Scientia Agricultura Sinica ›› 2024, Vol. 57 ›› Issue (10): 1943-1960.doi: 10.3864/j.issn.0578-1752.2024.10.008

• SOIL & FERTILIZER·WATER-SAVING IRRIGATION·AGROECOLOGY & ENVIRONMENT • Previous Articles     Next Articles

Model Simulation Research of Soil Organic Carbon Dynamics of Long-Term Conservation Tillage in Black Soil

WANG WenJun1,2(), LIANG AiZhen1,2(), ZHANG Yan1,2, CHEN XueWen1,2, HUANG DanDan1,2   

  1. 1 Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences/Key Laboratory of Mollisols Agroecology, Changchun 130102
    2 University of Chinese Academy of Sciences, Beijing 100049
  • Received:2023-06-12 Accepted:2023-08-03 Online:2024-05-16 Published:2024-05-23
  • Contact: LIANG AiZhen

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Abstract:

【Objective】 Conservation tillage is an important measure for restoring and enhancing soil fertility, and soil organic carbon (SOC) plays a crucial role in maintaining soil fertility, supporting crop growth, and protecting soil environment. However, there is currently a lack of long-term monitoring platforms for conservation tillage in China, so using modeling methods can help study SOC dynamics under long-term conservation tillage. 【Method】 A long-term tillage experiment was established in the black soil region in 2001 with three treatments: no-tillage (NT), moldboard plow (MP), and ridge-tillage (RT). The structures and parameters of process-based models (RothC, AMG model) and statistical models (MLPNN model) were optimized. The changes in SOC under long-term conservation tillage were simulated and compared. The effectiveness of different models in simulating and predicting the SOC dynamics under conservation tillage was evaluated, and the long-term response and influencing factors of SOC in black soil in Northeast China to conservation tillage were revealed. 【Result】 After optimizing parameters for carbon pool allocation, errors of the RothC and AMG models were significantly reduced. During the first 11 years (2001-2012) of conservation tillage, there was no significant difference in the simulation of SOC between RothC and AMG models, indicating that the structural complexity of process models does not have significant impacts on the simulation results for relatively short term. The simulation results of the statistical model MLPNN were similar to process models, proving the application of statistical models in small-scale regions. Over the next 100 years, RothC and AMG models predicted similar trends in SOC changes, but AMG model significantly overestimated the increase in SOC stocks, which may be attributed to SOC saturation and the influence of tillage practices. Both RothC and AMG models showed high sensitivity to carbon input, but they responded differently to climate and soil factor changes. 【Conclusion】 It is necessary to choose appropriate models based on local conditions while using models to simulate SOC in long-term conservation tillage. For short-term prediction of SOC under conservation tillage, a relatively simple AMG modelcan be used, while for long-term prediction, a more complex RothC model can be used. Under specific conditions, statistical models show similar effects to process models in simulating soil organic carbon at a small-scale regions, such as plots and fields.

Key words: soil organic carbon, conservation tillage, model simulation, black soil, process-based model, statistical model, Northeast China

Fig. 1

Schematic of field trial plots Blank areas represent fields irrelevant with this research in long-term tillage experiment; NTMM, MPMM and RTMM represent no-tillage, moldboard plow and ridge-tillage under maize and soybean rotation, respectively"

Fig. 2

Structure of RothC model[24]"

Table 1

Parameters used in RothC model"

模型参数Model parameter 数值Value
月均温度
Monthly temperature (℃)		 -14.1（一月 January），-9.4（二月 February），-1.6（三月 March）,8.8（四月 April），16.4（五月 May），21.7（六月 June），23.4（七月 July），22.6（八月 August），17.1（九月 September），8.9（十月 October），-2.4（十一月 November），-12.3（十二月 December）
月降水量
Monthly precipitation (mm)		 3.3（一月 January），6.1（二月 February），15.3（三月 March），26.8（四月 April），56（五月 May），100.7（六月 June），183（七月 July），127.5（八月 August），32.9（九月 September），27.6（十月 October），21.6（十一月 November），9.5（十二月 December）
月蒸散量
Monthly evaporation (mm)		 0（一月 January），0（二月 February），2.45（三月 March），55.83（四月 April），101.85（五月 May），133.34（六月 June），143.44（七月 July），138.84（八月 August），106.11（九月 September），56.65（十月 October），0.031（十一月 November），0（十二月 December）
黏粒含量Clay content (%) 36.03（实测值 Measured value）
DPM/RPM 1.44（默认值 Default value）
年碳投入量
Plant residue input (t C·hm-2)		 4.34（NT），4.25（MP），4.37（RT），由（2）式计算得来
4.34（NT），4.25（MP），4.37（RT），calculated from equation (2)
有机肥料月投入量 Manure input (FYM) 全年为0 0 for the whole year
取样深度 Depth of soil sample (cm) 0-20
土壤覆盖 Soil cover 详见1.2 不同耕作处理秸秆覆盖情况 Straw covering in different tillage treatments in 1.2

Table 2

Parameters needed for calculation of parameter k in AMG model"

模型参数 Model parameter 含义Meaning
T 年平均气温 Average temperature (℃)
a1 温度函数参数，取20 Temperature function parameter, take 20
b1 温度函数参数，取0.120 K-1 Temperature function parameter, take 0.120 K-1
Tref 对照温度，取15 ℃ Control temperature, take 15 ℃
A 土壤黏粒含量 Soil clay content (g·kg-1)
a2 黏土函数参数，取2.519×10-3 Clay function parameter, take 2.519×10-3
H 土壤湿度，取降水量P和灌溉水量IW之和与潜在蒸散量PET的差值
Soil moisture, taken as the difference between the sum of precipitation P and irrigation water IW and the potential evapotranspiration PET (mm)
a3 湿度函数参数，取 3.0×10-2 Moisture function parameter, take 3.0×10-2
b3 湿度函数参数，取5.247 m-1 Moisture function parameter, take 5.247 m-1
CaCO3 土壤CaCO3含量 Soil CaCO3 content (g·kg-1)
a4 碳酸钙函数参数，取1.50×10-3 CaCO3 function parameter, take 1.50×10-3
pH 土壤pH Soil pH
a5 pH函数参数，取0.112 pH function parameter, take 0.112
b5 pH函数参数，取8.5 pH function parameter, take 8.5
C/N 土壤碳氮比 Soil carbon to nitrogen ratio
a6 C/N函数参数，取0.060 C/N function parameter, take 0.060
b6 C/N函数参数，取11 C/N function parameter, take 11

Fig. 3

Structure of neural network model Ax, Bx, Cx represent different parameters in the same type respectively, i.e. soil factors, nutrient factors and climate factors"

Table 3

Parameters used by MLPNN model"

参数类型
Type of parameter 		参数
Parameter
土壤因素
Soil factor		 土壤氮含量 Soil nitrogen content (t C·hm-2)
年碳投入量 Carbon input per year (t C·hm-2)
养分投入因素
Nutrient factor		 氮施入量 Nitrogen input (t C·hm-2)
磷施入量Phosphorus input (t C·hm-2)
钾施入量 Potassium input (t C·hm-2)
气候因素
Climate factor		 平均温度 Average temperature (℃)
平均降水 Average precipitation (mm)
平均蒸散量 Average evaporation (mm)

Fig. 4

Results of optimization of RothC model Grey lines represent RMSE values of default DPM/RPM; Dashed lines represent RMSE value of optimized DPM/RPM"

Table 4

Comparation between optimized parameters and default parameters in AMG model"

处理 Treatment 参数 Parameter 默认值Default value 优化值 Optimized value RMSE RMSE0
NT Cs/C0 0.65 0.65 2.388597 2.393864
k0 0.29 0.26
h0 0.21 0.21
MP Cs/C0 0.65 0.175 1.204417 2.681656
k0 0.29 0.2925
h0 0.21 0.2075
RT Cs/C0 0.65 0.75 2.754996 3.008224
k0 0.29 0.175
h0 0.21 0.2175

Fig. 5

Errors of simulation of conservation tillage’s SOC using the MLPNN model varying with the number of neurons in the hidden layer The data was normalized before putting into model, thus TMSE values are no more than 0.06"

Fig. 6

Trends of SOC stock of long-term conservation tillage in black soil of Northeast China simulated by RothC, AMG and MLPNN models In the legend, the number after each treatment represents the repetition of that treatment. For example, NT1 represents the first repetition of the NT treatment. The treatment number followed by “-O” represents the observed value for that treatment. The simulated values and observed values of the same repetition have the same color"

Fig. 7

Comparison of simulation performance between RothC, AMG and MLPNN model"

Fig. 8

Responses of SOC stock in black soil in Northeast China after several years of conservation tillage predicted by RothC and AMG model The solid lines in the graph represent the means of each treatment and repetition; Grey points represent time points when SOC stock has reached equilibrium"

Fig. 9

The sensitivity analysis of RothC and AMG model"

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