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Driving force analysis of irrigation water consumption using principal component regression analysis

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  • Chen, Mengting
  • Luo, Yufeng
  • Shen, Yingying
  • Han, Zhenzhong
  • Cui, Yuanlai

Abstract

The effective management of irrigation water consumption is one of the main countermeasures to combat water shortages. This paper introduced an integrated approach to determine the major factors influencing irrigation water consumption in China. It combined multiple linear regression and principal component analysis to analyze the relationship between irrigation water consumption and influencing factors and then applied analytic hierarchy process and cluster analysis to analyze the spatial variation in driving factors of irrigation water consumption. Based on statistical data from the 31 provinces of China from 2000 to 2015, the results showed that irrigation water consumption was positively affected by the planting size, the ratio of surface water in water consumption (RSW), the planting structure, the annual ET0 (AE) and the annual average temperature (AAT); in contrast, consumption was generally negatively affected by irrigation technique, economic development, and annual rainfall (AR). The water consumption structure, irrigation technique and planting structure were major influential factors in most provinces of China, and there were significant differences in different regions; thus, regions should be restructured to be studied as subregions. For the total consumption of irrigation water, Central China was mainly affected by the water consumption structure, irrigation technique and climatic conditions, and North and Northwest China were hardly influenced by planting structure. Northeast, Southwest and southeastern coastal China were slightly affected by climatic conditions. For the per unit area irrigation water consumption, Central China was mainly affected by the water consumption structure, irrigation technique, planting size and climatic conditions, Southwest, South, East and Northeast China were mainly affected by the planting structure and planting size, and Northwest and North China were mainly influenced by the irrigation technique, water consumption structure and planting size.

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  • Chen, Mengting & Luo, Yufeng & Shen, Yingying & Han, Zhenzhong & Cui, Yuanlai, 2020. "Driving force analysis of irrigation water consumption using principal component regression analysis," Agricultural Water Management, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:agiwat:v:234:y:2020:i:c:s0378377419318207
    DOI: 10.1016/j.agwat.2020.106089
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    Cited by:

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    2. Miaomiao Hu & Tan Yigitcanlar & Fei Li & Shengqi Deng & Yabo Yang, 2024. "Sustainable Development of Production–Living–Ecological Spaces: Insights from a 30-Year Remote Sensing Analysis," Sustainability, MDPI, vol. 16(21), pages 1-27, November.
    3. Wu, Di & Cui, Yuanlai & Li, Dacheng & Chen, Manyu & Ye, Xugang & Fan, Guofu & Gong, Lanqiang, 2021. "Calculation framework for agricultural irrigation water consumption in multi-source irrigation systems," Agricultural Water Management, Elsevier, vol. 244(C).
    4. Wenbo Li & Alim Samat & Jilili Abuduwaili & Wei Wang, 2024. "Spatiotemporal Pattern, Evolutionary Trend, and Driving Forces Analysis of Ecological Quality in the Irtysh River Basin (2000–2020)," Land, MDPI, vol. 13(2), pages 1-28, February.
    5. Xin Liu & Xuefeng Sang & Jiaxuan Chang & Yang Zheng & Yuping Han, 2021. "The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-21, August.
    6. Awais, Minahil & Afzal, Ayesha & Firdousi, Saba & Hasnaoui, Amir, 2023. "Is fintech the new path to sustainable resource utilisation and economic development?," Resources Policy, Elsevier, vol. 81(C).
    7. Gao, Zitian & Guo, Danlu & Ryu, Dongryeol & Western, Andrew W., 2024. "Exploring key factors driving farm-level seasonal irrigation water usage with Bayesian hierarchical modelling," Agricultural Water Management, Elsevier, vol. 294(C).

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