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基于水足迹理论的县域农业用水评价及影响因素分析

包雄鹏 辛永君 周冬梅 金银丽 周凡 杨静 马静 张军

包雄鹏,辛永君,周冬梅,等.基于水足迹理论的县域农业用水评价及影响因素分析:以甘肃省景泰县为例[J].农业工程,2022,12(6):90-99. doi: 10.19998/j.cnki.2095-1795.2022.06.017
引用本文: 包雄鹏,辛永君,周冬梅,等.基于水足迹理论的县域农业用水评价及影响因素分析:以甘肃省景泰县为例[J].农业工程,2022,12(6):90-99. doi: 10.19998/j.cnki.2095-1795.2022.06.017
BAO Xiongpeng,XIN Yongjun,ZHOU Dongmei,et al.Evaluation and influencing factors of agricultural water in a county based on water footprint theory:taking Jingtai County of Gansu Province as an example[J].Agricultural Engineering,2022,12(6):90-99. doi: 10.19998/j.cnki.2095-1795.2022.06.017
Citation: BAO Xiongpeng,XIN Yongjun,ZHOU Dongmei,et al.Evaluation and influencing factors of agricultural water in a county based on water footprint theory:taking Jingtai County of Gansu Province as an example[J].Agricultural Engineering,2022,12(6):90-99. doi: 10.19998/j.cnki.2095-1795.2022.06.017

基于水足迹理论的县域农业用水评价及影响因素分析以甘肃省景泰县为例

doi: 10.19998/j.cnki.2095-1795.2022.06.017
基金项目: 甘肃省高等学校创新基金项目(2021A-061);甘肃省自然科学基金项目(21JR7RA811);甘肃省林业和草原资源保护与发展项目“环县人工造林地生态系统服务评价及优化调控”
详细信息
    作者简介:

    包雄鹏,硕士生,主要从事农业资源利用研究 E-mail:1477690855@qq.com

    张军,通信作者,教授,主要从事农业生态与资源可持续利用研究 E-mail:zhangjun@gsau.edu.cn

  • 中图分类号: S279

Evaluation and Influencing Factors of Agricultural Water in a County Based on Water Footprint TheoryTaking Jingtai County of Gansu Province as an Example

  • 摘要:

    水资源是一个地区发展的基础和重要保障,对区域水资源进行评价与分析研究,对于制定合理的水资源战略有重要的意义。利用水足迹理论,对甘肃省景泰县的农业水足迹进行了结构分析、时空变化分析和水足迹综合影响因子分析和评价。水足迹结构变化分析显示,景泰县农业水足迹2010—2019年整体上总量呈现不断增长的变化趋势,其中经济作物水足迹最高,畜产品水足迹次之,粮食作物水足迹最低;从2010—2019年农业水足迹增长变化来看,粮食作物水足迹增加了102%,畜产品水足迹增加了38%,经济作物水足迹增加了22%。农业水足迹空间变化分析结果显示,2010—2019年景泰县农业水足迹从东向西转移,农业水资源消耗重心由东向西转移;各乡镇农业水足迹增长型主要为景泰县西部区域,下降型为中东部地区,波动型区域为东北部地区。主成分影响因素分析结果显示,第1主成分影响贡献率高达64.997%,第2主成分和第3主成分分别为11.333%、10.504%,说明经济因素和农业生产因素对于景泰县的农业水足迹影响最大,而气候因素和人口因素影响相对较低。未来景泰县需从调整农业结构出发,发展高水效农业,促进区域农业水资源可持续利用。

     

  • 图 1  景泰县农业水足迹总量与人均量

    Figure 1.  Total and per capita agricultural water footprint in Jingtai County

    图 2  景泰县水足迹效益预测

    Figure 2.  Projected water footprint benefits for Jingtai County

    图 3  景泰县2010—2019年水足迹空间变化

    Figure 3.  Spatial variation of water footprint in Jingtai County from 2010 to 2019

    图 4  景泰县农业水足迹变化区域

    Figure 4.  Areas of change in agricultural water footprint in Jingtai County

    图 5  景泰县农业水足迹重心空间变化

    Figure 5.  Spatial variation of agricultural water footprint centre of gravity in Jingtai County

    表  1  景泰县农畜产品虚拟水含量

    Table  1.   Virtual water content of agricultural and livestock products in Jingtai County 单位:m3/kg

    产品类别虚拟水含量
    农产品小麦1.176
    玉米0.746
    豆类2.982
    油料2.740
    蔬菜1.152
    水果1.152
    动物产品猪肉3.561
    牛肉19.989
    羊肉18.005
    牛奶2.201
    禽蛋8.651
    下载: 导出CSV

    表  2  景泰县农业水足迹结构

    Table  2.   Structure of agricultural water footprint in Jingtai County 单位:106m3

    年份玉米小麦豆类蔬菜油料水果猪肉牛肉羊肉牛奶禽蛋
    201047.60856.1547.267119.65722.01039.44831.7930.71286.0148.93910.476
    201153.11345.8295.81226.21722.37845.05533.0100.80089.12512.32611.171
    201272.83153.9298.863131.69038.89250.00933.5620.99992.0428.47411.830
    201380.90332.7957.771145.67440.31952.87634.8950.99997.6055.39211.987
    201490.04237.9007.267116.94540.79350.35536.0910.955110.0806.82312.838
    201598.62247.9817.801115.97529.19354.22034.4281.071120.6697.30612.467
    201687.71549.3928.298144.31840.45755.58239.8431.407129.4267.35214.414
    201785.26951.5427.656156.84933.13458.78334.1271.529144.60813.97114.445
    2018116.31053.39162.234101.07531.19043.90032.8054.014119.5183.87415.859
    2019100.58446.86377.711116.47720.89984.29531.7414.268126.2139.30218.885
    下载: 导出CSV

    表  3  GM(1,1)模型检验及预测结果

    Table  3.   GM (1,1) model test and prediction results

    序号原始值预测值残差相对误差/%级比偏
    20102.6832.68300
    20112.6023.198−0.59622.90−0.041
    20123.4373.2700.1674.850.235
    20133.3803.3430.0371.08−0.027
    20143.8263.4170.40910.690.108
    20153.8433.4920.3519.14−0.006
    20163.5743.5670.0070.20−0.086
    20173.6573.6430.0140.380.013
    20183.4793.720−0.2416.92−0.062
    20193.6493.797−0.1484.070.037
    下载: 导出CSV

    表  4  农业水足迹与影响因素的相关系数

    Table  4.   Correlation coefficients between agricultural water footprint and influencing factors

    YX1X2X3X4X5X6X7X8X9X10X11
    Y1
    X10.686*1
    X20.838**0.669*1
    X30.857**0.664*0.969**1
    X40.5150.3370.2390.3411
    X5−0.747*−0.381−0.822**−0.913**−0.3141
    X6−0.470−0.385−0.490−0.420−0.3200.2861
    X70.4060.5780.735*0.5950.047−0.335−0.5261
    X80.760*0.6240.872**0.909**0.351−0.845**−0.6290.5171
    X90.862**0.750*0.929**0.890**0.338−0.687*−0.4820.709*0.778**1
    X100.2230.4670.6240.601−0.326−0.454−0.0600.5180.5700.4601
    X110.0420.309−0.231−0.1120.3340.170−0.012−0.4190.048−0.237−0.0281
    注:*代表p<0.05;**代表p<0.01。
    下载: 导出CSV

    表  5  主成分特征值及贡献率

    Table  5.   Eigenvalues and contribution of principal components

    成分特征值贡献率/%累计贡献积/%
    15.85064.99764.997
    21.02011.33376.330
    30.94510.50486.833
    下载: 导出CSV

    表  6  主成分因子载荷矩阵

    Table  6.   Principal component factor loading matrix

    影响因子主成分1主成分2主成分3
    X1 0.753 0.112 0.229
    X2 0.968 0.117−0.172
    X3 0.962−0.080−0.250
    X4 0.399−0.753 0.436
    X5−0.809 0.287 0.459
    X6−0.605−0.073−0.591
    X7 0.715 0.556 0.214
    X8 0.926−0.125−0.073
    X9 0.934 0.092−0.012
    下载: 导出CSV
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  • 收稿日期:  2021-10-14
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  • 出版日期:  2022-06-20

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