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Journal of Arid Land  2020, Vol. 12 Issue (5): 837-853    DOI: 10.1007/s40333-020-0073-y
Research article     
Can climate change influence agricultural GTFP in arid and semi-arid regions of Northwest China?
FENG Jian1, ZHAO Lingdi1,2,*(), ZHANG Yibo3, SUN Lingxiao4, YU Xiang4, YU Yang4,5,*()
1School of Economics, Ocean University of China, Qingdao 266100, China
2Institute of Marine Development, Ocean University of China, Qingdao 266100, China
3School of Foreign Languages, Ocean University of China, Qingdao 266100, China
4Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
5University of Chinese Academy of Sciences, Beijing 100049, China
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There are eight provinces and autonomous regions (Gansu Province, Ningxia Hui Autonomous Region, Xinjiang Uygur Autonomous Region, Inner Mongolia Autonomous Region, Tibet Autonomous Region, Qinghai Province, Shanxi Province, and Shaanxi Province) in Northwest China, most areas of which are located in arid and semi-arid regions (northwest of the 400 mm precipitation line), accounting for 58.74% of the country's land area and sustaining approximately 7.84×106 people. Because of drought conditions and fragile ecology, these regions cannot develop agriculture at the expense of the environment. Given the challenges of global warming, the green total factor productivity (GTFP), taking CO2 emissions as an undesirable output, is an effective index for measuring the sustainability of agricultural development. Agricultural GTFP can be influenced by both internal production factors (labor force, machinery, land, agricultural plastic film, diesel, pesticide, and fertilizer) and external climate factors (temperature, precipitation, and sunshine duration). In this study, we used the Super-slacks-based measure (Super-SBM) model to measure agricultural GTFP during the period 2000-2016 at the regional level. Our results show that the average agricultural GTFP of most provinces and autonomous regions in arid and semi-arid regions underwent a fluctuating increase during the study period (2000-2016), and the fluctuation was caused by the production factors (input and output factors). To improve agricultural GTFP, Shaanxi, Shanxi, and Gansu should reduce agricultural labor force input; Shaanxi, Inner Mongolia, Gansu, and Shanxi should decrease machinery input; Shaanxi, Inner Mongolia, Xinjiang, and Shanxi should reduce fertilizer input; Shaanxi, Xinjiang, Gansu, and Ningxia should reduce diesel input; Xinjiang and Gansu should decrease plastic film input; and Gansu, Shanxi, and Inner Mongolia should cut pesticide input. Desirable output agricultural earnings should be increased in Qinghai and Tibet, and undesirable output (CO2 emissions) should be reduced in Inner Mongolia, Xinjiang, Gansu, and Shaanxi. Agricultural GTFP is influenced not only by internal production factors but also by external climate factors. To determine the influence of climate factors on GTFP in these provinces and autonomous regions, we used a Geographical Detector (Geodetector) model to analyze the influence of climate factors (temperature, precipitation, and sunshine duration) and identify the relationships between different climate factors and GTFP. We found that temperature played a significant role in the spatial heterogeneity of GTFP among provinces and autonomous regions in arid and semi-arid regions. For Xinjiang, Inner Mongolia, and Tibet, a suitable average annual temperature would be in the range of 7°C-9°C; for Gansu, Shanxi, and Ningxia, it would be 11°C-13°C; and for Shaanxi, it would be 15°C-17°C. Stable climatic conditions and more efficient production are prerequisites for the development of sustainable agriculture. Hence, in the agricultural production process, reducing the redundancy of input factors is the best way to reduce CO2 emissions and to maintain temperatures, thereby improving the agricultural GTFP. The significance of this study is that it explores the impact of both internal production factors and external climatic factors on the development of sustainable agriculture in arid and semi-arid regions, identifying an effective way forward for the arid and semi-arid regions of Northwest China.

Key wordsclimate change      agricultural GTFP      Super-slacks-based measure (Super-SBM) model      Geodetector      CO2 emissions      arid regions      semi-arid regions     
Received: 06 February 2019      Published: 10 September 2020
Corresponding Authors: Lingdi ZHAO,Yang YU     E-mail:;
About author: *Corresponding author: ZHAO Lingdi (E-mail:; YU Yang (E-mail:
Cite this article:

FENG Jian, ZHAO Lingdi, ZHANG Yibo, SUN Lingxiao, YU Xiang, YU Yang. Can climate change influence agricultural GTFP in arid and semi-arid regions of Northwest China?. Journal of Arid Land, 2020, 12(5): 837-853.

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Source of carbon Emission coefficient Reference source
Fertilizer (kg CE/kg) 0.8956 Oak Ridge National Laboratory (ORNL), United States
Pesticide (kg CE/kg) 4.9341 Oak Ridge National Laboratory (ORNL), United States
Plastic film (kg CE/kg) 5.1800 Institute of Resource, Ecosystem and Environment of Agriculture of Nanjing Agricultural University (IREEA), China
Diesel (kg CE/kg) 0.5927 Intergovernmental Panel on Climate Change (IPCC), United Nations
Irrigation (kg/km2) 266.4800 Duan et al. (2011), China
Plowing (kg/km2) 312.6000 China Agricultural University (CAU), China
Table 1 CO2 emission coefficients during the cultivation process
Variable type Variable Mean Standard deviation
Input factors Number of agricultural labor force (×104 persons) 1165.3802 787.6478
Total power of agricultural machinery (×104 kW) 1424.8896 960.7656
Volume of effective component of fertilizer (×104 t) 89.1750 71.8066
Use of agricultural diesel (×104 t) 32.3338 25.3809
Use of agricultural plastic film (×104 t) 5.1745 6.0462
Use of agricultural pesticide (×104 t) 1.5174 1.6592
Total sown area of farm crops (×103 hm2) 3121.5603 2140.6714
Output factors Gross output value of agriculture (×108 CNY, at 2000 constant price) 306.0458 234.7596
CO2 emissions (×104 t) 217.4791 157.7604
Table 2 Descriptive statistics for variables used in the Super-slacks-based measure (Super-SBM) model
Region Province/
Autonomous region
(×104 persons)
(×104 kW)
(×104 t)
(×104 t)
(×104 t)
(×104 t)
(×103 hm2)
(×108 CNY)
(×104 t)
Arid Gansu 1799.4180 1752.1940 81.5529 26.8824 11.3788 4.1811 3909.8470 388.6244 273.9565
Ningxia 355.0647 627.2294 33.3706 17.2529 1.0324 0.2182 1179.5180 96.6187 78.3374
Xinjiang 1136.8940 1530.3290 151.1882 59.6412 16.1241 1.8047 4461.7940 531.6966 383.4737
Semi- Tibet 226.8471 345.2588 4.4353 2.7294 0.0859 0.0929 239.1765 25.8114 12.9417
arid Shanxi 2001.4290 2470.2410 103.7412 27.6823 3.8182 2.4718 3781.5060 380.3657 243.2440
Qinghai 323.8235 365.8294 8.0941 5.8235 0.3171 0.1871 522.7882 37.3417 27.3607
Shaanxi 2289.5120 1773.7060 180.0824 65.1411 3.1341 1.1465 4217.3530 525.5955 335.4848
Inner Mongolia 1190.0530 2534.3290 150.9353 53.5177 5.5053 2.0365 6660.5000 462.3124 385.0342
Average 1165.3802 1424.8896 89.1750 32.3338 5.1745 1.5174 3121.5603 306.0458 217.4791
Table 3 Average values of agricultural input and output factors in arid and semi-arid regions during 2000-2016
Region Province/
Autonomous region
Growth rate (%)
Machinery Fertilizer Diesel Plastic
Pesticide Land Earnings CO2
Arid Gansu -28.9125 80.1400 44.8062 183.3333 203.8941 513.1579 14.4511 145.9105 74.5302
Ningxia -24.0474 52.5223 72.4576 95.6522 214.5833 62.5000 21.0231 232.7867 57.0062
Xinjiang 35.6767 199.8355 215.9091 121.1587 202.1566 102.9412 80.6876 134.5649 148.8370
Semi- Tibet 7.4723 454.6725 136.0000 785.7143 1700.0000 57.1428 10.6880 -13.6474 86.7169
arid Shanxi -30.5950 2.5275 34.5977 28.6344 77.1739 74.8571 -6.3576 174.4045 18.5313
Qinghai -14.7356 79.0008 22.2222 8.4746 1216.6670 0.0000 -0.7585 117.6976 20.6629
Shaanxi -38.4843 108.2558 77.6677 66.3082 72.7273 28.1553 -6.7415 168.9692 39.7402
Inner Mongolia -28.9657 146.6933 213.6364 170.9030 176.3006 262.9213 29.2709 119.9713 98.7764
Table 4 Growth rate of agricultural input and output factors in arid and semi-arid regions during 2000-2016
Fig. 1 Estimation results for regional agricultural green total factor productivity (GTFP) in arid (a) and semi-arid (b) regions during 2000-2016
Fig. 2 Dynamic changes of agricultural input and output slacks in arid and semi-arid regions during 2000-2016. (a), labor force; (b), machinery; (c), fertilizer; (d), diesel; (e), plastic film; (f), pesticide; (g), land; (h), earnings; (i), CO2 emissions. Negative value means redundancy and positive value means deficiency.
Statistic Temperature Precipitation Sunshine duration
q statistic 0.2442 0.0173 0.0203
P value 0.9817 1.0000 1.0000
Table 5 Spatial heterogeneity of agricultural GTFP caused by climate factors
Temperature Precipitation Sunshine duration
Precipitation Y
Sunshine duration Y N
Table 6 Comparison of climate factor influences on agricultural GTFP
Temperature Precipitation Sunshine duration
Temperature 0.2442
Precipitation 0.3004 0.0173
Sunshine duration 0.2981 0.0550 0.0203
Table 7 Interaction influence of climate factors on agricultural GTFP
Climate factor Graphical representation Interaction
Temperature∩Precipitation Enhances, nonlinear
Temperature∩Sunshine duration Enhances, nonlinear
Precipitation∩Sunshine duration Enhances, nonlinear
Table 8 Interaction types of climate factors
Temperature 5°C-7°C 7°C-9°C 9°C-11°C 11°C-13°C 13°C-15°C 15°C-17°C
Average GTFP 0.3960 0.6615 0.4857 0.6785 0.5673 0.6748
7°C-9°C Y
9°C-11°C Y Y
11°C-13°C Y N Y
13°C-15°C Y N N N
Over 15°C Y N Y N N
Precipitation 0-200 mm 200-400 mm 400-600 mm 600-800 mm Over 800 mm
Average GTFP 0.5073 0.5749 0.5911 0.5772 0.4411
0-200 mm
200-400 mm N
400-600 mm N N
600-800 mm N N N
Over 800 mm N N N N
Sunshine 1000-1500 h 1500-2000 h 2000-2500 h
Average GTFP 0.5803 0.5827 0.5804
1000-1500 h
1500-2000 h N
2000-2500 h N N
Table 9 Risk detector results
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