Evaluation and Influencing Factors of Agricultural Water in a County Based on Water Footprint TheoryTaking Jingtai County of Gansu Province as an Example
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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。
Abstract:Water resources are foundation and important guarantee for development of a region, evaluation and analysis of regional water resources is of great significance for formulation of a reasonable water resources strategy．Water footprint theory was introduced and structural analysis, spatial and temporal change analysis and comprehensive impact factor analysis and evaluation of agricultural water footprint of Jingtai County were carried out．Analysis of structural changes of water footprint showed that, total agricultural water footprint of Jingtai County as a whole showed a growing trend from 2010 to 2019, among which water footprint of cash crops was the highest, water footprint of livestock products was the second highest, and water footprint of food crops was the lowest．From 2010 to 2019, water footprint of food crops increased by 102%, water footprint of livestock products increased by 38%, and water footprint of cash crops increased by 22%．Analysis results of spatial change of agricultural water footprint showed that, between 2010 and 2019, agricultural water footprint of Jingtai County showed a shift from east to west, and center of gravity of agricultural water consumption shifted from east to west．Growth type of agricultural water footprint of each township was mainly in the western region of Jingtai County, decline type was in the central and eastern region, fluctuating type was in the northeast region．Principal component influence factor analysis results showed that, the first principal component influence contribution rate was as high as 64.997%, while the second and third principal components were 11.333% and 10.504% respectively．It showed that agricultural water footprint of Jingtai County was mainly influenced by the first principal component factors, with economic factors and agricultural production factors having the greatest influence on agricultural water footprint of Jingtai County, which were main influencing factors; followed by the second and third principal component influencing factors, which were climatic factors and demographic factors respectively, with relatively low influence．There is more room for improvement in the use of agricultural water resources in Jingtai County．Starting from structure and influencing factors of agricultural water footprint, specific influencing factors were analyzed to adjust agricultural structure and promote sustainable use of regional agricultural water resources.
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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 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 
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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 2010 47.608 56.154 7.267 119.657 22.010 39.448 31.793 0.712 86.014 8.939 10.476 2011 53.113 45.829 5.812 26.217 22.378 45.055 33.010 0.800 89.125 12.326 11.171 2012 72.831 53.929 8.863 131.690 38.892 50.009 33.562 0.999 92.042 8.474 11.830 2013 80.903 32.795 7.771 145.674 40.319 52.876 34.895 0.999 97.605 5.392 11.987 2014 90.042 37.900 7.267 116.945 40.793 50.355 36.091 0.955 110.080 6.823 12.838 2015 98.622 47.981 7.801 115.975 29.193 54.220 34.428 1.071 120.669 7.306 12.467 2016 87.715 49.392 8.298 144.318 40.457 55.582 39.843 1.407 129.426 7.352 14.414 2017 85.269 51.542 7.656 156.849 33.134 58.783 34.127 1.529 144.608 13.971 14.445 2018 116.310 53.391 62.234 101.075 31.190 43.900 32.805 4.014 119.518 3.874 15.859 2019 100.584 46.863 77.711 116.477 20.899 84.295 31.741 4.268 126.213 9.302 18.885
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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 2010 2.683 2.683 0 0 − 2011 2.602 3.198 −0.596 22.90 −0.041 2012 3.437 3.270 0.167 4.85 0.235 2013 3.380 3.343 0.037 1.08 −0.027 2014 3.826 3.417 0.409 10.69 0.108 2015 3.843 3.492 0.351 9.14 −0.006 2016 3.574 3.567 0.007 0.20 −0.086 2017 3.657 3.643 0.014 0.38 0.013 2018 3.479 3.720 −0.241 6.92 −0.062 2019 3.649 3.797 −0.148 4.07 0.037
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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
Y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 Y 1 X1 0.686* 1 X2 0.838** 0.669* 1 X3 0.857** 0.664* 0.969** 1 X4 0.515 0.337 0.239 0.341 1 X5 −0.747* −0.381 −0.822** −0.913** −0.314 1 X6 −0.470 −0.385 −0.490 −0.420 −0.320 0.286 1 X7 0.406 0.578 0.735* 0.595 0.047 −0.335 −0.526 1 X8 0.760* 0.624 0.872** 0.909** 0.351 −0.845** −0.629 0.517 1 X9 0.862** 0.750* 0.929** 0.890** 0.338 −0.687* −0.482 0.709* 0.778** 1 X10 0.223 0.467 0.624 0.601 −0.326 −0.454 −0.060 0.518 0.570 0.460 1 X11 0.042 0.309 −0.231 −0.112 0.334 0.170 −0.012 −0.419 0.048 −0.237 −0.028 1 注：*代表p<0.05；**代表p<0.01。
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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/% 1 5.850 64.997 64.997 2 1.020 11.333 76.330 3 0.945 10.504 86.833
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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
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