4종의 diamide계열 살충제를 이용한 배추좀나방과 파밤나방의 지역계통별 감수성을 조사하였다. 배추좀나방의 경우 성주와 거창지역 집단 에서 4종의 약제 모두 추천농도에서 100%의 살충활성을 보인 반면, 평창지역 집단에서는 chlorantraniliprole에 대해 42.3%의 낮은 살충활성을 보였다. 감수성계통 배추좀나방과 저항성비를 비교한 결과 cyclaniliprole은 비교적 4지역에서 유사하거나 낮은 저항성비(0.1~6.3배)를 보인 반면, 평창지역 집단은 chlorantraniliprole (1,196.3배)와 cyantraniliprole (105.6배), flubendiamide (191.6배)은 매우 높은 저항성비를 보였다. 파밤나방의 경우 청주와 진도, 영광지역의 집단 모두 4종의 약제에 낮은 감수성을 나타났는데, 특히 청주와 진도지역의 집단은 flubendiamide에 대해 100,000배 이상의 저항성비를 보였다. 채집 연도(2014년과 2017년)에 따른 파밤나방에 대한 chlorantraniliprole의 감수성을 비교한 결과 2014년에 채집된 집단들은 모두 100%의 살충률을 보인 반면 2017년에 채집된 집단들은 살충활성이 매우 낮게 나타났다. 본 연구결과는 diamide계열 살충제가 빠르게 저항성이 발현되고 있으며 작용기작이 다른 약제와의 교호살포 등 종합적 방제전략을 수립하는데 기초자료가 될 수 있을 것이다.

In this paper, scale efficiencies and relative efficiencies of R&D projects in Industrial Technology Program, sponsored by Ministry of Trade, Industry and Energy, Korea, are calculated and compared. For the process, various DEA (Data Envelopment Analysis) models are adopted as major techniques. For DEA, two stage input oriented models are utilized for calculating the efficiencies. Next, the calculated efficiencies are grouped according to their subprograms (Industrial Material, IT Fusion, Nano Fusion, Energy Resources, and Resources Technology) and recipient types (Public Enterprise, Large Enterprise, Medium Enterprise, Small Enterprise, Lab., Univ., and etc.) respectively. Then various subprograms and recipient types are compared in terms of scale efficiencies (CCR models) and relative efficiencies (BCC models). In addition, the correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies is calculated, from which the causal relationship between them can be inferred. Statistical analysis shows that the amount of input, in general, should increase in order to be scale efficient (CCR models) regardless of the subprograms and recipient types, that the 1st and 2nd stage relative efficiencies are different in terms of the programs and recipient types (BCC models), and that there is no significant correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies. However, the results should be used only as reference because the goal each and every subprogram has is different and the situation each and every recipient type faces is different. In addition, the causal link between the 1st stage relative efficiencies and the 2nd relative efficiencies is not considered, which, in turn, is the limitation of this paper.

When evaluating effectiveness of a program, there is a tendency to simply compare the performances of the treated before and after the program or to compare the differences in the performances of the treated and the untreated before-after the program. However, these ways of evaluating effectiveness have problems because they can’t account for environmental changes affecting the treated and/or effects coming from the differences between the treated and the untreated. Therefore, in this paper, panel data analysis (fixed effects model) is suggested as a means to overcome these problems and is utilized to evaluate the effectiveness of fusion technology program conducted by Ministry of Trade, Industry and Energy, Korea. As a result, it turns out that the program has definitely positive impacts on the beneficiary in terms of sales, R&D expenditure, and employment.

In this paper, efficiencies of core technology development projects, conducted by Ministry of Trade, Industry and Energy, are compared. In the process, DEA (Data Envelopment Analysis) is utilized as a main technique for comparing efficiencies. For DEA, input oriented BCC Model is adopted with government grant, recipient expenditure, the number of participating institutions, and project duration as input factors, and the number of patents, the number of papers, and occurred sales as output factors. As a result, next generation mobile communication project turns out to be the most efficient project of all. Therefore, next generation mobile communication project should be benchmarked for the other projects to follow. However, these results should be used only for reference data since every project has a different objective and, of course, is run under a different environment.