Digital transformation: A great science in the making.
Few studies have determined the effect of foliar fungicides on the incidence of soybean seed infection by the pathogens Diaporthe spp. and Cercospora spp. in Arkansas. Three fungicides (propiconazole, pyraclostrobin, and thiophane-methyl) were compared to an untreated control (water) at two locations in Arkansas in 2018 and 2019. The fungicides were applied at the R3, R5, or at the R3 and R5 growth stages to an early maturity group 4 (MG4) cultivar (CZ4044LL in 2018 and CZ4105LL in 2019) and a late MG4 cultivar (CZ4748LL). Yellow pods were sampled from the lower half of the plants at physiological maturity (PM) and placed in a moist chamber for three days, the seeds removed, surface disinfested, placed on Petri dishes filled with solidified potato dextrose agar and the incidence of seed infected with Diaporthe spp. and Cercospora spp. determined. At harvest, yield data were collected, and seeds were assessed for the incidence of discolored seed (chalky, brown, purple, and total) and the incidence of seed infection. There was no significant effect of fungicide on Diaporthe spp. seed infection. Cercospora sp. seed infection under propiconazole and thiophanate-methyl treatments was significantly greater than the control at PM, but significantly lower than the control at harvest. The incidence of Cercospora spp. seed infection increased significantly from PM to harvest. All fungicides significantly reduced the incidence of purple seed and CZ4748LL had significantly more Cercospora spp. seed infection and seed discoloration than CZ4044LL/CZ4105LL. Yields were significantly greater with pyraclostrobin than the control.
Beyond informing human resources (HR) policies and practices, information gleaned from predictive analytics, visualized via dashboards, can increase awareness and prompt employee and management actions based on identified variables often related to intent to leave and employee wellness. While considerable research, relevant measurements, and tools are available within the field of human resource management that focus on measuring retention and mitigating turnover, there is minimal application of such tools in higher education. Analytics, particularly those with predictive abilities, can contribute to an increased understanding and improvement in the retention of the education workforce. Higher education institutions have the potential to proactively impact the health and well-being of employees and foster a culture of wellness. For example, risk stratification using objective and subjective data can be utilized to assign risk levels to employees. In addition, risk visualized through real-time dashboards alerts management to implement early interventions that mitigate burnout and other wellness concerns for at-risk employees. This article provides an overview of established metrics, explores analytical system design, and outlines practices related to the creation and implementation of a dashboard model that can provide at-a-glance views and risk stratification of key performance and predictive indicators (KPIs) (e.g., compensation data, workload, wellness, etc.) of employees’ intent to leave and overall wellness. This proactive approach will allow employees and management to collaborate to enable early recognition and engagement, improve educator retention, and minimize intent to leave.
