• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.135-143
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• 2020

Shortwave radiation and sunshine hours (SHOUR) are important variables having many applications, including crop growth. However, observational data for these variables have low horizontal resolution, rendering its application to related research and decision making on f arming practices challenging. In the present study, hourly solar radiation data were physically generated using the Land-Atmosphere Modeling Package (LAMP) at the National Center f or Agro-Meteorology, and then daily SHOUR fields were calculated through statistical downscaling. After data quality evaluation, including case studies, the SHOUR data were added to the existing publically accessible LAMP daily database. The LAMP daily dataset, newly updated with SHOUR, has been provided operationally as input data to the "Gyeonggi-do Agricultural Drought Prediction System," which predicts agricultural weather disasters and field crop growth status.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.171-175
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• 2020

Studies in socioeconomic impacts of air pollution are inevitable to merge data of the air pollutant density, weather, and socioeconomic variables. Due to their spatiotemporal disparities in units, to combine these data are time and effort consuming generically. The data described in this article aims to provide the major variables of air pollution and weather at the Si-Gun-Gu level to meet the data needs from social science. The latest (August 2020) data distributed are the balanced panel of 250 Si-Gun-Gu in South Korea for 2001-2018. The weather variables in this data are directly applicable to other social science topics, which are not limited to air pollution research.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.143-146
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• 2016

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.171-177
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• 2022

In this paper, we propose a service method that can provide insight into multi-source agricultural data, way to cluster environmental factor which supports data analysis according to time flow, and curate crop environmental factors. The proposed curation service consists of four steps: collection, preprocessing, storage, and analysis. First, in the collection step, the service system collects and organizes multi-source agricultural data by using an OpenAPI-based web crawler. Second, in the preprocessing step, the system performs data smoothing to reduce the data measurement errors. Here, we adopt the smoothing method for each type of facility in consideration of the error rate according to facility characteristics such as greenhouses and open fields. Third, in the storage step, an agricultural data integration schema and Hadoop HDFS-based storage structure are proposed for large-scale agricultural data. Finally, in the analysis step, the service system performs DTW-based time series classification in consideration of the characteristics of agricultural digital data. Through the DTW-based classification, the accuracy of prediction results is improved by reflecting the characteristics of time series data without any loss. As a future work, we plan to implement the proposed service method and apply it to the smart farm greenhouse for testing and verification.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.1-5
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• 2016

• Journal of Distribution Science
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• v.20 no.11
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• pp.79-88
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• 2022

Purpose: The purpose of this study was to determine the effect of agricultural exports and imports on economic growth in Bangladesh and propose an upgraded and customized model of the supply chain for agribusiness growth in Bangladesh to achieve plain sailing and systematic operation and financial gains at home and abroad. Research design, data, and methodology: All data in the research have been collected from secondary sources. Gross domestic product was used as the dependent variable and exports and imports of agricultural products were used as independent variables. Pairwise Granger causality was utilized to see the impact of the variable responsible for the economic growth in Bangladesh and the causal relationship between the variables analyzed was measured using Johansen co-integration test. Results: From the empirical analysis, the researchers observed that agricultural commodity imports and exports have a unidirectional impact on economic growth in Bangladesh and a long-run causal link with economic growth in Bangladesh. The suggested supply chain model of agribusiness aids in achieving smooth operations, systematic management, and monetary gains both domestically and internationally. Conclusions: This paper contributes to the development of a more effective and profitable agribusiness supply chain in Bangladesh systematically through their theoretical and practical implications.

• Advances in nano research
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• v.14 no.3
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• pp.225-234
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• 2023

In this study, we aim to use big data resources and statistical analysis to obtain a reliable instruction to reach high-quality and high yield agricultural yields. In this regard, soil type data, raining and temperature data as well as wheat production in each year are collected for a specific region. Using statistical methodology, the acquired data was cleaned to remove incomplete and defective data. Afterwards, using several classification methods in machine learning we tried to distinguish between different factors and their influence on the final crop yields. Comparing the proposed models' prediction using statistical quantities correlation factor and mean squared error between predicted values of the crop yield and actual values the efficacy of machine learning methods is discussed. The results of the analysis show high accuracy of machine learning methods in the prediction of the crop yields. Moreover, it is indicated that the random forest (RF) classification approach provides best results among other classification methods utilized in this study.

• Korean Journal of Environmental Agriculture
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• v.38 no.3
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• pp.159-165
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• 2019

BACKGROUND: The water footprint (WF) is an indicator of freshwater use that appears not only at direct water use of a consumer or producer, but also at the indirect water use. As an indicator of 'water use', the water footprint includes the green, blue, and grey WF, and differs from the classical measure of 'water withdrawal' because of green and grey WF. This study was conducted to assess and estimate the water footprint of the soybean and Chinese cabbage. METHODS AND RESULTS: APEX model with weather data, soil and water quality data from NAS (National Institute of Agricultural Sciences), and farming data from RDA (Rural Development Administration) was operated for analyzing the WF of the crops. As the result of comparing the yield estimated from APEX with the yield extracted from statistic data of each county, the coefficients of determination were 0.83 for soybean and 0.97 for Chinese cabbage and p-value was statistically significant. The WFs of the soybean and Chinese cabbage at production procedure were 1,985 L/Kg and 58 L/Kg, respectively. This difference may have originated from the cultivation duration. The WF ratios of soybean were 91.1% for green WF and 8.9% for grey WF, but the WF ratios of Chinese cabbage were 41.5% for green WF and 58.5% for grey WF. CONCLUSION: These results mean that the efficiency of water use for soybean is better than that for Chinese cabbage. The results could also be useful as an information to assess environmental impact of water use and agricultural farming on soybean and Chinese cabbage.

• Journal of Drive and Control
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• v.19 no.4
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• pp.46-53
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• 2022

RIn this study, a test tractor that could measure various types of agricultural operational loads was developed, and its performance was verified. This tractor could be used to measure the load generated during agricultural work and convert the related data into a database. A test tractor was developed using an 80-kW-rated load tractor, and it could measure various types of field test data, such as engine torque and rpm, wheel torque, PTO(power take-off) torque, hexometer, IMU/INS sensor, steering angle sensor, hydraulic pressure, and flow sensor data. To verify the developed test tractor, a verification test using an agriculture rotavator was performed. The test conditions were L1, L2, and L3 based on the tractor's main and sub-transmission stages, and stages 1 and 2 were selected as the PTO. In a comparison of the analyzed test data, similar tendencies in the test results of this research and other research (Kim's research) were seen. Through this, the developed test tractor was verified. In the future, we plan to conduct research on the tractor developed in this study using various attached working machines.

• Atmosphere
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• v.34 no.1
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• pp.69-81
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• 2024

In this paper, Annual flux variations in the Boseong Tall Tower (BTT) from 2016 to 2020 were analyzed using data from three levels (2.5 m, 60 m, and 300 m). BTT was installed in Boseong-gun, Jeollanam-do in February 2014 and continued to conduct energy exchange observations such as CO2, sensible heat, and latent heat using the eddy covariance method until March 2023. The BTT was located in a very flat and uniform paddy field, and flux observations were conducted at four levels: 2.5 m, 60 m, 140 m, and 300 m above ground. Surface energy balance was confirmed from observed data of net radiation flux, soil heat flux, sensible heat flux, and latent heat flux. Additionally, 2.5 m height surface fluxes, which are most influenced by agricultural land, were compared with data from Local Data Assimilation and Prediction System (LDAPS) of the Korea Meteorological Administration to evaluate the accuracy of LDAPS flux data. The correlation coefficient between LDAPS flux data and observed values was 0.95 or higher. Excluding summer latent heat flux data, there was a general tendency for LDAPS data to be higher than observed values. The footprint areas estimated below 60 m height mainly covered agricultural land, and flux observations at 2.5 m and 60 m heights showed typical agricultural characteristics. In contrast, the footprint estimated at 300 m height did not show agricultural characteristics, indicating that observations at this height encompassed a wide range, including mountains, sea, and roads. The analysis results of long-term flux observations can contribute to understanding the energy and carbon dioxide fluxes in agricultural fields. Furthermore, these results can be utilized as essential data for validating and improving numerical models related to such fluxes.