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摘要: 华北地区是我国冬小麦和夏玉米主产区, 过去40多年间, 随着大水漫灌和过量施肥等现象发生, 该地区农田氮淋失呈现加重趋势, 已经对地下水水质产生了严重影响。为探明华北地区面源污染的成因, 进而提出相应阻控措施, 本研究收集了1980—2021年国内外发表的华北地区夏玉米氮淋失研究文献, 选取环境条件和农田管理措施作为自变量, 基于线性模型、指数模型、多项式模型和多元回归模型等对氮淋失量进行模拟预测。结果表明, 氮淋失与水分和肥料氮之间存在较大关联性, 与土壤全氮、有机质含量和黏粒含量呈正相关关系, 与秸秆还田、土层深度、土壤pH、砂粒含量呈现负相关关系。在单变量预测模型中, 氮淋失量与施氮量呈指数关系, 说明在华北地区夏玉米生产中应特别注重优化肥料用量。本研究所获得的多元逐步回归模型(Y总氮淋失量=−23.07+1.14X有机质含量+0.34X黏粒含量−0.13X砂粒含量+0.06X总施氮量+0.18X水分渗漏量, 拟合优度R2=0.414)优于指数模型、线性模型和多项式模型, 具有较好的定量预测效果。考虑到水分渗漏测定过程复杂及方程的可应用性低, 可以采用水分投入量替换水分渗漏量, 但预测精度会受到影响。改善土壤物理条件(如质地)、秸秆还田和优化氮肥和灌溉, 是今后华北地区夏玉米生产中降低氮淋失的关键措施。Abstract: North China has seen intensive flood irrigation and excessive nitrogen (N) fertilization over the past four decades as a main cereal crop-producing region in China. N leaching from farmland in this region has rapidly increased with agricultural intensification, and the non-point source pollution has become increasingly prominent. It is necessary to quantify the amount of N leaching during crop production systematically. Literature on N leaching loss from summer maize production in North China published from 1980–2021 was screened, and soil properties and agricultural management practices were chosen as independent variables to predict N leaching loss based on linear, exponential, polynomial, and multiple regression models. Soil properties included soil organic matter, total N, clay content, sand content, pH, and depth, and agricultural management practices included straw incorporation, N application, and soil water. The results showed that soil water and N fertilizer input significantly influenced N leaching loss. Soil organic matter, soil total N, and clay content positively correlated with the total N leaching amount, whereas straw incorporation, soil depth, pH, and sand content negatively correlated with the total N leaching amount. For the single-factor simulation model, the exponential equation was more appropriate for quantifying total N leaching loss with fertilizer N input than the linear equation, indicating the importance of optimizing fertilizer N in summer maize production in North China. It also indicated that the risk of excess N leaching from summer maize production in North China was relatively high after a certain threshold of fertilizer N input, and optimization of N fertilization should be adopted as an important practice. Unlike many previous studies that directly selected fertilizer N input for predicting N leaching loss, this study combined N (total N rate, N surplus) and water (water input, water balance, water percolation) in various combinations to obtain an optimal prediction combination. The combination of the total N rate and water percolation had the highest R2 (0.3413). The stepwise regression equation of Ytotal N leaching loss=−23.07+1.14Xsoil organic matter+0.34Xclay content−0.13Xsand content+0.06Xtotal N rate+0.18Xwater percolation (R2=0.414) was better than the prediction effects of exponential, linear, and polynomial models. The standardized regression coefficients of the predictive variables were 0.18, 0.11, 0.07, 0.23, and 0.31 for soil organic matter, clay content, sand content, total N rate, and water percolation, respectively, which showed that water percolation was the most important, followed by total N rate and soil organic matter. Considering the complexity of the water percolation calculation process, the water input can be used to replace water percolation in the equation, that is, Ytotal N leaching loss=−18.60+0.64Xsoil organic matter−10.27Xstraw incorporation−0.30Xsand content+0.13Xtotal N rate+0.04Xwater input; however, the prediction accuracy of the regression equation was affected. Future research on predicting N leaching loss in North China should focus on accurately quantifying water percolation. The quantitative model obtained in this study provides technical support for precise N management and effective pollution prevention in North China.
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图 1 夏玉米农田总氮淋失量数据分布直方图
Figure 1. Data distribution of total N leaching loss in summer maize fields
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图 2 夏玉米农田总氮淋失量与预测变量的相关关系
Figure 2. Correlation between total N leaching loss and predictive variables in summer maize fields
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图 3 夏玉米农田总氮淋失量与总施氮量、氮盈余间的回归模拟
Figure 3. Regression simulation of total N leaching loss with total N rate and N surplus in summer maize fields
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图 4 夏玉米农田总氮淋失量与水分渗漏量间的回归模拟
Figure 4. Regression simulation of total N leaching loss with water percolation in summer maize fields
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图 5 夏玉米农田总氮淋失量与水分投入量、水平衡间的回归模拟
Figure 5. Regression simulation of total N leaching loss with water input and water balance in summer maize fields
表 1 夏玉米农田总氮淋失量与氮水两预测变量间的多元回归模拟
Table 1 Multiple regression simulation between total N leaching loss and pairwise indexes of N and water in summer maize fields
预测变量组合 Predictive variables combination 决定系数 R2 回归方程 Regression equation 总施氮量+水分投入量 Total N rate+Water input 0.2435 YNL=−20.55+0.13XNR+0.04XWI 总施氮量+水平衡 Total N rate+Water balance 0.2206 YNL=−3.33+0.13XNR+0.02XWB 总施氮量+水分渗漏量 Total N rate+Water percolation 0.3413 YNL=−5.46+0.06XNR+0.19XWP 氮盈余+水分投入量 N surplus+Water input 0.2450 YNL=−2.14+0.13XNS+0.04XWI 氮盈余+水平衡 N surplus+Water balance 0.2188 YNL= 16.00+0.13XNS+0.02XWB 氮盈余+水分渗漏量 N surplus+Water percolation 0.3001 YNL= 3.98+0.05XNS+0.19XWP 回归方程中, 总施氮量、氮盈余分别用NR、NS表示; 水分投入量、水平衡、水分渗漏量分别用WI、WB、WP表示; 总氮淋失量用NL表示。NR: total N rate; NS: N surplus; WI: water input; WB: water balance; WP: water percolation; NL: total N leaching loss. 
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表 2 夏玉米农田总氮淋失量的多元回归模型参数
Table 2 Parameters of multiple regression model for total N leaching loss in summer maize fields
系数预测值
Estimate
coefficient标准差
Std. errort值
t valueP值
P value截距 Intercept −18.13 20.36 −0.89 0.375 有机质含量
Soil organic matter0.67 0.49 1.37 0.172 土壤全氮
Soil total N13.83 10.17 1.36 0.176 黏粒含量
Clay content0.40 0.19 2.11 0.037 砂粒含量
Sand content−0.14 0.07 −2.02 0.045 土壤pH
Soil pH−0.61 2.38 −0.26 0.798 土层深度
Soil depth−0.03 0.03 −1.11 0.268 秸秆还田
Straw incorporation−4.45 3.11 −1.43 0.155 总施氮量
Total N rate0.06 0.01 4.78 3.72×10−6 水分渗漏量
Water percolation0.16 0.02 7.03 4.38×10−11 F值 F statistic: 15.74 P值 P value: 2.2×10−16 拟合优度 Multiple R2: 0.4432 修正拟合优度 Adjusted R2: 0.4150
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表 3 夏玉米农田总氮淋失量的逐步回归模型参数
Table 3 Parameters of stepwise regression model for total N leaching loss in summer maize fields
系数预测值
Estimated
coefficient标准差
Std. errort值
t valueP值
P value截距 Intercept −23.07 6.28 −3.68 3.12×10−4 有机质含量
Soil organic matter1.14 0.23 4.96 1.61×10−6 黏粒含量
Clay content0.34 0.15 2.20 0.029 砂粒含量
Sand content−0.13 0.07 −2.02 0.045 总施氮量
Total N rate0.06 0.01 5.13 7.35×10−7 水分渗漏量
Water percolation0.18 0.02 8.42 1.11×10−14 F值 F statistic: 27.36 P值 P value: 2.2×10−16 拟合优度 Multiple R2: 0.4291 修正拟合优度 Adjusted R2: 0.4140
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