivated soils regarding the soil type (CL or AR).
The SOCS analysis showed that on average, the SOCS content in CL was 20.9 Mg ha−1 and 13.1 Mg ha−1 in topsoil (0–40 cm depth) for NC-CL and OC-CL, respectively. In sub-soil (1 m depth), the SOCS was 43.0 Mg ha−1 (NC-CL) and 32.2 Mg ha−1 (OC-CL). In this line, [62] in dryland of southeastern Spain found 52 Mg ha−1 and 70 Mg ha−1 for 0.5 m and 1 m depth, respectively, and [63] in southern Spain reported 50.1 Mg ha−1 at 0.25 m depth and 82 Mg ha−1 at 0.75 m depth. These differences in SOCS between our results and the results reported by [62,63] could be explained by the impact of tillage that increases the SOC depletion and reduces the biomass generation. In addition, these discrepancies (low SOCS content in the studied soils) may reflect a combination of different factors such as intensive farming practices, climate conditions, soil types, soil age, and topography area [64,65].
In the AR case, on average, the SOCS contents were as follows: 14.9 Mg ha−1 and 25.1 Mg ha−1 for 0.4 m and 1 m depth, respectively, in NC-AR, and 15.5 Mg ha−1 and 41.2 Mg ha−1 at 0.4 m and 1 m depth, respectively, in OC-AR. Other authors such as [66], for soil groups in Peninsular Spain (FAO soil map of Peninsular Spain), reported low SOCS content in AR (22.2 Mg ha−1) in soil profiles deeper than 1 m. Furthermore, [8] indicated that the SOC content (average values) in the upper 1 m was 31 Mg ha−1 for sandy AR. These differences between our data and the data indicated by other authors may be due to soil texture (sandy soils) and tillage. In NC-AR, the SOCS at surface depth (Ap horizon) was higher than in the C horizon. In this line [67], argued that the SOCS on surface horizon was greater than in deep soil due to tillage and in turn increasing the physical protection of native SOC from microbial decomposition. We can increase C inputs into surface soil by enhancing crop biomass and in turn residue return. With respect to OC-AR, this relation was inverse (SOCS increased in AC horizon); this effect can be explained by soil texture (sandy soils) and tillage, because native SOC can be reduced on the surface, which may be attributed to soluble organic compounds that can leach into deeper layers, increasing the soil aggregates [68,69].

3.4. Effects of Management Time on Soil Organic Carbon Stock

The results indicated important differences between SOCS (0–100 cm) in newly and previously cultivated soils regarding the soil type (CL or AR). In NC-CL, the SOCS (0–100 cm) content ranged from 38.7 Mg ha−1 (NL-CL-AT) to 49.4 Mg ha−1, (NC-CL-PT); however, lower SOCS were obtained for OC-CL, varying between 25.7 Mg ha−1 (OC-CL-OG) and 39.8 Mg ha−1 (OC-CL-OG). In the CL case, it is important to note that the land use affected the SOCS content, since on average (0–100 cm), the centenary OG (OC-CL) had a 25.6% lower SOCS than the young OG (NC-CL). However, in depth (40–100 cm), no differences were found according to the land use age (OC and NC) with respect to SOCS. Another aspect to highlight is that in the different crops developed (olive, almond, and pistachio) in NC-CL, the pistachio (PT) had the highest SOCS content (49.4 Mg ha−1), whereas the lowest SOCS values were found in almond tree (AT) (38.7 Mg ha−1).
However, the study of AR has shown that the SOCS content on average in the Ap horizon (0–40 cm) in NC and OC were very similar (OC-AR: 15.5 Mg ha−1; NC-AR: 14.9 Mg ha−1), but nevertheless, in depth (40–100 cm: AC/BC/C horizon), significant differences were found (OC-AR: 25.6 Mg ha−1; NR-AR: 10.2 Mg ha−1). When comparing both soils (AR and CL), significant differences were observed between NC-CL and NC-AR; however, no significant differences were observed between OC-CL and OC-AR. The highest SOCS contents were obtained in NC-CL regardless of the land tillage time.
In OC-CL, the SOCS (32.2 Mg ha−1) was significantly lower compared to NC-CL (43 Mg ha−1). However, the SOCS in OC-AR (41.2 Mg ha−1) increased with respect to NC-AR (25.1 Mg ha−1) (Table 6 and Table 7). These results indicate that the SOCS in CL and AR are linked to land management duration (OC > 100 years and NC < 12 years). In this line, our results are accordance with [70] in CL in Turkey, which justified an SOC reduction with intensive soil management under strong wind and water erosion conditions, clarifying that under these conditions, the SOM is rapidly mineralized in the soil. In addition, soil management over time may deteriorate the aggregates’ stability [69] and reduce the CL quality, affecting the soil water storage capacity in rainfed OG [70], these processes therefore could explain the SOM reduction and the SOCS decreasing in the surface horizon.
Several studies have found an increase in the SOC content in AR cultivated more than 20 years in different arid areas [71,72,73]. This is in concordance with our study; however, according to [74], the SOC could be depleted under intensive tillage, which is in discrepancy with our findings. In this sense, [75], in the same study area as [76], showed the olive roots’ presence within the sands; however, it was observed that olive roots were absent in the calcareous crusts. In the study soils, more than 90% of AR had sandy texture; for this reason, we suggest that the SOC increase could be due to the olive roots’ presence since the calcareous crusts are not present. Moreover, the tillage can affect a large part in the accumulation of the olive roots (along the profile), which may explain our findings. In the literature, the tillage is assumed to alter the SOCS and also to reduce the SOC quantity and to deteriorate the aggregates in the soil [10,69]. However, long cultivation periods with heavy tillage showed a positive effect on SOCS in AR, but due to the climatic conditions (semi-desert), more research should be carried out in AR to be able to identify the mechanism that explains this SOCS increase in AR.
Another question to highlight is that OC could be influenced by the abundance of the olive root mass. In this sense, it should be noted that the olive tree renews its roots every year and that the root system is better developed in the ventilated area (AR) than in compacted soils (CL). Another issue to point out is the addition of the organic amendments, so that some sources of organic amendments may accelerate the SOC mineralization. In the study area, in some plots, different organic amendments were added (B1, A1, A2, and M1), the main consequence was a SOCS reduction in the soil surface with respect to the plots without amendments (C1, A4, A5, and M3) (Table 2 and Table 3). However, in other cases, there was a SOCS increase in the soil surface in modified plots (A3, B2, C2, and M2) compared to the unmodified plots (M4). This SOCS variability could be explained by the results obtained by [76], who suggested that depending on the organic amendment type, the SOC balance (gain or loss of SOC) is conditioned by the incubation period and the organic amendment type. The increase in SOC of green manure derived from olive pruning residues (dried and crushed shoots and leaves of olive trees) is too limited (0.13 mg g−1), and the SOC balance (between the day after amendment and 120 days after) (−0.45) compared to the SOC gain of compost of manure and olive husk and palm-leaf-based compost are (0.28 mg g−1) and (0.31 mg g−1), respectively. The SOC balance is (−0.02) and (−0.05) in both compost types. These results could explain the SOC variability in the study area. However, we must be careful, as the organic amendments addition can be confounding as they lead to variability of the SOC content.
In Egypt (semi-desert conditions), Ref. [77] proposed a direct relation between the cropping history and SOCS (longer cultivation period implies the higher SOCS). This result is in agreement with AR and in disagreement with CL. We suggest that the soil type plays an important role in the depletion or the gain of SOC. In the study area, AR are efficient for SOCS accumulation, especially after 100 years of cultivation and tillage, and they could be carbon sink and may be involved in reducing carbon dioxide (CO2) emissions. In fact, it has been reported that soil might represent a sink for atmospheric CO2 [78,79,80].

3.5. Uncertainties and Bias

The complexity of these soils study is due to the continuous movement of the surface particles by the wind (eolian processes). For this reason, in demi-desert areas, few studies have been developed to define SOCS thresholds. In this line, all soil samples were taken at the same time (synchronic approach) under different management practices at known durations from an initial reference state, and the SOCS was compared under this initial reference state [53]. However, due to eolian processes (displacement of particles by the wind), it could be useful to carry out different samplings throughout the year, to reduce the wind effect, this question is relevant to establish the thickness of the surface horizon.
However, despite the mistakes, the SOCS study in these soils type (AR and CL) under these climatic conditions could help us to promote strategies to combat the desertification and climate change. So, this research constitutes a preliminary assessment of SOCS estimation with different land uses and different management practice durations.

4. Conclusions

The main conclusions derived from this research depend on land use (olive grove, almond tree, and pistachio tree), land management (tillage and amendments) the practice’s duration (old plantations > 100 years and new plantations < 12 years) and soil type (Arenosols and Calcisols) in semi-desert conditions. In this line, the crop type, tillage, and tillage duration affect to soil bulk density, pH, and SOC for the same soil type.
On average, the SOCS for woody crops in semi-desert areas in CL is 43.0 Mg ha−1 and 32.2 Mg ha−1 (0–100 cm depth) for NC and OC, respectively, and with 40 cm depth, the SOCS is 20.9 Mg ha−1 (NC) and 13.2 Mg ha−1 (OC). In the case of the AR, the SOCS is 25.1 Mg ha−1 and 41.2 Mg ha−1 (0–100 cm depth) for NC and OC, respectively, and with 40 cm depth, the SOCS is 14.9 Mg ha−1 (NC) and 15.5 Mg ha−1 (OC). According to this, in dryland (semi-desert conditions), some soils could have a good capacity to increase soil organic carbon with certain management practices and duration specifically, the AR could increase the SOCS after 100 years of cultivation and tillage; however, the CL the SOC can be reduced.
Thus, SOCS content variations were detected and have established pistachio as the most efficient woody crop related to carbon storage (49.4 Mg ha−1) under the soils and climatic conditions analyzed in the short term. Therefore, good information to better understand the dynamics of soil organic carbon storage could help to develop and consolidate the Framework of the United Nation Convention to Combat Desertification (UNCCD) on the one hand and to minimize greenhouse gases on the other.

Author Contributions

Conceptualization, F.B., R.C., H.R., H.B.M., K.G., N.R., N.B., M.Ś., M.G.-R., L.P.-A., and Á.S.-B., methodology, F.B., R.C., H.R., H.B.M., K.G., N.R., N.B., M.Ś., M.G.-R., L.P.-A., and Á.S.-B., software, F.B., R.C., H.R., H.B.M., K.G., N.R., N.B., M.Ś., M.G.-R., L.P.-A., and Á.S.-B., validation, F.B., R.C., H.R., H.B.M., K.G., N.R., N.B., M.Ś., M.G.-R., L.P.-A., and Á.S.-B., formal analysis, F.B., R.C., H.R., H.B.M., K.G., N.R., N.B., M.Ś., M.G.-R., L.P.-A., and Á.S.-B., investigation, F.B., R.C., H.R., H.B.M., K.G., N.R., N.B., M.Ś., M.G.-R., L.P.-A., and Á.S.-B., data curation, F.B., R.C., H.R., H.B.M., K.G., N.R., N.B., M.Ś., M.G.-R., L.P.-A., and Á.S.-B., writing—original draft preparation, F.B., R.C., H.R., H.B.M., K.G., N.R., N.B., M.Ś., M.G.-R., L.P.-A., and Á.S.-B., writing—review and editing, F.B., R.C., H.R., H.B.M., K.G., N.R., N.B., M.Ś., M.G.-R., L.P.-A., and Á.S.-B., visualization, F.B., R.C., H.R., H.B.M., K.G., N.R., N.B., M.Ś., M.G.-R., L.P.-A., and Á.S.-B., supervision, F.B., R.C., H.R., H.B.M., K.G., N.R., N.B., M.Ś., M.G.-R., L.P.-A., and Á.S.-B. All authors have read and agreed to the published version of the manuscript.

Funding

The Ministry of Agriculture and Water Resources and the Ministry of Higher Education and Scientific Research (Tunisia) supported this study.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data that support this study cannot be publicly shared due to ethical or privacy reasons and may be shared upon reasonable request to the corresponding author if appropriate.

Acknowledgments

The study design and data collection were carried out at the Research Laboratory of Sustainable Olive and Arboriculture Growing of Olive Institute (Tunisia). Analysis of soil samples were carried out at the Department of Evolutionary Biology, Ecology and Environmental Science of the University of Barcelona. The facilities and services of the Olive Institute of Sfax (Tunisia), NCU in Toruń (Poland), and the Universities of Barcelona, Cadiz, and Cordoba (Spain) are gratefully acknowledged. The authors thank Nabil Soua (Olive) Institute for his expertise, hard work and all contribute to the field and in the laboratory. The authors are grateful to the members, specifically Mouhamed Fakhfekh, of the organic farm located in the Menzel Chaker region in Sfax, Tunisia. We are grateful to Souha Hammouda for her support and for her assistance in improving the English quality of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lal, R. Carbon sequestration. Philos. Trans. R. Soc. Lond. 2008, 63, 815–830. [Google Scholar] [CrossRef] [PubMed]
  2. Stockmann, U.; Adams, M.A.; Crawford, J.W.; Field, D.J.; Henakaarchchi, N.; Jenkins, M.; Minasny, B.; McBratney, A.B.; Courcelles, V.; Singh, K.; et al. The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agric. Ecosyst. Environ. 2013, 164, 80–99. [Google Scholar] [CrossRef]
  3. Brahim, N.; Ibrahim, H. Effect of Land Use on Organic Carbon Distribution in a North African Region: Tunisia Case Study. In Soil Management and Climate Change; Academic Press: Cambridge, MA, USA, 2018; pp. 15–24. [Google Scholar] [CrossRef]
  4. Pansu, M.; Ibrahim, H.; Hatira, A.; Brahim, N.; Drevon, J.J.; Harmand, J.M.; Chotte, J.L.; Blavet, D. Modelling the continuous exchange of nitrogen between microbial decomposers, the organs and symbionts of plants, soil reserves and the atmosphere. Soil Biol. Biochem. 2018, 125, 185–196. [Google Scholar] [CrossRef]
  5. Lal, R.; Smith, P.; Jungkunst, H.F.; Mitsch, W.J.; Lehmann, J.; Nair, P.K.R.; McBratney, A.B.; de Moraes Sá, J.C.; Schneider, J.; Zinn, Y.L.; et al. The carbon sequestration potential of terrestrial ecosystems. J. Soil Water Conserv. 2018, 73, 145A–152A. [Google Scholar] [CrossRef] [Green Version]
  6. Lefevre, C.; Rekik, F.; Alcantara, V.; Wiese, L. Soil Organic Carbon: The Hidden Potential; FAO: Rome, Italy, 2017. [Google Scholar]
  7. Salcedo, I.H.; Sampaio, V.S.B. Matéria orgânica do solo no Bioma Caatinga. In Fundamentos da Materia orgânica do Solo, Ecossistemastropicais e Subtropicais; Santos, G., de Silva, A.L.S., da Canellas, L.P., de Camargo, F.A.O., Eds.; Metrópole: Porto Alegre, Brasil, 2008; Volume 22, pp. 419–441. [Google Scholar]
  8. Batjes, N.H. Total carbon and nitrogen in the soils of the world. Eur. J. Soil Sci. 1996, 47, 151–163. [Google Scholar] [CrossRef]
  9. Mörchen, R.; Lehndorff, E.; Diaz, F.A.; Moradi, G.; Bol, R.; Fuentes, B.; Klumpp, E.; Amelung, W. Carbon accrual in the Atacama Desert. Glob. Planet. Chang. 2019, 181, 102993. [Google Scholar] [CrossRef]
  10. González-Rosado, M.; Parras-Alcántara, L.; Aguilera-Huertas, J.; Lozano-García, B. Long-term evaluation of the initiative 4‰ under different soil managements in Mediterranean olive groves. Sci. Total Environ. 2021, 758, 143591. [Google Scholar] [CrossRef] [PubMed]
  11. Conant, R.T.; Ryan, M.G.; Ågren, G.I.; Birge, H.E.; Davidson, E.A.; Eliasson, P.E.; Evans, S.E.; Frey, S.D.; Giardina, C.P.; Hopkins, F.M.; et al. Temperature and soil organic matter decomposition rates—Synthesis of current knowledge and a way forward. Glob. Chang. Biol. 2011, 17, 3392–3404. [Google Scholar] [CrossRef]
  12. Hammad, H.M.; Khaliq, A.; Abbas, F.; Farhad, W.; Fahad, S.; Aslam, M.; Bakhat, H.F. Comparative effects of organic and inorganic fertilizers on soil organic carbon and wheat productivity under arid region. Commun. Soil Sci. Plant Anal. 2020, 51, 1406–1422. [Google Scholar] [CrossRef]
  13. Gargouri, K.; Rigane, H.; Arous, I.; Touil, F. Evolution of soil organic carbon in an olive orchard under arid climate. Sci. Hortic. 2013, 152, 102–108. [Google Scholar] [CrossRef]
  14. González-Rosado, M.; Lozano-García, B.; Aguilera-Huertas, J.; Parras-Alcántara, L. Short-term effects of land management change linked to cover crop on soil organic carbon in Mediterranean olive grove hillsides. Sci. Total Environ. 2020, 744, 140683. [Google Scholar] [CrossRef] [PubMed]
  15. Noy-Meir, I. Desert Ecosystems: Environment and Producers. Ann. Rev. Ecol. Syst. 1973, 4, 25–51. [Google Scholar] [CrossRef]
  16. Campbell, A.; Miles, L.; Lysenko, I.; Huges, A.; Gibbs, H. Carbon Storage in Protected Areas; Technical Report; UNEP World Conservation Monitoring Center: Cambridge, UK, 2008. [Google Scholar]
  17. Pointing, S.B.; Belnap, J. Microbial Colonization and Controls in Dryland Systems. Nat. Rev. Microbiol. 2012, 10, 551–562. [Google Scholar] [CrossRef] [PubMed]
  18. Koyama, A.; Harlow, B.; Evans, R.D. Greater Soil Carbon and Nitrogen in a Mojave Desert Ecosystem after 10 Years Exposure to Elevated CO2. Geoderma 2019, 355, 113915. [Google Scholar] [CrossRef]
  19. Zhao, K.; Kong, W.; Wang, F.; Long, X.E.; Guo, C.; Yue, L. Desert and Steppe Soils Exhibit Lower Autotrophic Microbial Abundance but Higher Atmospheric CO2 Fixation Capacity Than Meadow Soils. Soil Biol. Biochem. 2018, 127, 230–238. [Google Scholar] [CrossRef]
  20. Brahim, N.; Ibrahim, H.; Hatira, A. Tunisian Soil Organic Carbon Stock—Spatial and Vertical Variation. Procedia Eng. 2014, 69, 1549–1555. [Google Scholar] [CrossRef]
  21. Bayer, C.; Martin-Neto, L.; Mielniczuk, J.; Pavinato, A.; Diekow, J. Carbon sequestration in two Brazilian Cerrado soils under no-till. Soil Tillage Res. 2006, 86, 237–245. [Google Scholar] [CrossRef]
  22. FAO. Organizing Committee of the First Symposium on the Management of Tropical Sandy Soils through the Land Management Officer; FAO Regional Office for Asia and the Pacific: Bangkok, Thailand, 2005. [Google Scholar]
  23. Azlan, A.; Aweng, E.R.; Ibrahim, C.O.; Noorhaidah, A. Correlation between Soil Organic Matter, Total Organic Matter and Water Content with Climate and Depths of Soil at Different Land use in Kelantan. Malays. J. Appl. Sci. Environ. Manag. 2012, 16, 353–358. [Google Scholar]
  24. Ding, Z.; Kheir, A.M.; Ali, M.G.; Ali, O.A.; Abdelaal, A.I.; Zhou, Z.; He, Z. The integrated effect of salinity, organic amendments, phosphorus fertilizers, and deficit irrigation on soil properties, phosphorus fractionation and wheat productivity. Sci. Rep. 2020, 10, 1–13. [Google Scholar] [CrossRef] [Green Version]
  25. Ben Mbarek, H.; Gargouri, K.; Mbadra, C.; Ben Mahmoud, I.; Chaker, R.; Maktouf, S.; Abbas, O.; Baeten, V.; Rigane, H. Effects of combination of tillage with olive mill wastewater on soil organic carbon groups in arid soils. Arab. J. Geosci. 2020, 13, 255. [Google Scholar] [CrossRef]
  26. Park, C.E.; Jeong, S.J.; Joshi, M.; Osborn, T.J.; Ho, C.H.; Piao, S.; Feng, S. Keeping global warming within 1.5 C constrains emergence of aridification. Nat. Clim. Chang. 2018, 8, 70–74. [Google Scholar] [CrossRef] [Green Version]
  27. Ma, X.; Zhu, J.; Yan, W.; Zhao, C. Projections of desertification trends in Central Asia under global warming scenarios. Sci. Total Environ. 2021, 781, 146777. [Google Scholar] [CrossRef] [PubMed]
  28. Moussadek, R.; Mrabet, R.; Dahan, R.; Zouahri, A.; El Mourid, M.; Ranst, E.V. Tillage System Affects Soil Organic Carbon Storage and Quality in Central Morocco. Appl. Environ. Soil Sci. 2014, 1–8. [Google Scholar] [CrossRef]
  29. Jaouadi, J.; Brahim, N.; Hatira, A.; Aichi, H.; Ben Hassine, H. Distribution of organic carbon stocks in cambisol under two different crops in semi-arid Tunisian climate. J. Res. Environ. Earth Sci. 2019, 8, 216–222. [Google Scholar]
  30. Znaidi, A.; Brahim, N.; Ibrahim, H.; Bol, R.; Chaouachi, M. Comparison of organic carbon stock of Regosols under two different climates and land use in Tunisia. Arab. J. Geosci. 2020, 13, 1011. [Google Scholar] [CrossRef]
  31. Macías, M.D.J.G.; Carbajal, N.; Vargas, J.T. Soil deterioration in the southern Chihuahuan Desert caused by agricultural practices and meteorological events. J. Arid Environ. 2020, 176, 104097. [Google Scholar] [CrossRef]
  32. Fallahzade, J.; Karimi, A.; Naderi, M.; Shirani, H. Soil mechanical properties and wind erosion following conversion of desert to irrigated croplands in central Iran. Soil Tillage Res. 2020, 204, 104665. [Google Scholar] [CrossRef]
  33. González-Rosado, M.; Parras-Alcántara, L.; Aguilera-Huertas, J.; Lozano-García, B. Soil Productivity Degradation in a Long-Term Eroded Olive Orchard under Semiarid Mediterranean Conditions. Agronomy 2021, 11, 812. [Google Scholar] [CrossRef]
  34. Hu, Y.; Wang, Z.; Zhang, Z.; Song, N.; Zhou, H.; Li, Y.; Hale, L. Alteration of desert soil microbial community structure in response to agricultural reclamation and abandonment. Catena 2021, 207, 105678. [Google Scholar] [CrossRef]
  35. Köberl, M.; Müller, H.; Ramadan, E.M.; Berg, G. Desert farming benefits from microbial potential in arid soils and promotes diversity and plant health. PLoS ONE 2011, 6, e24452. [Google Scholar] [CrossRef] [Green Version]
  36. Kottek, M.; Grieser, J.; Beck, C.; Rudolf, B.; Rubel, F. World map of the Köppen-Geiger climate classification updated. Meteorol. Z. 2006, 15, 259–263. [Google Scholar] [CrossRef]
  37. Previtali, F.; Gasmi, N.; Julitta, F.; Cantelli, D.; Deroma, M.; Madrau, S.; Zucca, C. The geomorphologic and pedo-morphologic maps of Skhirat (Feriana, Tunisia) at the 1:50000 scale. J. Maps 2014, 10, 73–81. [Google Scholar] [CrossRef]
  38. IUSS Working Group WRB. World Reference Base for soil resources 2014, update 2015. In International Soil Classification System for Naming Soils and Creating Legends for Soil Maps; World Soil Resources Report No. 106; FAO: Rome, Italy, 2015. [Google Scholar]
  39. Driessen, P.M.; Deckers, J.; Spaargaren, O. Lecture Notes on the Major Soils of the World; World Soil Resources Reports; Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2001; Volume 94. [Google Scholar]
  40. Missaoui, A.; Zerai, K.; Feridhi, A.; Baroudi, M.; Baroudi, J. Atlas du Governourat de Sfax; Direction Générale de l’aménagement du territoire: Tunisia, Tunisia, 2013; p. 105. [Google Scholar]
  41. Lal, R. Forest soils and carbon sequestration. For. Ecol. Manag. 2005, 220, 242–258. [Google Scholar] [CrossRef]
  42. Parras-Alcántara, L.; Lozano-García, B.; Brevik, E.C.; Cerdà, A. Soil organic carbon stocks assessment in Mediterranean natural areas: A comparison of entire soil profiles and soil control sections. J. Environ. Manag. 2015, 155, 219–228. [Google Scholar] [CrossRef] [PubMed]
  43. Álvaro-Fuentes, J.; Lóczy, D.; Thiele-Bruhn, S.; Zornoza, R. Handbook of Plant and Soil Analysis for Agricultural Systems; Crai uptc Editions: Cartagena, Spain, 2019; p. 389. [Google Scholar]
  44. Blake, G.R.; Hartge, K.H. Bulk density. In Methods of Soil Analysis, Part 1: Physical and Mineralogical Methods, 2nd ed.; Klute, Ed.; ASA, SSSA: Madison, WI, USA, 1986; pp. 377–382. [Google Scholar]
  45. USDA. Soil Survey Laboratory Methods Manual, Soil Survey Investigation Report no. 42, Version 4.0.; USDA-NCRS: Lincoln, NE, USA, 2004. [Google Scholar]
  46. Guitián, F.; Carballas, T. Técnicas de Análisis de Suelos; Picro Sacro: Santiago de Compostela, Spain, 1976. [Google Scholar]
  47. Nelson, D.W.; Sommers, L.E. Total carbon, organic carbon and organic matter. In Methods of Soil Analysis, Part 2. Chemical and Microbiological Properties. Agronomy Monograph; Page, A.L., Miller, R.H., Keeney, D., Eds.; ASA and SSSA: Madison, WI, USA, 1982; Volume 9, pp. 539–579. [Google Scholar]
  48. FAO. Standard operating procedure for soil calcium carbonate equivalent. In Volumetric Calcimeter Method; FAO: Roma, Italy; p. 2020.
  49. Estefan, G.; Sommer, R.; Ryan, J. Methods of Soil, Plant, and Water Analysis-A Manual for the West Asia and North Africa Region; MEL: Vermont, VI, Australia, 2013; pp. 170–176. [Google Scholar]
  50. Aguilera-Huertas, J.; Lozano-García, B.; González-Rosado, M.; Parras-Alcántara, L. Effects of Management and Hillside Position on Soil Organic Carbon Stratification in Mediterranean Centenary Olive Grove. Agronomy 2021, 11, 650. [Google Scholar] [CrossRef]
  51. Stolbovoy, V.; Montanarella, L.; Filippi, N.; Jones, A.; Gallego, J.; Grassi, G. Soil Sampling Protocol to Certify the Changes of Organic Carbon Stock in Mineral Soil of the European Union; Version 2; EUR 21576 EN/2. 56; Office for Official Publications of the European Communities: Luxembourg, 2007; ISBN 978-92-79-05379. [Google Scholar]
  52. IPCC. Intergovernmental Panel on Climate Change. In Good Practice Guidance for Land Use, Land Use Change and Forestry; Penman, J., Gytarsky, M., Hiraishi, T., Krug, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T., Tanabe, K., Eds.; IPCC/OECD/IEA/IGES: Hayama, Japan, 2003. [Google Scholar]
  53. Costa Junior, C.; Corbeels, M.; Bernoux, M.; Píccolo, M.C.; Siqueira Neto, M.; Feigl, B.J.; Cerri, C.E.P.; Cerri, C.C.; Scopel, E.; Lal, R. Assessing soil carbon storage rates under no-tillage: Comparing the synchronic and diachronic approaches. Soil Tillage Res. 2013, 134, 207–212. [Google Scholar] [CrossRef]
  54. Belaid, N.; Feki, S.; Cheknane, B.; Frank-Neel, C.; Baudu, M. Impacts of irrigation systems on vertical and lateral metals distribution in soils irrigated with treated wastewater: Case study of Elhajeb-Sfax. Agric. Water Manag. 2019, 225, 105739. [Google Scholar] [CrossRef]
  55. Belaid, N.; Neel, C.; Kallel, M.; Ayoub, T.; Ayadi, A.; Baudu, M. Long term effects of treated wastewater irrigation on calcisol fertility: A case study of Sfax-Tunisia. Agric. Sci. 2012, 3, 702–713. [Google Scholar] [CrossRef] [Green Version]
  56. Dewitte, O.; Jones, A.; Spaargaren, O.; Breuning-Madsen, H.; Brossard, M.; Dampha, A.; Deckers, J.; Gallali, T.; Hallett, S.; Jones, R.; et al. Harmonisation of the soil map of Africa at the continental scale. Geoderma 2013, 211, 138–153. [Google Scholar] [CrossRef] [Green Version]
  57. Świtoniak, M.; Dąbrowski, M.; Łyszkiewicz, A. The Influence of Human-induced Erosion on the Soil Organic Carbon Stock in Vineyards of Fordon Valley. Pol. J. Soil Sci. 2015, 48, 197. [Google Scholar] [CrossRef]
  58. Bormann, H.; Klaassen, K. Seasonal and land use dependent variability of soil hydraulic and soil hydrological properties of two Northern German soils. Geoderma 2008, 145, 295–302. [Google Scholar] [CrossRef]
  59. Ordoñez-Morales, K.; Cadena-Zapata, M.; Zermeño-González, A.; Campos-Magaña, S. Effect of Tillage Systems on Physical Properties of a Clay Loam Soil under Oats. Agriculture 2019, 9, 62. [Google Scholar] [CrossRef] [Green Version]
  60. Alletto, L.; Coquet, Y. Temporal and spatial variability of soil bulk density and near-saturated hydraulic conductivity under two contrasted tillage management systems. Geoderma 2009, 152, 85–94. [Google Scholar] [CrossRef]
  61. Ben Mbarek, H.; Gargouri, K.; Mbadra, C.; Chaker, R.; Souidi, Y.; Abbas, O.; Baeten, V.; Rigane, H. Change and spatial variability of soil organic matter humification after long-term tillage and olive mill wastewater application in arid regions. Soil Res. 2020, 58, 388. [Google Scholar] [CrossRef]
  62. Díaz-Hernández, J.L.; Fernández, E.B.; González, J.L. Organic and inorganic carbon in soils of semiarid regions: A case study from the Guadix–Baza basin (Southeast Spain). Geoderma 2003, 114, 65–80. [Google Scholar] [CrossRef]
  63. Muñoz-Rojas, M.; De la Rosa, D.; Zavala, L.M.; Jordán, A.; Anaya-Romero, M. Changes in land cover and vegetation carbon stocks in Andalusia, Southern Spain (1956–2007). Sci. Total Environ. 2011, 409, 2796–2806. [Google Scholar] [CrossRef] [PubMed]
  64. Hu, T.; Sørensen, P.; Olesen, J.E. Soil carbon varies between different organic and conventional management schemes in arable agriculture. Eur. J. Agron. 2018, 94, 79–88. [Google Scholar] [CrossRef]
  65. Yigini, Y.; Panagos, P. Assessment of soil organic carbon stocks under future climate and land cover changes in Europe. Sci. Total Environ. 2016, 557, 838–850. [Google Scholar] [CrossRef] [PubMed]
  66. Rodríguez-Murillo, J.C. Organic carbon content under different types of land use and soil in peninsular Spain. Biol. Fertil. Soils 2001, 33, 53–61. [Google Scholar] [CrossRef]
  67. Blanco-Canqui, H.; Lal, R. No-tillage and soil-profile carbon sequestration: An on farm assessment. Soil Sci. Soc. Am. J. 2008, 72, 693–701. [Google Scholar] [CrossRef] [Green Version]
  68. Diekow, J.; Mielniczuk, J.; Knicker, H.; Bayer, C.; Dick, D.P.; Kögel-Knabner, I. Soil C and N stocks as affected by cropping systems and nitrogen fertilisation in a southern Brazil Acrisol managed under no-tillage for 17 years. Soil Tillage Res. 2005, 81, 87–95. [Google Scholar] [CrossRef]
  69. González-Rosado, M.; Parras-Alcántara, L.; Aguilera-Huertas, J.; Benítez, C.; Lozano-García, B. Effects of land management change on soil aggregates and organic carbon in Mediterranean olive groves. Catena 2020, 195, 104840. [Google Scholar] [CrossRef]
  70. Akça, E.; Aydemir, S.; Kadir, S.; Eren, M.; Zucca, C.; Günal, H.; Previtali, F.; Zdruli, P.; Çilek, A.; Budak, M. (Eds.) The Soils of Turkey, World Soils Book Series; Springer International Publishing: Cham, Switzerland, 2018; pp. 139–167. [Google Scholar] [CrossRef]
  71. Polat, O.; Kapur, S. Soil quality parameters in Arenosols under Stone Pine (Pinus Pinea L.). Plantations in the Turan Emeksiz sand dune Area.Çukurova University Institute of Science. J. Sci. Eng. 2010, 3, 167–174. (in Turkish). [Google Scholar]
  72. Yaktı, S.; Akça, E.; Kapur, S. Management of coastal sand dunes by an agroecosystem approach. In Workshop Proceedings of Determining an Income-Product Generating Approach for Soil Conservation Management; Zdruli, P., Liuzzi, G.T., Hikmet, E.F., Eds.; CIHEAM: Marrakech, Moroco, 2004; pp. 227–234. [Google Scholar]
  73. Akça, E.; Bal, Y.; Kapur, S.; Eswaran, H. Human-induced late Holocene degradation of the Asi (Orontes) delta, Samandag, Southern Turkey. In Land Degradation and Rehabilitation: Dryland Ecosystems. In Proceedings of the Fourth International Conference on Land Degradation, Cartagena, Murcia, Spain, 12–17 September 2004; Catena Verlag: Schweizerbart, Germany, 2004; pp. 17–26. ISBN 3-923381-54. [Google Scholar]
  74. Maia, S.M.F.; Ogle, S.M.; Cerri, C.C.; Cerri, C.E.P. Changes in soil organic carbon storage under different agricultural management systems in the Southwest Amazon Region of Brazil. Soil Tillage Res. 2010, 106, 177–184. [Google Scholar] [CrossRef]
  75. Gargouri, K.; Bentaher, H.; Rhouma, A. A novel method to assess drought stress of olive tree. Agron. Sustain. Dev. 2012, 32, 735–745. [Google Scholar] [CrossRef] [Green Version]
  76. Chaker, R.; Gargouri, K.; Ben Mbarek, H.; Baraket, F.; Maktouf, S.; Soua, N.; Khlifi, M.; Bouzid, J. Effect of biochemical fraction of exogenous organic matter on CO2 emission from arid soil: Original Research Article: Effect of biochemical fraction of exogenous organic matter on CO2 emission from arid soil. Greenh. Gas Sci. Technol. 2018, 8, 721–733. [Google Scholar] [CrossRef]
  77. Arshad, M.; Khedher, K.M.; Ayed, H.; Mouldi, A.; Moghanm, F.S.; El Ouni, M.H.; Benkahla, N.; Laatar, E.; Bilal, M.; Abdel Zaher, M. Effects of land use and cultivation histories on the distribution of soil organic carbon stocks in the area of the Northern Nile Delta in Egypt. Carbon Manag. 2020, 11, 341–354. [Google Scholar] [CrossRef]
  78. Ogle, S.M.; Breidt, F.J.; Paustian, K. Agricultural management impacts on soil organic carbon storage under moist and dry climatic conditions of temperate and tropical regions. Biogeochemistry 2005, 72, 87–121. [Google Scholar] [CrossRef]
  79. Corbeels, M.; Scopel, E.; Cardoso, A.; Bernoux, M.; Douzet, J.M.; Neto, M.S. Soil carbon storage potential of direct seeding mulch-based cropping systems in the Cerrados of Brazil: Soil c storage under dam in the cerrados. Glob. Chang. Biol. 2006, 12, 1773–1787. [Google Scholar] [CrossRef]
  80. Don, A.; Schumacher, J.; Freibauer, A. Impact of tropical land-use change on soil organic carbon stocks—a meta-analysis: Soil organic carbon and land-use change. Glob. Chang. Biol. 2011, 17, 1658–1670. [Google Scholar] [CrossRef] [Green Version]
Agriculture 11 01267 g001 550
Figure 1. Maps of the study area: (a) Tunisia map, (b) Sfax location, and (c) Menzel-Chaker district with the location of soil profiles https://earth.google.com/web/@36.6638314,7.9704552,553.33197981a,3949044.97759938d,35y,0h,0t,0r (Google Earth Pro—Access: 27 September 2021).
Figure 1. Maps of the study area: (a) Tunisia map, (b) Sfax location, and (c) Menzel-Chaker district with the location of soil profiles https://earth.google.com/web/@36.6638314,7.9704552,553.33197981a,3949044.97759938d,35y,0h,0t,0r (Google Earth Pro—Access: 27 September 2021).
Agriculture 11 01267 g001
Agriculture 11 01267 g002 550
Figure 2. Different land uses in the experimental farm: Centenary olive grove, young olive grove, almond tree, and pistachio tree. (1): Centenary olive grove—old cultivated > 100 years; (2): young olive grove—newly cultivated < 12 years; (3): almond tree—newly cultivated < 12 years; (4): pistachio tree—newly cultivated < 12 years.
Figure 2. Different land uses in the experimental farm: Centenary olive grove, young olive grove, almond tree, and pistachio tree. (1): Centenary olive grove—old cultivated > 100 years; (2): young olive grove—newly cultivated < 12 years; (3): almond tree—newly cultivated < 12 years; (4): pistachio tree—newly cultivated < 12 years.
Agriculture 11 01267 g002
Table
Table 1. Analytical methods used in this study (field measurements, laboratory analysis, and parameters calculated).
Table 1. Analytical methods used in this study (field measurements, laboratory analysis, and parameters calculated).
ParametersMethod
Field measurements
 Bulk density (Mg m−3)Core method [44] a
Laboratory analysis
 Particle size distributionRobinson pipette method [45] b
 pH—H2OSuspension in water 1:2.5 [46]
 Total Organic C (g kg−1)Walkley and Black method [47]
 CaCO3 (%)Soil Calcium carbonate equivalent [48,49]
Parameters calculated
 SOC-S (Mg ha−1)SOC-S = SOC concentration × BD × d × (1 − δ2 mm%) × 10−1 [50,51,52] c
 T-SOC-S (Mg ha−1)T-SOC-S = Σ soil horizon 1… n SOC-S soil horizons [52] d
For all the parameters studied, the recommendations of the Handbook of Plant and Soil Analysis for Agricultural Systems have been followed [43]. a 3 cm in diameter, 10 cm in length, and 70.65 cm3 in volume. b Prior to determination of particle size distribution, samples were treated with H2O2 (6%) to remove organic matter (OM). Particles larger than 2 mm were determined by wet sieving, and smaller particles were classified according to USDA standards (2004). c Where SOC is the organic carbon content (g kg−1), d is the thickness of the soil layer (cm), δ2mm is the fractional percentage (%) of soil mineral particles >2 mm in size in the soil, and BD is the soil bulk density (Mg m−3). d T-SOC-S: Total SOC stock determined by adding all the soil horizons considered.
Table
Table 2. Land use, managements, and soil qualifiers of the soils (Arenosols) in the study area.
Table 2. Land use, managements, and soil qualifiers of the soils (Arenosols) in the study area.
Soil SPSoil
Type		HorDepth
(cm)		Land
Use		Management
AROC-ARB1ARcaAp0–40OGDensity: 17 trees ha−1; Amendments: sheep manure mixture; Olive pomace 3.5 Mg ha−1 every 10 years
AC40–100
B2ARcaAp0–40OGDensity: 20 trees ha−1; Amendments: Olive pomace 3.5 Mg ha−1 every 10 years
C40–100
B3AReuAp0–40OGDensity: 17 trees ha−1; Amendments: sheep manure mixture; Olive pomace 3.5 Mg ha−1 every 10 years
AC40–100
NC-ARC1ARcaAp0–40ATDensity: 68 trees ha−1; The plowing is Canadian type (plowing just with the tails of the washers).
Without organic amendment
C40–100
C2ARcaAp0–40OGDensity: 34 trees ha−1; Without organic amendment
BC40–100
C3AReuAp0–40OGDensity: 34 trees ha−1; Without organic amendment
C40–100
ARca: Calcaric Arenosols; AReu: Eutric Arenosols; SP: Soil profile; OC-AR: Old cultivated (tilled > 100 years) Arenosols; NCAR: Newly cultivated (tilled < 12 years) Arenosols; SP: Soil Sampling; Hor: Horizon; OG: Olive grove; AT: Almond tree. C2 and C3: The plowing is Canadian type (plowing just with the tails of the washers when the OG is big, sometimes using the blade types. They are plowed 4 times/year.
Table
Table 3. Land use, managements, and soil qualifiers of the soils (Calcisols) in the study area.
Table 3. Land use, managements, and soil qualifiers of the soils (Calcisols) in the study area.
SoilMSPSoil
Type		HorDepth
(cm)		Land
Use		Management
CLOC-CLM1ha CLAp0–40OGDensity: 20 trees ha−1; Amendments: sheep manure mixture 3.5 Mg ha−1 every 10 years
2Ck40–100
M2ha CLAp0–40OGDensity: 17 trees ha−1; Amendments: sheep manure mixture; Olive pomace 3.5 Mg ha−1 every 10 years
2Bk40–100
M3ha CLAp0–40OGDensity: 17 trees ha−1; No Amendments
Ck40–100
M4ha CLApk0–40OGDensity: 17 trees ha−1; No Amendments
2Ck40–100
M5ha CLAp0–40OGDensity: 17 trees ha−1; No Amendments
2Ck40–100
NC-CLA1ha CLAp0–40OGDensity: 34 trees ha−1; Amendments: sheep manure mixture 1 Mg ha−1 every 10 years
2Ck40–100
A2ha CLAp0–40OGDensity: 34 trees ha−1; Amendments: sheep manure mixture 1 Mg ha−1 every 10 years
Ck40–100
A3ha CLAp0–40OGDensity: 34 trees ha−1; Amendments: sheep manure mixture 1 Mg ha−1 every 10 years
2Ck40–100
A4ha CLAp0–40ATDensity: 68 trees ha−1; The plowing is Canadian type (plowing just with the tails of the washers)
Ck40–100
A5ha CLAp0–40PTDensity: 39 trees ha−1; The plowing is Canadian type (plowing just with the tails of the washers)
2Bk40–100
ha CL: Haplic Calcisols; OC-CL: Old cultivated (tilled > 100 years) Calcisols; M: Management; SP: Soil Profile; NC-CL: Newly cultivated (tilled < 12 years) Calcisols; SP: Soil Sampling; Hor: Horizon; OG: Olive grove; AT: Almond tree; PT: Pistachio tree. A1, A2, and A3: The plowing is Canadian type (plowing just with the tails of the washers when the OG will be big, they use sometimes the blade types. They are plowed 4 times/year. M1, M2, M3, M4, and M5: The plowing is of the Canadian type. In the fall, they use the tails of the two-row pucks, in the winter they use the blade (teeth), one row only, and in the spring, they use the tails of the pucks (two rows) for weeding. During the summer, they use the blade (teeth), a single row.
Table
Table 4. Principal soil properties evaluated (average ± SD *) in the soil profile by soil control section in Arenosols.
Table 4. Principal soil properties evaluated (average ± SD *) in the soil profile by soil control section in Arenosols.
SoilMSPLUHorDepth
(cm)		Gravel
(%)		Sand
(%)		Silt
(%)		Clay
(%)		BD
(Mg m−3)		pH
(H2O)		CaCO3
(%)		OC
(%)
AROC-AR
n = (3 × 2)		B1OGAp0–401.1596.371.172.461.7019.182.640.125
AC40–1000.6092.994.622.391.6459.142.780.330
B2OGAp0–401.9194.661.693.651.6929.254.060.290
C40–1006.7492.952.444.611.6739.197.420.171
B3OGAp0–401.4898.220.341.441.7059.090.510.282
AC40–1002.6098.050.491.461.6819.100.550.290
X Ap0–401.51 ± 0.2896.42 ± 1.781.07 ± 0.682.52 ± 1.111.70 ± 0.019.17 ± 0.082.40 ± 1.790.23 ± 0.09
AC40–1003.31 ± 3.1394.66 ± 2.932.52 ± 2.072.82 ± 1.621.67 ± 0.029.14 ± 0.053.58 ± 3.510.26 ± 0.08
NC-AR
n = (3 × 2)		C1ATAp0–402.9880.5110.758.741.5608.954.110.270
C40–1004,3282.588.389.041.5308.946.490.165
C2OGAp0–400.6688.915.225.871.6328.813.030.290
BC40–1000.8895.321.333.351.6818.682.630.095
C3OGAp0–400.0994.012.233.761.6709.121.040.145
C40–1000.4491.582.096.331.6909.170.810.065
X Ap0–401.24 ± 1.5387.81 ± 6.826.07 ± 4.326.12 ± 2.501.62 ± 0.068.96 ± 0.162.73 ± 1.560.24 ± 0.08
C40–1001.88 ± 2.1389.83 ± 6.553.93 ± 3.876.24 ± 2.851.63 ± 0.098.93 ± 0.253.31 ± 2.900.11 ± 0.05
SD *: Standard deviation; M: Management; SP: Soil sampling; Hor: Horizon; BD: Bulk density; OC: Organic carbon; n = Sample size. OC-AR: Old cultivated (tilled > 100 years) Arenosols; NC-AR: Newly cultivated (tilled < 12 years) Arenosols.
Table
Table 5. Principal soil properties evaluated (average ± SD *) in the soil profile by soil control section in Calcisol.
Table 5. Principal soil properties evaluated (average ± SD *) in the soil profile by soil control section in Calcisol.
SoilMSPLUHorDepth
(cm)		Gravel
(%)		Sand
(%)		Silt
(%)		Clay
(%)		BD
(Mg m−3)		pH
(H2O)		CaCO3
(%)		OC
(%)
CLOC-CL
n = (3 × 2)		M1OGAp0–406.9191.433.774.801.6219.265.660.131
2Ck40–1007.4774.288.6917.031.5919.1020.470.201
M2OGAp0–400.8192.142.855.011.6109.194.270.260
2Bk40–1002.3856.4121.1722.421.3428.8122.030.295
M3OGAp0–4023.5473.9812.8913.131.3308,7811.610.540
Ck40–10041.8663.5914.4521.961.4518.9921.970.230
M4OGApk0–4016.1690.975.793.241.5709.1210.080.115
2Ck40–1006.2063.323.9432.741.5018.7454.480.255
M5OGAp0–400.7391.036.312.661.5319.291.990.215
2Ck40–1002.2866.1431.162.701.4209.1921.940.255
X Ap0–409.63 ± 10.0087.91 ± 7.806.32 ± 3.945.77 ± 4.241.53 ± 0.129.13 ± 0.216.72 ± 4.020.25 ± 0.17
2Bk/Ck40–10012.04 ± 16.8364.75 ± 6.4415.88 ± 10.7019.37 ± 10.931.46 ± 0.098.97 ± 0.1928.18 ± 14.720.25 ± 0.04
NC-CL
n = (3×2)		A1OGAp0–401.5682.939.087.991.5708.779.340.271
2Ck40–1003.8061.0416.7222.241.3518.7426.160.285
A2OGAp0–404.2878.688.7612.561.5418.727.800.351
Ck40–1000.6460.0122.2517.741.3558.7422.760.265
A3OGAp0–4010.8569.6412.8517.511.4128.9517.130.395
2Ck40–10021.6051.5818.8329.591.3718.8335.410.402
A4ATAp0–4014.4753.3025.6421.061.0328.9019.410.490
Ck40–1009.8365.5314.8119.661.4618.9321.260.271
A5PTAp0–403.2477.0613.759.191.3708.839.800.560
2Bk40–10011.9954.2218.7127.071.3558.8732.370.275
X Ap0–406.88 ± 5.5172.32 ± 11.6714.02 ± 6.8713.66 ± 5.541.39 ± 0.228.83 ± 0.0912.70 ± 5.210.41 ± 0.11
2Bk/2Ck40–1009.57 ± 8.1258.48 ± 5.5818.26 ± 2.7723.26 ± 4.981.38 ± 0.058.82 ± 0.0827.59 ± 6.110.30 ± 0.06
SD *: Standard deviation; M: Management; SP: Soil sampling; Hor: Horizon; BD: Bulk density; OC: Organic carbon; n = Sample size. OC-CL: Old cultivated (tilled > 100 years) Calcisols; NC-CL: Newly cultivated (tilled < 12 years) Calcisols.
Table
Table 6. Soil organic carbon stock in Arenosols.
Table 6. Soil organic carbon stock in Arenosols.
SoilMSPLUHorTh
(cm)		Gravel
(%)		BD
(Mg m−3)		OC
(g kg−1)		SOCS
Mg ha−1		T-SOCS
Mg ha−1
AROC-AR
n = (3 × 2)		B1OGAp401.151.7011.258.40740.783
AC600.601.6453.3032.376
B2OGAp401.911.6922.9019.25235.260
C606.741.6731.7116.008
B3OGAp401.481,7052.8218.94747.436
AC602.601.6812.9028.489
X Ap401.51 ± 0.281.70 ± 0.012.32 ± 0.9315.535 ± 6.17541.159 ± 7.363
AC603.31 ± 3.131.67 ± 0.022.64 ± 0.8325.624 ± 8.552
NC-AR
n = (3 × 2)		C1ATAp402.981.5602.7016.34630.839
C604,321.5301.6514.493
C2OGAp400.661.6322.9018.80628.303
BC600.881.6810.959,497
C3OGAp400.091.6701.459.67716.239
C600.441.6900.656.562
X Ap401.24 ± 1.531.62 ± 0.062.35 ± 0.7914.943 ± 4.72325.127 ± 4.367
C601.88 ± 2.131.63 ± 0.091.08 ± 0.5110.184 ± 4.010
M: Management; SP: Soil sampling; BD: Bulk density; OC: Organic carbon; SOCS: Soil organic carbon stock; T-SOCS: Total SOCS; n = Sample size. OC-CL: Old cultivated (tilled > 100 years) Calcisols; NC-CL: Newly cultivated (tilled < 12 years) Calcisols.
Table
Table 7. Soil organic carbon stock in Calcisol.
Table 7. Soil organic carbon stock in Calcisol.
SoilMSPLUHorTh
(cm)		Gravel
(%)		BD
(Mg m−3)		OC
(g kg−1)		SOCS
Mg ha−1		T-SOCS
Mg ha−1
CLOC-CL
n = (3 × 2)		M1OGAp406.911.6211.317.90725.661
2Ck607.471.5912.0117.754
M2OGAp400.811.6102.6016.60839.796
2Bk602.381.3422.9523.188
M3OGAp4023.541.3305.4021.96533.607
Ck6041.861.4512.3011.642
M4OGAp4016.161.5701.156.05527.596
2Ck606.201.5012.5521.541
M5OGAp400.731.5312.1513.07034.301
2Ck602.281.4202.5521.231
X Ap409.63 ± 10.001.53 ± 0.122.52 ± 1.7213.121 ± 6.47132.192 ± 5.536
2Bk/Ck6012.04 ± 16.831.46 ± 0.092.47 ± 0.3519.071 ± 4.600
NC-CL
n = (3 × 2)		A1OGAp401.561.5702.7116.75338.977
2Ck603.801.3512.8522.224
A2OGAp404.281.5413.5120.71042.117
Ck600.641.3552.6521.407
A3OGAp4010.851.4123.9519.88945.815
2Ck6021.601.3714.0225.926
A4ATAp4014.471.0324.9017.30038.721
Ck609.831.4612.7121.421
A5PTAp403.241.3705.6029.69449.371
2Bk6011.991.3552.7519.677
X Ap406.88 ± 5.511.39 ± 0.224.13 ± 1.1420.869 ± 5.20943.000 ± 3.7625
2Bk/2Ck609.57 ± 8.121.38 ± 0.053.00 ± 0.5822.131 ± 2.316
M: Management; SP: Soil sampling; BD: Bulk density; OC: Organic carbon; SOCS: Soil organic carbon stock; T-SOCS: Total SOCS; n = Sample size. OC-CL: Old cultivated (tilled > 100 years) Calcisols; NC-CL: Newly cultivated (tilled < 12 years) Calcisols.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Baraket, F.; González-Rosado, M.; Brahim, N.; Roca, N.; Mbarek, H.B.; Świtoniak, M.; Chaker, R.; Sánchez-Bellón, Á.; Rigane, H.; Gargouri, K.; et al. Short and Long-Term Effect of Land Use and Management on Soil Organic Carbon Stock in Semi-Desert Areas of North Africa-Tunisia. Agriculture 2021, 11, 1267. https://doi.org/10.3390/agriculture11121267

AMA Style

Baraket F, González-Rosado M, Brahim N, Roca N, Mbarek HB, Świtoniak M, Chaker R, Sánchez-Bellón Á, Rigane H, Gargouri K, et al. Short and Long-Term Effect of Land Use and Management on Soil Organic Carbon Stock in Semi-Desert Areas of North Africa-Tunisia. Agriculture. 2021; 11(12):1267. https://doi.org/10.3390/agriculture11121267

Chicago/Turabian Style

Baraket, Fatma, Manuel González-Rosado, Nadhem Brahim, Núria Roca, Hadda Ben Mbarek, Marcin Świtoniak, Rayda Chaker, Ángel Sánchez-Bellón, Hafedh Rigane, Kamel Gargouri, and et al. 2021. "Short and Long-Term Effect of Land Use and Management on Soil Organic Carbon Stock in Semi-Desert Areas of North Africa-Tunisia" Agriculture 11, no. 12: 1267. https://doi.org/10.3390/agriculture11121267

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop