Bioengineering Sciences Research Group, Department of Mechanical Engineering, School of Engineering, Faculty of Engineering and Physical Sciences, University of Southampton, SO17 1BJ, UK.
School of Natural Science, Environment Centre Wales, Bangor University, Bangor, Gwynedd LL57 2UW, UK.
Sci Total Environ. 2021 Feb 20;756:144051. doi: 10.1016/j.scitotenv.2020.144051. Epub 2020 Nov 24.
Typically, half of the nitrogen (N) fertiliser applied to agricultural fields is lost to the wider environment. This inefficiency is driven by soil processes such as denitrification, volatilisation, surface run-off and leaching. Rainfall plays an important role in regulating these processes, ultimately governing when and where N fertiliser moves in soil and its susceptibility to gaseous loss. The interaction between rainfall, plant N uptake and N losses, however, remains poorly understood. In this study we use numerical modelling to predict the optimal N fertilisation strategy with respect to rainfall patterns and offer mechanistic explanations to the resultant differences in optimal times of fertiliser application. We developed a modelling framework that describes water and N transport in soil over a growing season and assesses nitrogen use efficiency (NUE) of split fertilisations within the context of different rainfall patterns. We used ninety rainfall patterns to determine their impact on optimal N fertilisation times. We considered the effects of root growth, root N uptake, microbial transformation of N and the effect of soil water saturation and flow on N movement in the soil profile. On average, we show that weather-optimised fertilisation strategies could improve crop N uptake by 20% compared to the mean uptake. In drier years, weather-optimising N applications improved the efficiency of crop N recovery by 35%. Further analysis shows that maximum plant N uptake is greatest under drier conditions due to reduced leaching, but it is harder to find the maximum due to low N mobility. The model could capture contrasting trends in NUE seen in previous arable cropping field trials. Furthermore, the model predicted that the variability in NUE seen in the field could be associated with precipitation-driven differences in N leaching and mobility. In conclusion, our results show that NUE in cropping systems could be significantly enhanced by synchronising fertiliser timings with both crop N demand and local weather patterns.
通常，农业用地施用到土壤中的氮肥有一半会损失到更广泛的环境中。这种低效性是由土壤过程驱动的，如反硝化、挥发、地表径流和淋溶。降雨在调节这些过程中起着重要作用，最终控制着氮肥在土壤中的移动时间和位置，以及其易气态损失的程度。然而，降雨、植物氮吸收和氮损失之间的相互作用仍了解甚少。在这项研究中，我们使用数值模型来预测与降雨模式相关的最佳氮肥施肥策略，并为施肥应用的最佳时间的差异提供机制解释。我们开发了一个模型框架，该框架描述了一个生长季节中土壤中的水和氮运移，并评估了不同降雨模式下的氮肥分期施肥的氮利用效率（NUE）。我们使用了 90 种降雨模式来确定它们对最佳氮肥施肥时间的影响。我们考虑了根生长、根氮吸收、氮的微生物转化以及土壤水分饱和和流动对土壤剖面中氮运移的影响。平均而言，我们表明与平均吸收相比，天气优化的施肥策略可以将作物氮吸收提高 20%。在干旱年份，优化氮应用可将作物氮回收效率提高 35%。进一步的分析表明，由于淋溶减少，在较干燥的条件下植物最大氮吸收量最大，但由于氮移动性低，更难找到最大氮吸收量。该模型可以捕捉到以前在旱地作物田间试验中看到的氮效率变化的相反趋势。此外，该模型预测，田间氮效率的可变性可能与降雨驱动的氮淋溶和移动性差异有关。总之，我们的研究结果表明，通过将施肥时间与作物氮需求和当地天气模式同步，可以显著提高作物系统中的氮效率。
