农户粮食生产规模效率及其影响因素分析——基于黑、豫、川三省玉米种植户的调查数据

doi:10.18402/resci.2017.05.12

摘要/Abstract

摘要：

农业经营规模和效率及效率的影响因素分析一直是学术界的研究热点。本文基于黑龙江、河南和四川三省的517份玉米种植户的调查数据,采用DEA方法对玉米种植规模效率进行了测算,并用Tobit方法对影响规模效率的因素进行了分析。结果表明,总体上玉米种植规模效率较高,达到0.90,但仍有进一步提升的空间;玉米种植规模效率省际差异明显,黑龙江省最高,四川省最低;针对不同规模分组的考察,发现规模效率随着规模的增加呈现先上升后下降的态势,说明推进规模经营应该因地制宜,并且不能盲目追求规模的扩大。Tobit分析表明,户主的教育程度越高、户主出县打过工对规模效率产生显著正向影响,家庭非农收入占纯收入的比重与规模效率显著负相关。因此,要加大对农民的培训,培育新型职业农民,为规模经营农户提供更好的科技支撑。

关键词: 粮食生产, 规模效率, 影响因素, 玉米种植户

Abstract:

Farm size and scale efficiency are hot issues. In recent years,farm size has expanded rapidly in China. Due to the lack of scale management experience,farmers have not realized yield or income growth and farmers' enthusiasm for production has declined. It is therefore necessary to study the scale efficiency of grain production. Here,we discuss farmer's scale efficiency using maize farming households as an example. The data was collected from three maize producing provinces,Heilongjiang,Henan and Sichuan,located in Northeast,North and Southwest China. Based on 517 surveys we employed the DEA approach to estimate maize scale efficiency and utilized a Tobit regression to analyze influencing factors. The results indicated that overall scale efficiency is 0.90. It is high but there is room for efficiency improvements. The scale efficiency of maize production is significantly different in the provinces. The highest scale efficiency is in Heilongjiang and the lowest is in Sichuan. By dividing the data according to farmer scale we found that scale efficiency drops with increasing scale after rising first. Based on these findings we should promote scale management suiting local conditions instead of expanding blindly. Tobit regression shows that the higher the education level of household head,household head as a migrant worker in cities have a significant positive impact on the scale efficiency. The proportion of non-farming income to net income of household is negatively correlated with scale efficiency. Therefore we should strengthen the training of farmers,cultivate new professional farmers and provide better technical support for scale farmers.

Key words: grain production, scale efficiency, influencing factor, maize farming households

贾琳, 夏英. 农户粮食生产规模效率及其影响因素分析——基于黑、豫、川三省玉米种植户的调查数据[J]. 资源科学, 2017, 39(5): 924-933.

Lin JIA, Ying XIA. Scale efficiency of grain production and influencing factors based on survey data from Heilongjiang,Henan and Sichuan[J]. Resources Science, 2017, 39(5): 924-933.

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	调查县/市	调查乡（镇）数	调查村庄数	有效样本数/户数
黑龙江	肇东市、龙江县、汤原县、宁安市	8	22	178
河南	西平县、夏邑县、许昌县、安阳县	8	16	252
四川	资阳市、邻水县、南部县、中江县	9	42	87
合计	12	25	80	517

	户均耕地面积 /hm²	非农就业劳动力比例/%	耕地流转比例 /%	玉米播种面积最大值/hm²	玉米播种面积最小值/hm²
黑龙江（N=22）	1.73	28.95	39.21	68.33	0.35
河南（N=16）	0.30	40.64	41.34	80.00	0.07
四川（N=42）	0.22	46.85	30.02	4.13	0.02

变量名称	变量说明	平均值	标准差	最小值	最大值
产出
玉米产量y	14%左右含水量的玉米干粮产量/万kg	2.38	5.48	0.01	107.11
投入
土地x1	玉米播种面积/hm²	2.34	5.77	0.02	80.00
劳动x2	自有和雇佣劳动力投入/h	264.39	564.30	1.00	9 455.00
种子x3	玉米种子投入/元	2 267.42	6 175.30	22.00	92 250.00
农药x4	防治病虫害、除草剂、生长调节剂的投入/元	1 020.18	2 378.12	4.00	31 200.00
化肥x5	化肥总投入/元	5 467.79	14 288.49	0	190 210.00
机械x6	自有和雇佣机械投入/h	65.36	111.51	0.20	1 679.00

变量	变量含义	变量类型	均值	标准差	最小值	最大值
规模效率	Deap2.1软件测算出的规模效率值,取值[0,1]	截断数据[0,1]	0.90	0.14	0.20	1.00
户主年龄/岁		连续变量	51.06	11.02	24.00	76.00
户主受教育程度	文盲=0,小学=1,初中=2,高中及以上=3	离散变量	1.74	0.75	0	3.00
是否出县打过工	户主是否出县打过工（是=1;否=0）	虚拟变量	0.50	0.50	0	1.00
从事粮食生产时间	户主从事粮食生产时间/年	连续变量	30.86	13.65	0	61.00
2011-2013年家庭成员是否参加过农业技术讲座	是=1;否=0	虚拟变量	0.47	0.50	0	1.00
2012-2014年农户年均非农收入占纯收入比重		连续变量	0.46	0.34	0	1.00
2014年耕地肥力	好=1,中=2,差=3	离散变量	1.63	0.61	1.00	3.00
2014年耕地能否灌溉	是=1,否=0	虚拟变量	0.65	0.48	0	1.00
地区控制变量	D1=1,河南;D1=0其他;D2=1,黑龙江,D2=0,其他;D3=1,四川,D3=0,其他	虚拟变量	-	-	-	-

组别	规模区间 /hm²	样本数量 /户	样本平均规模 /hm²	综合技术效率	纯技术效率	规模效率
1	<0.67	218	0.28	0.53	0.68	0.80
2	0.67~1.33	66	0.90	0.63	0.64	0.98
3	1.33~2.00	55	1.56	0.70	0.71	0.99
4	2.00~2.67	54	2.28	0.68	0.70	0.98
5	2.67~3.33	29	2.92	0.74	0.76	0.97
6	3.33~5.00	47	4.16	0.64	0.67	0.96
7	5.00~6.67	22	5.72	0.68	0.72	0.95
8	6.67~10.00	10	7.74	0.69	0.75	0.92
9	10.00~13.33	6	10.94	0.83	0.90	0.91
10	13.33~20.00	6	17.06	0.69	0.84	0.83
11	22.80	1	22.80	1.00	1.00	1.00
12	55.13	1	55.13	0.65	1.00	0.65
13	68.33	1	68.33	0.90	1.00	0.90
14	80.00	1	80.00	0.63	1.00	0.63