The rice-wheat rotation in southern China is characterized by frequent flooding-draining water regime and heavy nitrogen(N)fertilization. There is a substantial lack of studies into the behavior of dissolved organic n...The rice-wheat rotation in southern China is characterized by frequent flooding-draining water regime and heavy nitrogen(N)fertilization. There is a substantial lack of studies into the behavior of dissolved organic nitrogen(DON) in the intensively managed agroecosystem. A 3-year in situ field experiment was conducted to determine DON leaching and its seasonal and yearly variations as affected by fertilization, irrigation and precipitation over 6 consecutive rice/wheat seasons. Under the conventional N practice(300kg N ha-1for rice and 200 kg N ha-1for wheat), the seasonal average DON concentrations in leachate(100 cm soil depth) for the three rice and wheat seasons were 0.6–1.1 and 0.1–2.3 mg N L-1, respectively. The cumulative DON leaching was estimated to be1.1–2.3 kg N ha-1for the rice seasons and 0.01–1.3 kg N ha-1for the wheat seasons, with an annual total of 1.1–3.6 kg N ha-1. In the rice seasons, N fertilizer had little effect(P > 0.05) on DON leaching; precipitation and irrigation imported 3.6–9.1 kg N ha-1of DON, which may thus conceal the fertilization effect on DON. In the wheat seasons, N fertilization had a positive effect(P < 0.01)on DON. Nevertheless, this promotive effect was strongly influenced by variable precipitation, which also carried 1.8–2.9 kg N ha-1of DON into fields. Despite a very small proportion to chemical N applied and large variations driven by water regime, DON leaching is necessarily involved in the integrated field N budget in the rice-wheat rotation due to its relatively greater amount compared to other natural ecosystems.展开更多
A laboratory-based aerobic incubation was conducted to investigate nitrogen （N） isotopic fractionation related to nitrification in five agricultural soils after application of ammonium sulfate （（NH4）2804）. The s...A laboratory-based aerobic incubation was conducted to investigate nitrogen （N） isotopic fractionation related to nitrification in five agricultural soils after application of ammonium sulfate （（NH4）2804）. The soil samples were collected from a subtropical barren land soil derived from granite （RGB）, three subtropical upland soils derived from granite （RQU）, Quaternary red earth （RGU）, Quaternary Xiashu loess （YQU） and a temperate upland soil generated from alluvial deposit （FAU）. The five soils varied in nitrification potential, being in the order of FAU 〉 YQU 〉 RGU 〉 RQU 〉 RGB. Significant N isotopic fractionation accompanied nitrification of NH4＋. 615N values of NH4＋ increased with enhanced nitrification over time in the four upland soils with NH4＋ addition, while those of NO3 decreased consistently to the minimum and thereafter increased. 515N values of NH4＋ showed a significantly negative linear relationship with NH4＋-N concentration, but a positive linear relationship with NO3-N concentration. The apparent isotopic fractionation factor calculated based on the loss of NH4＋ was 1.036 for RQU, 1.022 for RGU, 1.016 for YQU, and 1.020 for FAU, respectively. Zero- and first-order reaction kinetics seemed to have their limitations in describing the nitrification process affected by NH4＋ input in the studied soils. In contrast, N kinetic isotope fractionation was closely related to the nitrifying activity, and might serve as an alternative tool for estimating the nitrification capacity of agricultural soils.展开更多
极端天气事件的发生会导致电力负荷产生突增或突降,对电网的稳定性和供电能力带来挑战。然而,现有的超短期负荷预测方法对极端天气下非线性和动态变化的负荷特征预测能力有限。为应对极端天气下负荷突变性强及波动剧烈导致的预测精度降...极端天气事件的发生会导致电力负荷产生突增或突降,对电网的稳定性和供电能力带来挑战。然而,现有的超短期负荷预测方法对极端天气下非线性和动态变化的负荷特征预测能力有限。为应对极端天气下负荷突变性强及波动剧烈导致的预测精度降低的问题,提出了一种考虑极端天气的二次重构分解去噪和双向长短时记忆网络(bidirectional long short-term memory,BiLSTM)的超短期电力负荷预测方法。首先,利用最大信息系数选取出能够最大程度反映对负荷影响的气候特征。然后,通过二次重构分解去噪方法提取到负荷多个频段的特征,降低数据复杂性,为BiLSTM模型提供更干净和信息量更清晰的输入序列,从而改善模型的训练效果和预测能力。最后基于比利时、福建省某区域以及得土安市的历史数据集进行算例分析,不同算例中平均绝对百分比误差分别下降到1.024%、0.875%、1.270%和1.009%,实验结果验证了所提方法在极端天气发生时的电力负荷超短期预测方面具有较好的预测性能和广阔的应用前景。展开更多
基金supported by the Jiangsu Provincial Natural Science Foundation of China(No.BK-2010612)the Foundation of State Key Laboratory of Soil and Sustainable Agriculture,China(No.Y05-2010034)the National Natural Science Foundation of China(No.41001147)
文摘The rice-wheat rotation in southern China is characterized by frequent flooding-draining water regime and heavy nitrogen(N)fertilization. There is a substantial lack of studies into the behavior of dissolved organic nitrogen(DON) in the intensively managed agroecosystem. A 3-year in situ field experiment was conducted to determine DON leaching and its seasonal and yearly variations as affected by fertilization, irrigation and precipitation over 6 consecutive rice/wheat seasons. Under the conventional N practice(300kg N ha-1for rice and 200 kg N ha-1for wheat), the seasonal average DON concentrations in leachate(100 cm soil depth) for the three rice and wheat seasons were 0.6–1.1 and 0.1–2.3 mg N L-1, respectively. The cumulative DON leaching was estimated to be1.1–2.3 kg N ha-1for the rice seasons and 0.01–1.3 kg N ha-1for the wheat seasons, with an annual total of 1.1–3.6 kg N ha-1. In the rice seasons, N fertilizer had little effect(P > 0.05) on DON leaching; precipitation and irrigation imported 3.6–9.1 kg N ha-1of DON, which may thus conceal the fertilization effect on DON. In the wheat seasons, N fertilization had a positive effect(P < 0.01)on DON. Nevertheless, this promotive effect was strongly influenced by variable precipitation, which also carried 1.8–2.9 kg N ha-1of DON into fields. Despite a very small proportion to chemical N applied and large variations driven by water regime, DON leaching is necessarily involved in the integrated field N budget in the rice-wheat rotation due to its relatively greater amount compared to other natural ecosystems.
基金Supported by the Natural Science Foundation of Jiangsu Province,China(No.BK2010612)the Foundation of State Key Laboratory of Soil and Sustainable Agriculture(No.Y052010034)the Knowledge Innovation Program of the Institute of Soil Science,Chinese Academy of Sciences(No.ISSASIP0723)
文摘A laboratory-based aerobic incubation was conducted to investigate nitrogen （N） isotopic fractionation related to nitrification in five agricultural soils after application of ammonium sulfate （（NH4）2804）. The soil samples were collected from a subtropical barren land soil derived from granite （RGB）, three subtropical upland soils derived from granite （RQU）, Quaternary red earth （RGU）, Quaternary Xiashu loess （YQU） and a temperate upland soil generated from alluvial deposit （FAU）. The five soils varied in nitrification potential, being in the order of FAU 〉 YQU 〉 RGU 〉 RQU 〉 RGB. Significant N isotopic fractionation accompanied nitrification of NH4＋. 615N values of NH4＋ increased with enhanced nitrification over time in the four upland soils with NH4＋ addition, while those of NO3 decreased consistently to the minimum and thereafter increased. 515N values of NH4＋ showed a significantly negative linear relationship with NH4＋-N concentration, but a positive linear relationship with NO3-N concentration. The apparent isotopic fractionation factor calculated based on the loss of NH4＋ was 1.036 for RQU, 1.022 for RGU, 1.016 for YQU, and 1.020 for FAU, respectively. Zero- and first-order reaction kinetics seemed to have their limitations in describing the nitrification process affected by NH4＋ input in the studied soils. In contrast, N kinetic isotope fractionation was closely related to the nitrifying activity, and might serve as an alternative tool for estimating the nitrification capacity of agricultural soils.
文摘极端天气事件的发生会导致电力负荷产生突增或突降,对电网的稳定性和供电能力带来挑战。然而,现有的超短期负荷预测方法对极端天气下非线性和动态变化的负荷特征预测能力有限。为应对极端天气下负荷突变性强及波动剧烈导致的预测精度降低的问题,提出了一种考虑极端天气的二次重构分解去噪和双向长短时记忆网络(bidirectional long short-term memory,BiLSTM)的超短期电力负荷预测方法。首先,利用最大信息系数选取出能够最大程度反映对负荷影响的气候特征。然后,通过二次重构分解去噪方法提取到负荷多个频段的特征,降低数据复杂性,为BiLSTM模型提供更干净和信息量更清晰的输入序列,从而改善模型的训练效果和预测能力。最后基于比利时、福建省某区域以及得土安市的历史数据集进行算例分析,不同算例中平均绝对百分比误差分别下降到1.024%、0.875%、1.270%和1.009%,实验结果验证了所提方法在极端天气发生时的电力负荷超短期预测方面具有较好的预测性能和广阔的应用前景。
