Quantifying nitrogen leaching response to fertilizer additions in China's cropland

Gao, S., Xu, P., Zhou, F., Yang, H., Zheng, C., Cao, W., Tao, S., Piao, S., Zhao, Y., Shang, Z., & Chen, M. (2016). Quantifying nitrogen leaching response to fertilizer additions in China's cropland. Environmental Pollution 21 241-251. 10.1016/j.envpol.2016.01.010.

Full text not available from this repository.

Abstract

Agricultural soils account for more than 50% of nitrogen leaching (LN) to groundwater in China. When excess levels of nitrogen accumulate in groundwater, it poses a risk of adverse health effects. Despite this recognition, estimation of LN from cropland soils in a broad spatial scale is still quite uncertain in China. The uncertainty of LN primarily stems from the shape of nitrogen leaching response to fertilizer additions (Nrate) and the role of environmental conditions. On the basis of 453 site-years at 51 site across China, we explored the nonlinearity and variability of the response of LN to Nrate and developed an empirical statistical model to determine how environmental factors regulate the rate of N leaching (LR). The result shows that LN-Nrate relationship is convex for most crop types, and varies by local hydro-climates and soil organic carbon. Variability of air temperature explains a half (~52%) of the spatial variation of LR. The results of model calibration and validation indicate that incorporating this empirical knowledge into a predictive model could accurately capture the variation in leaching and produce a reasonable upscaling from site to country. The fertilizer-induced LN in 2008 for China's cropland were 0.88 1 0.23 TgN (1.), significantly lower than the linear or uniform model as assumed by Food and Agriculture Organization and MITERRA-EUROPE models. These results also imply that future policy to reduce N leaching from cropland needs to consider environmental variability rather than solely attempt to reduce Nrate.

Item Type:	Article
Uncontrolled Keywords:	nitrogen leaching; nonlinearity; variability; spatial pattern; Bayesian inference
Research Programs:	Ecosystems Services and Management (ESM)
Depositing User:	Luke Kirwan
Date Deposited:	21 Jan 2016 11:34
Last Modified:	27 Aug 2021 17:40
URI:	https://pure.iiasa.ac.at/11826

Actions (login required)

View Item