Ling et al. (2025). Soil organic carbon thresholds control fertilizer effects on carbon accrual in croplands worldwide

, Jennifer A. J. Dungait

2,3

, Manuel Delgado-Baquerizo

4

,

Zhenling Cui

1

, Ranran Zhou

1

, Wushuai Zhang

5

, Qiang Gao

6

, Yuanxue Chen

7

,

Shanchao Yue

8

, Yakov Kuzyakov

9,10

, Fusuo Zhang

, Xinping Chen

1

&

Jing Tian

Initiatives to restore soil fertility and mitigate global warming rely on
rebuilding soil organic carbon (SOC). Nitrogen (N) fertilization is crucial for
crop yields but affects SOC unpredictably due to varying responses of parti-
culate organic carbon (POC) and mineral-associated organic carbon (MAOC)
pools to initial SOC levels. To clarify these effects, here, by combining a global
meta-analysis with continental-scale

ﬁ

eld experiments, we determine that an

initial SOC threshold of 15 g C kg

−

1

controls the effect of N fertilization on POC

and MAOC. In SOC-poor soils (< 15 g C kg

−

1

), N fertilizer increases plant-derived

C inputs and promotes soil aggregation, favouring POC accumulation. Con-
versely, in SOC-rich soils, N fertilizer stimulates microbial metabolic ef

ﬁ

ciency,

leading to larger necromass production and stabilization by mineral protec-
tion, observed as more pronounced MAOC accrual. Our

ﬁ

ndings reveal how

SOC thresholds shape the response of active and stable carbon pools to N
fertilization, with consequences for SOC accrual in cropland soils globally.

Arable lands have lost up to half of organic carbon (C) stocks since the
Industrial Revolution, with a cumulative depletion nearing 62 Pg C,
limiting soil fertility and threatening food security

–

. Soils depleted in

organic C by intensive agriculture are often regarded as a potential
global C sink. Building SOC in croplands is a challenging task which is,
however, essential to restore soil functionality and ensure food
production

. Nitrogen (N) fertilization is essential for agricultural

productivity to meet the human demand for food

6

, but can strongly

affect SOC dynamics

7

8

. In general, N fertilization facilitates the

production of plant biomass, increasing C inputs to soil that should
increase SOC

9

. However, variable responses of SOC accrual to N

addition are reported, ranging from decreases

10

to no change

11

to

increases

12

. The wide variation in the response of SOC to N fertilization

has important implications for safeguarding soils to meet both Sus-
tainable Development Goal (SDG) 2 (Zero Hunger) and SDG 13 (Climate
Action) under current circumstances that require increased food
production, optimizing fertilizer use and increasing C capture/
storage

13

14

. Understanding anthropogenic impacts on SOC is assisted

Received: 20 August 2024

Accepted: 7 March 2025

Check for updates

1

State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, PR

China.

2

Geography, Faculty of Environment, Science and Economy, University of Exeter, Rennes Drive, Exeter EX4 4RJ, UK.

3

Carbon Management Centre,

SRUC-Scotland

’

s Rural College, Edinburgh EH9 3JG, UK.

4

Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y

Agrobiología de Sevilla (IRNAS), CSIC, 41012 Sevilla, Spain.

5

College of Resources and Environment, Academy of Agricultural Science, Interdisciplinary

Research Center for Agriculture Green Development in Yangtze River Basin, Southwest University, 400715 Chongqing, China.

6

College of Resources and

Environment, Jilin Agricultural University, 130118 Changchun, China.

7

College of Resources and Environment, Sichuan Agricultural University, 611134

Chengdu, China.

8

Institute of Eco-Environment and Industrial Technology, Shanxi Agricultural University, 030031 Taiyuan, China.

9

Department of Soil

Science of Temperate Ecosystems, University of Göttingen, 37077 Göttingen, Germany.

10

Bioeconomy Research Institute, Vytautas Magnus University,

Agriculture Academy, Kaunas Reg., Lithuania.

e-mail:

chenxp2017@swu.edu.cn

;

tianj@igsnrr.ac.cn

| (2025) 16:3009

1

12

3

4

5

6

78

12

3

4

5

6

7

8

by considering the total SOC pool as a combination of particulate
organic carbon (POC) and mineral-associated organic carbon (MAOC)
that differ broadly in their chemical composition, turnover, and eco-
logical functioning

15

–

17

. Where N availability to plants is limited,

N-induced increases plant C inputs into soils is a necessary

ﬁ

rst step to

SOC regeneration

18

19

. However, simply increasing the precursor

materials is not suf

ﬁ

cient to achieve long-term soil C sequestration:

protection of the newly formed SOC from decomposition is crucial to
ensure its long-term persistence. Therefore, understanding the
mechanisms underlying variation in the response of SOC pools and its
functionality to N fertilizer is now critical.

Initial SOC content has recently emerged as an important factor

that modi

ﬁ

es the responses of POC and MAOC pools to N fertilization

via direct effects on microbial metabolism and indirect effects on soil
physico-chemical and mineralogical traits

20

. Ecological stoichiometry

theory (EST) predicts that the growth and reproduction of microbial
communities are driven by microbial demand and resource supplies

22

Fertilizer N inputs to SOC-poor soils may cause an stoichiometric
imbalance (low C: high N) between microbial demands and soil
resource availability, leading to the reallocation of a greater propor-
tion of the substrate to maintenance metabolism

23

, observed as ele-

vated microbial respiration

24

. In contrast, fertilizer N inputs to SOC-

rich soil satisfy microbial nutrient demand

25

, stimulate microbial

activity by providing nutrients and energy to both accelerate decom-
position of plant residues and invest in the synthesis of microbial
biomass and, subsequently, necromass production

26

. The accumula-

tion of microbial necromass C and its stabilization as MAOC is also
contingent on wider soil geochemical properties that could undergo
signi

ﬁ

cant changes that might be substantially altered by N fertilizer

application

27

. SOC-rich soils buffer N-induced acidi

ﬁ

cation due to the

cation exchange capacity (CEC) of soil organic matter, at the cost of
increased leaching of base cations (e.g., Ca

2+

and Mg

2+

) and greater

solubility of hydrolyzed cations (e.g., Fe

3+

and Al

3+

)

28

. Compared to

ﬁ

ner-textured (clayey) soils, coarse-textured (sandy) soils tend to be

C-poor with smaller CEC and thus poorer buffering capacity against
cation leaching caused by N fertilizer use

28

. Variations in mineral cation

chemistry in

ﬂ

uence adsorption and co-precipitation reactions with

organic compounds in soils, including those derived from microbial
necromass, causing variation in the effect of N fertilizer on MAOC
formation

20

29

. Additionally, N fertilizer may strengthen macro-

aggregate formation by stimulating root and mycorrhizal growth,
which, together with plant-derived C, contribute to POC pool and its
stabilization as intra-aggregate POC

20

29

. Therefore, the relationship

between initial SOC and functional pools responses to N fertilization
incorporates myriad variations of complex interactions between mul-
tiple mechanistic drivers, including microbial metabolic and geo-
chemical factors that affect the balance between POC and MAOC
pools. Assessing the relationship between initial SOC and C seques-
tration in response to fertilization is critical to better forecast the
changes in POC and MAOC pools in a changing world.

Herein, we combined a global meta-analysis with new data from

decades-long continental-scale N fertilization

ﬁ

eld experiments in four

sites across China (Fig.

a and Supplementary Fig. 1) to investigate how

initial SOC content may interact with N fertilization to in

ﬂ

uence the

SOC accrual of global cropland soils. We hypothesized: 1) N fertiliza-
tion promotes plant productivity and macroaggregate formation, and
thereby increases POC accumulation and stabilization; 2) an imbalance
between resource stoichiometry and microbial nutrient demand in
SOC-poor soils hinders the formation of microbial necromass C and
the decomposition of POC, weakening MAOC accrual; 3) Greater
microbial metabolic ef

ﬁ

ciency and necromass production coupled

with mineral protection, all of which increase MAOC in SOC-rich soils
under increased N availability. Here, we address these hypotheses
using two comprehensive approaches: a global meta-analysis of POC
and MAOC contents from 118 fertilization experiments, and the

analysis of four coordinated long-term

ﬁ

eld experiments with N ferti-

lization to further evaluate the underlying mechanisms, including
plant input, microbial transformation, and protection by aggregate
and minerals. We identi

ﬁ

ed a critical soil carbon threshold (i.e.,

15 g kg

−

1

SOC) controlling the in

ﬂ

uence of N fertilization on C seques-

tration. Below this C threshold, the sequestration in response to fer-
tilization tends towards the formation of POC derived directly from
plant C, while surpassing this point shifts the balance towards MAOC
accumulation derived from soil microorganisms. By identifying how
speci

ﬁ

c SOC components respond to N fertilization and how carbon

thresholds in

ﬂ

uence these responses through plant-microbe-soil

interactions, this study paves the way for more targeted, effective
soil management strategies that can bolster global food security while
combating climate change.

Results

Fertilizer impacts on POC and MAOC depend on initial SOC in
global croplands

To determine whether initial SOC content affects global changes of
POC and MAOC in response to N fertilization, we compiled a database
including 609 paired observations of POC and MAOC changes from
118

ﬁ

eld sites worldwide (Fig.

a), covering a very wide range of initial

SOC content (g kg

−

1

; 0.79 to 46.1). Overall, POC had 5.0% negative, 41%

neutral and 54% positive responses to N addition (average effect size =
0.22,

P

= 0.02); while MAOC had 8.0% negative, 64% neutral and 28%

positive responses to N addition (average effect size = 0.06,

P

= 0.02;

Supplementary Fig. 2). To help explain changes in POC and MAOC
responses to N fertilizer and drivers underlying, we considered 8 cli-
matic, management and edaphic variables as moderators. Variance
partition analysis showed that the relative importance of edaphic
properties and management practices were higher than that of climate
conditions (Supplementary Fig. 3). Among them, signi

ﬁ

cant predictors

in our model included individual effects of initial SOC, N fertilizer and
their interactions (

P

< 0.01; Supplementary Fig. 4), suggesting that the

N fertilization on POC and MAOC was modulated by initial SOC levels
across different regions. The result showed that the nonlinear model
was more suitable for POC and MAOC to initial C content (Supple-
mentary Fig. 5 and Supplementary Table 1), which indicated that there
were SOC thresholds where abrupt changes occurred.

Further threshold analysis assessed the responses of POC and

MAOC to various initial SOC contents, identifying speci

ﬁ

c SOC

levels at which their responses exhibited abrupt changes. By com-
paring six nonlinear models, we found that the N-induced POC and
MAOC changes responded in a stegmented manner to increases in
initial SOC content (Supplementary Table 1). Speci

ﬁ

cally, abrupt

changes in POC and MAOC occurred over a small range of initial C
content of ~13

–

15 g kg

−

1

(Fig.

b, c). A sharp rise in POC was detected

before an initial C content of 14.8 g kg

−

1

and then slightly decreased

(Fig.

b). However, at low levels of initial C, MAOC decreased

abruptly. Once a 13.2 g kg

−

1

SOC threshold was crossed, MAOC

slightly decreased as SOC increased (Fig.

c). This notion is further

supported by heterogeneity analysis, demonstrating clear differ-
ences in the POC and MAOC responses before and after reaching the
SOC threshold under N addition (Q

M

= 19.5,

P

< 0.001 for POC;

Q

M

= 9.24,

P

< 0.001 for MAOC; Fig.

d, e). Note that the POC

increase under N fertilization was more important before the
threshold than afterward, whereas the MAOC response followed the
opposite trend.

Divergent responses of POC and MAOC to a decade of N addition
in experimental sites

To disentangle the potential mechanisms behind how initial SOC
content in

ﬂ

uenced N-induced POC and MAOC changes, we col-

lected soil samples from four coordinated long-running

ﬁ

eld N

fertilizer experiments network (Supplementary Fig. 1). These

Article

https://doi.org/10.1038/s41467-025-57981-6

| (2025) 16:3009

2

experiments were located in four cropland sites with different initial
SOC contents: Quzhou (QZ, 9.49 g kg

−

1

), Changwu (CW, 9.51 g kg

−

1

),

Siping (SP, 16.3 g kg

−

1

) and Yaan (YA, 17.4 g kg

−

1

). A decade of N

fertilizer affected SOC fractions across four sites (

P

< 0.001; Sup-

plementary Table 2). The optimized and high N treatments (OPT
and HN) increased POC content at QZ and CW (

P

< 0.01), while

increased MAOC content at the SP and YA (

P

< 0.01; Supplementary

Fig. 6). Given the consistent results from both OPT and HN, we
merged these

ﬁ

ndings under

‘

N fertilizer effects

’

in our analysis,

enhancing interpretability for readers. Interestingly, the responses
of POC and MAOC contents to N fertilizer divided the four sites into
two categories: POC accumulation response and MAOC accumula-
tion response (Fig.

). Along with individual effects of N fertilizer

and initial SOC content, there were interactive effects of N × Initial C
content on SOC functional pools (

P

< 0.001; Supplementary

Table 2), suggesting that changes of POC and MAOC contents to N
addition strongly depended on initial SOC content. Based on the
results of the meta-analysis, the initial SOC threshold for the
mutation of POC and MAOC was determined to be between
13

–

15 g kg

−

1

. The initial SOC content of QZ and CW was found to be

less than 13 g kg

−

1

, while that of SP and YA was greater than 15 g kg

−

1

.

Consequently, the soils at the four points were classi

ﬁ

ed into two

categories: C-poor soils and C-rich soils. Notably, a strong contrast
exists in the changes of POC and MAOC contents between C-poor
and C-rich soils (Fig.

b, d). Further analysis revealed that N fertilizer

had positive effect on POC content in C-poor soils, demonstrating
an average increase of 105% compared to the soil without N fertili-
zation (N0) (

P

< 0.001; Fig.

a, b). However, N addition increased

MAOC content by 15% in C-rich soils relative to N0 (

P

< 0.001;

Fig.

https://www.ngdc.noaa.gov/mgg/shorelines/data/gshhg/

c, d).

0

1

2

0

10

20

30

40

Initial SOC content (g kg

-1

)

RR of POC

0.00

0.02

0.04

Threshold = 14.8

0

1

2

RR of POC

-0.5

0.0

0.5

1.0

10

20

30

40

Initial SOC content (g kg

-1

)

RR of MAOC

-0.06

-0.04

-0.02

0.00

-0.4

0.0

0.4

0.8

RR of MAOC

This study
Literature data

P

< 0.001

Changing rate

of POC

Changing rate

of MAOC

P

< 0.001

a

b

c

d

e

Threshold = 13.2

threshold

After

threshold

Before

threshold

After

threshold

Before threshold

After threshold

Q

M

= 19.5

P

< 0.001

Q

M

= 9.24

P

< 0.01

0°

90° W

90° E

50° N

50° S

0°

Before

Fig. 1 | Global distribution of study sites and the interactions between nitrogen
fertilization and initial soil organic carbon (SOC)on particulate organic carbon
and mineral-associated organic carbon (POC and MAOC) in global cultivated
upland soil. a

Global distribution of study sites.

b

–

e

nonlinear responses of POC

and MAOC attributes to initial soil organic carbon content. In

b

,

c

black long-

dashed lines represent the smoothed trend

ﬁ

tted by a generalized additive model

and blue solid lines represent the linear

ﬁ

ts at both sides of each threshold. Inset red

numbers and the vertical dashed lines describe the initial SOC identi

ﬁ

ed. Violin

diagrams show bootstrapped slopes or values of the predicted

ﬁ

tted trend at the

threshold of the two regressions existing at each side of the threshold (green:
before the threshold; brown, after the threshold). Signi

ﬁ

cant Q

M

(between-group

heterogeneity) indicates that the response ratios differ between before and after
the threshold of SOC (

P

< 0.05). Basemap in (

a

) from Natural Earth (

https://www.

naturalearthdata.com/

). The coastline boundaries in the map are derived from

the GSHHG database (

latest/

). Source data are provided as a Source Data

ﬁ

le.

Article

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| (2025) 16:3009

3

Plant-microbe-soil matrix interactions underpin contrasting
responses of POC and MAOC to N fertilization in C-poor and
C-rich soils

To investigate the underlying mechanisms in governing the dynamics
of POC and MAOC contents in response to N fertilizer, we analyzed a
set of variables associated with C pools, including plant and microbial
biomarkers, soil microbial properties, aggregate and mineral protec-
tion as well as SOC chemical composition. As compared to N0, N fer-
tilizer increased root biomass in C-poor (~148%) and C-rich (~115%)
soils, respectively (

P

< 0.001; Fig.

a and Supplementary Fig. 7). N

fertilizer altered both lignin phenols and necromass C, depending on
initial C content (

P

< 0.001; Supplementary Table 2). As compared to

N0, N fertilizer increased lignin phenols by 18.4% (

P

= 0.121) but

decreased microbial necromass C in C-poor soils (

P

< 0.001; Fig.

a and

Supplementary Fig. 8). Lignin phenols and microbial necromass C had
contrasting responses to N fertilizer in C-rich soils: lignin phenols
exhibited a negative response, while microbial necromass C showed a
positive response (

P

< 0.01; Fig.

a and Supplementary Fig. 8). Conse-

quently, N fertilizer increased microbial necromass C by 10.2% com-
pared by N0, con

ﬁ

rming the accumulation of MAOC in C-rich soils

(

P

< 0.01; Fig.

a).

Microbial properties associated with C transformation exhibited

pronounced contrastive effects in response to N fertilizer between the
C-poor and C-rich soils (

P

< 0.05; Fig.

a and Supplementary Table 2).

Speci

ﬁ

cally, N fertilizer aggregated microbial C limitation, relative to

N0 in C-poor soils (

P

< 0.05; Fig.

a and Supplementary Fig. 9). By

contrast, N fertilizer alleviated microbial C and resources limitation,
leading to 13.6% decrease of microbial metabolic quotient (qCO

2

) in

C-rich soils (

P

< 0.001; Fig.

a and Supplementary Fig. 10), indicating a

more ef

ﬁ

cient microbial growth. Additionally, N addition reduced the

oxidases (phenol oxidase and peroxidase) by 26.1% relative to N0
(

P

< 0.01; Fig.

a and Supplementary Table. 3). We then measured the

mean weight diameter (MWD) of aggregates and the content of
exchangeable cations to quantify SOC protection by aggregates and
minerals, respectively. The fertilization effects on aggregate stability
and mineral protection often depended on initial SOC (

P

< 0.05; Sup-

plementary Table 2). N fertilizer had a positive effect on MWD in
C-poor soils (20.7% increase), but decreased in C-rich soils (

P

< 0.001;

Fig.

a and Supplementary Fig. 11). In contrast, N fertilizer enhanced

the content of Fe and Al oxides in C-rich soils, thereby augmenting the
soil

’

s mineral protection capacity (

P

< 0.05; Fig.

a and Supplementary

Fig. 11).

13

C-NMR spectroscopy analysis further showed that N fertilizer

increased aliphaticity and recalcitrance from 3.0% to 10.0% in C-rich
soils, indicating that N may contribute to the stabilization of organic
carbon in such soils (

P

< 0.05; Fig.

a and Supplementary Table 4).

Partial correlation analysis showed that, in C-poor soils, despite

the signi

ﬁ

cant correlations between POC and the four types of factors

(i.e., SOC origin, microbial transformation, stability and structure;
zero-order in Fig.

b), the correlation coef

ﬁ

cients of POC and Mg

exe

decreased by 203% after controlling for the SOC origin (Fig.

b). By

contrast, root biomass and MWD were always closely related to the
POC in C-poor soils (

P

< 0.001; Fig.

d), even after controlling for the

other three or two types of factors. Speci

ﬁ

cally, increasing root bio-

mass and MWD was coupled with increasing POC (R

2

= 0.68,

P

< 0.001;

R

2

= 0.45,

P

< 0.001) in C-poor soils (Fig.

d). In C-rich soils, after con-

trolling SOC stability, the correlation coef

ﬁ

cients between MAOC and

C origin (characterized by plant C input and biomarkers) became
insigni

ﬁ

cant (Fig.

c). Moreover, the root biomass and Fe

p

+ Al

p

con-

sistently increased with the MAOC (Fig.

e; R

2

= 0.58,

P

< 0.001;

R

2

= 0.65,

P

< 0.001). Accordingly, plant C input was the single control

in all soils, exerting strong positive effects on POC and MAOC. Physical
protection and mineral protection were strongly associated with POC
accumulation in C-poor soils and MAOC accumulation in C-rich soils,
respectively (Fig.

).

Changes in global farmland POC and MAOC under current N
fertilization scenarios

Brie

ﬂ

y, after the standard process of model metric evaluation (Sup-

plementary Table 5), the optimized gradient-boosting decision trees
model was chosen as the optimal model for POC and MAOC. We then
applied our predictive model to scale up the site-level observations
across the globe at a ~ 10-km resolution to quantify changes in POC
and MAOC under current N fertilizer application rate relative to a no-
fertilization scenario. Over the past decade, our global estimates
indicate that the application of N fertilizer has led to an
average increase in POC and MAOC by 26% and 8.3%, respectively

Quzhou

Changwu

Siping

Yaan

0

2

4

6

0

4

8

12

-0.2

0.0

0.2

0.4

0.6

0.8

-0.1

0.0

0.1

POC (g kg

-1

)

MAOC (g kg

-1

)

**

POC (Effect size)

MAOC (Effect size)

C-poor

C-rich

a

b

d

c

N0

N

N0

N

N0

N

N0

N

Fig. 2 | Effects of long-term nitrogen fertilization on carbon pools across four

ﬁ

eld experiments, with results categorized into C-poor and C-rich soils from

four sites. a, c

Effects of N fertilization on POC and MAOC across four

ﬁ

eld

experiments. Asterisk indicate a statistical signi

ﬁ

cance of the effect based on non-

parametric Wilcoxon test (

P

< 0.05) using the Benjamini and Hochberg (BH)

method (

n

= 4 for N0,

n

= 8 for N).

b

,

d

Effects of N fertilization on POC and MAOC in

both C-poor and rich soils. The estimated effect sizes are regression coef

ﬁ

cients

based on rescaled response variables (with zero mean and unit standard deviation)

in the linear mixed-effects model. The points and shades represent mean ± s.e.m. of
effect sizes in B and D. Statistical signi

ﬁ

cance is based on Wald type II

χ

² tests, and

95% CI not overlapping zero indicated signi

ﬁ

cant. POC: particulate organic carbon;

MAOC: mineral-associated organic carbon. N0: control (No N addition); N: N
addition (results of combining optimized N addition and high N addition). C-poor:
combining QZ and CW results; C-rich: combining SP and YA results. Source data are
provided as a Source Data

ﬁ

le.

Article

https://doi.org/10.1038/s41467-025-57981-6

| (2025) 16:3009

4

(Fig.

a and b). Moreover, we partitioned worldwide farmland into two

categories based on SOC thresholds (SOC threshold of our meta-
analysis data): C-poor and C-rich farmland. N fertilizer application
increased POC by 26% in C-poor soils and 22% in C-rich soils, compared
to unfertilized soils (Supplementary Fig. 12). However, the MAOC
increase induced by fertilization was 7% in C-poor soils and 8% in C-rich
soils (Supplementary Fig. 12). Across ecozones, we found that the
bene

ﬁ

cial impacts of N fertilizer on POC were greatest in the sub-

tropical (27%), followed by temperate (26%) and boreal (25%), and least
in tropical zones (24%; Supplementary Fig. 13). Speci

ﬁ

cally, substantial

POC accrual due to fertilization is most evident in east Asia and north
America. In contrast, the response of POC to fertilization in Africa was
minimal or neutral. Our study revealed a relatively uniform enhance-
ment in MAOC, induced by N fertilizer, across diverse ecozones, with
increases varying between 7% and 10 %. The temperate zone observed
the most signi

ﬁ

cant rise, whereas the tropical zone exhibited the

smallest increase (Supplementary Fig. 13).

Discussion

Restoring SOC in agricultural land is pivotal for enhancing sustainable
agricultural production and offering a viable strategy to mitigate

global warming

. Soil carbon is not a uniform entity, differentiating it

into POC and MAOC can help understand SOC formation and accrual,
and thus provide more precise guidance for managers and policy-
makers. However, current assessments of the impact of N fertilizers on
POC and MAOC depending on soil fertility levels are insuf

ﬁ

cient, which

impedes the advancement of sustainable agriculture and the optimi-
zation of carbon sequestration bene

ﬁ

ts. Combining a global meta-

analysis of published literature and experimental evidence from four
experiments, we identi

ﬁ

ed a critical soil C threshold (i.e., 1.5% SOC)

controlling the in

ﬂ

uence of N fertilization on C accrual. Below this

crucial soil C threshold, C accrual in response to fertilization is inclined
towards the formation of POC, while surpassing this point shifts the
balance towards more MAOC accumulation (Fig.

d, e). Initial SOC

content affects N-induced changes in POC and MAOC through direct
effects on plant C input and indirect effects on microbial transforma-
tion, physical and mineral protection (Fig.

https://BioRender.com/b56q841

).

Utilizing systematic research and diverse viewpoints, this study

delves into the mechanisms behind the formation of SOC functional
components mediated by N fertilizer and initial C, systematically
analyzing the sources, transformation processes, and stability of these
components. In agricultural systems, crop yields are often increased

Recalcitrance

Aliphaticity

Mg

exe

Ca

exe

Fe

p

+ Al

p

Fe

o

+ Al

o

Aggregate

(MWD)

C limitation

Resources

limitation

F:B

Oxidase

qCO

2

Necromass C

Lignin

phenols

Root biomass

−0.5

0.0

0.5

1.0

Effect size

C−rich soils

C−poor soils

No-significant
Sig

CO

2

Fe

O

Al

O

Fe

p

Al

p

Ca

2+

Mg

2+

Origin

Transformation

Stability

Structure

a

C rich soils-MAOC

0.76

-0

.74

0.17

0.63

-0

.51

-0.1

5

0.83

-0

.85

0.1

4

0.00

-0

.28

-0.47

-0.61

-0.53

0.0

7

-0.

18

-0.17

-0.59

-0.61

0.0

1

-0.

13

-0.23

-0.01

-0.11

-0.29

0.

36

0.24

0.11

0.1

9

-0

.25

0.

05

-0.22

-0

.7

4

0.22

0.81

0.20

0.10

-0

.5

6

0.03

0.70

0.09

0.13

-0

.7

3

0.2

8

0.80

0.38

0.33

-0

.1

7

0.41

0.43

0.51

0.

3

6

0

.4

2

-0.

13

0

.2

4

-0.

6

1

-0

.1

1

-0.

27

-0

.0

4

Z

ero

TS

ST

SA

Zero

ST

SA

OR

Z

e

ro

TS

ST

OR

Zer

o

TS

OR

SA

R

o

t b

io

ma

ss

Lig

nin

Nec

romass

qCO

2

Oxidase

F:B

R

e

s

o

u

rce

limit

a

ton

C limitation

MWD

Fe

p

+Al

p

Ca

exe

Mg

exe

Alip

hati

cit

y

R

ec

alcitr

ance

Fe

o

+Al

o

S

tr

u

c

tu

re

(S

T

)

Sta

bili

ty

(S

A)

Tran

sfo

rm

atio

n

(T

S

)

O

ri

gn

in

(O

R

)

c

b

S

tr

u

c

tu

re

(S

T

)

C poor soils-POC

0.

8

3

0.4

9

-0.59

0.

8

0

0.3

1

-0.

61

0.

6

0

-0

.3

9

-0.41

0.

7

0

0.0

6

-0.

42

0.13

-0.23

0.58

0.

34

0.46

-0.11

-0.13

-0.3

8

0.05

0.39

-0.16

-0.28

0.26

0.25

0.28

0.00

0.29

-0.16

-0.13

-0.38

0.

67

-0

.2

6

-0.35

0.56

-0.

82

0.

51

0.25

-0.05

0.49

-0.

67

0.

56

-0.1

9

-0.08

0.33

-0.

67

0.

29

0.

35

0.24

0.32

-0.

27

-0

.4

7

-0

.18

-0

.46

-0

.32

-0

.14

-0

.1

0

-0

.2

0

-0

.1

6

Zero

TS

SA

ST

Zero

SA

ST

OR

Z

e

ro

TS

ST

OR

Zero

TS

SA

OR

R

o

t b

io

ma

ss

Lignin

Necr

omass

qCO

2

Oxi

dase

F:

B

R

e

s

ou

rce

limita

to

n

C limitation

M

WD

Fe

p

+Al

p

Ca

exe

Mg

exe

Alip

hati

cit

y

R

e

c

alcitr

an

c

e

Fe

o

+Al

o

Sta

bili

ty

(S

A)

Tran

sfo

rm

atio

n

(T

S

)

O

ri

gn

in

(O

R

)

S

tr

u

c

tu

re

(S

T

)

−2

−1

0

1

2

−1.0

0.0

1.0

Scaled Root biomass

Scaled POC

−1.0

0.0

1.0

Scaled MWD

−1

0

1

2

−1.0

0.0

1.0

Scaled Root biomass

Scaled MA

OC

−1.0

0.0

1.0

Scaled Fe

p

+Al

p

e

d

R

2

= 0.68

P

< 0.001

R

2

= 0.45

P

< 0.001

R

2

= 0.58

P

< 0.001

R

2

= 0.65

P

< 0.001

N0
N

-1.0 -0.8 -0.6 -0.4 -0.2 0.0

0.2

0.4

0.6

0.8

1.0

Partial correlation
coefficient r

Fig. 3 | Effects of long-term nitrogen fertilization on the origin, transformation,
and stability of carbon pools, as well as key indicators of carbon pool
dynamics. a

Effects of N fertilization on SOC origin (root biomass, lignin phenols

and microbial necromass C), microbial transformation (qCO

2

, oxidase, F:B,

resource limitation and C limitation), SOC physical and biochemical stability
(aggregate stability, Fe

o

+ Al

o

, Fe

p

+ Al

p

, Ca

exe

and Mg

exe

) and structure (Aliphaticity

and Recalcitrance) between C-poor and C-rich soils and their difference. The esti-
mated effect sizes are regression coef

ﬁ

cients based on rescaled response variables

(with zero mean and 95% con

ﬁ

dence intervals) in the linear mixed-effects model.

The points and shades represent mean ± s.e.m. of effect sizes. Statistical sig-
ni

ﬁ

cance is based on Wald type II

χ

² tests, and 95% CI not overlapping zero indi-

cated signi

ﬁ

cant. Filled circles represent signi

ﬁ

cant effects by N fertilizer (

P

< 0.05).

b

,

c

Partial correlations between POC of C-poor soils and MAOC of C-rich soils

with four types of factors (SOC origin (OR), microbial transformation (TS), SOC
physical and mineral stability (SA) and structure (ST)). The outermost circle
represents the factors (i.e., OR, TS, SA and ST) under examination for their corre-
lations with POC or MAOC. The color of the fan shapes signi

ﬁ

es the strength and

direction of the correlation, with a black frame indicating statistical signi

ﬁ

cance at

the 0.05 level. Distinctions in color between the zero-order (Zero) and controlled

factors indicate the extent of dependency of the correlation between POC or MAOC
and the examined factor on the controlled variable (no change in circle color
between the controlled factor and zero-order = no dependency; a decrease/
increase in circle color intensity = loss/gain of correlation).

d

,

e

Correlations

between POC in C-poor soils and MAOC in C-rich soils with key predictors. Solid line
in each panel shows signi

ﬁ

cant model

ﬁ

t using linear regression (

P

< 0.05), and the

shading around the

ﬁ

tted line represents the 95% con

ﬁ

dence intervals. qCO

2

(

μ

g CO

2

-C

μ

g

−

1

MBC h

−

1

): metabolic quotient; F:B: fungal-bacterial biomass ratio;

MWD: aggregates mean weight diameter; Fe

o

+ Al

o

: po

o

rly crystalline Fe/Al oxides;

Fe

p

+ Al

p

: organically complexed Fe/Al oxides; Ca

exe

and Mg

exe

: the molar ratios of

exchangeable Ca

2+

and Mg

2+

. Aliphaticity: the ratio of the alkyl region divided by the

O-alkyl region; Recalcitrance: the ratio of (alkyl + aromatic C)/ (O-alkyl + carboxyl
C). POC: particulate organic carbon; MAOC: mineral-associated organic carbon. N0:
control (No N addition); N: N addition (results of combining optimized N addition
and high N addition). C-poor soils: combining Quzhou and Changwu results; C-rich
soils: combining Siping and Yaan results. Some elements in (

a

) were created in

BioRender. B, A. (2025)

. Source data are provided

as a Source Data

ﬁ

le.

Article

https://doi.org/10.1038/s41467-025-57981-6

| (2025) 16:3009

5

by N fertilization, and both above- and belowground biomass and root-
derived C inputs are generally larger

18

. In C-poor soils, the increased

POC was evidenced by larger root biomass and lignin phenols fol-
lowing N fertilization (Fig.

a and d), in agreement with hypothesis (1).

Typically, an increase in plant C input catalyzes a corresponding
upsurge in enzyme activities, attributable to the enriched availability
of substrates that

“

fuel

”

microbial processes

30

. In the present study,

soil oxidase activities decreased slightly with N addition in C-poor soils
(Supplementary Table 3), which is consistent with a recent meta-
analysis that N addition suppressed the activity of lignin-decomposing
enzymes

31

, resulting in the accrual of plant residues as lignin

macromolecules

32

. Other than extracellular enzymes, microbial com-

position and classi

ﬁ

cation also play a role in mediating lignin break-

down and subsequent POC formation. Fungal biomass and F:B were
increased under N fertilization (Fig.

a; Supplementary Fig. 14), sug-

gesting that microbial communities undergo a transition towards a
fungus-dominated ecosystem to raise the ef

ﬁ

cient breakdown of

lignin

33

, also observed by Man et al.

34

. Furthermore, according to EST,

when N is abundant in C-poor soils, an imbalance between resource
availability and microbial nutrient needs can lead to stoichiometric
imbalance

23

,

25

. This imbalance hinders the transformation of POC to

MAOC because microbes, the primary mediators in synthesizing
unstable C into MAOC, lead to higher POC retention

19

35

. In addition to

the aforementioned biological factors, soil aggregates play a pivotal
role in the physical protection of POC (Fig.

b, d)

36

. We found that N

fertilization increased the stability of soil aggregates (Fig.

a), which

may be a result of increased plant litter, rhizodeposition and fungi

biomass. The residues of litter and rhizodeposition have the capacity
to bind with fungal hyphae and soil particles, leading to the formation
of macroaggregates

37

38

. Soil texture, alongside aggregate formation, is

a key factor in

ﬂ

uencing C accumulation

39

–

41

. Our meta-analysis indi-

cated that clay and silt content strongly impact POC and MAOC
accumulation (Supplementary Fig. 4). Heterogeneity analysis revealed
that the effect size of POC is larger in clayey soils than clay loamy soils
(Supplementary Fig. 15), as clay has more reactive sites for C
adsorption

42

. Overall, the net bene

ﬁ

cial impact of N addition on POC in

C-poor soils is dependent upon interactions among increased root
biomass and lignin, alterations in extracellular enzyme activities, and
robust soil aggregate protection (Fig.

).

While global consensus on increasing C sequestration, especially

slow-turnover pools (e.g., MAOC), has gained importance over the past
decade, the direction and magnitude of changes in MAOC in response
to N remain understudied

,

. Here, we observed that N fertilization

had particularly positive effect on MAOC in C-rich soils (Fig.

c, d;

Supplementary Fig. 6). We also observed that N fertilization increased
newly formed microbial necromass, particularly facilitating the con-
tribution of fungal necromass to the stable C pool (Fig.

https://www.ngdc.noaa.gov/mgg/shorelines/data/

a and Sup-

plementary Fig. 8). This indicates a shift in the trade-off between labile
and stable SOC pools under N fertilization, which can be due to mul-
tiple overlapping mechanisms. First, the increase in root biomass and
rhizodeposition induced by N fertilizer increased the availability of
simple carbohydrates, which are more ef

ﬁ

ciently utilized to form

microbial biomass and subsequently transformed into MAOC

Second, aligning with our second hypothesis, increased N availability

a

b

ΔPOC (%)

20 25 30 35

ΔMAOC (%)

6

10 14

0°

60° E

120° E

60° W

120° W

50° N

50° S

0°

60° E

120° E

60° W

120° W

50° N

50° S

0°

Fig. 4 | Maps of predicted spatial distribution of the impacts of current nitro-
gen fertilization rates on POC and MAOC changes (%) across global cultivated
uplands and its latitudinal pattern, respectively. a

Global map of changes in

particulate organic carbon (POC) and its latitudinal distribution.

b

Global map of

changes in mineral-associated organic carbon (MAOC) changes and its latitudinal
distribution. The inset on the right-hand side shows the latitudinal averages (black

line) and standard deviations (gray shading) of POC or MAOC.

“

Δ

”

represents the

percentage change in POC or MAOC under the current fertilization level compared
to the unfertilized condition. Basemap in (

a

) from Natural Earth (

https://www.

naturalearthdata.com/

). The coastline boundaries in the map are derived

from the GSHHG database (

gshhg/latest/

). Source data are provided as a Source Data

ﬁ

le.

Article

https://doi.org/10.1038/s41467-025-57981-6

| (2025) 16:3009

6

can mitigate imbalances between microbial community and available
resources in C-rich soils (> 1.5% SOC), thereby alleviating microbial
nutrient limitations and subsequently increasing necromass C
production

23

45

. Our study is comparable with previous reports that

microorganisms are more C-limited and exhibit reduced metabolic
ef

ﬁ

ciency, in soils with low soil SOC content (< 1%), as evidenced by

elevated qCO

2

values

,

46

. We also determined lower qCO

2

values under

N addition in C-rich soils (Fig.

a). Of course, microbial stoichiometric

balance is in

ﬂ

uenced by variations in both C content and soil nutrient

availability

22

. While N fertilizer application rate can be a key factor

in

ﬂ

uencing microbial metabolic responses to fertilization

47

, the

responses are not dependent on the amount of N applied

48

, which

aligns with our results (Supplementary Fig. 9). Third, analysis of
extracellular enzymes provides evidence that elevated phenol oxidase
and peroxidase activities may increase the potential of microbes to
acquire C from SOC pools with slower turnover rates

31

,

49

. The notable

reduction in oxidase activities observed in this study may have shifted
the microbial C acquisition strategies, ultimately contributing to an
augmentation of MAOC under N addition (Fig.

c, e). Finally, the

adsorption of organic compounds onto mineral surfaces is a hallmark
of C-rich soils, rendering these soils particularly susceptible to acid-
i

ﬁ

cation driven by N fertilization

50

. N fertilization had signi

ﬁ

cant

positive effect on mineral protection in C-rich soils in this study, pri-
marily caused by increasing content of organic-aluminum/iron com-
plexes (Fe

p

and Al

p

; Fig.

a). Speci

ﬁ

cally, N-induced acidi

ﬁ

cation

increased overall MAOC as a consequence of heightened mineral
cations solubility and the subsequent formation of organo-metal
complexes (Supplementary Fig. 11)

27

. Our study demonstrates that

C-rich soils that contain larger clay and silt fractions (such as those
from SP and YA) with enhanced water-holding capacity support
microbial biomass production and thereby microbial necromass
formation

51

. The large surface area of

ﬁ

ne soil particles provides more

mineral surfaces and reactive sites to stabilize microbial-derived C and

promote MAOC accumulation

42

52

. Additionally, the augmented con-

tribution of stable C to the SOC pool was evidenced by an 4.1% - 8.7%
increase in aliphaticity following N addition (Supplementary Table 4),
since aliphaticity represents a comprehensive index for assessing
chemically

‘

recalcitrant

’

compounds

53

. Overall, N-induced MAOC

accumulation in C-rich soils mainly resulted from accumulated
necromass and increased stabilization on minerals (Figs.

d and

https://BioRender.com/i51j856

a).

Our results together underscore the crucial role of root biomass,
microbial properties and minerals as key predictors in

ﬂ

uencing the

observed accumulation in MAOC levels following N addition in
C-rich soils.

The ability to predict C pools and its functionality changes under

current N fertilization can guide the optimal fertilization application of
cropland-based climate solutions. Recent studies have forecasted a
wide range of SOC responses to inorganic fertilizers, which differ in
magnitude and sign

54

. The uncertainty is, in part, attributed to the

allocation of C into model pools that lack distinct biophysical de

ﬁ

ni-

tions and exhibit varying sensitivities to N addition.

Based on our

ﬁ

ndings, the application of N fertilizer promoted

the accrual of MAOC in C-rich arable soils (Supplementary Fig. 12),
and could help combat the projected soil C losses from croplands to
meet soil C sequestration targets. Several existing meta-analysis
studies have also shown that fertilization slightly promoted MAOC
accumulation

. However, this strategy should be implemented

with caution to mitigate potential global risks to the economy,
environment, and public health

56

. For instance, the bene

ﬁ

ts of

increasing the MAOC sink may be partly offset by increased N

2

O

emissions, fossil energy and the associated greenhouse gas emis-
sions during fertilizer manufacture

57

,

58

. Considering the greenhouse

gas emissions throughout the entire life cycle of N fertilizer, the
global warming potential of fertilized soil remains greater than that
of unfertilized soil

58

. This trade-off is often necessitated by the

imperative of ensuring food security. Developing a comprehensive

de

ath

Lignin

Oxidases

Metabolism

Necromass

Aggregates

Aliphaticity

Recalcitrance

Plant C input

C

Stability

Minerals

Ca

ex

e

Mg

ex

e

Fe

Al

C inputs

SOC sta

b

iliza

tion

dea

th

C-poor soils (

Plant-derived

)

Lignin

Oxidases

Metabolism

Necromass

Aggregates

Aliphaticity

Recalcitrance

Plant C input

C

N fertilizer

Biomass

Stability

MAOC

POC

SOC

C

N fertilizer

Biomass

Growth

qCO

2

Growth

qCO

2

Minerals

C

a

exe

M

g

ex

e

Fe

A

l

MAOC

POC

SOC

SOC tr

ansf

or

ma

tion

C-rich soils (

Microbial-derived

)

Fig. 5 | Conceptual diagram illustrating the N fertilization effects on SOC and
functional pools (POC and MAOC) as well as their regulation mechanisms
through C input origins, microbial transformation, and physical and chemical
stabilization in C-poor and C-rich soils.

Evidently, in C-poor soils with low SOC

content, N fertilization facilitates the POC retention and increased SOC content,
primarily through its in

ﬂ

uence on plant-derived C and protection within aggre-

gates. Conversely, in C-rich soils with high SOC content, N fertilizer stimulated the

sequestration of MAOC and increased SOC by more ef

ﬁ

cient microbial C cycling

(high metabolic ef

ﬁ

ciency and pump ef

ﬁ

ciency) and strong mineral protection

mechanisms. The red path represents the primary mechanism by which nitrogen
fertilizer affects carbon components under the current conditions. The direction of
the arrow indicates the change in the variable (upward/increase; downward/
decrease), while a horizontal line represents no change. Some elements of this

ﬁ

gure were created in BioRender. B, A. (2025)

.

Article

https://doi.org/10.1038/s41467-025-57981-6

| (2025) 16:3009

7

understanding of N fertilizer-induced C changes is essential to
accurately forecast the fate of SOC in a warmer world, for max-
imizing agricultural ability to mitigate climate change and provide
additional ecosystem services. Our global forecasts indicated that
fertilization leads to a signi

ﬁ

cantly greater increase in POC in C-poor

soils compared to C-rich soils (Fig.

a). Yet, due to POC

’

s rapid

turnover

and

vulnerability

—

lacking

protection

from

micro-

aggregates and minerals

—

this retention does not translate into

long-term

SOC

gains

59

,

particularly

under

global

warming

conditions

60

. Moreover, warming that increase microbial activity

will likely accelerate POC decomposition, as microbes process
available C sources, potentially leading to signi

ﬁ

cant carbon loss in

an ampli

ﬁ

ed manner

42

. This exacerbation leads to elevated emis-

sions of greenhouse gases, thereby creating a positive feedback
loop that further intensi

ﬁ

es the warming of our planet

’

s climate.

Although fertilization has been observed to increase C pools
(Fig.

), caution is necessary when assessing its future potential,

particularly in areas with overfertilization and high fertility, where
the outcomes are often suboptimal

54

. While our analysis provides a

global estimate of N fertilization-induced changes in C pools over
the past decade, several limitations must be acknowledged. First,
our global assessment, based on meta-analyzes with non-
standardized experimental and sampling protocols across studies,
restricts model outputs to correlative interpretations rather than
establishing causality. To address this limitation, future efforts
should prioritize standardized multi-region N fertilizer trials that
integrate plot-scale data with causal inference methods. Such fra-
meworks would elucidate the causal relationships underlying how N
fertilizer mediates plant-microbial-environmental feedbacks gov-
erning C pools dynamics. Second, sparse data coverage in Europe,
Canada, and Russia constrains model predictability and accuracy in
these regions, particularly given their high-latitude locations and
heightened susceptibility to climate change impacts. Addressing
this limitation will require concerted efforts from the scienti

ﬁ

c

community to perform large-scale, networked experiments across
continents. Third, our estimates rely on interpolated global datasets
with coarse spatiotemporal resolution (Supplementary Table 5), as
many plot-level metadata are unavailable. To reduce uncertainties
in such datasets, further efforts to enhance data quality and reso-
lution are necessary. Despite these limitations, the comprehensive
nature of our study, which incorporates a diverse range of data
sources and advanced analytical methods, provides a robust esti-
mation of N fertilizer-induced C pools changes.

In summary, our work provides new insights into the bene

ﬁ

ts of

N fertilizer application for SOC accrual in global cropping systems.
We discern two distinct mechanisms of global soil C accumulation
following N fertilizer across an initial C threshold of ~15 g kg

−

1

: in

C-poor soils (SOC < 15 g kg

−

1

), accumulation is primarily driven by

increased plant inputs and aggregate protection promoting POC;
conversely, in C-rich soils (SOC > 15 g kg

−

1

), accumulation mainly

occurs through an enhanced microbial C pump and increased
mineral protection, leading to a shift towards MAOC. As depicted by
Fig.

, by carrying out four long-term N fertilizer networking

experiments, we observed that N fertilization altered soil functional
C pools depended on initial SOC content, orchestrating an inte-
grated control that encompasses plant, geochemical, and microbial
factors. Our

ﬁ

ndings contribute to the nuanced understanding of N

fertilizer

’

s role in promoting SOC sequestration, laying the

groundwork for revising existing models of soil C dynamics. The
dichotomy in SOC accumulation mechanisms we have uncovered

–

more pronounced POC accumulation in C-poor soils versus MAOC
accrual in C-rich conditions - challenges and extends traditional
notions of N fertilization impacts. This divergence underscores the
complexity of plant-microbe-soil matrix interactions in mediating
these effects. Future theoretical frameworks should incorporate the

diversity of soil responses to N addition, emphasizing the interplay
between biological and geochemical factors.

Methods

Field site description

Four coordinated long-running

ﬁ

eld N fertilizer experiments network

were established between 2008 and 2011 in the four main maize pro-
duction regions in China and represent four dominant soil types
(Supplementary Fig. 1). The four sites were located in Siping (SP)
Experimental Station, Jilin Province in Northeast China (43°20

’

N,

124°20

′

E); Quzhou Experimental Station (QZ), Hebei Province in the

North China Plain (36°53

’

N, 115°12

′

E); Changwu Experimental Station

(CW), Shaanxi Province in Northwest China (35°28

’

N, 107°88

′

E); and

Yaan Experimental Station (YA), Sichuan Province in Southwest China
(29°59

’

N, 103°14

’

E). At Siping, the soil texture is silt clay with 16.3 g kg

−

1

SOC, 1.69 g kg

−

1

TN and pH 6.16. The area had an average air tem-

perature of 6.81 °C and mean annual precipitation of 551 mm. In Quz-
hou experimental station, the soil texture is clay loam with 9.49 g kg

−

1

SOC, 0.87 g kg

−

1

TN and pH 8.5. The area had an average air tempera-

ture of 13.2 °C and mean annual precipitation of 516 mm. In Changwu
experimental station, the soil texture is silt loam with 9.51 g kg

−

1

SOC,

0.95 g kg

−

1

TN and pH, 8.4. The area had an average air temperature of

9.2 °C and mean annual precipitation of 595 mm. In Yaan experimental
station, the soil texture is clay loam with 17.4 g kg

−

1

SOC, 1.29 g kg

−

1

TN

and pH, 6.3. The area had an average air temperature of 17.1 °C and
mean annual precipitation of 1635 mm. Detailed site information of the
four sampling regions is summarized in Supplementary Table 7.

The same fertilizer experiments were established in a randomized

complete-block design with four replicates at each site. N fertilizer was
added as urea [CO(NH

2

)

2

] at three N levels: (1) no N-fertilizer (hereafter

called

‘

Control

’

); (2) optimized N rate (hereafter called

‘

OPT

’

); (3) high

N rate (hereafter called

‘

HN

’

). In brief, the optimal N application rate

was determined based on the in-season root-zone N management to
closely synchronize the soil and environment N supply with demand
for N by the crop, while the high N rate is comparable to local farmers

’

practices

61

. Fertilizer was applied twice during the growing period:

initially as a basal application and subsequently as a top-dressing in all
four sites. Maize straw was chopped and incorporated into the soil
through tillage to a depth of ~20 cm.

Soil and plant sampling and analysis

Topsoils (0

–

20 cm) were sampled in September 2020, and

ﬁ

ve topsoil

cores taken randomly from each plot were composited as on sample
per plot. The soil samples were sieved through a 2-mm sieve with all
visible roots and rocks were removed except for the aggregate frac-
tionation and were divided into two parts: one was stored at 4 °C for
enzymes activities and microbial composition analysis within one
week, and the remaining soil was stored at room temperature for
determining soil physicochemical analyzes and further incubation
experiment.

The SOC and total nitrogen (TN) contents were determined by dry

combustion using an elemental analyzer (Elementar, Vario Max CN,
Germany). Before measuring the SOC and TN, the alkaline soil was
fumigated with concentrated HCl for 48 h to remove carbonates. The
soil pH was measured using an ULTRAMETERII

™

6PFCE (Mellon, Cali-

fornia, USA) after shaking the soil in deionized water (1:2.5 w/v) sus-
pensions for 30 min. Microbial biomass C (MBC) and N (MBN) was
extracted in 0.5 M K

2

SO

4

solution from chloroform fumigated and

unfumigated soil and determined by the difference in C and N con-
centration using a Shimadzu TOC-L analyzer

62

. The fungal-to-bacterial

ratio (F:B) was assessed by applying the phospholipid fatty acids
method

63

.

At maturity in 2018, four complete root systems were collected

from the topsoil (0

–

20 cm) in each plot. The roots were separated

from the soil by rinsing on a

ﬁ

ne mesh screen under a gentle water

Article

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| (2025) 16:3009

8

spray until most of the soil was removed. The cleaned roots were then
dried at 40 °C, sieved to retain the roots while dislodging any
remaining soil, and subsequently weighed.

Soil enzymes activities, microbial metabolic ef

ﬁ

ciency and

nutrient limitation

The hydrolytic enzymes,

α

-glucosidase (BG), cellobiohydrolase (CBH),

N

-acetyl-

β

-D-glucosaminidase (NAG), leucine amino peptidase (LAP),

acid and alkaline phosphatase (ACP and ALP) in fresh soil samples were
measured

ﬂ

uorometrically

using

methylumbelliferone-labeled

substrates

64

. Brie

ﬂ

y, 125 mL of buffer solution was added to 1.0 g dry

mass of fresh soil adjusted pH to 8.2 with HCl. For ALP and ALP ana-
lysis, the buffer was adjusted to pH 6.5 and 8.2, respectively. A total of
200

μ

L sample suspension and 50

μ

L substrate solutions were dis-

pensed into the wells of a black 96-well microplate. The microplates
were covered and incubated in the dark at 25 °C for 4 h and the

ﬂ

uorescence quanti

ﬁ

ed using a microplate

ﬂ

uorometer with 365-nm

excitation and 450-nm emission

ﬁ

lters. Polyphenol oxidase (PPO) and

peroxidase (POD) were measured spectrophotometrically in a clear
96-well microplate using the substrate of L-3,4-dihydroxy-phenylala-
nine (L-DOPA; German et al.

64

). A 1 mL aliquot of soil slurry was mixed

with 1 mL of 10 mM L-DOPA in 50 mM acetate buffer for PPO or 1 mL
L-DOPA and 0.2 mL of hydrogen peroxide for POD and then incubated
in the dark at 20 °C for 2 h. The suspensions were centrifuged, and the
activity quanti

ﬁ

ed by measuring absorbance at 450 nm.

Enzymatic stoichiometric was employed to assess potential C

limitations in the soil. The vector length were calculated according to
Moorhead et al.

65

. Vector length indicates the enzyme activity towards

C acquisition relative to N and P. A Higher vector length represents
larger microbial C limitation

65

.

V L

=

ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

x

2

+

y

2

p

ð

1

Þ

where

x

and

y

represent the relative C:P and C:N acquiring enzyme

ratios, respectively. C-acquiring enzyme: BG and CBH; N-acquiring
enzyme: NAG and LAP; P-acquiring enzyme: ACP for SP and YA, ALP for
QZ and CW.

We also assessed microbial resource limitation by calculating the

ratios of the resource C:N ratios (R

C:N

), normalized to the ratio of MBC

to MBN (B

C:N

. Given that biomass stoichiometry determines the

nutrient requirements of microbial decomposers, greater resource
limitation indicates increasing microbial N limitation.

Resource limitation

=

R

C

:

N

=

B

C

:

N

ð

2

Þ

where

R

C:N

represents the ratio of SOC-to-TN,

B

C:N

represents the ratio

of MBC-to-MBN.

Microbial metabolic ef

ﬁ

ciency was assessed using qCO2, with

higher qCO2 values indicating less carbon (C) allocation to biomass
and greater respiration losses

. Soil samples (equivalent to 30 g dry

weight) were adjusted to 60% water holding capacity, and placed into
250 ml jars, and incubated at 25 °C for 28 days. Headspace CO

2

release

was measured using a soil CO

2

ﬂ

ux system equipped with a portable

chamber (EGM-5, USA, PP SYSTEM) on days 1, 3, 5, 7, 14, 21, 28 after soil
incubation. Soil qCO

2

was expressed as

μ

g CO

2

-C

μ

g

−

1

MBC h

−

1

.

SOC fractionation

Particulate organic matter (POC) and mineral-associated organic car-
bon (MAOC) were determined with modi

ﬁ

cations to the method

described by Poeplau et al.

67

. Brie

ﬂ

y, 20 g of 2-mm sieved bulk soil was

shaken for 18-h with glass beads in 100 mL 0.5 % sodium hexameta-
phosphate to disrupt all aggregates. The resulting soil slurry was rinsed
with deionized water over a 53-

µ

m sieve with the fraction passing

through (< 53-

µ

m) representing MAOC and the fraction remaining on

the sieve (> 53-

µ

m) representing POC. Following physical fractionation,

total C in MAOC and POC fractions were measured using elemental
analysis.

Compound speci

ﬁ

c analysis of lignin phenols and amino sugars

Lignin phenols were used as a geochemical proxy for terrestrial plant C
input according to Otto & Simpson

68

. Soil samples were ground to pass

through a 0.15 mm mesh sieve and a subsample was transferred to a
Te

ﬂ

on vessel digestion for digestion using alkaline CuO under high

pressure and temperature to release constituent monomer phenols
from lignin polyphenols. Brie

ﬂ

y, 1 g of air-dried soil (<0.15 mm) was

solvent-extracted and base hydrolyzed to remove extractable and
hydrolysable lipids. The dried residues were mixed with 1 g CuO,
100 mg ammonium iron(II) sulfate hexahydrate, and 15 ml N

2

-purged

2 M NaOH in te

ﬂ

on-lined bombs. The bombs were

ﬂ

ushed with N

2

for

10 min and heated at 170 °C for 2.5 h. After cooling, lignin oxidation
products (LOPs) were spiked with ethyl vanillin, acidi

ﬁ

ed to pH 1 with

6 M HCl, and kept in the dark for 1 h. LOPs were extracted from the
supernatant with ethyl acetate, concentrated under N

2

, and dissolved

in a solution with trans-cinnamic acid. The mixture was dried under N

2

,

derivatized with N,O-bis-(trimethylsilyl) tri

ﬂ

uoroacetamide and pyr-

idine (70 °C, 1 h), and quanti

ﬁ

ed using a GC-MS QP 2010 PLUS (Shi-

madzu) with an HP-5 fused silica column and a

ﬂ

ame ionization

detector. This method released eight characteristic lignin monomer
phenols, including vanillyl (V; vanillin, acetovanillone and vanillic acid),
syringyl (S; syringaldehyde, acetosyringone and syringic acid) and
cinnamyl (C; p- coumaric acid and ferulic acid) monomers. Vanillyl (V),
syringyl (S) and cinnamyl (C) phenols were summed to represent lignin
phenols in soils.

Amino sugars analysis was used to quantify microbial necromass

contributed by bacteria or fungi according to Zhang & Amelung

69

Brie

ﬂ

y, weighed soil subsamples were hydrolyzed with 6 M HCl for 8 h

at 105 °C. Myo-inositol was added as an internal standard after the
hydrolysate cooled to room temperature. The hydrolysate was then

ﬁ

ltered, evaporated, pH adjusted (6.6

–

6.8), centrifuged and freeze-

dried. Amino sugars were re-dissolved in methanol and separated from
salts by centrifugation. After the addition of a quantitative standard
(methyl-glucamine), amino sugars were transformed into aldononitrile
derivatives. Then reacted with derivative reagent, acetic anhydride,
ethyl acetate, and hexane for derivatization. Gas chromatograph
(Agilent 6890 A, Agilent Technologies) was utilizing to separated and
quanti

ﬁ

ed the three amino sugars. Bacterial and fungal necromass C

were calculated using the following equations and microbial necro-
mass C refer to the sum of bacterial and fungal necromass C

63

,

70

:

Bacterial necromass C

=

muramic acid

× 45

ð

3

Þ

Fungal necromass C

=

glucosamine

2 ×

muramic acid

ð

Þ

× 179

:

17 × 9

ð

4

Þ

where 45 represents the conversion ratio of muramic acid to bacterial
necromass C; 179.17 is the glucosamine molecular weight; and 9 indi-
cates the conversion ratio from fungal glucosamine-to-fungal necro-
mass C

71

Soil aggregate size fractionation

Soil aggregates size fractions were determined using wet sieving
procedure

72

, in which 100 g soil was separated into four aggregate size

class: large macroaggregates (2

–

8 mm), small macroaggregates

(0.25

–

2 mm), microaggregates (0.053

–

0.25 mm) and < 0.053 mm

fractions. In brief, undisturbed air-dried soil samples were gently cru-
shed to pass an 8-mm sieve mesh. Then, 100 g samples of ~8 mm-sized
aggregates were placed in the top sieve of a set containing sieves with
2.0 mm, 0.25 mm, and 0.053 mm mesh sizes that was submerged and
oscillated (amplitude ~2.5 cm) in deionized water. The soil material was

Article

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| (2025) 16:3009

9

rinsed from each sieve and then oven-dried and weighed. The mean
weight diameter (MWD, mm) indicates aggregate stability, which is
calculated as follows:

MW D

=

X

n

i

= 1

X

i

×

W

i

ð

5

Þ

where

X

i

is the average diameter of aggregates in any size fraction;

W

i

is

the proportion of aggregates corresponding to

X

i

.

Solid-state

13

C nuclear magnetic resonance (NMR) analysis

The chemical composition of SOC was investigated by determining the
relative abundance of functional groups using solid-state

13

C cross-

polarization and magic-angle-spinning (CPMAS) NMR

53

. Soil samples

were treated with 10% HF-HCL solution to concentrate the organic
matter and to remove paramagnetic minerals. The

13

C-CPMAS NMR

spectra were acquired using an AVANCE III 400 WB spectrometer
(Bruker, Billerica, MA, USA). The sold-state

13

C-CPMAS NMR spectra

were divided into four common chemical shift regions: alkyl-C (0

–

45

ppm), O-alkyl-C (45

−

110 ppm), Aromatic-C (110

−

160 ppm) and

Carbonyl-C (160

–

220 ppm). Different indices of SOC stability were

calculated as follows:

Aliphaticity

=

alkyl C

=

O alkyl C

ð

6

Þ

Recalcitrance

=

alkyl

+

aromatic C

ð

Þ

=

O alkyl

+

carboxyl C

ð

Þ

ð

7

Þ

Mineral protection

Polyvalent cations provide a mechanism for carbon retention through
their role in cation bridging. We measured the content of exchange-
able cations including Fe/Al-(hydr) oxides

73

. To measure Fe/Al oxides,

pyrophosphate-extractable Fe/Al oxides (Fe

p

+ Al

p

) extracted using

0.2-M sodium pyrophosphate, and oxalate-extractable Fe/Al oxides
(Fe

o

+ Al

o

) extracted using 0.2-M oxalic acid-ammonium oxalate

(pH = 3), were used to represent organically complexed Fe/Al oxides
and poorly crystalline oxyhydroxides, respectively.

Exchangeable calcium (Ca

exe

) and magnesium (Mg

exe

) ions safe-

guard C in soils were extracted with ammonium chloride-70% ethanol
solution after removing the soluble chloride and sulfate by 70% etha-
nol solution

73

. The contents of Fe/Al, Ca

exe

and Mg

exe

mineral phases

were determined with an inductively coupled plasma optical emission
spectrometer (ICP-OES; iCAP 6300, Thermo Scienti

ﬁ

c, Waltham, USA).

Compilation of the global database

To investigate changes of soil C functional pools (SOC, POC and
MAOC) response to N fertilizer application under in

ﬂ

uences of dif-

ferent initial SOC levels in global agricultural land, we compiled
experimental research articles published prior to June 2024 from the
Web of Science and the China National Knowledge Infrastructure
Database using the following keywords: (

“

nitrogen

”

) AND (

“

soil carbon

fraction

”

OR

“

soil mineral-associated organic carbon

”

OR

“

soil mineral-

associated organic matter

”

OR

“

MAOC

”

OR

“

MOC

”

OR

“

MAOM

”

OR

“

MOM

”

OR

“

particulate organic matter

”

OR

“

particulate organic car-

bon

”

OR

“

POC

”

or POM

”

) AND (

“

farmland

”

OR

“

cropland

”

OR

“

agri-

culture

”

). For in inclusion in the database, published research must

have satis

ﬁ

ed the following criteria: (1) a N fertilization experiment was

conducted in the

ﬁ

eld and included N-addition and control treatments

within agroecosystems under the same environmental conditions; (2)
only studies conducted at upland

ﬁ

elds were included; (3) no other

organic manure was applied in all treatments during the experiments;
(4) C pools and at least one related variable were measured, and the
mean, standard deviation or standard error, and sample size were
reported for all treatments; To ensure comparability with our four-site

experiment, we selected only the data from trials conducted on dry-
land. Implementing these criteria, 118 published articles were selected
(Supplementary References). We compiled a

ﬁ

nal dataset containing

494 and149, measurements of POC and MAOC, respectively. Data were
also collected on site coordinates, climate, initial soil organic carbon,
initial soil pH, initial total nitrogen, initial silt and clay content (all initial
soil properties refer to those before the start of the experiment.), N
input rate, and duration. Crop yield values were also extracted if
available. When data were presented graphically only,

ﬁ

gures were

digitized to extract the numerical values using GETDATA GRAPH. If soil
and climatic variables were not reported, we supplemented the miss-
ing data using global databases (e.g., soilgrids and WorldClim data-
base). We classi

ﬁ

ed data into tropical, subtropical, temperate and

boreal ecozones based on site-speci

ﬁ

c coordinates and Food and

Agriculture Organization ecozones.

Predictive modeling of global POC and MAOC

To create a spatially predictive model of the effect of current N ferti-
lizer on POC and MAOC, we

ﬁ

rst sampled our prepared stack of

environmental covariates at each independent data point within the
compiled database. These layers included climatic, soil, and fertilizer
use (Supplementary Table 6). Speci

ﬁ

cally, values of all grids of envir-

onmental layers were extracted based on site-speci

ﬁ

c coordinates or

directly from original studies when available. All covariates were
resampled and reprojected to a uni

ﬁ

ed pixel grid in EPSG:4326

(WGS84) at

ﬁ

ve-arcminute spatial resolution. We selected four com-

mon and reliable machine-learning models (random forest model, the
extreme gradient-boosting model, the light gradient-boosting
machine model, and the gradient-boosting decision trees mode) to
predict POC and MAOC changes. The data used for training and vali-
dation are 80% and 20%, respectively. We used a grid-search proce-
dure, with

ﬁ

ve-fold cross-validation, to select the best model. The

predictive power of the models was measured using three key metrics:
the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE),
and the goodness of

ﬁ

t, as represented by the coef

ﬁ

cient of determi-

nation (R

2

). The results showed that the gradient-boosting decision

trees machine model performed best among all models (characterized
by the maximum R

2

and the minimum MAE and MSE; Supplementary

Table 5) and was therefore selected to predict the global variations in
POC/MAOC. All models were run in Python.

Statistical analysis

For meta-analysis, we calculated the mean effect size and 95% CI of the
overall effects of N fertilizer managements on SOC, POC and MAOC.
The effects of N fertilization were estimated based on the natural log-
transformed response ratio (

RR

)

74

, con

ﬁ

dence intervals (CIs) on the

weighted effect size were generated using boot-strapping (999 itera-
tions, function con

ﬁ

nt, package metafor):

ln

RR

= ln

X

t

=

X

c

= ln X

t

ln

ð

X

c

Þ

ð

8

Þ

Where

X

t

is the treatment mean, and

X

c

is the control mean. The var-

iance (

v

) associated with each value of lnRR was calculated as follows:

v

=

S

2

t

n

t

X

2

t

+

S

2

c

n

c

X

2

c

ð

9

Þ

Where

n

t

and

n

c

are the sample sizes for the treatment and control

groups, respectively; and

s

t

and

s

c

are the SDs for the treatment and

control groups, respectively.

We

ﬁ

rst

ﬁ

tted linear and non-linear (quadratic and general addi-

tive models [GAM]) regressions to the relationships between the effect
sizes of POC/MAOC and initial SOC content, and used the Akaike
information criterion (AIC) to decide which model was the best

ﬁ

t in

each case

75

76

. Thresholds could only be de

ﬁ

ned when non-linear

Article

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| (2025) 16:3009

10

regressions best

ﬁ

t the data. Then, we evaluated six different nonlinear

models to select the one that best aligned with our data (Supple-
mentary Table 1), and which each differed from each other by the
functions

ﬁ

tted either side of the estimated breakpoint

77

. The non-

linear models evaluated were: general additive models (GAM), step
(two intercept models, for which the differences in intercept were
tested at the breakpoint), segmented (two linear models in which the
slope is changed at breakpoint), stegmented (two linear models in
which both the slope and the intercept are changed at the breakpoint),
hinge model 12 (one linear model is

ﬁ

tted for the left part of the

breakpoint and a second degree polynomial is

ﬁ

tted for the right part),

and hinge model 22 (two different second degree polynomial models
are

ﬁ

tted at both sides of the breakpoint). The model exhibiting the

lowest AIC and Bayesian information criterion (BIC) was considered
the best model. Detailed AIC and BIC values for each regression model
are provided in Supplementary Table 1. We de

ﬁ

ned the breakpoint as

the initial SOC threshold.

To further test whether the thresholds identi

ﬁ

ed signi

ﬁ

cantly

affected the slope of the

ﬁ

tted regressions, we bootstrapped linear

regressions either side of each threshold for each variable. We then
extracted the slope and the predicted value of the variable evaluated
before and after the threshold and compared them using a Mann-
Whitney U test following the methods in refs.

75

https://doi.org/10.5281/zenodo.10374039

76

. We also used the

Q-statistic to test the heterogeneity of the N effect sizes on POC and
MAOC between before and after the SOC threshold. Signi

ﬁ

cant Q

M

values (between-group heterogeneity) indicated that the RR differed
between groups (

P

< 0.05).

We assessed the quality of our meta-analysis using the checklist

developed by Koricheva and Gurevitch (Supplementary Table 8

78

;).

Our meta-analysis met all the quality criteria established for meta-
analyzes in ecology. The assessment of publication bias conventionally
employs funnel plot analysis. However, numerous studies lack the
necessary sample variance information crucial for the construction of
informative funnel plots. Consequently, a histogram was generated to
examine the distribution of individual effect sizes across the dataset,
aiming to ascertain any indications of publication bias (Supplementary
Fig. 16). Egger

’

s test suggests that the impact of nitrogen fertilizer

management on POC may be slightly overestimated, yet it appears to
have no signi

ﬁ

cant effect on MAOC. The failsafe number for both POC

and MAOC exceeds the threshold of 5n + 10 (

N

is the sampling size in

this study), suggesting an absence of publication bias. We performed a
Jackknife sensitivity analysis to assess the impact of individual studies
on the overall robustness of the analysis

79

. In the Jackknife analysis,

each study received a unique identi

ﬁ

cation, and, in each iteration, data

from one study were systematically excluded from the dataset (Sup-
plementary Fig. 17). The sensitivity analysis demonstrated the robust-
ness of these

ﬁ

ndings, with no speci

ﬁ

c publication exerting a

disproportionate in

ﬂ

uence on the overall response. In addition, tem-

poral change tests show signi

ﬁ

cant correlation between POC with

publication year (

P

< 0.05; MAOC:

P

> 0.05), we can still infer that our

results are robust as the estimates are small-scale (

β

= 0.02 for POC;

Supplementary Fig. 18).

For the

ﬁ

eld, experiments, we used linear mixed-effect models

to determine the effects of N fertilizer practices, initial C conditions
and their interactions on root biomass, soil C pools, microbial
properties and necromass, lignin phenols and mineral and physical
protection. Statistical signi

ﬁ

cance is based on Wald type III

χ

² tests

and all estimated effect sizes (

β

) are based on rescaled response

variables. Speci

ﬁ

cally, the preceding responsive variables for each

experiment were tested with N fertilizer managements as

ﬁ

xed

effects, and site were tested as random effects (function lmer,
package

lme4

and

lmertest

). Pairwise treatment comparisons were

assessed via Tukey

’

s honest signi

ﬁ

cant difference (function

emmeans, package

emmeans

). The statistical analyzes were per-

formed in R version 4.2.1.

Partial correlation analysis was used to assess the relationships

between C fractions (POC and MAOC) with

ﬁ

ve groups of impact fac-

tors (i.e., C origin (root biomass, lignin phenols and microbial necro-
mass C), transform (qCO

2

, Oxidase, F:B, resource limitation and C

limitation), stability (MWD, Fe

p

+ Al

p

, Fe

o

+ Al

o

, Ca

exe

and Mg

exe

) and

structure (recalcitrance and aliphaticity). The greater the difference in
partial correlation coef

ﬁ

cient between zero-order and controlling

correlation, the stronger the effect of control factor. After identifying
the key predictors, the correlation between C fraction and these fac-
tors were analyzed by univariate linear regression.

Reporting summary

Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.

Data availability

The data used in this study are available in Source data and online in the
Zenodo database (

). Source

data are provided with this paper.

Code availability

The analysis code that supports the

ﬁ

ndings of this study is available

on GitHub (

https://github.com/ling9802/articles_code_backup/tree/

main/N_fertilizer_C%20pools

).

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Acknowledgements

This study was supported by the National Natural Science Foundation of
China (grant nos. U23A20158 and 32071629, J.T.) National Key R&D
Program of China (2023YFD1901500, 2022YFD1901300, J.T.), 2115 Talent
Development Program of China Agricultural University and Beijing
Advanced Disciplines (F.S.Z). M.D-B. acknowledges support from
TED2021

−

130908B-C41/AEI/10.13039/501100011033/Unión Europea

NextGenerationEU/PRTR and from the Spanish Ministry of Science and
Innovation for the I + D + i project PID2020

−

115813RA-I00 funded by

MCIN/AEI/10.13039/501100011033. Y. Kuzyakov acknowledges the
Ministry of Education, Science and Sports of the Republic of Lithuania
and the Research Council of Lithuania (LMTLT) under the Program

‘

University Excellence Initiative

’

Project

‘

Development of the Bioec-

onomy Research Center of Excellence

’

(BioTEC) (No S-A-UEI-23

−

14).

Author contributions

All authors contributed intellectual input and assistance to this study
and manuscript preparation. J.T. and X.P.C. designed the original con-
cept and experiment strategy. J.L. and R.R.Z. collected the data. J.L.
analyzed the data. Q.G., Y.X.C., S.C.Y., and W.S.Z. carried the

ﬁ

eld

experiment. J.L., J.A.J.D., M.D.B., Y.K., Z.L.C., F.S.Z., X.P.C., and J.T.
drafted the manuscript and all authors involved in critical revisions and
gave

ﬁ

nal approval for publication.

Competing interests

The authors declare no competing interests.

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and requests for materials should be addressed to

Xinping Chen or Jing Tian.

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