• Korean journal of applied entomology
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• v.51 no.4
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• pp.431-438
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• 2012

The developmental time of the nymphs of Myzus persicae was studied in the laboratory (six constant temperatures from 15 to $30^{\circ}C$ with 50~60% RH, and a photoperiod of 14L:10D) and in a green-pepper plastic house. Mortality of M. persicae in laboratory was high in the first(6.7~13.3%) and second instar nymphs(6.7%) at low temperatures and high in the third (17.8%) and fourth instar nymphs(17.8%) at high temperatures. Mortality was 66.7% at $33^{\circ}C$ in laboratory and $26.7^{\circ}C$ in plastic house. The total developmental time was the longest at $14.6^{\circ}C$ (14.4 days) and shortest at $26.7^{\circ}C$ (6.0 days) in plastic house. The lower threshold temperature of the total nymphal stage was $3.0^{\circ}C$ in laboratory. The thermal constant required for nymphal stage was 111.1DD. The relationship between developmental rate and temperature was fitted nonlinear model by Logan-6 which has the lowest value on Akaike information criterion (AIC) and Bayesian information criterion (BIC). The distribution of completion of each developmental stage was well described by the 3-parameter Weibull function ($r^2=0.95{\sim}0.97$). This model accurately described the predicted and observed occurrences. Thus the model is considered to be good for use in predicting the optimal spray time for Myzus persicae.

• Korean journal of applied entomology
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• v.51 no.4
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• pp.421-429
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• 2012

The developmental time period of Aphis gossypii was studied in laboratory (six constant temperatures from 15 to $30^{\circ}C$ with 50~60% RH, and a photoperiod of 14L:10D) and in a cucumber plastic house. The mortality of A. gossypii in the laboratory was high in the 2nd (20.0%) and 3rd stage(13.3%) at low temperature but high in the 3rd (26.7%) and 4th stage (33.3%) at high temperatures. Mortality in the plastic house was high in the 1st and 2nd stage but there was no mortality in the 4th stage at low temperature. The total developmental period was longest at $15^{\circ}C$ (12.2 days) in the laboratory and shortest at $28.5^{\circ}C$ (4.09 days) in the plastic house. The lower threshold temperature at the total nymphal stage was $6.8^{\circ}C$ in laboratory. The thermal constant required to reach the total nymphal stage was 111.1DD. The relationship between the developmental rate and temperature fit the nonlinear model of Logan-6 which has the lowest value for the Akaike information criterion(AIC) and Bayesian information criterion(BIC). The distribution of completion of each development stage was well described by the 3-parameter Weibull function ($r^2=0.89{\sim}0.96$). This model accurately described the predicted and observed outcomes. Thus it is considered that the model can be used for predicting the optimal spray time for Aphis gossypii.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.