• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.2
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• pp.7-14
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• 2012

To analyze any given situation, it is necessary to have information on elements which affect the situation. Particularly, there is greater variability in both frequency and magnitude of agricultural product prices as they are affected by various unpredictable factors such as weather conditions etc. This is the reason why it is difficult for the farmers to maintain their stable income through agricultural production and marketing. In this research, attempts are made to quantify the entropy of various situations inherent in the price changes so that the stability of farmers' income can be increased. Through this research, we developed an entropy model which can quantify the uncertainties of price changes using the probability distribution of price changes. The model was tested for its significance by comparing its simulation outcomes with actual ranges and standard deviations of price variations of the past using monthly agricultural product prices data. We confirmed that the simulation results reflected the features of the ranges and standard deviations of actual price variations. Also, it is possible for us to predict standard deviations for changed prices which will occur after a certain time using the information entropy obtained from relevant agricultural product price data before the time.

• Journal of Advanced Technology Convergence
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• v.3 no.2
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• pp.17-23
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• 2024

In this paper, we analyzed the impact of temperature and precipitation on agricultural product prices and predicted the prices of major agricultural products using TensorFlow. As a result of the analysis, the rise in temperature and precipitation had a significant effect on the rise in prices of cabbage, radish, green onion, lettuce, and onion. In particular, prices rose sharply when temperature and precipitation increased simultaneously. The prediction model was useful in predicting agricultural product price changes due to climate change. Through this, agricultural producers and consumers can prepare for climate change and prepare response strategies to price fluctuations. The paper can contribute to understanding the impact of climate change on agricultural product prices and exploring ways to increase the stability and sustainability of agricultural product markets. In addition, it provides important data to increase agricultural sustainability and ensure economic stability in the era of climate change. The research results will also provide useful insights to policy makers and can contribute to establishing effective agricultural policies in response to climate change.

• Asia Pacific Journal of Business Review
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• v.1 no.1
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• pp.1-22
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• 2016

The prediction of prices of agricultural products in the agriculture IT sector plays a significant role in the economic life of consumers and anyone engaged in agricultural business, and as these prices fluctuate more often than do other prices, the prediction of these prices holds a great deal of research promise. For this reason, academic literature has provided studies on the factors influencing the prices of agricultural products and the price index. However, as these factors vary, they are difficult to predict, resulting in the challenge of acquiring quantitative data. China is one example of a country without a reliable prediction system for prices of agricultural products. Fortunately, disclosed heterogeneous data can be found on the Internet, which allows for the effective collection of factors related to the prediction of these product prices through the use of text mining. The data provided online is valuable in that they reflect the opinions of the general public in real-time. Accordingly, this study aims to use heterogeneous data from the Internet and suggest a model predicting the prices of agricultural products before functional analyses. Toward this end, data analyses were conducted on the Chinese agricultural products market, one of the largest markets in the world.

• Korean Journal of Agricultural Science
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• v.44 no.4
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• pp.636-641
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• 2017

Water is an essential resource for human survival. According to the OECD Environmental Outlook to 2050, rapid industrialization and a global population increase by approximately two billion will likely increase global water use by 55% in 2050. However, water depletion has been getting worse than before and has been happening more quickly, as Earth's water resources are limited. The present study proposes water management measures by using the virtual water theory which enables water consumption measurement and the confirmation and recognition of water scarcity problems, and will support the development of counter-measures. As a method for estimating the value of agricultural water, virtual water theory was used to calculate the amount of agricultural water input for domestic rice and to apply prices of agricultural water in the United States and China to Korean water prices. When the Chinese price was applied to Korean water prices, the value of agricultural water represented 0.3% of the Korean rice producer's price. When the US price was applied to Korean water prices, the value of agricultural water represented 1.6% of the domestic rice producer's price. The study exposes the percentage of the value of agricultural water in agricultural product prices, as well as how this scare resource may affect future prices. In the future, if there are water charges to effectively manage agricultural water, this study, which uses the virtual water theory, can be used as a preliminary research.

• Agribusiness and Information Management
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• v.12 no.2
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• pp.40-54
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• 2021

Market integration and prices in pulse crops like black gram play an important role in determining the production decisions of the farmers and diversification towards high value nutritious crops. In this context, the present study explores extent of market integration and price transmission in selected major black gram markets in Andhra Pradesh using Johansen co-integration, Vector Error Correction Model and Granger causality test. The study used monthly prices data of black gram (Rs/quintal) sourced from selected markets of Srikakulam, Krishna and Kurnool spanning January, 1990 to December, 2019. The results of the study strongly buttressed the existence of co-integration and interdependence of selected black gram markets in Andhra Pradesh. However, the speed of adjustment of the prices found to be moderate in Krishna market and quite weaker in Srikakulam market and thereby prices correct a small percentage of the disequilibrium in these markets with the greatest percentage by the external and internal forces. So, it necessitates the need for future research, to investigate the influence of external and internal factors such as market infrastructure, Government policy and self-sufficient production, product characteristics and utilization towards market integration. As there exists only unidirectional causality from Krishna to Kurnool and from Krishna to Srikakulam markets, it calls for strengthening the information technology for flow of market information regularly to help the farmers for increasing their income.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.911-926
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• 2021

We devised a grape variety model to estimate the impact of lowering the Shine Muscat quality level on the grape market. Shine Muscat has become a popular grape variety in Korea. Accordingly, the area devoted to the harvesting of Shine Muscat has increased dramatically since 2016. Our study examines how a reduction in the quality of Shine Muscat affects other grapes such as Campbell Early, giant peak, and Muscat Bailey A (MBA). The Armington model was used to impose consumer preferences and product differentiation assumptions. We found that a decrease in the consumer preference for Shine Muscat realized by lowering the quality of Shine Muscat largely reduces the price of this variety. Also, the prices of other grape varieties fell via a substitute effect. Moreover, if grape varieties were more differentiated, the reduction in the price of Shine Muscat would be greater, while the decreases in the prices of other grape varieties would be smaller. These results imply that farmers of Shine Muscat must continue with quality management efforts to avoid the negative effect of changing consumer behavior with regard to Shine Muscat against a reduction in its quality. Our model introduces a product differentiation model for the fruit market and helps policymakers and farmers understand the impact of changing market conditions in the fruit market.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.83-91
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• 2021

For agricultural products, supply is irregular due to changes in meteorological conditions, and it has high price elasticity. For example, if the supply decreases by 10%, the price increases by 50%. Due to these fluctuations in the prices of agricultural products, the Korean government guarantees the safety of prices to producers through small merchants' auctions. However, when prices plummet due to overproduction, protection measures for producers are insufficient. Therefore, in this paper, we designed a business model that can be used in the electronic transaction system by predicting the price of agricultural products with an artificial intelligence algorithm. To this end, the trained model with the training pattern pairs and a predictive model was designed by applying ARIMA, SARIMA, RNN, and CNN. Finally, the agricultural product forecast price data was classified into short-term forecast and medium-term forecast and verified. As a result of verification, based on 2018 data, the actual price and predicted price showed an accuracy of 91.08%.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.252.2-252.2
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• 2010

Biomass is considered to be a major potential fuel and renewable resource for the future. In fact, there is high potential to produce the large amount of energy from biomass around the world. In this study, to obtain basic data for practical application of wood pellet and wood pellet boiler system as heating system in agriculture, agricultural biomass resources were surveyed, pellet was made of agricultural by-product such as stem of rape, oat and rice, ricehusk and sawdust and wood pellet boiler system with capacity of 116 kW was manufactured and installed in greenhouse of $38.5m{\times}32m$. High heating value, bulk density and ash content of pellet made of agricultural by-product and efficiency and heating performance of this system was estimated. Rice straw was the largest agricultural biomass in 2005 and the total amount of rice straw converted into energy of $131.71{\times}10^{11}$ kJ. And in 2005, total amount of forest' by-product converted into energy of $29,277.05{\times}10^{11}$ kJ. High heating values of pellets made of agricultural by-products of stem and seed of rape, stem of oat, rice straw and rice husk were 16,034, 16,026, 16,089, 15,650, 15,044 kJ/kg respectively. High heating values of pellets made of agricultural by-products were 83.6% compared to that of wood pellet. Average bulk density of pellets made of agricultural by-products of stem and seed of rape, stem of oat, rice straw and rice husk was 1,400 $kg/m^3$. Ash contents of the pellets were 6.6, 7.0, 6.2, 5.5, 33% respectively. Ash content of rice husk pellet was the largest compared to other kind of pellets. To increase efficiency of agricultural pellet boiler, the boiler adopted secondary heat exchanger. The agricultural pellet boiler designed and manufactured in this study had high efficiency of 84.2% compared to the conventional agricultural pellet boiler, when water flow rate, exhaust gas temperature and average combustion furnace temperature were 39L/min, $180^{\circ}C$, $680^{\circ}C$ respectively. And pellet supplying and pausing time were 13, 43 seconds respectively. In March of 2010, prices of wood pellet, agricultural tax free diesel, diesel, kerosene were 350 won/kg, 811 won/L, 1,422 won/L, 976 Won/L respectively. Also in terms of energy, prices per same heating value were 77.8, 90.1, 158, 108.4 Won/Mcal. Energy saving rate of wood pellet was 16, 50, 39% compared to agricultural tax free diesel, diesel and kerosene respectively.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.1-10
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• 2022

Vegetables such as cabbage are greatly affected by natural disasters, so price fluctuations increase due to disasters such as heavy rain and disease, which affects the farm economy. Various efforts have been made to predict the price of agricultural products to solve this problem, but it is difficult to predict extreme price prediction fluctuations. In this study, cabbage prices were analyzed using the ensemble Voting technique, a method of determining the final prediction results through various classifiers by combining a single classifier. In addition, the results were compared with LSTM, a time series analysis method, and XGBoost and RandomForest, a boosting technique. Daily data was used for price data, and weather information and price index that affect cabbage prices were used. As a result of the study, the RMSE value showing the difference between the actual value and the predicted value is about 236. It is expected that this study can be used to select other time series analysis research models such as predicting agricultural product prices

• Smart Media Journal
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• v.13 no.1
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• pp.9-17
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• 2024

This study aims to develop an integrated agricultural distribution network management system to improve the quality, profit, and decision-making efficiency of agricultural products. We adopt two key techniques: crop maturity detection based on the YOLOX target detection algorithm and market price prediction based on the Prophet model. By training the target detection model, it was possible to accurately identify crops of various maturity stages, thereby optimizing the shipment timing. At the same time, by collecting historical market price data and predicting prices using the Prophet model, we provided reliable price trend information to shipping decision makers. According to the results of the study, it was found that the performance of the model considering the holiday factor was significantly superior to that of the model that did not, proving that the effect of the holiday on the price was strong. The system provides strong tools and decision support to farmers and agricultural distribution managers, helping them make smart decisions during various seasons and holidays. In addition, it is possible to optimize the distribution network of agricultural products and improve the quality and profit of agricultural products.