基于GWR模型的渭北黄土旱塬粮食单产空间分异 及其影响因子分析——以陕西彬县为例

doi:10.3864/j.issn.0578-1752.2019.02.007

Abstract

Abstract:

【Objective】 This research was conducted to reveal the spatial differentiation characteristics and influencing factors of grain yield per hectare on the county scale in the Loess Plateau of Weibei, and to provide scientific references for similar researches on small scale and improvement of regional grain output. 【Method】 The spatial distribution characteristics of grain yield per hectare and spatial heterogeneity of its influencing factors were analyzed by using spatial autocorrelation, least square method and geographically weighted regression model in Binxian county of Shannxi province -a main grain producing county in Weibei Plateau. 【Result】 The Moran's I index of grain yield per hectare in Binxian County was 0.328, and the Z value of significance test was 5.51, and the characteristics of local spatial agglomeration were north high and south low. Slope, plough layer thickness, soil organic matter, road density and cost of fertilization had a positive effect on the grain yield in Binxian County. Soil type, erosion degree and groundwater depth had a negative influence on the grain yield in Binxian County. The relative range of regression coefficients for explanatory variables was between 0.55-14.11. In space, plough layer thickness, soil type, erosion degree, soil organic matter and road density had a stronger influence on the grain yield of the hilly and gully areas in the South and southeast in Binxian County than that in the northern Loess Plateau; while slope, groundwater depth and cost of fertilization showed opposite spatial non-stationary characteristics. The significance of regression coefficient of OLS model was negatively correlated with the relative range of regression coefficient of GWR model. The R ² of the GWR model was 0.04 higher than that of the OLS model, and the AIC value was reduced by 11.04. 【Conclusion】 There was a significant positive spatial correlation in grain yields per hectare of Binxian County. Soil organic matter, cost of fertilization and groundwater depth were the most important factors influencing grain yield per hectare in the county of Weibei Plateau. The influence degree of influencing factor on grain yield per hectare was of great difference in different spatial location, and the spatial non-stationarity of the influencing factors was the main reason for the lower significance level of regression coefficient of OLS model. The GWR model had better explanatory power and accuracy in modeling spatial non-stationary data than OLS model. And the spatial visualization of model estimation parameters could be realized by GWR model.

Key words: grain yield per hectare, spatial heterogeneity, GWR model, impact factors, county scale

QIU MengLong,CAO XiaoShu,ZHOU Jian,FENG XiaoLong,GAO XingChuan. Spatial Differentiation and Impact Factors of Grain Yield Per Hectare in Weibei Plateau Based on GWR Model: A Case Study of Binxian County, Shannxi[J].Scientia Agricultura Sinica, 2019, 52(2): 273-284.

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References 43

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变量 Variable	非标准化系数 Unstandardized coefficients	标准误差 Standard error	标准化系数 Standardization coefficient	t值 t value	P值 P value
常数项 Intercept	288.55	31.11	---	9.28	0.00
坡度 Slope	16.81	26.87	0.04	0.63	0.53
耕层厚度 Plough layer thickness	45.95	32.57	0.10	1.41	0.16
土壤类型 Soil type	-37.13	23.88	-0.08	-1.95	0.09
侵蚀程度 Erosion degree	-26.84	22.74	-0.07	-1.18	0.24
土壤有机质 Soil organic matter	218.80	32.83	0.45	6.66	0.00
地下水埋深 Groundwater depth	-87.97	17.81	-0.28	-4.94	0.00
道路密度 Road density	35.05	22.57	0.09	1.95	0.09
施肥成本 Cost of fertilization	178.33	22.61	0.42	7.89	0.00

参数 Parameters	平均值 Mean	最小值 Minimum	最大值 Maximum	上四分位值 Upper quartile	下四分位值 Lower quartile	中位数 Median	相对极差 Relative ranges
常数项 Intercept	297.57	246.90	329.88	286.52	314.55	299.85	0.28
坡度 Slope	5.18	-45.30	27.81	-9.53	22.16	11.83	14.11
耕层厚度 Plough layer thickness	42.30	33.35	66.25	37.63	45.30	42.05	0.78
土壤类型 Soil type	-35.94	-100.74	6.57	-51.11	-20.31	-33.44	2.99
侵蚀程度 Erosion degree	-24.85	-46.13	7.77	-38.97	-13.68	-25.11	2.17
土壤有机质 Soil organic matter	225.50	172.00	311.23	189.92	254.17	220.21	0.62
地下水埋深 Groundwater depth	-82.94	-116.71	-60.55	-98.08	-69.51	-79.40	0.68
道路密度 Road density	28.28	-18.36	70.14	11.46	48.93	25.63	3.13
施肥成本 Cost of fertilization	168.07	93.21	185.42	162.60	178.68	175.91	0.55
粮食单产实测值 Crop yield per unit (kg·hm^-2)	3257.88	1775.00	4950.00	2653.12	3893.75	3198.75	0.97
粮食单产预测值 Estimating crop yield per unit (kg·hm^-2)	3254.35	1888.21	5139.93	2773.27	3680.76	3246.47	1.00
条件数 Cond	22.10	19.64	29.73	20.74	23.02	21.78	0.46
Local R²	0.81	0.73	0.89	0.78	0.83	0.81	0.20
残差 Residual	3.53	-769.77	576.19	-172.61	160.68	19.67	381.29

模型 Model	信息准则 AIC	R²	调整后的R² Adjust R²	残差平方和 RRS	残差估计标准差 Sigma
OLS模型 OLS model	1080.07	0.80	0.78	198694.52	46.22
GWR模型 GWR model	1069.03	0.86	0.82	140028.34	41.53
GWR模型对模拟精度指标的提高 Improvement of simulation accuracy by GWR model	11.04	0.06	0.04	58666.18	4.69

Spatial Differentiation and Impact Factors of Grain Yield Per Hectare in Weibei Plateau Based on GWR Model: A Case Study of Binxian County, Shannxi

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