• Proceedings of the Korean Society of Crop Science Conference
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• 2005.04a
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• pp.198-199
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• 2005

• Proceedings of the Korean Society of Crop Science Conference
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• 2006.04a
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• pp.78-79
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• 2006

• KOREAN JOURNAL OF CROP SCIENCE
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• v.65 no.1
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• pp.1-21
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• 2020

Association mapping is widely used in rice and other crops to identify genes underlying important agronomic traits. Most association mapping studies use diversity panels comprising accessions with various geographical origins to exploit their wide genetic variation. While locally adapted breeding lines are rarely used in association mapping owing to limited genetic diversity, genes/alleles identified from elite germplasm are practically valuable as they can be directly utilized in breeding programs. In this study, we analyzed genetic diversity of 179 rice varieties (161 japonica and 18 Tongil-type) released in Korea from 1970 to 2006 using 192 microsatellite markers evenly distributed across the genome. The 161 japonica rice varieties were genetically very close to each other with limited diversity as they were developed mainly through elite-by-elite crosses to meet the specific local demands for high quality japonica rice in Korea. Despite the narrow genetic background, abundant phenotypic variation was observed in heading time, culm length, and amylose and protein content in the 161 japonica rice varieties. Using these varieties in association mapping, we identified six, seven, ten, and four loci significantly associated with heading time, culm length, and amylose and protein content, respectively. The sums of allelic effects of these loci showed highly significant positive correlation with the observed phenotypic values for each trait, indicating that the allelic variation at these loci can be useful when designing cross combinations and predicting progeny performance in local breeding programs.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.677-687
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• 2017

In this paper, we propose the use of hierarchical classification for winter crop mapping based on satellite imagery. A hierarchical classification is a classifier that maps input data into defined subsumptive output categories. This classification method can reduce mixed pixel effects and improve classification performance. The methodology are illustrated focus on winter cropsin Gimje city, Jeonbuk with Landsat-8 imagery. First, agriculture fields were extracted from Landsat-8 imagery using Smart Farm Map. And then winter crop fields were extracted from agriculture fields using temporal Normalized Difference Vegetation Index (NDVI). Finally, winter crop fields were then classified into wheat, barley, IRG, whole crop barley and mixed crop fields using signature from Unmanned Aerial Vehicle (UAV). The results indicate that hierarchical classifier could effectively identify winter crop fields with an overall classification accuracy of 98.99%. Thus, it is expected that the proposed classification method would be effectively used for crop mapping.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.148-148
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• 2017

Soybean is an important economical resource of protein and oil for human and animals. The genetic basis of seed protein and oil content has been separately characterized in soybean. However, the genetic relationship between seed protein and oil content remains to be elucidated. In this study, we used a combined analysis of phenotypic correlation and linkage mapping to dissect the relationship between seed protein and oil content. A $F_{10:11}$ RIL population containing 222 lines, derived from the cross between two Korean soybean cultivars Seadanbaek as female and Neulchan as male parent, were used in this experiment. Soybean seed analyzed were harvested in three different experimental environments. A genetic linkage map was constructed with 180K SoyaSNP Chip and QTLs of both traits were analyzed using the software QTL IciMapping. QTL analyses for seed protein and oil content were conducted by composite interval mapping across a genome wide genetic map. This study detected four major QTL for oil content located in chromosome 10, 13, 15 and 16 that explained 13.2-19.8% of the phenotypic variation. In addition, 3 major QTL for protein content were detected in chromosome 10, 11 and 16 that explained 40.8~53.2% of the phenotypic variation. A major QTLs was found to be associated with both seed protein and oil content. A major QTL were mapped to soybean chromosomes 16, which were designated qHPO16. These loci have not been previously reported. Our results reveal a signi cant genetic relationship between seed protein and oil fi content traits. The markers linked closely to these major QTLs may be used for selection of soybean varieties with improved seed protein and oil content.

• Journal of Ecology and Environment
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• v.45 no.4
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• pp.255-263
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• 2021

Background: Ecosystem service mapping is an important tool for decision-making in landscape planning and natural resource management. Today, pollination service mapping is based on the Lonsdorf model (InVEST software) that determines the availability of nesting and floral resources for each land cover and estimates pollination according to the foraging range of the desired species. However, it is argued that the Lonsdorf model has significant limitations in estimating pollination in a landscape that can affect the results of this model. Results: This paper presents a free software, named PollMap, that does not have the limitations of the Lonsdorf model. PollMap estimates the pollination service according to a modified version of the Lonsdorf model and assumes that only cells within the flight range of bees are important in the pollination mapping. This software is produced for estimating and mapping crop pollination in agricultural landscapes. The main assumption of this software is that in the agricultural landscapes, which are dominated by forest and agriculture ecosystems, forest patches serve only as a nesting habitat for wild bees and the surrounding fields provide floral resources. Conclusion: The present study provided new software for mapping crop pollination in agricultural landscapes that does not have the limitations of the Lonsdorf model. We showed that the use of the Lonsdorf model for pollination mapping requires attention to the limitations of this model, and by removing these limitations, we will need new software to obtain a reliable mapping of pollination in agricultural landscapes.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.179-190
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• 2024

This study aims to compare supervised classification methods with phenology-based approaches, specifically pixel-based and segment-based methods, for accurate crop mapping in agricultural landscapes. We utilized Sentinel-2A imagery, which provides multispectral data for accurate crop mapping. 31 normalized difference vegetation index (NDVI) images were calculated from the Sentinel-2A data. Next, we employed phenology-based approaches to extract valuable information from the NDVI time series. A set of 10 phenology metrics was extracted from the NDVI data. For the supervised classification, we employed the maximum likelihood (MaxLike) algorithm. For the phenology-based approaches, we implemented both pixel-based and segment-based methods. The results indicate that phenology-based approaches outperformed the MaxLike algorithm in regions with frequent rainfall and cloudy conditions. The segment-based phenology approach demonstrated the highest kappa coefficient of 0.85, indicating a high level of agreement with the ground truth data. The pixel-based phenology approach also achieved a commendable kappa coefficient of 0.81, indicating its effectiveness in accurately classifying the crop types. On the other hand, the supervised classification method (MaxLike) yielded a lower kappa coefficient of 0.74. Our study suggests that segment-based phenology mapping is a suitable approach for regions like South Korea, where continuous cloud-free satellite images are scarce. However, establishing precise classification thresholds remains challenging due to the lack of adequately sampled NDVI data. Despite this limitation, the phenology-based approach demonstrates its potential in crop classification, particularly in regions with varying weather patterns.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.140-140
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• 2023

• Proceedings of the Korean Society of Crop Science Conference
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• 2019.09a
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• pp.27-27
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• 2019

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.258-258
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• 2022