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Special Issue:
农业生态环境-氮素合辑Agro-ecosystem & Environment—Nitrogen
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| Statistical analysis of nitrogen use efficiency in Northeast China using multiple linear regression and random forest |
| LIU Ying-xia1, 2, 3, Gerard B. M. HEUVELINK2, 3, Zhanguo BAI3, HE Ping1, JIANG Rong1, HUANG Shao-hui1, XU Xin-peng1 |
1 Key Laboratory of Plant Nutrition and Fertilizer, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
2 Soil Geography and Landscape Group, Wageningen University, Wageningen 6700 AA, The Netherlands
3 International Soil Reference and Information Centre - World Soil Information, Wageningen 6700 AJ, The Netherlands
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摘要
了解作物氮（N）素利用率（NUE）的时空动态及其与环境变量的关系可以有效指导土地利用管理和相关政策制定。然而，有关应用统计模型评估作物NUE时空变化的解释变量的研究较少。因此，本研究采用逐步多元线性回归（SMLR）模型和随机森林（RF）模型来评价1990—2015年间中国东北地区（黑龙江、辽宁、吉林）县域尺度下NUE的时空变化。其中，NUE包括N素偏生产力（PFPN）和N素偏因子养分平衡（PNBN）两个指标，解释变量包括农业管理措施、地形、气候、经济、土壤和作物类型。结果表明，1990-2015年间，东北地区PFPN以北部较高，中部较低，PNBN由南向北逐渐增加。而多数县的NUE随着时间的变化逐渐降低。SMLR和RF的模型效率系数对于PFPN分别为0.44和0.84，PNBN分别为0.67和0.89。与SMLR模型相比，RF模型中土壤类型和气候的相对重要性较高，而作物类型的相对重要性较低。蔬菜和豆类种植面积指数、土壤粘土含量、饱和含水量、11-12月植被增强指数、土壤容重和年最低气温是NUE的主要解释变量。本文首次使用SMLR和RF模型对中国东北县级NUE解释变量的相对重要性进行定量研究。研究结果为改善作物NUE提供了重要参考，有利于氮素优化管理、农业可持续发展、保障粮食安全、缓解环境恶化和提高农民收入。
Abstract
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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Received: 01 September 2021
Accepted: 03 December 2021
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| Fund:
We are specially acknowledgeable financial support from the China Scholarship Council (CSC) (201903250115). This research was supported by the National Natural Science Foundation of China (31972515) and the China Agriculture Research System of MOF and MARA (CARS–09-P31).
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| About author: LIU Ying-xia, E-mail: yingxia.liu@wur.nl; Correspondence HE Ping, Tel: +86-10-82105638, E-mail: heping02@caas.cn |
Cite this article:
LIU Ying-xia, Gerard B. M. HEUVELINK, Zhanguo BAI, HE Ping, JIANG Rong, HUANG Shao-hui, XU Xin-peng.
2022.
Statistical analysis of nitrogen use efficiency in Northeast China using multiple linear regression and random forest. Journal of Integrative Agriculture, 21(12): 3637-3657.
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