Space-time statistical analysis and modelling of nitrogen use efficiency indicators at provincial scale in China

文献类型： 外文期刊

第一作者： Liu, Yingxia

作者： Liu, Yingxia;Heuvelink, Gerard B. M.;Liu, Yingxia;He, Ping;Xu, Xinpeng;Liu, Yingxia;Heuvelink, Gerard B. M.;Bai, Zhanguo;Ma, Jinchuan;Masiliunas, Dainius

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关键词： Nitrogen use efficiency; Space-time variation; Stepwise multiple linear regression; Temperature; Soil property; China

期刊名称：EUROPEAN JOURNAL OF AGRONOMY （ 影响因子：5.124； 五年影响因子：5.567 ）

ISSN： 1161-0301

年卷期： 2020 年 115 卷

页码：

收录情况： SCI

摘要： Nitrogen use efficiency (NUE) is crucial to establish efficient fertilizer application guidelines that balance crop yield, economic return and environmental sustainability. Although there are quite a few researches about the spatial and temporal variation of NUE, little work has been done on modelling NUE through deriving empirical relationships with explanatory environmental variables and exploring their relative importance quantitatively. The space-time patterns of NUE indicators (i.e., the Partial Factor Productivity of nitrogen, PFPN, and the Partial Nutrient Balance of nitrogen, PNBN) at provincial scale in China were derived and related to environmental covariates using stepwise multiple linear regression. PFPN was higher in east and south China than in central and west China and was smaller than 30 kg kg(-1) yr(-1) in most provinces, while PNBN was moderate in most provinces (0.41-0.50 kg kg(-1) yr(-1)) and low (< 0.40 kg kg(-1) yr(-1)) in south China. The national PFPN declined slightly from 32 kg kg(-1) in 1978 to 27 kg kg(-1) in 1995 and went up gradually to reach 38 kg kg(-1) in 2015. The national PNBN decreased from 0.53 to 0.36 kg kg(-1) from 1978 to 2003, thereafter stabilizing at around 0.40 kg kg(-1) yr(-1) between 2004 and 2015. The multiple linear regression models explained 74 % of the variance of PFPN and PNBN. The main explanatory variables of PFPN were planting area index of sugar crop (32 % of the R-square), followed by Arenosols (12 %), planting area index of oil crop (8 %), planting area index of vegetables (5 %), silt content (5 %) and total potassium (5 %). For PNBN, the variation was mainly attributed to mean annual daytime surface temperature (28 % of the R-square), planting area index of crops (beans 20 %, orchards 10 % and vegetables 9 %) and wet day frequency (5 %). The results of this study indicate that crop types, temperature and soil properties are important variables that determine NUE. These should be considered by policy makers when agricultural land development decisions are made in order to balance NUE and productivity (i.e., agronomy and environment).

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