Research and predictionanalyses of topsoil overburden depth and distribution

doi:10.6048/j.issn.1001-4330.2025.04.021

Abstract

Abstract:

【Objective】 It is important to study the main factors affecting the depth of overtopping on the surface of the plough body, so this project aims to establish a prediction model for overtopping, in order to improve the quality of ploughing and to provide a theoretical basis for the design of the surface of the plough body. 【Methods】 The orthogonal experimental design method was used to analyse the effects of structural parameters such as plough body push angle, trace lateral displacement, soil turning angle and soil turning curve on the distribution of top soil after ploughing and tilling based on discrete element simulation. A prediction model of top soil distribution after ploughing was established and validated using nonlinear regression analysis. 【Results】 The effect of the slope of the change in tangent inclination of the top of the tillage curve at the plough breast on the distribution of topsoil after ploughing was significant (P<0.05) in both cases, and the effect of the amount of trajectory lateral shift on the content of topsoil within the 100-200 mm soil layer above and below the surface was significant (P<0.05), and the effect of the slope of the change in tangent inclination of the top of the tillage curve at the plough breast on the content of topsoil within the subsoil layer below the surface was greater than the amount of trajectory lateral shift. 【Conclusion】 With the increase of the slope of the tangent inclination of the top of the tilling curve in the plough breast change, the topsoil content within the soil layer above the surface gradually increases; with the increase of the lateral shift of the trace, the topsoil content within the soil layer above the surface shows a tendency of decreasing first and then increasing. The prediction model of topsoil distribution after ploughing can predict the topsoil content in each soil layer after ploughing under different structural parameters of the plough body surface, and the average error between the predicted and simulated values is less than 6%.

Key words: moldboard plow; discrete element method; plough body surface; soil displacement

摘要：

【目的】研究影响犁体曲面表层土翻埋深度的主要因素,建立表层土翻埋预测模型,为提高耕作质量以及为犁体曲面设计提供理论依据。【方法】采用正交试验设计方法,基于离散元仿真分析犁体推土角、迹线侧移量、翻土角及翻土曲线等结构参数对耕翻后表层土壤分布的影响。利用非线性回归分析方法,建立耕翻后表层土壤分布预测模型并验证。【结果】翻土曲线顶部切线倾角在犁胸处变化的斜率对耕翻后表层土壤分布的影响均显著(P<0.05),迹线侧移量对地表上及地表下100~200 mm土层内表层土含量的影响显著(P<0.05),翻土曲线顶部切线倾角在犁胸变化的斜率对地表下土层内表层土含量的影响大于迹线侧移量。【结论】随着翻土曲线顶部切线倾角在犁胸处变化的斜率的增加,地表以上土层内表层土含量逐渐增大;随着迹线侧移量的增加,地表以上土层内表层土含量呈先减小后增大的趋势。耕翻后表层土分布预测模型可预测犁体曲面不同结构参数下耕翻后各土层内表层土含量,预测值与模拟值平均误差小于6%。

关键词: 铧式犁, 离散元, 犁体曲面, 土壤位移

CLC Number:

S222.12

SONG Yuying, ZHENG Xuan, HU Heyan, LIU Jinbao, YANG Huaijun, LI Fan. Research and predictionanalyses of topsoil overburden depth and distribution[J]. Xinjiang Agricultural Sciences, 2025, 62(4): 962-974.

宋禹莹, 郑炫, 胡赫岩, 刘进宝, 杨怀君, 李帆. 表层土壤翻埋深度及分布与预测分析[J]. 新疆农业科学, 2025, 62(4): 962-974.

Figures/Tables 22

Fig 1 Structure of the plough body Notes:1-Plough share,2-Countersunk head bolts,3-Plough moldboard,4-Plough support,5-Plough side plates

Fig.2 Surface formed by the movement of the tilting curve along the trajectory of the trench bottom

Fig.3 Changes law of the angle of the soil turning curve

Tab.1 Classification scheme of soil texture in China(%)

质地组 Texture group	质地名称 Texture name	颗粒组成 Particle composition
质地组 Texture group	质地名称 Texture name	砂粒 (1~0.05 mm)		粗粉粒 (0.05~ 0.01 mm)	细黏粒 (<0.001 mm)
砂土 Sandy soil	粗砂土		>70		<30
	细砂土		≥60~≤70
	面砂土		≥50~60
壤土 Soil	砂粉土		≥20	≥40
	粉土		<20	<40
	砂壤土		≥20
	壤土		<20
	砂黏土		≥50		≥30
黏土 Clay	粉黏土				≥30~35
	壤黏土				≥35~40
	黏土				≥40~≤60
	重黏土				>60

Fig.4 Determination of soil parameters

Tab.2 Soil modelling parameters

参数 Parametric	数值 Value	参数 Parametric	数值 Value
土壤密度 Soil density (kg/cm³)	1 482	犁体密度 Plough density (kg/cm³)	7 865
土壤剪切模量 Soil shear modulus (Pa)	1×10⁶	犁体剪切模量 Plough shear modulus (Pa)	7.9×10¹⁰
土壤泊松比 Poisson's ratio for soil	0.37	犁体泊松比 Poisson's ratio for plough	0.3
土壤与土壤间碰撞恢复系数 Coefficient of recovery for soil-soil collisions	0.57	土壤与犁体间碰撞恢复系数 Coefficient of recovery for soil-plough collisions	0.51
土壤与土壤间动摩擦系数 Coefficient of soil-soil dynamic friction	0.23	土壤与犁体间动摩擦系数 Coefficient of soil-plough dynamic friction	0.08
土壤与土壤间静摩擦系数 Coefficient of static soil-soil friction	0.65	土壤与犁体间静摩擦系数 Coefficient of static soil- plough friction	0.56
土壤与土壤间JKR表面能 Soil-to-soil JKR surface energy	4.49	土壤与犁体间JKR表面能 Soil-to-plough JKR surface energy	4.12

Fig.5 Test preparation

Tab.3 Orthogonal experimental design factor level table

水平 Level	j	tanθ	k₀₁	k₀₂	k₁₁	k₁₂	k₂₁	k₂₂
1	0	0.84	0.086	0.110	-0.029	0.154	0.203	0.073
2	75	0.92	0.09	0.114	-0.018	0.159	0.214	0.077
3	150	1	0.094	0.118	-0.007	0.164	0.225	0.081

Fig.6 A discrete meta-model of soil-plough interactions

Fig.7 Plough mapping process

Fig.8 The pattern of change in the angle of the plough body

Tab.4 Soil modelling parameters

示踪剂含量 Tracer concentration(g)	0	50	100	150	200	250	300
实测值 Measured value (10-3SI)	3.00	16.10	36.10	58.30	79.10	95.00	108.70
拟合值 Simulation value (10-3SI)	1.13	19.63	38.13	56.63	75.13	93.63	112.13
相对误差 Relative error(%)	16.70	18.00	5.30	3.00	5.3	1.50	3.10

Fig.9 Pre-tillage tracer filling back into tracer plots

Fig.10 Tracer cell magnetisation measurements

Tab.5 Soil modelling parameters

土层 Soil	T₁	T₂	T₃	T₄
实测值 Measured value (%)	19.61	34.79	32.62	12.98
仿真值 Simulation value (%)	26.59	34.90	32.55	5.97
误差 Errors (%)	6.98	0.11	0.07	7.01

Fig.11 Correlation analysis between the structural parameters of the plough body surface and the topsoil content within each soil layer

Tab.6 Orthogonal test results

编号	J	tanθ	k₁₁	k₂₁	k₂₂	T₁	T₂	T₃	T₄
1	0	0.84	-0.029	0.203	0.073	13.88	60.24	20.88	5.00
2	0	0.84	-0.018	0.225	0.081	14.40	45.63	32.96	7.01
3	0	0.84	-0.007	0.214	0.077	22.40	59.00	18.60	0.00
4	0	0.92	-0.018	0.203	0.077	15.25	42.17	34.75	7.83
5	0	0.92	-0.007	0.225	0.073	22.90	60.44	16.66	0.00
6	0	0.92	-0.029	0.214	0.081	10.42	57.75	31.83	0.00
7	0	1	-0.007	0.203	0.081	16.07	74.65	9.28	0.00
8	0	1	-0.029	0.225	0.077	14.57	60.35	20.08	5.00
9	0	1	-0.018	0.214	0.073	18.14	50.93	24.93	6.00
10	75	0.84	-0.007	0.225	0.077	10.78	74.10	15.11	0.00
11	75	0.84	-0.029	0.214	0.073	0.42	50.81	44.58	4.19
12	75	0.84	-0.018	0.203	0.081	11.52	43.78	32.96	11.73
13	75	0.92	-0.029	0.225	0.081	0.14	49.49	45.25	5.12
14	75	0.92	-0.018	0.214	0.077	4.85	47.52	38.23	9.40
15	75	0.92	-0.007	0.203	0.073	7.34	75.62	17.04	0.00
16	75	1	-0.018	0.225	0.073	11.77	46.40	37.95	3.88
17	75	1	-0.007	0.214	0.081	2.36	69.25	28.39	0.00
18	75	1	-0.029	0.203	0.077	0.00	49.07	43.85	7.08
19	150	0.84	-0.018	0.214	0.081	13.57	38.23	39.89	8.31
20	150	0.84	-0.007	0.203	0.077	17.59	70.08	12.33	0.00
21	150	0.84	-0.029	0.225	0.073	15.54	60.12	16.34	8.00
22	150	0.92	-0.007	0.214	0.073	23.09	63.59	13.32	0.00
23	150	0.92	-0.029	0.203	0.081	10.00	51.07	32.51	6.42
24	150	0.92	-0.018	0.225	0.077	20.79	40.43	33.93	4.85
25	150	1	-0.029	0.214	0.077	12.36	53.23	29.89	4.52
26	150	1	-0.018	0.203	0.073	16.88	39.9	31.86	11.36
27	150	1	-0.007	0.225	0.081	23.39	67.6	9.01	0

Fig.12 Extreme variance analysis of factor levels and topsoil content

Tab.7 Orthogonal experiment variance

因素 Consider- ations	f	T₁		T₂		T₃		T₄
因素 Consider- ations	f	F值	P值	F值	P值	F值	P值	F值	P值
j	2	48.035	<0.001	0.604	0.565	7.110	0.012	0.963	0.414
tanθ	2	0.116	0.892	0.407	0.676	0.757	0.494	0.601	0.567
k₁₁	2	17.565	0.001	35.745	<0.001	22.251	<0.001	26.743	<0.001
k₂₁	2	3.204	0.084	0.233	0.797	1.355	0.302	1.868	0.205
k₂₂	2	2.790	0.109	0.129	0.881	1.009	0.399	0.000	1.000

Tab.8 One-factor fitted relational equation

土层 Soil layer	因素 Factor	拟合关系式 Fit the relation ship equation	显著水平P Significant level	判定系数R² The coeffecient of determination
T₁	j	T₁=3×10^-5j²-3.37×10^-3j+0.203	0.037	0.963
T₁	k₁₁	T₁=13.7 k₁₁+0.481	0.003	0.963
T₂	k₁₁	T₂=1 922.31 $k 112$ +67.52 k₁₁+0.97	0.030	0.970
T₃	j	T₃=-2×10^-5j²+3.37×10^-3j+0.24	0.027	0.973
T₃	k₁₁	T₃=-1 268.95 $k 112$ -56.31 k₁₁-0.27	0.042	0.958
T₄	k₁₁	T₄=-399.53 $k 112$ -15.07 k₁₁-0.09	0.030	0.960

Tab.8 One-factor fitted relational equation

土层 Soil layer	因素 Factor	拟合关系式 Fit the relation ship equation	显著水平P Significant level	判定系数R² The coeffecient of determination
T₁	j	T₁=3×10^-5j²-3.37×10^-3j+0.203	0.037	0.963
T₁	k₁₁	T₁=13.7 k₁₁+0.481	0.003	0.963
T₂	k₁₁	T₂=1 922.31 $k 112$ +67.52 k₁₁+0.97	0.030	0.970
T₃	j	T₃=-2×10^-5j²+3.37×10^-3j+0.24	0.027	0.973
T₃	k₁₁	T₃=-1 268.95 $k 112$ -56.31 k₁₁-0.27	0.042	0.958
T₄	k₁₁	T₄=-399.53 $k 112$ -15.07 k₁₁-0.09	0.030	0.960

Fig.13 Comparison of predicted and modelled values of topsoil content

Fig.14 Comparison of predicted and measured topsoil contents

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