The impact of agricultural comprehensive development on agro-ecological efficiency of China - evidence from perspective of scale and structure

Zhang, Tongyue; Hou, Mengyang; Chu, Liqi; Wang, Lili

doi:10.1590/0103-8478cr20220096

ABSTRACT:

Promoting the agricultural sustainable development is one of the core objectives of agricultural comprehensive development (ACD), and the core of agricultural sustainable development lies in enhancing agro-ecological efficiency (AEE). Based on inter-provincial panel data from 2003-2017, the AEE was measured by a unexpected super-efficient SBM model. From the perspective of investment scale and investment structure, the impact of investment scale in ACD on AEE was examined using a panel econometric model, then characterizing the investment structure by the proportion of government funds in the investment of ACD, examining the non-linear relationship between investment structure of ACD and AEE seeking a reasonable proportion of government expenditure. Finally, a spatial econometric model was constructed to test the spatial spillover effect of the scale and structure of investment in ACD on AEE. Results showed that: (i) AEE of China is on the rise overall, but the efficiency level is still low and there is large scope for improvement. (ii) Due to the inefficient use of funds and the lag in the transformation of agricultural benefits, the scale of investment in ACD has a significant negative impact on AEE in the current period. (iii) The impact of investment structure of ACD on AEE presents a significant “inverted N” relationship, and the optimal proportion of government fund investment structure is 76.71%. The reasonable structure of investment in ACD at different food functional areas shows differences. (iv) After considering the spatial effect, the impact of ACD remains robust, but the spatial spillover effect prolongs the time lag of this impact of investment scale and starts to have a positive impact in the 2nd year after the investment. Guarantee the investment scale in ACD, reduce financial redundancy, establish a cooperation mechanism between ACD and agricultural ecological protection in neighboring areas, and increase the introduction of private capital can achieve sustainable development of agricultural economy.

Key words:
agro-ecological efficiency (AEE); agricultural comprehensive development (ACD); investment scale; investment structure; Spatial lag

[1] E-mail: wllxbnl@foxmail.com. ^*Corresponding author.

Index	Variable	Variable description	Remarks
Resources invested	Land input	Total sown area of crops/khm²	Reflect the actual cultivated area in agricultural production
	Labor input	Agricultural employees/10,000 Capita	Calculated by first industry employees * (gross agricultural output value/gross output value of agriculture, forestry, animal husbandry and fishery)
	Mechanical investment	Total power of agricultural machinery/10,000 kW	Agricultural machinery is a representative tool of agricultural modernization
	Water input	Effective irrigation area/khm²	Agricultural water is mainly used for irrigation, as a proxy
	Fertilizer input	Amount of agricultural chemical fertilizer applied/10,000t (reduced scalar amount)	The input of chemical fertilizers, pesticides, agricultural film, diesel, etc. are the main pollution sources in the agricultural production process
	Pesticide input	Pesticide usage/10,000 tons
	Agricultural film investment	Agricultural film usage/10,000 tons
	Energy input	Agricultural diesel consumption/10,000 tons
Expected output	Agricultural output	Agricultural output value/100 million yuan	Converted to the constant price in 1978 according to the CPI index to eliminate the impact of price changes
Unexpected output	pollutant emissions	Agricultural non-point source pollution discharge/10,000 tons	Pollution from fertilizer runoff, pesticide residues and agricultural film residues
	carbon emission	Agricultural carbon emissions/10,000 tons	Carbon source coefficient refer to related literature

Variables	Abbr	Units	Sample size	Average	S.D.	Min	Max
agro-ecological efficiency	AEE	-	420	0.536	0.225	0.239	1.073
Investment Scale of ACD (Fund Input)	FIACD	Billion yuan	420	14.854	9.297	2.180	57.220
Investment Structure of ACD (Fund Structure)	FSACD	%	420	68.817	13.050	29.990	96.330
Per capita disposable income of rural residents	PDIRR	yuan	420	6994.746	4352.435	1564.66	25520.40
Intensity of agricultural machinery input	MII	kW/hm²	420	5.628	2.662	1.644	14.156
Multiple cropping index	MCI	%	420	163.118	51.056	69.980	260.930
Crop planting structure	CPS	%	420	254.827	269.597	48.841	2224.890
Agricultural disaster rate	ADR	%	420	23.720	14.751	0.262	93.592
Urbanization	URBAN	%	420	50.158	15.046	21.047	90.300
Financial investment	FI	%	420	10.391	7.361	1.460	34.360

Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
lnFIACD	-0.039^** (0.016)	-0.038^** (0.015)	-0.026^*** (0.016)	-0.028^** (0.016)	-0.029^** (0.016)	-0.029^** (0.016)	-0.021 (0.018)	-0.016 (0.018)
l. lnFIACD							0.007 (0.019)
l2.lnFIACD								0.013 (0.009)
lnPDIRR	0.109^*** (0.013)	0.158^*** (0.015)	0.148^*** (0.015)	0.149^*** (0.015)	0.135^*** (0.016)	0.134^*** (0.019)	0.128^*** (0.023)	0.128^*** (0.025)
lnMII		-0.151^*** (0.026)	-0.157^*** (0.025)	-0.156^*** (0.026)	-0.152^*** (0.025)	-0.151^*** (0.025)	-0.149^*** (0.027)	-0.147^*** (0.030)
lnMCI			-0.129^*** (0.038)	-0.130^*** (0.039)	-0.129^*** (0.038)	-0.129^*** (0.038)	-0.134^*** (0.039)	0.123^*** (0.041)
lnCPS				-0.008 (0.019)	-0.008 (0.019)	-0.009 (0.019)	-0.008 (0.021)	-0.006 (0.025)
lnADR					-0.022^*** (0.006)	-0.022^*** (0.006)	-0.020^*** (0.007)	-0.017^** (0.007)
lnURBAN						0.007 (0.054)	-0.002 (0.058)	-0.026 (0.062)
C	-0.316^*** (0.090)	-0.499^*** (0.092)	0.226 (0.337)	0.182 (0.258)	0.352 (0.258)	0.337 (0.281)	0.400 (0.316)	0.452 (0.340)
rho	0.868	0.869	0.890	0.892	0.893	0.893	0.898	0.899
LogL	440.050	456.595	462.111	462.199	468.632	468.642	432.757	397.096

Variables	-------National level-------		Main grain producing areas		--Main grain sales areas--		---Grain balance areas---
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
lnFSACD	-0.231 (0.554)	-6.931^*** (0.056)	-1.061 (0.700)	27.886^** (13.42)	0.004 (1.105)	-32.613^** (12.583)	0.784 (0.919)	-34.709^** (15.864)
(lnFSACD)²	0.028 (0.065)	1.681^*** (0.079)	0.136 (0.085)	-6.966^** (3.289)	-0.001 (0.132)	8.132^* (4.542)	-0.099 (0.112)	8.743^** (3.948)
(lnFSACD)³		-0.136^*** (0.019)		0.579^** (0.268)		-0.675^* (0.345)		-0.732^** (0.327)
lnPDIRR	0.112^*** (0.018)	0.113^*** (0.018)	0.049^* (0.029)	0.047^* (0.029)	0.212^*** (0.025)	0.221^*** (0.026)	0.022 (0.038)	0.035 (0.038)
lnMII	-0.153^*** (0.026)	-0.153^*** (0.026)	-0.082^*** (0.032)	-0.080^** (0.031)	-0.476^*** (0.054)	-0.484^*** (0.054)	-0.015 (0.055)	-0.016 (0.054)
lnMCI	-0.146^*** (0.037)	-0.145^*** (0.037)	0.052 (0.047)	0.058 (0.047)	-0.185^*** (0.050)	-0.178^*** (0.050)	-0.272^** (0.122)	-0.231^** (0.120)
lnCPS	-0.001 (0.019)	-0.001 (0.019)	0.003 (0.026)	-0.004 (0.026)	-0.124^*** (0.036)	-0.129^*** (0.036)	-0.015 (0.048)	-0.037 (0.048)
lnADR	-0.022^*** (0.006)	-0.022^*** (0.006)	-0.015^* (0.009)	-0.015^* (0.008)	-0.015^** (0.007)	-0.013^** (0.007)	-0.055^*** (0.016)	-0.049^*** (0.016)
lnURBAN	0.013 (0.053)	0.008 (0.054)	0.160^** (0.069)	0.172^** (0.068)	0.128 (0.127)	0.102 (0.126)	0.097 (0.122)	0.034 (0.123)
C	1.033 (1.153)	1.063 (1.057)	1.380 (1.537)	-37.876^** (18.238)	0.700 (2.316)	46.237 (30.624)	0.025 (2.074)	47.363^** (21.216)
rho	0.901	0.901	0.844	0.841	0.936	0.937	0.902	0.904
LogL	467.044	467.285	250.752	253.060	128.621	129.719	141.506	143.973

Variables		--Rook adjacency weight matrix--			Geographic distanceweight matrix			Agricultural economic distance weight matrix
		Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9
FIACD	W^* lnAEE	0.215^*** (0.001)	0.227^*** (0.000)	0.225^*** (0.001)	0.705^*** (0.000)	0.769^*** (0.000)	0.821^*** (0.000)	0.933^*** (0.000)	1.012^*** (0.000)	1.043^*** (0.000)
	W^* lnFIACD	0.001 (0.840)	0.001 (0.909)	0.001 (0.768)	0.004 (0.197)	0.004 (0.265)	0.004 (0.264)	0.005^*** (0.000)	0.005^*** (0.000)	0.005^*** (0.000)
	lnFIACD	-0.034^** (0.026)			-0.027^* (0.083)			-0.025^* (0.089)
	l. lnFIACD		-0.010 (0.803)			-0.006 (0.727)			-0.004 (0.478)
	l2. lnFIACD			0.015 (0.509)			0.013 (0.462)			0.009 (0.602)

Brasil

Brasil

The impact of agricultural comprehensive development on agro-ecological efficiency of China - evidence from perspective of scale and structure

O impacto do desenvolvimento agrícola na eficiência agro-ecológica da China: evidência sob a perspectiva de escala e estrutura

ABSTRACT:

Variables		Rook adjacency weight matrix		Geographic distance weight matrix		Agricultural economic distance weight matrix
		Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
FSACD	W*lnAEE	0.022 (0.712)	0.023 (0.688)	0.552 (0.003)	0.575^*** (0.002)	0.628^*** (0.000)	0.655^*** (0.000)
	lnFSACD	-0.295 (0.705)	-19.137 (0.171)	-0.169 (0.825)	-22.078 (0.109)	-0.006 (0.994)	-23.415^* (0.088)
	(lnFSACD)²	0.035 (0.712)	4.685 (0.174)	0.021 (0.824)	5.428 (0.110)	0.001 (0.996)	5.779^* (0.086)
	(lnFSACD)³		-0.381 (0.177)		-0.444 (0.111)		-0.474^* (0.086)

Provinces	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017
Beijing	1	0.8742	0.5523	0.5942	1	0.6781	0.5472	0.4442	0.6140	1
Tianjin	1	1	1	1	1	1	1	1	1	1
Hebei	1	1	1	0.9775	0.7800	1	1	1	0.9402	1
Liaoning	0.6478	0.7058	0.6686	0.8157	0.9973	0.7844	0.8706	0.8542	0.8689	0.7770
Zhejiang	0.4580	0.4670	0.4653	0.5935	0.4636	0.8269	0.7969	0.7262	1	0.3435
Fujian	0.5569	0.6711	0.5364	0.7840	0.6305	1	0.7073	0.5422	0.6400	0.8848
Hunan	0.7216	0.7536	0.6401	0.7008	0.7124	0.6092	0.7711	0.7185	0.7671	0.8358
Guangdong	0.8455	0.7715	0.7148	0.8142	1	0.9640	1	0.7859	0.8918	0.9620
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Average	0.8763	0.8915	0.8408	0.8997	0.9250	0.9411	0.9184	0.8828	0.9156	0.9270