Please wait a minute...
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
Download: HTML     PDF(572KB)
Export: BibTeX | EndNote (RIS)      


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:
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.

URL:     OR

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
[1]   Andersen P, Petersen N C. 1993. A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10): 1261-1264.
[2]   Blancard S, Martin E. 2014. Energy efficiency measurement in agriculture with imprecise energy content information. Energy Policy, 66: 198-208.
[3]   Chen P C, Yu M M, Chang C C, et al. 2008. Total factor productivity growth in China's agricultural sector. China Economic Review, 19(4): 580-593.
[4]   Chen Z, Huffman W E, Rozelle S. 2009. Farm technology and technical efficiency: Evidence from four regions in China. China Economic Review, 20(2): 153-161.
[5]   Cheng G. 2014. Data Envelopment Analysis: Methods and MaxDEA Software. Beijing: Intellectual Property Right Press, 180-189. (in Chinese)
[6]   Dai A G. 2011. Drought under global warming: a review. Climate Change, 2(1): 45-65.
[7]   Deng X Z, Gibson J, Wang P. 2017. Management of trade-offs between cultivated land conversions and land productivity in Shandong Province. Journal of Cleaner Production, 142: 767-774.
[8]   Duan H P, Zhang Y, Zhao J B, et al. 2011. Carbon footprint analysis of farm land ecosystem in China. Journal of Soil and Water Conservation, 25(5): 203-208. (in Chinese)
[9]   Feng C P, Chu F, Ding J J, et al. 2015. Carbon Emissions Abatement (CEA) allocation and compensation schemes based on DEA. Omega-International Journal of Management Science, 53: 78-89.
[10]   Feng Z M, Yang Y Z, Zhang Y Q, et al. 2005. Grain-for-green policy and its impacts on grain supply in West China. Land Use Policy, 22(4): 301-312.
[11]   Fischer G, Winiwarter W, Ermolieva T, et al. 2010. Integrated modeling framework for assessment and mitigation of nitrogen pollution from agriculture: concept and case study for China. Agriculture, Ecosystems & Environment, 136(1-2): 116-124.
[12]   Fuinhas J A, Marques A C, Almeida P, et al. 2016. Two centuries of economic growth: international evidence on deepness and steepness. Transformations in Business & Economics, 15(3): 192-206.
[13]   Gan Y T, Kadambot H M S, Turner N C, et al. 2013. Ridge-furrow mulching systems—an innovative technique for boosting crop productivity in semiarid rain-fed environments. Advances in Agronomy, 118: 429-476.
[14]   Gollin D, Parente S L, Rogerson R. 2007. The food problem and the evolution of international income levels. Journal of Monetary Economics, 54(4): 1230-1255.
[15]   He F, Wang K, Li X L, et al. 2012. Effects of ridge and furrow rainfall harvesting system of on soil hydrothermal condition and yields of Elymus Sibiricus L. in arid and semiarid regions. Transactions of the Chinese Society of Agricultural Engineering, 28(12): 122-126. (in Chinese)
[16]   Heidari M D, Omid M, Mohammadi A. 2012. Measuring productive efficiency of horticultural greenhouses in Iran: a data envelopment analysis approach. Expert Systems with Applications, 39(1): 1040-1045.
[17]   Hu Q, Pan F F, Pan X B, et al. 2014. Effects of a ridge-furrow micro-field rainwater-harvesting system on potato yield in a semi-arid region. Field Crops Research, 166(9): 92-101.
[18]   Huang C, Santibanez-Gonzalez E D, Song M. 2018. Interstate pollution spillover and setting environmental standards. Journal of Cleaner Production, 170: 1544-1553.
[19]   Ito J. 2010. Inter-regional difference of agricultural productivity in China: distinction between biochemical and machinery technology. China Economic Review, 21(3): 394-410.
[20]   Jin G, Li Z H, Deng X Z, et al.2018. An analysis of spatiotemporal patterns in Chinese agricultural productivity between 2004 and 2014. Ecological Indicators, 2019: 591-600.
[21]   Kerstens K, Shen Z, Ignace V D W. 2018. Comparing Luenberger and Luenberger-Hicks-Moorsteen productivity indicators: how well is total factor productivity approximated? International Journal of Production Economics, 195: 311-318.
[22]   Kravchenko A N, Bullock D G. 2000. Correlation of corn and soybean grain yield with topography and soil properties. Agronomy Journal, 92(1): 75-83.
[23]   Lee E A, Tollenaar M. 2007. Physiological basis of successful breeding strategies for maize grain yields. Crop Science, 47(Suppl.): S202-S215.
[24]   Lei Y D, Zhang H L, Chen F, et al. 2016. How rural land use management facilitates drought risk adaptation in a changing climate—a case study in arid Northern China. Science of the Total Environment, 550: 192-199.
[25]   Leng G Y, Tang Q H, Rayburg S. 2015. Climate change impacts on meteorological, agricultural and hydrological droughts in China. Global and Planetary Change, 126: 23-34.
[26]   Li X Y, Gong J D. 2002. Effect of different ridge: furrow ratios and supplement irrigation on crop production in ridge and furrow rainfall harvesting system with mulches. Agricultural Water Management, 54(3): 243-254.
[27]   Liobikiene G, Mandravickaite J, Krepstuliene D, et al. 2017. Lithuanian achievements in terms of CO2 emissions based on production side in the context of the EU-27. Technological and Economic Development of Economy, 23(3): 483-503.
[28]   Liu J Y, Xu X L, Zhuang D F, et al. 2005. Impacts of LUCC processes on potential land productivity in China in the 1990s. Science in China Series D: Earth Sciences, 48(8): 1259-1269.
[29]   Liu T, Liu H, Qi Y J. 2015. Construction land expansion and cultivated land protection in urbanizing China: insights from national land surveys, 1996-2006. Habitat International, 46:13-22.
[30]   Liu Y, Zhang J B, Zhang L. 2018. Analysis of carbon emission efficiency of rice in China under different rice planting patterns based on the DEA-SBM model. Journal of China Agricultural University, 23(6): 177-186. (in Chinese)
[31]   Liu Z, Guan D B, Crawford-Brown D, et al. 2013. Energy policy: a low-carbon road map for China. Nature, 500(7461): 143-145.
doi: 10.1038/500143a pmid: 23925225
[32]   Liu Z, Davis S J, Feng K S, et al. 2016. Targeted opportunities to address the climate-trade dilemma in China. Nature Climate Change, 6(2): 201-206.
[33]   Lobell D B, Burke M B, Tebaldi C, et al. 2008. Prioritizing climate change adaptation needs for food security in 2030. Science, 319(5863): 607-610.
doi: 10.1126/science.1152339
[34]   Ma S Z, Feng H. 2013. Will the decline of efficiency in China's agriculture come to an end? An analysis based on opening and convergence. China Economic Review, 27:179-190.
doi: 10.1016/j.chieco.2013.04.003
[35]   MacDonald D, Crabtree J R, Wiesinger G, et al. 2000. Agricultural abandonment in mountain areas of Europe: environmental consequences and policy response. Journal of Environmental Management, 59(1): 47-69.
doi: 10.1006/jema.1999.0335
[36]   Makijenko J, Burlakovs J, Brizga J, et al. 2016. Energy efficiency and behavioral patterns in Latvia. Management of Environmental Quality, 27(6): 695-707.
[37]   Mueller L, Kay B D, Hu C S, et al. 2009. Visual assessment of soil structure: evaluation of methodologies on sites in Canada, China and Germany: part I: comparing visual methods and linking them with soil physical data and grain yield of cereals. Soil & Tillage Research, 103(1): 178-187.
[38]   NBSC (National Bureau of Statistical of China). 2001-2017a. China Rural Statistical Yearbook 2000-2016. Beijing: China Statistics Press. (in Chinese)
[39]   NBSC (National Bureau of Statistical of China). 2001-2017b. China Statistical Yearbook 2000-2016. Beijing: China Statistics Press. (in Chinese)
[40]   Nigussie Z, Tsunekawa A, Haregeweyn N, et al. 2017. Factors influencing small-scale farmers' adoption of sustainable land management technologies in north-western Ethiopia. Land Use Policy, 67: 57-64.
[41]   Olesen J E, Bindi M. 2002. Consequences of climate change for European agricultural productivity, land use and policy. European Journal of Agronomy, 16(4): 239-262.
[42]   Pang J X, Chen X P, Zhang Z L, et al. 2016. Measuring eco-efficiency of agriculture in China. Sustainability, 8(4): 1-15.
[43]   Peters C J, Wilkins J L, Fick G W. 2007. Testing a complete-diet model for estimating the land resource requirements of food consumption and agricultural carrying capacity: the New York State example. Renewable Agriculture and Food Systems, 22(2): 145-153.
[44]   Piao S L, Ciais P, Huang Y, et al. 2010. The impacts of climate change on water resources and agriculture in China. Nature, 467(7311): 43-51.
doi: 10.1038/nature09364 pmid: 20811450
[45]   Ponce C, Guillermo E, Moran M S, et al. 2013. Ecosystem resilience despite large-scale altered hydroclimatic condition. Nature, 470(1): 1-4.
[46]   Ren W, Tian H Q, Tao B, et al. 2012. China's crop productivity and soil carbon storage as influenced by multifactor global change. Global Change Biology, 18(9): 2945-2957.
doi: 10.1111/j.1365-2486.2012.02741.x pmid: 24501069
[47]   Rigoberto A L, Xi H, Eleonora D F. 2017. What drives China's new agricultural subsidies? World Development, 93: 279-292.
[48]   Saleska S R, Didan K, Huete A R, et al. 2007. Amazon forests green-up during 2005 drought. Science, 318(5850): 612-612.
doi: 10.1126/science.1146663 pmid: 17885095
[49]   Seiford L M, Zhu J. 2002. Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142: 16-20.
[50]   Sheffield J, Wood E F. 2008. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Climate Dynamics, 31(1): 79-105.
[51]   Shen Z Y, Baležentis T, Chen X L, et al. 2018. Green growth and structural change in Chinese agricultural sector during 1997-2014. China Economic Review, 51: 83-96.
[52]   Song M L, Wang S H, Cen L. 2015. Comprehensive efficiency evaluation of coal enterprises from production and pollution treatment process. Journal of Cleaner Production, 104: 374-379.
[53]   Song M L, Zheng W P, Wang Z Y. 2016. Environmental efficiency and energy consumption of highway transportation systems in China. International Journal of Production Economics, 181: 441-449.
[54]   Tao F L, Yokozawa M, Xu Y L, et al. 2006. Climate changes and trends in phenology and yields of field crops in China, 1981-2000. Agricultural and Forest Meteorology, 138(1-4): 82-92.
[55]   Tian X, Yu X H. 2012. The enigmas of TFP in China: a meta-analysis. China Economic Review, 23(2): 396-414.
[56]   Tone K. 2002. A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 143(1): 32-41.
[57]   Trenberth K E, Dai A G, Schrier G V D, et al. 2014. Global warming and changes in drought. Nature Climate Change, 4(1): 17-22.
[58]   Turner N C. 2004. Sustainable production of crops and pastures under drought in a Mediterranean environment. Annals of Applied Biology, 144(2): 139-174.
[59]   Van Ittersum M K, Leffelaar P A, Van Keulen H. 2003. On approaches and applications of Wageningen crop models. European Journal of Agronomy, 18(3-4): 201-234.
[60]   Wang A H, Lettenmaier D P, Sheffield J. 2011. Soil moisture drought in China, 1950-2006. Journal of Climate, 24(13): 3257-3271.
[61]   Wang F T, Yu C, Xiong L C, et al. 2019. How can agricultural water use efficiency be promoted in China? A spatial-temporal analysis. Resources, Conservation and Recycling, 145: 411-418.
[62]   Wang J F, Li X H, Christakos G, et al. 2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. International Journal of Geographical Information Science, 24(1): 107-127.
[63]   Wang J F, Zhang T L, Fu B J. 2016. A measure of spatial stratified heterogeneity. Ecological Indicators, 67: 250-256
[64]   Wang J F, Xu C D. 2017. Geographical detector: principles and prospects. Acta Geographica Sinica, 72(1): 116-134. (in Chinese)
[65]   Wang K, Wei Y M, Huang Z M. 2016. Potential gains from carbon emissions trading in China: A DEA based estimation on abatement cost savings. Omega-International Journal of Management Science, 63: 48-59.
[66]   Wang Y J, Xie Z K, Malhi S S, et al. 2011. Effects of gravel-sand mulch, plastic mulch and ridge and furrow rainfall harvesting system combinations on water use efficiency, soil temperature and watermelon yield in a semi-arid Loess Plateau of northwestern China. Agricultural Water Management, 10(1): 88-92.
[67]   Wu Y R. 1995. Productivity growth, technological progress, and technical efficiency change in China: A three-sector analysis. Journal of Comparative Economics, 21(2): 207-229.
[68]   Xiao G J, Zhang Q B, Zhang F J, et al. 2016. Warming influences the yield and water use efficiency of winter wheat in the semiarid regions of Northwest China. Field Crops Research, 199: 129-135.
[69]   Xu S W, Li G Q, Li Z M. 2015. China agricultural outlook for 2015-2024 based on China Agricultural Monitoring and Early-warning System (CAMES). Journal of Integrative Agriculture, 14(9): 1889-1902.
[70]   Xu Y B, Li J Y, Wan J M. 2017. Agriculture and crop science in China: innovation and sustainability. Crop Journal, 5(2): 95-99.
[71]   Yao Y B, Wang R Y, Yang J H, et al. 2011. Impact of climate warming on flax growth and water use efficiency in semiarid regions of the loess plateau. Journal of Applied Ecology, 22(10): 2635-2642.
pmid: 22263469
[72]   Zhang B C. 2008. Discussion on basic characteristics and developmental status of rainfall-harvesting technique in arid areas. Journal of Irrigation & Drainage, 27(2): 119-122.
[73]   Zhang C J, Liao Y M, Duan J Q, et al. 2016. The progresses of dry-wet climate divisional research in China. Research in Climate Change Progress, 12(4): 261-267.
[74]   Zhang L, Pang J X, Chen X P, et al. 2019. Carbon emissions, energy consumption and economic growth: Evidence from the agricultural sector of China's main grain-producing areas. Science of the Total Environment, 665: 1017-1025.
doi: 10.1016/j.scitotenv.2019.02.162
[75]   Zhao H, Wang R Y, Ma B L. 2014. Ridge-furrow with full plastic film mulching improves water use efficiency and tuber yields of potato in a semiarid rainfed ecosystem. Field Crops Research, 161: 137-148.
doi: 10.1016/j.fcr.2014.02.013
[76]   Zhou L M, Jin S L, Liu C A, et al. 2012. Ridge-furrow and plastic-mulching tillage enhances maize-soil interactions: opportunities and challenges in a semiarid agroecosystem. Field Crops Research, 126: 181-188.
doi: 10.1016/j.fcr.2011.10.010
[1] LIN Yanmin, HU Zhirui, LI Wenhui, CHEN Haonan, WANG Fang, NAN Xiongxiong, YANG Xuelong, ZHANG Wenjun. Response of ecosystem carbon storage to land use change from 1985 to 2050 in the Ningxia Section of Yellow River Basin, China[J]. Journal of Arid Land, 2024, 16(1): 110-130.
[2] ZHAO Xuqin, LUO Min, MENG Fanhao, SA Chula, BAO Shanhu, BAO Yuhai. Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change[J]. Journal of Arid Land, 2024, 16(1): 46-70.
[3] WANG Yinping, JIANG Rengui, YANG Mingxiang, XIE Jiancang, ZHAO Yong, LI Fawen, LU Xixi. Spatiotemporal characteristics and driving mechanisms of land use/land cover (LULC) changes in the Jinghe River Basin, China[J]. Journal of Arid Land, 2024, 16(1): 91-109.
[4] Mitiku A WORKU, Gudina L FEYISA, Kassahun T BEKETIE, Emmanuel GARBOLINO. Projecting future precipitation change across the semi-arid Borana lowland, southern Ethiopia[J]. Journal of Arid Land, 2023, 15(9): 1023-1036.
[5] QIN Guoqiang, WU Bin, DONG Xinguang, DU Mingliang, WANG Bo. Evolution of groundwater recharge-discharge balance in the Turpan Basin of China during 1959-2021[J]. Journal of Arid Land, 2023, 15(9): 1037-1051.
[6] MA Jinpeng, PANG Danbo, HE Wenqiang, ZHANG Yaqi, WU Mengyao, LI Xuebin, CHEN Lin. Response of soil respiration to short-term changes in precipitation and nitrogen addition in a desert steppe[J]. Journal of Arid Land, 2023, 15(9): 1084-1106.
[7] ZHANG Hui, Giri R KATTEL, WANG Guojie, CHUAI Xiaowei, ZHANG Yuyang, MIAO Lijuan. Enhanced soil moisture improves vegetation growth in an arid grassland of Inner Mongolia Autonomous Region, China[J]. Journal of Arid Land, 2023, 15(7): 871-885.
[8] ZHANG Zhen, XU Yangyang, LIU Shiyin, DING Jing, ZHAO Jinbiao. Seasonal variations in glacier velocity in the High Mountain Asia region during 2015-2020[J]. Journal of Arid Land, 2023, 15(6): 637-648.
[9] GAO Xiang, WEN Ruiyang, Kevin LO, LI Jie, YAN An. Heterogeneity and non-linearity of ecosystem responses to climate change in the Qilian Mountains National Park, China[J]. Journal of Arid Land, 2023, 15(5): 508-522.
[10] Reza DEIHIMFARD, Sajjad RAHIMI-MOGHADDAM, Farshid JAVANSHIR, Alireza PAZOKI. Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments[J]. Journal of Arid Land, 2023, 15(5): 545-561.
[11] Sakine KOOHI, Hadi RAMEZANI ETEDALI. Future meteorological drought conditions in southwestern Iran based on the NEX-GDDP climate dataset[J]. Journal of Arid Land, 2023, 15(4): 377-392.
[12] Mehri SHAMS GHAHFAROKHI, Sogol MORADIAN. Investigating the causes of Lake Urmia shrinkage: climate change or anthropogenic factors?[J]. Journal of Arid Land, 2023, 15(4): 424-438.
[13] ZHANG Yixin, LI Peng, XU Guoce, MIN Zhiqiang, LI Qingshun, LI Zhanbin, WANG Bin, CHEN Yiting. Temporal and spatial variation characteristics of extreme precipitation on the Loess Plateau of China facing the precipitation process[J]. Journal of Arid Land, 2023, 15(4): 439-459.
[14] WU Jingyan, LUO Jungang, ZHANG Han, YU Mengjie. Driving forces behind the spatiotemporal heterogeneity of land-use and land-cover change: A case study of the Weihe River Basin, China[J]. Journal of Arid Land, 2023, 15(3): 253-273.
[15] Adnan ABBAS, Asher S BHATTI, Safi ULLAH, Waheed ULLAH, Muhammad WASEEM, ZHAO Chengyi, DOU Xin, Gohar ALI. Projection of precipitation extremes over South Asia from CMIP6 GCMs[J]. Journal of Arid Land, 2023, 15(3): 274-296.