Impacts of Land Fragmentation and Cropping System on the Productivity and Efficiency of Grain Producers in the North China Plain: Taking Cangxian County of Hebei Province as an Example

doi:10.5814/j.issn.1674-764x.2020.06.005

Abstract

Abstract:

Land fragmentation is widely known to have an impact on farm performance. However, previous studies investigating this impact mainly focused on a single crop, and only limited data from China are available. This study considers multiple crops to identify the impact of land fragmentation (LF), as well as cropping system (CS), on farm productivity and the efficiency of grain producers in the North China Plain (NCP), using Cangxian County of Hebei Province as an example. Detailed household- and plot-level survey data are applied and four stochastic frontier and inefficiency models are developed. These models include different sets of key variables in either the production function or the inefficiency models, in order to investigate all possibilities of their influences on farm productivity and efficiency. The results show that LF plays a significant and detrimental role, affecting both productivity and efficiency. A positive effect is evident with respect to the CS variable, i.e., multiple cropping index (MCI), and the wheat-maize double CS, rather than the maize single CS, is usually associated with higher farm productivity and efficiency. In addition to LF and CS, four basic production input variables (labor, seed, pesticide and irrigation), also significantly affect farmers’ productivity, while the age of the household head and the ratio of the off-farm labor to total labor are significantly relevant to technical inefficiency. Policies geared toward the promotion of land transfer and the rational adjustment of cropping systems are recommended for boosting farm productivity and efficiency, and thus maintaining the food supply while mitigating the overexploitation of groundwater in the NCP.

Key words: land fragmentation, cropping system, stochastic production frontier analysis, grain production efficiency, North China Plain

WANG Xue, LI Xiubin. Impacts of Land Fragmentation and Cropping System on the Productivity and Efficiency of Grain Producers in the North China Plain: Taking Cangxian County of Hebei Province as an Example[J]. Journal of Resources and Ecology, 2020, 11(6): 580-588.

Figures/Tables 5

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Variable	Description	Mean	S.D.	Min.	Max.
Grain output	Per hectare grain output of the household (kg ha^-1)	7371.00	3701.68	750.00	20250.00
Labor	Per hectare input of family labor (person-day ha^-1)	31.80	19.83	3.00	123.96
Seed	Per hectare cost of seed (yuan ha^-1)	1437.42	790.62	197.37	6800.00
Machinery	Per hectare cost of machinery (yuan ha^-1)	3340.38	766.44	900.00	4986.67
Fertilizer	Per hectare cost of fertilizer (yuan ha^-1)	4121.32	1572.53	900.00	10016.13
Pesticide	Per hectare cost of pesticide (yuan ha^-1)	209.51	231.46	6.00	2000.00
Irrigation	Per hectare cost of irrigation (yuan ha^-1)	518.39	484.88	30.00	3290.00
Irrigation users	Dummy (1=Used irrigation, 0=No)	0.73	0.45	0.00	1.00
Average_size	Total agricultural land area divided by number of plots (ha)	0.16	0.06	0.05	0.40
MCI	Multiple cropping index	1.55	0.37	1.00	2.00
Qland	Average quality of farmland weighted by area	2.01	0.68	1.00	4.00
Age	Age of household head (year)	56.92	10.55	24.00	87.00
Education	Education status of the household head (1=illiterate; 2=primary school; 3=junior middle school; 4=senior middle school; 5=college or above)	2.29	0.79	1.00	5.00
Ragrilabor	Ratio of agricultural labor to family size	0.49	0.25	0.10	1.00
Routlabor	Ratio of off-farm labor to total labor	0.35	0.28	0.00	0.83
Aincome	Average income per off-farm labor (×10⁴ yuan person^-1 yr^-1)	1.24	0.86	0.00	4.56

Variable	Description	Mean	S.D.	Min.	Max.
Grain output	Per hectare grain output of the household (kg ha^-1)	7371.00	3701.68	750.00	20250.00
Labor	Per hectare input of family labor (person-day ha^-1)	31.80	19.83	3.00	123.96
Seed	Per hectare cost of seed (yuan ha^-1)	1437.42	790.62	197.37	6800.00
Machinery	Per hectare cost of machinery (yuan ha^-1)	3340.38	766.44	900.00	4986.67
Fertilizer	Per hectare cost of fertilizer (yuan ha^-1)	4121.32	1572.53	900.00	10016.13
Pesticide	Per hectare cost of pesticide (yuan ha^-1)	209.51	231.46	6.00	2000.00
Irrigation	Per hectare cost of irrigation (yuan ha^-1)	518.39	484.88	30.00	3290.00
Irrigation users	Dummy (1=Used irrigation, 0=No)	0.73	0.45	0.00	1.00
Average_size	Total agricultural land area divided by number of plots (ha)	0.16	0.06	0.05	0.40
MCI	Multiple cropping index	1.55	0.37	1.00	2.00
Qland	Average quality of farmland weighted by area	2.01	0.68	1.00	4.00
Age	Age of household head (year)	56.92	10.55	24.00	87.00
Education	Education status of the household head (1=illiterate; 2=primary school; 3=junior middle school; 4=senior middle school; 5=college or above)	2.29	0.79	1.00	5.00
Ragrilabor	Ratio of agricultural labor to family size	0.49	0.25	0.10	1.00
Routlabor	Ratio of off-farm labor to total labor	0.35	0.28	0.00	0.83
Aincome	Average income per off-farm labor (×10⁴ yuan person^-1 yr^-1)	1.24	0.86	0.00	4.56

Variables	Model 1		Model 2		Model 3		Model 4
Production frontier	Coef.	Std. Err.	Coef.	Std. Err.	Coef.	Std. Err.	Coef.	Std. Err.
ln(Labor)	0.197^***	0.040	0.213^***	0.039	0.167^***	0.042	0.175^***	0.041
ln(Seed)	0.122^***	0.032	0.128^***	0.031	0.098^***	0.030	0.104^***	0.029
ln(Machinery)	0.108	0.166	-0.107	0.161	-0.069	0.170	-0.090	0.167
ln(Fertilizer)	0.077	0.047	0.052	0.047	0.074	0.046	0.057	0.045
ln(Pesticide)	-0.020^**	0.009	-0.015^*	0.008	-0.018^**	0.004	-0.015^**	0.007
ln(Irrigation)	0.009^**	0.004	0.009^**	0.004	0.008^***	0.004	0.007^*	0.004
Average_size	-	-	0.926^***	0.224	-	-	0.757^***	0.225
MCI	-	-	-	-	0.241^***	0.052	0.229^***	0.051
Constant	7.120^***	0.450	7.012^***	0.409	7.363^***	0.441	7.326^***	0.429
Inefficiency predictors
Age	0.005^**	0.002	0.007^*	0.004	0.007^*	0.004	0.011	0.008
Education	-0.003	0.027	0.002	0.038	0.006	0.039	0.016	0.058
Ragrilabor	0.128	0.152	0.159	0.215	0.185	0.228	0.226	0.065
Routlabor	0.219^*	0.115	0.281^*	0.140	0.308^*	0.305	0.148	0.371
Aincome	-0.029	0.026	-0.024	0.035	-0.040	0.039	-0.034	0.055
Qland	0.150^***	0.038	0.195^***	0.068	0.211^***	0.075	0.283^**	0.137
Average_size	-1.521^***	0.493	-	-	-1.916^***	0.776	-	-
MCI	-0.293^***	0.092	-0.381^***	0.150	-	-	-	-
Constant	0.275	0.271	-0.276	0.493	-0.613	0.564	-1.638	1.246
Model diagnostics
${{\sigma }^{2}}=\sigma _{\mu }^{2}+\sigma _{v}^{2}$	0.077^***	0.016	0.100^***	0.032	0.107^***	0.035	0.148^**	0.071
$\gamma =\sigma _{\mu }^{2}/(\sigma _{\mu }^{2}+\sigma _{v}^{2})$	0.815^***	0.062	0.834^***	0.053	0.862^***	0.049	0.887^***	0.049
H0: No inefficiency component (Prob. ≤ z):
	0.000		0.000		0.000		0.000
Total number of observations	350		350		350		350

Variables	Model 1		Model 2		Model 3		Model 4
Production frontier	Coef.	Std. Err.	Coef.	Std. Err.	Coef.	Std. Err.	Coef.	Std. Err.
ln(Labor)	0.197^***	0.040	0.213^***	0.039	0.167^***	0.042	0.175^***	0.041
ln(Seed)	0.122^***	0.032	0.128^***	0.031	0.098^***	0.030	0.104^***	0.029
ln(Machinery)	0.108	0.166	-0.107	0.161	-0.069	0.170	-0.090	0.167
ln(Fertilizer)	0.077	0.047	0.052	0.047	0.074	0.046	0.057	0.045
ln(Pesticide)	-0.020^**	0.009	-0.015^*	0.008	-0.018^**	0.004	-0.015^**	0.007
ln(Irrigation)	0.009^**	0.004	0.009^**	0.004	0.008^***	0.004	0.007^*	0.004
Average_size	-	-	0.926^***	0.224	-	-	0.757^***	0.225
MCI	-	-	-	-	0.241^***	0.052	0.229^***	0.051
Constant	7.120^***	0.450	7.012^***	0.409	7.363^***	0.441	7.326^***	0.429
Inefficiency predictors
Age	0.005^**	0.002	0.007^*	0.004	0.007^*	0.004	0.011	0.008
Education	-0.003	0.027	0.002	0.038	0.006	0.039	0.016	0.058
Ragrilabor	0.128	0.152	0.159	0.215	0.185	0.228	0.226	0.065
Routlabor	0.219^*	0.115	0.281^*	0.140	0.308^*	0.305	0.148	0.371
Aincome	-0.029	0.026	-0.024	0.035	-0.040	0.039	-0.034	0.055
Qland	0.150^***	0.038	0.195^***	0.068	0.211^***	0.075	0.283^**	0.137
Average_size	-1.521^***	0.493	-	-	-1.916^***	0.776	-	-
MCI	-0.293^***	0.092	-0.381^***	0.150	-	-	-	-
Constant	0.275	0.271	-0.276	0.493	-0.613	0.564	-1.638	1.246
Model diagnostics
${{\sigma }^{2}}=\sigma _{\mu }^{2}+\sigma _{v}^{2}$	0.077^***	0.016	0.100^***	0.032	0.107^***	0.035	0.148^**	0.071
$\gamma =\sigma _{\mu }^{2}/(\sigma _{\mu }^{2}+\sigma _{v}^{2})$	0.815^***	0.062	0.834^***	0.053	0.862^***	0.049	0.887^***	0.049
H0: No inefficiency component (Prob. ≤ z):
	0.000		0.000		0.000		0.000
Total number of observations	350		350		350		350

Variables	Model 1	Model 2	Model 3	Model 4
Mean	0.737	0.778	0.776	0.802
S. D.	0.135	0.128	0.130	0.124
Min.	0.330	0.352	0.369	0.352
Max.	0.965	0.967	0.966	0.967