• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.63-72
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• 2020

With the implementation of integrated water management policies, the need for information sharing with respect to agricultural water use has increased, necessitating the quantification of irrigation water supply using monitoring data. This study aims to estimate the irrigation water supply amount based on the relationship between the water level and irrigation canal discharge, and evaluate the reliability of monitoring data for irrigation water supply in terms of hydrology. We conducted a flow survey in a canal and reviewed the applicability of the rating curve based on the exponential and parabolic curves. We evaluated the reliability of the monitoring data using a reservoir water balance analysis and compared the calculated results of the supply quantity in terms of the reservoir water reduction rate. We secured 26 readings of measurement data by varying the water levels within 80% of the canal height through water level control. The exponential rating curve in the irrigation canal was found to be more suitable than the parabolic curve. The irrigation water supplied was less than 9.3-28% of the net irrigation water from 2017 to 2019. Analysis of the reservoir water balance by applying the irrigation water monitoring data revealed that the estimation of the irrigation water supply was reliable. The results of this study are expected to be used in establishing an evaluation process for quantifying the irrigation water supply by using measurement information from irrigation canals in agricultural reservoirs.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• 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.