• Korean Journal of Agricultural Science
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• v.43 no.4
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• pp.547-559
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• 2016

The objectives of this study were to investigate the effects of excessive soil-water on crop growth and to predict decrease of yields caused by excessive soil-water. The following five crops were selected for investigation: foxtail millet, proso millet, sorghum, adzuki bean, and sesame. These were planted in pots and a soil-water table was set to 10cm for 10 days. Crop susceptibility (CS) factors and stress-day indexes (SDI) were calculated for each crop to estimate effects of excessive soil-water. SDI models were calculated using CS and SDI data for each crop and predicted the yields of crops cultivated in paddy fields. All crops were cultivated in paddy fields with different soil water contents to evaluate the yield-SDI models. Results showed that yields decreased most when crops were affected by excessive soil-water at the early development stage. Decrease of yields was the greatest when the excessive soil-water treatment was applied at early growth stage. In the field experiment, crops from soils with the greatest soil-water content had the smallest yield, while ones from soils with the smallest soil water contents showed the greatest yields. Observed yields from the field and predicted yields from SDI models showed the least correlation for proso millet, foxtail millet, and adzuki bean and the greatest correlation for sesame. In conclusion, proso millet, foxtail millet, and adzuki bean were more susceptible to soil water than other crops, while sorghum and sesame were more suitable to cultivation in paddy fields.

• Journal of Climate Change Research
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• v.6 no.1
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• pp.21-31
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• 2015

Various crop models have been extensively used for estimation of the crop yields. Compared to the other models, the EPIC model uses a unified approach to simulate more than 100 types of crops. It has been successfully applied in simulating crop yields for various combinations of weather conditions, soil properties, crops, and management schemes in many countries. The objective of this study was to estimate the rice and maize yield in South Korea using the EPIC model. The input datasets for the 30 types in the 11 categories were created for the EPIC model. The EPIC model simulated rice and maize yields. The performance of the EPIC model was evaluated with the goodness-of-fit measures including Root Mean Square Error (RMSE), Relative Error (RE), Nash-Sutcliffe Efficiency Coefficient (NSEC), Mean Absolute Error (MAE), and Pearson Correelation Coefficient (r). The rice yield showed to more high accuracy than maize yield on four type of method without NSEC. Theses results showed that the EPIC model better simulated rice yields than maize yields. The results suggest that the EPIC crop model can be useful to estimate crop yield in South Korea.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.337-337
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• 2017

Accurate and optimal water supply to cereal crop is critical in growing stalks and producing maximum yields. Excessive soil moisture may cause nutrient deficiencies and oxygen deficiency. Excessive soil water during crop growth stages results in decrease of yields. In Korea, the largest agricultural lands are paddy fields. Recently, upland crops are cultivated in paddy field soils to reduce overproduced rice in Korea. In order to success this policy, it is necessary to fully understand crop response to excessive soil moisture condition from paddy field soils. Adzuki bean is one of major legumes which provide protein in daily diet. Adzuki bean has been well know its weakness to excessive soil moisture condition, In order to obtain optimal yields of adzuki bean from paddy field cultivation, it is necessary to understand response of adzuki bean under different soil moisture conditions. This study investigated characteristics of growths, yields and response degree of water stress from adzuki bean. Three cultivars were selected for this study; Chungju, Hongeon, and Arari. All adzuki beans were cultivated in a paddy field which was divided into three sections with different soil moistures. The paddy field was located in Milyang, Gyeongsangnam during 2016. One section of the paddy field had the greatest average soil moisture content as 35.1% during adzuki bean cultivation (very poor). The second greatest soil moisture section had 32.6% (somewhat poor) and the smallest soil moisture section had 28.9% of soil moisture (somewhat well). During cultivation of three cultivar adzuki beans, soil moisture contents and groundwater levels were monitored. All the characteristics of growth and yield components were measured; height, thickness, 100 seed weights etc. Stress index values were calculated by Stress Day Index (SDI). All cultivars had the greatest yields from somewhat well section. Chungju had the greatest yields throughout all three sections compared to other cultivars. Chungju had 81% greater yield than Hongeon which had the smallest yield from somewhat well section. Arari set in middle from all sections. However there was no significant differences yields from very poor and somewhat poor sections. Leaf SPAD values tended to decrease and stable carbon isotope values increased as soil moisture increased. However, Chungju had no difference across different soil moistures in SPAD and stable carbon isotope values, while Hongeon had the greatest differences across sections. These trends followed by SDI values. Chungju had the smallest SDI values compared to other cultivars, which meant that Chungju was the strongest tolerance against excessive soil moisture than other cultivars. All three cultivars showed severe decrease of yields from very poor and somewhat poor sections. Arari and Hongeon showed great decrease from somewhat well section compared to yields from upland soil. These two cultivars may not be proper cultivating in paddy fields. In conclusion, adzuki bean is very sensitive to soil moisture condition and detailed soil managements are required to obtain optimal yields of adzuki bean from paddy field cultivation.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.61 no.2
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• pp.104-112
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• 2016

A double-cropping system with soybean (Glycine max) following the cultivation of potato, garlic, and onion is widely adopted in the southern region of Korea. For this system, marginal dates for planting must be determined for profitable soybean yields, because the decision to plant soybean as a second crop is occasionally delayed by harvest of the first crop and weather conditions. In order to investigate the effect of planting date on soybean yield, three cultivars (early and late maturity) were planted on seven different dates from May 1 to July 30 in both paddy and upland fields across 2012 and 2013. Soybean yields were significantly different among the planting dates and the cultivars; however, the interaction between cultivar and planting date was not significant. Based on linear regression, the maximum yield of soybean was reached with a June 10 planting date, with a sharp decline in yield for crops planted after this date. The results of this study were consistent with those of a previous one that recommends early and mid-June as the optimum planting period. Regardless of soybean ecotype, a reduction in yield of greater than 20% occurred when soybean was planted after mid-July. Frost during soybean growth can reduce yields, and the late maturity cultivars planted on July 30 were damaged by frost before completing maturation and harvest; however, early maturity cultivars were safely harvested. For sufficient time to develop and reach profitable yields, the planting of soybean before mid-July is recommended.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.65 no.4
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• pp.416-425
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• 2020

This study evaluated the productivity of early maturity silage corns during continuous monocropping and the possibility of expanding forage production. Continuous monocropping of three silage corns, such as Kwangpyeongok (medium maturity), Sinhwangok (early maturity), and Sinhwangok2 (early maturity) was planted twice, in April and July from 2018 to 2019 at Suwon. The number of days from the sowing date to the silking date was 78 for the preceding crop and 52 for the succeeding crop. The number of days from the silking date to the harvesting date was 26 for the preceding crop and 46 for the succeeding crop. The sums of the temperature from the sowing date to the silking date were 1,512℃ for the preceding crop, 1,246℃ for the succeeding crop. The sums of the temperature from the sowing date to the harvesting date were 2,198℃ for the preceding crop and 1,951℃ for the succeeding crop. The dry matter yield of the preceding crop (1,637 kg/10a) was similar to that of the succeeding crop (1,565 kg/10a). The dry matter yields of Sinhwangok2 (1,673 kg/10a), Sinhwangok (1,660 kg/10a) and Kwangpyeongok (1,579 kg/10a) were similar to those of the preceding crop. The dry matter yields of Sinhwangok (1,669 kg/10a) and Kwangpyeongok (1,651 kg/10a) were similar to those of the succeeding crop and Sinhwangok2 (1,374 kg/10a) was the lowest among the three corn varieties. The total digestible nutrients (TDN) yield of the preceding crop (1,135 kg/10a) was similar to that of the succeeding crop (1,037 kg/10a). The TDN yields of Sinhwangok2 (1,183 kg/10a), Sinhwangok (1,158 kg/10a), and Kwangpyeongok (1,063 kg/10a) were similar to those of the preceding crop. The TDN yields of Sinhwangok (1,150 kg/10a) and Kwangpyeongok (1,100 kg/10a) were similar for the succeeding crop and Sinhwangok2 (970 kg/10a) was the lowest among the three corn varieties. The total dry matter yields of Sinhwangok (3,329 kg/10a) and Kwangpyeongok (3,230 kg/10a) were similar, but Sinhwangok2 (3,047 kg/10a) was the lowest among the three corn varieties. The total TDN yields of Sinhwangok (2,307 kg/10a), Kwangpyeongok (2,162 kg/10a), and Sinhwangok2 (2,152 kg/10a) were similar. It was concluded that Sinhwangok and Sinhwangok2 have high TDN yields as well as early maturity, and therefore are advantageous for direct continuous monocropping.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.1
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• pp.37-43
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• 2001

Crop status monitoring and yield prediction at higher spatial resolution is a valuable tool in various decision making processes including agricultural policy making by the national and local governments. A prototype crop forecasting system was developed to project the size of rice crop across geographic areas nationwide, based on daily weather pattern. The system consists of crop models and the input data for 1,455 cultivation zone units (the smallest administrative unit of local government in South Korea called "Myun") making up the coterminous South Korea. CERES-rice, a rice crop growth simulation model, was tuned to have genetic characteristics pertinent to domestic cultivars. Daily maximum/minimum temperature, solar radiation, and precipitation surface on 1km by 1km grid spacing were prepared by a spatial interpolation of 63 point observations from the Korea Meteorological Administration network. Spatial mean weather data were derived for each Myun and transformed to the model input format. Soil characteristics and management information at each Myun were available from the Rural Development Administration. The system was applied to the forecasting of national rice production for the recent 3 years (1997 to 1999). The model was run with the past weather data as of September 15 each year, which is about a month earlier than the actual harvest date. Simulated yields of 1,455 Myuns were grouped into 162 counties by acreage-weighted summation to enable the validation, since the official production statistics from the Ministry of Agriculture and Forestry is on the county basis. Forecast yields were less sensitive to the changes in annual climate than the reported yields and there was a relatively weak correlation between the forecast and the reported yields. However, the projected size of rice crop at each county, which was obtained by multiplication of the mean yield with the acreage, was close to the reported production with the $r^2$ values higher than 0.97 in all three years.

• Proceedings of the Korean Society of Crop Science Conference
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• 2020.11a
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• pp.11-11
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• 2020

• Advances in nano research
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• v.14 no.3
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• pp.225-234
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• 2023

In this study, we aim to use big data resources and statistical analysis to obtain a reliable instruction to reach high-quality and high yield agricultural yields. In this regard, soil type data, raining and temperature data as well as wheat production in each year are collected for a specific region. Using statistical methodology, the acquired data was cleaned to remove incomplete and defective data. Afterwards, using several classification methods in machine learning we tried to distinguish between different factors and their influence on the final crop yields. Comparing the proposed models' prediction using statistical quantities correlation factor and mean squared error between predicted values of the crop yield and actual values the efficacy of machine learning methods is discussed. The results of the analysis show high accuracy of machine learning methods in the prediction of the crop yields. Moreover, it is indicated that the random forest (RF) classification approach provides best results among other classification methods utilized in this study.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.46-46
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• 2022

• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 2000.10b
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• pp.3-16
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• 2000

Protection agriculture is the essential choice for human to increase the efficiency of limited crop production area under harsh and changeable weather boundary conditions, extend growing season, maximize the crop yields, and then increase the sustainable income of the grower. The investment costs far greenhouses as well as labor and energy costs are much higher than for conventional plant production systems, so these can only be balanced by better crop yields, higher labor productivity, and higher energy efficiency. (omitted)