Introduction
Soil organic carbon (SOC), as a critical component of soil fertility,
not only affects soil quality and crop production (Vågen & 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-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-1 yr-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.