Abstract：[Background] Currently, the Yellow River Basin contributes one-third of China's total grain production, but its agricultural water management is inefficient, with irrigation water accounting for over 90% of total usage. Efficient utilization of agricultural irrigation water is crucial for sustainable agricultural development and environmental conservation. Implementing efficient irrigation system improves crop yield and quality, enhances drought resistance and adaptability, reduces costs, and mitigates soil salinization. Therefore, efficient agricultural irrigation water usage is necessary to promote sustainable agricultural development and ecological protection. [Methods] This article employed the three-stage DEA model and Malmquist index to conduct static and dynamic analysis of agricultural irrigation water use efficiency in the Yellow River Basin from 2007 to 2020. The three-stage DEA model can eliminate the impact of non-operational factors (including external environment and random errors) on agricultural irrigation water use efficiency, thereby more accurately reflecting the internal management level of decision units. The Malmquist index can analyze the trend changes in agricultural irrigation water use efficiency, which helps to better understand the development trend of agricultural irrigation water use efficiency. [Results] The First Stage of the DEA model revealed that, from 2007 to 2020, the annual average comprehensive efficiency, pure technical efficiency, and scale efficiency of agricultural irrigation water use in the Yellow River Basin were 0.786, 0.913, and 0.864, respectively. The regression outcomes from the Second Stage of SFA revealed that the regression coefficients of per capita gross domestic product (GDP) and per capita water resources with respect to the input slack variables associated with effective irrigated area pass the 1% significance test. In contrast, input slack variables associated with agricultural water use and personnel employment did not pass the significance test. Furthermore, fiscal expenditures on agricultural and forestry water affairs did not exhibit significance in relation to any of the input slack variables. In the Third Stage, the results of the DEA model indicate that, after controlling for external factors and random errors, the annual average comprehensive efficiency for this period decreased by 5.2%, while pure technical efficiency increased by 1.8%, and scale efficiency declined by 7.2%. The annual average comprehensive efficiency, pure technical efficiency, and scale efficiency in Henan province all remained above 1, reaching their maximum values. The Malmquist index revealed that, from 2007 to 2020, the overall trend in irrigation water use efficiency in the Yellow River Basin demonstrated a fluctuating upward trajectory with an average growth rate of 10.3%. Specifically, the change in technical progress exceeded 1, and the variation in scale efficiency corresponded to the changes observed in the Malmquist index. [Conclusions] Based on the above conclusions, the agricultural irrigation technology in the Yellow River Basin is at an advanced level. However, the pure technical efficiency of agricultural irrigation water use is underestimated, and the scale efficiency is overestimated, leading to the inappropriate scale of agricultural irrigation. Therefore, while ensuring the adoption of advanced irrigation technologies, it is imperative to prioritize the exploration of the scale efficiency potential in agricultural irrigation water use. This approach will further enhance the efficiency of irrigation water use, promoting the sustainable development of water and soil resources in the region.
焦雨萌, 曹建军, 陈洁, 王海茹, 李育梅. 2007—2020年黄河流域农业灌溉用水效率——基于3阶段DEA模型和Malmquist指数[J]. 中国水土保持科学, 2024, 22(1): 70-76.
JIAO Yumeng, CAO Jianjun, CHEN Jie, WANG Hairu, LI Yumei. Agricultural irrigation water efficiency in the Yellow River Basin from 2007 to 2020: Based on the three-stage DEA model and Malmquist index. SSWC, 2024, 22(1): 70-76.
GENNARI P,ROSERO-MONCAYO J,TUBIELLO F N. The FAO contribution to monitoring SDGs for food and agriculture [J]. Nature Plants,2019,5(12):1196.
[2]
CHEN Shuai,SONG Chaonan,MAO Xiaomin,et al. Modeling response of spring wheat yield to soil water and salt contents and its application in scheduling brackish water irrigation [J]. Computers and Electronics in Agriculture,2022,200:107216.
[3]
TRAN D N L,NGUYEN T D,PHAM T T,et al. Improving irrigation water use efficiency of Robusta Coffee (Coffea canephora) production in Lam Dong province,Vietnam [J]. Sustainability,2021,13(12):6603.
[4]
刘维哲,常明,王西琴. 基于随机前沿的灌溉用水效率及影响因素研究:以陕西关中地区小麦为例[J]. 中国生态农业学报,2018,26(9):1407. LIU Weizhe,CHANG Ming,WANG Xiqin. Irrigation water efficiency based on stochastic production frontier and influencing factors:An empirical study of wheat in Guanzhong region, Shaanxi [J]. Chinese Journal of Eco-Agriculture,2018,26(9):1407.
[5]
王学渊. 基于DEA和SFA方法的农户灌溉用水效率比较研究:以西北地区的实地调查数据为例[J]. 中国农村水利水电,2010(1):8. WANG Xueyuan. A comparative study of irrigation water efficiency of farmers based on DEA and SFA-by using survey data from Northwest China [J]. China Rural Water and Hydropower,2010(1):8.
[6]
AIGNER D,LOVELL C A K,SCHMIDT P. Formulation and estimation of stochastic frontier production function models [J]. Journal of Econometrics,1977,6(1):21.
[7]
卢曦,许长新. 基于三阶段DEA与Malmquist指数分解的长江经济带水资源利用效率研究[J]. 长江流域资源与环境,2017,26(1):7. LU Xi,XU Changxin. Study the utilization efficiency of water resouces in Yangtze River Economic Belt based on Three-Stage DEA and Malmquist index decomposition [J]. Resources and Environment in the Yangtze Basin,2017,26(1):7.
[8]
陈杰,许朗. 基于面板三阶段DEA-Malmquist模型的中国农业绿色水资源利用效率研究[J]. 地理科学,2023,43(4):709. CHEN Jie,XU Lang. Utilization efficiency of Chinese agricultural green water resources based on panel three-stage DEA-Malmquist model [J]. Scientia Geographica Sinica,2023,43(4):709.
[9]
FRIED H O,LOVELL C A K,SCHMIDT S S,et al. Accounting for environmental effects and statistical noise in data envelopment analysis [J]. Journal of Productivity Analysis,2002,17(1):157.
[10]
王韧,李志伟. 面板数据随机前沿分析及其应用[J]. 统计与决策,2021,37(19):25. WANG Ren,LI Zhiwei. Stochastic frontier analysis of panel data and its application [J]. Statistics and Decision,2021,37(19):25.
[11]
陈万旭,梁加乐,卞娇娇,等. 黄河流域景观破碎化对土壤保持服务影响研究[J]. 地理科学,2022,42(4):589. CHEN Wanxu,LIANG Jiale,BIAN Jiaojiao,et al. Impact of landscape fragmentation on soil conservation services in the Yellow River Basin [J]. Scientia Geographica Sinica,2022,42(4):589.
[12]
李魁明,王晓燕,姚罗兰. 黄河流域农业绿色发展水平区域差异及影响因素[J]. 中国沙漠,2022,42(3):85. LI Kuiming,WANG Xiaoyan,YAO Luolan. Regional differences and driving factors of agricultural green development level in the Yellow River Basin [J]. Journal of Desert Research,2022,42(3):85.
[13]
孟德友,陆玉麒. 高速铁路对河南沿线城市可达性及经济联系的影响[J]. 地理科学,2011,31(5):537. MENG Deyou,LU Yuqi. Impact of high-speed railway on accessibility and economic linkage of cities along the railway in Henan province, China [J]. Scientia Geographica Sinica,2011,31(5):537.
[14]
李穗英,孙新庆. 青海省近10年耕地面积动态变化及驱动因子分析研究[J]. 中国农业资源与区划,2009,30(5):39. LI Suiying,SUN Xinqing. Studies and analysis on dynamic changes of arable land and driving forces in the past 10 years in Qinghai province [J]. Chinese Journal of Agricultural Resources and Regional Planning,2009,30(5):39.
[15]
孙付华,陈汝佳,张兆方. 基于三阶段DEA-Malmquist区域农业水资源利用效率评价[J]. 水利经济,2019,37(2):53. SUN Fuhua,CHEN Rujia,ZHANG Zhaofang. Evaluation of utilization efficiency of regional agricultural water resources based on three-stage DEA-Malmquist model [J]. Journal of Economics of Water Resources, 2019,37(2):53.
[16]
WU Bingfang,TIAN Fuyou,ZHANG Miao,et al. Quantifying global agricultural water appropriation with data derived from earth observations [J]. Journal of Cleaner Production,2022,358:131891.
[17]
HE Jinyu,MA Bo,TIAN Juncang. Water production function and optimal irrigation schedule for rice (Oryza sativa L.) cultivation with drip irrigation under plastic film-mulched[J]. Scientific Reports,2022,12(1):17243.
2024, Vol. 22 
