保护性耕作机械能否带动保护性耕作净碳汇的空间外溢?

李园园; 薛彩霞; 柴朝卿; 李卫; 姚顺波

doi:10.12357/cjea.20230375

保护性耕作机械能否带动保护性耕作净碳汇的空间外溢?基于农机跨区服务视角

doi: 10.12357/cjea.20230375

1.
西北农林科技大学经济管理学院　杨凌　712100
2.
西北农林科技大学机械与电子工程学院　杨凌　712100

基金项目: 教育部人文社科项目(22YJA630042)、陕西省软科学项目(2023-CX-RKX-043)和西北农林科技大学经济管理学院研究生科技创新项目(JGYJSCXXM202206)资助

详细信息

作者简介:
李园园, 主要从事资源经济与环境管理研究工作。E-mail: lyy020924@nwafu.edu.cn

通讯作者:
薛彩霞, 主要从事农林经济管理、资源经济与环境管理研究工作。E-mail: xiaoxueacc@126.com

中图分类号: F323.3
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出版历程
- 收稿日期: 2023-07-05
- 录用日期: 2023-10-18
- 修回日期: 2023-10-17
- 网络出版日期: 2023-10-21

Can conservation tillage machinery drive the spatial spillover of the net carbon sink of conservation tillage?Based on the perspective of cross-zone service of agricultural machinery

1.
College of Economics and Management, Northwest A & F University, Yangling 712100, China
2.
College of Mechanical and Electronic Engineering, Northwest A & F University, Yangling 712100, China

Funds: This study was supported by Ministry of Education, Humanities and Social Science Project (22YJA630042), Shaanxi Soft Science Project (2023-CX-RKX-043) and Postgraduate Science and Technology Innovation Project in College of Economics and Management, Northwest A& F University (JGYJSCXXM202206)

More Information

Corresponding author: E-mail: lyy020924@nwafu.edu.cn

摘要

摘要: 农机跨区服务推动了以农业机械为载体的保护性耕作技术及其净碳汇的空间外溢。本文以2000—2020年中国30个省份(不含港澳台及西藏)为研究样本, 运用探索性空间数据分析法揭示保护性耕作机械及其净碳汇在空间上的集聚特征, 并通过空间杜宾模型定量分析保护性耕作机械对其净碳汇的空间溢出效应。研究发现: 1)保护性耕作机械动力及其净碳汇在2000—2020年期间均呈增长态势, 年均增长率分别为12.52%和7.42%, 两者在空间上主要表现为“高-高集聚”和“低-低集聚”的区域集聚特征。2)保护性耕作机械动力通过跨区服务能够显著带动保护性耕作净碳汇实现空间外溢。具体表现为保护性耕作机械动力对周边省份保护性耕作净碳汇会产生正向空间溢出效应, 且主要归因于秸秆还田机械的跨区作业。3)保护性耕作机械动力对其净碳汇的空间溢出效应因时间、地形、粮食作物主产区而异。具体来说, 其空间溢出效应在2004—2009年间和2010—2013年间显著为正, 并呈增加趋势; 在平原地区, 其空间溢出效应为正, 在丘陵山区则为负; 保护性耕作机械的空间溢出效应在水稻主产区更明显, 免耕机械的空间溢出效应在小麦主产区相对突出, 而秸秆还田机械的空间溢出效应在三大粮食作物主产区基本无差异。为此, 本研究提出加大保护性耕作推广力度、搭建农机服务信息化平台和提高保护性耕作农机装备水平的对策建议。
- 农机跨区服务 /
- 保护性耕作 /
- 净碳汇 /
- 空间溢出效应
Abstract: Conservation tillage is an environment-friendly agricultural cultivation technique that distinguishes itself from traditional tillage, and its implementation relies on agricultural machinery. China’s unique situation as a large country with many small-scale farms has led to the development of a distinctive path for agricultural machinery in the form of cross-regional agricultural machinery services. Therefore, it is worth exploring whether conservation tillage machinery drives the spatial spillover of the net carbon sink of conservation tillage in the context of cross-regional agricultural machinery services. This study used panel data from 30 provinces in China (excluding Hong Kong, Macao, Taiwan, and Tibet) from 2000 to 2020. First, an exploratory spatial data analysis was used to reveal the spatial agglomeration characteristics of conservation tillage machinery and its net carbon sink. Second, the spatial spillover effect of conservation tillage machinery on net carbon sink was quantitatively analyzed using the spatial Durbin model. Furthermore, this study analyzed the heterogeneity of the spatial spillover effect of conservation tillage machinery on its net carbon sink from the dimensions of time, topography, and major grain-producing areas. The study found that: 1) from 2000 to 2020, mechanical power and the net carbon sink of conservation tillage increased from 22.55 million kW and 7.93 million t C in 2000 to 238.63 million kW and 33.17 million t C in 2020, with average annual growth rates of 12.52% and 7.42%, respectively. The growth trends were significant, and their development was closely synchronized. The spatial correlation results indicated that both of them mainly exhibited regional agglomeration characteristics with ‘high-high’ and ‘low-low’, showing a significant positive spatial correlation. 2) In the context of cross-regional agricultural machinery services, conservation tillage mechanical power significantly drove the spatial spillover effect of net carbon sink of conservation tillage. This manifested as a positive spatial spillover effect of the mechanical power of conservation tillage on the corresponding net carbon sink in neighboring provinces. Specifically, straw-returning mechanical power exhibited a positive spatial spillover effect, whereas no-tillage mechanical power, owing to its long-term implementation, mainly showed a negative spatial spillover effect, which can lead to crop yield reduction. 3) The spatial spillover effect of conservation tillage mechanical power on the corresponding net carbon sink exhibited heterogeneity across different time periods, topographies, and major grain-producing areas. In the temporal dimension, the spatial spillover effect was significantly positive and increased during the 2004–2009 and 2010–2013 periods. In the topographic dimension, the spatial spillover effect was positive in plain areas but negative in hilly and mountainous regions. Among the major grain-producing areas, the spatial spillover effect of conservation tillage mechanical power on the corresponding net carbon sink was more pronounced in rice-producing areas. The spatial spillover effect of no-tillage mechanical power was relatively prominent in the wheat-producing areas. The spatial spillover effect of the straw-returning mechanical power was essentially the same across the three major grain-producing areas. This study proposes measures to promote conservation tillage, establish an agricultural machinery service information platform, and enhance the level of conservation tillage of agricultural machinery and equipment. Additionally, the research findings hold significant reference value for how the government can use conservation tillage to contribute to the dual-carbon target.
- Cross-regional service of agricultural machinery /
- Conservation tillage /
- Net carbon sink /
- Spatial spillover effect

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图 1 保护性耕作机械对保护性耕作净碳汇的影响机制

Figure 1. Influencing mechanism of conservation tillage machinery on related net carbon sink

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图 2 2000—2020年中国保护性耕作机械动力及其净碳汇的时序变化

Figure 2. Time series changes of mechanical power and the corresponding net carbon sink of conservation tillage in China from 2000 to 2020

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表 1 变量的描述性统计分析(n=630)

Table 1. Descriptive statistical analysis of variables (n=630)

	变量名称 Variable	代码 Code	平均值 Mean	标准差 S.D.	最小值 Minimum vlaue	最大值 Maximum value
被解释变量 Explained variable	保护性耕作净碳汇 Net carbon sink of conservation tillage (×10⁴ t C)	CTNCS	60.573	93.692	−39.538	453.640
核心解释变量 Core explanatory variable	保护性耕作机械动力 Conservation tillage mechanical power (×10⁴ kW)	CTMP	341.434	651.837	0.000	4356.868
	免耕机械动力 No-tillage mechanical power (×10⁴ kW)	NTMP	84.951	196.332	0.000	1177.958
	秸秆还田机械动力 Straw returning mechanical power (×10⁴ kW)	SRMP	256.402	476.065	0.000	3178.910
控制变量 Control variable	种植结构 Planting structure (%)	PLANT	47.214	29.335	0.000	95.605
	技术推广强度 Intensity of technology popularization (%)	CTEI	27.615	29.103	0.000	155.523
	受教育水平 Education level (a)	EDU	7.440	0.903	4.405	12.213
	经济发展水平 GDP per capita (×10⁴ ¥·cap.⁻¹)	PGDP	2.715	1.973	0.266	11.417

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表 2 保护性耕作机械动力和保护性耕作净碳汇的全局莫兰指数

Table 2. The global Moran's index for mechanical power and the corresponding net carbon sink of conservation tillage

年份 Year	机械动力 Mechanical power		净碳汇 Net carbon sink		年份 Year	机械动力 Mechanical power		净碳汇 Net carbon sink
年份 Year	莫兰指数 Moran’s I	P值 P value	莫兰指数 Moran’s I	P值 P value	年份 Year	莫兰指数 Moran’s I	P值 P value	莫兰指数 Moran’s I	P值 P value
2000	0.099	0.119	0.132	0.083	2011	0.342	0.001	0.187	0.034
2001	0.069	0.176	0.097	0.137	2012	0.328	0.001	0.181	0.038
2002	0.094	0.127	0.104	0.126	2013	0.338	0.001	0.178	0.040
2003	0.079	0.145	0.138	0.078	2014	0.343	0.001	0.179	0.041
2004	0.123	0.084	0.210	0.023	2015	0.359	0.000	0.216	0.021
2005	0.106	0.109	0.221	0.019	2016	0.373	0.000	0.240	0.013
2006	0.131	0.075	0.216	0.021	2017	0.382	0.000	0.232	0.015
2007	0.148	0.055	0.230	0.011	2018	0.382	0.000	0.194	0.032
2008	0.322	0.001	0.217	0.019	2019	0.393	0.000	0.165	0.053
2009	0.346	0.001	0.262	0.007	2020	0.395	0.000	0.164	0.053
2010	0.358	0.000	0.218	0.018

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表 3 2000年、2010年和2020年保护性耕作机械动力及其净碳汇的LISA集聚关系

Table 3. The relationship of LISA agglomeration for mechanical power and the corresponding net carbon sink of conservation tillage in 2000, 2010 and 2020

集聚类型 Agglomeration type	机械动力 Mechanical power			净碳汇 Net carbon sink
集聚类型 Agglomeration type	2000	2010	2020	2000	2010	2020
高-高 High-high	15	12, 15, 16	12, 15, 16	4, 5, 16	3, 4, 12, 15, 16	4, 12, 16, 17
低-低 Low-low	18, 19, 24	23	23	20, 21, 24	21	9
低-高 Low-high	2, 4, 5, 6	4	4	6, 8, 28
非研究区 Non study area	26, 32, 33, 34
不显著 Not significant	其他 Others	其他 Others	其他 Others	其他 Others	其他 Others	其他 Others
表中为10%显著性水平下的结果。高-低集聚类型无数据, 故未列出。1: 北京; 2: 天津; 3: 河北; 4: 山西; 5: 内蒙古; 6: 辽宁; 7: 吉林; 8: 黑龙江; 9: 上海; 10: 江苏; 11: 浙江; 12: 安徽; 13: 福建; 14: 江西; 15: 山东; 16: 河南; 17: 湖北; 18: 湖南; 19: 广东; 20: 广西; 21: 海南; 22: 重庆; 23: 四川; 24: 贵州; 25: 云南; 26: 西藏; 27: 陕西; 28: 甘肃; 29: 青海; 30: 宁夏; 31: 新疆; 32: 台湾; 33: 香港; 34: 澳门。Results in this table was at a significance level of 10%. There is no data for high-low agglomeration type, and this is not listed in the table. 1: Beijing; 2: Tianjin; 3: Hebei; 4: Shanxi; 5: Inner Mongolia; 6: Liaoning; 7: Jilin; 8: Heilongjiang; 9: Shanghai; 10: Jiangsu; 11: Zhejiang; 12: Anhui; 13: Fujian; 14: Jiangxi; 15: Shandong; 16: Henan; 17: Hubei; 18: Hunan; 19: Guangdong; 20: Guangxi; 21: Hainan; 22: Chongqing; 23: Sichuan; 24: Guizhou; 25: Yunnan; 26: Tibet; 27: Shaanxi; 28: Gansu; 29: Qinghai; 30: Ningxia; 31: Xinjiang; 32: Taiwan; 33: Hong Kong; 34: Macao.

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表 4 保护性耕作机械动力对其净碳汇空间计量模型检验

Table 4. The test of the spatial econometric model for conservation tillage mechanical power on the corresponding net carbon sink

检验方法 Calibration method	统计量值 t test	P值 P value
豪斯曼检验 Hausman test	27.016	0.005
沃尔德(滞后)检验 Wald-lag test	32.849	0.000
沃尔德(误差)检验 Wald-error test	35.268	0.000
似然比(滞后)检验 LR-lag test	36.786	0.000
似然比(误差)检验 LR-error test	38.209	0.000

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表 5 保护性耕作机械动力对其净碳汇空间杜宾模型的估计结果

Table 5. The estimation results of the spatial Durbin model for conservation tillage mechanical power on the corresponding net carbon sink

解释变量 Explanatory variable	模型1 Model 1		模型2 Model 2
解释变量 Explanatory variable	估计系数 Estimated coefficient	t值 t value	估计系数 Estimated coefficient	t值 t value
CTMP	0.012^**	1.961
NTMP			−0.038	−1.248
SRMP			0.005	0.468
PLANT	0.361	0.763	1.322^***	2.950
CTEI	1.901^***	10.896	2.117^***	13.030
EDU	−0.093	−1.207	−0.106	−1.492
PGDP	−0.141^***	−4.200	−0.188^***	−5.975
W×CTMP	0.029^***	2.624
W×NTMP			−0.533^***	−9.339
W×SRMP			0.215^***	10.167
W×PLANT	−2.393^***	−2.803	−0.127	−0.153
W×CTEI	−0.280	−0.731	0.649^*	1.786
W×EDU	0.160	1.002	0.192	1.302
W×PGDP	−0.192^***	−3.580	−0.311^***	−6.144
空间自回归系数 Spatial autoregressive coefficient	0.147^***	2.850	0.066	1.282
R²	0.838		0.862
观测值数量 Number of observed value	630		630
各变量的解释见表1。W表示空间权重, W×变量表示变量的空间滞后项。^*、^和^分别表示1%、5%和10%的显著性水平。The explanation of each variable is shown in Table 1. W represents the spatial weight, and W×variable represents the spatial lag term of the variable. ^, ^ and ^* represent the significance levels of 1%, 5% and 10%, respectively.

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表 6 保护性耕作机械动力对其净碳汇的空间杜宾模型回归结果及效应分解

Table 6. The spatial Durbin model regression results and effect decompotion of conservation tillage mechanical power on the corresponding net carbon sink

解释变量 Explanatory variable	直接效应 Direct effect		间接效应 Indirect effect		总效应 Total effect
解释变量 Explanatory variable	估计系数 Estimated coefficient	t值 t value	估计系数 Estimated coefficient	t值 t value	估计系数 Estimated coefficient	t值 t value
CTMP	0.013^**	2.137	0.035^***	2.917	0.049^***	4.138
NTMP	−0.045	−1.497	−0.569^***	−9.092	−0.614^***	−8.495
SRMP	0.009	0.782	0.228^***	9.752	0.237^***	9.843
PLANT	1.306^***	2.917	−0.002	−0.002	1.304	1.367
CTEI	2.132^***	13.173	0.830^**	2.201	2.962^***	6.939
EDU	−0.104	−1.469	0.191	1.233	0.087	0.488
PGDP	−0.190^***	−5.865	−0.343^***	−6.074	−0.533^***	−9.184
各变量的解释见表1。^*、^和^分别表示1%、5%和10%的显著性水平。The explanation of each variable is shown in Table 1. ^, ^ and ^* represent the significance levels of 1%, 5% and 10%, respectively.

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表 7 时间维度下保护性耕作机械动力对其净碳汇的空间杜宾模型回归结果及效应分解

Table 7. The spatial Durbin model regression results and effect decomposition of conservation tillage mechanical power on the corresponding net carbon sink in the temporal dimension

解释变量 Explanatory variable	2000—2003			2004—2009
解释变量 Explanatory variable	直接效应 Direct effect	间接效应 Indirect effect	总效应 Total effect	直接效应 Direct effect	间接效应 Indirect effect	总效应 Total effect
CTMP	0.068^***(4.253)	0.015 (0.634)	0.083^***(3.204)	0.058^***(5.336)	0.043^**(2.065)	0.101^***(5.793)
NTMP	0.057^*(1.744)	0.157^**(2.630)	0.213^***(2.714)	0.038 (1.536)	0.004 (0.062)	0.042 (0.574)
SRMP	0.088^***(4.741)	−0.045 (−1.512)	0.043 (1.411)	0.071^***(3.529)	0.050^*(1.828)	0.121^***(4.147)
控制变量 Control variable	已控制 Controlled
观测值数量 Number of observed value	120			270

解释变量 Explanatory variable	2010—2013			2014—2020
解释变量 Explanatory variable	直接效应 Direct effect	间接效应 Indirect effect	总效应 Total effect	直接效应 Direct effect	间接效应 Indirect effect	总效应 Total effect
CTMP	−0.005 (−0.347)	0.055^**(2.455)	0.049^**(2.293)	−0.007 (−0.614)	0.010 (0.530)	0.003 (0.198)
NTMP	0.101^**(2.230)	−0.272^**(−2.430)	−0.171 (−1.303)	−0.112^**(−2.198)	−0.124 (−1.257)	−0.236^*(−2.019)
SRMP	−0.061^***(−3.099)	0.181^***(4.255)	0.119^***(3.118)	0.036 (1.411)	0.056 (1.211)	0.092^*(1.961)
控制变量 Control variable	已控制 Controlled
观测值数量 Number of observed value	120			210
各变量的解释见表1。^*、^和^分别表示1%、5%和10%的显著性水平。括号内为t值。The explanation of each variable is shown in Table 1. ^, ^ and ^* represent the significance levels of 1%, 5% and 10%, respectively. The value in brackets is the t value.

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表 8 地形维度下保护性耕作机械动力对其净碳汇的空间杜宾模型回归结果及效应分解

Table 8. The spatial Durbin model regression results and effect decomposition of conservation tillage mechanical power on the corresponding net carbon sink in the topographic dimension

解释变量 Explanatory variable	平原地区 Plain area			丘陵山区 Hilly area
解释变量 Explanatory variable	直接效应 Direct effect	间接效应 Indirect effect	总效应 Total effect	直接效应 Direct effect	间接效应 Indirect effect	总效应 Total effect
CTMP	−0.004 (−0.551)	0.109^***(9.628)	0.105^***(10.168)	0.147^***(6.292)	−0.212^***(−4.823)	−0.064 (−1.428)
NTMP	0.045 (1.267)	0.086 (1.256)	0.131 (1.457)	−0.693^***(−8.416)	−0.323^***(−3.567)	−1.016^***(−12.316)
SRMP	−0.024 (−1.687)	0.127^***(6.646)	0.103^***(4.428)	0.239^***(8.654)	−0.057 (−1.174)	0.182^***(5.054)
控制变量 Control variable	已控制 Controlled
观测值数量 Number of observed value	252			378
各变量的解释见表1。^*、^和^分别表示1%、5%和10%的显著性水平。括号内为t值。据已有研究^[16,30], 将北京、天津、河北、内蒙古、辽宁、吉林、黑龙江、上海、江苏、安徽、山东和河南12个省划分为平原地区, 本研究中的其余18个省划分为丘陵山区。The explanation of each variable is shown in Table 1. ^, ^ and ^* represent the significance levels of 1%, 5% and 10%, respectively. The value in brackets is the t value. According to the existing literatures^[16,30], Beijing, Tianjin, Hebei, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Anhui, Shandong and Henan are divided into plain area, and the remaining 18 provinces of the selected areas in this study are divided into hilly area.

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表 9 粮食作物主产区维度下保护性耕作机械动力对其净碳汇的空间杜宾模型回归结果及效应分解

Table 9. The spatial Durbin model regression results and effect decomposition of conservation tillage mechanical power on the corresponding net carbon sink in the dimension of different major grain-producing areas

解释变量 Explanatory variable	小麦 Wheat			玉米 Maize			水稻 Rice
解释变量 Explanatory variable	直接效应 Direct effect	间接效应 Indirect effect	总效应 Total effect	直接效应 Direct effect	间接效应 Indirect effect	总效应 Total effect	直接效应 Direct effect	间接效应 Indirect effect	总效应 Total effect
CTMP	−0.028^*** (−3.225)	0.002 (0.169)	−0.026^** (−1.880)	−0.001 (−0.129)	0.024^* (1.988)	0.024^* (1.987)	−0.025^** (−2.781)	0.071^*** (4.620)	0.045^** (2.683)
NTMP	−0.153^*** (−5.340)	−0.325^*** (−6.474)	−0.478^*** (−8.783)	−0.007 (−0.249)	−0.191^*** (−2.975)	−0.198^*** (−2.879)	−0.023 (−0.644)	−0.048 (−0.762)	−0.072 (−0.923)
SRMP	0.001 (0.085)	0.097^*** (5.040)	0.098^*** (5.700)	0.008 (−0.582)	0.094^*** (3.821)	0.086^*** (3.666)	0.025 (−1.666)	0.098^*** (4.592)	0.073^** (2.695)
控制变量 Control variable	已控制 Controlled
观测值数量 Number of observed value	315			420			273
各变量的解释见表1。^*、^和^分别表示1%、5%和10%的显著性水平。括号内为t值。借鉴已有文献^[31,32], 小麦主产区包括河北、山西、内蒙古、黑龙江、江苏、安徽、山东、河南、湖北、四川、云南、陕西、甘肃、宁夏和新疆; 玉米主产区包括河北、山西、内蒙古、辽宁、吉林、黑龙江、江苏、安徽、山东、河南、湖北、广西、重庆、四川、贵州、云南、陕西、甘肃、宁夏和新疆; 水稻主产区包括辽宁、吉林、黑龙江、河北、山东、河南、江苏、浙江、安徽、湖北、内蒙古、宁夏和云南。The explanation of each variable is shown in Table 1. ^, ^ and ^* represent the significance levels of 1%, 5% and 10%, respectively. The value in brackets is the t value. According to the existing literatures^[31,32], the main wheat-producing areas include Hebei, Shanxi, Inner Mongolia, Heilongjiang, Jiangsu, Anhui, Shandong, Henan, Hubei, Sichuan, Yunnan, Shaanxi, Gansu, Ningxia and Xinjiang. The main maize-producing areas include Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Jiangsu, Anhui, Shandong, Henan, Hubei, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Ningxia and Xinjiang. The main rice-producing areas include Liaoning, Jilin, Heilongjiang, Hebei, Shandong, Henan, Jiangsu, Zhejiang, Anhui, Hubei, Inner Mongolia, Ningxia and Yunnan.

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表 10 保护性耕作机械动力对其净碳汇的空间杜宾模型回归结果稳健性检验

Table 10. The robustness test of the spatial Durbin model regression results for conservation tillage mechanical power on the corresponding net carbon sink

解释变量 Explanatory variable	模型1 Model 1		模型3 Model 3		模型4 Model 4
解释变量 Explanatory variable	估计系数 Estimated coefficient	t值 t value	估计系数 Estimated coefficient	t值 t value	估计系数 Estimated coefficient	t值 t value
CTMP	0.012^**	1.961			0.015^**	2.755
CTMN			1.724^***	4.661
PLANT	0.361	0.763	0.457	0.983	0.492	1.175
CTEI	1.901^***	10.896	1.701^***	9.675	1.693^***	10.977
EDU	−0.093	−1.207	−0.070	−0.933	−0.062	−0.904
PGDP	−0.141^***	−4.200	−0.123^***	−3.747	−0.115^***	−3.866
W×CTMP	0.029^**	2.624	1.460^**	1.956	0.033^***	3.315
W×PLANT	−2.393^**	−2.803	−1.949^**	−2.314	−2.258^***	−2.996
W×CTEI	−0.280	−0.371	−0.512	−1.336	−0.316	−0.934
W×EDU	0.160	1.002	0.137	0.874	0.161	1.145
W×PGDP	−0.192^***	−3.58	−0.174^***	−3.302	−0.210^***	−4.433
空间自回归系数 Spatial autoregressive coefficient	0.147^***	2.850	0.101^*	1.913	0.135^***	2.600
R²	0.838		0.844		0.862
观测值数量 Number of observed value			630
各变量的解释见表1。^*、^和^分别表示1%、5%和10%的显著性水平。 CTMN表示保护性耕作农机具数量。The explanation of each variable is shown in Table 1. ^, ^ and ^* represent the significance levels of 1%, 5% and 10%, respectively. CTMN represents the number of conservation tillage agricultural machinery.

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