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Agroforestry Systems

, Volume 92, Issue 2, pp 285–299 | Cite as

Soil carbon sequestration in agroforestry systems: a meta-analysis

  • Andrea De Stefano
  • Michael G. Jacobson
Article

Abstract

Agroforestry systems may play an important role in mitigating climate change, having the ability to sequester atmospheric carbon dioxide (CO2) in plant parts and soil. A meta-analysis was carried out to investigate changes in soil organic carbon (SOC) stocks at 0–15, 0–30, 0–60, 0–100, and 0 ≥ 100 cm, after land conversion to agroforestry. Data was collected from 53 published studies. Results revealed a significant decrease in SOC stocks of 26 and 24% in the land-use change from forest to agroforestry at 0–15 and 0–30 cm respectively. The transition from agriculture to agroforestry significantly increased SOC stock of 26, 40, and 34% at 0–15, 0–30, and 0–100 cm respectively. The conversion from pasture/grassland to agroforestry produced significant SOC stock increases at 0–30 cm (9%) and 0–30 cm (10%). Switching from uncultivated/other land-uses to agroforestry increased SOC by 25% at 0–30 cm, while a decrease was observed at 0–60 cm (23%). Among agroforestry systems, significant SOC stocks increases were reported at various soil horizons and depths in the land-use change from agriculture to agrisilviculture and to silvopasture, pasture/grassland to agrosilvopastoral systems, forest to silvopasture, forest plantation to silvopasture, and uncultivated/other to agrisilviculture. On the other hand, significant decreases were observed in the transition from forest to agrisilviculture, agrosilvopastoral and silvopasture systems, and uncultivated/other to silvopasture. Overall, SOC stocks increased when land-use changed from less complex systems, such as agricultural systems. However, heterogeneity, inconsistencies in study design, lack of standardized sampling procedures, failure to report variance estimators, and lack of important explanatory variables, may have influenced the outcomes.

Keywords

Agroforestry Carbon sequestration Soil organic carbon Climate change Meta-analysis 

Introduction

Anthropogenic carbon dioxide emission as cause of climate change

One of the recognized causes of climate change is the increasing concentration of atmospheric carbon dioxide (Nair et al. 2009a; Jose and Bardhan 2012). Since the industrial revolution, atmospheric carbon dioxide (CO2) concentration has increased more than 40%, rising from 280 ppm in 1750 to about 392 ppm in 2012, it was expected to exceed 400 ppm by 2015 (Hutchinson et al. 2007; Jose and Bardhan 2012). The most recent measurements confirm the expectations that atmospheric CO2 concentration exceed the 400 ppm threshold in 2016 (http://www.esrl.noaa.gov/gmd/ccgg/trends/weekly.html). Agriculture accounts for about 25% of the CO2, 50% of the CH4, and 70% of the N2O emitted on a global scale through anthropogenic sources (Hutchinson et al. 2007). One of the feasible strategies to reduce CO2 atmospheric concentration is carbon (C) sequestration, defined as the process of removing C from the atmosphere and depositing it in a reservoir (http://unfccc.int/essential_background/glossary/items/3666.php#C) (Nair et al. 2009a).

Climate change and land uses

C stock modifications associated with land-use change can occur naturally or as a result of human activities (Guo and Gifford 2002). Within human driven land-use change, agricultural and forestry practices have the potential to mitigate CO2 concentration through C sequestration, allowing the land acts as a ‘‘sink’’ for C (Hutchinson et al. 2007). In 1992, during the Kyoto Protocol, afforestation and reforestation were recognized as a form of GHG-offset activities (Nair et al. 2009a). Subsequently, different land management strategies, such as forest-, crop-, and grazing-land management, and revegetation, were added to the of land-use, land-use change and forestry (LULUCF) activities in 2001, including agroforestry practices (Nair et al. 2009a). With regard to those activities, C sequestration implies the removal of CO2 from atmosphere and its storage in long-lived pools of C, such as aboveground plant biomass, belowground biomass (roots, soil microorganisms), stable forms of organic and inorganic C in the soil, and long-lived products (i.e. timber products) (Sanchez 2000; Roshetko et al. 2002; Kirby and Potvin 2007; Nair et al. 2009a).

Soil as carbon sinks

Soil plays an important role in C sequestration, being able to store 1.5–3 times more C than in vegetation (Young 1997). The amount of C sequestered in the soil depends on a large number of factors, including the region, site quality, current land-use, previous land-use, and the portion of soil profile in case of land-use changes, (Nair et al. 2010). Generally, soil accounts for 60% of the total C stored in tree-based land-use systems (Lal 2004d; Lorenz and Lal 2005; Lal 2007; Nair et al. 2010). Soil C sequestration occurs in two different ways: (1) direct fixation of atmospheric CO2, which transforms CO2 into soil inorganic C compounds, (2) and indirect fixation of atmospheric CO2, in which atmospheric CO2 is incorporated in plant tissue through the photosynthetic process, and subsequently, part of plant biomass is indirectly sequestered as SOC during decomposition processes (Burras et al. 2001). On a global scale, the total soil C pool was estimated in the rage of 2157–2296 Pg, of which 1462–1548 Pg is soil organic C (SOC), and 659–748 Pg is soil inorganic C (SIC) (Batjes 1996). The total soil C pool is about three times the estimated atmospheric C pool, and 3.8 times the vegetation C pool (Lal 2004b; Nair et al. 2010); therefore, any variation in the soil carbon pool would have a significant impact on the global C budget. Among land uses, agricultural and degraded soil have a promising C sequestration potential: those soils were depleted of a significant part of their original organic C pool, and the adoption of specific management practices, such as the implementation of trees and permanent vegetation, could significantly boost their C sequestration potential (Albrecht and Kandji 2003; Lal 2004c).

The role of agroforestry and other land uses in mitigating climate change

Agroforestry could offer a viable opportunity to deal with climate change issues, having the potential to sequester and store atmospheric CO2 over long periods (Albrecht and Kandji 2003; Lorenz and Lal 2014). In sustainable-managed agroforestry systems, a large portion of organic C returns to the soil in the form of crop residues and tree litter (Oelbermann et al. 2004). Those inputs can help to stabilize soil organic matter (SOM) and decrease biomass decomposition rate and SOM destabilization, improving SOC stocks (Young 1997; Oelbermann et al. 2004; Lal 2004a; Sollins et al. 2007). Commonly, most agricultural soils act as a major source of greenhouse gasses (CO2, CH4, and N2O), having lost an important portion of their original soil organic carbon (Stavi and Lal 2013). Agroforestry can help to recover up to 35% of the original forest C stock lost due to slash and burn agriculture (Sanchez 2000). Previous individual studies and secondary quantitative reviews have investigated the overall effect of land-use change and management practices on SOC stocks, showing how the loss of soil coverage related to deforestation, forest clearing, land-use change, and other disturbance factors can negatively affect SOC stocks (Allen 1985; Detwiler 1986; Mann 1986; Schlesinger 1986; Neill et al. 1997; Fearnside and Imbrozio Barbosa 1998; Conant et al. 2001). Guo and Gifford (2002) found decreased SOC stocks after the conversion from pasture to plantation, native forest to crops, and pasture to crops. Afforestation, defined as the conversion of non-forested lands to forest plantations, decreased soil C due when Pinus species where used (Berthrong et al. 2009). Laganiere et al. (2010) investigated on the factors that contribute to restoring SOC stocks after afforestation, finding out that previous land-use, tree species planted, soil clay content, pre-planting disturbance, and climatic zone had a significant effect. Specifically, the positive impact of afforestation on SOC stocks was more evident in cropland soils than in pastures or natural grasslands. Don et al. (2011) found SOC losses from conversion of primary forest into cropland and perennial crops, forest into grassland. Furthermore, SOC losses were partly reversible with afforestation of agricultural land, and with cropland converted to grassland. Similar patterns were shown by Li et al. (2012): carbon stock increased following afforestation on cropland and pasture. The introduction of the cover crop in agricultural systems had a significant positive effect on SOC stock (Poeplau and Don 2015). Those results suggest that the introduction of trees may increase SOC stocks, supporting the potential pf agroforestry. However, despite the availability of important individual studies, there are few comprehensive quantitative reviews on the effect of agroforestry on SOC stocks during land-use change process. The potential of agroforestry in mitigating climate change is widely recognized, but appears to be based on opinions rather than quantifiable comprehensive data.

Objectives

The main objective of this paper was to investigate the effects of agroforestry on soil carbon stocks by summarizing the data from literature using meta-analytical techniques. Meta-analysis is defined as statistical procedure that allows one to compare results from different studies, in order to accomplish a statistical synthesis, finding common patterns, discrepancies or other interesting relationships that may come to light in the context of multiple studies (Borenstein et al. 2009). The core of meta-analysis is to identify a common measure, reflecting the magnitude of the strength of a phenomenon, called the effect size (Borenstein et al. 2009). Cohen (1992) defined an effect as a statistical measure that depicts the degree to which a given event is present in a sample, while Rosenberg et al. (2000) considered it as the different measures of a specific effect and their magnitude. More details about the effect size and its measure will be provided in Methods section. Specific objectives were to investigate the effects of: (1) overall agroforestry, (2) and specific agroforestry practices on SOC stocks as consequence of land-use change from non-agroforestry land uses to agroforestry.

Methods

Data collection

A literature survey of peer-reviewed publications was carried out using ISI-Web of Science, CAB Direct, and Google Scholar (Google Inc., Mountain View, CA, USA). The first step was the identification of the potential studies to be included in the analysis through the examination of the abstract, using agroforestry, C sequestration, soil C sequestration, soil C stock, soil C pool, and their combination their combination, as keywords for the search. The result yielded a total 250 observations from 52 publications (see Supplemental Material), encompassing over 20 countries, mostly located in Northern, Central, and Southern America, Africa, and Asia. To be included, studies had to contain information about soil C concentration or stocks per unit land area (i.e. Mg of C ha−1 or in other appropriate equivalent), for both non-agroforestry and agroforestry land uses (see Supplemental Material). When C concentration was reported as (Mg C Mg−1), SOC stocks (Mg C ha−1) were calculated as follows (Don et al. 2011):
$$SOC \;Stock = \mathop \sum \limits_{i = 1}^{n} SOC ({\text{Mg}}\; {\text{C}}\; {\text{Mg}}^{ - 1} ) \times BD \times SV$$
(1)
where n is the number of soil layers, BD is soil bulk density (Mg cm−3), and SV is soil volume (m3 ha−1). Studies considered different soil depths, ranging form 7.5 to 250 cm. SOC content increases with sampling depth, and its vertical distribution is function of several features, such as climate, soil texture, clay content, and vegetation type (Jobbágy and Jackson 2000, 2001). To reduce the variability due to the different sampling depths, the dataset was divided into the following sampling depths: (1) 0–15 cm, (2) 0–30 cm, (3) 0–60 cm, (4) 0–100 cm, and (5) 0 ≥ 100 cm. Studies that reported information for different sampling depths were included in more than one category. Details about the studies and the number of observations relative to each sampling depth are listed in the Supplemental Material.

Variance estimators and weighting function

One of the challenges in conduction of a systematic review deals with incomplete reporting of outcomes, which often results in the omission of estimates of variations—usually standard deviation (SD) or standard error (SE)—in the included studies (Furukawa et al. 2006; Wiebe et al. 2006). In meta-analysis, effect size estimates and related inferences are dependent on the weighting method (Hungate et al. 2009). The weighting function conventionally used in meta-analyses is based on the inverse of pooled variance (van Groenigen et al. 2011), and omitting studies that fail to report estimates of variance can be detrimental for the analysis itself, and may induce bias in the outcomes (Wiebe et al. 2006). Different approaches can be used to impute SD, to mitigate any loss in statistical power and to avoid bias. Authors were contacted to obtain non-reported or missing data. When no variance data were obtained, means and variation estimates from the all studies included in the meta-analysis were to calculate the coefficient of variation (CV) as follows (Bracken 1992):
$$CV = \frac{{SD_{mean} }}{{Mean_{mean} }} \times 100$$
(2)
where SD mean and Mean mean are the mean of the SD and the mean of the mean of all the studies included in the meta-analysis. Missing SD are computed as follows:
$$SD_{missing} = \frac{{CV \times Mean_{missing \;SD} }}{100}$$
(3)
where Mean missing SD is the mean of the study with missing SD.

This approach allowed to include of all experimental comparisons contained in the data set.

Categories

Due to the high variability of agroforestry systems and land uses in general, categories were created. Agroforestry systems were grouped according to the composition and arrangement of the system’s elements, functions of the system, its socioeconomic scale, level of management, and its ecological spread (Nair 1987). Three main agroforestry groups (treatment) were considered: (1) agrisilviculture (crops + trees); (2) silvopastoral (pasture/animals + trees); (3) agrosilvopastoral (crops + pasture/animals + trees) (Table 1). Non-agroforestry land uses (control) were grouped into five categories, according to the information provided by the authors: (1) agriculture; (2) forest; (3) forest plantation; (4) pasture/grassland; (5) uncultivated land/other. The last category includes uncultivated land, marginal or degraded land, and control (no tree), because of the lack of an adequate number of studies/observations needed for using those land uses as standalone categories. For a similar reason grassland and herbland (natural ecosystem) were merged with pasture (human-induced ecosystems) into one single category (pasture/grassland).
Table 1

Agroforestry grouping according to Nair (1987)

Category

Components

Systems

Agrisilvicultural systems

Woody perennials, agricultural species

Improved fallow species in shifting cultivation, alley cropping, multispecies tree gardens, multipurpose trees/shrubs farmlands, plantation crops and other crops, mixtures of plantation crops, shade trees for commercial plantation crops, agroforestry for fuelwood production, shelterbelts, windbreaks, riparian buffers, and soil conservation edges

Silvopastoral systems

Woody perennials, pasture/animals

Protein bank (multipurpose fodder trees on or around farmlands, living fences or fodder hedges and shrubs, trees and shrubs on pastures, and integrated production of animals and wood products

Agrosilvopastoral systems

Woody perennials, agricultural species, pasture/animals

Tree-livestock-crop mix around the homestead (home gardens), crop-animal-wood integrated production, woody hedgerows for browse, green manure, and soil conservation

Data analysis

An effect size estimator widely used in meta-analysis is the magnitude of the experimental treatment mean (X e ) relative to the control treatment mean (X c ). A common effect size metric is the response ratio R = X e /X c . To be statistically useful, R is often log transformed such that ln R = ln X e ln X c . When the distribution of X e and X c is normal, and X c is unlikely to be negative, R will be approximately normally distributed with a mean approximately equal to the true response log ratio (Gurevitch and Hedges 2001). The effect of agroforestry on SOC stock was quantified by calculating the natural log of the response ratio using MetaWin 2.0 (Rosenberg et al. 2000). A random-effects model was used for analysis of all data sets, based on the assumption that random variation in SOC stocks occurred between observations. Mean effect size for each observation was calculated with 95% confidence intervals (CIs) generated by a bootstrapping procedures (4999 interactions) (Efron 1979). Positive effect size indicates a positive impact of agroforestry. Likewise, the effect of agroforestry is considered negative for negative values of effect size. To facilitate the interpretation of effect size, it was transformed as percentage change ([ln R − 1] × 100).

Categorical meta-analysis

One of the specific objectives was to determine the effect of specific agroforestry practices on SOC stocks. For agroforestry practices, the difference in mean response among various categories of agroforestry and land-use changes was statistically tested, following a procedure similar to analysis of variance (ANOVA). In random-effects models, the heterogeneity of true effect size among studies is supposed to be due to random variation around the mean effect size of the population of studies, allowing different study-specific effect sizes (Borenstein et al. 2009). The total heterogeneity for a group of comparisons (Q T ) is partitioned into within-class heterogeneity (Q w ) and between class heterogeneity (Q b ), such that Q T  = Q w  + Q b . To assess whether there is significant heterogeneity in a sample, Q is tested against a Chi square distribution with k − 1 degrees of freedom, where k is the number of categories. The null hypothesis for this test is that all effect sizes are equal (Gurevitch and Hedges 2001; Borenstein et al. 2009). A significant Q indicates that the variance among effect sizes is greater than expected by sampling error (Cooper 1998). Between-group heterogeneity (Q b ) was analyzed across the data and the overall mean and confidence intervals (CIs) were calculated. Means were considered significant when the CIs did not overlap with zero (Gurevitch and Hedges 2001).

Datasets

Natural forests and vegetation have the ability to increase the SOM and consequently SOC, due to their organic matter inputs (litter, decomposing material) and soil protection provided by trees, shrubs, and perennial plants reduces C losses due to precipitation leaching and other disturbances factors (Guo and Gifford 2002; Don et al. 2011; Poeplau and Don 2015). Therefore, outcomes also included results deriving from the exclusion of observations relative to conversions from forest (but not forest plantations) and uncultivated land/other. For each sampling depth, two meta-analyses were performed: (1) a meta-analysis investigating the overall effect of agroforestry, (2) a meta-analysis investigating the effect of each agroforestry systems listed in Table 1. Publications used in the meta-analysis presented various experimental designs such as paired sites, pseudo-replication, chronosequence, and repeated measures. An explanation of experimental designs was provided by Laganiere et al. (2010). In paired site design, a treatment site is compared to an adjacent control site, allowing comparisons among different treatments. Paired site design assumes certain variables to be fixed between fixed and control sites, and the sampling constitutes a single measurement in time. A chronosequence is defined as the combination of a series of paired sites, supposedly having similar characteristics, spread out over time to simulate a succession. The basic assumption of this design is that each site in the sequence differs only in age, and that each has the same history.

Results

0–15 cm

Analysis of the effect size revealed no significant difference in the transition to agroforestry (Fig. 1a). However, the conversion from agriculture to agroforestry showed significant differences (Fig. 1a), increasing SOC stocks of 26% (Fig. 1b). The conversion from forest to agroforestry indicated a negative and significant effect (Fig. 1a), with a decrease of 26% (Fig. 1b). No significant differences in SOC stocks were found in the shift from pasture/grassland and uncultivated/other to agroforestry (Fig. 1a). Removing natural forest and uncultivated/other from the analysis the effect size produced a significant effect in the land-use change towards agroforestry (Fig. 1a), with a 13% increase in SOC stocks (Fig. 1b). Change from agriculture to agroforestry increased the SOC stocks by 25%, while there was no difference in the conversion from agroforestry to pasture/grassland (Fig. 1a, b). Test for heterogeneity was not significant for both full (Q = 30.917, df = 32, p value 0.521) and reduced datasets (Q = 9.509, df = 14, p-value 0.797) (See Supplemental Material).
Fig. 1

Effects of agroforestry (AF) on SOC stocks (0–15 cm sampling depth). On the left a values are effect size and 95% bootstrap confidence intervals (CIs). On the right b values are percentage change of SOC stocks. Effect size is considered significant when confidence intervals did not overlap with zero. Numbers in parentheses indicate number of observations, and * denotes results generated when land-use changes from forest and uncultivated/other to agroforestry were excluded from the analysis

Looking at specific agroforestry systems, significant differences on SOC stocks were detected in the transition from forest to agrisilviculture (12% decrease), forest to silvopasture (44% decrease), and agriculture to agrisilviculture (25% increase), while the overall agroforestry effect produced no significant differences (Fig. 2a, b). Land-use change towards agroforestry showed a significant effect on SOC stocks (9% increase), when natural forest and uncultivated/other were removed from the analysis (Fig. 2a, b). No noticeable variations were observed in the transition from pasture/grassland to agrisilviculture, pasture/grassland to silvopasture, and agriculture to agrisilviculture (Fig. 2a, b). Test for heterogeneity was significant for the full dataset (Q = 62.949, df = 32, p-value 0.001), while was not significant for the reduced dataset (Q = 9.199, df = 14, p-value 0.818) (See Supplemental Material).
Fig. 2

Effects of specific agroforestry systems on SOC stocks (0–15 cm sampling depth). On the left a values are effect size and 95% bootstrap confidence intervals (CIs). On the right b values are percentage change of SOC stocks. Effect size is considered significant when confidence intervals did not overlap with zero. Numbers in parentheses indicate number of observations, and * denotes results generated when land-use changes from forest and uncultivated/other to agroforestry were excluded from the analysis

0–30 cm

Land-use change to agroforestry denoted a positive and significant effect size on SOC stocks, with an increase of 12% (Fig. 3a, b). Other significant effect sizes on SOC stocks were observed in the transition from forest to agroforestry (22% decrease), pasture/grassland to agroforestry (9% increase), agriculture to agroforestry (40% increase), and uncultivated/other to agroforestry (25% increase), (Fig. 3a, b). The removal of forest and natural/uncultivated categories confirmed the positive trend, increasing the magnitude of effect size for the conversion to overall agroforestry (26% increase), while the results for pasture/grassland and agriculture to agroforestry were identical (Fig. 3a, b). Test for heterogeneity was not significant for both the full (Q = 30.986, df = 44, p-value 0.931) and the reduced dataset (Q = 11.229, df = 27, p-value 0.997) (See Supplemental Material).
Fig. 3

Effects of agroforestry (AF) on SOC stocks (0–30 cm sampling depth). On the left a values are effect size and 95% bootstrap confidence intervals (CIs). On the right b values are percentage change of SOC stocks. Effect size is considered significant when confidence intervals did not overlap with zero. Numbers in parentheses indicate number of observations, and * denotes results generated when land-use changes from forest and uncultivated/other to agroforestry were excluded from the analysis

Significant differences on SOC stocks were detected in the transition from forest to agrisilviculture (24% decrease), agriculture to agrisilviculture (40% increase), pasture/grassland to agrosilvopastoral systems (13% increase), uncultivated/other to agrisilviculture (55% increase), uncultivated/other to agrosilvopastoral systems (7% increase), and overall agroforestry effect (12%) (Fig. 4a, b). Removing forest and uncultivated/other form the analysis doubled the SOC stocks % change due to overall agroforestry effect (from 12% to 25%) (Fig. 4a, b). Test for heterogeneity was not significant for both the full (Q = 29.230, df = 42, p-value 0.918) and the reduced dataset (Q = 10.916, df = 26, p-value 0.996) (See Supplemental Material).
Fig. 4

Effects of specific agroforestry systems on SOC stocks (0–30 cm sampling depth). On the left a values are effect size and 95% bootstrap confidence intervals (CIs). On the right b values are percentage change of SOC stocks. Effect size is considered significant when confidence intervals did not overlap with zero. Numbers in parentheses indicate number of observations, and * denotes results generated when land-use changes from forest and uncultivated/other to agroforestry were excluded from the analysis

0–60 cm

The conversion of pasture/grassland to agroforestry significantly increased SOC stocks by 10% (Fig. 5a, b). On the other hand, the conversion of uncultivated/other to agroforestry decreased SOC stocks by (23%). No significant effect sizes on SOC stocks were found in the conversion from forest to agroforestry, forest plantation to agroforestry, agriculture to agroforestry, and in the overall agroforestry effect (Fig. 5a, b). The removal of forest and uncultivated/other did not noticeably affected the previous outcomes, but changed the overall agroforestry effect from not significant to significant, increasing SOC stocks by 10% (Fig. 5a, b). Test for heterogeneity was significant for both the full (Q = 101.479, df = 50, p-value 0.000) and the reduced dataset (Q = 59.030, df = 35, p-value 0.006) (See Supplemental Material).
Fig. 5

Effects of agroforestry (AF) on SOC stocks (0–60 cm sampling depth). On the left a values are effect size and 95% bootstrap confidence intervals (CIs). On the right b values are percentage change of SOC stocks. Effect size is considered significant when confidence intervals did not overlap with zero. Numbers in parentheses indicate number of observations, and * denotes results generated when land-use changes from forest and uncultivated/other to agroforestry were excluded from the analysis

Significant changes in SOC stocks were reported in the conversion from forest plantation to silvopasture (17% increase), forest to agrosilvopastoral systems (27% decrease), agriculture to agrosilvopastoral systems (21% increase), agriculture to silvopasture (66% increase), pasture/grassland to agrisilviculture (8% increase) (Fig. 6a, b). The removal of forest and uncultivated/other changed the effect size of overall agroforestry from not significant to significant, with an 11% increase in SOC stocks (Fig. 6a, b). Test for heterogeneity was significant for both the full (Q = 113.950, df = 47, p-value 0.000) and the reduced dataset (Q = 65.062, df = 34, p-value 0.001) (See Supplemental Material).
Fig. 6

Effects of specific agroforestry systems on SOC stocks (0–60 cm sampling depth). On the left a values are effect size and 95% bootstrap confidence intervals (CIs). On the right b values are percentage change of SOC stocks. Effect size is considered significant when confidence intervals did not overlap with zero. Numbers in parentheses indicate number of observations, and * denotes results generated when land-use changes from forest and uncultivated/other to agroforestry were excluded from the analysis

0–100 cm

The land-use change from agriculture to agroforestry significantly increased the SOC stocks by 34%, and the overall effect of agroforestry on SOC stocks was found significant (8% increase) (Fig. 7a, b). On the other hand, there was no significant difference in the land-use change from forest, pasture/grassland, forest plantation, and uncultivated/other to agroforestry (Fig. 7a, b). Removing forest and uncultivated/other from the analysis increased the SOC stocks due the overall agroforestry effect by 5 percentage points (from 8 to 13%) (Fig. 7a, b). Test for heterogeneity was significant for both the full dataset (Q = 80.108, df = 80, p-value 0.476), while was significant for the reduced dataset (Q = 84.383, df = 51, p-value 0.002) (See Supplemental Material).
Fig. 7

Effects of agroforestry (AF) on SOC stocks (0–100 cm sampling depth). On the left a values are effect size and 95% bootstrap confidence intervals (CIs). On the right b values are percentage change of SOC stocks. Effect size is considered significant when confidence intervals did not overlap with zero. Numbers in parentheses indicate number of observations, and * denotes results generated when land-use changes from forest and uncultivated/other to agroforestry were excluded from the analysis

The effect of agroforestry was found significant and positive, showing an increase SOC stock of 9% (Fig. 8a, b). Significant effect on land-use change on SOC stocks were found in the conversion from agriculture to agrisilviculture (10% increase), agriculture to agrosilvopastoral systems (30% increase), pasture to agrisilviculture (11% decrease), forest to agrosilvopastoral systems (36% decrease), forest to silvopasture (32% increase), agriculture to silvopasture (31% increase), uncultivated/other to silvopasture (47% decrease), and forest plantation to silvopasture (3% increase) (Fig. 8a, b). Removing forest and uncultivated/other had a positive influence on overall agroforestry effect, increasing the SOC stocks from 9 to 14% (Fig. 8a, b). Test for heterogeneity was significant for both the full (Q = 113.303, df = 79, p-value 0.006) and the reduced dataset (Q = 86.623, df = 50, p-value 0.001) (See Supplemental Material).
Fig. 8

Effects of specific agroforestry systems on SOC stocks (0–100 cm sampling depth). On the left a values are effect size and 95% bootstrap confidence intervals (CIs). On the right b values are percentage change of SOC stocks. Effect size is considered significant when confidence intervals did not overlap with zero. Numbers in parentheses indicate number of observations, and * denotes results generated when land-use changes from forest and uncultivated/other to agroforestry were excluded from the analysis

0 ≥ 100 cm

No significant influence on SOC stocks was detected in land-use conversions to agroforestry, nor in the overall effect of agroforestry (Fig. 9a, b). The removal of forest and uncultivated/other from the analysis did not affect the previous outcome (Fig. 9a, b). Test for heterogeneity was significant for both the full (Q = 110.457, df = 65, p-value 0.000) and the reduced dataset (Q = 47.838, df = 30, p-value 0.021) (See Supplemental Material).
Fig. 9

Effects of agroforestry (AF) on SOC stocks (0 ≥ 100 cm sampling depth). On the left a values are effect size and 95% bootstrap confidence intervals (CIs). On the right b values are percentage change of SOC stocks. Effect size is considered significant when confidence intervals did not overlap with zero. Numbers in parentheses indicate number of observations, and * denotes results generated when land-use changes from forest and uncultivated/other to agroforestry were excluded from the analysis

The same non-significant results were observed in the effect of specific agroforestry systems on SOC stocks with or without the removal of forest and uncultivated/other from the analysis (Fig. 10a, b). Test for heterogeneity was significant for both the full (Q = 105.846, df = 64, p-value 0.001) and the reduced dataset (Q = 42.744, df = 29, p-value 0.048) (See Supplemental Material).
Fig. 10

Effects of specific agroforestry systems on SOC stocks (0 ≥ 100 cm sampling depth). On the left a values are effect size and 95% bootstrap confidence intervals (CIs). On the right b values are percentage change of SOC stocks. Effect size is considered significant when confidence intervals did not overlap with zero. Numbers in parentheses indicate number of observations, and * denotes results generated when land-use changes from forest and uncultivated/other to agroforestry were excluded from the analysis

Discussion

Overall effect of agroforestry

According to our results and considering a full dataset with forest and uncultivated/other land-uses included, agroforestry revealed a significant and positive effect on SOC stocks at 0–30 and 0–100 cm depths. In the reduced dataset, the significant positive effect of agroforestry was observed at all depths, except for 0 ≥ 100 cm. Overall, and confirming other studies, incorporating trees on land leads to an increase in SOC stocks (Haile et al. 2008; Nair et al. 2009a). This is possibly due to changes in quantity and quality of litter inputs (Jobbágy and Jackson 2000), and soil characteristics related to SOC sequestration dynamics and storage, such as humification, aggregation, translocation of biomass into subsoil by root system, and leaching of inorganic C into groundwater (Lal 2001). Nair et al. (2009a) ranked SOC stocks as follows: forests > agroforests > tree plantations > arable crops, and our findings seemed to be in line with the pattern.

Forest to agroforestry

Land-use conversion from forest to agroforestry decreased the SOC stocks at 0–15 and 0–30 cm depths, while no significant effect was detected at the other investigated depths. The trend was somehow expected, since forests retain the most part of their SOC in the topsoil, and generally the conversion from forest to another land-use, such as agriculture, caused loss in SOC, especially in those upper layers (Brown and Lugo 1990; Guo and Gifford 2002; Leuschner et al. 2013). Specifically, significant decreases in SOC carbon were observed in the conversion from forest to agrisilviculture (0–15, 0–30 cm). Despite the presence of woody plants, agroforestry systems lack diversification, density, and structural complexity typical of natural ecosystems. However, the effect of natural vegetation on SOC is less evident in deeper layers, where no significant changes in SOC stocks were observed in the land-use change from forest to agrisilviculture (0–60, 0–100, 0 ≥ 100 cm), and forest to agrosilvopasture (0–100 cm). On the other hand, perennial grasses present in silvopastoral systems seem to be more efficient than woody plants in storing C in soil. Generally trees deposit a larger fraction of OM on the soil surface than grasses; here the decomposition process is dominant, and might lead to less formation of SOM and consequently less SOC (Post and Kwon 2000; Guo and Gifford 2002). Higher SOC stocks have been found in grasslands than forests (Brown and Lugo 1990; Jobbágy and Jackson 2000; Conant et al. 2001). This study showed silvopastoral systems having increased SOC stocks than forest at 0–100 cm of depth.

Agriculture to agroforestry

Findings suggested that the conversion of agricultural land to agroforestry significantly increased SOC stocks at 0–15, 0–30, 0–100, but not at 0–60 and > 100 cm. Different authors (Brown and Lugo 1990; Guo and Gifford 2002) also indicated that in the conversion from cropland to systems with trees, C sequestration increased. Again, trees appear to have a positive effect on SOC, thanks to higher inputs, deeper deposition, and reduced decomposability of OM (Post and Kwon 2000). Successful examples of SOC stock recovery after afforestation and reforestation are common in literature (Detwiler 1986; Houghton 1995; Don et al. 2011). Generally, agroforestry reduce tillage and soil disturbance regimes, which can help to maintain or even increase SOC pools (Aslam et al. 1999). Under proper agroforestry management, considerable litter inputs and vegetation residues from pruning are returned to the soil, which might increase SOC (Montagnini and Nair 2004). However, the results for 0–60 and 0 ≥ 100 cm deviated from the general trend, and further investigation with additional studies included are recommended. Among agroforestry systems, positive significant increases of SOC stocks were observed in the change from agriculture to agrisilviculture (0–15, 0–30, 0–100 cm), agriculture to agrosilvopastoral systems (0–60, 0–100 cm), and agriculture to silvopasture (0–100 cm). The inclusion of perennial grasses in agrosilvopastoral systems and silvopasture seems to increase SOC stocks as a consequence of their highly developed root system, allowing higher belowground translocation of C (Kuzyakov and Domanski 2000). In addition, grasses provide a continuous soil coverage, reducing soil temperatures, and can have higher belowground productivity and turnover rates that increase SOM (Brown and Lugo 1990; Conant et al. 2001).

Pasture/grassland to agroforestry

The establishment of agroforestry systems requires a certain level of inputs, although minimized under sustainable management, such as site preparation, tree planting, and other activities, which could reduce original grasslands and pastures SOC stocks (Guo and Gifford 2002). Findings seems to support agroforestry: no significant differences were observed in the conversion from pasture/grassland to agroforestry (0–15, 0–30, 0–100, 0 ≥ 100 cm), while a significant increase was observed at 0–60 cm. SOC stocks were found significantly reduced when pasture/grassland were converted into agrisilvicultural systems (0–100 cm), probably due to the lack of perennial grasses. In pastures, the annual turnover of organic matter from roots is higher than trees, which deposit more recalcitrant material (Jobbágy and Jackson 2001), increasing SOC stocks. Legumes are often sown to increase forage production, and they can increase soil nitrogen, improve soil fertility, increase belowground productivity, and consequently belowground C inputs (Watson 1963; Vallis 1972; Boddey et al. 1997; Conant et al. 2001).

Uncultivated/other to agroforestry

The category encompassed all land-uses that are not classified as forest by the authors and other without additional information available, such as bare land or control plots. Results indicated no significant difference (0–15, 0–100, 0 ≥ 100 cm), significant increase (0–30 cm), and significant decrease (0–60 cm) in SOC stocks. The conversion from uncultivated/other to agrisilviculture and agrosilvopastoral systems in SOC at 0–30 cm, while a significant reduction was observed in the transition from uncultivated/other to agrisilviculture at 0–60 cm. Findings were contrasting for the top layers, where both significant and not significant effects were detected. It is possible that the wide variation of the land-uses included in uncultivated/other category was somehow responsible of the diverging results. Hence, more detailed information about control groups is needed, in particular avoiding land-uses such as bare land or no vegetation as only control group.

Forest plantation to agroforestry

No significant differences in SOC stocks were detected in the conversion from forest plantation to agroforestry at 0–60 and 0–100 cm. Similarly, the land-use change from forest plantation to silvopasture did not produce significant results at the same depths. Unfortunately, the database for this category had observations only from a few studies (see Supplemental Material for more information). Guo and Gifford (2002) observed a decrease in SOC stocks in the transition from pasture to forest plantation, recognizing the negative impacts of site preparation activities, which can break soil aggregates, disturb soil structure, and disrupt physical protection provided by vegetation coverage. They also found how tree species may influence C storage: decreases in SOC stocks were observed in the conversion of pastures with conifers plantations, while little effects were reported in broadleaf plantations. Although fertilization inputs can enhance decomposition and reduce belowground C allocation (Haynes and Gower 1995), N-fixing species and fertilization were found, in certain case, to increase SOC sequestration rates, due to the additional nitrogen inputs (Ewers et al. 1996; Guo and Gifford 2002). Therefore, species and management practices may play an important role in SOC stocks in agroforestry systems compared to forest plantations.

Methodological issues

In the last decades studies investigating agroforestry SOC stocks have pointed out critical methodological challenges linked to sampling, analysis, computations, and interpretation of results (Nair 2011). The heterogeneous nature of agroforestry in terms of site, soil type, tree species, and management practices, produced an inconsistent estimate of agroforestry C stocks (Montagnini and Nair 2004; Nair 2011, 2012). Nair (2011, 2012) have largely discussed about methodological discrepancies and issues related to (1) soil sampling depth, (2) preparation of samples, (3) experimental design, and (4) calculation/presentation of results, pointing out the lack of rigorous standards. The consistency of effects across studies is a desirable requisite in meta-analysis (Higgins et al. 2003) and Liberati et al. (2001) advocated for it. The lack of standardized methodologies certainly influences findings, in particular heterogeneity, which might bias outcomes (Borenstein et al. 2009). Inconsistency across the studies is noticeable in wide confidence intervals, such as in Fig. 10a, b. One way to deal with the problem is to include more precise and consistent studies: increasing the sample size increases precision, reduces the variation, and the point estimate is more likely to represent the true mean value in the investigated population (Zlowodzki et al. 2007). However, the available database is unfortunately limited. The adoption of consistent standards in agroforestry research will increase the precision of future studies, and strongly suggest it includes (1) variance estimators, (2) detailed information about previous land-uses, and (3) follow the guidelines suggested by Nair (2011, 2012). Also, studies should include important explanatory variables, such as age of the systems and time to have C sequestration rates (quantity of C per area unit per time) rather than SOC stock quantities (Kim et al. 2016), adopted management practices (fertilization, irrigation, tillage, etc.), depth-relative BD, soil texture, pH, silt and clay content values (Brown and Lugo 1990; Laganiere et al. 2010), climatic factors (Brown and Lugo 1990); and vegetation species (Guo and Gifford 2002).

Conclusions

The conversion from forest to agroforestry lead to losses in SOC stocks in the top layers, while no significant differences were detected when deeper layers were included. On the other hand, the conversion from agriculture to agroforestry increased SOC stocks in most of the cases. Significant increases were also observed in the transition from pasture/grassland to agroforestry in the top layers, especially with the inclusion perennial in the systems, such as in silvopasture and agrosilvopastoral systems. Finally, the conversion from uncultivated/other land-uses to agroforestry produced inconsistent results, perhaps due to the high variability of the category, and the little available land-use history. Overall, SOC stocks increased when land-use changed from less complex systems, such as agricultural systems.

The purpose of the study was to provide an empirical foundation to support agroforestry systems as a strategy to reduce atmospheric CO2 concentration and mitigate climate change, as proposed by several authors (Albrecht and Kandji 2003; Montagnini and Nair 2004; Nair et al. 2009a, b, 2010; Mosquera-Losada et al. 2011; Nair 2012). However, important methodological issues, lack of information, and knowledge gaps might bias the outcome of the meta-analysis. Specific efforts are needed to build a more robust database for future research, in particular the adoption of unified and rigorous standards in study design, sampling collection and preparation, information completeness, and data presentation.

Supplementary material

10457_2017_147_MOESM1_ESM.pdf (149 kb)
Supplementary material 1 (PDF 148 kb)

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.School of Renewable Natural ResourcesLouisiana State UniversityBaton RougeUSA
  2. 2.Department of Ecosystem Science and ManagementThe Pennsylvania State UniversityUniversity ParkUSA

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