Volunteer community service providers during the COVID‐19 crisis response in China: What are their personal needs and how to respond?

The needs of volunteer community service providers (VCSPs), who are the main responders to community crises, have received significantly less attention for the contributions they have been making during the COVID‐19 crisis. A mixed‐method research framework was used in this study, which involved semi‐structured interviews with 13 NGOs and questionnaire responses from 430 VCSPs in Hubei, China to assess the VCSPs' personal needs based on Maslow's hierarchy of needs. It was found that the VCSPs had safety, love, belonging, self‐esteem, and self‐actualization personal needs, all of which were closely related to family, partners, organizations, society and the government. The discussions revealed that the more experienced VCSPs needed special attention and family support was extremely significant for VCSPs in crisis. Several recommendations to meet VCSPs' personal needs are proposed that could have valuable reference value for emergency managers when organizing and supporting VCSPs in contingencies. [ FROM AUTHOR] Copyright of Journal of Contingencies & Crisis Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Remote Prediction of Oilseed Rape Yield via Gaofen-1 Images and a Crop Model

The fast and accurate prediction of crop yield at the regional scale is of great significance to food policies or trade. In this study, a new model is developed to predict the yield of oilseed rape from high-resolution remote sensing images. In order to derive this model, the ground experiment and remote sensing data analysis are carried out successively. In the ground experiment, the leaf area index (LAI) of four growing stages are measured, and a regression model is established to predict yield from ground LAI. In the remote sensing analysis, a new model is built to predict ground LAI from Gaofen-1 images where the simple ratio vegetation index at the bolting stage and the VARIgreen vegetation index at the flowering stage are used. The WOFOSTWOrld FOod STudy (WOFOST) crop model is used to generate time-series ground LAI from discontinuous ground LAI, which is calibrated coarsely with the MODerate resolution imaging spectroradiometer LAI product and finely with the ground-measured data. By combining the two conclusive formulas, an estimation model is built from Gaofen-1 images to the yield of oilseed rape. The effectiveness of the proposed model is verified in Wuxue City, Hubei Province from 2014 to 2019, with the pyramid bottleneck residual network to extract oilseed rape planting areas, the proposed model to estimate yields, and the China statistical yearbooks for comparison. The validation shows that the prediction error of the proposed algorithm is less than 5.5%, which highlights the feasibility of our method for accurate prediction of the oilseed rape yield in a large area. [ FROM AUTHOR] Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Investor Sentiment and Stock Market Reactions to COVID-19: Evidence from China

This paper treats the outbreak of coronavirus disease 2019 (COVID-19) as a natural experiment that can provide insights into the effects of investor sentiment on stock market reactions. Employing the event study methodology (ESM) and taking the date of the Wuhan lockdown as the event date, we find that average abnormal return (AAR) and cumulative abnormal return (CAR) are significantly negative, and average trading volume excesses far more than before within two days of the outbreak. Further, we establish a difference-in-differences (DID) model to investigate the differences between Hubei and non-Hubei listed companies. The results show that for Hubei listed companies, the change of excessive trading volume (ETV) between pre-event and post-event period is significantly higher than that of non-Hubei listed companies, while there exhibits no relationship between the change of AAR and registration place. Overall, our findings provide new evidence for the interaction of local bias and investor sentiment affecting stock market reactions. [ FROM AUTHOR] Copyright of Discrete Dynamics in Nature & Society is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)