Even though ecologists and agronomists have considered the spatial root distribution of plants to be important for interspecific interactions in natural and agricultural ecosystems, few experimental studies have quantified patterns of root distribution dynamics and their impacts on interspecific interactions. A field experiment was conducted to investigate the relationship between root distribution and interspecific interactions between intercropped plants. Roots were sampled twice by auger and twice by the monolith method in wheat (Triticum aestivum L.)/maize (Zea mays L.) and faba bean (Vicia faba L.)/maize intercropping and in sole wheat, maize, and faba bean up to 100 cm depth in the soil profile. The results showed that the roots of intercropped wheat spread under maize plants, and had much greater root length density (RLD) at all soil depths than sole wheat. The roots of maize intercropped with wheat were limited laterally, but had a greater RLD than sole-cropped maize. The RLD of maize intercropped with faba bean at different soil depths was influenced by intercropping to a smaller extent compared to maize intercropped with wheat. Faba bean had a relatively shallow root distribution, and the roots of intercropped maize spread underneath them. The results support the hypotheses that the overyielding of species showing benefit in the asymmetric interspecific facilitation results from greater lateral deployment of roots and increased RLD, and that compatibility of the spatial root distribution of intercropped species contributes to symmetric interspecific facilitation in the faba bean/maize intercropping.
A field experiment was carried out to quantify biological nitrogen fixation (BNF) using the 15 N isotope natural abundance method in maize (Zea mays L.)/faba bean (Vicia faba L.) and wheat (Triticum aestivum L.)/faba bean intercropping systems. Faba bean was yielding more in the maize/faba bean intercropping, but not in the wheat/faba bean intercropping. Biomass, grain yield and N acquisition of faba bean were significantly increased when intercropped with maize, and decreased significantly with wheat, irrespective of N-fertilizer application, indicating that the legume could gain or lose productivity in an intercropping situation. There was yield advantage of maize/faba bean intercropping, but no in wheat/faba bean intercropping. The grain yield of the faba bean intercropped with maize was greater than that of faba bean monoculture due to increases of the stems per plant and the pods per stem of faba bean. N fertilization inhibited N fixation of faba bean in maize/faba bean and wheat/faba bean intercropping and faba bean monoculture. The responses of different cropping systems to N-fertilizer application, however, were not identical, with competitive intercropping (wheat/faba bean) being more sensitive than facilitative intercropping (maize/faba bean). Intercropping increased the percentage of N derived from air (%Ndfa) of the wheat/faba bean system, but not that of the maize/faba bean system when no N fertilizer was applied. When receiving 120 kg N/ha, however, intercropping did not significantly increase %Ndfa either in the wheat/faba bean system or in the maize/faba bean system in comparison with faba bean in monoculture. The amount of shoot N derived from air (Ndfa), however, increased significantly when intercropped with maize, irrespective of N-fertilizer application. Ndfa decreased when intercropped with wheat, albeit not significantly at 120 kg N/ha. Ndfa was correlated more closely with dry matter yield, grain yield and competitive ratio, than with %Ndfa. This indicates that that total dry matter yield (sink strength), not %Ndfa, was more critical for the legume to increase Ndfa. The results suggested that N fixation could be improved by yield maximization in an intercropping system.
Time series forecasting for streaming data plays an important role in many real applications, ranging from IoT systems, cyber-networks, to industrial systems and healthcare. However the real data is often complicated with anomalies and change points, which can lead the learned models deviating from the underlying patterns of the time series, especially in the context of online learning mode. In this paper we present an adaptive gradient learning method for recurrent neural networks (RNN) to forecast streaming time series in the presence of anomalies and change points. We explore the local features of time series to automatically weight the gradients of the loss of the newly available observations with distributional properties of the data in real time. We perform extensive experimental analysis on both synthetic and real datasets to evaluate the performance of the proposed method.
Surging interests exist in double‐atom catalysts (DACs), which not only inherit the advantages of single‐atom catalysts (SACs) (e.g., ultimate atomic utilization, high activity, and selectivity) but also overcome the drawbacks of SACs (e.g., low loading and isolated active site). The design of DACs, however, remains cost‐prohibitive for both experimental and computational studies, due to their huge design space. Herein, by means of density functional theory (DFT) and topological information‐based machine‐learning (ML) algorithms, we present a data‐driven high‐throughput design principle to evaluate the stability and activity of 16 767 DACs for oxygen evolution (OER) and oxygen reduction (ORR) reactions. The rational design reveals 511 types of DACs with OER activity superior to IrO2 (110), 855 types of DACs with ORR activity superior to Pt (111), and 248 bifunctional DACs with high catalytic performance for both OER and ORR. An intrinsic descriptor is revealed to correlate the catalytic activity of a DAC with the electronic structures of the DAC and its bonding carbon surface structure. This data‐driven high‐throughput approach not only yields remarkable prediction precision (>0.926 R‐squared) but also enables a notable 144 000‐fold reduction of screening time compared with pure DFT calculations, holding promise to drastically accelerate the design of high‐performance DACs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.