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Shifts in soil structure and soil organic matter in a chronosequence of set-aside fields
Soil & Tillage Research 174 (2017) 113–119
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Shifts in soil structure and soil organic matter in a chronosequence of setaside fields Antonios Apostolakis, Sotiria Panakoulia, Nikolaos P. Nikolaidis, Nikolaos V. Paranychianakis
MARK
⁎
Technical University of Crete, School of Environmental Engineering, Polytechnioupolis 73100 Chania, Greece
A R T I C L E I N F O
A B S T R A C T
Keywords: Drylands Aggregates Fertility restoration C sequestration CAST model
Set-aside is a commonly used practice worldwide to improve soil quality and fertility. However, the benefits of set-aside have not been quantified in a detailed manner and particularly in semi-arid environments where primary production, and hence C inputs to the soil, is constrained by water availability. Using a chronosequence of set-aside fields (0, 6 and 50 years), data were collected regarding the soil structure, addressed with the distribution of aggregate size classes, soil organic carbon (SOC), and total nitrogen content at the surface soil layer (0–15 cm). Soil structure showed a rapid recovery in the 6-years set-aside field and approached the levels of 50-years set-aside field. The recovery of soil structure, however, was not accompanied by a significant increase in bulk soil or in any aggregate size class SOC. In the 50-years set-aside field, the SOC content was 62% higher compared to the 0-years and 6-years set-aside fields. In this field the SOC content reached identical contents in all aggregate size classes except for the silt-clay fraction. In the latter, lower SOC content was assessed which did not differ from the 0-years and 6-years set-aside fields. In a following step, the Carbon, Aggregation, and Structure Turnover (CAST) model was employed to simulate the evolution of soil structure and C sequestration with time. Overall, the CAST model successfully simulated the evolution of soil structure and SOC stocks and provided critical information about the time required to approach the optimum thresholds under the prevailing environmental conditions. The estimated low rates of C sequestration question the effectiveness of the set-aside practice to restore soil fertility in semi-arid environments. These findings outline the need to evaluate additional practices and/or combinations of practices (e.g addition of organic amendments) to improve its effectiveness. Future work should aim to evaluate such practices considering climatic constraints and soil properties.
1. Introduction Soils in (semi)-arid climates are characterized by low soil organic carbon (SOC) content that renders them particularly vulnerable to further degradation and fertility loss (Lal, 2004). The low inputs of organic-C, the intense soil management practices (tillage), and the high rates of erosion are recognized as the major factors constraining SOC accumulation and degrading soil quality in semi-arid ecosystems (Moraetis et al., 2015). Thus, the adoption of soil management practices that stimulate SOC accumulation are of paramount importance to prevent further deterioration of soil quality and to maintain their productivity in the long term (Lal et al., 2015). Agricultural practices aiming to protect and promote soil structure development, like minimum or no-tillage have been associated with gains in C sequestration by several studies across the world (Lal et al., 2015). The positive effect of soil structure on C sequestration has been explained by the physical protection of SOC from decomposition that ⁎
Corresponding Author. E-mail address: [email protected] (N.V. Paranychianakis).
http://dx.doi.org/10.1016/j.still.2017.07.004 Received 27 December 2016; Received in revised form 2 July 2017; Accepted 4 July 2017 0167-1987/ © 2017 Elsevier B.V. All rights reserved.
arises from the compartmentalization of SOC, microorganisms, and exoenzymes (Lutzow et al., 2006; Dungait et al., 2012). The magnitude of C sequestration varies substantially across different sites and this variance has been partitioned to climatic conditions, soil properties, crops, management practices, and the time elapsed since the adoption of the restoration practices (Six et al., 2002b; West and Post, 2002; Lal et al., 2015; Gregorich et al., 2016; Han et al., 2016). Compared to other climates, a limited number of studies has dealt with the interactions of soil structure and SOM in soils evolved in semi-arid environments (Fuentes et al., 2012; Lopez-Bellido et al., 2017). Lopez-Bellido et al. (2017) reported no-tillage as a key practice for improving C sequestration in a semi-arid Vertisol in the Mediterranean and this positive influence was mainly attributed to the encapsulation of C and N within microaggregates. Critical questions, however, remain yet unanswered including the effect of natural restoration on soil structure development and its potential feedbacks with C sequestration, the influence of soil physical and biochemical properties as well as the time
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of water-stable aggregates (WSA) among various size classes was performed by wet-sieving adopting the protocols developed by Elliott (1986) and Cambardella and Elliott (1993). Before sieving, small rocks and plant-origin debris were manually removed from the samples followed by air drying at 45 °C for 48 h. Briefly, 100 g of dried soil was placed in a 2000 μm sieve and was sunk into water for 60 times. The soil fraction passing the screen was placed in a 1000- μm sieve and was sunk into water for 40 times. The same procedure was repeated with a 250-μm and a 53-μm sieve for 20 and 10 times, respectively. The fraction < 53 μm was collected through centrifugation. The aggregate size classes were subjected to sand correction according to the methodology developed by Elliott et al. (1991). The SOC and TN contents were measured in an elementary analyzer (Analytik Jena Multi N/C 2100S). Ammonium (NH4+-N) and nitrates (NO3−-N) were extracted with 2 M KCl and 0.01 M CaCl2, respectively. Then, they were measured colorimetrically in a Perkin-Elmer spectrophotometer (Lambda 25) with the Nessler reagent and the Cd-reduction method, accordingly.
required to reach the optimum levels of soil fertility depending on the prevailing environmental conditions. Regarding the latter, our knowledge has been limited until recently due to the lack of appropriate tools which account for the influence of soil structure, primary production, and soil properties on SOC turnover (Malamoud et al., 2009; Segoli et al., 2013; Stamati et al., 2013). The soil Carbon, Aggregation, and Structure Turnover (CAST) model is based on the aggregate hierarchy concept (Stamati et al., 2013) and has been successfully used to simulate soil structure and SOC interactions in sites with disparate climatic and edaphic conditions (Giannakis et al., 2014; Panakoulia et al., 2017). Soil conservation practices and the C inputs from primary production stimulate the sequestration of C to the soil. There is, however, evidence that the combined effect of these processes is non-linear and depends on the state of physical structure and the amount of C inputs (Kong et al., 2005; Reinhart and Vermeire, 2016; Wiesmeier et al., 2016). Understanding, thus, the feedbacks between soil structure and C inputs remains a critical issue for planning appropriate management practices and maximizing their effectiveness regarding the C sequestration. We, thus, hypothesized that if the disrupted physical structure, due to tilling, constrains the C sequestration in the Mediterranean landscapes, then a rapid accumulation of C would occur shortly following the adoption of restoration practices. By contrast, if C input is the limiting factor, then both physical structure and SOC would not or would slowly respond to the adopted practices. To shed light on the interactions between soil structure and C sequestration under Mediterranean conditions a chronosequence of adjacent fields subjected to set-aside practice for different periods of time (0, 6 and 50 years) was selected. The soil in each field was investigated for the distribution of aggregate size classes and basic soil chemical properties. To our knowledge, this study is one of the few which have employed a chronosequence of fields to get insights on the potential of agricultural practices (set-aside, no-tilling) to stimulate natural restoration processes under Mediterranean conditions (Álvaro-Fuentes et al., 2014). Finally, the CAST model (Stamati et al., 2013) was employed to determine the time for soil structure restoration and to evaluate the evolution of C sequestration for a period of 100 years.
2.2. The CAST model The CAST model is a mathematical model that simulates the mechanisms of aggregate and carbon turnover (Stamati et al., 2013). It has been built on the concept of aggregates (de)formation introduced by Tisdall and Oades (1982). The CAST model postulates that micro-aggregates are formed within the macro-aggregates and considers three main aggregate size classes: i) the macro-aggregates (AC3: > 250 μm), ii) the micro-aggregates (AC2: 53–250 μm) and iii) the silt-clay sized aggregates (AC1: < 53 μm). The plant residues entering the soil are colonized by microbial decomposers which stimulate the formation of macro-aggregates around particulate organic matter (POM). Further decomposition of POM results in the formation of micro-aggregates within the macro-aggregates by encapsulating the finely fragmented POM in silt-clay sized aggregates. The incorporated organic matter is subjected to further decomposition and as consequence, the microbial activity decreases gradually. With the progress of time, the stability of macro-aggregates weakens due to the depletion of microbial-origin polymers that are responsible for the binding of the structural components of macroaggregates. The unstable macro-aggregates are sensitive to slaking events, which leads to their disruption and the simultaneous release of stable microaggregates and silt-clay sized aggregates. The C pools of the Roth C model (Coleman and Jenkinson, 1996) are also considered in the individual aggregate size classes of the CAST model. These include the decomposable plant material (DPM), the resistant plant material (RPM), the microbial biomass (BIO), the humified organic matter (HUM), and the inert organic matter (IOM). From the above-mentioned C pools, the AC1 aggregates include BIO, HUM, and IOM, the AC2 aggregates include BIO, HUM, IOM, and fine DPM and RPM, and the AC3 aggregates include BIO, HUM, IOM, and fine and coarse derived DPM and RPM. The decomposition of each C pool is described by a first-order kinetic that produces CO2, BIO, and HUM. The proportion of C that is transformed to CO2, BIO and HUM is determined by the soil clay content (Coleman and Jenkinson, 1996). All the abbreviations reported in the previous paragraphs are summarized in Table 2. The CAST model requires also inputs of climatic conditions, aboveground plant-derived C, and basic soil properties (particle size distribution, BD, soil depth) (Stamati et al., 2013). In addition, the model requires data for the mass distribution of WSA in the size classes reported previously (AC1, AC2, AC3) and their SOC content. The aggregate size classes used in the simulations have been corrected for the sand content since sand does not correspond to real aggregates (Elliott et al., 1991). All this information is summarized in Table 1 and Table 3. The parameters used for model calibration, like decomposition rates of the different C pools, rates of macro- and micro-aggregate formation, proportional contribution of the components (DPM, RPM, AC1 and
2. Materials and methods The study area is located at the prefecture of Heraklion in Crete, and has an elevation of approximately 600 m. The area has a semi-arid climate with annual mean precipitation and temperature 654 mm and 17.3 °C, respectively. A chronosequence of soils differing in the duration of set-aside practice was examined in the present work. The close vicinity of the selected fields minimized the variance of climatic conditions and soil properties and allowed for the accurate estimation of the effect of the set-aside practice on soil fertility restoration. The first field was a vineyard subjected to tillage, twice per year, for more than 30 years and receiving conventional fertilization early in the growing season (typically during March). The second field was a vineyard of the same age that had been abandoned (not subjected to tillage and fertilization) the last six years. The last one was an uncultivated field for 50 years that was dominated by annual grasses and subjected to restricted grazing. 2.1. Soil physical and chemical analyses Soil samples were taken three times from each field, between the end of March and May 2015 from the upper 15 cm of the fields. Particle size analysis was carried out by the Bouyoucos hydrometer method (Bouyoucos, 1962). The soil in the study area was characterized as clayey with clay and silt content 53.0 ± 1.2% and 35.7 ± 1.7%, respectively with slight variations between fields (Table 1). The bulk density (BD) of the soils was determined by drying undisturbed soil cores of known volume at 105 °C to a constant weight. The distribution 114
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Table 1 Basic soil properties of the treatments 0, 6 and 50 years from cultivation. Soil samples were collected from the upper 15 cm. Treatment
BD (g cm−3)
Clay (%)
Silt (%)
Sand (%)
pH
NO3−-N (mg kg−1)
NH4+eN (mg kg−1)
Moisture (%)
0 years 6 years 50 years
1.43 ± 0.11 1.28 ± 0.02 1.12 ± 0.01
54.3 54.1 50.6
38.7 33.0 35.3
7.0 12.9 14.1
8.2 8.1 7.9
3.1 ± 0.7 2.6 ± 1.3 3.1 ± 0.2
15.8 ± 3.5 21.3 ± 2.1 11.2 ± 1.7
14.4 ± 5.3 14.9 ± 4.4 12.8 ± 4.5
Table 2 Summary of the abbreviations used in the text.
Table 4 Calibrated values of the rate constants (RC) and turnover time (RC−1 corrected with the ‘abc’ factor which equals 0.4836) of the model for the natural restoration of a vineyard in Crete, Greece.
Abbreviation
Definition
SOC SOM TN BD CAST WSA AC1 ΑC2 AC3 POM DPM RPM BIO HUM IOM
Soil Organic Carbon Soil Organic Matter Total Nitrogen Bulk Density Carbon Aggregation and Structure Turnover model Water-Stable Aggregates silt-clay sized aggregates (< 53 μm) microaggregates (53–250 μm) macroaggregates (> 250 μm) Particulated Organic Matter Decomposable Organic Matter Resistant Organic Matter Microbial biomass Humified organic matter Inert organic matter
Rate constants [y−1]
Turnover time[y]
RPM RPMc RPMcinAC3 DPMcinAC3 DPMc RPM RPMc RPMf RPMinAC3 RPMfinAC3 DPMcinAC3 DPMfinAC3 BIOinAC2withinAC3 HUMinAC2withinAC3 RPMfinAC2withinAC3 DPMfinAC2withinAC3 BIOinAC1withinAC3 HUMinAC1withinAC3 BIOinAC2withinAC3 HUMinAC2withinAC3 RPMfinAC2 DPMinAC2 BIOinAC1 HUMinAC1
13.00 0.00 0.10 0.50 10.450 0.305 0.305 0.305 0.010 0.010 2.800 1.500 0.800 0.008 0.120 1.500 0.600 0.003 0.800 0.056 0.380 2.000 0.600 0.030
0.2 – 20.7 4.1 – 3.4 4.4 – 10.3 10.3 – 0.2 6.8 6.8 6.8 206.8 206.8 0.7 1.4 4.1 667.0 2.6 258.5 17.2
RPMc DPMc RPMfinAC2withinAC3 DPMfinAC2withinAC3 RPMcaggr DPMcaggr AC1aggr AC2aggr RPMfinAC3aggr Microaggregation, percent composition of AC2inAC3DPMinAC3aggr AC1inAC3aggr formed [%] Criterion for macroaggregate (AC3) disruption (DPM within AC3) [%] Criterion for microaggregate (AC2inAC3) disruption (DPM + RPM)inAC2withinAC3 [%] Criterion for microaggregate disruption (AC2) ((DPM + RPM)inAC2) [%] AC1 Correction factor AC2aggr to adjust for AC1 within silt-clay flow AC3 f1 (AC2) Distribution of f2 (AC3) sand mass in aggregates Particle density of the mineral phase [g cm−3] Organic matter particle density [g cm3]
0.25 0.47 0.20 0.20 20.0 10.0 45.0 25.0 30.0 0.0 70.0 0.0014
1.4 2.6 41.4 6.9 1.0 1.0 24.3 – – – – –
0.0015
–
0.0015
–
0.80 2.00 0.14
– – –
0.33 0.67
– –
2.20
–
0.70
–
Fragmentation
Decomposition
Plant litter pool
AC3 aggregate type
Table 3 Monthly values of temperature, precipitation, evaporation and plant-derived C. Month
Temperature (°C)
Precipitation (mm)
Evaporation (mm)
Plant-derived C (tC ha−1)
January February March April May June July August September October November December Total
10.0 10.2 11.7 14.8 19.1 23.4 25.7 25.6 22.6 18.2 14.7 11.7 –
135.1 114.5 49.8 26.4 12.1 11.2 0.1 0.1 28.0 71.2 59.4 146.3 654.3
68.1 76.3 107.2 149.2 211.3 270.3 295.9 265.5 189.5 125.2 84.9 72.7 –
0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 1.92
AC2 aggregate type AC1 aggregate type Macroaggregation (AC3 formation) Microaggregates (AC2inAC3 formation) Macroaggregation, percent composition of AC3 formed [%]
AC2) in the considered aggregate sizes, and the fragmentation rates of plant material are shown in Table 4. The distribution of WSA in the AC1, AC2, and AC3 size classes and their SOC content were simulated for a period of 100 years. The (tilled) vineyard was defined as the starting point of the natural restoration (set aside) practice, while the abandoned one and the grassland functioned as calibration points with known soil physico-chemical properties in the future. To evaluate the simulating prowess of the calibrated CAST model, plots depicting the simulated soil structure and SOC stocks as a function of the observed values were created (Fig. 1). The coefficients of determination for the soil structure and SOC stocks were 0.996 and 0.998 (Fig. 1), respectively, indicating a strong correlation between the simulated and the observed values. The Root Mean Square Error (RMSE) was estimated to 1.7% and 0.8 tC ha−1 for the soil structure and SOC stocks, respectively providing further support for the goodness of fit of the calibration.
2.3. Statistical analysis One-way ANOVA was applied to determine whether there were significant differences among the means of aggregate size classes 115
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Fig. 1. Simulated and observed (a) mass distribution and (b) SOC stocks in the different aggregate size classes in 6-years and 50years set-aside fields. The 1:1 line (dashed line), the linear regression line (solid line), its equation and the coefficient of determination (R2) are also depicted. The RMSE was estimated to 1.7% and 0.8 tC ha−1 for the mass distribution and SOC stocks, respectively.
distribution and SOC content. The Tukey’s Honest Significance test was used to determine the differences between means (p: < 0.05). All statistical analyses were performed with the R statistical platform (R Development Core and Team, 2013). The bar-plots and scatter-plots were constructed using the ggplot2 package. The RMSE was calculated using the hydroGOF package.
3. Results The mass distribution of aggregates in various size classes diverged greatly among the chronosequence of set-aside soils. The 0-years setaside field (tilled vineyard) showed the lowest distribution of aggregates to large macroaggregates (> 2 mm) approaching 19.2% (Fig. 2). The corresponding proportions of large macroaggregates in the 6-years and 50-years set-aside fields were 36.7% and 44.8%, respectively. The 0-years set-aside field also showed the lowest distribution of aggregates in the 2000–1000 μm size class compared to the other fields (Fig. 2). There was no significant effect of the duration of set-aside practice in the 250–1000 μm size class. By contrast, the 0-years setaside field showed a greater proportion of microaggregates (53–250 μm) and silt-clay sized aggregates compared to the 6-years and 50-years set-aside fields (Fig. 2). A diverged pattern, compared to that of soil structure recovery, was observed for soil C and N contents in the soil chronosequence. The SOC content of the bulk soil did not change in the field subjected to set-aside
Fig. 3. Soil Organic Carbon (SOC) content in the bulk soil (BS) and the aggregate size classes in 0-years, 6-years and 50-years set-aside fields. Bars represent the mean value of three samplings ± SE. Small letters depict significant differences among the aggregate size classes of the same land use and capital letters depict significant differences in the corresponding aggregate class among the different fields in the chronosequence. Samples were collected from the upper 15 cm of the soil profile.
Fig. 4. Total Nitrogen (TN) content in the bulk soil (BS) and the aggregate size classes in 0-years, 6-years and 50-years set-aside fields. Bars represent the mean value of three samplings ± SE. Samples were collected from the upper 15 cm of the soil profile. Fig. 2. Mass distribution of the different aggregate size classes in 0-years, 6-years and 50years set-aside fields. Bars represent the mean value of three samplings ± SE. Small letters depict significant differences among the aggregate size classes of the same land use and capital letters depict significant differences in the corresponding aggregate class among the different fields in the chronosequence. Samples were collected from the upper 15 cm of the soil profile.
practice for six years, but a profound increase, from 13.1 to 20.9 mg kg−1, was observed in the 50-years set-aside field (Fig. 3). Regarding the N forms, TN in the bulk soil was not significantly affected by the time elapsed since the adoption of set-aside practice (Fig. 4), 116
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Fig. 7. Simulated evolution of SOC stocks in the bulk soil (Bulk soil), the silt-clay sized class (AC1), the microaggregates (AC2) and the cPOM and macroaggregates (AC3 and cPOM) of the upper 15 cm. The dashed horizontal line indicates the fertility threshold according to Quiroga et al. (2006).
The SOC stock of the bulk soil increased rapidly from approximately 28.8 to 35.5 tC ha−1 in the first 20 years following tillage cessation. Afterwards, the increase in SOC stock slowed down (slope: 0.07 tC ha−1 y−1) reaching 41.6 tC ha−1 at the end of the simulation period (Fig. 7). Likewise, the SOC stock of AC3 and cPOM fraction showed a profound increase from 12.8 to 28.0 tC ha−1 during the first 13 years of the setaside practice. In the rest of the simulation period (year 14 to 100) no significant changes were found in the SOC stock of the AC3 and cPOM fraction. The SOC stock of the AC2 fraction decreased during the first 12 years from 6.3 tC ha−1 to < 1.0 tC ha−1 and then followed a gradual increase throughout the simulation period to 9.2 tC ha−1 (Fig. 7). Finally, the SOC stock of the AC1 fraction decreased from 9.7 to 4.7 tC ha−1 during the first 12 years and then followed a milder decrease to approximately 2.7 tC ha−1.
Fig. 5. Ratio of C:N in the bulk soil (BS) and the aggregate size classes in 0-years, 6-years and 50-years set-aside fields. Bars represent the mean value of three samplings ± SE.
while higher content of NH4+-N was assessed in the 0-years set-aside field (p: < 0.05) compared to that of 50-years (Table 1), possibly due to recent N applications of fertilizer and/or the greater C mineralization rates stimulated by the destruction of soil structure. By contrast, NO3−N content did not differ among the fields (Table 1). Significant effects were observed in the SOC content of the different aggregate size classes between and within the treatments. The 50-years set-aside field showed the highest and identical concentrations of SOC in all size classes except for the silt-clay sized aggregates (Fig. 3). Slightly greater contents of SOC were assessed in the 1000–2000 μm and 250–1000 μm size classes in the 6-years compared to 0-years set-aside field. By contrast, no effects were found for the TN content (Fig. 4). Regarding the C/N ratio, no significant differences were observed in the bulk soil or in any aggregate size class in the chronosequence of soils (Fig. 5). To obtain an accurate picture of soil structure and C sequestration evolution with time that would allow us to infer if the 50-year set-aside soil has reached a steady-state and to assess the time required to reach steady-state under the prevailing environmental conditions the CAST model was employed. The simulations obtained by the model showed that the mass distribution of AC3 fraction increased from about 33.6% to 75% of soil mass within a period of 12 years in response to no tillage. Then, it followed a decreasing trend with slight annual ( ± 2.5% per year) variations (Fig. 6). During the same period, the mass distribution of silt-clay fraction (AC1) decreased abruptly from 45.7% to 22.1%, and then followed a milder decrease to approximately 11.3% at the end of the simulation period and with annual variations of about ± 2%. Regarding the AC2 fraction, it decreased sharply from 20.7% to 2.5% after 12 years and then increased gradually to 21% at the end of the simulation period (Fig. 6).
4. Discussion The sustainable management of soils in areas with (semi)-arid climates remains a great challenge. A high proportion of drylands has been strongly degraded by the synergistic action of physical factors and intensive agricultural practices (Acín-Carrera et al., 2013; ÁlvaroFuentes et al., 2014; Moraetis et al., 2015). Currently, various management practices capable to restore soil quality and fertility are evaluated with strong emphasis to be given on the minimal-tillage and the incorporation of organic amendments, but with important issues to remain yet unanswered (Álvaro-Fuentes et al., 2014). The findings from the chronosequence of set-aside practice investigated in this work revealed a nearly complete recovery of soil structure in fields subjected to tillage for more than three decades within only six years following tillage cessation. Acín-Carrera et al. (2013) also reported improvement in the structure of a sandy-loam soil in vineyards which have been abandoned for four years. However, the methodology used (CND index) did not allow for a direct comparison of the two studies. Cessation of tillage for six years increased the content of macroaggregates (> 250 μm) up to 40% compared to tilled fields in a severely degraded soil with low clay content (Hontoria et al., 2016). The field data revealed an uncoupling of soil structure recovery, addressed with an increase in the mass of macroaggregates, and C sequestration (Figs. 2 and 3). This finding from a first sight seems to contrast the conceptual model of aggregates formation proposed by Tisdall and Oades (1982) in which organic matter and microorganisms have a profound role in aggregate formation as well as the several studies reporting strong correlations between the mass distribution of macroaggregates and SOC content (Six et al., 2000; Wilson et al., 2009; Spohn and Giani, 2010; Veum et al., 2012). The formation of aggregates, however, is regulated by the interplay of numerous factors, including the composition of SOC, the structure of microbial community and its activity, as well as by abiotic factors including among others minerals composition, clay content and type, pH, and environmental
Fig. 6. Simulated evolution of the distribution of WSAs among the silt-clay sized class (AC1), the microaggregate size class (AC2) and the macroaggregate size class (AC3) in the upper 15 cm. The dashed horizontal line indicates the fertility threshold for the soil structure according to Banwart et al. (2011).
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protected by decomposition and suggest that additional practices are required to be considered to improve SOC sequestration in short-term time intervals. Simulations performed in a chronosequence of dryland soils in Spain with the CENTURY model also confirmed the slow rates of C accumulation in the Mediterranean landscapes and estimated the time required for soil to reach steady-state to approximately 20 years (Álvaro-Fuentes et al., 2014) that is in good agreement with the predictions obtained in this study.
conditions (Tisdall and Oades, 1982; Leifheit et al., 2014; Yin et al., 2016). Barto et al. (2010), by applying structural equation modeling, found that when the abiotic factors involved in the formation of aggregates were considered the influence of biotic factors was minor. The differentiation observed in this study could be attributed to the high clay content of the fields investigated. Clay content promotes soil aggregation directly by acting as binding agent or indirectly through swelling and shrinking (Leifheit et al., 2014) due to the alteration of wetting and drying cycles of soils. Complementary, a slight increase in SOC content early in the restoration period, although statistically no detectable, might have stimulated soil structure recovery (Lehmann et al., 2007; Liu et al., 2013). The lag in the accumulation of C compared to soil structure recovery strengthens the existing evidence that soil structure is a necessary condition for C sequestration by decreasing the accessibility and availability of SOC to exoenzymes and microorganisms (Six et al., 2002a; Dungait et al., 2012). Likewise, no difference in SOC content was observed in vineyards in central Spain which have been abandoned for four years and tilled vineyards, despite the strong improvement in soil structure that followed tillage cessation (Acín-Carrera et al., 2013). This delay in C sequestration could be explained by the low turnover rates of aggregates in soils with a high clay content in absence of mechanical disturbance. A remarkable finding of this study was the identical SOC content in all aggregate size classes of the 50-years set-aside field, except for the silt-clay fraction. This finding might indicate that the soil has reached a steady-state under the prevailing environmental conditions. The uniform SOC content provides evidence for a converge of the mechanisms involved in C sequestration, independently of the aggregate size class and possibly indicates the occlusion of finely fragmented POM as the principal mechanism. This hypothesis is strengthened by the similar C:N ratios in all aggregate size classes as well as the lack of C accumulation in the silt-clay fraction (Fig. 3) in which microbial-origin organic matter is mainly accumulated through interactions with the minerals (Vogel et al., 2014). Several indicators have been proposed to evaluate soil quality status. Emphasizing on soil structure, Banwart et al. (2011) suggested the fraction of WSA > 250 μm as quality indicator defining a threshold value of > 60% to distinguish agronomically valuable soils. This threshold has been exceeded only in the 50-years set-aside field. Quiroga et al. (2006) proposed the SOM/(silt + clay) ratio as indicator of soil quality and defined a threshold value of 4.4, which subsequently was adjusted to 4.5 by Giannakis et al. (2014) to account for soils with a high clay content. Considering the properties of soils investigated in this work, a SOC mass of 64.3 t ha−1 is needed to reach the threshold value, a condition which, however, cannot be achieved even after 100 years of natural restoration. The CAST model provided successful simulations regarding the soil structure and SOC stocks (Fig. 1), in accordance with simulations reported in earlier studies from diverge environments, soil properties and climatic conditions (Giannakis et al., 2014; Panakoulia et al., 2017). The simulations of CAST model revealed that the macroaggregates (AC3), the fraction with the greatest contribution in soil mass, had approached a steady state at year 12 (Fig. 6) but not the other fractions (Fig. 6). Simulations showed that a time interval of approximately 8 years of non-tillage is needed to reach the threshold (AC3 mass: > 60%) that satisfies the soil quality indicator introduced by Banwart et al. (2011). Regarding SOC content, the CAST model revealed that the AC3 and cPOM pool contributes the most to the SOC stocks of the bulk soil. Moreover, the CAST model predicted that SOC of the AC3 and POM fraction approaches steady-state approximately 13 years after tillage cessation (Fig. 7). The estimated low rates of C sequestration question the effectiveness of set-aside practice in dryland environments to restore its fertility. These findings indicate that C inputs limit the accumulation of SOC, even at soils with a high clay content where C is well
5. Conclusions The combination of field data and modeling tools allowed us to track the evolution of soil structure recovery and C sequestration in a chronosequence of set-aside fields under Mediterranean climatic conditions. The results indicated a rapid recovery of soil structure which, however was followed by low rates of C sequestration. Simulations with the CAST model showed that even after 100 years of the set-aside practice some soil quality indicators (e.g Quiroga index) did not reach the suggested thresholds. These findings outline the need to investigate additional soil restoration practices or to combine set-aside practice with other practices to restore and maintain the productivity of soils in the long term and to minimize degrading threats. Acknowledgements The study has been founded by the Technical University of Crete. References Álvaro-Fuentes, J., Plaza-Bonilla, D., Arrúe, J.L., Lampurlanés, J., Cantero-Martínez, C., 2014. Soil organic carbon storage in a no-tillage chronosequence under mediterranean conditions. Plant Soil 376, 31–41. Acín-Carrera, M., José Marques, M., Carral, P., Álvarez, A.M., López, C., Martín-López, B., González, J.A., 2013. Impacts of land-use intensity on soil organic carbon content, soil structure and water-holding capacity. Soil Use Manage. 29, 547–556. Banwart, S., Bernasconi, S.M., Bloem, J., Blum, W., Brandao, M., Brantley, S., Chabaux, F., Duffy, C., Kram, P., Lair, G., Lundin, L., Nikolaidis, N., Novak, M., Panagos, P., Ragnarsdottir, K.V., Reynolds, B., Rousseva, S., de Ruiter, P., van Gaans, P., van Riemsdijk, W., White, T., Zhang, B., 2011. Soil processes and functions in critical zone observatories: hypotheses and experimental design. Vadose Zone J. 10, 974. Barto, E.K., Alt, F., Oelmann, Y., Wilcke, W., Rillig, M.C., 2010. Contributions of biotic and abiotic factors to soil aggregation across a land use gradient. Soil Biol. Biochem. 42, 2316–2324. Bouyoucos, G.J., 1962. Hydrometer method improved for making particle size analyses of soils. Agron. J. 54, 464–465. Cambardella, C.A., Elliott, E.T., 1993. Methods for physical separation and characterization of soil organic matter fractions. Geoderma 56, 449–457. Coleman, K., Jenkinson, D.S., 1996. RothC-26.3 − A Model for the turnover of carbon in soil. In: Powlson, D.S., Smith, P., Smith, J.U. (Eds.), Evaluation of Soil Organic Matter Models: Using Existing Long-Term Datasets. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 237–246. Dungait, J.A.J., Hopkins, D.W., Gregory, A.S., Whitmore, A.P., 2012. Soil organic matter turnover is governed by accessibility not recalcitrance. Global Change Biol. 18, 1781–1796. Elliott, E.T., Palm, C.A., Reuss, D.E., Monz, C.A., 1991. Organic matter contained in soil aggregates from a tropical chronosequence: correction for sand and light fraction. Agric. Ecosyst. Environ. 34, 443–451. Elliott, E.T., 1986. Aggregate structure and carbon, nitrogen, and phosphorus in native and cultivated soils1. Soil Sci. Soc. Am. J. 50, 627–633. Fuentes, M., Hidalgo, C., Etchevers, J., León, F., Guerrero, A., Dendooven, L., Verhulst, N., Govaerts, B., 2012. Conservation agriculture, increased organic carbon in the top-soil macro-aggregates and reduced soil CO2 emissions. Plant Soil 355, 183–197. Giannakis, G.V., Panakoulia, S.K., Nikolaidis, N.P., Paranychianakis, N.V., 2014. Simulating soil fertility restoration using the CAST Model. Procedia. Earth Planet. Sci. 10, 325–329. Gregorich, E.G., Henry Janzen, H., Ellert, B.H., Helgason, B.L., Qian, B., Zebarth, B.J., Angers, D.A., Beyaert, R.P., Drury, C.F., Duguid, S., May, W.E., McConkey, B.G., Dyck, M.F., 2016. Litter decay controlled by temperature, not soil properties, affecting future soil carbon. Glob. Chang. Biol. Han, P., Zhang, W., Wang, G., Sun, W., Huang, Y., 2016. Changes in soil organic carbon in croplands subjected to fertilizer management: a global meta-analysis. Sci. Rep. 6, 27199. Hontoria, C., Gómez-Paccard, C., Mariscal-Sancho, I., Benito, M., Pérez, J., Espejo, R., 2016. Aggregate size distribution and associated organic C and N under different tillage systems and Ca-amendment in a degraded Ultisol. Soil Tillage Res. 160, 42–52. Kong, A.Y.Y., Six, J., Bryant, D.C., Denison, R.F., Van Kessel, C., 2005. The relationship between carbon input, aggregation, and soil organic carbon stabilization in
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Shifts in soil structure and soil organic matter in a chronosequence of setaside fields Antonios Apostolakis, Sotiria Panakoulia, Nikolaos P. Nikolaidis, Nikolaos V. Paranychianakis
MARK
⁎
Technical University of Crete, School of Environmental Engineering, Polytechnioupolis 73100 Chania, Greece
A R T I C L E I N F O
A B S T R A C T
Keywords: Drylands Aggregates Fertility restoration C sequestration CAST model
Set-aside is a commonly used practice worldwide to improve soil quality and fertility. However, the benefits of set-aside have not been quantified in a detailed manner and particularly in semi-arid environments where primary production, and hence C inputs to the soil, is constrained by water availability. Using a chronosequence of set-aside fields (0, 6 and 50 years), data were collected regarding the soil structure, addressed with the distribution of aggregate size classes, soil organic carbon (SOC), and total nitrogen content at the surface soil layer (0–15 cm). Soil structure showed a rapid recovery in the 6-years set-aside field and approached the levels of 50-years set-aside field. The recovery of soil structure, however, was not accompanied by a significant increase in bulk soil or in any aggregate size class SOC. In the 50-years set-aside field, the SOC content was 62% higher compared to the 0-years and 6-years set-aside fields. In this field the SOC content reached identical contents in all aggregate size classes except for the silt-clay fraction. In the latter, lower SOC content was assessed which did not differ from the 0-years and 6-years set-aside fields. In a following step, the Carbon, Aggregation, and Structure Turnover (CAST) model was employed to simulate the evolution of soil structure and C sequestration with time. Overall, the CAST model successfully simulated the evolution of soil structure and SOC stocks and provided critical information about the time required to approach the optimum thresholds under the prevailing environmental conditions. The estimated low rates of C sequestration question the effectiveness of the set-aside practice to restore soil fertility in semi-arid environments. These findings outline the need to evaluate additional practices and/or combinations of practices (e.g addition of organic amendments) to improve its effectiveness. Future work should aim to evaluate such practices considering climatic constraints and soil properties.
1. Introduction Soils in (semi)-arid climates are characterized by low soil organic carbon (SOC) content that renders them particularly vulnerable to further degradation and fertility loss (Lal, 2004). The low inputs of organic-C, the intense soil management practices (tillage), and the high rates of erosion are recognized as the major factors constraining SOC accumulation and degrading soil quality in semi-arid ecosystems (Moraetis et al., 2015). Thus, the adoption of soil management practices that stimulate SOC accumulation are of paramount importance to prevent further deterioration of soil quality and to maintain their productivity in the long term (Lal et al., 2015). Agricultural practices aiming to protect and promote soil structure development, like minimum or no-tillage have been associated with gains in C sequestration by several studies across the world (Lal et al., 2015). The positive effect of soil structure on C sequestration has been explained by the physical protection of SOC from decomposition that ⁎
Corresponding Author. E-mail address: [email protected] (N.V. Paranychianakis).
http://dx.doi.org/10.1016/j.still.2017.07.004 Received 27 December 2016; Received in revised form 2 July 2017; Accepted 4 July 2017 0167-1987/ © 2017 Elsevier B.V. All rights reserved.
arises from the compartmentalization of SOC, microorganisms, and exoenzymes (Lutzow et al., 2006; Dungait et al., 2012). The magnitude of C sequestration varies substantially across different sites and this variance has been partitioned to climatic conditions, soil properties, crops, management practices, and the time elapsed since the adoption of the restoration practices (Six et al., 2002b; West and Post, 2002; Lal et al., 2015; Gregorich et al., 2016; Han et al., 2016). Compared to other climates, a limited number of studies has dealt with the interactions of soil structure and SOM in soils evolved in semi-arid environments (Fuentes et al., 2012; Lopez-Bellido et al., 2017). Lopez-Bellido et al. (2017) reported no-tillage as a key practice for improving C sequestration in a semi-arid Vertisol in the Mediterranean and this positive influence was mainly attributed to the encapsulation of C and N within microaggregates. Critical questions, however, remain yet unanswered including the effect of natural restoration on soil structure development and its potential feedbacks with C sequestration, the influence of soil physical and biochemical properties as well as the time
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of water-stable aggregates (WSA) among various size classes was performed by wet-sieving adopting the protocols developed by Elliott (1986) and Cambardella and Elliott (1993). Before sieving, small rocks and plant-origin debris were manually removed from the samples followed by air drying at 45 °C for 48 h. Briefly, 100 g of dried soil was placed in a 2000 μm sieve and was sunk into water for 60 times. The soil fraction passing the screen was placed in a 1000- μm sieve and was sunk into water for 40 times. The same procedure was repeated with a 250-μm and a 53-μm sieve for 20 and 10 times, respectively. The fraction < 53 μm was collected through centrifugation. The aggregate size classes were subjected to sand correction according to the methodology developed by Elliott et al. (1991). The SOC and TN contents were measured in an elementary analyzer (Analytik Jena Multi N/C 2100S). Ammonium (NH4+-N) and nitrates (NO3−-N) were extracted with 2 M KCl and 0.01 M CaCl2, respectively. Then, they were measured colorimetrically in a Perkin-Elmer spectrophotometer (Lambda 25) with the Nessler reagent and the Cd-reduction method, accordingly.
required to reach the optimum levels of soil fertility depending on the prevailing environmental conditions. Regarding the latter, our knowledge has been limited until recently due to the lack of appropriate tools which account for the influence of soil structure, primary production, and soil properties on SOC turnover (Malamoud et al., 2009; Segoli et al., 2013; Stamati et al., 2013). The soil Carbon, Aggregation, and Structure Turnover (CAST) model is based on the aggregate hierarchy concept (Stamati et al., 2013) and has been successfully used to simulate soil structure and SOC interactions in sites with disparate climatic and edaphic conditions (Giannakis et al., 2014; Panakoulia et al., 2017). Soil conservation practices and the C inputs from primary production stimulate the sequestration of C to the soil. There is, however, evidence that the combined effect of these processes is non-linear and depends on the state of physical structure and the amount of C inputs (Kong et al., 2005; Reinhart and Vermeire, 2016; Wiesmeier et al., 2016). Understanding, thus, the feedbacks between soil structure and C inputs remains a critical issue for planning appropriate management practices and maximizing their effectiveness regarding the C sequestration. We, thus, hypothesized that if the disrupted physical structure, due to tilling, constrains the C sequestration in the Mediterranean landscapes, then a rapid accumulation of C would occur shortly following the adoption of restoration practices. By contrast, if C input is the limiting factor, then both physical structure and SOC would not or would slowly respond to the adopted practices. To shed light on the interactions between soil structure and C sequestration under Mediterranean conditions a chronosequence of adjacent fields subjected to set-aside practice for different periods of time (0, 6 and 50 years) was selected. The soil in each field was investigated for the distribution of aggregate size classes and basic soil chemical properties. To our knowledge, this study is one of the few which have employed a chronosequence of fields to get insights on the potential of agricultural practices (set-aside, no-tilling) to stimulate natural restoration processes under Mediterranean conditions (Álvaro-Fuentes et al., 2014). Finally, the CAST model (Stamati et al., 2013) was employed to determine the time for soil structure restoration and to evaluate the evolution of C sequestration for a period of 100 years.
2.2. The CAST model The CAST model is a mathematical model that simulates the mechanisms of aggregate and carbon turnover (Stamati et al., 2013). It has been built on the concept of aggregates (de)formation introduced by Tisdall and Oades (1982). The CAST model postulates that micro-aggregates are formed within the macro-aggregates and considers three main aggregate size classes: i) the macro-aggregates (AC3: > 250 μm), ii) the micro-aggregates (AC2: 53–250 μm) and iii) the silt-clay sized aggregates (AC1: < 53 μm). The plant residues entering the soil are colonized by microbial decomposers which stimulate the formation of macro-aggregates around particulate organic matter (POM). Further decomposition of POM results in the formation of micro-aggregates within the macro-aggregates by encapsulating the finely fragmented POM in silt-clay sized aggregates. The incorporated organic matter is subjected to further decomposition and as consequence, the microbial activity decreases gradually. With the progress of time, the stability of macro-aggregates weakens due to the depletion of microbial-origin polymers that are responsible for the binding of the structural components of macroaggregates. The unstable macro-aggregates are sensitive to slaking events, which leads to their disruption and the simultaneous release of stable microaggregates and silt-clay sized aggregates. The C pools of the Roth C model (Coleman and Jenkinson, 1996) are also considered in the individual aggregate size classes of the CAST model. These include the decomposable plant material (DPM), the resistant plant material (RPM), the microbial biomass (BIO), the humified organic matter (HUM), and the inert organic matter (IOM). From the above-mentioned C pools, the AC1 aggregates include BIO, HUM, and IOM, the AC2 aggregates include BIO, HUM, IOM, and fine DPM and RPM, and the AC3 aggregates include BIO, HUM, IOM, and fine and coarse derived DPM and RPM. The decomposition of each C pool is described by a first-order kinetic that produces CO2, BIO, and HUM. The proportion of C that is transformed to CO2, BIO and HUM is determined by the soil clay content (Coleman and Jenkinson, 1996). All the abbreviations reported in the previous paragraphs are summarized in Table 2. The CAST model requires also inputs of climatic conditions, aboveground plant-derived C, and basic soil properties (particle size distribution, BD, soil depth) (Stamati et al., 2013). In addition, the model requires data for the mass distribution of WSA in the size classes reported previously (AC1, AC2, AC3) and their SOC content. The aggregate size classes used in the simulations have been corrected for the sand content since sand does not correspond to real aggregates (Elliott et al., 1991). All this information is summarized in Table 1 and Table 3. The parameters used for model calibration, like decomposition rates of the different C pools, rates of macro- and micro-aggregate formation, proportional contribution of the components (DPM, RPM, AC1 and
2. Materials and methods The study area is located at the prefecture of Heraklion in Crete, and has an elevation of approximately 600 m. The area has a semi-arid climate with annual mean precipitation and temperature 654 mm and 17.3 °C, respectively. A chronosequence of soils differing in the duration of set-aside practice was examined in the present work. The close vicinity of the selected fields minimized the variance of climatic conditions and soil properties and allowed for the accurate estimation of the effect of the set-aside practice on soil fertility restoration. The first field was a vineyard subjected to tillage, twice per year, for more than 30 years and receiving conventional fertilization early in the growing season (typically during March). The second field was a vineyard of the same age that had been abandoned (not subjected to tillage and fertilization) the last six years. The last one was an uncultivated field for 50 years that was dominated by annual grasses and subjected to restricted grazing. 2.1. Soil physical and chemical analyses Soil samples were taken three times from each field, between the end of March and May 2015 from the upper 15 cm of the fields. Particle size analysis was carried out by the Bouyoucos hydrometer method (Bouyoucos, 1962). The soil in the study area was characterized as clayey with clay and silt content 53.0 ± 1.2% and 35.7 ± 1.7%, respectively with slight variations between fields (Table 1). The bulk density (BD) of the soils was determined by drying undisturbed soil cores of known volume at 105 °C to a constant weight. The distribution 114
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Table 1 Basic soil properties of the treatments 0, 6 and 50 years from cultivation. Soil samples were collected from the upper 15 cm. Treatment
BD (g cm−3)
Clay (%)
Silt (%)
Sand (%)
pH
NO3−-N (mg kg−1)
NH4+eN (mg kg−1)
Moisture (%)
0 years 6 years 50 years
1.43 ± 0.11 1.28 ± 0.02 1.12 ± 0.01
54.3 54.1 50.6
38.7 33.0 35.3
7.0 12.9 14.1
8.2 8.1 7.9
3.1 ± 0.7 2.6 ± 1.3 3.1 ± 0.2
15.8 ± 3.5 21.3 ± 2.1 11.2 ± 1.7
14.4 ± 5.3 14.9 ± 4.4 12.8 ± 4.5
Table 2 Summary of the abbreviations used in the text.
Table 4 Calibrated values of the rate constants (RC) and turnover time (RC−1 corrected with the ‘abc’ factor which equals 0.4836) of the model for the natural restoration of a vineyard in Crete, Greece.
Abbreviation
Definition
SOC SOM TN BD CAST WSA AC1 ΑC2 AC3 POM DPM RPM BIO HUM IOM
Soil Organic Carbon Soil Organic Matter Total Nitrogen Bulk Density Carbon Aggregation and Structure Turnover model Water-Stable Aggregates silt-clay sized aggregates (< 53 μm) microaggregates (53–250 μm) macroaggregates (> 250 μm) Particulated Organic Matter Decomposable Organic Matter Resistant Organic Matter Microbial biomass Humified organic matter Inert organic matter
Rate constants [y−1]
Turnover time[y]
RPM RPMc RPMcinAC3 DPMcinAC3 DPMc RPM RPMc RPMf RPMinAC3 RPMfinAC3 DPMcinAC3 DPMfinAC3 BIOinAC2withinAC3 HUMinAC2withinAC3 RPMfinAC2withinAC3 DPMfinAC2withinAC3 BIOinAC1withinAC3 HUMinAC1withinAC3 BIOinAC2withinAC3 HUMinAC2withinAC3 RPMfinAC2 DPMinAC2 BIOinAC1 HUMinAC1
13.00 0.00 0.10 0.50 10.450 0.305 0.305 0.305 0.010 0.010 2.800 1.500 0.800 0.008 0.120 1.500 0.600 0.003 0.800 0.056 0.380 2.000 0.600 0.030
0.2 – 20.7 4.1 – 3.4 4.4 – 10.3 10.3 – 0.2 6.8 6.8 6.8 206.8 206.8 0.7 1.4 4.1 667.0 2.6 258.5 17.2
RPMc DPMc RPMfinAC2withinAC3 DPMfinAC2withinAC3 RPMcaggr DPMcaggr AC1aggr AC2aggr RPMfinAC3aggr Microaggregation, percent composition of AC2inAC3DPMinAC3aggr AC1inAC3aggr formed [%] Criterion for macroaggregate (AC3) disruption (DPM within AC3) [%] Criterion for microaggregate (AC2inAC3) disruption (DPM + RPM)inAC2withinAC3 [%] Criterion for microaggregate disruption (AC2) ((DPM + RPM)inAC2) [%] AC1 Correction factor AC2aggr to adjust for AC1 within silt-clay flow AC3 f1 (AC2) Distribution of f2 (AC3) sand mass in aggregates Particle density of the mineral phase [g cm−3] Organic matter particle density [g cm3]
0.25 0.47 0.20 0.20 20.0 10.0 45.0 25.0 30.0 0.0 70.0 0.0014
1.4 2.6 41.4 6.9 1.0 1.0 24.3 – – – – –
0.0015
–
0.0015
–
0.80 2.00 0.14
– – –
0.33 0.67
– –
2.20
–
0.70
–
Fragmentation
Decomposition
Plant litter pool
AC3 aggregate type
Table 3 Monthly values of temperature, precipitation, evaporation and plant-derived C. Month
Temperature (°C)
Precipitation (mm)
Evaporation (mm)
Plant-derived C (tC ha−1)
January February March April May June July August September October November December Total
10.0 10.2 11.7 14.8 19.1 23.4 25.7 25.6 22.6 18.2 14.7 11.7 –
135.1 114.5 49.8 26.4 12.1 11.2 0.1 0.1 28.0 71.2 59.4 146.3 654.3
68.1 76.3 107.2 149.2 211.3 270.3 295.9 265.5 189.5 125.2 84.9 72.7 –
0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 1.92
AC2 aggregate type AC1 aggregate type Macroaggregation (AC3 formation) Microaggregates (AC2inAC3 formation) Macroaggregation, percent composition of AC3 formed [%]
AC2) in the considered aggregate sizes, and the fragmentation rates of plant material are shown in Table 4. The distribution of WSA in the AC1, AC2, and AC3 size classes and their SOC content were simulated for a period of 100 years. The (tilled) vineyard was defined as the starting point of the natural restoration (set aside) practice, while the abandoned one and the grassland functioned as calibration points with known soil physico-chemical properties in the future. To evaluate the simulating prowess of the calibrated CAST model, plots depicting the simulated soil structure and SOC stocks as a function of the observed values were created (Fig. 1). The coefficients of determination for the soil structure and SOC stocks were 0.996 and 0.998 (Fig. 1), respectively, indicating a strong correlation between the simulated and the observed values. The Root Mean Square Error (RMSE) was estimated to 1.7% and 0.8 tC ha−1 for the soil structure and SOC stocks, respectively providing further support for the goodness of fit of the calibration.
2.3. Statistical analysis One-way ANOVA was applied to determine whether there were significant differences among the means of aggregate size classes 115
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Fig. 1. Simulated and observed (a) mass distribution and (b) SOC stocks in the different aggregate size classes in 6-years and 50years set-aside fields. The 1:1 line (dashed line), the linear regression line (solid line), its equation and the coefficient of determination (R2) are also depicted. The RMSE was estimated to 1.7% and 0.8 tC ha−1 for the mass distribution and SOC stocks, respectively.
distribution and SOC content. The Tukey’s Honest Significance test was used to determine the differences between means (p: < 0.05). All statistical analyses were performed with the R statistical platform (R Development Core and Team, 2013). The bar-plots and scatter-plots were constructed using the ggplot2 package. The RMSE was calculated using the hydroGOF package.
3. Results The mass distribution of aggregates in various size classes diverged greatly among the chronosequence of set-aside soils. The 0-years setaside field (tilled vineyard) showed the lowest distribution of aggregates to large macroaggregates (> 2 mm) approaching 19.2% (Fig. 2). The corresponding proportions of large macroaggregates in the 6-years and 50-years set-aside fields were 36.7% and 44.8%, respectively. The 0-years set-aside field also showed the lowest distribution of aggregates in the 2000–1000 μm size class compared to the other fields (Fig. 2). There was no significant effect of the duration of set-aside practice in the 250–1000 μm size class. By contrast, the 0-years setaside field showed a greater proportion of microaggregates (53–250 μm) and silt-clay sized aggregates compared to the 6-years and 50-years set-aside fields (Fig. 2). A diverged pattern, compared to that of soil structure recovery, was observed for soil C and N contents in the soil chronosequence. The SOC content of the bulk soil did not change in the field subjected to set-aside
Fig. 3. Soil Organic Carbon (SOC) content in the bulk soil (BS) and the aggregate size classes in 0-years, 6-years and 50-years set-aside fields. Bars represent the mean value of three samplings ± SE. Small letters depict significant differences among the aggregate size classes of the same land use and capital letters depict significant differences in the corresponding aggregate class among the different fields in the chronosequence. Samples were collected from the upper 15 cm of the soil profile.
Fig. 4. Total Nitrogen (TN) content in the bulk soil (BS) and the aggregate size classes in 0-years, 6-years and 50-years set-aside fields. Bars represent the mean value of three samplings ± SE. Samples were collected from the upper 15 cm of the soil profile. Fig. 2. Mass distribution of the different aggregate size classes in 0-years, 6-years and 50years set-aside fields. Bars represent the mean value of three samplings ± SE. Small letters depict significant differences among the aggregate size classes of the same land use and capital letters depict significant differences in the corresponding aggregate class among the different fields in the chronosequence. Samples were collected from the upper 15 cm of the soil profile.
practice for six years, but a profound increase, from 13.1 to 20.9 mg kg−1, was observed in the 50-years set-aside field (Fig. 3). Regarding the N forms, TN in the bulk soil was not significantly affected by the time elapsed since the adoption of set-aside practice (Fig. 4), 116
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Fig. 7. Simulated evolution of SOC stocks in the bulk soil (Bulk soil), the silt-clay sized class (AC1), the microaggregates (AC2) and the cPOM and macroaggregates (AC3 and cPOM) of the upper 15 cm. The dashed horizontal line indicates the fertility threshold according to Quiroga et al. (2006).
The SOC stock of the bulk soil increased rapidly from approximately 28.8 to 35.5 tC ha−1 in the first 20 years following tillage cessation. Afterwards, the increase in SOC stock slowed down (slope: 0.07 tC ha−1 y−1) reaching 41.6 tC ha−1 at the end of the simulation period (Fig. 7). Likewise, the SOC stock of AC3 and cPOM fraction showed a profound increase from 12.8 to 28.0 tC ha−1 during the first 13 years of the setaside practice. In the rest of the simulation period (year 14 to 100) no significant changes were found in the SOC stock of the AC3 and cPOM fraction. The SOC stock of the AC2 fraction decreased during the first 12 years from 6.3 tC ha−1 to < 1.0 tC ha−1 and then followed a gradual increase throughout the simulation period to 9.2 tC ha−1 (Fig. 7). Finally, the SOC stock of the AC1 fraction decreased from 9.7 to 4.7 tC ha−1 during the first 12 years and then followed a milder decrease to approximately 2.7 tC ha−1.
Fig. 5. Ratio of C:N in the bulk soil (BS) and the aggregate size classes in 0-years, 6-years and 50-years set-aside fields. Bars represent the mean value of three samplings ± SE.
while higher content of NH4+-N was assessed in the 0-years set-aside field (p: < 0.05) compared to that of 50-years (Table 1), possibly due to recent N applications of fertilizer and/or the greater C mineralization rates stimulated by the destruction of soil structure. By contrast, NO3−N content did not differ among the fields (Table 1). Significant effects were observed in the SOC content of the different aggregate size classes between and within the treatments. The 50-years set-aside field showed the highest and identical concentrations of SOC in all size classes except for the silt-clay sized aggregates (Fig. 3). Slightly greater contents of SOC were assessed in the 1000–2000 μm and 250–1000 μm size classes in the 6-years compared to 0-years set-aside field. By contrast, no effects were found for the TN content (Fig. 4). Regarding the C/N ratio, no significant differences were observed in the bulk soil or in any aggregate size class in the chronosequence of soils (Fig. 5). To obtain an accurate picture of soil structure and C sequestration evolution with time that would allow us to infer if the 50-year set-aside soil has reached a steady-state and to assess the time required to reach steady-state under the prevailing environmental conditions the CAST model was employed. The simulations obtained by the model showed that the mass distribution of AC3 fraction increased from about 33.6% to 75% of soil mass within a period of 12 years in response to no tillage. Then, it followed a decreasing trend with slight annual ( ± 2.5% per year) variations (Fig. 6). During the same period, the mass distribution of silt-clay fraction (AC1) decreased abruptly from 45.7% to 22.1%, and then followed a milder decrease to approximately 11.3% at the end of the simulation period and with annual variations of about ± 2%. Regarding the AC2 fraction, it decreased sharply from 20.7% to 2.5% after 12 years and then increased gradually to 21% at the end of the simulation period (Fig. 6).
4. Discussion The sustainable management of soils in areas with (semi)-arid climates remains a great challenge. A high proportion of drylands has been strongly degraded by the synergistic action of physical factors and intensive agricultural practices (Acín-Carrera et al., 2013; ÁlvaroFuentes et al., 2014; Moraetis et al., 2015). Currently, various management practices capable to restore soil quality and fertility are evaluated with strong emphasis to be given on the minimal-tillage and the incorporation of organic amendments, but with important issues to remain yet unanswered (Álvaro-Fuentes et al., 2014). The findings from the chronosequence of set-aside practice investigated in this work revealed a nearly complete recovery of soil structure in fields subjected to tillage for more than three decades within only six years following tillage cessation. Acín-Carrera et al. (2013) also reported improvement in the structure of a sandy-loam soil in vineyards which have been abandoned for four years. However, the methodology used (CND index) did not allow for a direct comparison of the two studies. Cessation of tillage for six years increased the content of macroaggregates (> 250 μm) up to 40% compared to tilled fields in a severely degraded soil with low clay content (Hontoria et al., 2016). The field data revealed an uncoupling of soil structure recovery, addressed with an increase in the mass of macroaggregates, and C sequestration (Figs. 2 and 3). This finding from a first sight seems to contrast the conceptual model of aggregates formation proposed by Tisdall and Oades (1982) in which organic matter and microorganisms have a profound role in aggregate formation as well as the several studies reporting strong correlations between the mass distribution of macroaggregates and SOC content (Six et al., 2000; Wilson et al., 2009; Spohn and Giani, 2010; Veum et al., 2012). The formation of aggregates, however, is regulated by the interplay of numerous factors, including the composition of SOC, the structure of microbial community and its activity, as well as by abiotic factors including among others minerals composition, clay content and type, pH, and environmental
Fig. 6. Simulated evolution of the distribution of WSAs among the silt-clay sized class (AC1), the microaggregate size class (AC2) and the macroaggregate size class (AC3) in the upper 15 cm. The dashed horizontal line indicates the fertility threshold for the soil structure according to Banwart et al. (2011).
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protected by decomposition and suggest that additional practices are required to be considered to improve SOC sequestration in short-term time intervals. Simulations performed in a chronosequence of dryland soils in Spain with the CENTURY model also confirmed the slow rates of C accumulation in the Mediterranean landscapes and estimated the time required for soil to reach steady-state to approximately 20 years (Álvaro-Fuentes et al., 2014) that is in good agreement with the predictions obtained in this study.
conditions (Tisdall and Oades, 1982; Leifheit et al., 2014; Yin et al., 2016). Barto et al. (2010), by applying structural equation modeling, found that when the abiotic factors involved in the formation of aggregates were considered the influence of biotic factors was minor. The differentiation observed in this study could be attributed to the high clay content of the fields investigated. Clay content promotes soil aggregation directly by acting as binding agent or indirectly through swelling and shrinking (Leifheit et al., 2014) due to the alteration of wetting and drying cycles of soils. Complementary, a slight increase in SOC content early in the restoration period, although statistically no detectable, might have stimulated soil structure recovery (Lehmann et al., 2007; Liu et al., 2013). The lag in the accumulation of C compared to soil structure recovery strengthens the existing evidence that soil structure is a necessary condition for C sequestration by decreasing the accessibility and availability of SOC to exoenzymes and microorganisms (Six et al., 2002a; Dungait et al., 2012). Likewise, no difference in SOC content was observed in vineyards in central Spain which have been abandoned for four years and tilled vineyards, despite the strong improvement in soil structure that followed tillage cessation (Acín-Carrera et al., 2013). This delay in C sequestration could be explained by the low turnover rates of aggregates in soils with a high clay content in absence of mechanical disturbance. A remarkable finding of this study was the identical SOC content in all aggregate size classes of the 50-years set-aside field, except for the silt-clay fraction. This finding might indicate that the soil has reached a steady-state under the prevailing environmental conditions. The uniform SOC content provides evidence for a converge of the mechanisms involved in C sequestration, independently of the aggregate size class and possibly indicates the occlusion of finely fragmented POM as the principal mechanism. This hypothesis is strengthened by the similar C:N ratios in all aggregate size classes as well as the lack of C accumulation in the silt-clay fraction (Fig. 3) in which microbial-origin organic matter is mainly accumulated through interactions with the minerals (Vogel et al., 2014). Several indicators have been proposed to evaluate soil quality status. Emphasizing on soil structure, Banwart et al. (2011) suggested the fraction of WSA > 250 μm as quality indicator defining a threshold value of > 60% to distinguish agronomically valuable soils. This threshold has been exceeded only in the 50-years set-aside field. Quiroga et al. (2006) proposed the SOM/(silt + clay) ratio as indicator of soil quality and defined a threshold value of 4.4, which subsequently was adjusted to 4.5 by Giannakis et al. (2014) to account for soils with a high clay content. Considering the properties of soils investigated in this work, a SOC mass of 64.3 t ha−1 is needed to reach the threshold value, a condition which, however, cannot be achieved even after 100 years of natural restoration. The CAST model provided successful simulations regarding the soil structure and SOC stocks (Fig. 1), in accordance with simulations reported in earlier studies from diverge environments, soil properties and climatic conditions (Giannakis et al., 2014; Panakoulia et al., 2017). The simulations of CAST model revealed that the macroaggregates (AC3), the fraction with the greatest contribution in soil mass, had approached a steady state at year 12 (Fig. 6) but not the other fractions (Fig. 6). Simulations showed that a time interval of approximately 8 years of non-tillage is needed to reach the threshold (AC3 mass: > 60%) that satisfies the soil quality indicator introduced by Banwart et al. (2011). Regarding SOC content, the CAST model revealed that the AC3 and cPOM pool contributes the most to the SOC stocks of the bulk soil. Moreover, the CAST model predicted that SOC of the AC3 and POM fraction approaches steady-state approximately 13 years after tillage cessation (Fig. 7). The estimated low rates of C sequestration question the effectiveness of set-aside practice in dryland environments to restore its fertility. These findings indicate that C inputs limit the accumulation of SOC, even at soils with a high clay content where C is well
5. Conclusions The combination of field data and modeling tools allowed us to track the evolution of soil structure recovery and C sequestration in a chronosequence of set-aside fields under Mediterranean climatic conditions. The results indicated a rapid recovery of soil structure which, however was followed by low rates of C sequestration. Simulations with the CAST model showed that even after 100 years of the set-aside practice some soil quality indicators (e.g Quiroga index) did not reach the suggested thresholds. These findings outline the need to investigate additional soil restoration practices or to combine set-aside practice with other practices to restore and maintain the productivity of soils in the long term and to minimize degrading threats. Acknowledgements The study has been founded by the Technical University of Crete. References Álvaro-Fuentes, J., Plaza-Bonilla, D., Arrúe, J.L., Lampurlanés, J., Cantero-Martínez, C., 2014. Soil organic carbon storage in a no-tillage chronosequence under mediterranean conditions. Plant Soil 376, 31–41. Acín-Carrera, M., José Marques, M., Carral, P., Álvarez, A.M., López, C., Martín-López, B., González, J.A., 2013. Impacts of land-use intensity on soil organic carbon content, soil structure and water-holding capacity. Soil Use Manage. 29, 547–556. Banwart, S., Bernasconi, S.M., Bloem, J., Blum, W., Brandao, M., Brantley, S., Chabaux, F., Duffy, C., Kram, P., Lair, G., Lundin, L., Nikolaidis, N., Novak, M., Panagos, P., Ragnarsdottir, K.V., Reynolds, B., Rousseva, S., de Ruiter, P., van Gaans, P., van Riemsdijk, W., White, T., Zhang, B., 2011. Soil processes and functions in critical zone observatories: hypotheses and experimental design. Vadose Zone J. 10, 974. Barto, E.K., Alt, F., Oelmann, Y., Wilcke, W., Rillig, M.C., 2010. Contributions of biotic and abiotic factors to soil aggregation across a land use gradient. Soil Biol. Biochem. 42, 2316–2324. Bouyoucos, G.J., 1962. Hydrometer method improved for making particle size analyses of soils. Agron. J. 54, 464–465. Cambardella, C.A., Elliott, E.T., 1993. Methods for physical separation and characterization of soil organic matter fractions. Geoderma 56, 449–457. Coleman, K., Jenkinson, D.S., 1996. RothC-26.3 − A Model for the turnover of carbon in soil. In: Powlson, D.S., Smith, P., Smith, J.U. (Eds.), Evaluation of Soil Organic Matter Models: Using Existing Long-Term Datasets. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 237–246. Dungait, J.A.J., Hopkins, D.W., Gregory, A.S., Whitmore, A.P., 2012. Soil organic matter turnover is governed by accessibility not recalcitrance. Global Change Biol. 18, 1781–1796. Elliott, E.T., Palm, C.A., Reuss, D.E., Monz, C.A., 1991. Organic matter contained in soil aggregates from a tropical chronosequence: correction for sand and light fraction. Agric. Ecosyst. Environ. 34, 443–451. Elliott, E.T., 1986. Aggregate structure and carbon, nitrogen, and phosphorus in native and cultivated soils1. Soil Sci. Soc. Am. J. 50, 627–633. Fuentes, M., Hidalgo, C., Etchevers, J., León, F., Guerrero, A., Dendooven, L., Verhulst, N., Govaerts, B., 2012. Conservation agriculture, increased organic carbon in the top-soil macro-aggregates and reduced soil CO2 emissions. Plant Soil 355, 183–197. Giannakis, G.V., Panakoulia, S.K., Nikolaidis, N.P., Paranychianakis, N.V., 2014. Simulating soil fertility restoration using the CAST Model. Procedia. Earth Planet. Sci. 10, 325–329. Gregorich, E.G., Henry Janzen, H., Ellert, B.H., Helgason, B.L., Qian, B., Zebarth, B.J., Angers, D.A., Beyaert, R.P., Drury, C.F., Duguid, S., May, W.E., McConkey, B.G., Dyck, M.F., 2016. Litter decay controlled by temperature, not soil properties, affecting future soil carbon. Glob. Chang. Biol. Han, P., Zhang, W., Wang, G., Sun, W., Huang, Y., 2016. Changes in soil organic carbon in croplands subjected to fertilizer management: a global meta-analysis. Sci. Rep. 6, 27199. Hontoria, C., Gómez-Paccard, C., Mariscal-Sancho, I., Benito, M., Pérez, J., Espejo, R., 2016. Aggregate size distribution and associated organic C and N under different tillage systems and Ca-amendment in a degraded Ultisol. Soil Tillage Res. 160, 42–52. Kong, A.Y.Y., Six, J., Bryant, D.C., Denison, R.F., Van Kessel, C., 2005. The relationship between carbon input, aggregation, and soil organic carbon stabilization in
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