黑土区保护性耕作土壤有机碳动态的模型模拟研究

doi:10.3864/j.issn.0578-1752.2024.10.008

摘要/Abstract

摘要：

【目的】保护性耕作是恢复和提高土壤肥力的重要措施，土壤有机碳在保持土壤肥力、维持作物生长与保护土壤环境方面具有非常重要的作用。然而，目前国内仍然缺少研究长期耕作的监测平台，因此采用模型的方法有助于研究长期保护性耕作下土壤有机碳的动态。【方法】依托东北黑土地保护性耕作长期定位试验地，选取免耕（NT）、秋翻（MP）、垄作（RT）3种处理下的土壤为研究对象，优化过程模型（RothC、AMG模型）与统计模型（MLPNN模型）的结构与参数，对比模拟长期保护性耕作下土壤有机碳储量的动态变化，评估不同模型对保护性耕作土壤有机碳动态模拟与预测的效果，并揭示东北黑土有机碳对保护性耕作的长期响应及其影响因素。【结果】优化有机碳在模型中各碳库的分配系数后，RothC与AMG模型模拟误差显著降低。保护性耕作实施的前11年（2001—2012），RothC与AMG过程模型对土壤有机碳的动态变化模拟效果没有显著差异，说明过程模型复杂的结构对持续时间相对较短的试验模拟效果没有显著影响，统计模型MLPNN的模拟结果与过程模型相似，验证了统计模型在田块与样地等小空间尺度内的应用效果。未来100年RothC与AMG模型预测的MP处理土壤有机碳变化趋势相同，但RothC模型预测结果得出，NT与RT处理土壤有机碳表现出先增长后达到平衡的趋势，但AMG模型模拟的结果却是有机碳一直处于增长趋势，这可能与土壤碳饱和效应与耕作处理的影响有关。RothC与AMG模型对碳投入都具有很高的灵敏度，但对气候因素与土壤因素变化的响应却存在差异。【结论】模型模拟长期保护性耕作土壤有机碳动态时需要因地制宜选择合适的预测模型。在预测保护性耕作土壤有机碳的短期变化时可以采用结构较为简单的AMG过程模型；在进行长期预测时，可以采用结构较为复杂的RothC模型。在特定条件下，统计模型MLPNN在田块与样地等小空间尺度上对土壤有机碳的模拟与过程模型具有相近的效果。

关键词: 土壤有机碳, 保护性耕作, 模型模拟, 黑土, 过程模型, 统计模型, 东北

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

王文俊, 梁爱珍, 张延, 陈学文, 黄丹丹. 黑土区保护性耕作土壤有机碳动态的模型模拟研究[J]. 中国农业科学, 2024, 57(10): 1943-1960.

WANG WenJun, LIANG AiZhen, ZHANG Yan, CHEN XueWen, HUANG DanDan. Model Simulation Research of Soil Organic Carbon Dynamics of Long-Term Conservation Tillage in Black Soil[J]. Scientia Agricultura Sinica, 2024, 57(10): 1943-1960.

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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

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
T_ref	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^-2Moisture 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

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)
土壤因素 Soil factor	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)

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