Zhang Yan, Wang Feiyu, Du Zhenjie, Dou Ming, Liang Zhijie, Gao Yun, Li Ping
Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China.
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Plants (Basel). 2023 Jan 28;12(3):580. doi: 10.3390/plants12030580.
To reveal the relationship between agricultural water resource consumption and grain production in the Yellow River Basin, the irrigation water productivity (WPI), crop water productivity (WPC), total inflow water productivity (WPT), and eleven influencing factors were selected. The spatial and temporal distribution characteristics and driving factors of water productivity of the main crops in the Yellow River Basin were analyzed with the spatial autocorrelation analysis, grey correlation analysis, sensitivity analysis, and relative contribution rate. The results showed that the minimum mean values of WPI, WPC, and WPT were 0.22, 0.35, and 0.18 kg/m in Qinghai, respectively, the maximum mean value of WPI was 2.11 kg/m in Henan, and the maximum mean values of WPC and WPT were 0.71 and 0.61 kg/m in Shandong, respectively. The changing trends in WPI and WPT in Qinghai and in WPC in Shandong were insignificant, whereas the WPI, WPC, and WPT in other provinces showed a significant increasing trend. Water productivity displayed a certain spatial clustering feature in the Yellow River Basin in different years, such as a high-high (H-H) aggregation in Henan in 2005, and an H-H aggregation in Shanxi in 2015 for WPI. The water productivity had a significant positive correlation with the consumption of chemical fertilizer with a 100% effective component (CFCEC), effective irrigated area (EIA), plastic film used for agriculture (PFUA), and total power of agricultural machinery (AMTP), while it had a significant negative correlation with the persons engaged in rural areas (PERA). There was a large grey correlation degree between the water productivity and the average annual precipitation (AAP), CFCEC, PFUA, consumption of chemical pesticides (CFC), and AMTP in the Yellow River Basin, but their sensitivity was relatively small. The main driving factors were EIA (8.98%), agricultural water (AW, 15.55%), AMTP (12.64%), CFCEC (12.06%), and CPC (9.77%) for WPI; AMTP (16.46%), CFCEC (13.25%), average annual evaporation (AAE, 12.94%), EIA (10.49%), and PERA (10.19%) for WPC; and EIA (14.26%), AMTP (13.38%), AAP (12.30%), CFCEC (10.49%), and PFUA (9.69%) for WPT in the Yellow River Basin. The results can provide support for improving the utilization efficiency of agricultural water resources, optimizing the allocation of water resources, and implementing high-quality agricultural developments in the Yellow River Basin.
为揭示黄河流域农业水资源消耗与粮食生产之间的关系，选取了灌溉水生产率（WPI）、作物水生产率（WPC）、总入境水生产率（WPT）以及11个影响因素。运用空间自相关分析、灰色关联分析、敏感性分析和相对贡献率，对黄河流域主要作物水分生产率的时空分布特征及驱动因素进行了分析。结果表明，青海的WPI、WPC和WPT的最小均值分别为0.22、0.35和0.18kg/m，河南的WPI最大均值为2.11kg/m，山东的WPC和WPT最大均值分别为0.71和0.61kg/m。青海的WPI和WPT以及山东的WPC变化趋势不显著，而其他省份的WPI、WPC和WPT呈显著上升趋势。不同年份黄河流域水分生产率呈现出一定的空间集聚特征，如2005年河南的高高（H-H）集聚，2015年山西的WPI高高集聚。水分生产率与有效成分100%的化肥施用量（CFCEC）、有效灌溉面积（EIA）、农用塑料薄膜使用量（PFUA）和农业机械总动力（AMTP）呈显著正相关，而与农村从业人数（PERA）呈显著负相关。黄河流域水分生产率与年均降水量（AAP）、CFCEC、PFUA、化学农药施用量（CFC）和AMTP之间的灰色关联度较大，但敏感性相对较小。黄河流域WPI的主要驱动因素为EIA（8.98%）、农业用水（AW，15.55%）、AMTP（12.64%）、CFCEC（12.06%）和CPC（9.77%）；WPC的主要驱动因素为AMTP（16.46%）、CFCEC（13.25%）、年均蒸发量（AAE，12.94%）、EIA（10.49%）和PERA（10.19%）；WPT的主要驱动因素为EIA（14.26%）、AMTP（13.38%）、AAP（12.30%）、CFCEC（10.49%）和PFUA（9.69%）。研究结果可为提高黄河流域农业水资源利用效率、优化水资源配置和实施高质量农业发展提供支撑。
