Figures
Abstract
Soil organic matter models are widely used to study soil organic carbon (SOC) dynamics. Here, we used the CENTURY model to simulate SOC in wheat-corn cropping systems at three long-term fertilization trials. Our study indicates that CENTURY can simulate fertilization effects on SOC dynamics under different climate and soil conditions. The normalized root mean square error is less than 15% for all the treatments. Soil carbon presents various changes under different fertilization management. Treatment with straw return would enhance SOC to a relatively stable level whereas chemical fertilization affects SOC differently across the three sites. After running CENTURY over the period of 1990–2050, the SOC levels are predicted to increase from 31.8 to 52.1 Mg ha−1 across the three sites. We estimate that the carbon sequestration potential between 1990 and 2050 would be 9.4–35.7 Mg ha−1 under the current high manure application at the three sites. Analysis of SOC in each carbon pool indicates that long-term fertilization enhances the slow pool proportion but decreases the passive pool proportion. Model results suggest that change in the slow carbon pool is the major driver of the overall trends in SOC stocks under long-term fertilization.
Citation: Cong R, Wang X, Xu M, Ogle SM, Parton WJ (2014) Evaluation of the CENTURY Model Using Long-Term Fertilization Trials under Corn-Wheat Cropping Systems in the Typical Croplands of China. PLoS ONE 9(4): e95142. https://doi.org/10.1371/journal.pone.0095142
Editor: Julio Vera, University of Erlangen-Nuremberg, Germany
Received: September 26, 2013; Accepted: March 24, 2014; Published: April 21, 2014
Copyright: © 2014 Cong et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Financial supports are from the National Science Foundation of China (41171239) and the National Basic Research Program (2011CB100501). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Soil organic carbon (SOC) is one of the most important terrestrial pools for C storage. It is estimated that the total soil carbon pool is around 1400–1500 Pg C, which is approximately three times greater than the atmospheric pool (750 Pg C) [1], [2]. The SOC pool represents a dynamic equilibrium resulting from changes in gains and losses. Even small changes in SOC at a site may potentially add up to significant changes in large-scale carbon cycling across a region [3]. Furthermore, SOC is relatively dynamic and can be greatly influenced by agricultural practices. Increases in SOC storage in cropland soils would benefit soil productivity and environmental health [4], [5], and so alternative farming management practices have been evaluated to identify their potentials for increasing SOC in the agroecosystems [4]–[7].
Long-term experiments are crucial for determining fundamental crop, soil and ecological processes and their impacts on the environment [6]–[9]. Data from long-term experiments provide a unique resource to investigate long-term influences of climate, crop rotation and crop residue management on soil fertility [6]–[12]. However, SOC change is affected by complex interactions that vary across space and time depending on the environmental conditions and agricultural management practices. A weakness of long-term experiments is that they are typically restricted to small subset of the entire set of environmental conditions and management practices that exists [13].
Process-based models are an effective way to evaluate SOC changes across a broader set of environmental conditions and management practices [14]. In recent decades, the development and evaluation of soil organic matter models has improved the understanding of factors controlling SOC dynamics, and thus increased our ability to predict future SOC trends. A number of SOC models have been developed, but applying these models requires adequate evaluation with measured SOC trends from experimental for different environmental conditions and management practices [15]. For example, the CENTURY model [16] has been widely used to simulate SOC changes under different management conditions in long-term experiments (e.g., [17], [18] and [19]). With the development of CENTURY, the model has been successfully employed in long-term fertilizer, irrigation, pest management, and site-specific farming applications [20], [21]. In China, CENTURY model has been used in grassland [22], forest [23], and regional farmland [24]. However, CENTURY modeling research was still limited in farmland especially under the double cropping rotations and in the acidic soil.
Here, we evaluate the CENTURY with data from three long-term experiments with wheat-corn cropping rotations and different fertilization practices. Specifically, our objectives were (i) to evaluate the performance of CENTURY with evaluation of modeled SOC stocks for different fertilizations and under acidic soil; (ii) to study the effect of fertilization practices on different SOC pools in the modeling framework; and (iii) to predict soil carbon potential under long-term fertilization.
Materials and Methods
Long-term Experiment
Three long-term experiments were utilized for this study, which were located at Changping (40°13′N, 116°15′E), Yangling (34°17′N, 108°00′E) and Qiyang (26°45′N, 111°52′E) in China. Climate conditions varied from semi-humid (Changping site) to humid warm-temperate (Yangling sites) to humid subtropical climate (Qiyang site). Annual mean temperature was 13.1°C at the Changping site, 14.9°C at the Yangling site, and 18.1°C at the Qiyang site. Annual precipitation was generally low at Changping (515 mm) and Yangling (525 mm) sites but 1445 mm at Qiyang. However, annual evaporation was much higher, varying from 993 mm to 1470 mm [25].
The experimental sites had double cropping systems, i.e., winter wheat and summer corn. Winter wheat was seeded in early November and harvested in early May at the Qiyang site. For the other two sites, winter wheat was seeded around October 20th, and harvested around June 1st. The wheat seeding rates ranged from 165 to 225 kg ha−1. Summer corn was sown around June 10th, with the rate of 63 000–75 000 seeds per hectare, and harvested around October 1st at most sites. For the Qiyang site, summer corn was sown in holes between the wheat strips in early April, and harvested in the middle of July. Thus, there was a short period of inter-cropping for wheat and corn at the Qiyang site. However, in the CENTURY modeling, wheat was harvested in April and corn was sown in May at the Qiyang site since inter-cropping could not be fitted in the model. We believed that there would be little impact on the belowground biomass of wheat and corn, and thus to the soil organic carbon turnover.
Seven treatments were selected in this study: (i) control (no fertilizer); (ii) mineral nitrogen (N); (iii) mineral nitrogen and phosphorus combination (NP); (iv) mineral nitrogen, phosphorus and potassium combination (NPK); (v) NPK combinations with livestock or farmyard (i.e., livestock manure mixed with soil and/or crop residue) manure (NPKM); (vi) 1.5 times’ application rate of NPKM (hNPKM); and (vii) mineral NPK combined with crop straw (NPKS). The total nitrogen applied (i.e., mineral plus organic) was equal (i.e., nitrogen balanced) in each of the fertilizer treatments (i.e., N, NP, NPK, NPKM and NPKS treatments). The NPKM and hNPKM treatments had 30% of total N applied from mineral fertilizer and the remaining 70% from organic manure. The treatment plots were initially randomized and isolated by 100-cm-cement baffle plates. There was no replicate for the treatments at these sites due to field availability. However, the plot size was relative large (196 m2–400 m2) at each study site. The durations of experiments were from 1990 to 2005 at the Changping site, from 1990 to 2008 at the Qiyang sites, and from 1990 to 2009 at the Yangling site.
Urea, calcium superphosphate, and potassium chloride were used as sources of mineral N, P, and K, respectively. All P and K fertilizers and nearly half of the mineral N fertilizer were applied as basal fertilizers prior to seeding. The remaining mineral N fertilizer was applied as top dressing during the growing season. Manure was generally applied once each year before wheat sowing. However, at the Qiyang site, 30% of manure was applied before wheat seeding and remaining 70% was applied before corn seeding. The sources of organic manure were farmyard manure mixed with crop residue at Changping site, cattle manure at Yangling site, and pig manure at Qiyang site [25]. For the NPKS treatment, half amount (2000 kg ha−1 yr−1) of both wheat and corn straw was incorporated at the Qiyang site, whereas 2250 kg ha−1 yr−1 and 4500 kg ha−1 yr−1 of corn straw were incorporated at the Changping and Yangling sites, respectively [26]. Carbon and nutrient contents of manure are measured in 2009 and 2010. Nutrient contents of straw are measured annually over the period of experiment.
There was no irrigation at the Qiyang site since sufficient precipitation occurs during the growing season (i.e., from March to August). However, at the other two sites, irrigations were applied 2–3 times during wheat growing season: 5 mm before seeding, 4 mm for the over wintering stage if needed, and 5 mm at jointing stage. Irrigation was applied once during corn growing season, with 5 mm before seeding.
Plots were ploughed to a depth of 20 cm twice a year, usually in early June at most sites after wheat harvest and early October after corn harvest. In contrast, at the Qiyang site, the whole plot area was ploughed (20 cm depth) in late October before wheat seeding. Then, area between the wheat belts was ploughed (20 cm depth) in early April before the corn seeding.
Aboveground biomass was removed with negligible stubble left in the field. Thus, organic carbon input was mainly from manure application (i.e., NPKM and hNPKM treatments), residue addition (i.e., NPKS treatment) and from root system during crop growing season. All harvested biomass was removed from the plots for determining crop grain and residue yields. The grain and residue samples were air-dried, threshed, oven-dried at 70°C to a uniform moisture level, and then weighed separately.
During the experimental periods, surface soil (0–20 cm) was collected in each plot annually approximately 15 days after corn harvest. Five cores of soil from each plot were randomly taken, and soils were mixed thoroughly, and air dried for seven days after removing discrete plant residues and visible soil organism. Air-dried soil was sieved through 2 mm screen to determine pH (1∶1w/v water). Representative subsamples were crushed to 0.25 mm for measurements of SOC [27]. Soil bulk density was measured in situ once every five or ten years. Surface soil SOC stock expressed as Mg ha−1 was calculated by multiplying the SOC content (g kg−1) by averaged soil bulk density (g cm−3) and depth (20 cm).
Model Description and Application
The simulation of SOC dynamics was performed with the CENTURY model (version 4.5) which was described by Parton and Rasmussen [28]. As shown in Fig. 1 [29], the arrows showing the CO2 evolved in the transformations was indicative of the microbial growth efficiencies. The first-order decay rates for each of the pools corresponded to turnover times of roughly 3 and 0.5 years for the structural and metabolic components; 1.5 years for the active fraction, 25 years for the slow pool, and 1000 years for the passive pool [28]. The actual turnover times of each carbon pool was a function of the maximum turnover time of specific carbon pool and DEFAC. The value of DEFAC was calculated by multiplying the soil moisture factor (function of precipitation and stored soil water) and the soil temperature factor (function of the average monthly soil surface temperature). The turnover rate of active carbon pool was also a function of soil texture (higher for sandy soils), while the stabilization of active carbon into slow carbon was a function of the silt plus clay content [28].
The diagram showed the major factors which control the flows [29].
Model simulations were set-up based on the historical crop rotations and farm practices’ investigation from the local farmers at these sites. Five distinct periods were modeled (Table 1), including (1) an initialization period, i.e., 4000 years of native vegetation to reach an equilibrium; (2) the first cultivation period, i.e., plowing of the native grassland in 1901, and planting of corn with removal of straw until 1960; (3) a second cultivation period, i.e., corn-wheat cropping with removal of straw from 1961 to 1988; (4) pre-experimental treatment period, i.e., uniform tillage with no crop in 1989 prior to establishing the long-term experiment; and (5) experiment period, i.e., the long-term corn-wheat fertility experiment from 1990 to 2050. Management during the experiment (1990–2010) was repeated for remainder of the experimental period until the end time of simulation to evaluate future trends. As shown in Table 2, carbon inputs from manure application were around 500 kg C ha−1 before the long-term experiment. Then carbon input varied based on measured data at different sites.
Weather data from 1990–2010 were obtained from China meteorological sharing service system (http://cdc.cma.gov.cn/). For the future simulations (i.e., 2011–2050), we used monthly mean climate data from the 1990–2010 period. Soil properties parameters were soil texture (sand, silt and clay content), soil pH, and bulk density (Table 3). Soils from the Changping and Yangling sites had high soil pH (∼8.5) and silt loam texture with similar clay minerals (e.g., Hydromica and Montmorillonite). On the contrary, soil at the Qiyang site, developed from Quaternary red clay, had a lower soil pH (5.7) and heavy texture with the main clay mineral of Kaolinite.
As shown in Fig. 2, Changping and Yangling sites had similar climate condition whereas temperature and precipitation were much higher at the Qiyang site. The humid and warm climate at Qiyang site would accelerate decomposition of soil organic matter [28]. After setting up all the information (e.g., climate condition, soil texture and etc.) and modeling crop growth successfully, we modeled soil organic carbon pool by calibrating the dec4 value (i.e., maximum decomposition rate of soil organic matter with slow turnover) in the fix.100 file. By raising the parameter 10% higher for every test compared with the measured SOC, we finally got the sound fixed parameter as 0.0045 at the Qiyang site but 0.0023 (i.e., default value in the CENTURY model) at the other two sites [28].
Error bars are standard deviations for the mean values from 1990–2010.
For the plant production submodel, there were pools for live shoots and roots, and standing dead plant material. The potential production was a function of a genetic maximum (PRDX(1)) defined for each crop and 0–1 scalars depending on soil temperature, moisture status, shading by dead vegetation, and seedling growth. The maximum potential production of a crop, unlimited by temperature, moisture or nutrient stresses, was primarily determined by the level of photosynthetically active radiation, the maximum net assimilation rate of photosynthesis, the efficiency of conversion of carbohydrate into plant constituents, and the maintenance respiration rate [30]. Thus, the parameter for maximum potential production (PRDX(1)) had both genetic and environmental components. However, in CENTURY, the seasonal distribution of production was primarily controlled by the temperature response function rather than the seasonal variation in photosynthetically active radiation, so the maximum potential production parameter would reflect aboveground crop production in optimal summer conditions. This parameter would frequently be used to calibrate the predicted crop production for different environments, species, and varieties [31].
The CENTURY model had no function for pH effects on aboveground growth. Based on the 20 years of field measurement at the Qiyang site, soil pH had no impact on crop growth as long as the value remains above pH 5.5, and would completely stop growth in wheat and corn if the value declined to less than pH 4.0 [32]. We added the following soil pH factor (fpH) in the crop growth sub-model to account for this effect:(1)where δ and β were the maximum (i.e., δ = 5.5) and minimum (i.e., β = 4.0) pH value that effected plant growth, respectively.
Model Evaluation
We compared the simulated SOC (0–20 cm) with field measured values. Both visual examination of graphic output and several statistical tests were used to evaluate the CENTURY model performance. Four statistical parameters were selected [33]–[36] for the evaluation, including (i) the sample coefficients of determination (R2), (ii) the normalized-root mean square error (n-RMSE, equation 2), and (iii) the index of agreement (d, equation 3), as a descriptive measure of the average relative error [37].(2)where Mi were the measured values, Si were the simulated values,
was the mean of the measured data and n was the number of the paired values. The index of agreement is computed with the following equation:
(3)
where and
.
The classical R2 statistic (0<R2<1) provided the percentage of data variance that was accounted for by the model. The RMSE evaluated the difference between observed and modeled values in the original units of the data, while the normalized-RMSE (n-RMSE, equation 2) removed the influence of the units, and placed the results on a percentage scale for comparison of model performance among variables with different units. For an ideal fit, R2 would equal 100%, RMSE would equal zero and d would equal 1. In our study, RMSE<15%, d>0.5 and significant R2 were used to bound the best case parameter sets for both calibration and validation simulations [38].
Results
Model Evaluation
We evaluated the model results for both grain yields and the SOC stock predictions. We were focusing on the SOC stock predictions of the model. In summary for the grain yield results (Fig. 3), we found that the model had correlation coefficients (R2) ranging from 0.57 to 0.92 (p<0.05), indicating that CENTURY model simulates the grain yields with reasonable accuracy.
The modeled SOC follows the trend of the measurements reasonably well in all treatments across the sites (Figs. 4, 5, and 6). Model results and measured SOC demonstrated that carbon levels decrease in the control treatment at all sites but increase under the fertilization treatments, except N only. For the N treatment, SOC is maintained at a stable level at most sites but did decline at the Qiyang site. The reason was related to a low soil pH that caused a decline in crop production and thus decline in residue input to the soil [25].
Linear regressions of observed vs. simulated SOC stocks (Fig. 7) are all highly significant (P<0.001) under various fertilization treatments. Approximately 77%–84% of the variability in the observed SOC stocks can be explained by the simulation. Therefore, in our experiment, the CENTURY model performs well and is suitable for predicting SOC dynamics for different types of N fertilizer (i.e., manure versus mineral) and N rates. The coefficients of determination (R2) are higher at the Yangling and Qiyang sites than the Changping site.
RMSE is useful for evaluation of precision in model results [33]. Several studies have emphasized the need to estimate the precision in model predictions to evaluate model uncertainty [14], [39], [40]. In this study, we used several parameters for quantitative statistical analysis of measured and simulated SOC (Table 4). The n-RMSE ranges from 4% to 7% at the Changping site, 9%–14% at the Yangling site, and 6%–11% at the Qiyang site, respectively. Similarly, Fallon and Smith [41] calculated n-RMSE ranging from 1.8% to 16.4% when they modeled SOC in several long-term experiments in Europe. Also Álvaro et al. [20] had a low n-RMSE (below 6%) when they used the CENTURY model to evaluate tillage effects on SOC. Overall, the n-RMSE value is less than 15% for all the treatments in our study, demonstrating that the model is relatively precise in predicting the trends for different fertilization practices that are common in Chinese agricultural systems.
The d values and coefficients of determination (R2) are highest in the NPKM, hNPKM and NPKS treatments at most sites. However, the R2 value was much lower in the control (∼0.07) or N (∼0.19) treatments. The reason was that inter-annual fluctuations in crop yield were larger in the control and N treatments due to the long-term nutrient deficiency [25]. Therefore, carbon input from biomass was fluctuated and thus the simulation was not satisfied as other treatments.
Fertilization Effects on Organic Carbon Pools
Long-term fertilization has a variety of effects on SOC pools (Fig. 8 and 9). Generally, the active pool is the smallest carbon pool [16], and our results are consistent with past studies with an active pool containing only 1–3 Mg ha−1 across the sites (first row in Fig. 8). By 2010, the largest active pool is at the Qiyang site (Fig. 8_c1), followed by Changping (Fig. 7_a1) and Yangling (Fig. 8_b1) sites.
Long-term manure fertilization significantly increased the proportion of slow soil organic matter at all sites (Fig. 8). By the end of 2010, the slow pool accounted for ∼42% of the total SOC at the Changping site, ∼65% at the Yangling site, and ∼48% at the Qiyang site (Fig. 9). In addition, trends in the slow pool are consistent with the overall change in total SOC stock at each of the sites (Figs. 4, 5, and 6). These results suggest that the carbon dynamics associated with the slow pool are key drivers of the trend in total SOC.
The percentage of SOC in the passive soil organic matter is 75%–87% under the control treatment but only 32%–48% under the hNPKM treatment (Fig. 9). Our modeling results suggest that a higher proportion of the organic carbon is contained in the passive pool with the slowest turnover rate if no fertilizer is added. However, long-term fertilization generally has a smaller effect on the passive pool compared to the slow pool according to the experimental data (third row in Fig. 8). The reason would be corresponded to turnover times of 25 years for the slow pool but 1000 years for the passive pool in the CENTURY [28].
Soil Carbon Dynamics and Carbon Sequestration Potential
Long-term fertilization has different effects on soil organic carbon across the three sites (Fig. 10). For example, the SOC stock (0–20 cm) increases and then decreases to a relative stable level in the control and N treatments at Changping site (Fig. 10a). Plots with balanced fertilization or manure application and straw return (i.e., NPK, NPKM, hNPKM and NPKS) have an increase in SOC and then remain relatively stable. By 2050, the SOC stocks reach 30.5–31.8 Mg ha−1 with organic fertilization and 26.8 Mg ha−1 with chemical fertilization (NPK).
For the Yangling site (Fig. 10b), SOC contents increase over time in the NPK, NPKM, hNPKM, and NPKS treatments. By the end of simulated period, SOC reaches 42.6 Mg ha−1 and 52.1 Mg ha−1 under the NPKM and hNPKM treatments, respectively. With straw return, the SOC level reaches 34.3 Mg ha−1. Balanced chemical fertilization (NPK) also yields relatively high SOC contents at 24.6 Mg ha−1 by 2050. There is little difference in SOC stocks between N and NP treatments. SOC increases to 20.1 Mg ha−1 in 1994, then declines to 15.3 Mg ha−1 in 2050 for the control treatment.
Similar to the Yangling site, SOC levels increase through the entire period of simulation under the NPKM, hNPKM, and NPKS treatments for the Qiyang site (Fig. 10c). By 2050, SOC stocks are 40.2 Mg ha−1, 44.2 Mg ha−1, and 28.2 Mg ha−1 for the NPKM, hNPKM, and NPKS treatments, respectively. For all chemical fertilization treatments, we assume no decrease in pH after year 2010. Therefore, soil pH has not negative impact on carbon input due to a reduction in crop production associated with pH, and applying NPK fertilizers maintain SOC levels at 21.7 Mg ha−1.
The SOC trends are relatively stable by the end of 2050 in the different treatments (Fig. 10). In order to estimating the carbon sequestration potential (i.e., the SOC increment by the end of simulation) with manure addition and regular straw incorporation, we further evaluated the SOC change under the hNPKM and NPKS treatments in the period of 1990–2050 (Table 5). The initial SOC level is highest at the Changping site (22.4 Mg ha−1) and lowest at the Yangling site (16.4 Mg ha−1). In contrasts, the highest simulated SOC level in 2050 happens at the Yangling site and lowest at the Changping site. Compared with the NPKS treatment, carbon sequestration potential is greater with hNPKM management, ranging from 9.4 Mg ha−1 to 35.7 Mg ha−1. For the NPKS treatment, SOC increases to 17.9 Mg ha−1 at the Yangling site but only 7.1 Mg ha−1–8.1 Mg ha−1 at the other two sites.
Discussion
Studies has proved that optimizing fertilization managements can raise crop yield as well as biomass, which would enhance biomass input into the soil from crop straw and roots [28], [42], [43]. Halvorson et al [44] reported that increasing nitrogen fertilizer rate would increase SOC levels in the 0–7.5 cm soil depth after 11 years cropping. In our study, chemical fertilization (expect N treatment) would also increase crop yield and thus increase SOC in the first 20 years of crop rotation at two of three sites. However, most chemical fertilization treatment would only maintain the SOC level in the long-term run (Fig. 10).
Straw incorporation would also increase carbon input to the soil. Li et al [45] found that cropland soil lost 1.6% of SOC due the less (25%) aboveground residue’s returning. Using the DNDC model, Tang et al. [46] predicted that the soil carbon loss in China’s cropland would be partly reversed if straw return and no tillage practices were expanded widely. In our study, averaged carbon sequestration rate would reach to 118 kg ha−1 yr−1 to 298 kg ha−1 yr−1 with straw returning during the period of 1990–2050 under current returning rate of straw residue (i.e., 2000–4500 kg ha−1 yr−1). The results are lower than the estimated rate (108–728 kg ha−1 yr−1) in the similar region by Lu et al [47], in which the estimated straw returning rates were also higher (4530–8110 kg ha−1 yr−1).
Moreover, there are differences in the influence of manure on SOC trends among the sites. At the Changping site, SOC stocks reach a relatively stable level (30.0 Mg ha−1–31.0 Mg ha−1) by 2013 (Fig. 10a). In contrast, plots from the other two sites continue to increase in SOC over the entire simulated period. The reason for the site difference in the simulated trends is related to the manure quality between the sites, and the associated decomposition rates of the organic matter [28], [48]. For the Changping site, farmyard manure is livestock manure mixed with soil and/or crop residue. The lignin content in farmyard manure is lower and decompose at a faster rate [49]. When we enhanced the lignin content of farmyard manure in CENTURY, the simulation of SOC rose to the much higher level (data not shown).
Many modeling research also focus on the regional estimation of SOC sequestration. Follett [50] estimated soil carbon sequestration potential would be 11–56 Tg C yr−1 for residue management in the USA. In China, Yan et al [51] estimated 32.5 Tg C yr−1 to be sequestered in croplands if 50% of the crop residue was returned to soils, and no tillage was adopted on 50% of the arable lands. However, these results are criticized for the models that are calibrated against few or no field experimental data. Our study estimated carbon sequestration potential using long-term experiment data, providing credible data to evaluate carbon sequestration response to different fertilization managements.
Conclusion
We used the CENTURY model (version 4.5) to simulate soil organic carbon (SOC) dynamics for long-term fertilization experiments with wheat-corn cropping systems in China. Our study indicated that CENTURY can simulate fertilization effects on SOC trends for different climatic conditions and soil properties that are common for wheat-corn systems in China. For most sites, the model was more precise in predicting the SOC trends for treatments with balanced nutrient additions (NPK, NPKM, hNPKM, and NPKS) than the control and unbalanced fertilization treatments (N and NP). After simulating SOC dynamics from 1990–2050, the SOC levels increased from 31.8–52.1 Mg ha−1 across the four sites. We estimate that carbon sequestration potential ranges from 9.4–35.7 Mg ha−1 under the high manure application practice (i.e., hNPKM treatment). Analysis of the proportion of organic carbon in each of the soil organic matter pools indicates that long-term fertilization enhances the slow pool and leads to a decline in the proportion of carbon in the passive pool. Model results suggest that changes in slow organic matter drive the overall trends in SOC stocks associated with long-term fertilization.
Acknowledgments
The authors would like to thank Dr. Sigen Chen and Ms. Cynthia Keough for the technical support on the CENTURY model. We acknowledge all the colleagues for their unremitting efforts on the long-term experiments from these sites.
Author Contributions
Conceived and designed the experiments: RHC MGX XJW. Performed the experiments: RHC SMO WJP. Analyzed the data: RHC MGX XJW. Contributed reagents/materials/analysis tools: SMO WJP. Wrote the paper: RHC.
References
- 1.
Schlesinger WH (1997) Biogeochemistry: an analysis of global change: New York Academic Press.
- 2. Schuman GE, Janzen HH, Herrick JE (2002) Soil carbon dynamics and potential carbon sequestration by rangelands. Environmental pollution 116: 391–396.
- 3. Manzoni S, Porporato A (2009) Soil carbon and nitrogen mineralization: Theory and models across scales. Soil Biology & Biochemistry 41: 1355–1379.
- 4. Lal R, Follett F, Stewart BA, Kimble JM (2007) Soil carbon sequestration to mitigate climate change and advance food security. Soil Science 172: 943–956.
- 5. Lal R (2004) Soil Carbon Sequestration Impacts on Global Climate Change and Food Security. Science 304: 1623–1627.
- 6. Körschens M (2006) The importance of long-term field experimentsfor soil science and environmental research - a review. Plant, soil and environment 52: 1–8.
- 7.
Zhang W, Xu M, Wang X, Huang Q, Nie J, et al.. (2012) Effects of organic amendments on soil carbon sequestration in paddy fields of subtropical China. Journal of Soils and Sediments: 1–14.
- 8. Powlson D, Zhao BQ, Li XY, Li XP, Shi XJ, et al. (2010) Long-Term Fertilizer Experiment Network in China: Crop Yields and Soil Nutrient Trends. Agronomy Journal 102: 216–230.
- 9. Edmeades DC (2003) The long-term effects of manures and fertilisers on soil productivity and quality: a review. Nutrient Cycling in Agroecosystems 66: 165–180.
- 10. Thomsen IK, Christensen BT (2004) Yields of wheat and soil carbon and nitrogen contents following long-term incorporation of barley straw and ryegrass catch crops. Soil Use and Management 20: 432–438.
- 11. Bruun S, Christensen BT, Hansen EM, Magid J, Jensen LS (2003) Calibration and validation of the soil organic matter dynamics of the Daisy model with data from the Askov long-term experiments. Soil Biology & Biochemistry 35: 67–76.
- 12. Shirato Y, Yokozawa M (2005) Applying the Rothamsted Carbon Model for long-term experiments on Japanese paddy soils and modifying it by simple mining of the decomposition rate. Soil Science and Plant Nutrition 51: 405–415.
- 13. Yang Y, Luo Y, Finzi AC (2011) Carbon and nitrogen dynamics during forest stand development: a global synthesis. New Phytologist 190: 977–989.
- 14. Smith P, Smith JU, Powlson DS, McGill WB, Arah JRM, et al. (1997) A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments. Geoderma 81: 153–225.
- 15. Chilcott CR, Dalal RC, Parton WJ, Carter JO, King AJ (2007) Long-term trends in fertility of soils under continuous cultivation and cereal cropping in southern Queensland. IX*. Simulation of soil carbon and nitrogen pools using CENTURY model. Australian Journal of Soil Research 45: 206–217.
- 16. Denef K, Six J, Merckx R, Paustian K (2004) Carbon sequestration in microaggregates of no-tillage soils with different clay mineralogy. Soil Science Society of America Journal 68: 1935–1944.
- 17. Kelly RH, Parton WJ, Crocker GJ, Grace PR, Klir J, et al. (1997) Simulating trends in soil organic carbon in long-term experiments using the century model. Geoderma 81: 75–90.
- 18. Falloon P, Smith P (2002) Simulating SOC changes in long-term experiments with RothC and CENTURY: model evaluation for a regional scale application. Soil Use and Management 18: 101–111.
- 19. Bhattacharyya T, Pal DK, Williams S, Telpande BA, Deshmukh AS, et al. (2010) Evaluating the Century C model using two long-term fertilizer trials representing humid and semi-arid sites from India. Agriculture, Ecosystems & Environment 139: 264–272.
- 20. Álvaro-Fuentes J, López MV, Arrúe JL, Moret D, Paustian K (2009) Tillage and cropping effects on soil organic carbon in Mediterranean semiarid agroecosystems: Testing the Century model. Agriculture, Ecosystems & Environment 134: 211–217.
- 21. Cerri CEP, Easter M, Paustian K, Killian K, Coleman K, et al. (2007) Simulating SOC changes in 11 land use change chronosequences from the Brazilian Amazon with RothC and Century models. Agriculture, Ecosystems & Environment 122: 46–57.
- 22. Feng XM, Zhao YS (2011) Grazing intensity monitoring in Northern China steppe: Integrating CENTURY model and MODIS data. Ecological Indicators 11: 175–182.
- 23. Fang D, Zhou G, Jiang Y, Jia B, Xu Z, et al. (2012) Impact of fire on carbon dynamics of Larix gmelinii forest in Daxingan Mountains of Northeast China:A simulation with CENTURY model. The Journal of Applied Ecology 23: 2411–2421.
- 24. Wang SH, Shi XZ, Zhao YC, Weindorf DC, Yu DS, et al. (2011) Regional Simulation of Soil Organic Carbon Dynamics for Dry Farmland in East China by Coupling a 1:500 000 Soil Database with the Century Model. Pedosphere 21: 277–287.
- 25. Cong RH, Wang XJ, Xu MG, Zhang WJ, Xie LJ, et al. (2012) Dynamics of soil carbon to nitrogen ratio changes under long-term fertilizer addition in wheat-corn double cropping systems of China. European Journal of Soil Science 63: 341–350.
- 26. Cong R, Xu M, Wang X, Zhang W, Yang X, et al. (2012) An analysis of soil carbon dynamics in long-term soil fertility trials in China. Nutrient Cycling in Agroecosystems 93: 201–213.
- 27. Walkley A, Black IA (1934) An examination of Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Science 37: 29–38.
- 28. Parton WJ, Rasmussen PE (1994) Long-Term Effects of Crop Management in Wheat-Fallow: II. CENTURY Model Simulations. Soil Science Society of America Journal 58: 530–536.
- 29.
Alister K Metherell, Laura A Harding, C. Vernon Cole, Parton WJ (1993) CENTURY Soil Organic Matter Model Environment.
- 30.
van Heemst HDJ (1986) Physiological principles. In: Keulen; HV, Wolf J, editors. Modelling of agricultural production: weather, soils and crops. Pudoc Wageningen. pp. 13–26.
- 31.
Metherell AK, Harding LA, Cole CV, Parton WJ (1993) Plant Production Submodels. CENTURY Soil Organic Matter Model Environment Technical Documentation Agroecosystem Version 40.
- 32. Zhang H, Wang B, Xu M (2008) Effects of Inorganic Fertilizer Inputs on Grain Yields and Soil Properties in a Long-Term Wheat-corn Cropping System in South China. Communications in Soil Science and Plant Analysis 39: 1583–1599.
- 33.
Smith JU, Smith P, Addiscott TM (1996) Quantitative methods to evaluate and compare soil organic matter (SOM) models. In: Powlson DS, Smith P, Smith JU, editors. Evaluation of Soil Organic Matter Models Using Existing, Long-term Datasets. Heidelberg: Springer-Verlag. pp. 181–200.
- 34. Cheng WX, Zhang QL, Coleman DC, Carroll CR, Hoffman CA (1996) Is available carbon limiting microbial respiration in the rhizosphere? Soil Biology & Biochemistry 28: 1283–1288.
- 35. Kong AYY, Six J, Bryant DC, Denison RF, van Kessel C (2005) The Relationship between Carbon Input, Aggregation, and Soil Organic Carbon Stabilization in Sustainable Cropping Systems. Soil Sci Soc Am J 69: 1078–1085.
- 36. Domke GM, Woodall CW, Walters BF, Smith JE (2013) From Models to Measurements: Comparing Downed Dead Wood Carbon Stock Estimates in the U.S. Forest Inventory. PLoS One 8: e59949.
- 37. Willmott CJ (1982) Some Comments on the Evaluation of Model Performance. Bulletin of the American Meteorological Society 63: 1309–1313.
- 38. Tonitto C, David MB, Drinkwater LE, Li CS (2007) Application of the DNDC model to tile-drained Illinois agroecosystems: model calibration, validation, and uncertainty analysis. Nutrient Cycling in Agroecosystems 78: 51–63.
- 39. Blagodatskaya E, Kuzyakov Y (2008) Mechanisms of real and apparent priming effects and their dependence on soil microbial biomass and community structure: critical review. Biology and Fertility of Soils 45: 115–131.
- 40. Ogle SM, Breidt FJ, Easter M, Williams S, Paustian K (2007) An empirically based approach for estimating uncertainty associated with modelling carbon sequestration in soils. Ecological Modelling 205: 453–463.
- 41. Falloon P, Smith P (2003) Accounting for changes in soil carbon under the Kyoto Protocol: need for improved long-term data sets to reduce uncertainty in model projections. Soil Use and Management 19: 265–269.
- 42. Halvorson AD, Wienhold BJ, Black AL (2002) Tillage, nitrogen, and cropping system effects on soil carbon sequestration. Soil Science Society of America Journal 66: 906–912.
- 43. Dumanski J, Desjardins RL, Tarnocai C, Monreal C, Gregorich EG, et al. (1998) Possibilities for future carbon sequestration in Canadian agriculture in relation to land use changes. Climatic Change 40: 81–103.
- 44. Halvorson AD, Reule CA, Follett RF (1999) Nitrogen fertilization effects on soil carbon and nitrogen in a dryland cropping system. Soil Science Society of America Journal 63: 912–917.
- 45. Li C, Zhuang Y, Frolking S, Galloway J, Harriss R, et al. (2003) Modeling Soil Organic Carbon Change in Croplands of China. Ecological Applications 13: 327–336.
- 46. Tang HJ, Qiu JJ, Van Ranst E, Li CS (2006) Estimations of soil organic carbon storage in cropland of China based on DNDC model. Geoderma 134: 200–206.
- 47. Lu F, Wang XK, Han B, Ouyang ZY, Duan XN, et al. (2009) Soil carbon sequestrations by nitrogen fertilizer application, straw return and no-tillage in China’s cropland. Global Change Biology 15: 281–305.
- 48. Paustian K, Parton W, Persson J (1992) Modeling Soil Organic Matter in Organic-Amended and Nitrogen-Fertilized Long-Term Plots. Soil Science Society of America Journal 56: 476–488.
- 49. Zhang WJ, Wang XJ, Xu MG, Huang SM, Liu H, et al. (2010) Soil organic carbon dynamics under long-term fertilizations in arable land of northern China. Biogeosciences 7: 409–425.
- 50. R.F F (2001) Soil management concepts and carbon sequestration in cropland soils. Soil and Tillage Research 61: 77–92.
- 51. Yan HM, Cao MK, Liu JY, Tao B (2007) Potential and sustainability for carbon sequestration with improved soil management in agricultural soils of China. Agriculture Ecosystems & Environment 121: 325–335.