中国农户环境友好型农药施用行为影响因素的Meta分析

doi:10.18402/resci.2018.01.08

摘要/Abstract

摘要：

为进一步揭示影响农户农药施用行为的关键因素并深入分析研究层面分歧的原因,以国内外43篇已公开发表的期刊文献为基础数据库,运用Meta分析方法剖析中国范围内农户农药施用行为的关键因素是否完全同质,在允许异质性存在的条件下影响农户农药施用的关键因素及不同因素影响强度随时间的变化规律。结果表明：①农户农药施用行为的关键因素并非完全同质,年龄、家庭年收入、对农药知识的了解等存在显著异质性。②农户性别、教育程度、风险偏好、对农药知识的了解、种植年限、农业收入占比、是否参加过农药施用培训和是否有农药残留检测是影响农户施药行为的关键因素。③随时间推移,受教育程度、风险偏好、对农药知识的了解、是否参加农药施用培训和政府宣传支持对农户施药行为影响强度逐渐增加;性别和农业收入占比影响显著且趋势较为稳定;其余变量影响强度均不同程度的减弱。最后基于实证结果提出政策和学术两个层面的建议。

关键词: 农药施用行为, 农户, 环境友好, 影响因素, Meta分析

Abstract:

The main purposes of this study were to reveal key factors influencing farmers’ pesticide application behavior, and reasons for differences in research level. A total of 43 papers were selected from China and abroad. We explored the following issues: whether key factors in pesticide application in China are completely homogeneous; key factors influencing the application of pesticide in farmers under the condition of allowing the existence of heterogeneity; and trends in key factors over time. The meta-analysis method includes effect size selection, fixed or random effect model selection depending on the existence of heterogeneity, publication bias tests and correction, sensitivity analysis, cumulative meta-analysis and meta-regression analysis. The Q statistic was used to test whether there was heterogeneity. If variables are homogeneous in farmers, the fixed effect model is used, if variables are heterogenous, then the random effect model is used. Results showed that farmers’ pesticide application behavior was not completely homogeneous. There was significant heterogeneity among age, household income, knowledge of pesticide use, scale of cultivation and ratio of agricultural income. The key factors influencing behavior included gender, educational level, risk appetite, knowledge of pesticide use, farming experience, proportion of agricultural income, pesticide application training and pesticide residue detection. The trend in educational level, risk appetite, pesticide related knowledge, participation in pesticide application technical training and government propaganda support increased over time. However, variables including gender and agricultural income accounted for a significant impact, while the trend was nearly stable. The remaining variables gradually decreased. Based on these results, two suggestions spanning policy and academic are proposed.

Key words: pesticide application behavior, farmers, environment-friendly, influence factors, Meta-analysis

李昊, 李世平, 南灵, 李晓庆. 中国农户环境友好型农药施用行为影响因素的Meta分析[J]. 资源科学, 2018, 40(1): 74-88.

Hao LI, Shiping LI, Ling NAN, Xiaoqing LI. A Meta-analysis of farmers’ environment-friendly pesticide application behavior in China[J]. Resources Science, 2018, 40(1): 74-88.

图/表 8

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文献编码"

第一作者	发表年	期刊来源	影响因子	所属地域	种植类型	研究方法	样本量	因变量类型
张慧静^[32]	2016	北大核心	0.619	东部	经济作物	二项Logit	293	意愿
瞿逸舟^[33]	2013	省级	0.292	东部	NA	二项Logit	237	行为
胡定寰^[11]	2006	CSSCI	2.111	东部	经济作物	二项Logit	126	行为
张复宏^[34]	2013	CSSCI	3.345	东部	经济作物	二项Logit	479	意愿
方秋平^[35]	2011	CSSCI	2.230	东部	经济作物	二项Logit	490	行为
王洪丽^[36]	2014	省级	0.413	中部	粮食作物	二项Logit	293	意愿
任重^[20]	2016	CSSCI	1.859	东部	粮食作物	二项Logit	609	意愿
方学伟^[37]	2014	北大核心	0.461	西部	经济作物	二项Logit	400	行为
牛亚丽^[22]	2014	北大核心	0.759	东部	经济作物	二项Logit	484	行为
毛飞^[7]	2011	CSSCI	2.416	西部	经济作物	二项Logit	462	行为
李红梅^[38]	2007	CSSCI	2.416	西部	NA	二项Logit	214	意愿
崔新蕾^[39]	2011	北大核心	0.847	中部	粮食作物	二项Logit	183	意愿
王志刚^[40]	2012	CSSCI	3.088	东部	NA	Probit	390	意愿
娄博杰^[41]	2014	CSSCI	1.184	东部	NA	二项Logit	474	行为
吴雪莲^[42]	2016	北大核心	1.232	中部	NA	二项Logit	467	意愿
王永强^[17]	2013	CSSCI	1.762	西部	经济作物	Probit	285	行为
朱淀^[43]	2014	CSSCI	2.111	东部	粮食作物	二项Logit	648	行为
乔立娟^[44]	2014	省级	0.609	东部	NA	Probit	283	意愿
田云^[45]	2015	CSSCI	1.888	中部	NA	二项Logit	387	行为
傅新红^[46]	2010	CSSCI	2.416	西部	NA	二项Logit	406	意愿
江激宇^[47]	2012	CSSCI	2.416	东部	经济作物	Probit	151	行为
蔡荣^[48]	2012	北大核心	1.232	东部	经济作物	Probit	348	行为
李秀义^[21]	2013	CSSCI	0.535	东部	经济作物	Probit	335	行为
吴林海^[49]	2011	CSSCI	0.489	中部	粮食作物	二项Logit	233	行为
邢永华^[50]	2014	北大核心	0.565	西部	经济作物	二项Logit	249	行为
王常伟^[19]a	2013	CSSCI	4.600	东部	经济作物	Probit	643	行为
王志刚^[51]	2012	CSSCI	3.088	东部	经济作物	Probit	392	行为
魏欣^[52]	2012	CSSCI	0.908	西部	经济作物	二项Logit	220	行为
贾雪莉^[14]	2011	北大核心	0.783	东部	经济作物	二项Logit	182	行为
朱淀^[5]	2014	CSSCI	3.088	东部	经济作物	二项Logit	294	意愿
蔡书凯^[53]	2011	北大核心	2.188	中部	粮食作物	Tobit	596	行为
赵连阁^[54]	2013	CSSCI	2.111	中部	粮食作物	联立方程	287	行为
李光泗^[55]	2007	CSSCI	1.184	东部	经济作物	线性回归	440	行为
周峰^[56]	2008	CSSCI	1.401	东部	经济作物	二项Logit	181	行为
钱韵旭^[57]	2013	北大核心	1.960	西部	经济作物	二项Logit	231	意愿
童霞^[9]	2011	CSSCI	2.416	东部	NA	偏最小二乘法	237	行为
周峰^[58]	2007	CSSCI	2.230	东部	经济作物	二项Logit	331	行为
高晨雪^[59]	2013	CSSCI	2.416	中部	粮食作物	协方差模型	264	行为
Liangxin Fan^[1]	2015	SCI	3.976	西部	粮食作物	结构方程模型	79	行为
Dongpo LI^[60]	2012	SCI	0.216	东部	NA	线性回归	199	行为
Yazhen Gong^[18]	2016	SCI	1.739	西部	NA	Tobit	259	行为
Jiehong Zhou^[61]	2009	SCI	3.388	东部	经济作物	两组比较	507	行为
Yongqiang Wang^[3]	2015	SCI	3.388	西部	经济作物	泊松回归	472	行为

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变量	变量含义	测量方式	预期方向
1. 个体特征
性别	男或女	二分类变量	-
年龄	户主年龄	有序分类或连续型变量	-
教育程度	户主受教育水平	有序分类、连续或二分类变量	+
风险偏好	风险规避、中性和偏好	有序分类或二分类变量	-
2. 家庭特征
家庭人口数	家庭总人数	有序分类或连续型变量	+/-
家庭年收入	调查年农户家庭全部收入	有序分类、连续或二分类变量	+/-
劳动力人数	家庭农业劳动力人数	有序分类或连续型变量	+
3. 认知特征
环境污染	农药施用对环境污染的认知	有序分类或二分类变量	+
人体健康	农药施用对人体健康的影响	有序分类或二分类变量	+
农药知识	对农药施用知识的了解	有序分类或二分类变量	+
4. 种植特征
种植规模	种植农作物总面积	有序分类、连续或二分类变量	+/-
种植年限	从事农业生产年数	有序分类或连续型变量	-
农业收入占比	农业收入占家庭总收入比例	有序分类、百分比或二分类变量	-
安全农产品价格预期	对安全农产品市场价格的预测	有序分类或二分类变量	+
5. 外部因素
技术培训	是否接受过农药施用培训	二分类变量	+
合作社	是否参加了合作社	二分类变量	+
农药残留检测	是否有农药残留检测	二分类变量	+
生产过程检测	是否有生产过程检测	二分类变量	+
政府宣传支持	政府是否宣传和支持环境友好型施药	有序分类或二分类变量	+

变量	效应量	标准误	Q	P_Q	τ²	Z_B	P_B	n	N
性别	-0.137**	0.054	29.402	0.134	0.000	0.983	0.326	23	8 055
年龄	0.011	0.028	71.386	0.002***	0.011	-2.821	0.005***	42	14 550
教育程度	0.155***	0.028	55.166	0.101	0.000	1.474	0.140	44	14 286
风险偏好	0.094**	0.047	4.373	0.358	0.000	1.318	0.188	5	2 268
家庭人口数	-0.015	0.044	7.934	0.968	0.000	0.689	0.491	18	4 508
家庭年收入	0.035	0.064	43.087	0.007***	0.028	1.083	0.279	24	6 745
劳动力人数	0.006	0.022	20.629	0.112	0.000	-0.241	0.810	15	5 096
对环境污染的认知	0.059	0.101	2.050	0.915	0.000	1.257	0.209	7	1 714
对人体健康的影响	-0.148	0.105	3.377	0.497	0.000	1.473	0.141	5	1 213
对农药知识的了解	0.373***	0.090	46.757	0.002***	0.072	-0.628	0.530	24	7 544
种植规模	0.011	0.012	39.048	0.125	0.000	2.732	0.006***	31	10 070
种植年限	-0.059**	0.026	16.923	0.658	0.000	0.487	0.626	21	7 827
农业收入占比	-0.164***	0.061	51.795	0.000***	0.032	-0.879	0.379	21	7 839
安全农产品价格预期	0.013	0.038	4.992	0.661	0.000	1.198	0.231	8	3 528
是否参加培训	0.315***	0.051	30.662	0.164	0.000	1.463	0.144	25	7 234
是否参加合作社	0.222	0.146	5.910	0.949	0.000	0.825	0.410	14	4 971
是否有农药残留检测	0.504***	0.180	6.663	0.247	0.000	2.187	0.029**	6	2 906
是否有生产过程检测	0.241	0.187	1.069	0.899	0.000	0.183	0.855	5	2 597
政府的宣传和支持	0.116	0.137	3.443	0.632	0.000	0.111	0.911	6	2 642

变量	效应量	标准误	Q	P_Q	τ²	方向	N_S	SE_N
年龄	0.056	0.039	109.992	0.000***	0.017	右侧	12	4.213
种植规模	0.004	0.012	71.210	0.001***	0.000	左侧	7	3.693
农药残留检测	0.351**	0.167	12.935	0.074*	0.000	左侧	2	1.784

变量	指标	系数	标准误	变量	系数	标准误	变量	系数	标准误
年龄	截距	0.065	0.342	对农药知识的了解	-4.708**	2.340	家庭年收入	6.495**	2.540
	连续型变量	0.108	0.115					0.543	0.814
	二分类变量	0.160	0.414		1.098	0.807		0.803	0.643
	抽样年份	-0.017	0.017		0.373**	0.190		-0.725**	0.343
	核心期刊	0.352	0.219					-1.945***	0.701
	CSSCI	0.205	0.187		-0.773	1.053		-2.913**	1.126
	SCI	0.001	0.029		-3.624	2.509		-0.090	0.552
	影响因子	0.014	0.035		1.040	0.669		1.899***	0.728
	中部地区	-0.500**	0.247					-0.247	0.697
	西部地区	0.068	0.084		0.478	0.783		-3.007**	1.346
	经济作物	-0.137	0.197
	Probit模型	0.125	0.114		-0.233	0.675		-2.190**	0.977
	Tobit模型	0.502***	0.143					-3.818**	1.769
	结构方程模型	-0.120	0.237					-2.722**	1.090
	行为	-0.225	0.286		0.793	0.543		-1.447***	0.555
	异质性检验	Q=13.180	P_Q=0.356		Q=3.436	P_Q=0.488		Q=15.395	P_Q=0.181
种植规模	截距	2.778*	1.538	农业收入占比	1.386	1.257
	连续型变量	-0.624	0.388		-0.655**^a	0.332
	二分类变量				-0.106	0.305
	抽样年份	-0.102	0.072		-0.075	0.099
	核心期刊	-0.049	1.028		-0.453	0.614
	CSSCI	-0.710	0.750		-0.901	0.597
	SCI				-0.597	0.543
	影响因子	-0.032	0.177		0.171	0.198
	中部地区	-0.926	0.587		-0.538	0.479
	西部地区	-0.137	0.524		0.490	0.387
	经济作物	-1.154*	0.686		-0.750	0.587
	Probit模型	0.695	0.460		-0.420***	0.112
	Tobit模型	0.361	0.387
	结构方程模型
	行为	-0.099	0.240		0.380	0.308
	异质性检验	Q=26.899	P_Q=0.043		Q=7.660	P_Q=0.467