Summer cabbages in an alpine area are very sensitive to the fluctuations in supply and demand. Yield variability due to weather conditions dictates the market fluctuations of cabbage price. This study reports an empirical relationship based on weather conditions to estimate the growth and harvestable biomass of cabbages, factors that are critical for supply of summer cabbages. Based on experimental results testing sowing date effects over the two years from 1997 to 1998, a logistic equation was parameterized to predict leaf area expansion of summer cabbages. This logistic model for leaf area expansion was then combined with an empirical allometric relationship to predict total biomass. The final equation for estimating fresh weight accumulation of Chinese cabbage is given by: Fresh weight =3500/(1+exp(5.175-1.153×(6/(1+exp(6.367-0.0064×PHU))))) Where PHU is potential heat units ( o C). The model performance was tested using weather data from 2003 to 2006 to predict fresh harvestable biomass. Overall the model performance was satisfactory with the correlation efficient ranging between 0.89 and 0.94 for each year.
For more than 50 years, satellite images have been used to monitor crop growth. Currently, unmanned aerial vehicle (UAV) imagery is being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of growth estimating equation for highland Kimchi cabbage using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main districts producing highland Kimchi cabbage. UAV imagery was taken in the Anbandeok ten times from early June to early September. Meanwhile, three plant growth parameters, plant height (P.H.), leaf length (L.L.) and outer leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation during growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, NDVI UAV and rainfall in the model explain 93% of the P.H. and L.L. with a root mean square error (RMSE) of 2.22, 1.90 cm. And NDVI UAV and accumulated temperature in the model explain 86% of the L.N. with a RMSE of 4.29. These lead to the result that the characteristics of variations in highland Kimchi cabbage growth according to NDVI UAV and other agro-meteorological factors were well reflected in the model.
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 × GDD -136 × LW + 116 × PH + 155 × HH -423 × HW + 0.28 × HH × HW × 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) × (number of plants) × (head weight estimated with growth variables and GDD) × (compensation terms derived relationship of GDD and growth by soil moisture) × (marketable head rate).Additional key words: Brassica campestris ssp. pekinesis, excel spreadsheet model, regression model, relative growth rate, soil moisture
Remote sensing can be used to provide information about the monitoring of crop growth condition. Recently Unmanned Aerial Vehicle (UAV) technology offers new opportunities for assessing crop growth condition using UAV imagery. The objective of this study was to assess weather UAV aerial images are suitable for the monitoring of highland Kimchi cabbage. This study was conducted using a fixed-wing UAV (Model : Ebee) with Cannon S110, IXUS/ELPH camera during farming season from 2015 to 2016 in the main production area of highland Kimchi cabbage, Anbandegi, Maebongsan, and Gwinemi. The Normalized Difference Vegetation Index (NDVI) by using UAV images was stable and suitable for monitoring of Kimchi cabbage situation. There were strong relationships between UAV NDVI and the growth parameters (the plant height and leaf width) (R 2 ≥ 0.94). The tendency of UAV NDVI according to Kimchi cabbage growth was similar in the same area for two years (2015~2016). It means that if UAV image may be collected several years, UAV images could be used for estimation of the stage of growth and situation of Kimchi cabbage cultivation.
The areas of soybean (Glycine max (L.) Merrill) cultivation in Gangwon-do have increased due to the growing demand for well-being foods. The soybean barcode system is a useful tool for cultivar identification and diversity analysis, which could be used in the seed production system for soybean cultivars. We genotyped cultivars using 202 insertion and deletion (InDel) markers specific to dense variation blocks (dVBs), and examined their ability to identify cultivars and analyze diversity by comparison to the database in the soybean barcode system. The genetic homology of "Cheonga," "Gichan," "Daewang," "Haesal," and "Gangil" to the 147 accessions was lower than 81.2%, demonstrating that these barcodes have potentiality in cultivar identification. Diversity analysis of one hundred and fifty-three soybean cultivars revealed four subgroups and one admixture (major allele frequency <0.6). Among the accessions, "Heugcheong," "Hoban," and "Cheonga" were included in subgroup 1 and "Gichan," "Daewang," "Haesal," and "Gangil" in the admixture. The genetic regions of subgroups 3 and 4 in the admixture were reshuffled for early maturity and environmental tolerance, respectively, suggesting that soybean accessions with new dVB types should be developed to improve the value of soybean products to the end user. These results indicated that the two-dimensional barcodes of soybean cultivars enable not only genetic identification, but also management of genetic resources through diversity analysis.
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