基于模型集成的中国耕地非农化影响因素及其时空特征研究

doi:10.3864/j.issn.0578-1752.2018.22.010

Abstract

Abstract:

【Objective】The purpose of this paper was to reveal the temporal and spatial characteristics of the factors affecting farmland conversion, and to provide decision-making information support for policy making for the protection and utilization of farmland.【Method】Based on the panel data of farmland conversion from 2006 to 2015, an integrated model of "ordinary regression model-panel model-geographically weighted regression-geographically and temporally weighted regression" (abbreviately named “OPGT” ) was established to analyze the factors of farmland conversion【Result】The ordinary regression model, GWR and GTWR model results showed that urban population growth, fixed asset investment, economy, arable and industrial structure variables all passed the significance test; Moran's I of farmland conversion was 0.740, and passed significance test at the 1% level. The results showed that there was a significant positive spatial correlation of farmland conversion. Ordinary regression model, GWR and GTWR models were used to estimate the equations, and the fit goodness of the equations were 0.689, 0.785 and 0.858, respectively. The interpretation ability of GWR and GTWR models was improved significantly under the condition of adding spatio-temporal weight information. The results of GWR and GTWR models showed that the elastic coefficients of factors were spatio-temporal non-stationary. The results of spatial analysis showed that the influence of urban population growth and farmland resource endowment on farmland conversion was declining from west to east in longitude direction, and reversed U-shaped curve in latitude direction. The influence of fixed assets investment and level of economic development was increasing from west to east in longitude direction, and U-shaped curve in latitude direction. The influence of industrial structure was increasing from west to east in longitude direction, and declining from north to south in latitude direction. From the perspective of temporal evolution, the coefficients of urban population growth, fixed assets investment and level of economic development had a downward trend, while coefficients of farmland resource endowment tended to increase. Coefficients of industrial structure had been reduced in some provinces.【Conclusion】(1) OPGT was an organic whole, each part was mutually tested and complementary, which could describe the spatio-temporal effect of factors in more detail. (2) In terms of the overall action intensity of the factors, the largest elastic coefficient was industrial structure, followed by level of economic development, fixed asset investment and farmland resource endowment, and the smallest was urban population growth. (3) In terms of the spatial characteristics of factor intensities, the influence of urban population growth and farmland resource endowment on farmland conversion was declining from Western China to Eastern China, while fixed assets investment, level of economic development and industrial structure increasing. (4) From the perspective of temporal evolution, the influence of urban population growth, fixed assets investment and level of economic development on farmland conversion had a downward trend. The relationship between farmland resource endowment and farmland conversion tended to strengthen. Although the influence of industrial structure had been reduced in some provinces, its degree of overall influence was still relatively high.

Key words: farmland conversion, integrated model, factors, temporal and spatial characteristics

CUI XuFeng,MA YunMeng,ZHANG GuangHong. The Factors of Farmland Conversion and Its Temporal and Spatial Characteristics: An Integrated Model[J].Scientia Agricultura Sinica, 2018, 51(22): 4316-4327.

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Figures/Tables 9

Fig. 1

Table 1

Table 2

Table 3

Table 4

Table 5

Table 6

Table 7

The estimated results of GTWR of cultivated land non-agricultural-transformation (2006, 2010, 2015)"

省（地区） Province (Region)	lnUrb_popu			lnFixed_ass			lnEconomy			lnArable			lnIndu_struc
省（地区） Province (Region)	2006	2010	2015	2006	2010	2015	2006	2010	2015	2006	2010	2015	2006	2010	2015
北京Beijing	0.15	0.07	-0.10	1.06	0.78	0.78	-0.89	-0.83	-0.58	0.01	0.21	0.48	2.97	3.25	3.42
天津Tianjin	0.16	0.06	-0.10	1.06	0.77	0.79	-0.88	-0.81	-0.58	0.01	0.21	0.47	2.99	3.19	3.29
河北Hebei	0.15	0.07	-0.10	1.06	0.77	0.77	-0.88	-0.82	-0.57	0.01	0.21	0.48	2.98	3.11	3.39
山西Shanxi	0.14	0.09	-0.05	1.05	0.79	0.71	-0.84	-0.75	-0.50	0.01	0.21	0.50	2.80	2.29	3.39
内蒙古Inner Mongolia	0.16	0.07	-0.07	1.04	0.86	0.72	-0.91	-0.92	-0.50	0.01	0.19	0.52	2.94	3.39	3.49
辽宁Liaoning	0.17	0.04	-0.13	1.08	0.78	0.87	-0.90	-0.83	-0.61	0.01	0.21	0.43	2.87	3.46	2.85
吉林Jilin	0.16	0.03	-0.14	1.11	0.78	0.88	-0.90	-0.80	-0.60	0.00	0.23	0.42	2.48	3.58	2.46
黑龙江Heilongjiang	0.18	0.03	-0.14	1.13	0.79	0.87	-0.90	-0.85	-0.55	-0.01	0.25	0.44	2.01	4.12	2.09
上海Shanghai	0.10	0.05	-0.12	1.18	0.69	0.93	-0.83	-0.72	-0.63	0.01	0.21	0.30	3.14	2.91	2.23
江苏Jiangsu	0.12	0.06	-0.12	1.14	0.72	0.91	-0.83	-0.73	-0.62	0.02	0.21	0.34	3.12	2.76	2.31
浙江Zhejiang	0.06	0.05	-0.11	1.22	0.68	0.94	-0.82	-0.66	-0.63	0.02	0.22	0.29	3.11	2.67	2.15
安徽Anhui	0.11	0.06	-0.11	1.14	0.73	0.89	-0.83	-0.70	-0.60	0.02	0.21	0.35	3.08	2.31	2.21
福建Fujian	0.02	0.05	-0.09	1.25	0.69	0.94	-0.85	-0.64	-0.64	0.02	0.22	0.26	3.02	1.92	1.93
江西Jiangxi	0.05	0.07	-0.08	1.19	0.72	0.90	-0.84	-0.65	-0.62	0.02	0.21	0.30	3.02	1.68	2.01
山东Shandong	0.15	0.07	-0.12	1.09	0.75	0.86	-0.86	-0.77	-0.61	0.01	0.20	0.41	3.06	2.77	2.77
河南Henan	0.12	0.08	-0.08	1.09	0.77	0.81	-0.82	-0.72	-0.55	0.01	0.20	0.42	2.90	1.95	2.50
湖北Hubei	0.09	0.09	-0.05	1.12	0.78	0.80	-0.82	-0.69	-0.51	0.01	0.21	0.39	2.87	1.46	2.12
湖南Hunan	0.07	0.09	-0.03	1.15	0.78	0.82	-0.84	-0.69	-0.53	0.02	0.21	0.35	2.95	1.16	1.96
广东Guangdong	0.01	0.06	-0.04	1.23	0.75	0.87	-0.91	-0.68	-0.57	0.01	0.22	0.29	2.96	1.02	1.92
广西Guangxi	0.08	0.09	0.03	1.13	0.82	0.76	-0.87	-0.68	-0.44	0.02	0.24	0.36	3.04	0.44	1.76
海南Hainan	0.04	0.07	0.02	1.22	0.81	0.80	-0.90	-0.64	-0.46	0.02	0.26	0.30	3.03	0.29	1.72
重庆Chongqing	0.11	0.13	0.03	1.06	0.83	0.68	-0.79	-0.72	-0.34	0.01	0.22	0.46	2.80	1.08	2.08
四川Sichuan	0.16	0.21	0.12	0.95	0.83	0.53	-0.74	-0.59	-0.02	0.01	0.29	0.56	2.71	0.59	1.68
贵州Guizhou	0.12	0.13	0.05	1.07	0.84	0.68	-0.81	-0.72	-0.36	0.01	0.24	0.43	2.97	0.59	1.91
云南Yunnan	0.18	0.21	0.17	0.94	0.87	0.55	-0.82	-0.52	0.04	0.01	0.35	0.51	3.32	-0.99	1.08
西藏Tibet	0.42	0.74	0.30	0.48	0.21	0.19	-0.61	0.24	0.66	-0.02	0.82	0.82	2.90	-0.23	1.10
陕西Shaanxi	0.12	0.12	0.00	1.04	0.78	0.64	-0.78	-0.69	-0.34	0.01	0.24	0.52	2.57	1.89	2.76
甘肃Gansu	0.14	0.24	0.16	0.95	0.74	0.39	-0.67	-0.51	0.09	0.00	0.34	0.68	2.04	1.94	2.85
青海Qinghai	0.22	0.40	0.28	0.80	0.62	0.22	-0.60	-0.30	0.41	-0.01	0.46	0.77	1.99	1.46	2.21
宁夏Ningxia	0.13	0.15	0.06	1.00	0.75	0.50	-0.75	-0.64	-0.19	0.01	0.28	0.61	2.41	2.24	3.24
新疆Xinjiang	0.24	0.60	0.31	0.70	0.41	0.02	-0.69	0.35	0.74	-0.04	0.74	0.94	3.06	0.07	1.16

Table 7

Fig. 2

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变量 Variable	最小值 Minimum	最大值 Maximum	均值 Mean	标准差 Standard deviation	方差 Variance
Non_agr (hm²)	2.60	23872.92	6724.08	4826.32	23293353.33
Urb_popu (×10⁴)	-60.00	487.00	69.31	61.49	3781.28
Fixed_ass (×10⁸yuan)	78.86	49935.93	9556.29	8928.17	79712305.52
Economy (yuan/people)	5750.00	57310.00	22779.46	11648.92	135697256.59
Arable (×10³ hm²)	187.60	15865.90	4255.16	3125.47	9768536.21
Indu_struc (%)	0.70	0.99	0.89	0.06	0.003

	(6-a) lnNon_agr	(6-b) lnNon_agr	(6-c) lnNon_agr	(6-d) lnNon_agr
lnUrb_popu	0.474^***(0.0552)	0.294^***(0.0516)	0.401^***(0.0527)	0.358^***(0.0530)
lnFixed_ass	0.357^***(0.0515)	0.742^***(0.0598)	0.277^**(0.0997)	0.314^**(0.0983)
lnEconomy		-0.878^***(0.0900)	-0.128(0.158)	-0.358^*(0.167)
lnArable			0.393^***(0.0689)	0.414^***(0.0678)
lnIndu_struc				2.919^***(0.810)
C	3.500^***(0.350)	7.899^***(0.544)	2.379^*(1.103)	-9.199^**(3.389)
R²	0.535	0.647	0.681	0.695
Adjusted. R²	0.532	0.643	0.677	0.689

	(7-a) lnNon_agr	(7-b) lnNon_agr	(7-c) lnNon_agr	(7-d) lnNon_agr
lnUrb_popu	0.235^**(0.0721)	0.175^*(0.0729)	0.173^*(0.0716)	0.167^*(0.0719)
lnFixed_ass	0.0505(0.0431)	-0.217^*(0.0880)	-0.253^**(0.0875)	-0.239^**(0.0887)
lnEconomy		1.454^***(0.420)	1.438^***(0.421)	1.593^***(0.449)
lnArable			1.577^***(0.443)	1.570^***(0.443)
lnIndu_struc				-2.728(2.758)
C	7.076^***(0.424)	-4.798(3.456)	-16.86^***(4.786)	-6.207(11.79)
R²	0.048	0.089	0.129	0.132
Adjusted. R²	-0.063	-0.022	0.019	0.019

变量 Variables	GWR		GTWR
变量 Variables	均值Mean	标准误Standard error	均值Mean	标准误Standard error
lnUrb_popu	0.3119***	0.0640	0.14491***	0.0094
lnFixed_ass	0.2397***	0.0723	0.66776***	0.0151
lnEconomy	-0.5029***	0.1118	-0.51308***	0.0205
lnArable	0.3487***	0.0587	0.27513***	0.0128
lnIndu_struc	4.5003***	1.0738	2.33906***	0.0893
	R²=0.785		R²=0.858

省（地区）Province (Region)	lnUrb_popu	lnFixed_ass	lnEconomy	lnArable	lnIndu_struc
北京 Beijing	0.472	0.064	-0.355	0.619	7.164
天津Tianjin	0.460	-0.053	-0.153	0.707	6.569
河北Hebei	0.531	-0.022	-0.206	0.648	6.344
山西Shanxi	0.439	-0.113	0.215	0.658	0.821
内蒙古Inner Mongolia	0.272	0.316	-0.709	0.439	6.364
辽宁Liaoning	0.103	0.373	-0.920	0.626	11.803
吉林Jilin	0.058	0.633	-0.978	0.380	7.285
黑龙江Heilongjiang	0.048	0.694	-0.972	0.303	5.507
上海Shanghai	0.059	0.021	-1.026	0.733	17.048
江苏Jiangsu	0.025	0.274	-1.668	0.676	22.710
浙江Zhejiang	0.069	-0.191	-0.503	0.830	13.625
安徽Anhui	0.037	-0.074	-0.623	0.780	13.813
福建Fujian	0.001	-0.472	0.624	0.816	2.289
江西Jiangxi	-0.020	-0.287	0.385	0.640	2.498
山东Shandong	0.186	-0.212	0.210	0.826	3.223
河南Henan	0.094	0.155	0.038	0.501	-2.134
湖北Hubei	0.096	0.558	-0.670	0.044	-2.950
湖南Hunan	0.096	0.540	-0.626	0.025	-1.707
广东Guangdong	0.044	0.841	-1.417	0.006	3.280
广西Guangxi	0.082	1.002	-1.463	0.035	1.511
海南Hainan	0.056	0.956	-1.425	0.042	2.208
重庆Chongqing	0.245	0.603	-0.816	0.026	0.118
四川Sichuan	0.474	0.476	-0.895	0.027	3.078
贵州Guizhou	0.123	0.673	-0.874	0.016	0.746
云南Yunnan	0.199	0.711	-1.153	0.024	3.159
西藏Tibet	1.237	-0.197	0.108	0.109	-1.318
陕西Shaanxi	0.422	0.360	-0.374	0.016	-1.227
甘肃Gansu	0.847	0.041	-0.045	-0.004	3.761
青海Qinghai	1.256	-0.248	0.146	0.053	3.327
宁夏Ningxia	0.713	0.080	0.128	-0.064	2.989
新疆Xinjiang	0.936	-0.065	0.409	0.249	-2.379

The Factors of Farmland Conversion and Its Temporal and Spatial Characteristics: An Integrated Model

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