Constructing cropland ecological stability assessment method based on disturbance-resistance-response processes and classifying cropland ecological types.

The cropland ecosystem stability (CES) has received increasing attention, especially in ecologically fragile areas, because of its impact on cropland quality, agricultural production and its ability to resist external disturbances. In this study, we first introduced the concepts of resilience and resistance, proposed the ecosystem disturbance-resistance-response process, and established a framework for evaluating the spatial and temporal dynamics of the CES based on RS data, and innovatively combined the RS assessment results of CES with soil field samples data to further classify cropland ecological types (CET) in a key agricultural areas of the Qinghai-Tibetan Plateau, which can effectively identify those croplands in need of priority ecological protection. Results indicate that the combined interactions of disturbance, resistance and response systems affect CES, forming a complex process with significant fluctuations and spatial variations. We also conclude that the disturbance system is positively influenced by topography and precipitation, while slope negatively affects resistance system. Hydrothermal conditions positively influence resistance system, while the response system is influenced by environmental factors at a lower intensity in six periods. It was interesting to note that soil α-biodiversity indicators are significantly and positively correlated with CES at the end of the study period. Therefore, based on the CES assessment results, we further combined the soil α-biodiversity indicators to classify the type of spatial pattern of CET and found that the eastern and northern areas have better quality, which implied an increase in the CES and a higher level of soil biodiversity, which was ideal for cropland expansion. On the contrary, we concluded that the ecosystem maintenance of the Huangshui headwaters and the northern mountainous areas needs to be strengthened in order to reverse the ecological fragility here and safeguard the cropland productive capacity.

Virtual teams in a gig economy.

While the gig economy provides flexible jobs for millions of workers globally, a lack of organization identity and coworker bonds contributes to their low engagement and high attrition rates. To test the impact of virtual teams on worker productivity and retention, we conduct a field experiment with 27,790 drivers on a ride-sharing platform. We organize drivers into teams that are randomly assigned to receiving their team ranking, or individual ranking within their team, or individual performance information (control). We find that treated drivers work longer hours and generate significantly higher revenue. Furthermore, drivers in the team-ranking treatment continue to be more engaged 3 mo after the end of the experiment. A machine-learning analysis of 149 team contests in 86 cities suggests that social comparison, driver experience, and within-team similarity are the key predictors of the virtual team efficacy.