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Variation in Cropping Intensity in Northern China from 1982 to 2012 Based on GIMMS-NDVI Data

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  • Mingjun Ding

    (Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, School of Geography and Environment, Jiangxi Normal University, No. 99 Ziyang Ave, Gaoxing District, Nanchang 330022, China
    Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, No. 101 David L. Boren Blvd, Norman, OK 73019, USA)

  • Qian Chen

    (Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, School of Geography and Environment, Jiangxi Normal University, No. 99 Ziyang Ave, Gaoxing District, Nanchang 330022, China)

  • Xiangming Xiao

    (Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, No. 101 David L. Boren Blvd, Norman, OK 73019, USA
    Institute of Biodiversity Science, Fudan University, Shanghai 200433, China)

  • Liangjie Xin

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), CAS, Beijing 100101, China)

  • Geli Zhang

    (Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, No. 101 David L. Boren Blvd, Norman, OK 73019, USA)

  • Lanhui Li

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), CAS, Beijing 100101, China)

Abstract

Cropping intensity is an important indicator of the intensity of cropland use and plays a very important role in food security. In this study, we reconstructed a normalized difference vegetation index (NDVI) time-series from 1982 to 2012 using the Savitzky-Golay (S-G) technique and used it to derive a multiple cropping index (MCI) combined with land use data. Spatial–temporal patterns of variation in the MCI of northern China were as follows: (1) The MCI in northern China increased gradually from north-west to south-east; from 1982 to 2012, the mean cropping index across grid-cells over the study area increased by 4.36% per 10 years ( p < 0.001) with fluctuations throughout the study period; (2) The mean MCI across grid-cells over the whole of northern China increased from 107% to 115% with all provinces showing an increasing trend throughout the 1980s and 1990s. Aside from Tianjin, Hebei, Beijing, and Shandong, all provinces also displayed an increasing trend between the 1990s and 2000s. Arable slope played an important role in the variation of the MCI; regions with slope ≤3° and the regions with slope >3° were characterized by inverse temporal MCI trends; (3) Drivers of change in the MCI were diverse and varied across different spatial and temporal scales; the MCI was affected by the changing agricultural population, deployment of food policies, and methods introduced for maximizing farmer benefits. For the protection of national food security, measures are needed to improve the MCI. However, more attention should also be given to the negative impacts that these measures may have on agricultural sustainability, such as soil pollution by chemical fertilizers and pesticides.

Suggested Citation

  • Mingjun Ding & Qian Chen & Xiangming Xiao & Liangjie Xin & Geli Zhang & Lanhui Li, 2016. "Variation in Cropping Intensity in Northern China from 1982 to 2012 Based on GIMMS-NDVI Data," Sustainability, MDPI, vol. 8(11), pages 1-16, November.
  • Handle: RePEc:gam:jsusta:v:8:y:2016:i:11:p:1123-:d:81890
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    References listed on IDEAS

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    Cited by:

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    2. Shen, Ge & Yu, Qiangyi & Zhou, Qingbo & Wang, Cong & Wu, Wenbin, 2023. "From multiple cropping frequency to multiple cropping system: A new perspective for the characterization of cropland use intensity," Agricultural Systems, Elsevier, vol. 204(C).
    3. Fritz, Steffen & See, Linda & Bayas, Juan Carlos Laso & Waldner, François & Jacques, Damien & Becker-Reshef, Inbal & Whitcraft, Alyssa & Baruth, Bettina & Bonifacio, Rogerio & Crutchfield, Jim & Rembo, 2019. "A comparison of global agricultural monitoring systems and current gaps," Agricultural Systems, Elsevier, vol. 168(C), pages 258-272.
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    5. Diana Mariana Cocârţă & Mihaela Alexandra Stoian & Aykan Karademir, 2017. "Crude Oil Contaminated Sites: Evaluation by Using Risk Assessment Approach," Sustainability, MDPI, vol. 9(8), pages 1-16, August.

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