Changes in the soil organic carbon (SOC) stock are determined by the
balance between the carbon input from organic materials and the
output from the decomposition of soil C. The fate of SOC in cropland
soils plays a significant role in both sustainable agricultural
production and climate change mitigation. The spatiotemporal changes
of soil organic carbon in croplands in response to different
carbon (C) input management and environmental conditions across the
main global cereal systems were studied using a modeling
approach. We also identified the key variables that drive SOC
changes at a high spatial resolution (0.1
On a global scale, the soil is the largest terrestrial carbon (C)
pool, and it stores approximately three times the quantity of C that
is in the atmosphere. Consequently, a small variation in soil carbon
stock can lead to substantial changes in atmospheric carbon dioxide
(
Cropland SOC is a balance of carbon inputs (mainly dependent on biomass productivity that is controlled by the climate and management conditions) and outputs (strongly regulated by climatic conditions). Since the start of the 1960s, the “green revolution”, which aims to provide more food to feed the increasing population, has been widely launched across the global agricultural systems (Evenson and Gollin, 2003). During this period, numerous efforts regarding crop variety improvement, and the applications of irrigation and nitrogen fertilization have been taken to enhance the global crop production (Fischer and Edmeades, 2010; Evenson and Gollin, 2003). As a result, the global crop production tripled from 1961 to 2010, which is mostly due to greater yields per unit area (Zeng et al., 2014). Increases in crop production provide more carbon inputs (e.g., organic matter from crop roots and residues) into soils, thereby substantially affecting the SOC sequestration (Wang et al., 2016). However, the degrees of these impacts at fine spatiotemporal resolutions on a global scale are still unclear and have seldom been comprehensively studied.
Over the past several decades, a number of agricultural system models
have been developed and used to reproduce the dynamic processes,
including carbon flows, that occur between the agroecosystems and the
atmosphere (Li et al., 1994; Parton et al., 1994; Keating et al.,
2003; Huang et al., 2009). These models have been reported to be able
to capture the soil C changes under different environmental and
management conditions, thereby providing an opportunity for
quantifying the soil C dynamics at larger spatial and temporal
scales. Based on the process-based models, efforts have already been
taken to quantify the soil C dynamics in croplands at the national and
continental scales. For example, using the CENTURY model, Ogle
et al. (2010) and Lugato et al. (2014) estimated that the average soil
C density increased under improved management at rates of
1.3
Currently, most existing process-based models require many detailed parameters for the model inputs, which are not readily obtainable at a large scale. As one of the most classic and widely used soil C turnover models, the RothC model (Jenkinson et al., 1990), however, requires only a few easily obtainable parameters and input data. The model has been widely and frequently adopted to simulate the soil C changes under different management treatments and soil and climate conditions across the world's cropping systems (Falloon and Smith, 2002; Guo et al., 2007; Yang et al., 2003; Bhattacharyya et al., 2011; Skjemstad et al., 2004; Smith et al., 2005). More recently, by adopting the model's original default parameters, the RothC model has been tested against the measurements obtained from 16 long-term experimental sites across the global croplands and showed a generally good performance in representing the SOC dynamics under different treatments at different sites (Wang et al., 2016).
In this study, we simulated the spatiotemporal soil C dynamics across
the main global cereal (i.e., wheat, maize and rice) cropping systems,
using the RothC model and state-of-the-art databases of soil and
climate. The soil C revolutions were simulated under different
scenarios of C inputs (calculated from crop residues, roots and
manure) on a monthly time step from 1961 to 2014 at a high spatial
resolution of
The study area covered the main cereal (i.e., wheat, maize and rice)
cropping regions of the world (Fig. S1 in the Supplement). We selected the wheat, maize
and rice cropping areas because they are the most widely planted
(covering approximately 72 % of the global cereal cropping
areas) and productive (constituting approximately 80 % of the
global cereal yield) cereals in the world (FAOSTAT, 2017). The
geographic distribution of the global croplands (
The Rothamsted carbon model (RothC, version 26.3) was used to simulate the soil C dynamics in croplands in the present study. The RothC model is a widely used soil organic matter (SOM) decomposition model used to simulate the C dynamics in agricultural soils under various environments and management practices (Smith et al., 2005; Guo et al., 2007; Skjemstad et al., 2004). Recently, Wang et al. (2016) evaluated the model's performance in simulating soil C variations using observations from 16 long-term experimental sites across the world's wheat-growing regions. The validated results suggested that the model could reasonably reproduce the SOC dynamics under a wide range of soil and climatic conditions and agricultural management practices. Detailed information on the RothC model description can be found in Jenkinson et al. (1990).
The soil carbon pool in the RothC model is divided into five
conceptual components, i.e., decomposable plant material
(DPM), resistant plant material (RPM), microbial
biomass (BIO), humified organic matter (HUM), and
inert organic matter (IOM). These conceptual pools are
difficult to measure directly in most cases and can only be
empirically initialized because only the quantity of total soil
organic carbon is obtainable without finer level partitioning among
the subpools. In the present study, following Wang et al. (2016), we
adopted the approach of Weihermüller et al. (2013), who developed
a validated set of pedotransfer functions to initialize C pools in the
RothC model:
The default yearly decomposition rates for the five abovementioned
soil C subpools were divided by 12 to run the model on a monthly time
step (Jenkinson et al., 1990). The annual carbon inputs to soils from
crop residue, root and manure were assumed to occur after harvest,
which is acceptable because the model is insensitive to the time of C
input, particularly in long-term simulations (Smith et al., 2005). The
default value of the
The soil parameters used in the present study, such as soil carbon
density and the clay fraction in the top 30
The global climate data layers with a
Carbon inputs are mainly sourced from crop residues, roots and manure
(Yu et al., 2012). We derived this information at a high spatial
resolution from the various sources of existing data sets. Firstly, the
global crop yields for wheat, maize and rice in 2005 at
a
The annual carbon input from manure application at a global scale was
derived from Zhang et al. (2017), who used the Global Livestock Impact
Mapping System (GLIMS) data set in conjunction with the
country-specific annual livestock population to reconstruct the manure
nitrogen production and application of global croplands during
1860–2014 at a high spatial resolution of
The crop residue that is retained in the system after harvest can benefit the sequestration of soil carbon in the croplands. The amount of above-ground residue that is retained in the system, however, shows vast spatial disparity and uncertainty across the global croplands. In developing regions such as Asia and Africa, it has been suggested that only approximately 30 % of the crop residues are retained in the soils after harvest (Jiang et al., 2012; Baudron et al., 2014). In developed regions such as Europe and North America, however, the crop residue retention rate can reach over 60 % (Scarlat et al., 2010; Lokupitiya et al., 2012). Furthermore, in Australia, it has been reported that 100 % of the crop residue was retained across 72–100 % of the cropping area of the country from 2010 to 2014 (National Inventory Report 2013, 2015). However, this information is based on rough estimations and statistical data. To the best of our knowledge, detailed information on the residue retention rates over a meaningfully large scale of both time and space across different countries and continents is still lacking. Consequently, a scenario modeling approach was adopted to assess the dynamics of SOC as determined by various potential management practices on crop residues. We specified three crop residue retention rates in the present study, i.e., 30, 60 and 90 % (hereafter simply denoted as R30, R60 and R90).
In total, we ran 461 586 (three crop residue retention
scenarios
On a global average, SOC generally increased
over time under the different specified crop residue retention rates
in the present study (Fig. 1). The median SOC increased from
46.2
Temporal changes in the soil organic carbon
(
Figure 2 shows the spatial patterns of the estimated SOC changes under R30 (Fig. 2a), R60 (Fig. 2b) and R90 (Fig. 2c). Among the three scenarios, a relatively higher increase in SOC generally occurred in the middle latitudes of the Northern Hemisphere, such as the central parts of the USA, western Europe and the northern regions of China (Fig. 2). A relatively small increase in SOC generally occurred in the high-latitude regions of both the Northern and Southern Hemisphere, while the SOC decreased across the equatorial zones of Asia, Africa and America (Fig. 2). On a global average, 69, 82 and 89 % of the study area acted as a net carbon sink during the study period under R30 (Fig. 2a), R60 (Fig. 2b) and R90 (Fig. 2c), respectively.
Spatial distribution of the SOC change (1961–2014,
The quantified SOC changes also showed large spatiotemporal disparities across different continents (Fig. 3). In general, among the three scenarios, the SOC of the cropland across Europe, Asia and North America showed a linearly increasing trend over time (Fig. 3). In Oceania, the SOC increased faster in the first two decades and showed a relatively lower increasing rate during the latter three decades (Fig. 3). In South America and Africa, the SOC decreased in the first few decades and increased or remained relatively stable during the latter periods under R30 (Fig. 3a) and R60 (Fig. 3b). Under R90, however, the average SOC on all continents increased over time (Fig. 3c). In general, the regions with higher annual C input rates (e.g., Europe and North America) experienced higher SOC increases than the areas with relatively lower C input rates (e.g., Oceania and Africa) across all three crop residue retention scenarios (Figs. 3 and S4).
SOC evolution of five continents in the main global cereal
cropping regions under different above-ground crop residue retention
rates of 30 %
The quantified SOC changes were regulated by soil, climate and
management practices. The initial SOC was significantly but negatively
correlated (
Spearman's rank correlation coefficients between SOC change
(1961–2014,
Response of SOC change (1961–2014,
Soil organic carbon change is a balance between C input from crops and
manures and C output through decomposition. The linear increase in the
global average SOC that was quantified in this study (Fig. 1) can be
mainly attributed to the increasing rate of C input throughout the
study period (Fig. S3). This is associated with the increased crop
production that began at the start of the “green revolution,” which
was launched during the 1960s (Fischer and Edmeades, 2010; Evenson and
Gollin, 2003). In the present study, we found that the crop residue
retention rate is strongly and positively correlated with the change
in SOC (Fig. 4). This is similar to the findings of our previous
studies (Wang et al., 2016, 2015), which found that higher amounts of
C input can lead to higher soil C sink capacities. On a global
average, the total amounts of C input to soils are 1.7, 2.7 and
3.7
Apart from the residue retention rate, the initial SOC is one of the major
controlling factors of SOC change. The results in Figs. 4 and 5 indicate that
under otherwise similar environmental and managed conditions, soils with
lower initial SOC contents would experience greater SOC increases or smaller
soil C losses. This negative correlation between SOC change and initial SOC
content has also been documented in other studies (Zhao et al., 2013; Wang
et al., 2014). The relationship is further supported by the distribution of
global SOC changes (Fig. 2) and the global initial SOC densities that are
quantified in this study (Fig. S2). For example, soils with lower initial SOC
contents in western Europe generally showed higher SOC increases than the
soils in eastern Europe with relatively higher initial SOC contents (Figs. 2
and S2). Spatial patterns of lower initial SOC associated with higher SOC
changes in neighboring areas can also be found in other regions such as the
USA and China (Figs. 2 and S2). The soil clay fraction has been suggested to
benefit C stabilization through the mineralogical protection of soil C
(Oades, 1988; Amato and Ladd, 1992). However, we identified a negligible but
negative correlation between soil C accumulation and soil clay fraction in
this study (Fig. 4). The adverse effects of soil clay could be a result of
the strong correlation between initial soil C content and the soil clay
fraction (
The negative effects of higher temperature and precipitation on SOC change identified in the present study (Figs. 4 and 5) can be attributed to the higher SOC decomposition rates in warmer and wetter soils, which is consistent with the description of the RothC model (Jenkinson et al., 1990) and the other findings by Bond-Lamberty and Thomson (2010). Here, it should be noted that such correlations between the climate and SOC changes might only be valid in a soil carbon turnover model that only consists of the dynamic C processes in the soil (e.g., RothC model). In other agricultural model simulations, climatic variables may play a different role in affecting the SOC change through jointly regulating both crop productions and soil C dynamics. For example, Wang et al. (2014) used a process-based agricultural system model (i.e., Agro-C model) to simulate the SOC dynamics in the semi-arid regions of the North China Plain and found positive effects of temperature and precipitation on SOC accumulation. This is because, in temperature and water deficient areas (e.g., the North China Plain), increased temperature and precipitation promote crop production and hence increases the C input to soils, which favors SOC sequestration.
Can we estimate the actual historical soil C dynamics across the
world? A large challenge exists due to a lack of data availability,
particularly for the two main RothC model inputs, initial SOC content
and annual C input. Firstly, the soil properties presented by the HWSD
were derived from different sources with unevenly sampled soil
profiles over time and space. As such, the value of initial SOC
content can hardly, if at all, represent the actual SOC content at the
beginning of the study period. However, the modeled dynamics of the
SOC in the present study may be appropriate, to a certain extent, to
represent the spatiotemporal patterns of the soil C source and sink
processes. Secondly, a lack of detailed information on crop residue
management across both time and space remains, which hinders our
ability to accurately characterize the SOC changes on a large scale at
fine spatiotemporal resolutions. However, we can roughly assume that
the above-ground residue retention rates were approximately 30 %
in developing regions such as Asia and Africa (Jiang et al., 2012;
Baudron et al., 2014; Erenstein, 2011) and 60 % in other regions
(Lokupitiya et al., 2012; Scarlat et al., 2010; Baudron et al.,
2015). Based on these assumptions, we further quantified that the
global average SOC increased at a rate of
0.34
By extrapolating these results to the global total cropland area of
1400
Several uncertainties and limitations should be considered when interpreting the simulation results in this study. Firstly, the SOC change modeled in the present study could be biased due to the spatial inconsistency in the time of soil sampling, which varied widely over the second half of the twentieth century (Fao and Isric, 2012). In some places, the initial soil C information derived from the HWSD only represented the actual soil C levels during the periods after the early 1960s. For example, the soil profile measurements used to produce the soil map of China, which is included in the HWSD data sets, were generally collected in the late 1970s and early 1980s (Yu et al., 2007). Considering that the spatial patterns of cropland SOC could have substantially changed over the study period under the changing environments and management practices (Figs. 1 and 2), the initial SOC used in the present study (derived from HWSD) might significantly differ from the actual soil C levels in the early 1960s. In addition, it has been reported that soils with higher initial C contents would experience smaller increases or greater C losses under otherwise similar conditions, and vice versa (Zhao et al., 2013; Wang et al., 2015). Consequently, for those regions with soil sampling times much later than the early 1960s, our quantified SOC changes may be underestimations in the areas where substantial soil C increases had occurred before measurements were collected. In contrast, the SOC changes could be overestimated in the areas that are accompanied by a previous significant decrease in soil C.
Secondly, the RothC model was developed to simulate the soil organic matter turnover in upland soils (Jenkinson et al., 1990), and it generally performs well in the global wheat systems with non-waterlogged soils (Wang et al., 2016). In the paddy soils, particularly during the rice-growing seasons, the soil C decomposition rate might be reduced when subjected to anaerobic conditions. For example, Shirato and Yokozawa (2005) used the RothC model to simulate the C changes in Japanese paddy soils and suggested that the model's performance can be improved by modifying the SOC decomposition rates during the rice growing season. As such, the default parameters adopted in the present study may bias the simulations of the SOC changes across the rice systems that are mainly distributed in southeast Asia. In the present study, we adopted the model's default parameters rather than the modified factors from Shirato and Yokozawa (2005), mainly because the rice-growing areas in Japan constitute approximately 1 % of the world's total (FAOSTAT, 2017), and the associated climatic and edaphic conditions differ significantly from the other rice systems. We highlight the need to robustly calibrate the model's soil C decomposition rates against the long-term experimental data across the rice paddy soils to represent the different patterns in climate, soil and management conditions of southeast Asia in the future.
Finally, the limitations of the current first-order decay model (e.g., RothC) may cause significant bias in the model simulations. For example, our results suggested a general linear relation between C input and SOC variation (Figs. 1 and S3), which contradicts previous findings that increasing the incorporated amount of crop residue may affect the SOC change in a variety of ways other than linearly (Powlson et al., 2011). Moreover, it has been reported that, although soil can accumulate a significant amount of C when the preexisting soil C content is low, the SOC reaches a threshold level (i.e., carbon saturation state) where little or no significant further changes occur even when more C is added (Stewart et al., 2007; Qin et al., 2013). Without considering the C saturation state, the first-order decay model might overestimate the SOC in longer timescale simulations, particularly in regions where the C input is higher and the SOC decomposition rate is lower.
The data mentioned in this paper are available upon request (contact: wanggc@mail.iap.ac.cn).
The authors declare that they have no conflict of interest.
This research was funded by the National Natural Science Foundation of China (grant no. 41590870, 31370492). Edited by: Jianping Huang Reviewed by: two anonymous referees