Spatial Variability Characteristics of Farmland Soil Organic Matter in Xi'an

doi:10.11924/j.issn.1000-6850.casb2022-0594

Abstract

Abstract:

Accurately identifying the spatial variability of soil organic matter (SOM) and its affecting factors in regional farmland can provide guidance for agricultural production. Taking Xi’an as an example, the spatial variability of farmland SOM and its affecting factors were analyzed by using geostatistics and GIS technology. The results showed that the average content of SOM in the study area was 17.07±4.37 g/kg, and the coefficient of variation (CV) was 25.60%. The best theoretical model of SOM semi-variance function was the exponential model, and the nugget coefficient of this model was 49.83%, which indicated that SOM spatial variability in the study area was affected by the dual effects of structure factors and random factors. Taking the northeast—southwest direction as the axis, the spatial distribution of SOM content in the study area generally showed a distribution trend of higher in the middle and lower in the east and west. The SOM content in the study area was affected by soil type, soil texture, landform type and land use type. Meanwhile, SOM content had significantly negative correlations with pH and altitude. Overall, the SOM abundance level in the study area was moderate, and the difference in SOM content of farmland soil in different counties was obvious. In agricultural production, differential fertilization management needs to be carried out in different regions.

Key words: geostatistics, GIS, soil organic matter, spatial variability, soil type, soil texture, landform type, land use type

CLC Number:

S141

SUN Xijun, DENG Rui, LV Shuang, GAO Ying, CAI Miao, GOU Qiaohong, ZHAO Juan. Spatial Variability Characteristics of Farmland Soil Organic Matter in Xi'an[J]. Chinese Agricultural Science Bulletin, 2022, 38(35): 43-53.

Figures/Tables 11

References 60

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区域	样点数		最小值/(g/kg)		最大值/(g/kg)		平均值/(g/kg)		中位数/(g/kg)			标准差		变异系数/%		偏度	峰度
周至县	960		6.90		34.50		15.35		15.10			4.22		27.49		0.791	4.212
鄠邑区	1171		7.40		34.40		16.87		16.80			3.60		21.34		0.413	4.336
长安区	982		8.20		34.00		19.12		18.50			4.56		23.85		0.657	3.805
蓝田县	789		6.90		26.00		15.67		15.60			2.96		18.89		0.059	3.156
临潼区	1044		7.00		30.50		17.35		17.10			3.85		22.19		0.250	3.284
灞桥区	534		8.90		35.20		19.51		18.60			5.45		27.93		0.871	3.307
阎良区	847		8.00		31.70		16.69		16.40			4.71		28.22		0.280	2.520
高陵区		481		7.00		29.10		16.42		16.70	4.18		25.46		-0.058		2.601
研究区		6808		6.90		35.20		17.07		16.80	4.37		25.60		0.624		4.029
Box-Cox变换数据		6808													0.000		3.328

区域	样点数		最小值/(g/kg)		最大值/(g/kg)		平均值/(g/kg)		中位数/(g/kg)			标准差		变异系数/%		偏度	峰度
周至县	960		6.90		34.50		15.35		15.10			4.22		27.49		0.791	4.212
鄠邑区	1171		7.40		34.40		16.87		16.80			3.60		21.34		0.413	4.336
长安区	982		8.20		34.00		19.12		18.50			4.56		23.85		0.657	3.805
蓝田县	789		6.90		26.00		15.67		15.60			2.96		18.89		0.059	3.156
临潼区	1044		7.00		30.50		17.35		17.10			3.85		22.19		0.250	3.284
灞桥区	534		8.90		35.20		19.51		18.60			5.45		27.93		0.871	3.307
阎良区	847		8.00		31.70		16.69		16.40			4.71		28.22		0.280	2.520
高陵区		481		7.00		29.10		16.42		16.70	4.18		25.46		-0.058		2.601
研究区		6808		6.90		35.20		17.07		16.80	4.37		25.60		0.624		4.029
Box-Cox变换数据		6808													0.000		3.328

理论模型	块金值(C₀)	基台值(C₀+C)	块金系数[C₀/(C₀+C)]/%	变程/km	决定系数(R²)	残差(RSS)
指数模型	0.1487	0.2984	49.83	2.343	0.948	7.656×10^-4
球状模型	0.0119	0.2858	4.16	0.059	0.525	4.474×10^-3
高斯模型	0.0393	0.2856	13.76	0.050	0.528	4.449×10^-3

理论模型	块金值(C₀)	基台值(C₀+C)	块金系数[C₀/(C₀+C)]/%	变程/km	决定系数(R²)	残差(RSS)
指数模型	0.1487	0.2984	49.83	2.343	0.948	7.656×10^-4
球状模型	0.0119	0.2858	4.16	0.059	0.525	4.474×10^-3
高斯模型	0.0393	0.2856	13.76	0.050	0.528	4.449×10^-3

影响因子	偏差平方和	自由度	均方	F
土壤类型	3008.759	11	273.524	14.621
土壤质地	1260.982	2	630.491	33.294
地貌类型	966.164	3	322.055	16.965
土地利用类型	420.014	3	210.007	11.018