The effects of exogenous sodium nitroprusside (SNP, nitric oxide donor) on the growth, yield, photosynthetic characteristics, and antioxidant enzyme activity of kimchi cabbage (Brassica rapa L. subsp. pekinensis (Lour.) Hanelt) was studied under the low temperature conditions. Kimchi cabbages were treated with SNP of three concentrations (7.5, 15, 30 mg·L-1) for three times at four-day intervals and exposed to low temperature (16/7°C) stress for seven days. SNP treatment induced increases of net photosynthetic rate (Pn), stomatal conductance (Gs), intracellular CO2 concentration (Ci) and transpiration rate (Tr) under the stress condition with the highest level after the third treatment. The contents of malondialdehyde (MDA) and H2O2 were significantly lower in the treatment of SNP compared to the non-treated control. The activity of ascorbate peroxidase (APX), catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD), increased in treated plants by up to 38, 187, 24 and 175%, respectively compared to the non-treated control. SNP-treated and untreated plants had similar growth characteristics. Compared to the control group, SNP-treatment increased fresh weight and leaf area by 5%. Overall, our findings suggest that the application of sodium nitroprusside to the leaves contributes to reducing physiological damage and enhancing the activities of antioxidant enzymes, thereby improving low temperature stress tolerance in kimchi cabbage.

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.