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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (3): 396-414    DOI: 10.1007/s40333-024-0094-z     CSTR: 32276.14.s40333-024-0094-z
Research article     
Spatiotemporal characteristics of cultivated land use eco-efficiency and its influencing factors in China from 2000 to 2020
LI Shaoting1,2, MU Na1,2, REN Yanjun1,2,3,*(), Thomas GLAUBEN3
1College of Economics and Management, Northwest A&F University, Yangling 712100, China
2Sino-German Center for Agricultural and Food Economics, Northwest A&F University, Yangling 712100, China
3Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle 06120, Germany
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Abstract  

Improving cultivated land use eco-efficiency (CLUE) can effectively promote agricultural sustainability, particularly in developing countries where CLUE is generally low. This study used provincial-level data from China to evaluate the spatiotemporal evolution of CLUE from 2000 to 2020 and identified the influencing factors of CLUE by using a panel Tobit model. In addition, given the undesirable outputs of agricultural production, we incorporated carbon emissions and nonpoint source pollution into the global benchmark-undesirable output-super efficiency-slacks-based measure (GB-US-SBM) model, which combines global benchmark technology, undesirable output, super efficiency, and slacks-based measure. The results indicated that there was an upward trend in CLUE in China from 2000 to 2020, with an increase rate of 2.62%. The temporal evolution of CLUE in China could be classified into three distinct stages: a period of fluctuating decrease (2000-2007), a phase of gradual increase (2008-2014), and a period of rapid growth (2015-2020). The major grain-producing areas (MPAs) had a lower CLUE than their counterparts, namely, non-major grain-production areas (non-MPAs). The spatial agglomeration effect followed a northeast-southwest strip distribution; and the movement path of barycentre revealed a "P" shape, with Luoyang City, Henan Province, as the centre. In terms of influencing factors of CLUE, investment in science and technology played the most vital role in improving CLUE, while irrigation index had the most negative effect. It should be noted that these two influencing factors had different impacts on MPAs and non-MPAs. Therefore, relevant departments should formulate policies to enhance the level of science and technology, improve irrigation condition, and promote sustainable utilization of cultivated land.

Key words： cultivated land use eco-efficiency (CLUE)      slacks-based measure (SBM) model      barycentre model      standard deviation ellipse (SDE)      panel Tobit model      carbon emissions      nonpoint source pollution     
Received: 22 September 2023      Published: 31 March 2024
Corresponding Authors: *REN Yanjun (E-mail: yanjun.ren@nwafu.edu.cn)
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LI Shaoting, MU Na, REN Yanjun, Thomas GLAUBEN. Spatiotemporal characteristics of cultivated land use eco-efficiency and its influencing factors in China from 2000 to 2020. Journal of Arid Land, 2024, 16(3): 396-414.

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http://jal.xjegi.com/10.1007/s40333-024-0094-z     OR     http://jal.xjegi.com/Y2024/V16/I3/396

Fig. 1 Connotation of cultivated land use eco-efficiency (CLUE) in this study
Indicator Definition Mean SD
Input Land Total sown area of crops (×103 hm2) 5154.20 3733.38
Labor Labor was calculated as the employee numbers in the primary industry multiplied by the proportion of total agricultural output value to the gross output value of primary industry (×104 persons). 496.30 389.30
Agricultural machinery Total power of agricultural machinery (×104 kW) 2763.18 2687.36
Pesticide Usage of pesticide (×104 t) 5.06 4.22
Chemical fertilizer Net amount of chemical fertilizer usage (×104 t) 169.62 138.49
Agricultural Film Usage of agricultural film (×104 t) 6.78 6.35
Desirable output Total agricultural output value Total agricultural output value (×108 CNY) 769.98 630.59
Total grain output Total grain output (×104 t) 1818.10 1587.47
Undesirable output Carbon emissions Total carbon emissions (×104 t) 255.10 194.22
Nonpoint source pollution Total loss of fertilizer nitrogen and phosphorus, pesticide, and agricultural film (×104 t) 20.84 16.27
Table 1 Evaluation indicator system of cultivated land use eco-efficiency (CLUE) in this study
Classification Carbon source Coefficient Reference
The first source Pesticide (kg) 4.9341 kg/kg Post and Kwon (2000)
Chemical fertilizer (kg) 0.8956 kg/kg West and Marland (2002)
Agricultural film (kg) 5.1800 kg/kg Li et al. (2011)
The second source Diesel fuel (kg) 0.5927 kg/kg Li et al. (2011)
The third source Irrigation (hm2) 25.0000 kg/hm2 Dubey and Lal (2009)
The fourth source Plowing (hm2) 3.1260 kg/hm2 Wu et al. (2007)
Table 2 Carbon emission coefficient of each kind of carbon source evaluated by this study
Dimension Variable Description Mean SD Minimum Maximum
Natural condition MCI Proportion of total sown area of crops to total cultivated area (%) 1.25 0.39 0.48 2.29
Regional economic development level GPC Gross domestic product (GDP) per capita (×104 CNY/person) 2.13 1.44 0.27 8.57
Agricultural production condition II Proportion of effective irrigated area to total cultivated area (%) 0.52 0.23 0.14 1.18
Science and technology level STI Proportion of local expenditures on science and technology to general budgetary expenditures of local governments (%) 1.83 0.01 0.30 7.20
Agricultural business scale SAL Total sown area of crops divided by agricultural labour (hm2/person) 1.17 0.59 0.47 4.41
Table 3 Selected influencing factors of cultivated land use eco-efficiency (CLUE) by this study
Fig. 2 Boxplot of CLUE in China from 2000 to 2020. The upper and lower boundaries of box indicate the 75% and 25% quantiles, respectively. The line within the box represents the median and the cross represents the average value. The upper boundary of error bar is equal to the 75th percentile plus 1.5 times interquartile range, while the lower boundary of error bar is equal to the 25th percentile minus 1.5 times interquartile range.
Fig. 3 Temporal change in CLUE of each province, autonomous region, and municipality in China from 2000 to 2020
Fig. 4 Spatial distribution of CLUE in China in 2000 (a), 2007 (b), 2014 (c), and 2020 (d). Note that this map is based on the standard map (No. GS(2019)1823) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 5 Spatial evolution of CLUE in China from 2000 to 2020. Note that this map is based on the standard map (No. GS(2019)1823) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified
Year Latitude Longitude Location Moving direction Moving distance (km) Moving speed (km/a)
2000 32°36′00′′N 110°06′00′′E Shiyan City, Hubei Province - - -
2007 34°11′24′′N 110°58′12′′E Sanmenxia City, Henan Province 69°22′12′′
east by north		 196.09 28.01
2014 34°36′00′′N 112°52′12′′E Zhengzhou City, Henan Province 19°03′00′′
east by north		 178.14 25.45
2020 33°56′24′′N 111°18′36′′E Luoyang City, Henan Province 32°00′00′′
west by south		 159.88 26.65
Table 4 Barycentre parameters of CLUE in China from 2000 to 2020
Year Area (×105 km2) XStdDist (km) YStdDist (km) Azimuth (°) y/x-axis
2000 47.45 1339.96 1127.28 55.24 0.84
2001 49.38 1393.16 1128.21 48.43 0.81
2002 49.68 1451.66 1089.45 49.08 0.75
2003 49.72 1420.75 1114.04 58.76 0.78
2004 46.23 1381.06 1065.29 50.24 0.77
2005 44.79 1338.47 1064.88 54.06 0.80
2006 46.50 1373.37 1077.54 50.75 0.78
2007 47.55 1395.04 1084.99 67.01 0.78
2008 48.55 1439.49 1073.64 57.05 0.75
2009 44.91 1311.08 1090.46 58.88 0.83
2010 47.61 1342.38 1129.05 61.64 0.84
2011 46.47 1354.24 1092.38 49.93 0.81
2012 46.12 1339.03 1096.46 51.52 0.82
2013 45.67 1343.34 1082.25 48.35 0.81
2014 44.86 1325.58 1077.22 46.40 0.81
2015 43.99 1060.93 1319.96 44.42 1.24
2016 41.73 1017.41 1305.56 38.24 1.28
2017 42.14 1025.77 1307.82 41.30 1.27
2018 42.04 1050.66 1273.60 43.19 1.21
2019 42.86 1053.97 1294.60 42.60 1.23
2020 45.31 1278.14 1128.47 60.01 0.88
Table 5 Standard deviation ellipse (SDE) parameters of CLUE in China from 2000 to 2020
Variable Regression coefficient
Whole country MPAs Non-MPAs
MCI 0.0980±0.0442** 0.0306±0.0554 0.1130±0.0587*
GPC 0.0788±0.0074*** 0.0671±0.0110*** 0.0765±0.0101***
II -0.2240±0.0783*** -0.3020±0.1010*** -0.2180±0.1030**
STI 2.0760±0.8870** 3.8660±1.2210*** 1.1420±1.2700
SAL 0.0931±0.0202*** 0.0944±0.0256*** 0.1100±0.0263***
Constant 0.1870±0.0549*** 0.3020±0.0673*** 0.1740±0.0692**
Wald χ2 test 341.69*** 158.98*** 155.41***
Likelihood ratio test 302.29*** 154.48*** 137.96***
n 651 273 378
Table 6 Regression results of the panal Tobit model for CLUE in China from 2000 to 2020
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