• Korean Journal of Poultry Science
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• v.49 no.2
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• pp.53-60
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• 2022

This study investigated correlations between egg quality indicators to identify external egg quality traits to predict internal egg quality using non-destructive and convenient measurements. Thirteen indicators, including Haugh unit, albumen height, eggshell breaking strength, eggshell thickness, eggshell color (CIE L^*, CIE a^*, CIE b^*), and reflectivity value, egg weight, egg length, egg width, shape index, and yolk color, were investigated. A total of 180 brown eggs were obtained from one 27-week-old flock of Hy-line brown-laying hens raised in a cage system. Correlations were evaluated using Pearson's correlation coefficient (r). The results showed strong correlations between Haugh unit and albumen height, eggshell color CIE L^* and reflectivity, egg weight and width, egg weight and length, eggshell color CIE L^* and CIE a^*, eggshell color CIE a^* and reflectivity, and shape index and egg length (P<0.001). Moderate correlations were observed between eggshell breaking strength and eggshell thickness, eggshell color CIE a^* and CIE b^*, and shape index and egg width (P<0.001). Eggshell color CIE L^* was correlated with eggshell breaking strength (P<0.01), and eggshell color CIE a^* was correlated with Haugh unit, albumen height (P<0.01), and eggshell breaking strength (P<0.001). The present study showed significant correlations between eggshell color and other quality indicators. Thus, this study suggests that eggshell colors based on reflectiveness and the CIE L^*a^*b^* value can be used to estimate the Haugh unit, albumen height, eggshell breaking strength, and thickness.

• Journal of The Korean Society of Grassland and Forage Science
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• v.38 no.1
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• pp.44-52
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• 2018

This experiment was conducted at Suwon, Korea from 2013 to 2015. The objective of this study was to establish the optimum seeding rate, and to clarify the nitrogen fertilizer level for rye seed production in central and north area of Korea. We used Korean rye cultivar 'Gogu' for this test. We employed a split-plot design with three replications. The main plots were designed by three seeding levels (3, 5 and $7kg\;10a^{-1}$), but other sub-plots were randomly seeded. The plots were treated with three different nitrogen fertilizer levels (3, 6 and $9kg\;10a^{-1}$). The percentage of productive tiller, number of grain per spike, fertility rate, 1 liter weight, and 1000-grain weight decreased as seeding rate increased from $3kg\;10a^{-1}$ to $7kg\;10a^{-1}$, whereas the number of spike per $m^2$ increased. Therefore the grain yields of rye had less of an effect by increasing seeding rate. There was an increase in number of spike per $m^2$, number of grain per spike, and fertility rate as nitrogen fertilizer level increased from $3kg\;10a^{-1}$ to $9kg\;10a^{-1}$, but grain yields significantly not affected by the interaction of seeding rate ${\times}$ nitrogen fertilizer levels. However, the best seeding rate and nitrogen fertilizer level for rye seed production were 5 kg and $5{\sim}6kg\;10a^{-1}$, respectively, considering seed and fertilizer reduction and the prevention of pollution by excess fertilization.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.133-149
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• 2022

Existing domestic studies on estimating rice yield were mainly implemented at the level of cities and counties in the entire nation using MODIS satellite images with low spatial resolution. Unlike previous studies, this study tried to estimate rice yield at the level of eup-myon-dong in Gimje-si, Jeollabuk-do using Sentinel-2 satellite images with medium spatial resolution, rainfall and soil data, and then to evaluate its accuracy. Five vegetation indices such as NDVI, LAI, EVI2, MCARI1 and MCARI2 derived from Sentinel-2 images of August 1, 2018 for Gimje-si, Jeollabuk-do, rainfall and paddy soil-type data were aggregated by the level of eup-myon-dong and then rice yield was estimated with gamma generalized linear model, an expanded variant of multi-variate regression analysis to solve the non-normality problem of dependent variable. In the rice yield model finally developed, EVI2, rainfall days in September, and saline soils ratio were used as significant independent variables. The coefficient of determination representing the model fit was 0.68 and the RMSE for showing the model accuracy was 62.29kg/10a. This model estimated the total rice production in Gimje-si in 2018 to be 96,914.6M/T, which was very close to 94,470.3M/T the actual amount specified in the Statistical Yearbook with an error of 0.46%. Also, the rice production per unit area of Gimje-si was amounted to 552kg/10a, which was almost consistent with 550kg/10a of the statistical data. This result is similar to that of the previous studies and it demonstrated that the rice yield can be estimated using Sentinel-2 satellite images at the level of cities and counties or smaller districts in Korea.