• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.3
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• pp.143-150
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• 2016

In this study, we analyzed the relationship between rice yield and abnormal meteorological features for El $Ni{\tilde{n}}o$ years (with more than 1.0 Oceanic $Ni{\tilde{n}}o$ Index) since 1980 in South Korea. The national averaged rice yield of El $Ni{\tilde{n}}o$ years (n=14) was $4,663kg\;ha^{-1}$, which was less than that of non El $Ni{\tilde{n}}o$ years (n=16) by $102kg\;ha^{-1}$, but the difference was not significant statistically (t=1.215, p=0.234). The averaged rice yield of El $Ni{\tilde{n}}o$ end years ($4,558kg\;ha^{-1}$) was analyzed to be less than those of El $Ni{\tilde{n}}o$ start years and non El $Ni{\tilde{n}}o$ years by 209 and $206kg\;ha^{-1}$, respectively. But, the trend was not significant statistically (df=2, f=2.355, p=0.114). When meteorological anomalies were analyzed based on seasonal meteorological values, 18 meteorological events in total were observed for the past 30 years (1981-2010). In detail, abnormally much precipitation occurred 6 times, most often, followed by 5 times of abnormally low temperature during the past 30 years. Occurrence of meteorological anormalies of El $Ni{\tilde{n}}o$ end years was 0.71 events per year on average, which was higher than those of El $Ni{\tilde{n}}o$ start years ($0.43yr^{-1}$) and non El $Ni{\tilde{n}}o$ years ($0.63yr^{-1}$), even if the differences were not significant statistically (df=2, f=0.321, p=0.727).

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Korean Journal of Agricultural and Forest Meteorology
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• v.5 no.4
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• pp.255-260
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• 2003

Recently, both climate change and unusual meteorological disasters are becoming a more frequent and serious threat to agricultural production. Destruction of the stabilizing base of agricultural productivity in Korea is a concern. This study provides basic information for stabilizing agricultural production by clarifying and analyzing the features of agro-meteorological disasters which have occurred recently in Korea. The occurrence of meteorological disasters has increased rapidly since the 1940s. A 19-fold increase in occurrence is noted over the past 60 years from 1941 to 2000. Meteorological disasters occurred mostly in August, then in July, and least often in October, In terms of regional occurrences, the frequency of meteorological disasters was the highest in Gangwon (751 times) and in Jeonnam (703 times) provinces, and the lowest in Jeju (459 times) province for the 97 years from 1904 to 2000. Agro-meteorological disasters which caused the most serious damage to cropland were rain storms and typhoons for the 10 years from 1991 to 2000, and they occurred 52 and 18 times during this period, respectively. Agro-meteorological disasters occurred mainly during the summer season (from June to September) when major crops are cultivated in Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.337-347
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• 2018

Weather causes much of the risk of agricultural activity. For efficient farming, we need to use weather information. Modern agriculture has been developed to create high added value through convergence with state-of-the-art Information and Communication Technology (ICT). This study deals with the quality control algorithms of weather monitoring equipment through Ubiquitous Sensor Network (USN) observational equipment for efficient cultivation of cabbage. Accurate weather observations are important. To achieve this goal, the Korea Meteorological Administration, for example, developed various quality control algorithms to determine regularity of the observation. The research data of this study were obtained from five USN stations, which were installed in Anbandegi and Gwinemi from 2015 to 2017. Quality control algorithms were developed for flat line check, temporal outliers check, time series consistency check and spatial outliers check. Finally, the quality control algorithms proposed in this study can also identify potential abnormal observations taking into account the temporal and spatial characteristics of weather data. It is expected to be useful for efficient management of highland cabbage production by providing quality-controlled weather data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.245-257
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• 2023

The representative crop in the Republic of Korea, rice, is cultivated over extensive areas every year, which resulting in reduced resistance to pests and diseases. One of the major rice diseases, rice blast disease, can lead to a significant decrease in yields when it occurs on a large scale, necessitating early detection and effective control of rice blast disease. Drone-based crop monitoring techniques are valuable for detecting abnormal growth, but frequent image capture for potential rice blast disease occurrences can consume significant labor and resources. The purpose of this study is to early detect rice blast disease using remote sensing data, such as drone and satellite images, along with weather data. Satellite images was helpful in identifying rice cultivation fields. Effective detection of paddy fields was achieved by utilizing vegetation and water indices. Subsequently, air temperature, relative humidity, and number of rainy days were used to calculate the risk of rice blast disease occurrence. An increase in the risk of disease occurrence implies a higher likelihood of disease development, and drone measurements perform at this time. Spectral reflectance changes in the red and near-infrared wavelength regions were observed at the locations where rice blast disease occurred. Clusters with low vegetation index values were observed at locations where rice blast disease occurred, and the time series data for drone images allowed for tracking the spread of the disease from these points. Finally, drone images captured before harvesting was used to generate spatial information on the incidence of rice blast disease in each field.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.2
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• pp.108-125
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• 2015

The agro-climatic index is one of the ways to assess the climate resources of particular agricultural areas on the prospect of agricultural production; it can be a key indicator of agricultural productivity by providing the basic information required for the implementation of different and various farming techniques and practicalities to estimate the growth and yield of crops from the climate resources such as air temperature, solar radiation, and precipitation. However, the agro-climate index can always be changed since the index is not the absolute. Recently, many studies which consider uncertainty of future climate change have been actively conducted using multi-model ensemble (MME) approach by developing and improving dynamic and statistical downscaling of Global Climate Model (GCM) output. In this study, the agro-climatic index of Korean Peninsula, such as growing degree day based on $5^{\circ}C$, plant period based on $5^{\circ}C$, crop period based on $10^{\circ}C$, and frost free day were calculated for assessment of the spatio-temporal variations and uncertainties of the indices according to climate change; the downscaled historical (1976-2005) and near future (2011-2040) RCP climate sceneries of AR5 were applied to the calculation of the index. The result showed four agro-climatic indices calculated by nine individual GCMs as well as MME agreed with agro-climatic indices which were calculated by the observed data. It was confirmed that MME, as well as each individual GCM emulated well on past climate in the four major Rivers of South Korea (Han, Nakdong, Geum, and Seumjin and Yeoungsan). However, spatial downscaling still needs further improvement since the agro-climatic indices of some individual GCMs showed different variations with the observed indices at the change of spatial distribution of the four Rivers. The four agro-climatic indices of the Korean Peninsula were expected to increase in nine individual GCMs and MME in future climate scenarios. The differences and uncertainties of the agro-climatic indices have not been reduced on the unlimited coupling of multi-model ensembles. Further research is still required although the differences started to improve when combining of three or four individual GCMs in the study. The agro-climatic indices which were derived and evaluated in the study will be the baseline for the assessment of agro-climatic abnormal indices and agro-productivity indices of the next research work.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.327-336
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• 2019

Soybean is one of the most important crops of which the grains contain high protein content and has been consumed in various forms of food. Soybean plants are generally cultivated on the field and their yield and quality are strongly affected by climate change. Recently, the abnormal climate conditions, including heat wave and heavy rainfall, frequently occurs which would increase the risk of the farm management. The real-time assessment techniques for quality and growth of soybean would reduce the losses of the crop in terms of quantity and quality. The objective of this work was to develop a simple model to estimate the growth of soybean plant using a multispectral sensor mounted on a rotor-wing unmanned aerial vehicle(UAV). The soybean growth model was developed by using simple linear regression analysis with three phenotypic data (fresh weight, dry weight, leaf area index) and two types of vegetation indices (VIs). It was found that the accuracy and precision of LAI model using GNDVI (R²= 0.789, RMSE=0.73 ㎡/㎡, RE=34.91%) was greater than those of the model using NDVI (R²= 0.587, RMSE=1.01 ㎡/㎡, RE=48.98%). The accuracy and precision based on the simple ratio indices were better than those based on the normalized vegetation indices, such as RRVI (R²= 0.760, RMSE=0.78 ㎡/㎡, RE=37.26%) and GRVI (R²= 0.828, RMSE=0.66 ㎡/㎡, RE=31.59%). The outcome of this study could aid the production of soybeans with high and uniform quality when a variable rate fertilization system is introduced to cope with the adverse climate conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.188-198
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• 2016

In soybeans, responses of high temperature according to shift of sowing dates during the growing season was explored using the crop model, CROPGRO-soybean. In addition, it analyzed impact on change of sowing dates affects yield potential of soybean under future climate scenario (2041-2070). In Jeonju and Miryang during 1981-2010, if sowing at 15 or ten days ahead from 10 June, namely in shorten of the sowing day (i.e. when sown on 25 or 30 May), the yield potential reduced. However, the yield potential increased when sown 5 June. In the case of delay of sowing day (i.e. when sown on 15 or 20 June), reduction of yield potential in the average -5% was higher than increase in the average +2%. In particular, the relative changes for shorten of the sowing day or delay of the sowing day do not be shown in normal years which high temperatures did not abnormally occur during the growing season from 2003 to 2010 except when sown on 25 May. In abnormal years which high temperatures occurred during the critical period, especially R5 to R7, shorten of the sowing day affected to the increase of yield potential in Miryang, while the yield potential decreased in Jeonju except when sown on 5 June. However, delay of the sowing day influenced on the reduction of yield potential both in two sites. In future climate scenario of Representative Concentration Pathway (RCP) 8.5 during from 2041 to 2070, the increase and decrease of yield potential for shorten of the sowing day were +10/-9% for RCP 8.5 of Jeonju, and +14/-9% for RCP 8.5 of Miryang, respectively. Additionally, it showed +10/-17% for RCP 8.5 in Jeonju, and +10/-29% for RCP 8.5 in Miryang, respectively in the increase and decrease of yield potential for delay of the sowing day.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.145-154
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• 2022

Due to abnormal weather conditions caused by climate change, natural disasters and damages are gradually increasing around the world. Global climate change as accompanied by warming is projected to exert adverse impact on production of potato, which is known as cool season crop. Even though, role of potato as a food security crop is expected to increase in the future, the climate change impacts on potato and adaption strategies are not sufficiently established. Therefore, this study was conducted to analyze the damage pattern of potatoes due to high temperature treatment and to evaluate the response of cultivars. T he high temperature treatment (35~38℃) induced heat stress by sealing the plastic house in midsummer (July), and the quantity and quality characteristics of potatoes were compared with the control group. T otal yield, marketable yield (>80 g) and the number of tubers per plants decreased when heat treatment was performed, and statistical significance was evident. In the heat treatment, 'Jayoung' cultivar suffered a high heat damage with an 84% reduction in yield of >80 g compared to the control group. However, in Jopung cultivar, the decrease was relatively small at 26%. Tuber physiological disturbances (Secondary growth, Tuber cracking, Malformation) tended to increase in the heat stress. Under heat conditions, the tubers were elongated overall, which means that the marketability of potatoes was lowered. T he tuber firmness and dry matter content tended to decrease significantly in the heat-treated group. T herefore, the yield and quality of tubers were damaged when growing potatoes in heat conditions. T he cultivar with high heat adaptability was 'Jopung'. T his result can be used as basic data for potato growers and breeding of heat-resistant cultivars.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.337-345
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• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.