• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.90-90
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• 2022

Nicaragua is located in Central America, climatic conditions are considered tropical dry forest. Statistics reflex that in Nicaragua exits 24,000 rice farmers. National rice production only covers 73% of the national consumption. It exists two sowing system: irrigation and rainfed. Varieties used in both systems are mid-late maturity (120-135 days), there are 14 released varieties for irrigation, eight for rainfed, and eight landraces used in rainfed. The current breeding system (introduction of lines from Colombia) has increased the national production, however, has some limitation due to the lack of enough variability, reducing the proability of finding good genotypes and therefore the possibility of satisfying 100% of the demand. The purpose of this study was to analyze the problems that must be resolved in the short and long term to improve rice productivity in Nicaragua. In this paper we explain some proposal for an improvement plan. The selection of varieties with high adaptability to various cultivation environmental conditions it is necessary, also to thoroughly manage seed purity to supply certified seeds. In rice cultivation technology, it needs to improve seedling standing and weeding effect by improving soil leveling and water-saving cultivation technology. Also, proper fertilization and planting density must be established in irrigated and rain-fed areas. Furthermore, capacity must be strengthened by collecting and training with the most recent agricultural technology information, as well as by revitalizing the union rather than the individual farmer. It is necessary to develop varieties highly adaptable to the Nicaraguan cultivation environment, as well as to expand irrigation facilities and cultivation technology suitable for weather conditions in rain-fed areas. Last, it is necessary to maintain the consistency of agricultural policy for continuous and stable rice production in response to climate change events such as drought or intermittent heavy rain.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.233-233
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• 2022

As the world's population grows and food needs diversify, the demand for horticultural crops for beneficial traits is increasing. In order to meet this demand, it is necessary to develop suitable cultivars and breeding methods accordingly. Breeding methods have changed over time. With the recent development of sequencing technology, the concept of genomic selection (GS) has emerged as large-scale genome information can be used. GS shows good predictive ability even for quantitative traits by using various markers, breaking away from the limitations of Marker Assisted Selection (MAS). Moreover, GS using machine learning (ML) and deep learning (DL) has been studied recently. In this study, we aim to build a system that selects phenotype-related markers using the genomic information of the pepper population and trains a genomic selection model to select individuals from the validation population. We plan to establish an optimal genome wide association analysis model by comparing and analyzing five models. Validation of molecular markers by applying linkage markers discovered through genome wide association analysis to breeding populations. Finally, we plan to establish an optimal genome selection model by comparing and analyzing 12 genome selection models. Then We will use the genome selection model of the learning group in the breeding group to verify the prediction accuracy and discover a prediction model.

• 한국약용작물학회:학술대회논문집
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• 2011.09a
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• pp.162-163
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• 2011

• KOREAN JOURNAL OF CROP SCIENCE
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• v.44 no.1
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• pp.26-31
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• 1999

This experiment was conducted to evaluate genetic variation in 48 rice accessions (Oryza sativa L.) using AFLP and RAPD markers. For AFLP, a total of 928 bands were generated with 11 primer combinations and 327 bands (35.2%) of them were polymorphic among 48 accessions. In RAPD analyses using 22 random primers 145 bands were produced, and 121 (83.4%) were polymorphic among 48 accessions. Each accession revealed a distinct fingerprint by two DNA marker systems. Cluster analysis using AFLP-based genetic similarity tended to classify rice cultivars into different groups corresponding to their varietal types and breeding pedigrees, but not using RAPD-based genetic similarity. The AFLP marker system was more sensitive than RAPD in fingerprinting of rice cultivars with narrow genetic diversity.

• 한국약용작물학회:학술대회논문집
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• 2008.05a
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• pp.108-109
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• 2008

• Proceedings of the Korean Society of Crop Science Conference
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• 1994.06a
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• pp.64-65
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• 1994

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.351-351
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• 2017

Arbuscular mycorrhizal fungi (AMF) are particular soil fungi that benefit many crops and require a symbiosis with plant roots to survive. In our previous study, there was a positive correlation between AMF root colonization and soybean grain yield in a four-year consecutive winter cover crop-soybean rotational system without phosphorus fertilizer. It is suggested that higher AMF root colonization can be a better solution for improving soybean growth and grain yield in P-limited soil. Our purpose in this study was to test the hypothesis that a P application is the main factor improving soybean growth, P nutrition and grain yield, and the benefit from AMF to soybean P uptake and growth in a P-limited soil. Impact of a P application on AMF root colonization and communities in soybean roots and their potential contribution to soybean growth and P nutrition under a five-year P-unfertilized crop rotational system were investigated over two-years. In this study, four cover crop treatments included 1) wheat (Triticum aestivum); 2) red clover (Trifolium pratense); 3) rapeseed (Brassica napus); and 4) fallow in the crop rotation. The amount of triple superphosphate as a P fertilizer applied rate after cultivation of cover crops was 120 and $360k\;ha^{-1}$ in 2014 and 2015, respectively. Soybean roots were sampled at full-flowering and analyzed for AMF communities using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and quantitative real-time PCR (qPCR) techniques. The AMF root colonization in the soybean roots at full bloom stage was significantly influenced by cover crop and P application throughout the two-year rotation. The two-year rotation of different cover crops or fallow impacted the molecular diversity of AMF communities colonizing roots of soybean. Redundancy analysis (RDA) indicated that AMF communities colonizing roots of soybean were significantly different among cover crop rotations. The AMF communities colonizing roots of soybean were clearly influenced by a P application in the two-year trial. Moreover, a P application may have positively impacts on the AMF communities under P-deficit soil due to the continuous cover crop-soybean rotational system without a P fertilizer.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.4
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• pp.614-621
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• 2012

Although there is a broad dispute over genetically modified foods on safety, the worldwide adoption of transgenic crops is rapidly increasing. The objectives of this study were to identify trends in the effects of transgenic on crop yields and examine the effect of agricultural variables including crop type, biotech trait, tillage system, and yield environment on corn and soybean yield. A meta-analysis from the 34 peer-reviewed scientific literatures was conducted to compare the crop yield between transgenic crops and conventional varieties. Results showed that the yield of transgenic corn and soybean was strongly dependent on growing conditions. Transgenic hybrids had higher yield potential in the low crop yield environments such as high weeds and/or insect infestation, low soil water, and cool temperature conditions, while transgenic crops did not have yield advantages in high yield environments. The results from this study suggest that producers should consider the potential yield environmental conditions and possible yield reductions when producers choose crop hybrids in their fields.

• Journal of Environmental Science International
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• v.33 no.1
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• pp.27-41
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• 2024

In this study, we evaluated productivity, soil physiochemical properties, and soil microbial characteristics in Kimchi cabbage(Brassica rapa subsp. pekinensis) cultivation within a highland environment during summer. Specifically, we examined the effect of different cropping systems, namely monoculture and rotation with soybean, over an 8-year cropping period. The results of our investigation revealed that significant differences were absent in terms of yield and soil physiochemical properties between the two cropping systems. However, microbial characteristics exhibited distinctive patterns. Bacterial diversity was significantly higher in the rotation system that in the monoculture, whereas fungal diversity demonstrated a preference for rotation although the result was not significant. Our findings identified the presence of Bradyrhizobium stylosanthis, a nitrogen-fixation symbiont, as an indicator ASV (amplicon sequence variant) in the rotation system, where it displayed significantly higher abundances. These observations suggest a potential positive effect of the rotation system on nitrogen fixation. Notably, throughout the cultivation period, both cropping systems did not exhibit critical disease incidences. However, Fusarium oxysporum, a well-known pathogen responsible for inducing fusarium wilt disease in Kimchi cabbage, was detected with significantly higher abundance in the monoculture system. This finding raises concerns about the potential risk associated with Kimchi cabbage cultivation in a long-term monoculture system.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.80-80
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• 2022

Estimation of heading date of paddy rice is laborious and time consuming. Therefore, automatic estimation of heading date of paddy rice is highly essential. In this experiment, deep learning classification models were used to classify two difference categories of rice (vegetative and reproductive stage) based on the panicle initiation of paddy field. Specifically, the dataset includes 444 slanted view images belonging to two categories and was then expanded to include 1,497 images via IMGAUG data augmentation technique. We adopt two transfer learning strategies: (First, used transferring model weights already trained on ImageNet to six classification network models: VGGNet, ResNet, DenseNet, InceptionV3, Xception and MobileNet, Second, fine-tuned some layers of the network according to our dataset). After training the CNN model, we used several evaluation metrics commonly used for classification tasks, including Accuracy, Precision, Recall, and F1-score. In addition, GradCAM was used to generate visual explanations for each image patch. Experimental results showed that the InceptionV3 is the best performing model in terms of the accuracy, average recall, precision, and F1-score. The fine-tuned InceptionV3 model achieved an overall classification accuracy of 0.95 with a high F1-score of 0.95. Our CNN model also represented the change of rice heading date under different date of transplanting. This study demonstrated that image based deep learning model can reliably be used as an automatic monitoring system to detect the heading date of rice crops using CCTV camera.