BACKGROUND: Coal combustion bottom ash(BA) has high carbon and calcium content, and alkaline pH, which might improve nutrient cycling in soil related to microbial enzyme activities as it is used as soil amendment. However, it contains heavy metals such as copper(Cu), manganese (Mn), and zinc(Zn), which could cause heavy metals accumulation in soil. Compost might play a role that stabilize BA. The objective of this study was to evaluate effect of combined application of BA and compost as soil amendment on heavy metals concentration, enzyme activities, chemical properties, and crop yield in upland soil.
BACKGROUND:The utilization of green manures as alternatives to reduce the use of chemical fertilizers is considered a good agricultural practice. Effect of incorporation of green manure to soil on change of inorganic nitrogen (N) is well literatured. However, there have been few studies on examining entire dynamic of N including inorganic N and N gases in soil incorporated with green manure. The objective of this study was to examine the changes of inorganic N and N gases with single and mixture applications of hairy vetch and barley in the soil. METHODS AND RESULTS: Hairy vetch(H) and barley (B) were applied at the mixture ratio of B:H=0:0, B:H= 100:0, B:H=0:100, and B:H=50:50 in soil. The soil-green manure mixtures were incubated in the dark at 25°C for 17 weeks under aerobic conditions. Cumulative emission of NH 3 and N 2 O from soils amended with mixture of barley and hairy vetch(B:H=50:50) were less than those from amended with mono hairy vetch(B:H=0:100). Incorporation of single hairy vetch or mixture of barley and hair vetch
Purpose: The purpose of this study was to redefine a competency model for each rank of a university’s staff and to assess the training needs of each competency in the Digital Transformation era. Methods: To redefine a competency model, a six-step procedure was designed using a modified generic model overlay method developed by Dubois(1993). A literature review and extant interviews were executed to design roles, responsibilities, and competency structures of each rank. Research team workshops and expert workshops were also conducted to derive a competency model draft which included competency definitions and behavior indicators. Following this, the Delphi method was conducted to secure the validity of the derived model. To assess training needs for university staff, each rank’s competencies and common competencies were utilized. Results: As a result, twenty-five competencies by rank and five common competencies were confirmed. Five common competencies were problem solving, communication, digital literacy, business planning,and documentation. With the assessment of training needs, the highest competencies for each rank were derived. The derived competencies are as follows from the highest rank to common competency:Strategic thinking, vision suggestion/organizational management/conflict and management/creative thinking/responsibility/problem solving. Conclusion: In order to increase the work efficiency and the competitiveness of university staff,universities need to reform their job structure based on the competencies and should implement talent transformation for the Digital Transformation era.
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