• KOREAN JOURNAL OF CROP SCIENCE
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• v.18
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• pp.54-87
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• 1975

The effects of various weather factors on yield components of rice, year variation of yield components within regions, and regional differences of yield components within year were investigated at three Crop Experiment Stations O.R.D., Suweon, Iri, Milyang, and at nine provincial Offices of Rural Development for eight years from 1966 to 1973 for the purpose of providing information required in improving cultural practices and predicting the yield level of rice. The experimental results analyzed by standard partial regression analysis are summarized as follows: 1. When rice was grown in ordinary seasonal culture the number of panicles greatly affected rice yield compared to other yield components. However, when rice was seeded in ordinary season and transplanted late, and transplanted in ordinary season in the northern area the ratio of ripening was closely related to the rice yield. 2. The number of panicles showed the greatest year variation when the Jinheung variety was grown in the northern area. The ripening ratio or 1, 000 grain weight also greatly varied due to years. However, the number of spikelets per unit area showed the greatest effects on yield of the Tongil variety. 2. Regional variation of yield components was classified into five groups; 1) Vegetation dependable type (V), 2) Partial vegetation dependable type (P), 3) Medium type (M), 4) Partial ripening dependable type (P.R), and 5) Ripening dependable type (R). In general, the number of kernel of rice in the southern area showed the greatest partial regression coefficient among yield components. However, in the mid-northern part of country the ripening ratio was one of the component!; affecting rice yield most. 4. A multivariate equation was obtained for both normal planting and late planting by log-transforming from the multiplication of each component of four yield components to additive fashion. It revealed that a more accurate yield could be estimated from the above equation in both cases of ordinary seasonal culture and late transplanting. 5. A highly positive correlation coefficient was obtained between the number of tillers from 20 days after transplanting and the number of panicles at each(tillering) stage 20 days after transplanting in normal planting and late planting methods. 6. A close relationship was found between the number of panicles and weather factors 21 to 30 days, after transplanting. 7. The average temperature 31 to 40 days after transplanting was greatly responsible for the maximum number of tillers while the number of duration of sunshine hours per day 11 to 30 days after transplantation was responsible for that character. The effect of water temperature was negligible. 8. No reasonable prediction for number of panicles was calculated from using either number of tillers or climatic factors. The number of panicles could early be estimated formulating a multiple equation using number of tillers 20 days after transplantation and maximum temperature, temperature range and duration of sunshine for the period of 20 days from 20 to 40 days after transplantation. 9. The effects of maximum temperature and day length 25 to 34 days before heading, on kernel number per panicle, were great in the mid-northern area. However, the minimum temperature and day length greatly affected the kernel number per panicle in the southern area. The maximum temperature had a negative relationship with the kernel number per panicle in the southern area. 10. The maximum temperature was highly responsible for an increased ripening ratio. On the other hand, the minimum temperature at pre-heading and early ripening stages showed an adverse effect on ripening ratio. 11. The 1, 000 grain weight was greatly affected by the maximum temperature during pre- or mid-ripening stage and was negatively associated with the minimum temperature over the entire ripening period.

• Journal of Bio-Environment Control
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• v.21 no.3
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• pp.271-275
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• 2012

The average annual and winter ambient air temperatures in Korea have risen by $0.7^{\circ}C$ and $1.4^{\circ}C$, respectively, during the last 30 years. The continuous rise in temperature presents a challenge in growing certain horticultural crops. Chinese cabbage, one most important cool season crop, may well be used as a model to study the influence of climate change on plant growth, because it is more adversely affected by elevated temperatures than warm season crops. This study examined the influence of transplanting time, nitrogen fertilizer level and climate parameters, including air temperature and growing degree days (GDD), on the performance of a Chinese cabbage cultivar (Chunkwang) during the spring growing season to estimate crop yield under the unfavorable environmental conditions. The chinese cabbage plants were transplanted from Apr. 8 to May 13, 2011 when 3~4 leaves were occurred, at internals of 7 days and cultivated with 3 levels of nitrogen fertilization. The data from plants transplanted on Apr. 22 and 29, 2012 were used for the prediction of yield as affected by planting date and nitrogen fertilization for spring production. In our study, plant dry weight was higher when the seedlings were transplanted on 15th (168 g) than on 22nd (139 g) of April. There was no significant difference in the yield when plants were grown with different levels of nitrogen fertilizer. The values of correlation coefficient ($R^2$) between GDD and number of leaves, and between GDD and dry weight of the above-ground plant parts were 0.9818 and 0.9584, respectively. Nitrogen fertilizer did not provide a good correlation with the plant growth. Results of this study suggest that the GDD values can be used as a good indicator in predicting the top biomass yield of Chinese cabbage.

• Korean Journal of Weed Science
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• v.30 no.1
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• pp.43-49
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• 2010

This study was conducted to predict the rice yield loss and determine the economic threshold levels for water direct seeded rice from competition of the most serious weeds, Scirpus juncoides Roxb. (bulrush) and Echinochlor crusgalli L. (barnyardgrass) in Daegu of Korea. To predict crop yield as a function of weed density used a rectangular hyperbola, and determine their economic threshold levels used the equation developed by Cousens. The rice yield loss model of S. juncoides was predicted as y = 466 / (1+0.00188x), $R^2$ = 0.933 and that of E. crusgalli was y = 458 / (1+0.02402x), $R^2$ = 0.973. In comparison of the competitiveness represented by parameter ${\beta}$, it was 0.001884 in S. juncoides and 0.02402 in E. crusgalli. Economic threshold calculated using Cousens' equation was negatively related to the competitiveness of weed. So that the economic threshold of S. juncoides was 13.4 and that of E. crusgalli was 1.07 plants per $m^2$.

• Korean Journal of Weed Science
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• v.31 no.3
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• pp.289-293
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• 2011

Field experiments were conducted to predict rice yield losses caused by Echinochloa crus-galli (L.)P. Beauv., Bidens frondosa L. and Aeschynomeme indica L. at a range of plant densities under machine transplanted rice cultivation in different regions of Korea in 2006, and to determine their economic threshold levels (ET). All data were fitted to Cousens' rectangular hyperbola to estimate parameters for predicting rice yield loss. The rice yield loss models of Bidens frondosa L. was predicted as y=5.43/(1+0.0113x), $R^2$=0.963, A. indica was y=5.47/(1+0.0332x), $R^2$=0.976 and E. crus-galli y=5.43/(1+0.01552x), $R^2$=0.950. The mean competitivities represented by the parameter, whose reciprocal ($1/{\beta}$) is a weed density reducing crop yield by 50%. Those of E. crus-galli, B. frondosa and A. indica were 0.01552, 0.01113 and 0.0332 in normal-season machine transplanting of Korea, respectively. Single year mean economic thresholds (ET) of A. indica were 0.5, 0.6 and 0.7 plant $m^{-2}$ with the application of flucetosulfuron, flucetosulfuron+imazosulfuron GR and flucetosulfuron+imazosulfuron+carfentrazone GR herbicides, respectively. Meanwhile ET values of 1.6, 1.9 and 1.9 plants $m^{-2}$ for B. frondosa, 1.2, 1.4, and 1.4 plants $m^{-2}$ for E. crus-galli.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.883-893
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• 2023

While South Korea's dependence on imported grains is very high, droughts impacts from exporting countries have been overlooked. Using the Evaporative Stress Index (ESI), this study globally analyzed frequency, extent, and long-term trends of agricultural droughts and their relation to natural oscillations and global crop prices. Results showed that global-scale correlations were found between ESI and soil moisture anomalies, and they were particularly strong in crop cultivation areas. The high correlations in crop cultivation areas imply a strong land-atmosphere coupling, which can lead to relatively large yield losses with a minor soil moisture deficits. ESI showed a clear decreasing trend in crop cultivation areas from 1991 to 2022, and this trend may continue due to global warming. The sharp increases in the grain prices in 2012 and 2022 were likely related to increased drought areas in major grain-exporting countries, and they seemed to elevate South Korea's producer price index. This study suggests the need for drought risk management for grain-exporting countries to reduce socioeconomic impacts in South Korea.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.26 no.4
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• pp.311-317
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• 1981

As the low labor-productivity is being floated up to be the most limiting factor in barley cropping, the endeavor of many researchers concerned have been concentrated on recovering from that. For this reason, in this experiment 8 kinds of sowing methods were employed to survey the yield productivity and the labor-requirement of each methods. And with promising the possibility of consistent mechanization, both prediction of the assurable requirement of labor investment and calculation of the labor productivity in each method were estimated so that the possibility of labor-saving in barley cultivation investigated. Also sowing methods for labor-saving cultivation were presented according to the labor-structure of farm as divided with the phases.

• Journal of Plant Biotechnology
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• v.48 no.3
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• pp.148-155
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• 2021

Gibberellins (GAs) are essential phytohormones for plant growth that influence developmental processes and crop yields. Recent functional genomic analyses of model plants have yielded good characterizations of the canonical GA signaling pathways and related genes. Although Panax ginseng has long been considered to have economic and medicinal importance, functional genomic studies of the GA signaling pathways in this crucial perennial herb plant have been rarely conducted. Here, we identified and performed functional analysis of the GA signaling-related genes, including PgGID1s, PgSLY1s, and PgRGAs. We confirmed that the physiological role of GA signaling components in P. ginseng was evolutionarily conserved. In addition, the important functional domains and amino acid residues for protein interactions among active GA, GID1, SCF^SLY1, and RGA were also functionally conserved. Prediction and comparison of crystallographic structural similarities between PgGID1s and AtGID1a supported their function as GA receptors. Moreover, the subcellular localization and GA-dependent promotion of DELLA degradation in P. ginseng was similar to the canonical GA signaling pathways in other plants. Finally, we found that overexpression of PgRGA2 and PgSLY1-1 was sufficient to complement the GA-related phenotypes of atgid1a/c double- and rga quintuple-mutants, respectively. This critical information for these GA signaling genes has the potential to facilitate future genetic engineering and breeding of P. ginseng for increased crop yield and production of useful substances.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.391-404
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• 2021

This study developed a long-term prediction model for the potential yield of garlic based on a genetic algorithm (GA) by utilizing global climate reanalysis data. The GA is used for digging the inherent signals from global climate reanalysis data which are both directly and indirectly connected with the garlic yield potential. Our results indicate that both deterministic and probabilistic forecasts reasonably capture the inter-annual variability of crop yields with temporal correlation coefficients significant at 99% confidence level and superior categorical forecast skill with a hit rate of 93.3% for 2 × 2 and 73.3% for 3 × 3 contingency tables. Furthermore, the GA method, which considers linear and non-linear relationships between predictors and predictands, shows superiority of forecast skill in terms of both stability and skill scores compared with linear method. Since our result can predict the potential yield before the start of farming, it is expected to help establish a long-term plan to stabilize the demand and price of agricultural products and prepare countermeasures for possible problems in advance.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.3
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• pp.261-269
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• 2015

The purpose of this research is to identify the significance of climate factors related to the significance of change of dry matter yield (DMY) of whole crop maize (WCM) by year through the exploratory data analysis. The data (124 varieties; n=993 in 7 provinces) was prepared after deletion and modification of the insufficient and repetitive data from the results (124 varieties; n=1027 in 7 provinces) of import adaptation experiment done by National Agricultural Cooperation Federation. WCM was classified into early-maturity (25 varieties, n=200), mid-maturity (40 varieties, n=409), late-maturity (27 varieties, n=234) and others (32 varieties, n=150) based on relative maturity and days to silking. For determining climate factors, 6 weather variables were generated using weather data. For detecting DMY and climate factors, SPSS21.0 was used for operating descriptive statistics and Shapiro-Wilk test. Mean DMY by year was classified into upper and lower groups, and a statistically significant difference in DMY was found between two groups (p<0.05). To find the reasons of significant difference between two groups, after statistics analysis of the climate variables, it was found that Seeding-Harvesting Accumulated Growing Degree Days (SHAGDD), Seeding-Harvesting Precipitation (SHP) and Seeding-Harvesting Hour of sunshine (SHH) were significantly different between two groups (p<0.05), whereas Seeding-Harvesting number of Days with Precipitation (SHDP) had no significant effects on DMY (p>0.05). These results indicate that the SHAGDD, SHP and SHH are related to DMY of WCM, but the comparison of R2 among three variables (SHAGDD, SHP and SHH) couldn't be obtained which is needed to be done by regression analysis as well as the prediction model of DMY in the future study.

• Horticultural Science & Technology
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• v.33 no.6
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• pp.911-922
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• 2015

This study was carried out to evaluate growth characteristics of Kimchi cabbage cultivated in various highland areas, and to create a predicting model for the production of highland Kimchi cabbage based on the growth parameters and climatic elements. Regression model for the estimation of head weight was designed with non-destructive measured growth variables (NDGV) such as leaf length (LL), leaf width (LW), head height (HH), head width (HW), and growing degree days (GDD), which was $y=6897.5-3.57{\times}GDD-136{\times}LW+116{\times}PH+155{\times}HH-423{\times}HW+0.28{\times}HH{\times}HW{\times}HW$, ($r^2=0.989$), and was improved by using compensation terms such as the ratio (LW estimated with GDD/measured LW ), leaf growth rate by soil moisture, and relative growth rate of leaf during drought period. In addition, we proposed Excel spreadsheet model for simulation of yield prediction of highland Kimchi cabbage. This Excel spreadsheet was composed four different sheets; growth data sheet measured at famer's field, daily average temperature data sheet for calculating GDD, soil moisture content data sheet for evaluating the soil water effect on leaf growth, and equation sheet for simulating the estimation of production. This Excel spreadsheet model can be practically used for predicting the production of highland Kimchi cabbage, which was calculated by (acreage of cultivation) ${\times}$ (number of plants) ${\times}$ (head weight estimated with growth variables and GDD) ${\times}$ (compensation terms derived relationship of GDD and growth by soil moisture) ${\times}$ (marketable head rate).