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生态学杂志 ›› 2026, Vol. 45 ›› Issue (5): 1744-1750.doi: 10.13292/j.1000-4890.202605.004

• 技术与方法 • 上一篇    下一篇

基于BP-DEMATEL模型的黑龙江省大豆水足迹影响因素

张璐阳1,2*,赵佳怡1,3,陈晶4,唐梓灵1,李莎1   

  1. (1东北农业大学资源与环境学院, 哈尔滨 150030; 2中国气象局中国农业大学农业应对气候变化联合实验室, 北京 100193; 3中国南方航空股份有限公司南阳基地, 河南南阳 473009; 4哈尔滨市呼兰区气象局, 哈尔滨 150500)

  • 出版日期:2026-05-10 发布日期:2026-05-12

Identifying critical drivers for soybean water footprint variations in Heilongjiang Province using the BP-DEMATEL model.

ZHANG Luyang1,2*, ZHAO Jiayi1,3, CHEN Jing4, TANG Ziling1, LI Sha1   

  1. (1School of Resources and Environment, Northeast Agricultural University, Harbin 150030, China; 2Joint Laboratory of Agriculture Coping with Climate Change of China Meteorological Administration (CMA) and China Agricultural University (CAU), Beijing 100193, China; 3China Southern Airlines Nanyang Base, Nanyang 473009, Henan, China; 4Meteorological Bureau of Hulan District in Harbin City, Harbin 150500, China).

  • Online:2026-05-10 Published:2026-05-12

摘要: 评估作物水足迹并识别其影响因素对于区域水资源管理和农业可持续发展具有重要意义。本研究对2004—2020年黑龙江省各地区大豆水足迹演变特征进行了分析,并基于BP神经网络与决策实验室分析模型(BP-DEMATEL)辨识影响大豆水足迹的驱动型因素和特征型因素,以揭示各因素之间的内在作用机制。结果表明:黑龙江省各地区年均大豆水足迹在1.99~2.72 m3·kg-1,并呈现绿水足迹(71.4%)>灰水足迹(17.9%)>蓝水足迹(10.7%)的特点;齐齐哈尔、鹤岗和大兴安岭地区水足迹波动较其他地区更明显;影响黑龙江省大豆水足迹的因素中,关键驱动因素为平均相对湿度,一般驱动因素有日照时数、降水量、农业机械总动力和化肥用量;关键特征因素包括平均气温、人均农业GDP和平均风速;黑龙江省大豆水足迹主要受当地气象条件和人均农业GDP影响,化肥和农业机械的使用对大豆水足迹影响程度次之。提高大豆种植水资源利用效率的首要途径为合理利用当地气候资源调整种植制度及重视农业经济发展水平,辅助条件为合理施肥和投入农业机械动力。


关键词: 大豆, 水足迹, BP神经网络, 决策实验室分析

Abstract: For effective regional water resource management and sustainable agricultural development, it is crucial to assess the water footprint of crops and clarify the determinants. We analyzed the spatiotemporal variations of soybean water footprints across Heilongjiang Province from 2004 to 2020. By employing the BP Neural Network and Decision-making Trial and Evaluation Laboratory (DEMATEL) (BP-DEMATEL) model, we identified the driving forces and typological determinants within the causal network that affect the water footprint of soybean, and elucidated the hierarchical interdependencies among them. The results showed that the average annual soybean water footprints ranged from 1.99 to 2.72 m3·kg-1 across Heilongjiang Province, with compositional dominance being green water (71.4%) > grey water (17.9%) > blue water (10.7%). Qiqihar, Hegang, and Daxing’anling exhibited stronger interannual variability than other areas. Among the factors influencing soybean water footprint, the key driving factor was the average relative humidity, while the moderate driving factors included the sunshine duration, precipitation, the total power of agricultural machinery and fertilizer consumption. The key typological determinants encompassed average temperature, per capita agricultural GDP, and average wind speed. The soybean water footprint in Heilongjiang Province was primarily influenced by local meteorological conditions and  per capita agricultural GDP, with the chemical fertilizer application and agricultural machinery having the secondary impact on soybean water footprint. The primary means to improve water resource utilization efficiency in soybean cultivation are to optimize planting systems through localized climate resource utilization and enhancing agricultural economic capacity. Supplementary measures are implementing precision fertilizer management and mechanization intensity regulation.


Key words: soybean, water footprint, back propagation neural network, Decision-making Trial and Evaluation Laboratory (DEMATEL)