Development of Growth Models as Affected by Cultivation Season and Transplanting Date and Estimation of Prediction Yield in Kimchi Cabbage

Lee, Jin Hyoung; Lee, Hee Ju; Kim, Sung Kyeom; Kim, Tae Hyun; Lee, Hee Su; Choi, Chang Kyoung

doi:10.12791/ksbec.2017.26.4.235

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…In the Republic of Korea, kimchi cabbage is typically transplanted for the autumn season in late August to early September and harvested between late November and early December. A study on kimchi cabbage weight prediction models based on transplanting dates indicated that kimchi cabbages transplanted in early September had lower weights compared to cabbages transplanted two weeks earlier [60]. When applying the growth model for each transplanting date with a two-year average cultivation duration.…”

Section: Suggestion 1: Early Transplanting Strategymentioning

confidence: 99%

Agrivoltaic Farming Insights: A Case Study on the Cultivation and Quality of Kimchi Cabbage and Garlic

Ko,

Chae,

Moon

et al. 2023

Agronomy

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Agrivoltaic systems, which combine the cultivation of crops with solar panel installations, offer a novel solution to the dual challenges of energy production and agricultural productivity. This research verifies the impact of agrivoltaic (APV) conditions on the growth and quality of garlic and kimchi cabbage over two consecutive years in Naju-si, Jeollanam Province, Republic of Korea. In the 2019–2020 cultivation season, both kimchi cabbage and garlic grown under APV conditions experienced weight reductions of 18% and 15%, respectively, when compared to those grown in conventional settings. Intriguingly, despite the altered light conditions of APV leading to microenvironmental changes (mainly 41% light reduction), the quality of these crops, particularly in terms of their sulfur compound concentrations, remained consistent. This suggests that there was no discernible difference in the sensory quality of APV-grown kimchi cabbage and garlic compared to their traditionally grown counterparts. These findings highlight the potential of APV systems in promoting sustainable agriculture by balancing both crop yield and quality. Based on these results, the study suggests three innovative cultivation techniques to enhance crop growth in APV environments.

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Section: Suggestion 1: Early Transplanting Strategymentioning

confidence: 99%

Agrivoltaic Farming Insights: A Case Study on the Cultivation and Quality of Kimchi Cabbage and Garlic

Ko,

Chae,

Moon

et al. 2023

Agronomy

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Growth Characteristics of Hydroponically Grown Melon according to Volume of Granular Rockwool and Substrates of Coir and Rockwool

Jung

Oh²,

Kim

et al. 2023

J. Bio-Env. Con.

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Melons, a rich source of vitamins and fibers, are commonly grown in the soil. Hydroponic cultivation could improve yield and quality of melon and selection of substrate volume and the kind of substrates is important for hydroponic cultivation of melons. This study investigated the effect on melon growth according to volume of granular rockwool and substrates of coir and rockwool slab. 'Geumsegye' melon (Cucumis melo L. cv. Geumsegye) was cultivated hydroponically according to volume of granular rockwool to 1.0, 1.5, 2.0, 3.0, and 4.0 L, and was also cultivated using coir and rockwool slabs. Logistic model was applied to estimate the growth characteristics of melons such as plant height, leaf length, leaf width, and the characteristics of fruit. The growth characteristics of melons were significantly increased at 4.0 L compared to those grown of 1.0 L volume of on granular rockwool, and the results were the highest in coir and rockwool slabs. Melons grown in rockwool slabs showed the largest fruit fresh weight, fruit length, and fruit width. During hydroponic cultivation, growth characteristics of melon appropriate at the 4.0 L volume of granular rockwool, and the highest at coir and rockwool slabs. This study provides a basis for understanding the effect of root zone environment to the growth characteristics and fruit quality of non-netted melon.

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Development of Kimchi Cabbage Growth Prediction Models Based on Image and Temperature Data

Kang,

Shim,

Lee

et al. 2023

J. Bio-Env. Con.

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This study was conducted to develop a model for predicting the growth of kimchi cabbage using image data and environmental data. Kimchi cabbages of the 'Cheongmyeong Gaual' variety were planted three times on July 11th, July 19th, and July 27th at a test field located at Pyeongchang-gun, Gangwon-do (37°37′ N 128°32′ E, 510 elevation), and data on growth, images, and environmental conditions were collected until September 12th. To select key factors for the kimchi cabbage growth prediction model, a correlation analysis was conducted using the collected growth data and meteorological data. The correlation coefficient between fresh weight and growth degree days (GDD) and between fresh weight and integrated solar radiation showed a high correlation coefficient of 0.88. Additionally, fresh weight had significant correlations with height and leaf area of kimchi cabbages, with correlation coefficients of 0.78 and 0.79, respectively. Canopy coverage was selected from the image data and GDD was selected from the environmental data based on references from previous researches. A prediction model for kimchi cabbage of biomass, leaf count, and leaf area was developed by combining GDD, canopy coverage and growth data. Single-factor models, including quadratic, sigmoid, and logistic models, were created and the sigmoid prediction model showed the best explanatory power according to the evaluation results. Developing a multi-factor growth prediction model by combining GDD and canopy coverage resulted in improved determination coefficients of 0.9, 0.95, and 0.89 for biomass, leaf count, and leaf area, respectively, compared to single-factor prediction models. To validate the developed model, validation was conducted and the determination coefficient between measured and predicted fresh weight was 0.91, with an RMSE of 134.2 g, indicating high prediction accuracy. In the past, kimchi cabbage growth prediction was often based on meteorological or image data, which resulted in low predictive accuracy due to the inability to reflect on-site conditions or the heading up of kimchi cabbage. Combining these two prediction methods is expected to enhance the accuracy of crop yield predictions by compensating for the weaknesses of each observation method.

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Development of Growth Models as Affected by Cultivation Season and Transplanting Date and Estimation of Prediction Yield in Kimchi Cabbage

Cited by 6 publications

References 10 publications

Agrivoltaic Farming Insights: A Case Study on the Cultivation and Quality of Kimchi Cabbage and Garlic

Agrivoltaic Farming Insights: A Case Study on the Cultivation and Quality of Kimchi Cabbage and Garlic

Growth Characteristics of Hydroponically Grown Melon according to Volume of Granular Rockwool and Substrates of Coir and Rockwool

Development of Kimchi Cabbage Growth Prediction Models Based on Image and Temperature Data

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