http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Esayas Mendesil,Abush Tesfaye 한국응용곤충학회 2009 Journal of Asia-Pacific Entomology Vol.12 No.3
The seasonal incidence of the coffee berry moth, Prophantis smaragdina (Butler) (Lepidoptera: Pyralidae), was investigated on Coffea arabica L. in Jimma, Ethiopia. Our results showed that; the coffee berry moth was present throughout the study period except during November and December. The average incidence was 24.5% and the peak incidence (61%) was in September. Multiple correlation analysis was used to estimate the strength of association between weather variables and the incidence of the pest, and stepwise (both forward and backward) regression analysis was used to select the best explanatory variable. There were strong associations among the explanatory weather variables, indicating the potential problem of multicollinearity in the regression analysis. Relative humidity had a highly significant regression coefficient of 2.228 and was selected in the stepwise regression analysis as the best explanatory variable. The results of the study can be used in designing an integrated pest management strategy against the coffee berry moth.
Abebe Desta,Mohammed Wassu,Tesfaye Abush 한국작물학회 2023 Journal of crop science and biotechnology Vol.26 No.1
Determination of the genotype x environment interaction (GEI) and stability of upland rice varieties for grain yield provides the basis to identify high-yielding and stable upland rice varieties across diferent environments and to delineate and identify rice mega environments in Ethiopia. Twenty rice varieties were laid out in a randomized complete block design with three replications and evaluated across six locations that represent the major rice growing agro-ecologies in the country. The combined analysis of variance over environments revealed signifcant diferences among genotypes, environments and GEI for grain yield. The signifcant GEI implicated the diferential response of the genotypes in diferent environments and demonstrated the remarkable efect of GEI on the performance of the genotypes. The partitioning of GEI based on the AMMI model revealed that only the frst two terms of AMMI were signifcant. The E and GEI had a higher contribution than G for most of the traits, and GEI had larger contribution than G and E to the variations in the studied varieties for yield. The Interaction Principal Component Axis one (IPC1) and two (IPC2) contributed to 55.1% and 24.8% of the GEI sum of squares, respectively. NERICA-3, Hidasse and Chewaqa varieties were identifed as responsive to changing environments and the frst three best varieties across poor to most favorable environments. NERICA-12 and ADET were identifed as the most stable and widely adapted varieties based on most of the stability parameters. NERICA-4, NERICA-13 and Getachew varieties were identifed as stable varieties by most of the stability parameters and selected as the frst three best varieties at poor to average environments. In conclusion, this study revealed that GE interactions were an important source of rice yield variation, and its AMMI biplots were forceful for visualizing the response of genotypes to environments.