耕地地块细碎程度及其对山区农业生产成本的影响

doi:10.31497/zrzyxb.20191214

摘要/Abstract

摘要：

在务农成本持续上涨和省工性技术普及的背景下,山区地块细碎化逐渐成为抑制农民增收的重要因素。厘清山区耕地细碎程度及其对农业生产的影响,对于降低农业生产成本具有指导价值。基于重庆市武隆区、巫山县和酉阳县的1015份农户调研数据,分别从地块权属和空间分布两个角度测算耕地细碎程度,并采用半对数经济计量模型评估耕地细碎差异对农业生产成本的影响。结果表明：研究区耕地细碎程度严重,辛普森指数和地块距离指数分别为0.71和0.19,户均耕地面积6.19亩、地块数8.89块,地块与家庭平均相距430 m,即呈现出“块数多、规模小和距离短”的特征;耕地细碎程度加剧提高了农业生产成本,当辛普森指数和地块距离指数每增加一个标准差时,单位产量总成本依次上升33.8%和16.6%;具体到各单项成本,耕地细碎程度加剧显著提高了劳动力、化肥和种子成本,而限制机械及其他要素的使用。分作物的实证显示,耕地细碎化效应存在明显的作物异质性。此外,优质耕地能缓解地块细碎带来的成本上升,同时扩大地块面积有助于降低生产成本。政策启示：降低农业经营成本是缓解山区耕地撂荒的重要举措,政府应建立低成本的农地流转网络,并鼓励农户或村庄之间的土地互换,提高地块规模和减少耕作距离,同时政府还应推广和普及适合山区农业的微耕机械。

关键词: 耕地细碎化, 山区, 农业成本, 撂荒, 土地流转

Abstract:

Under the background of the continuous increase of agricultural cost and the wide use of labor-saving technology, in the mountainous areas, cropland fragmentation has gradually become a key factor to curb the increase of farmers' income. The clarification of the degree of cropland fragmentation and its impact on agricultural production cost has guiding value for reducing agricultural costs. Degree of cropland fragmentation was measured from the perspective of land ownership and spatial distribution, and the semi-logarithm econometric models were developed to evaluate the impact of land fragmentation on agricultural costs based on the field survey data of 1015 farm households in counties of Wulong, Wushan and Youyang of Chongqing, China. The results showed that the cultivated land in the study areas was severely fragmented, with the Simpson Index and the Plot Distance Index being 0.71 and 0.19, respectively. The cropland area per farm household was 6.19 mu, the number of parcels was 8.89 and the distance between the parcels and families was about 430 m, the characteristics of cropland fragmentation demonstrated "more parcels, small scale and short distance". The increased degree of cropland fragmentation has improved the production cost of agriculture. When the Simpson Index and the Plot Distance Index increased by one standard deviation, the total cost per unit of production increased by 33.8% and 16.6%, respectively. To be specific, the increased degree of land fragmentation has significantly increased labor, fertilizer and seed costs, while limiting the use of machinery and other factors. The study further found the heterogeneity in the impact of land fragmentation on production costs in different crops. In addition, high-quality cropland can alleviate the cost increase caused by the fragmentation of the plots, while expanding the scale of the plots will help to reduce production costs. The policy enlightenment was that reducing agricultural operation costs was an important measure to alleviate the cropland abandonment in mountainous areas. The government should establish a low-cost cropland transfer network and encourage land exchange between farmers or villages to expand the size of the plots and reduce the distance of cultivation, and the government should also promote the wide use of micro-farming machinery suitable for mountain agriculture.

Key words: cropland fragmentation, mountainous area, agricultural cost, land abandonment, land circulation

王亚辉, 李秀彬, 辛良杰. 耕地地块细碎程度及其对山区农业生产成本的影响[J]. 自然资源学报, 2019, 34(12): 2658-2672.

WANG Ya-hui, LI Xiu-bin, XIN Liang-jie. Characteristics of cropland fragmentation and its impact on agricultural production costs in mountainous areas[J]. JOURNAL OF NATURAL RESOURCES, 2019, 34(12): 2658-2672.

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县/区	乡镇	村庄	农户		地块
县/区	乡镇	村庄	数量/户	占比/%	数量/户	占比/%
武隆县	白马镇、长坝镇	车盘村、东升村、鹅冠村、前进村	317	31.23	3460	40.96
巫山县	龙溪镇、福田镇	老鸦村、莲花村、双河村、双塘村	374	36.85	3095	36.65
酉阳县	木叶乡、毛坝乡	大板营村、梨耳村、双龙村、天苍村	324	31.92	1892	22.39

变量	代码	单位	平均值	标准差	最小值	最大值	样本量
总成本	TC	元/t	784.55	622.22	64.20	6498.81	950
劳动力成本	LC	元/t	458.24	408.59	22.93	4252.63	950
化肥成本	FC	元/t	145.97	121.74	0.20	2010.00	950
种子成本	SC	元/t	104.64	254.61	2.20	5062.67	950
机械等其他成本	MC	元/t	75.70	206.30	0	3198.00	950
核心解释变量
辛普森指数	si	—	0.71	0.18	0	0.97	950
地块距离指数	pdi	—	0.19	0.34	0	1.00	950
耕地经营规模	Area	亩	6.19	4.92	0.08	33.40	950
户主特征
户主年龄	Age	岁	54.95	12.18	19.00	84.00	950
户主健康状况	Health	—	3.31	0.81	1.00	4.00	950
户主受教育程度	Edu	—	5.83	2.98	0	15.00	950
是否参加过农业培训	Atrain	—	0.22	0.42	0	1.00	950
家庭特征
劳动力成本	Flabor	元/日	78.23	43.11	34.20	220.32	950
家庭有效劳动力数量	Nlabor	—	3.09	1.18	0	7.00	950
是否能够贷款	Finance	—	0.35	0.48	0	1.00	950
家庭固定资产总额	Fixedasset	元	4628.58	13152.82	10.00	167480	950
地块特征
地块质量等级	Lquality	—	3.41	0.76	1.00	5.00	8447
地块灌溉条件	Irrigation	—	0.47	0.50	0	1.00	8447
地块微地貌类型	Topography	—	0.28	0.45	0	1.00	8447

县	村	辛普森指数		地块距离指数		户均地块数/块		户均规模/亩		块均规模/亩		地块与家庭距离/m
县	村	均值	方差	均值	方差	均值	方差	均值	方差	均值	方差	均值	方差
武隆区	车盘村	0.59	0.21	0.31	0.41	3.28	1.88	7.77	6.14	2.10	2.01	339.86	401.08
	东升村	0.84	0.05	0.32	0.09	13.83	5.61	9.59	4.50	0.73	0.64	455.13	412.22
	鹅冠村	0.85	0.05	0.30	0.09	14.36	5.72	8.60	3.50	0.65	0.52	323.01	325.73
	前进村	0.65	0.06	0.04	0.07	14.16	6.46	8.18	5.64	0.63	0.55	450.65	461.92
巫山县	老鸦村	0.65	0.06	0.17	0.27	9.58	4.49	5.19	2.79	0.55	0.36	267.86	232.94
	莲花村	0.63	0.10	0.03	0.35	6.18	2.57	2.25	1.60	0.38	0.32	492.19	482.65
	双河村	0.77	0.07	0.17	0.23	11.91	6.41	5.96	3.08	0.51	0.46	444.94	469.71
	双塘村	0.69	0.07	0.22	0.31	7.62	3.16	3.54	2.01	0.45	0.27	371.13	343.38
酉阳县	大板营村	0.73	0.11	0.13	0.17	7.96	4.19	4.25	3.39	0.53	0.56	552.90	407.34
	梨耳村	0.65	0.10	0.14	0.24	6.03	2.78	7.53	5.31	1.30	1.26	534.81	506.59
	双龙村	0.73	0.09	0.12	0.18	6.88	2.51	8.11	5.08	1.20	1.03	528.08	425.28
	天苍村	0.62	0.15	0.03	0.29	4.76	2.09	4.16	2.91	0.89	0.81	409.53	399.28
合计		0.71	0.11	0.19	0.23	8.89	4.79	6.19	4.08	0.81	0.72	430.75	420.21

变量	变量系数	T值	变量系数	T值
si	0.338^**	2.06
pdi			0.166^**	2.07
Area	-0.08^*	-1.83	-0.02	-0.05
Health	-0.022	-0.86	-0.006	-0.17
Age	-0.002	-0.01	-0.002	-0.77
Edu	-0.002	-0.26	0.004	0.62
Atrain	-0.032	-0.73	-0.044	-0.89
Flabor	0.047^*	1.69	0.054	1.34
Nlabor	-0.011	-0.64	0.007	0.35
Finance	-0.100^**	-2.38	-0.152^***	-3.02
log(Fixedasset)	-0.038^***	-2.80	-0.028^*	-1.74
region dummies	Yes		Yes
F-value	5.81		3.62
Prob>F	0.000		0.000
R²	0.071		0.050
Number of observations	950		950

变量	劳动力	化肥	种子	机械及其他	劳动力	化肥	种子	机械及其他
si	0.386^**	0.118^***	0.062	-0.279
si	(2.17)	(3.28)	(0.30)	(-0.69)
pdi					0.232^*	0.072^***	0.168^***	-0.164^**
pdi					(1.76)	(3.20)	(3.31)	(-2.01)
Area	-0.003	-0.007^***	-0.001	-0.003	0.004	-0.005^***	0.008	-0.014
	(-0.63)	(-5.20)	(-0.27)	(-0.28)	(0.76)	(-3.30)	(1.47)	(-1.10)
Health	-0.059^**	-0.009^*	0.020	0.065	-0.029	-0.004	0.027	0.005
Health	(-2.06)	(-1.93)	(0.59)	(0.91)	(-0.77)	(-0.44)	(0.79)	(0.05)
Age	-0.001	-0.001	-0.004^*	0.006	-0.002	0.001^*	-0.005^*	0.001
	(-0.24)	(0.89)	(-1.72)	(1.24)	(-0.76)	(1.66)	(-1.78)	(0.21)
Edu	-0.004	0.001	0.005	-0.022	-0.001	-0.001	0.017^**	-0.024
Edu	(-0.49)	(-0.46)	(0.62)	(-1.17)	(0.01)	(-0.37)	(1.98)	(-1.07)
Flabor	-0.011	-0.002	-0.038^**	0.104^**	0.002	0.005	-0.019	0.115^**
Flabor	(-0.61)	(-0.66)	(-2.01)	(2.10)	(0.08)	(0.87)	(-0.88)	(1.98)
Finance	0.122^***	0.003	0.099^*	-0.066	0.197^***	-0.002	0.115^*	-0.071
	(2.69)	(0.43)	(1.80)	(-0.60)	(3.42)	(-0.15)	(1.93)	(-0.54)
log(Fixedasset)	-0.044^***	0.001	-0.031^*	0.021	-0.027	-0.003	-0.032^*	0.019
	(-2.83)	(0.23)	(-1.85)	(0.63)	(-1.34)	(-0.83)	(-1.73)	(0.45)
region dummies	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
R²	0.063	0.108	0.035	0.414	0.045	0.119	0.062	0.436
Number of observations	950	950	950	950	950	950	950	950