Statistical analysis of nitrogen use efficiency in Northeast China using multiple linear regression and Random Forest

文献类型： 外文期刊

第一作者： Liu Ying-xia

作者： Liu Ying-xia;He Ping;Jiang Rong;Huang Shao-hui;Xu Xin-peng;Liu Ying-xia;Heuvelink, Gerard B. M.;Liu Ying-xia;Heuvelink, Gerard B. M.;Bai, Zhanguo

作者机构：

关键词： partial factor productivity of N; partial nutrient balance of N; stepwise multiple linear regression; Random Forest; county scale; Northeast China

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：4.8； 五年影响因子：4.8 ）

ISSN： 2095-3119

年卷期： 2022 年 21 卷 12 期

页码：

收录情况： SCI

摘要： Understanding the spatial-temporal dynamics of crop nitrogen ( N) use efficiency (NUE) and the relationship with explanatory environmental variables can support land-use management and policymaking. Nevertheless, the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under- researched. In this study, stepwise multiple linear regression ( SMLR) and Random Forest (RF) were used to evaluate the spatial and temporal variation of NUE indicators (i. e., partial factor productivity of N (PFPN); partial nutrient balance of N (PNBN)) at county scale in Northeast China (Heilongjiang, Liaoning and Jilin provinces) from 1990 to 2015. Explanatory variables included agricultural management practices, topography, climate, economy, soil and crop types. Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990-2015 period. The NUE indicators decreased with time in most counties during the study period. The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN, and 0.67 and 0.89 for PNBN, respectively. The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model. The planting area index of vegetables and beans, soil clay content, saturated water content, enhanced vegetation index in November & December, soil bulk density, and annual minimum temperature were the main explanatory variables for both NUE indicators. This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR. This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development, ensuring food security, alleviating environmental degradation and increasing farmer's profitability.

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