基于根系加权土壤水分有效性的冬小麦水分生产函数  

Water production function of winter wheat based on root-weighted soil water availability

作　　者：吴训 许艳奇 石建初 张婷 王利春 薛绪掌 左强 Wu Xun;Xu Yanqi;Shi Jianchu;Zhang Ting;Wang Lichun;Xue Xuzhang;Zuo Qiang(College of Land Science and Technology,China Agricultural University,Beijing 100193,China;National Research Center of Intelligent Equipment for Agriculture,Beijing 100097,China)

机构地区：[1]中国农业大学土地科学与技术学院,北京100193 [2]国家农业智能装备工程技术研究中心,北京100097

出　　处：《农业工程学报》2022年第8期124-134,共11页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(U1706211,51790532)。

摘　　要：为了准确评估作物水分亏缺程度及其敏感性动态对作物产量的影响,该研究结合基于根系加权土壤水分有效性的植物水分亏缺指数(Plant Water Deficit Index,PWDI)与基于归一化热单元指数的S型累积水分敏感指数,建立了3种不同形式的作物水分生产函数(Crop Water Production Function,CWPF),即Blank加法模型(PWDI-B)、Jensen(PWDI-J)和Rao(PWDI-R)乘法模型。通过2 a冬小麦栽培田间蒸渗仪试验(北京昌平)和1 a冬小麦栽培田间滴灌试验(山东黄河三角洲),优化了土壤水分胁迫修正系数中参数,进而对PWDI估算精度及CWPF产量估算效果进行检验与评价。结果表明:蒸渗仪试验基于根系加权估算的PWDI与实测值吻合良好,决定系数R^(2)为0.78,标准化均方根误差(Normalized Root Mean Squared Error,NRMSE)为0.16;滴灌试验PWDI均值与作物株高(r=−0.95)、生物量及产量(r≤−0.79)均具有较好的相关性,表明根系加权PWDI能较准确地反映不同试验条件下冬小麦的水分亏缺程度及其对作物生长的影响;此外,无论是蒸渗仪试验还是滴灌试验,所建的3个CWPF对冬小麦产量的估算精度均在可接受范围内(R^(2)≥0.78,NRMSE≤0.11),且PWDI-R估算精度依次高于PWDI-J、PWDI-B、以及线性回归模型(即PWDI均值与产量的线性拟合模型)。因此,根系加权PWDI与S型水分敏感指数累积函数融合可用于合理构建冬小麦水分生产函数,其中PWDI-R乘法模型可优先推荐用于研究区冬小麦产量估算和灌溉制度优化,从而为当地冬小麦田间水分管理提供理论依据。In order to accurately evaluate the effects of the crop water deficit extent and the water stress sensitivity on crop yield,a novel Crop Water Production Function(CWPF)was proposed through combining a Root-Weighted Plant Water Deficit Index(PWDI)and an S-shaped cumulative function of water stress sensitivity index as a function of the normalized heat unit index in this study.Three forms of CWPFs,including Blank additive model(PWDI-B),Jensen(PWDI-J)and Rao(PWDI-R)multiplicative models,were considered.A two-year field lysimetric experiment and a one-year field drip irrigation experiment for winter wheat,respectively conducted in Changping District,Beijing(40°10′31′′N,116°26′10′′E)from September 2014 and 2015 to June the next year and Yellow River Delta,Shandong Province,China(37°19'17"N,118°38'41"E)from October 2020 to June 2021,were employed to optimize the fitting parameter in the nonlinear soil water stress reduction function,and to test the root-weighted PWDI estimation method and to compare and evaluate three different CWPFs.Thirteen and six irrigation treatments with various water supply levels were respectively designed in lysimetric and drip irrigation experiments.The experimental observations included meteorological data,soil water content distributions,daily transpiration,leaf area index,plant height,aboveground dry matter and grain yield of winter wheat.Three statistical indicators were used to evaluate the model performance,including the coefficient of determination(R^(2)),Root Mean Squared Error(RMSE)and Normalized RMSE(NRMSE).Optimized using a nonlinear least-squares method by minimizing the residual between measured and estimated daily or stage-cumulative transpiration,the fitting parameter in the soil water stress reduction function was used to estimate the PWDI under various irrigation treatments.The results showed the estimated PWDIs were in a good agreement with the measured values,with an R^(2) of 0.78,a RMSE of 0.10,and a NRMSE of 0.16 in the lysimetric experiment,and the estima

关 键 词：土壤水分 亏缺 模型 水分敏感指数 冬小麦 

分 类 号：S274.3[农业科学—农业水土工程]