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Soil carbon sequestration in agroforestry systems: a meta-analysis

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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.
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Soil carbon sequestration in agroforestry systems:
a meta-analysis
Andrea De Stefano .Michael G. Jacobson
Received: 12 October 2016 / Accepted: 22 October 2017
ÓSpringer Science+Business Media B.V. 2017
Abstract Agroforestry systems may play an impor-
tant role in mitigating climate change, having the
ability to sequester atmospheric carbon dioxide (CO
2
)
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 C100 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 agri-
culture 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 agro-
forestry 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 agrosilvopas-
toral systems, forest to silvopasture, forest plantation
to silvopasture, and uncultivated/other to agrisilvicul-
ture. On the other hand, significant decreases were
observed in the transition from forest to agrisilvicul-
ture, 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. How-
ever, 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
Electronic supplementary material The online version of
this article (https://doi.org/10.1007/s10457-017-0147-9) con-
tains supplementary material, which is available to authorized
users.
A. De Stefano
School of Renewable Natural Resources, Louisiana State
University, Baton Rouge, LA 70808, USA
M. G. Jacobson (&)
Department of Ecosystem Science and Management, The
Pennsylvania State University, University Park,
PA 16802, USA
e-mail: mgj2@psu.edu
123
Agroforest Syst
DOI 10.1007/s10457-017-0147-9
dioxide (Nair et al. 2009a; Jose and Bardhan 2012).
Since the industrial revolution, atmospheric carbon
dioxide (CO
2
) 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 CO
2
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 CO
2
, 50% of the CH
4
,
and 70% of the N
2
O emitted on a global scale through
anthropogenic sources (Hutchinson et al. 2007). One
of the feasible strategies to reduce CO
2
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 CO
2
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 recog-
nized 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 CO
2
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 CO
2
,
which transforms CO
2
into soil inorganic C com-
pounds, (2) and indirect fixation of atmospheric CO
2
,
in which atmospheric CO
2
is incorporated in plant
tissue through the photosynthetic process, and subse-
quently, 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 CO
2
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
Agroforest Syst
123
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 (CO
2
,CH
4
, and
N
2
O), 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 planta-
tions, 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 previ-
ous 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 compre-
hensive quantitative reviews on the effect of agro-
forestry 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 compre-
hensive 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, dis-
crepancies 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 magni-
tude 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 agro-
forestry, (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
Agroforest Syst
123
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 ¼X
n
i¼1
SOCðMg C Mg1ÞBD SV
ð1Þ
where nis the number of soil layers, BD is soil bulk
density (Mg cm
-3
), and SV is soil volume (m
3
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 (Jobba
´gy 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
C100 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 stan-
dard 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 ¼SDmean
Meanmean
100 ð2Þ
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:
SDmissing ¼CV Meanmissing SD
100 ð3Þ
where Mean
missing SD
is the mean of the study with
missing SD.
This approach allowed to include of all experimen-
tal comparisons contained in the data set.
Categories
Due to the high variability of agroforestry systems and
land uses in general, categories were created. Agro-
forestry systems were grouped according to the
composition and arrangement of the system’s ele-
ments, functions of the system, its socioeconomic
scale, level of management, and its ecological spread
(Nair 1987). Three main agroforestry groups (treat-
ment) were considered: (1) agrisilviculture (crop-
s?trees); (2) silvopastoral (pasture/
animals ?trees); (3) agrosilvopastoral (crops ?pas-
ture/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 (hu-
man-induced ecosystems) into one single category
(pasture/grassland).
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
Agroforest Syst
123
R=X
e
/X
c
. To be statistically useful, Ris 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, Rwill 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 quanti-
fied 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 bootstrap-
ping 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] 9100).
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 agro-
forestry and land-use changes was statistically tested,
following a procedure similar to analysis of variance
(ANOVA). In random-effects models, the heterogene-
ity 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 kis 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-anal-
ysis investigating the overall effect of agroforestry, (2)
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
Agroforest Syst
123
a meta-analysis investigating the effect of each
agroforestry systems listed in Table 1. Publications
used in the meta-analysis presented various experi-
mental designs such as paired sites, pseudo-replica-
tion, 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 chronose-
quence 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 agro-
forestry showed significant differences (Fig. 1a),
increasing SOC stocks of 26% (Fig. 1b). The conver-
sion 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). Remov-
ing 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, pvalue 0.521) and
reduced datasets (Q=9.509, df =14, p-value 0.797)
(See Supplemental Material).
Looking at specific agroforestry systems, signifi-
cant 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, pas-
ture/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. 1 Effects of agroforestry (AF) on SOC stocks (0–15 cm
sampling depth). On the left avalues are effect size and 95%
bootstrap confidence intervals (CIs). On the right bvalues 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
Agroforest Syst
123
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), pas-
ture/grassland to agroforestry (9% increase), agricul-
ture 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).
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 sys-
tems (13% increase), uncultivated/other to agrisilvi-
culture (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 dou-
bled the SOC stocks % change due to overall
agroforestry effect (from 12% to 25%) (Fig. 4a, b).
Fig. 2 Effects of specific agroforestry systems on SOC stocks
(0–15 cm sampling depth). On the left avalues are effect size
and 95% bootstrap confidence intervals (CIs). On the right
bvalues 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
Fig. 3 Effects of agroforestry (AF) on SOC stocks (0–30 cm
sampling depth). On the left avalues are effect size and 95%
bootstrap confidence intervals (CIs). On the right bvalues 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
Agroforest Syst
123
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).
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 signifi-
cant 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).
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 sys-
tems (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
Fig. 4 Effects of specific agroforestry systems on SOC stocks
(0–30 cm sampling depth). On the left avalues are effect size
and 95% bootstrap confidence intervals (CIs). On the right
bvalues 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
Fig. 5 Effects of agroforestry (AF) on SOC stocks (0–60 cm
sampling depth). On the left avalues are effect size and 95%
bootstrap confidence intervals (CIs). On the right bvalues 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
Agroforest Syst
123
(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).
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).
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), agricul-
ture to agrosilvopastoral systems (30% increase),
pasture to agrisilviculture (11% decrease), forest to
agrosilvopastoral systems (36% decrease), forest to
silvopasture (32% increase), agriculture to silvopas-
ture (31% increase), uncultivated/other to silvopasture
(47% decrease), and forest plantation to silvopasture
Fig. 6 Effects of specific agroforestry systems on SOC stocks
(0–60 cm sampling depth). On the left avalues are effect size
and 95% bootstrap confidence intervals (CIs). On the right
bvalues 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
Fig. 7 Effects of agroforestry (AF) on SOC stocks (0–100 cm
sampling depth). On the left avalues are effect size and 95%
bootstrap confidence intervals (CIs). On the right bvalues 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
Agroforest Syst
123
(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).
0C100 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).
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. 8 Effects of specific agroforestry systems on SOC stocks
(0–100 cm sampling depth). On the left avalues are effect size
and 95% bootstrap confidence intervals (CIs). On the right
bvalues 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
Fig. 9 Effects of agroforestry (AF) on SOC stocks
(0 C100 cm sampling depth). On the left avalues are effect
size and 95% bootstrap confidence intervals (CIs). On the right
bvalues 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
Agroforest Syst
123
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
0C100 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 (Jobba
´gy and Jackson 2000), and soil charac-
teristics related to SOC sequestration dynamics and
storage, such as humification, aggregation, transloca-
tion 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 diversifi-
cation, 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 C100 cm), and
forest to agrosilvopasture (0–100 cm). On the other
hand, perennial grasses present in silvopastoral sys-
tems 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; Jobba
´gy 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
Fig. 10 Effects of specific agroforestry systems on SOC stocks
(0 C100 cm sampling depth). On the left avalues are effect
size and 95% bootstrap confidence intervals (CIs). On the right
bvalues 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
Agroforest Syst
123
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, con-
siderable 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 C100 cm deviated from the
general trend, and further investigation with additional
studies included are recommended. Among agro-
forestry 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 con-
version from pasture/grassland to agroforestry (0–15,
0–30, 0–100, 0 C100 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 (Jobba
´gy 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 below-
ground 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 differ-
ence (0–15, 0–100, 0 C100 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 unculti-
vated/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 Supple-
mental Material for more information). Guo and
Gifford (2002) observed a decrease in SOC stocks in
the transition from pasture to forest plantation, recog-
nizing the negative impacts of site preparation activ-
ities, 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
Agroforest Syst
123
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 hetero-
geneous nature of agroforestry in terms of site, soil
type, tree species, and management practices, pro-
duced 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/presenta-
tion 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 influ-
ences findings, in particular heterogeneity, which
might bias outcomes (Borenstein et al. 2009). Incon-
sistency 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 unfortu-
nately 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 manage-
ment 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 conver-
sion 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 sil-
vopasture and agrosilvopastoral systems. Finally, the
conversion from uncultivated/other land-uses to agro-
forestry 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 CO
2
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, informa-
tion completeness, and data presentation.
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... We based estimated changes in soil C primarily on data from global meta-analysis of soil C impacts of land use transitions to agroforestry [39][40][41] (Table SI.2). These estimates show increases in soil C with transitions from cropland to agroforestry, but uncertain impacts with transitions from pasture or grassland, and little to no data on transitions from shrubland. ...
... Another clear finding is that the greatest benefits for soil C will be in areas that were formerly intensively cultivated under plantation agriculture, representing over a third of the restoration area. This is in line with broader literature on the influence of both agroforestry and other reforestation projects on soil C [39][40][41]45 , which suggest the greatest benefits happen where projects occur on cultivated or highly altered lands 46 . In Hawaiʻi, the greatest benefits for overall carbon will be seen with restoration of invasive grasslands or sparsely vegetated non-native systems that were formerly in plantation agriculture given that high above-ground C and soil C gains are expected. ...
... In other areas, including grasslands and shrublands which were never used for intensive agriculture, but often were used for pasture, there is wide uncertainty over the likely influence of transitions to agroforestry on soil C. In general, the global literature is mixed on the influence of reforestation and transitions from pasture or grassland to agroforestry on soil C [39][40][41]45 , and there is little evidence for transitions from shrubland to agroforestry. However, given that above-ground C increases in these transitions, if soil C increases or stays the same, these areas would also be viable for carbon sequestration and likely expand the extent of positive carbon benefits. ...
Article
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There are growing efforts to incorporate agroforestry into ecosystem service incentive programs. Indigenous and other place-based multi-strata agroforestry systems are important conservation and agricultural strategies, yet their ecosystem services, including carbon sequestration benefits, have received little research attention. To fill this gap, we draw on interviews with agroforestry practitioners and ecosystem service modeling in Hawaiʻi to: (1) create future scenarios of where fallow unmanaged agricultural and non-native dominated conservation lands could be transitioned to multi-strata agroforestry under current and future climates; and (2) quantify the potential above-ground carbon and soil carbon benefits and tradeoffs of transitions across these scenarios. We found that about half of unmanaged fallow agricultural lands, representing >1,500 km² , was suitable for agroforestry transitions under current rainfall and over a third, representing >1,200 km², remained suitable under a dry climate change scenario, RCP 8.5 mid-century. Mean above-ground carbon in modeled agroforestry systems was estimated to be 92–125 Mg C ha⁻¹ (337–458 Mg CO2 ha⁻¹) with ~75% of the potential restoration area projected to significantly increase above-ground carbon storage. Considering both above-ground and soil carbon, overall carbon benefits are expected across over a third of the potential restoration area with just 5% of the area with expected overall losses. These results provide evidence for potential carbon hotspots for agroforestry transitions, as well as to the need for further study of soil carbon changes with multi-strata agroforestry transitions across varying climates and soil types. With potential carbon sequestration similar to or greater than that of native forest restoration, restoration through agroforestry represents an important pathway to achieving carbon benefits through multi-benefit forest-agricultural systems on large areas of unmanaged agricultural lands, offering a pathway to support inclusive and effective natural climate solutions.
... Additionally, they exhibit deeper rooting systems compared to crops, improving soil stability [6] and water retention levels by reducing runoff and promoting water infiltration [22]. Moreover, this habitat-enhancing vegetation stores more carbon in biomass and soil compared to croplands [23], and could thus help to mitigate agricultural GHG emissions, which contribute around 10 % of annual GHG emissions in Europe [24]. Research indicates that introducing habitat-enhancing measures in ground-mounted solar parks, such as the planting of grasses, shrubs, and flowering plants, significantly increased plant diversity (P = 0.0001) in UK solar farms compared to control sites [25]. ...
Article
Agrivoltaics offers a promising solution to the dual challenge of ensuring food security and expanding renewable energy infrastructure while optimising land use and bolstering climate resilience. This study addresses a research gap by evaluating habitat-enhancing strategies for agrivoltaics. Using the InVEST modelling framework, the effectiveness of these strategies on key ecosystem services-carbon storage, sediment retention, water retention, and pollinator supply-was assessed. Fifty-one utility-scale solar farms in NorthEastern Germany served as a hypothetical case study to analyse the potential ecosystem service benefits between habitat-enhanced and conventional farming practices in agrivoltaics. The Mini and Midi scenarios, aligned with the German agrivoltaic standard, integrated up to 15 % of habitat-enhancing elements in the field, while Maxi incorporated 22 %. Eco-Horticulture and Agriforst Orchard explored agricultural diversification by combining annual and perennial crops with habitat-enhancing features. Model results revealed significant ecosystem service gains compared to conventional farming practices: a 33-88 % increase in pollinator supply, 9-22 % in water retention, 7.5-20 % in sediment retention, and up to 8 % in carbon storage. Notably, the diversification approaches demonstrated exceptional potential to enhance biodiversity while providing income diversification for farmers. The study provides actionable insights for policymakers to scale agrivoltaics in line with countries' biodiversity targets and inform future agrivoltaic standards, balancing renewable energy deployment, land use efficiency and biodiversity conservation, aligned with multiple SDGs. Integrating habitat-enhancing features in agrivoltaics could improve the aesthetic appeal of solar infrastructure, fostering public acceptance. Further field studies are recommended to validate outcomes in agrivoltaic-specific microclimatic conditions and refine strategies to local contexts.
... Pengelolaan yang tepat dalam sistem agroforestri diharapkan dapat memaksimalkan sumberdaya yang ada dan meningkatkan kesejahteraan masyarakat tanpa menimbulkan kerusakan lahan dan degradasi lingkungan (Wanderi et al. 2019). Agroforestri memberikan beberapa manfaat lingkungan dalam meningkatkan kualitas tanah (Guimaraes et al. 2014;De Stefano, Jacobson 2017). Berdasarkan latar belakang tersebut, tujuan dari penelitian ini adalah: 1. Menganalisis karakteristik vegetasi dan sifat fisika tanah pada berbagai tutupan lahan agroforestri dan posisi kelerengan di Hutan Mandiangin. 2. Menganalisis hubungan antara karakteristik vegetasi dengan sifat tanah pada berbagai tutupan lahan agroforestri dan posisi kelerengan di Hutan Mandiangin. ...
Article
Full-text available
Perubahan penggunaan lahan pada kawasan hutan untuk tujuan tertentu (KHDTK) di Mandiangin, Kalimantan Selatan berdampak pada hilangnya keanekaragaman vegetasi dan perubahan sifat fisik tanah. Penelitian ini bertujuan untuk menganalisis karakteristik vegetasi dan sifat fisik tanah pada berbagai tipe tutupan lahan (karet/RB, lahan kosong/BL, agroforestri sederhana/SA, agroforestri kompleks/CA, dan hutan alam/NF) pada berbagai posisi lereng (atas, tengah). , dan bawah). Metode yang digunakan adalah purposive sampling untuk analisis vegetasi pada 5 jenis tutupan lahan dan 3 posisi lereng dengan tiga kali ulangan (45 plot). Parameter yang diamati sifat fisik tanahnya adalah Bulk Density (BD), Particle Density (PD) dan Porositas. Jumlah individu pada NF, SA, CA, RB, dan BL secara berurutan adalah (1.713.333, 1.035.000, 768.333, 444.167, 375.000 individu /ha individu). Jenis yang dominan pada hutan alam adalah alaban dan bangkal gunung, agroforestri kompleks yaitu rambutan, durian, nangka, alpukat, matoa dan mahoni, agroforestri sederhana yaitu jengkol, kemiri dan mahoni, perkebunan karet yaitu karet, lahan kosong yaitu karamunting. BD tertinggi terdapat pada hutan alam dan karet (1,21 g/cm³). Porositas tertinggi terdapat pada lahan gundul (53,34%) dan terendah pada karet (44,57%). Hasil ini menunjukkan bahwa variasi tutupan lahan dan kemiringan lahan berdampak signifikan terhadap sifat fisik tanah, yang penting bagi pengelolaan lahan berkelanjutan. Kata kunci: Agroforestri, Tutupan Lahan, Sifat fisika tanah
... A meta-analysis published in Soil Use Management found that agroforestry practices increased soil organic carbon by an average of 19% compared to conventional agriculture [15]. Another metaanalysis by study [16] found that transitioning from agriculture to agroforestry increased soil organic carbon stocks at 0-15 cm depth by 26%, 0-30 cm by 40%, and 0-100 cm by 34%. Agroforestry systems significantly increased biodiversity compared to monoculture systems. ...
Chapter
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Integrated farming systems aim to achieve synergies by deliberately combining crop, livestock, and other agricultural enterprises within a farm system. Well-managed integration can lead to greater productivity, more efficient resource utilization, and environmental benefits compared to specialized or single-enterprise systems. This chapter provides an overview of key principles, components, major examples, benefits, challenges, and future opportunities associated with integrated farming approaches. The background section defines integrated farming systems as those featuring purposeful complementarities between agricultural, horticultural, and aquatic production components. Several examples are provided, including crop–livestock integration through rotational grazing or stall-feeding of residues, the use of pond water and waste from aquaculture to support crop irrigation and soil nutrient management, and agroforestry combining woody perennials with agricultural crops or livestock. By taking an integrated approach that combines crops, livestock, forestry, aquaculture, and other farm enterprises, integrated farming systems can contribute to multiple sustainable development goals (SDGs). This chapter outlines considerations in enterprise selection along with different resource availability and agroecological contexts. Furthermore, it discusses planning for nutrient balancing, waste and by-product utilization, and coordinated production cycles and infrastructure requirements. While integrated systems can bolster productivity, economic returns, and climate resilience, challenges exist, including heightened management complexity, higher labor and skill requirements, and the need for supporting infrastructure. To realize the full advantages of integrated farming techniques, it is often necessary to expand the scale of farmers’ current operations. This expansion requires facilitating farmers’ access to knowledge-sharing platforms, financial incentives, new market openings, and transportation infrastructure that can handle multiple agricultural goods. Overall, well-designed and managed integrated farming systems hold promise toward developing multifunctional landscapes yielding environmental gains alongside sustainable livelihoods. The concluding section summarizes the challenges and potential for sustainable intensification of important future directions of food and nutrition security through purposefully managed integrated farming systems.
Article
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Hedgerow planting is recommended by biodiversity policies and those that promote the inclusion of woody plants in agricultural landscapes to sequester atmospheric carbon into the soil. However, the extent and variability of soil organic carbon (SOC) sequestration under hedges are not known. We measured SOC stock beneath hedges in five pedoclimatic conditions in the UK to quantify the SOC sequestration potential associated with hedgerow planting. We measured SOC stocks in 10 cm intervals in the top 50 cm of soil or to bedrock, comparing 46 hedges of different age classes and their adjacent grassland fields. We assessed how additional SOC stocks and SOC sequestration rates under hedges varied with covariates of climate and soil properties. The mean additional SOC stock under hedges was consistent across pedoclimatic conditions at~40 Mg C ha − 1 more than improved grassland fields. On average, SOC stocks beneath hedges were 40 % higher than in adjacent fields at 0-50 cm depth, with older hedges storing greater additional SOC stock at depth than younger hedges. The additional stock was driven by an increase in light particulate organic matter (l-POM), due to increased leaf and root litter inputs under woody vegetation. The mean SOC sequestration rate of mature hedges was 1.5 (1.0-2.0) Mg C ha − 1 yr − 1 while the net SOC sequestration rates over time since hedgerow planting declined from 4.2 to 0.2 Mg CO 2 km − 1 yr − 1 within the first 20 years. Our results will aid future land-use related carbon accounting and inform climate change mitigation practice.
Article
Glomalin is a glycoprotein produced by mycorrhizal fungi and is an important biotic agent in forming soil aggregates. Despite this, few studies have focused on its contribution to stabilizing soil organic carbon (SOC), especially in coffee cultivation systems. We aim to answer the following questions: do agroforestry and coffee monoculture systems influence the aggregate formation pathways? How is this influence reflected in the organic carbon and glomalin contents of biogenic and physiogenic soil aggregates? Thus, three coffee cultivation systems were evaluated (AFSG – coffee agroforestry system with grevillea, AFSC – coffee agroforestry system with cedar, and CM – coffee monoculture), and a native forest (NF) used as a reference. Soil was collected by removing monoliths which were subjected to dry aggregate fractionation in the field. The > 6 mm fractions were separated according to their morphological pattern (biogenic, physiogenic and intermediate aggregates). The SOC, labile carbon (LC), easily extractable glomalin and total glomalin (TG) contents were determined in the morphological classes. AFSG presented the highest carbon contents in whole soil (67.4 g kg−1), maintaining similar values to NF, while AFSC and CM did not vary from each other (average of 44.6 g kg−1). AFSG was also the coffee system with the highest root mass, LC and TG. The coffee systems did not affect the aggregate formation and distribution. AFSG proved to be more favorable to maintaining SOC and glomalin contents associated with biogenic and physiogenic aggregates.
Article
Land use change, mostly from forest to conventional agriculture, has a detrimental impact on soil health and production. However, the impact of such LUC on soil biological characteristics is unknown. This study aimed to evaluate some of the physicochemical and biological properties of soil with varied land uses in the southwestern Khorramabad area. The research locations comprised diverse land use types including coniferous forest, broadleaf forest, farmland, and rangeland. According to the findings, there was no significant variation in bulk density (ρb) and bulk density at 33 kPa (ρb33) for various land uses, but there was a significant difference between different soil layers. The amount of clay and silt varies dramatically across land uses. However, the quantity of sand used did not differ significantly across the usage (p Farmland (0.05%)> coniferous forest (0.03%). The findings also suggested that the quantity of microbial respiration has considerably declined in all locations as land use has shifted from forest to pasture and farmland. Notably, farmland includes the greatest population of fungi, bacteria, and actinomycetes, with a significant difference from other uses (p
Research
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Final report on the StartClim project “Agroforestry - How trees in the field can contribute to solving the biodiversity and climate crisis”
Chapter
Ecological research and the way that ecologists use statistics continues to change rapidly. This second edition of the best-selling Design and Analysis of Ecological Experiments leads these trends with an update of this now-standard reference book, with a discussion of the latest developments in experimental ecology and statistical practice. The goal of this volume is to encourage the correct use of some of the more well known statistical techniques and to make some of the less well known but potentially very useful techniques available. Chapters from the first edition have been substantially revised and new chapters have been added. Readers are introduced to statistical techniques that may be unfamiliar to many ecologists, including power analysis, logistic regression, randomization tests and empirical Bayesian analysis. In addition, a strong foundation is laid in more established statistical techniques in ecology including exploratory data analysis, spatial statistics, path analysis and meta-analysis. Each technique is presented in the context of resolving an ecological issue. Anyone from graduate students to established research ecologists will find a great deal of new practical and useful information in this current edition.
Chapter
Organic matter in the world’s soils contains about three times as much carbon as the land vegetation. Soil organic matter is labile and is likely to change as a result of human activities. Agricultural clearing, for example, results in a decline in soil organic matter. At the present time, there may be a net release of 0.85 × 1015 g C • yr−1 from soils of the world due to agricultural clearing (Houghton et al. 1983; Schlesinger 1984), or about 15% of the annual release from fossil fuels. The release of carbon may have been greater near the turn of the century as a result of more rapid agricultural expansion into virgin areas (Stuiver 1978, Wilson 1978). It is the purpose of this chapter (1) to review briefly the present estimates of the size of the pool of carbon in world soils and (2) to offer a review and analysis of what is known about the effects of agriculture on soil carbon storage.