\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,greek,english]{babel}



\usepackage{float}








\begin{document}

\title{Spatiotemporal changes in cropland soil organic carbon in a rapidly
urbanizing area of southeastern China during 1980-2015}



\author[1]{Enze Xie}%
\author[1]{Xiu Zhang}%
\author[1]{Fangyi Lu}%
\author[1]{Yuxuan Peng}%
\author[2]{Yongcun Zhao}%
\affil[1]{Institute of Soil Science Chinese Academy of Sciences}%
\affil[2]{Institute of Soil Science, Chinese Academy of Sciences}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Understanding the spatiotemporal changes in soil organic carbon (SOC)
and their driving factors is an important prerequisite for
decision-making in maintaining sustainable agricultural development and
addressing climate change. A total of 1219 cropland topsoil SOC data
(0-20 cm) collected from southern Jiangsu Province of China in 1980,
2000, and 2015, and geostatistical sequential Gaussian simulation were
used to identify the changes in the spatiotemporal patterns of SOC
during the period of 1980-2015. Results showed that the changes in SOC
within the different time periods were significantly different, with a
net increment of 3.65 g kg-1 during the period of 1980-2000 and a net
decrement of 2.32 g kg-1 during the period of 2000-2015. Significant SOC
accumulation occurred throughout the study area during 1980-2000, while
SOC decline became predominant in the southeast during 2000-2015.
Overall, the SOC contents for 60\% of the study area increased
significantly over the entire 35-year period. The SOC increase during
the first two decades (1980-2000) was largely attributed to the
increasing soil C input that resulted from the enhanced crop
productivity by chemical fertilizers, while the stagnant soil carbon
inputs associated with the rapid urban expansion were the primary reason
for constraining cropland SOC accumulation in the subsequent 15 years
(2000-2015). These findings highlight the importance of balancing
agricultural development and urbanization processes to maintain SOC
levels, and may also provide some guidance for planning cropland soil C
management strategies in many areas that are undergoing similar
urbanization processes.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{introduction}}

Soil organic carbon (SOC), as a critical component of soil fertility,
not only affects soil quality and crop production (V\selectlanguage{ngerman}ågen \& Winowiecki,
2013; Jiang et al., 2014; Zhao et al., 2018), but also plays an
important role in the global carbon cycle (Wei et al., 2011; Borrelli et
al., 2018). The SOC levels are influenced by natural factors and human
activities (Bolinder et al., 1997; Wang et al., 2015a; Fujisaki et al.,
2018; Fan et al., 2019), while management practices usually have more
profound influences on the cropland SOC levels (Nandwa, 2001; Li et al.,
2016; Sandeep et al., 2016). For example, unreasonable management
practices (i.e., overuse of chemical fertilizers and straw burning) may
cause soil nutrient imbalances, and further accelerate cropland SOC loss
in a short time (de Blécourt et al., 2019; Keel et al., 2019; Yang et
al., 2019). Consequently, information and knowledge on the
spatiotemporal changes in cropland SOC and their driving factors are
extremely important for developing effective management practice
recommendations and addressing climate change.

Multiple factors affect the spatiotemporal changes in cropland SOC,
including natural factors such as climate, soil types, and topography
(Meersmans et al., 2011; Ou et al., 2017; Yang et al., 2019), and
anthropogenic factors such as agricultural management practices (i.e.,
tillage, fertilization) (VandenBygaart et al., 2004; Han et al., 2018;
Zhou et al., 2019) and land use change (Guo \& Gifford, 2002; Luo et
al., 2019). The systematic review of existing SOC studies (Schillaci et
al., 2018; Ramesh et al., 2019; Wiesmeier et al., 2019) shows that
natural factors are generally recognized as important contributors to
spatial variability of SOC in cropland. For example, Li et al., (2018b)
found that topographic factors such as slope and aspect significantly
influenced the spatial distributions of cropland SOC in central Iowa and
explained more than 62\% of the spatial variability of SOC in that area.
The spatial prediction of topsoil SOC with the random forest model in an
intensively cultivated area in eastern China demonstrated that soil
properties, climate, and geographic locations were the most important
predictors for mapping SOC distributions (Deng et al., 2018). However,
numerous studies have indicated that anthropogenic factors, in
particular management practices and land use changes, have even more
profound influences on the spatiotemporal changes in cropland SOC (Bas
et al., 2010; Leifeld et al., 2013; Zhao et al., 2018; Shi et al.,
2019), because anthropogenic activities usually alter the balance
between soil carbon inputs and decomposition over a relatively short
time period (Jiang et al., 2014). For example, a significant difference
in the annual SOC change rates across different agricultural regions of
China (-2.0\% to 0.6\% yr\textsuperscript{-1}) was mainly caused by
regional differences in management practices, such as cultivation,
fertilization, and straw/residue return rate (Yan et al., 2011). The
CENTURY model analysis of the SOC changes in the agricultural soils of
northeastern Spain also revealed that the soil carbon inputs associated
with management practices were the primary reasons for SOC increases
(rate of 0.27 Mg C ha\textsuperscript{-1} yr\textsuperscript{-1}) over
the period of 1977-2007(Álvaro-Fuentes et al., 2011). Qiu et al., (2013)
identified spatiotemporal changes in SOC in eastern China over the
period of 1979-2006, and found that the changes in SOC were closely
related to the conversion from croplands to urban lands due to rapid
urban expansion.

China has been experiencing rapid economic growth since the 1980s (Bao
et al., 2019). Meanwhile, in the rapidly urbanizing area, the urban
expansion and the industry-dominated economic growth have caused a
significant shrinkage of cropland area and a decrease of economic
investment in cropland management (Bren d'Amour et al., 2017; van Vliet
et al., 2017; Zhong et al., 2020). However, the impacts of these changes
on the spatiotemporal changes of SOC and their implications remain
unclear, in particular over different time periods. Therefore, the
specific objectives of this study were (1) to map the spatial
distribution of cropland SOC in a representative area that has been
experiencing rapid urbanization; (2) to quantify the changes in SOC
spatial distributions over different time periods while considering the
associated mapping uncertainties; and (3) to identify the primary
driving factors of the SOC spatiotemporal changes.

\section*{2. Material and methods}

{\label{material-and-methods}}

\textbf{\textless{}Fig. 1\textgreater{}}

\subsection*{2.1 Study area}

{\label{study-area}}

The study area is located in southern Jiangsu Province of southeastern
China, including three prefecture-level cities (Suzhou, Wuxi, and
Changzhou) and two county-level cities (Danyang and Dantu), with a total
land area of 18,700 km\textsuperscript{2} (Fig. 1a). The area has a
subtropical monsoon climate with a mean annual rainfall of 900-1200 mm,
and an average temperature of 15.6 °C (Song et al., 2019). Most parts of
the study area are lowlands with elevations ranging from 0 to 10 m above
sea level, while the mountains with elevations \textgreater{}100 m are
mainly located in the southwest (Fig. 1b). Paddy soil (Stagnic
Anthrosols) is the dominant soil type, and fluvo-aquic soil (Aquic
Cambosols) and yellow brown soil (Ferri-Udic Argosols) are the other two
major soil types (Fig. 1c). The parent materials for paddy soils in the
area were mainly composed of alluvial materials, loess, and lacustrine
deposits, while the fluvo-aquic soils in the northern parts of the study
area were developed from the Yangtze River alluvium. Yellow brown soils
derived from residual and slope deposits were mainly distributed in the
low-mountain areas.

Southern Jiangsu Province has thousand years of agriculture planting
history, while the rapid urbanization only began in the early 1980s. The
rapid urbanization process and economic growth due to industrial
development have greatly influenced the soil quality in the area (Liu et
al., 2010).

\subsection*{2.2 Soil sampling and
analysis}

{\label{soil-sampling-and-analysis}}

Cropland topsoil data (0-20 cm) in 1980 were collected from the soil
survey reports of each city within the study area (Fig. 1a). These
legacy data have been further checked for quality control, such as the
correction of likely wrong samples and outlier detection (Schillaci et
al., 2019), resulting in a total of 399 data on soil organic matter
(SOM) being collected. The SOM in 1980 was determined by using the
potassium dichromate oxidation method with external heating (Institute
of Soil Science Chinese Academy of Sciences, 1978), and was converted to
SOC by the SOM×0.58 based on the Van Bemmelen factor (Yan et al., 2011)
(denoted as SOC\textsubscript{1980} hereafter). Soil data recorded in
these soil survey reports, including the text descriptions of the soil
type, topography, bulk density, rock fragments with the fraction
\textgreater{}2 mm (coarse fraction), and sampling locations, were the
findings of the second National Soil Survey of China. So far, these
legacy data were still the most comprehensive and detailed historical
data that were available for extracting information about the soil
properties of the study area in 1980.

In 2000, a total of 413 topsoil samples were collected for better
coverage of the study area. These sampling locations were chosen to (i)
match the 1980's sampling locations as closely as possible, (ii) be on
the identical soil types and topographic characteristics, and (iii) have
the most typical cropping systems around the sites. In 2015, the
sampling locations were determined by the same rules as the sampling
campaign in 2000. However, because of the significant changes in land
use (i.e., cropland converted to urban land over the period of
2000-2015), some sampling locations in 2015 were slightly different from
those in 2000, resulting in a total of 407 soil samples being collected
in 2015. Hand-held GPS (global positioning system, positioning error
\textless{}10 meters) was used to record the sample locations in 2000
and 2015, and the related information such as soil types, land use, and
topographic characteristics were also recorded in detail. The average
sampling density over the three sampling campaigns is approximately one
sample per 33 km\textsuperscript{2}. And the SOC contents in 2000 and
2015, which were denoted as SOC\textsubscript{2000} and
SOC\textsubscript{2015} hereafter, were all determined by the same
method as SOC\textsubscript{1980.}

\subsection*{2.3 SOC change mapping and uncertainty
assessment}

{\label{soc-change-mapping-and-uncertainty-assessment}}

The geostatistical ordinary kriging (OK) may reflect the mechanism of
spatial variations in soil properties and is relatively transparent and
straightforward, compared to the complicated algorithm such as machine
learning (Veronesi \& Schillaci, 2019). Therefore, the OK method was
employed to map the spatial distributions of SOC\textsubscript{1980},
SOC\textsubscript{2000}, and SOC\textsubscript{2015}, and then, the
spatial patterns of the SOC changes over the three time periods were
determined by subtracting the OK-predicted SOC\textsubscript{1980} from
the OK-predicted SOC\textsubscript{2000}, the OK-predicted
SOC\textsubscript{2000} from the OK-predicted SOC\textsubscript{2015},
and the OK-predicted SOC\textsubscript{1980} from the OK-predicted
SOC\textsubscript{2015}, respectively.

During the OK prediction, the experimental variogram that summarized the
spatial relations in SOC data was first calculated. For a set of SOC
data z(\emph{x\textsubscript{i}} ) at location
\emph{x\textsubscript{i}}(i=1,2,\ldots{}n), the semivariance \selectlanguage{greek}\emph{γ}
\selectlanguage{english}(\emph{h} ) for a spatial distance \emph{h} can be estimated by using
the following equation (Veronesi \& Schillaci, 2019):

(1),

where \emph{N} (\emph{h} ) is the number of pair observations
z(\emph{x\textsubscript{i}} ) and z(\emph{x\textsubscript{i}} +\emph{h}
) that separated by the spatial distance \emph{h} . The experimental
variogram can be fitted by some theoretical variogram models such as
spherical, exponential, and Gaussian models. The sill of the fitted
model is the semivariance value at which the fitted variogram model
first flattens out (represents the spatial variance of the data), and
the range is the distance at which the variogram reaches the sill
(represents the distance at which the data are no longer
auto-correlated), while the nugget is the model-fitted semivariance
value when the distance is zero (represents the influences of
measurement error and/or small-scale variations).

In this study, the variogram models were fitted to the experimental
variograms of SOC\textsubscript{1980}, SOC\textsubscript{2000}, and
SOC\textsubscript{2015}, respectively, and then the optimal model for
each of the three sampling dates was chosen according to the goodness of
fit (the value of R\textsuperscript{2}) of spherical, exponential, and
Gaussian models (Goovaerts, 1997). Sequential Gaussian Simulation (SGS)
was used to generate equiprobable realizations of SOC for quantifying
the uncertainties associated with the OK-predicted SOC. Based on the
best-fitted variogram models from the OK method\textsubscript{,} the SGS
was first conducted 1000 times for each of the SOC\textsubscript{1980},
SOC\textsubscript{2000}, and SOC\textsubscript{2015} values, and then,
random subtractions of the 1000 times SOC\textsubscript{1980} maps from
the 1000 times SOC\textsubscript{2000} maps were used to obtain the 1000
equiprobable realizations of the SOC changes during 1980-2000. This
process was repeated to generate the SOC change realizations for the
periods of 2000-2015 and 1980-2015. Finally, the realizations of the SOC
changes were used to quantify the uncertainty intervals of the SOC
changes.

Ensure that the sample data are normal or approximately normal
distribution, and the SGS algorithm used in this study was summarized as
follows (Webster \& Oliver, 2007):

\begin{enumerate}
\tightlist
\item
  Model the variogram model of the SOC data.
\item
  Define a random path visiting all grid nodes across the study area.
\item
  For each un-sampled node, determine a searching window to obtain the
  conditional dataset (sample data and simulated values within the
  searching window) for the node.
\item
  Estimate the mean and variance of SOC for the node location using
  kriging and the conditional dataset, and build the conditional
  cumulative distribution function (ccdf) of SOC based on the assumption
  of Gaussian behavior;
\item
  Draw a simulated value randomly from the ccdf and add the simulated
  value to the conditioning dataset;
\item
  The calculation proceeds to the next node until all grid nodes have
  been simulated.
\end{enumerate}

Since the sampling locations in 1980 were recorded by text descriptions,
some location errors may have occurred; thus, the assumption of a 500 m
location description error was applied during the SGS of
SOC\textsubscript{1980} based on our field-work experiences in the area.
Specifically, the SOC\textsubscript{1980} sampling locations were first
perturbed randomly within a buffer circle of 500 m in diameter, and then
the SGS was conducted to generate 200
SOC\textsubscript{1980}realizations. A total of 1000 SGS-generated
SOC\textsubscript{1980}realizations were obtained by repeating this
procedure 5 times (the sampling locations in SOC\textsubscript{1980}
were randomly perturbed 5 times). Thus, the uncertainties resulting from
the location description error of SOC\textsubscript{1980} were
considered in this study. While for SOC\textsubscript{2000} and
SOC\textsubscript{2015}, the SGS method was directly used.

\section*{3. Results}

{\label{results}}

\subsection*{3.1 Descriptive statistics of the SOC
contents}

{\label{descriptive-statistics-of-the-soc-contents}}

\textbf{\textless{}Fig. 2\textgreater{}}

Descriptive statistics of the SOC contents on the three sampling dates
(Fig. 2) showed that the mean topsoil SOC contents increased from 13.34
g kg\textsuperscript{-1} in 1980 to 16.99 g kg\textsuperscript{-1} in
2000, and then decreased to 14.67 g kg\textsuperscript{-1} in 2015. The
net increment of SOC contents during the period of 1980-2000 was 3.65 g
kg\textsuperscript{-1}, while the net decrement during the subsequent 15
years (2000-2015) was 2.32 g kg\textsuperscript{-1}. The ranges between
the minimum and maximum values for SOC\textsubscript{1980},
SOC\textsubscript{2000}, and SOC\textsubscript{2015} were 35.22 g
kg\textsuperscript{-1}, 29.29 g kg\textsuperscript{-1}, and 30.62 g
kg\textsuperscript{-1}, respectively. The coefficients of variation (CV)
for SOC\textsubscript{1980}, SOC\textsubscript{2000}, and
SOC\textsubscript{2015} were all moderate (CVs ranging from 25\% to
36\%). However, all CVs exceeded 20\%, indicating considerable
variations in the SOC contents among the three sampling dates. The value
distribution of SOC\textsubscript{1980} was slightly positively skewed,
with a skewness of 1.39. While for SOC\textsubscript{2000} and
SOC\textsubscript{2015}, their value distributions were symmetrical,
with small skewness values of 0.36 and 0.06, respectively. The normal
Q-Q graphs further indicated that SOC\textsubscript{2000} and
SOC\textsubscript{2015} were both approximately normal distributions,
while the value distribution of SOC\textsubscript{1980} was
approximately normal after 2 outliers were removed (Figure S1).

The experimental variograms of SOC in the three sampling dates were
presented in Fig. 3. Fig. 3 showed that the nugget to still ratios for
the SOC contents in 1980, 2000, and 2015 were 38\%, 55\%, and 84\%,
respectively, indicating an overall trend of decreasing SOC spatial
autocorrelation over the past 35 years. The range of the spatial
autocorrelation continuously decreased from 38km in 1980 to 33km in 2015
(35km in 2000), and the R-squared values for the fitted variogram models
of SOC\textsubscript{1980}, SOC\textsubscript{2000}, and
SOC\textsubscript{2015} were 0.95, 0.83, and 0.29, respectively.
Therefore, compared to the spatial structures of
SOC\textsubscript{1980}and SOC\textsubscript{2000}, the relatively short
range of spatial autocorrelation and the worse goodness of fit for
SOC\textsubscript{2015} indicated that external factors, such as
anthropogenic activities, may have a significant impact on the spatial
variability of SOC\textsubscript{2015} in the area.

\textbf{\textless{}Fig. 3\textgreater{}}

\subsection*{3.2 Spatial distribution patterns of
SOC}

{\label{spatial-distribution-patterns-of-soc}}

\textbf{\textless{}Fig. 4\textgreater{}}

The spatial distribution maps of the SOC contents derived by the OK
method were presented in Fig. 4, with a kriging error range of 1.16-3.67
g kg\textsuperscript{-1}, 1.11-4.41 g kg\textsuperscript{-1}, and
1.57-2.29 g kg\textsuperscript{-1} for SOC\textsubscript{1980},
SOC\textsubscript{2000}, and SOC\textsubscript{2015}, respectively
(Figure S2). Overall, the SOC distribution patterns on the three
sampling dates were similar, with high SOC contents in the southeast
(mainly surrounding Taihu Lake) and low SOC contents in other parts of
the study area. However, differences in the local details of the spatial
patterns of SOC still existed among the three sampling dates. The
high-value areas with SOC\textsubscript{1980} exceeding 18 g
kg\textsuperscript{-1} were mainly distributed in the southeast of the
study area, while the low-value areas with
SOC\textsubscript{1980}\textless{} 10 g kg\textsuperscript{-1} were
scattered along the southern side of the Yangtze River and the areas
near the western boundary of the study area. The SOC\textsubscript{1980}
in most parts of the study area ranged from 10 to 18 g
kg\textsuperscript{-1}. However, in 2000, the high-value areas with SOC
\textgreater{} 18 g kg\textsuperscript{-1} expanded significantly across
the study area; these areas were located throughout the whole
southeastern parts of the study area and in several small areas in
Jiangyin, Yixing, and Jintan city. The areas with moderate values of
SOC\textsubscript{2000} between 10-18 g kg\textsuperscript{-1} were
observed in the northwest (Dantu, Danyan), in the southwest (Liyang,
Jintan), and near the Yangtze River (Zhangjiagang, Taicang). In 2015,
the SOC contents in most parts of the study area varied from 10 g
kg\textsuperscript{-1} to 18 g kg\textsuperscript{-1}, while the
high-value areas with SOC\textsubscript{2015} \textgreater{} 18 g
kg\textsuperscript{-1} were very limited and were only distributed in
Yixing and southwestern Changshu.

\subsection*{3.3 Temporal changes in SOC}

{\label{temporal-changes-in-soc}}

\textbf{\textless{}Fig. 5\textgreater{}}

The spatial patterns of the SOC changes during the three time periods
were presented in Fig. 5. In the first two decades (1980-2000), the
areas with relatively large SOC increments (\textgreater{} 5 g
kg\textsuperscript{-1}) were mainly distributed in the north (along the
Yangtze River) and southwest of the study area, while the areas with
relatively large SOC decrements (\textgreater{} 5 g
kg\textsuperscript{-1}) were scattered in the northeastern parts (brown
areas in Fig. 5a). Changes in the SOC contents in the major croplands of
the study area varied from -12.4 to 15.1 g kg\textsuperscript{-1}. In
contrast, the SOC contents for most of the study area decreased during
the period of 2000-2015; in particular, the areas with SOC decrements
\textgreater{} 5 g kg\textsuperscript{-1} expanded significantly (mainly
located in the east, Fig. 5b). A slight decrease in the SOC contents
(decrement\textless{} 3 g kg\textsuperscript{-1}) across the study area
could be intuitively observed (yellow areas), while the increases in SOC
mainly occurred in the northeastern, northwestern, and southeastern
parts of study area (green areas in Fig. 5b), with a small increment of
less than 5 g kg\textsuperscript{-1}. Over the entire 35 years (Fig.
5c), changes in the SOC contents in the topsoil of the major croplands
varied from -21.3 to 10.2 g kg\textsuperscript{-1}, and the SOC for most
parts of the study area increased, except for that in the southeastern
parts (close to Changshu city), where SOC declined significantly (with
SOC decrement \textgreater{} 5 g kg\textsuperscript{-1}).

\subsection*{3.4 Probability of SOC
increase/decrease}

{\label{probability-of-soc-increasedecrease}}

\textbf{\textless{}Fig. 6\textgreater{}}

The probability maps for the increases/decreases in SOC over the three
time periods were calculated based on the SGS-generated SOC realizations
(\textbf{Text S1} and Fig. 6). During 1980-2000, the areas with
significant SOC increases were mainly distributed throughout the study
area, except for several areas (green regions in Fig. 6a) scattered
around Taihu Lake. However, from 2000-2015, the probabilities of SOC
increase in most areas were less than 0.40, while the areas with higher
probabilities (0.50-0.70) of SOC increase were mainly distributed in the
western parts and the northeastern corner of the study area (Fig. 6b).
Over the entire 35-year period (1980-2015), the areas with significantly
increased SOC were identified in the western and northeastern parts near
the Yangtze River (Fig. 6c). However, for the probability of SOC
decreases (Fig. 6d-f), a high probability of SOC decreases over the
period of 2000-2015 in the southeastern parts of the study area can be
intuitively observed. Over the entire 35-year period (1980-2015), the
areas with significantly decreased SOC appeared to be very limited and
were mainly located in northwestern Kunshan city.

Based on the uncertainty intervals of the changes in SOC (Fig. 7), the
average area percentages of the decreases in SOC over the periods of
1980-2000, 2000-2015, and 1980-2015 were estimated at 21\%, 62\%, and
40\%, respectively. This global information on the area proportions of
the SOC changes across the study area implied that the SOC changes went
through two distinct stages: SOC increases were predominant over the
first two decades (the SOC for 79\% of the study area increased during
1980-2000, Fig. 7, left) while SOC decreases became predominant in the
subsequent 15 years (the SOC for 62\% of the study area decreased during
2000-2015, Fig. 7, middle). However, over the entire 35-year period
(1980-2015), the SOC for 60\% of the study area increased (Fig. 7,
right), indicating an overall trend of SOC accumulation in the area.

\textbf{\textless{}Fig. 7\textgreater{}}

\section*{4. Discussion}

{\label{discussion}}

\subsection*{4.1 Driving factors of spatiotemporal changes in
SOC}

{\label{driving-factors-of-spatiotemporal-changes-in-soc}}

The differing parent materials or bedrocks of the soil types have a
profound influence on SOC levels (Araujo et al., 2017; Angst et al.,
2018). Fig. 8a showed that the average SOC contents for the paddy soils
(13.62 g kg\textsuperscript{-1}, 17.73 g kg\textsuperscript{-1}, and
14.79 g kg\textsuperscript{-1} in 1980, 2000, and 2015, respectively)
were the highest among the three predominant soil types in the area,
followed by those of the yellow-brown soils (13.62 g
kg\textsuperscript{-1}, 17.73 g kg\textsuperscript{-1}, and 14.79 g
kg\textsuperscript{-1} in 1980, 2000, and 2015, respectively) and the
fluvo-aquic soils (10.31 g kg\textsuperscript{-1}, 12.71 g
kg\textsuperscript{-1}, and 13.39 g kg\textsuperscript{-1} in 1980,
2000, and 2015, respectively). The sorting order of the SOC contents for
each soil type remained unchanged among the different sampling dates,
indicating that the influences of soil types on the SOC levels in the
area were still important. The paddy soils in southern Jiangsu Province
were mainly developed from alluvial materials and lacustrine deposits.
The high SOC contents were mainly caused by the waterlogged soils in
paddy fields where the anaerobic conditions decreased the rate of SOC
decomposition (Liu et al., 2006; Li et al., 2018a). Moreover, the
physical and chemical properties of the paddy soils were significantly
different from those of the other two soil types (fluvo-aquic soils and
yellow-brown soils) due to the generally more intensive agricultural
activities in the paddy soils (Liu et al., 2006; Liu et al., 2019).
During the periodic submergence and drainage, a large amount of
amorphous Fe and Al oxides resulting from the frequent rice cultivation
significantly enhanced the soil aggregate stability, thereby promoting
the accumulation of SOC in the paddy soils (Gong, 1999; Heng et al.,
2010; Wei et al., 2017; Xue et al., 2019). However, the fluvo-aquic
soils and yellow-brown soils were saturated with water for a relatively
short time, and the amorphous Fe and Al oxides concentrations were
significantly lower than those in paddy soils (Li et al., 2003; Heng et
al., 2010). Therefore, the average SOC in the fluvo-aquic and
yellow-brown soils was relatively low. In addition, compared to the
fluvo-aquic and yellow-brown soils, the average SOC in the paddy soils
differed significantly among the three sampling dates, indicating a
stronger influence of agricultural management practices on the SOC
changes in the paddy soils in the area.

Agricultural management practices, in particular the application of
chemical fertilizers, play an essential role in affecting SOC changes
(Lu et al., 2009; Alavaisha et al., 2019). For example, the use of
chemical fertilizers has been identified as one of the dominant
anthropogenic contributors to SOC accumulation on the North China Plain
(Han et al., 2018), in the Yellow River basin (Gong et al., 2011), in
the Loess Plateau region (Guo et al., 2011), and on the Huang-Huai-Hai
Plains of China (Kong et al., 2013). However, in southern Jiangsu
Province, chemical fertilizer inputs to the soils went through two
distinct stages over the past three decades. The application of chemical
fertilizers, including synthetic N fertilizers, increased continuously
before 2000, and declined thereafter (Fig. 8b). An increase in the
inputs of chemical fertilizers (in particular synthetic N fertilizers)
before 2000 made a significant contribution to the increased SOC because
the enhanced crop biomass resulted in proportionally increased soil
carbon inputs from the aboveground residues and roots over the first two
decades (Li et al., 2013; Zhao et al., 2018). Since 1999, the chemical
fertilizer inputs in the area have continuously declined, which was
closely related to the decreased cropland areas due to the rapid
urbanization process (Lu et al., 2017). Consequently, the importance of
the changes in soil carbon input resulting from the land use change
should not be overlooked when identifying the dominant driving factors
of the SOC change in the area.

\textbf{\textless{}Fig. 8\textgreater{}}

Crop-derived carbon is the major source of carbon input in cropland
soils (Wiesmeier et al., 2014; Wang et al., 2015b). Fig. 9a showed that
the average soil carbon inputs in the major croplands in the area went
through two distinct stages over the past three decades. The soil carbon
inputs from the crops (straw/stover returned to the soils plus roots)
increased continuously before 2000, and fluctuated greatly thereafter.
The rapid increase of carbon inputs before 2000 was the significant
contributor to SOC accumulation over the first two decades because the
initial SOC content (13.34 g kg\textsuperscript{-1}) in 1980 was still
relatively low. In 2000, the SOC levels in the area reached a relatively
high level (16.99 g kg\textsuperscript{-1}). However, the cropland areas
have decreased significantly since 2000 due to the rapid urban
expansion, resulting in the strong fluctuations in the soil carbon
inputs in the area. The mean soil carbon inputs of 1.44 t
ha\textsuperscript{-1} yr\textsuperscript{-1} after 2000 may still be
insufficient for maintaining the high SOC levels in 2000. The soil
carbon inputs after 2000 differed significantly among the 14
county-level cities (Fig. 9b), with increased carbon inputs occurring in
the west (Danyang, Liyang, Jintan, Yixing), while the areas with
declining carbon inputs mainly surrounded Taihu Lake (Wujiang, Kunshan,
Wuxian). This regional difference in soil carbon inputs was closely
related to the rapid urbanization process in the study area (Fig. 10a).

Urban expansion may lead to fragmentation of the cropland landscape,
which was a dominant factor limiting soil carbon inputs (Liu et al.,
2011; Hao, 2012; Xia et al., 2017). Fig. 10b showed that the ratio
between urban land area and cropland area was negatively correlated with
the soil carbon inputs during the period of 2000-2015, indicating that
the cropland soil C inputs in the areas with higher urbanization rates
were low. Moreover, socioeconomic development actively pushes forward
the marketization process of land use (Bao et al., 2019), particularly
for industries and tertiary industries, resulting in the reduction of
agricultural investment. For example, the number of people employed in
agriculture in the area declined significantly during 2000-2015 (from
2.85 million to 1.42 million), while the employees in the tertiary
industry increased by two times, and was almost 6 times higher than
those in agriculture in southern Jiangsu Province (Jiangsu Bureau of
Statistics, 2001-2016), implying that staple food agriculture in the
area was becoming less important. This also caused poor management of
the cropland soils. Therefore, although the increasing soil C input
resulting from the enhanced crop productivity due to chemical
fertilizers benefited the SOC accumulation over the first two decades,
the stagnant soil carbon inputs associated with rapid urbanization and
economic growth constrained the SOC accumulation in the subsequent 15
years (2000-2015).

\textbf{\textless{}Fig. 9\textgreater{}}

\textbf{\textless{}Fig. 10\textgreater{}}

\subsection*{4.2 Challenge for soil carbon
sequestration}

{\label{challenge-for-soil-carbon-sequestration}}

Our results, together with the existing studies in the area, provide
more details on the regional differences of the changes in SOC during
the period of 2000-2015 (Fig. 11), which may provide more insights into
the future C sequestration in the area. Fig. 11a showed that a
continuous decline in SOC during the period of 2000-2015 mainly occurred
in the eastern part of the study area (i.e., Changshu, Wujiang), where
the reduction in cropland area was more severe than that in the western
parts, although the SOC levels in the west decreased first and then
increased steadily (i.e., Jintan, Yixing) (Fig. 11b). The trend of the
SOC changes in the western parts of the study area can be attributed to
the large-scale implementation of the crop straw/stover return policy
since 2000 (Zhao et al., 2018).

Moreover, the SOC stock in croplands of the study area increased from
30.95 t ha\textsuperscript{-1} in 1980 to 42.12 t
ha\textsuperscript{-1}in 2000, and then decreased to 37.53 t
ha\textsuperscript{-1} in 2015, which was highly consistent with the
changes in SOC contents (Table 1,\textbf{Text S2} , and Figure S3). The
increase rate of SOC stock over the past 35 years was 0.19 t
ha\textsuperscript{-1}yr\textsuperscript{-1}, which was slightly higher
than the estimate of Jiangsu Province (0.16 t ha\textsuperscript{-1}
yr\textsuperscript{-1}) by (Liao et al., 2009) and the estimate of
Chinese cropland soils (0.14 t ha\textsuperscript{-1}
yr\textsuperscript{-1}) by (Zhao et al., 2018), indicating a
considerable SOC sequestration potential in the study area. However,
although cropland SOC accumulation can be achieved through enhancing the
C input (i.e., improving the straw/stover management), future SOC
sequestration in the area is still facing big challenges, in particular
the balance between agricultural development and the urbanization
process for ensuring soil carbon input to maintain the SOC levels. A
high SOC level not only benefits the biodiversity of agroecosystems
(Wiesmeier et al., 2019), but also improves key soil functions, such as
the capacity for acid buffering (Ritchie \& Dolling, 1985), which are
especially important for areas with accelerated soil acidification,
e.g., this study area (Xie et al., 2019). Therefore, management
practices and policies for enhancing the soil carbon input should still
be given the top priority for maintaining the stability of SOC levels.

\textbf{\textless{}Fig. 11\textgreater{}}

\subsection*{4.3 Implications and
limitations}

{\label{implications-and-limitations}}

Our study explored the spatiotemporal changes of cropland SOC in a
rapidly urbanizing area and identified their primary drivers. We found
that, although the enhanced soil C input benefited the cropland SOC
accumulation during 1980-2000, the rapid urban expansion and industrial
economic growth during the subsequent 15 years may lowering SOC due to
the decreased soil C input. The urbanizing and industrializing area may
overlook the development of agriculture, and the low economic investment
in cropland management often caused decreased C input in cropland soils,
which implied that the coordinated development of industry, agriculture,
and urbanization process is particularly important for cropland SOC
sequestration.

Our study investigated the spatiotemporal changes of SOC at a time scale
of more than 10 years. However, using the geostatistical method to
explore changes in SOC over a shorter time period (i.e., a time period
of fewer than five years) may be not feasible, because the detectable
changes in SOC still can not be directly measured due to the high
stability of SOC pools and slow turnover time of SOC (Yang et al., 2015;
Yang et al., 2020). Therefore, the development of new methodologies for
incorporating SOC dynamical mechanism into the geostatistical mapping
framework (i.e., through the ``space for time'' method) is necessary.

\section*{5. Conclusion}

{\label{conclusion}}

Our results revealed that the mean topsoil soil organic carbon (SOC)
contents in 1980, 2000, and 2015 were 13.34 g kg\textsuperscript{-1},
16.99 g kg\textsuperscript{-1}, and 14.67 g kg\textsuperscript{-1},
respectively. The SOC changes went through two distinct stages, with a
net increment of 3.65 g kg\textsuperscript{-1} in the period of
1980-2000 and a net decrement of 2.32 g kg\textsuperscript{-1} over the
period of 2000-2015. The areas with high SOC contents were identified in
the southeast of the study area, while the SOC contents in other parts
of the study area were relatively low. The spatial patterns of the SOC
changes were significantly different, with an overall SOC increase
throughout the study area during the period of 1980-2000, the areas with
declines in SOC were mainly distributed in the southeast during
2000-2015. The SOC increase over the first two decades (1980-2000) was
largely attributed to the enhanced soil carbon inputs resulting from the
increase in fertilizer application (in particular low-cost N
fertilizers), while the stagnation of soil carbon inputs associated with
the rapid urbanization and economic growth constrained the SOC
accumulation in the subsequent 15 years (2000-2015).

With the rapid urbanization and socioeconomic development, significant
regional differences in the SOC change between the east and west of the
study area occurred in the last 15 years (2000-2015). Thus, we suggest
that maintaining the stability of SOC by enhanced the soil carbon inputs
is becoming a priority issue and needs to be addressed not only in the
scientific realm, but also within the policy arena. Scientific
management practices and powerful economic policy can be used as a guide
for sustainable potential of SOC sequestration, especially in areas
experiencing rapid urbanization processes.

\section*{Acknowledgments}

{\label{acknowledgments}}

This work was supported by the Natural Science Foundation of China
(41971067) and the National Key Research and Development Program of
China (2017YFA0603002).

\section*{References}

{\label{references}}

Alavaisha, E., Manzoni, S. and Lindborg, R. (2019). Different
agricultural practices affect soil carbon, nitrogen and phosphorous in
Kilombero -Tanzania. Journal of Environment management, 234,
159-166.\emph{http://doi.org/10.1016/j.jenvman.2018.12.039}

\selectlanguage{ngerman}Álvaro-Fuentes, J., Easter, M., Cantero-Martinez, C. and Paustian, K.
(2011). Modelling soil organic carbon stocks and their changes in the
northeast of Spain. European Journal of Soil Science, 62,
685-695.\emph{http://doi.org/10.1111/j.1365-2389.2011.01390.x}

Angst, G., Messinger, J., Greiner, M., Häusler, W., Hertel, D., Kirfel,
K., Kögel-Knabner, I., Leuschner, C., Rethemeyer, J. and Mueller, C. W.
(2018). Soil organic carbon stocks in topsoil and subsoil controlled by
parent material, carbon input in the rhizosphere, and microbial-derived
compounds. Soil Biology and Biochemistry, 122,
19-30.\emph{http://doi.org/10.1016/j.soilbio.2018.03.026}

Araujo, M. A., Zinn, Y. L. and Lal, R. (2017). Soil parent material,
texture and oxide contents have little effect on soil organic carbon
retention in tropical highlands. Geoderma, 300,
1-10.\emph{http://doi.org/10.1016/j.geoderma.2017.04.006}

Bao, J., Gao, S. and Ge, J. (2019). Dynamic land use and its policy in
response to environmental and social-economic changes in China: A case
study of the Jiangsu coast (1750--2015). Land Use Policy, 82,
169-180.\emph{http://doi.org/10.1016/j.landusepol.2018.12.008}

Bas, V. W., Keith, P., Jeroen, M., Esther, G., Gabriela, B. and Mark, E.
(2010). Agricultural management explains historic changes in regional
soil carbon stocks. Proceedings of the National Academy of Sciences of
the United States of America, 107,
14926-14930.\emph{http://doi.org/10.1073/pnas.1002592107}

Bolinder, M. A., Angers, D. A. and Dubuc, J. P. (1997). Estimating shoot
to root ratios and annual carbon inputs in soils for cereal crops.
Agriculture, Ecosystems \& Environment, 63,
61-66.\emph{http://doi.org/10.1016/S0167-8809(96)01121-8}

Borrelli, P., Panagos, P., Lugato, E., Alewell, C., Ballabio, C.,
Montanarella, L. and Robinson, D. A. (2018). Lateral carbon transfer
from erosion in noncroplands matters. Global Change Biology, 24,
3283-3284.\emph{http://doi.org/10.1111/gcb.14125}

Bren d'Amour, C., Reitsma, F., Baiocchi, G., Barthel, S., Guneralp, B.,
Erb, K. H., Haberl, H., Creutzig, F. and Seto, K. C. (2017). Future
urban land expansion and implications for global croplands. Proceedings
of the National Academy of Sciences of the United States of America,
114, 8939-8944.\emph{http://doi.org/10.1073/pnas.1606036114}

de Blécourt, M., Gröngröft, A., Baumann, S. and Eschenbach, A. (2019).
Losses in soil organic carbon stocks and soil fertility due to
deforestation for low-input agriculture in semi-arid southern Africa.
Journal of Arid Environments, 165,
88-96.\emph{http://doi.org/10.1016/j.jaridenv.2019.02.006}

Deng, X., Chen, X., Ma, W., Ren, Z., Zhang, M., Grieneisen, M. L., Long,
W., Ni, Z., Zhan, Y. and Lv, X. (2018). Baseline map of organic carbon
stock in farmland topsoil in East China. Agriculture, Ecosystems \&
Environment, 254,
213-223.\emph{http://doi.org/10.1016/j.agee.2017.11.022}

Ding, W. (2009). GIS-based evaluation of farmland productivity of Liyang
city, Jiangsu Province (In Chinese). Nanjing Agriculture University,
Nanjing.

Fan, J., McConkey, B. G., Liang, B. C., Angers, D. A., Janzen, H. H.,
Kröbel, R., Cerkowniak, D. D. and Smith, W. N. (2019). Increasing crop
yields and root input make Canadian farmland a large carbon sink.
Geoderma, 336,
49-58.\emph{http://doi.org/10.1016/j.geoderma.2018.08.004}

Fujisaki, K., Chevallier, T., Chapuis-Lardy, L., Albrecht, A.,
Razafimbelo, T., Masse, D., Ndour, Y. B. and Chotte, J.-L. (2018). Soil
carbon stock changes in tropical croplands are mainly driven by carbon
inputs: A synthesis. Agriculture, Ecosystems \& Environment, 259,
147-158.\emph{http://doi.org/10.1016/j.agee.2017.12.008}

Gao, J., Han, G., Xu, M., Chen, X., Ding, R., Huang, H. and Ren, T.
(2016). The problems on the quality of cultivated land in southern
Jiangsu Province, China (In Chinese). Shanghai Agricultural Science and
Technology, 4,
86-88.\emph{http://doi.org/CNKI:ISSN:1001-0106.0.2016-04-054}

Gao, J., Pan, J., Liu, S. and Jiang, X. (2011). Influence of land use
change on topsoil organic carbon storage of paddy fields in Wujiang
city. Geo-information Science, 13,
164-169.\emph{http://doi.org/10.3724/sp.j.1047.2011.00164}

Gao, Z., Peng, B., Yu, J., Pu, L. and Zhang, L. (2005). Effect of land
use changes on soil conditions in the developed areas: a case of Kunshan
in Jiangsu Province (In Chinese). Journal of Natural resources, 20,
44-51.\emph{http://doi.org/CNKI:ISSN:1000-3037(2005)-01-0044-08}

Gong, W., Yan, X. and Wang, J. (2011). The effect of chemical fertilizer
on soil organic carbon renewal and CO\textsubscript{2} emission---a pot
experiment with maize. Plant and Soil, 353,
85-94.\emph{http://doi.org/10.1007/s11104-011-1011-8}

Gong, Z. (1999). Chinese soil taxonomy: theory approaches and
application (In Chinese). China Science Press, Beijing.

Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation.
Oxford University Press, New York.

Guo, L. B. and Gifford, R. M. (2002). Soil carbon stocks and land use
change: a meta analysis. Global Change Biology, 8,
345-360.\emph{http://doi.org/10.1046/j.1354-1013.2002.00486.x}

Guo, S., Wu, J., Coleman, K., Zhu, H., Li, Y. and Liu, W. (2011). Soil
organic carbon dynamics in a dryland cereal cropping system of the Loess
Plateau under long-term nitrogen fertilizer applications. Plant and
Soil, 353, 321-332.\emph{http://doi.org/10.1007/s11104-011-1034-1}

Guo, Z., Zuo, Q., Li, M., Min, Y., Xu, J., Shen, W. and Gu, Y. (2007).
Soil fertility investigation and quality assessment in Taicang, changes
of soil pH, organic matter, total N, available P, rapidly available K
and CEC (In Chinese). Soils, 39,
318-321.\emph{http://doi.org/10.3321/j.issn:0253-9829.2007.02.029}

Han, D., Wiesmeier, M., Conant, R. T., Kuhnel, A., Sun, Z.,
Kogel-Knabner, I., Hou, R., Cong, P., Liang, R. and Ouyang, Z. (2018).
Large soil organic carbon increase due to improved agronomic management
in the North China Plain from 1980s to 2010s. Global Change Biology, 24,
987-1000.\emph{http://doi.org/10.1111/gcb.13898}

Han, S., Pu, L., Chen, F., Zhang, C., Zhang, J. and Peng, B. (2007).
Response of soil properties to change in land use in a typical area of
the Yangtze River Delta, a case study of Xishan city, Jiangsu Province,
China (In Chinese). Acta Ecologica Sinica, 44,
612-619.\emph{http://doi.org/10.11766/trxb200608210405}

Hao, C. (2012). Research of land use and soil organic carbon dynamics in
Binhai new area of China (In Chinese). Nankai University, Tianjin.

He, L. and Cao, H. (2014). Soil organic matter content and its
distribution in Jintan city (In Chinese). Shanghai Agricultural Science
and Technology, 5,
109.\emph{http://doi.org/CNKI:ISSN:1001-0106.0.2014-05-072}

Heng, L., Wang, D., Jiang, X., Rao, W., Zhang, W., Guo, C. and Li, T.
(2010). Relationship between Fe, Al oxides and stable organic carbon,
nitrogen in the Yellow-Brown soils (In Chinese). Environmental Science,
31, 2748-2755.\emph{http://doi.org/10.13227/j.hjkx.2010.11.040}

Hu, J., Wang, D., Sun, R., Wang, C. and Liu, Q. (2008). Variation of
soil fertility of farmlands in typical regions in the lower-reaches of
the Yangtze River, a case study of Changshu county (In Chinese). Acta
Ecologica Sinica, 45,
1087-1094.\emph{http://doi.org/trxb10.11766/20007170611}

Institute of Soil Science Chinese Academy of Sciences (1978). Soil
Physics and Chemistry Analysis. Shanghai Science \& Technology Press,
Shanghai.

Jiang, G., Xu, M., He, X., Zhang, W., Huang, S., Yang, X., Liu, H.,
Peng, C., Shirato, Y., Iizumi, T., Wang, J. and Murphy, D. V. (2014).
Soil organic carbon sequestration in upland soils of northern China
under variable fertilizer management and climate change scenarios.
Global Biogeochemical Cycles, 28,
319-333.\emph{http://doi.org/10.1002/2013gb004746}

Jiangsu Bureau of Statistics (2001-2016). Jiangsu Statistical Yearbook
2001-2016. China Statistics Press, Beijing.

Keel, S. G., Anken, T., Büchi, L., Chervet, A., Fliessbach, A., Flisch,
R., Huguenin-Elie, O., Mäder, P., Mayer, J., Sinaj, S., Sturny, W.,
Wüst-Galley, C., Zihlmann, U. and Leifeld, J. (2019). Loss of soil
organic carbon in Swiss long-term agricultural experiments over a wide
range of management practices. Agriculture, Ecosystems \& Environment,
286, 106654.\emph{http://doi.org/10.1016/j.agee.2019.106654}

Kong, X., Li, B., Lal, R., Li, K. and Bai, Y. (2013). Soil Organic
Carbon Pool under Diverse Chemical Fertilizer Management in
Huang-Huai-Hai Plains, China. Agricultural Research, 2,
68-80.\emph{http://doi.org/10.1007/s40003-013-0055-9}

Leifeld, J., Angers, D. A., Chenu, C., Fuhrer, J., Katterer, T. and
Powlson, D. S. (2013). Organic farming gives no climate change benefit
through soil carbon sequestration. Proceedings of the National Academy
of Sciences of the United States of America, 110,
E984.\emph{http://doi.org/10.1073/pnas.1220724110}

Li, H. Y., Wang, H., Wang, H. T., Xin, P. Y., Xu, X. H., Ma, Y., Liu, W.
P., Teng, C. Y., Jiang, C. L., Lou, L. P., Arnold, W., Cralle, L., Zhu,
Y. G., Chu, J. F., Gilbert, J. A. and Zhang, Z. J. (2018a). The
chemodiversity of paddy soil dissolved organic matter correlates with
microbial community at continental scales. Microbiome, 6,
187.\emph{http://doi.org/10.1186/s40168-018-0561-x}

Li, S., Zhou, J., Wang, H., Chen, X. and Du, C. (2003). Characteristics
of fixation and release of phosphorus in three soils (In Chinese). Acta
Pedologica Sinica, 40,
908-914.\emph{http://doi.org/10.11766/trxb200206240616}

Li, X., McCarty, G. W., Karlen, D. L. and Cambardella, C. A. (2018b).
Topographic metric predictions of soil redistribution and organic carbon
in Iowa cropland fields. Catena, 160,
222-232.\emph{http://doi.org/10.1016/j.catena.2017.09.026}

Li, Z. T., Li, X. G., Li, M., Yang, J. Y., Turner, N. C., Wang, X. Y.
and Li, F. M. (2013). County-scale changes in soil organic carbon of
croplands in southeastern Gansu province of China from the 1980s to the
Mid-2000s. Soil Science Society of America Journal, 77,
2111.\emph{http://doi.org/10.2136/sssaj2013.05.0192}

Li, Z. W., Liu, L., Nie, X. D., Chang, X. F., Liu, C. and Xiao, H. B.
(2016). Modeling soil organic carbon loss in relation to flow velocity
and slope on the Loess Plateau of China. Soil Science Society of America
Journal, 80, 1341.\emph{http://doi.org/10.2136/sssaj2016.03.0092}

Liang, Y. (2004). The actualities, problems and countermeasures of soil
and fertilizer in Yixing city, Tai Lake area (In Chinese). Nanjing
Agriculture University, Nanjing.

Liao, Q., Zhang, X., Li, Z., Pan, G., Smith, P., Yang, J. and Wu, X.
(2009). Increase in soil organic carbon stock over the last two decades
in China's Jiangsu Province. Global Change Biology, 15,
861--875.\emph{http://doi.org/10.1111/j.1365-2486.2008.01792.x}

Liu, K., Huang, J., Li, D., Yu, X., Ye, H., Hu, H., Hu, Z., Huang, Q.
and Zhang, H. (2019). Comparison of carbon sequestration efficiency in
soil aggregates between upland and paddy soils in a red soil region of
China. Journal of Integrative Agriculture, 18,
1348-1359.\emph{http://doi.org/10.1016/s2095-3119(18)62076-3}

Liu, Q., Shi, X., Weindorf, D. C., Yu, D., Zhao, Y., Sun, W. and Wang,
H. (2006). Soil organic carbon storage of paddy soils in China using the
1:1,000,000 soil database and their implications for C sequestration.
Global Biogeochemical Cycles, 20,
1-5.\emph{http://doi.org/10.1029/2006gb002731}

Liu, W., Zhang, L., Ye, Y., Zou, J., Wang, Y. and Qi, Y. (2011). The
content and density of soil organic carbon under different land use
types in Shenzhen (In Chinese). Ecological Science, 30,
486-492.\emph{http://doi.org/CNKI:ISSN:1008-8873.0.2011-05-004}

Liu, Y. S., Wang, J. Y. and Long, H. L. (2010). Analysis of arable land
loss and its impact on rural sustainability in Southern Jiangsu Province
of China. Journal of Environment management, 91,
646-653.\emph{http://doi.org/10.1016/j.jenvman.2009.09.028}

Lu, F., Wang, X., Han, B., Ouyang, Z., Duan, X., Zheng, H. and Miao, H.
(2009). Soil carbon sequestrations by nitrogen fertilizer application,
straw return and no-tillage in China's cropland. Global Change Biology,
15, 281-305.\emph{http://doi.org/10.1111/j.1365-2486.2008.01743.x}

Lu, X., Shi, Y., Chen, C. and Yu, M. (2017). Monitoring cropland
transition and its impact on ecosystem services value in developed
regions of China: A case study of Jiangsu Province. Land Use Policy, 69,
25-40.\emph{http://doi.org/10.1016/j.landusepol.2017.08.035}

Luo, Y., Li, Q., Shen, J., Wang, C., Li, B., Yuan, S., Zhao, B., Li, H.,
Zhao, J., Guo, L., Li, S. and He, Y. (2019). Effects of agricultural
land use change on organic carbon and its labile fractions in the soil
profile in an urban agricultural area. Land Degradation \& Development,
30, 1875-1885.\emph{http://doi.org/10.1002/ldr.3388}

Meersmans, J., Van Wesemael, B., Goidts, E., Van Molle, M., De Baets, S.
and De Ridder, F. (2011). Spatial analysis of soil organic carbon
evolution in Belgian croplands and grasslands, 1960-2006. Global Change
Biology, 17,
466-479.\emph{http://doi.org/10.1111/j.1365-2486.2010.02183.x}

Nandwa, S. M. (2001). Soil organic carbon (SOC) management for
sustainable productivity of cropping and agro-forestry systems in
Eastern and Southern Africa. Nutrient Cycling in Agroecosystems, 61,
143-158.\emph{http://doi.org/10.1023/A:1013386710419}

Ou, Y., Rousseau, A. N., Wang, L. and Yan, B. (2017). Spatio-temporal
patterns of soil organic carbon and pH in relation to environmental
factors---A case study of the Black Soil Region of Northeastern China.
Agriculture, Ecosystems \& Environment, 245,
22-31.\emph{http://doi.org/10.1016/j.agee.2017.05.003}

Qiu, L., Zhu, J., Zhu, Y., Hong, Y., Wang, K. and Deng, J. (2013). Land
use changes induced soil organic carbon variations in agricultural soils
of Fuyang County, China. Journal of Soils and Sediments, 13,
981-988.\emph{http://doi.org/10.1007/s11368-013-0684-4}

Ramesh, T., Bolan, N. S., Kirkham, M. B., Wijesekara, H.,
Kanchikerimath, M., Srinivasa Rao, C., Sandeep, S., Rinklebe, J., Ok, Y.
S., Choudhury, B. U., Wang, H., Tang, C., Wang, X., Song, Z. and Freeman
Ii, O. W. (2019). Soil organic carbon dynamics: Impact of land use
changes and management practices: A review. Advances in Agronomy, 156,
1-107.\emph{http://doi.org/10.1016/bs.agron.2019.02.001}

Ritchie, G. S. P. and Dolling, P. J. (1985). The role of organic matter
in soil acidification. Australian Journal of Soil Research, 23,
569-576.\emph{http://doi.org/10.1071/SR9850569}

Sandeep, S., Manjaiah, K. M., Pal, S. and Singh, A. K. (2016). Soil
carbon fractions under maize-wheat system: effect of tillage and
nutrient management. Environmental Monitoring and Assessment, 188,
14.\emph{http://doi.org/10.1007/s10661-015-4995-3}

Schillaci, C., Acutis, M., Vesely, F. and Saia, S. (2019). A simple
pipeline for the assessment of legacy soil datasets: An example and test
with soil organic carbon from a highly variable area. Catena, 175,
110-122.\emph{http://doi.org/10.1016/j.catena.2018.12.015}

Schillaci, C., Saia, S. and Acutis, M. (2018). Modelling of soil organic
carbon in the Mediterranean area: a systematic map. Rendiconti Online
Societa Geologica Italiana, 46,
161-166.\emph{http://doi.org/10.3301/ROL.2018.68})

Shi, P., Zhang, Y., Li, P., Li, Z., Yu, K., Ren, Z., Xu, G., Cheng, S.,
Wang, F. and Ma, Y. (2019). Distribution of soil organic carbon impacted
by land-use changes in a hilly watershed of the Loess Plateau, China.
Science of the Total Environment, 652,
505-512.\emph{http://doi.org/10.1016/j.scitotenv.2018.10.172}

Song, X., Yang, J., Zhao, M., Zhang, G., Liu, F. and Wu, H. (2019).
Heuristic cellular automaton model for simulating soil organic carbon
under land use and climate change: A case study in eastern China.
Agriculture, Ecosystems \& Environment, 269,
156-166.\emph{http://doi.org/10.1016/j.agee.2018.09.034}

Vågen, T. G. and Winowiecki, L. A. (2013). Mapping of soil organic
carbon stocks for spatially explicit assessments of climate change
mitigation potential. Environmental Research Letters, 8,
015011.\emph{http://doi.org/10.1088/1748-9326/8/1/015011}

van Vliet, J., Eitelberg, D. A. and Verburg, P. H. (2017). A global
analysis of land take in cropland areas and production displacement from
urbanization. Global Environmental Change, 43,
107-115.\emph{http://doi.org/10.1016/j.gloenvcha.2017.02.001}

VandenBygaart, A. J., Gregorich, E. G., Angers, D. A. and Stoklas, U. F.
(2004). Uncertainty analysis of soil organic carbon stock change in
Canadian cropland from 1991 to 2001. Global Change Biology, 10,
983-994.\emph{http://doi.org/10.1111/j.1365-2486.2004.00780.x}

Veronesi, F. and Schillaci, C. (2019). Comparison between geostatistical
and machine learning models as predictors of topsoil organic carbon with
a focus on local uncertainty estimation. Ecological Indicators, 101,
1032-1044.\emph{http://doi.org/10.1016/j.ecolind.2019.02.026}

Wan, Y., Zhou, S. and Zhao, Q. (2006). Study on spatial and temporal
variability of soil basic properties in typical area of southern Jiangsu
Province: a case study in Kunshan city (In Chinese). Geographical
Research, 25, 303-310.\emph{http://doi.org/10.11821/yj2006020014}

Wang, G., Huang, Y., Zhang, W., Yu, Y. and Sun, W. (2015a). Quantifying
carbon input for targeted soil organic carbon sequestration in China's
croplands. Plant and Soil, 394,
57-71.\emph{http://doi.org/10.1007/s11104-015-2508-3})

Wang, J., Wang, X., Xu, M., Feng, G., Zhang, W. and Lu, C. a. (2015b).
Crop yield and soil organic matter after long-term straw return to soil
in China. Nutrient Cycling in Agroecosystems, 102,
371-381.\emph{http://doi.org/10.1007/s10705-015-9710-9}

Webster, R. and Oliver, M. A. (2007). Geostatistics for Environmental
Scientists. John Wiley \& Sons, New York.

Wei, J., Xiao, D., Zhang, X. and Li, X. (2011). Topography and land use
effects on the spatial variation of soil organic carbon: A case study in
a typical small watershed of the black soil region in northeast China.
Eurasian Soil Science, 41,
39-47.\emph{http://doi.org/10.1134/s1064229308010055}

Wei, Z., Ji, J., Li, Z. and Yan, X. (2017). Changes in organic carbon
content and its physical and chemical distribution in paddy soils
cultivated under different fertilisation practices. Journal of Soils and
Sediments, 17, 2011-2018.\emph{http://doi.org/10.1007/s11368-017-1676-6}

Wiesmeier, M., Hübner, R., Dechow, R., Maier, H., Spörlein, P., Geuß,
U., Hangen, E., Reischl, A., Schilling, B., von Lützow, M. and
Kögel-Knabner, I. (2014). Estimation of past and recent carbon input by
crops into agricultural soils of southeast Germany. European Journal of
Agronomy, 61, 10-23.\emph{http://doi.org/10.1016/j.eja.2014.08.001}

Wiesmeier, M., Urbanski, L., Hobley, E., Lang, B., von Lützow, M., Marin
Spiotta, E., van Wesemael, B., Rabot, E., Ließ, M., Garcia Franco, N.,
Wollschläger, U., Vogel, H. J. and Kögel Knabner, I. (2019). Soil
organic carbon storage as a key function of soils - A review of drivers
and indicators at various scales. Geoderma, 333,
149-162.\emph{http://doi.org/10.1016/j.geoderma.2018.07.026}

Wu, C. (2014). Analysis of cultivated land quality and agricultural
production safety in Dantu city (In Chinese). Agricultural Equipment \&
Techonlogy, 40,
55-57.\emph{http://doi.org/CNKI:ISSN:1671-6337.0.2014-06-020}

Xia, X., Yang, Z., Xue, Y., Shao, X., Yu, T. and Hou, Q. (2017). Spatial
analysis of land use change effect on soil organic carbon stocks in the
eastern regions of China between 1980 and 2000. Geoscience Frontiers, 8,
597-603.\emph{http://doi.org/10.1016/j.gsf.2016.06.003}

Xie, E., Zhao, Y., Li, H., Shi, X., Lu, F., Zhang, X. and Peng, Y.
(2019). Spatio-temporal changes of cropland soil pH in a rapidly
industrializing region in the Yangtze River Delta of China, 1980--2015.
Agriculture, Ecosystems \& Environment, 272,
95-104.\emph{http://doi.org/10.1016/j.agee.2018.11.015}

Xie, J., Zheng, W. and Tan, H. (2009). Technical report on evaluation of
cultivated land and formulated fertilization by soil testing in Danyang
City (In Chinese). Agricultural Equipment \& Techonlogy, 35,
41-44.\emph{http://doi.org/CNKI:ISSN:1671-6337.0.2009-02-029}

Xu, J. and Gong, K. (2014). Soil fertility condition and fertilization
measures in farmland in Wuxi, city (In Chinese). Modern Agricultural
Science and Technology, 24,
202-203.\emph{http://doi.org/CNKI:ISSN:1007-5739.0.2014-24-120}

Xu, M., Wang, X., Jiang, J. and Shen, Q. (2006). Changes in cultivated
land utilization pattern and countermeasures in south Jiangsu (In
Chinese). Soils, 38,
825-829.\emph{http://doi.org/10.3321/j.issn:0253-9829.2006.06.028}

Xu, X., Liu, J., Zhang, S., Li, R., Yan, C. and Wu, S. (2018). Remote
sending monitoring dataset of multi-period land cover in
China\emph{http://www.resdc.cn/DOI/doi.aspx?DOIid=54}.

Xue, B., Huang, L., Huang, Y., Zhou, F., Li, F., Kubar, K. A., Li, X.,
Lu, J. and Zhu, J. (2019). Roles of soil organic carbon and iron oxides
on aggregate formation and stability in two paddy soils. Soil and
Tillage Research, 187,
161-171.\emph{http://doi.org/10.1016/j.still.2018.12.010}

Yan, X., Cai, Z., Wang, S. and Smith, P. (2011). Direct measurement of
soil organic carbon content change in the croplands of China. Global
Change Biology, 17,
1487-1496.\emph{http://doi.org/10.1111/j.1365-2486.2010.02286.x}

Yang, J., Li, A., Yang, Y., Li, G. and Zhang, F. (2020). Soil organic
carbon stability under natural and anthropogenic-induced perturbations.
Earth-Sci Rev, 205,
103199.\emph{http://doi.org/10.1016/j.earscirev.2020.103199}

Yang, L., Song, M., Zhu, A. X., Qin, C., Zhou, C., Qi, F., Li, X., Chen,
Z. and Gao, B. (2019). Predicting soil organic carbon content in
croplands using crop rotation and Fourier transform decomposed
variables. Geoderma, 340,
289-302.\emph{http://doi.org/10.1016/j.geoderma.2019.01.015}

Yang, Y., Ji, C., Chen, L., Ding, J., Cheng, X., Robinson, D. and
Whitehead, D. (2015). Edaphic rather than climatic controls
over\textsuperscript{13}C enrichment between soil and vegetation in
alpine grasslands on the Tibetan Plateau. Functional Ecology, 29,
839-848.\emph{http://doi.org/10.1111/1365-2435.12393}

Zhang, X. and Wang, X. (2013). The relationship between the soil organic
matter and total nitrogen in cropland in Taicang city (In Chinese).
Anhui Agricultural Science Bulletin, 19,
57-58.\emph{http://doi.org/10.16377/j.cnki}

Zhao, Y., Wang, M., Hu, S., Zhang, X., Ouyang, Z., Zhang, G., Huang, B.,
Zhao, S., Wu, J., Xie, D., Zhu, B., Yu, D., Pan, X., Xu, S. and Shi, X.
(2018). Economics- and policy-driven organic carbon input enhancement
dominates soil organic carbon accumulation in Chinese croplands.
Proceedings of the National Academy of Sciences of the United States of
America, 115, 4045-4050.\emph{http://doi.org/10.1073/pnas.1700292114}

Zhong, C., Hu, R., Wang, M., Xue, W. and He, L. (2020). The impact of
urbanization on urban agriculture: Evidence from China. Journal of
Cleaner Production, 276,
122686.\emph{http://doi.org/10.1016/j.jclepro.2020.122686}

Zhou, Y., Hartemink, A. E., Shi, Z., Liang, Z. and Lu, Y. (2019). Land
use and climate change effects on soil organic carbon in North and
Northeast China. Science of the Total Environment, 647,
1230-1238.\emph{http://doi.org/10.1016/j.scitotenv.2018.08.016}

\section*{Table}

{\label{table}}

\textbf{Table 1} Changes in SOC stock in the study area over the three
time periods\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.32\columnwidth}\raggedright\strut
Time period\strut
\end{minipage} & \begin{minipage}[b]{0.32\columnwidth}\raggedright\strut
SOC stock change (t ha\textsuperscript{-1})\strut
\end{minipage} & \begin{minipage}[b]{0.32\columnwidth}\raggedright\strut
Annual changes (t ha\textsuperscript{-1} yr\textsuperscript{-1})\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
1980-2000\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
11.17\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.56\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
2000-2015\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
-4.59\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
-0.31\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
1980-2015\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
6.58\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.19\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\section*{Figure Captions}

{\label{figure-captions}}

\textbf{Fig. 1 The sampling location (a), elevation (b), and soil type
map (c) in the study area.}

\textbf{Fig. 2 Histograms and descriptive statistics of the topsoil SOC
contents (g kg-1) in 1980 (a), 2000 (b), and 2015 (c).}

SD represents standard deviation, and CV represents coefficient of
variation.

\textbf{Fig. 3 The experimental (dots) and fitted semi-variograms (solid
lines) of SOC in 1980, 2000, and 2015.}

\textbf{Fig. 4 Spatial distribution maps of the cropland SOC in 1980
(a), 2000 (b), and 2015 (c).}

\textbf{Fig. 5 Spatial patterns of the SOC changes in cropland over the
period of 1980-2000 (a), 2000-2015 (b), and 1980-2015 (c).}

\textbf{Fig. 6 Probability maps of the increases/decreases in cropland
SOC over the three time periods}

Fig. 6(a)-(c) represent the probability maps of the SOC increases over
the period of 1980-2000, 2000-2015, and 1980-2015, respectively; Fig.
6(d)-(f) represent the probability maps of the SOC decreases over the
period of 1980-2000, 2000-2015, and 1980-2015, respectively.

\textbf{Fig. 7 The cumulative frequency curves and uncertainty intervals
of the SGS-generated SOC changes over the period of 1980-2000 (a),
2000-2015 (b), and 1980-2015 (c).}

\textbf{Fig. 8 Average SOC contents for the different soil types (a) and
the amounts of chemical fertilizers use over the past three decades in
southern Jiangsu Province (b).}

In Fig. 8a, the bold values represent the average SOC contents, and the
error bars represent the standard deviation of the SOC contents. In Fig.
8b, data was sourced from the Jiangsu Bureau of
Statistics,\emph{http://tj.jiangsu.gov.cn}.

\textbf{Fig. 9 Crop-derived carbon inputs over the period of 1980-2015
in southern Jiangsu Province (a), in county-level cities (b} ),

\textbf{and the relationship between annual SOC change and carbon inputs
among these county-level cities (c).}

In Fig. 9c, annual SOC change represents the average SOC changes over
the past 35 years; carbon inputs represent the average carbon inputs
during 1980-2015; Carbon inputs were estimated using the crop yield data
recorded by county-level agricultural census (Zhao et al., 2018).

\textbf{Fig. 10 Changes in the industrial and residential lands over the
period of 2000-2010 (a), the relationship between urban expansion and
the annual carbon inputs during 2000-2015 (b)} .

UL represents the area of urban land, CL represents the cropland area;
land use data were obtained from Xu et al., (2018).

\textbf{Fig. 11 SOC contents from previous studies during 2000-2015 in
the east of study area (a), and in the west (b).}

SOC data from previous studies were obtained from Ding, (2009), Gao et
al., (2016), Gao et al., (2011), Gao et al., (2005), Guo et al., (2007),
Han et al., (2007), He and Cao, (2014), Hu et al., (2008), Liang,
(2004), Wan et al., (2006), Wu, (2014), Xie et al., (2009), Xu and Gong,
(2014), Xu et al., (2006), and Zhang and Wang, (2013).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{\textless{}Fig. 1\textgreater{}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{\textless{}Fig. 2\textgreater{}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}



\textbf{\textless{}Fig. 3\textgreater{}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{\textless{}Fig. 4\textgreater{}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{\textless{}Fig. 5\textgreater{}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{\textless{}Fig. 6\textgreater{}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{\textless{}Fig. 7\textgreater{}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{\textless{}Fig. 8\textgreater{}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{\textless{}Fig. 9\textgreater{}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

\textbf{\textless{}Fig. 10\textgreater{}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

\textbf{\textless{}Fig. 11\textgreater{}}

\selectlanguage{english}
\FloatBarrier
\end{document}

