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Start date: 1984 × DDC: 500 × Type: Text × Version: publishedVersion × Subject: Humans × WGL Institute: PIK ×

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    Cost-effective mitigation of nitrogen pollution from global croplands
    (London [u.a.] : Nature Publ. Group, 2023) Gu, Baojing; Zhang, Xiuming; Lam, Shu Kee; Yu, Yingliang; van Grinsven, Hans J. M.; Zhang, Shaohui; Wang, Xiaoxi; Bodirsky, Benjamin Leon; Wang, Sitong; Duan, Jiakun; Ren, Chenchen; Bouwman, Lex; de Vries, Wim; Xu, Jianming; Sutton, Mark A.; Chen, Deli
    Cropland is a main source of global nitrogen pollution1,2. Mitigating nitrogen pollution from global croplands is a grand challenge because of the nature of non-point-source pollution from millions of farms and the constraints to implementing pollution-reduction measures, such as lack of financial resources and limited nitrogen-management knowledge of farmers3. Here we synthesize 1,521 field observations worldwide and identify 11 key measures that can reduce nitrogen losses from croplands to air and water by 30–70%, while increasing crop yield and nitrogen use efficiency (NUE) by 10–30% and 10–80%, respectively. Overall, adoption of this package of measures on global croplands would allow the production of 17 ± 3 Tg (1012 g) more crop nitrogen (20% increase) with 22 ± 4 Tg less nitrogen fertilizer used (21% reduction) and 26 ± 5 Tg less nitrogen pollution (32% reduction) to the environment for the considered base year of 2015. These changes could gain a global societal benefit of 476 ± 123 billion US dollars (USD) for food supply, human health, ecosystems and climate, with net mitigation costs of only 19 ± 5 billion USD, of which 15 ± 4 billion USD fertilizer saving offsets 44% of the gross mitigation cost. To mitigate nitrogen pollution from croplands in the future, innovative policies such as a nitrogen credit system (NCS) could be implemented to select, incentivize and, where necessary, subsidize the adoption of these measures.
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    The stability of memristive multidirectional associative memory neural networks with time-varying delays in the leakage terms via sampled-data control
    (San Francisco, California, US : PLOS, 2018) Wang, Weiping; Yu, Xin; Luo, Xiong; Wang, Long; Li, Lixiang; Kurths, Jürgen; Zhao, Wenbing; Xiao, Jiuhong
    In this paper, we propose a new model of memristive multidirectional associative memory neural networks, which concludes the time-varying delays in leakage terms via sampled-data control. We use the input delay method to turn the sampling system into a continuous time-delaying system. Then we analyze the exponential stability and asymptotic stability of the equilibrium points for this model. By constructing a suitable Lyapunov function, using the Lyapunov stability theorem and some inequality techniques, some sufficient criteria for ensuring the stability of equilibrium points are obtained. Finally, numerical examples are given to demonstrate the effectiveness of our results.