Crop Diversity for Yield Increase

Traditional farming practices suggest that cultivation of a mixture of crop species in the same field through temporal and spatial management may be advantageous in boosting yields and preventing disease, but evidence from large-scale field testing is limited. Increasing crop diversity through intercropping addresses the problem of increasing land utilization and crop productivity. In collaboration with farmers and extension personnel, we tested intercropping of tobacco, maize, sugarcane, potato, wheat and broad bean – either by relay cropping or by mixing crop species based on differences in their heights, and practiced these patterns on 15,302 hectares in ten counties in Yunnan Province, China. The results of observation plots within these areas showed that some combinations increased crop yields for the same season between 33.2 and 84.7% and reached a land equivalent ratio (LER) of between 1.31 and 1.84. This approach can be easily applied in developing countries, which is crucial in face of dwindling arable land and increasing food demand

Seismology for urban activity monitoring in Singapore under the impact of COVID-19

Urban activities cause minute vibrations of the earth surface that can be detected by highly sensitive seismometers. In the island city-state Singapore, human activities have been dramatically changed since April 2020 by government measures to suppress the spread of COVID-19. By analyzing the high- frequency seismic signals, the impact of the pandemic and its corresponding mitigation measures were quantified as traffic flow at the exit of the National University of Singapore (NUS) and at the intersection between Pasir Panjang Port Terminal and West Coast Park, representing activities at nonessential workplaces, essential workplaces, and recreational areas. The anonymity of seismic data enabled an unprecedented spatial and temporal resolution that is pivotal to understand the heterogeneity and evolution of pandemic responses in different sectors of an urban society. The rich information extracted from seismic data provide an opportunity for real time activity monitoring and dynamic policy making in order to ensure a successful pandemic mitigation and a less disturbed urban lifestyle

Quantifying Urban Activities Using Nodal Seismometers in a Heterogeneous Urban Space

Earth’s surface is constantly vibrating due to natural processes inside and human activities on the surface of the Earth. These vibrations form the ambient seismic fields that are measured by sensitive seismometers. Compared with natural processes, anthropogenic vibrations dominate the seismic measurements at higher frequency bands, demonstrate clear temporal and cyclic variability, and are more heterogeneous in space. Consequently, urban ambient seismic fields are a rich information source for human activity monitoring. Improving from the conventional energy-based seismic spectral analysis, we utilize advanced signal processing techniques to extract the occurrence of specific urban activities, including motor vehicle counts and runner activities, from the high-frequency ambient seismic noise. We compare the seismic energy in different frequency bands with the extracted activity intensity at different locations within a one-kilometer radius and highlight the high-resolution information in the seismic data. Our results demonstrate the intense heterogeneity in a highly developed urban space. Different sectors of urban society serve different functions and respond differently when urban life is severely disturbed by the impact of the COVID-19 pandemic in 2020. The anonymity of seismic data enabled an unprecedented spatial and temporal resolution, which potentially could be utilized by government regulators and policymakers for dynamic monitoring and urban management

Quantifying Urban Activities Using Nodal Seismometers in a Heterogeneous Urban Space

Earth’s surface is constantly vibrating due to natural processes inside and human activities on the surface of the Earth. These vibrations form the ambient seismic fields that are measured by sensitive seismometers. Compared with natural processes, anthropogenic vibrations dominate the seismic measurements at higher frequency bands, demonstrate clear temporal and cyclic variability, and are more heterogeneous in space. Consequently, urban ambient seismic fields are a rich information source for human activity monitoring. Improving from the conventional energy-based seismic spectral analysis, we utilize advanced signal processing techniques to extract the occurrence of specific urban activities, including motor vehicle counts and runner activities, from the high-frequency ambient seismic noise. We compare the seismic energy in different frequency bands with the extracted activity intensity at different locations within a one-kilometer radius and highlight the high-resolution information in the seismic data. Our results demonstrate the intense heterogeneity in a highly developed urban space. Different sectors of urban society serve different functions and respond differently when urban life is severely disturbed by the impact of the COVID-19 pandemic in 2020. The anonymity of seismic data enabled an unprecedented spatial and temporal resolution, which potentially could be utilized by government regulators and policymakers for dynamic monitoring and urban management

Seismic Attenuation Extraction From Traffic Signals Recorded by a Single Seismic Station

Abstract Seismic ambient noise contains rich information about the physical properties of the critical zone. From the motor vehicle noise, we extract the daily seismic attenuation by quantifying the linear relationship between the frequency and amplitude ratio of paired instantaneous spectra. After verifying the reliability of the proposed method, we apply it to seismic ambient noise data collected at three urban sites. The estimated attenuation is compared against three environmental variables: rainfall, temperature, and traffic volume. The results show that the estimated seismic attenuation correlates most strongly with precipitation with positive time lags, suggesting a high attenuation sensitivity to changes in soil moisture and groundwater system. Furthermore, differences in attenuation‐precipitation correlations indicate that near‐surface physical properties may vary significantly due to local site conditions. Our statistical method extracts reliable near‐surface information from highly complex urban ambient noise data, achieving unprecedented spatial and temporal resolution compared to existing ambient noise imaging methods