Immunoassay for Detection of Zearalenone in Agricultural Commodities

1990 ◽  
pp. 170-175
Author(s):  
Glenn A. Bennett
Keyword(s):  
Agricultural Commodities

International Price Changes of Agricultural Commodities and Their Impacts on Income Distribution and Poverty in Brazil

2006 ◽  
Author(s):  
Joaquim Guilhoto ◽  
Carlos Roberto Azzoni ◽  
Fernando Gaiger Silveira ◽  
Tatiane A. de Menezes ◽  
Marcos M. Hasegawa ◽  
...  
Keyword(s):  
Income Distribution ◽  
Agricultural Commodities ◽  
Price Changes ◽  
International Price

Optimal Trading and Shipping of Agricultural Commodities

2014 ◽  
Author(s):  
Nicolas Merener ◽  
Ramiro Alberto Moyano ◽  
Nicolas E. Stier-Moses ◽  
Pablo Watfi
Keyword(s):  
Agricultural Commodities ◽  
Optimal Trading

Prediction of Future Market Price for Agricultural Commodities

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Sagar Pathane ◽  
Uttam Patil ◽  
Nandini Sidnal
Keyword(s):  
Mobile Application ◽  
Market Price ◽  
Commodity Prices ◽  
Future Price ◽  
Future Market ◽  
Graphical Interface ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Web Based ◽  
Agriculture Products

The agricultural commodity prices have a volatile nature which may increase or decrease inconsistently causing an adverse effect on the economy. The work carried out here for predicting prices of agricultural commodities is useful for the farmers because of which they can sow appropriate crop depending on its future price. Agriculture products have seasonal rates, these rates are spread over the entire year. If these rates are known/alerted to the farmers in advance, then it will be promising on ROI (Return on Investments). It requires that the rates of the agricultural products updated into the dataset of each state and each crop, in this application five crops are considered. The predictions are done based on neural networks Neuroph framework in java platform and also the previous years data. The results are produced on mobile application using android. Web based interface is also provided for displaying processed commodity rates in graphical interface. Agricultural experts can follow these graphs and predict market rates which can be informed to the farmers. The results will be provided based on the location of the users of this application.

Does Commodity Derivatives Function Effectively? A lengthy Discussion

2021 ◽  
pp. 227797522098574
Author(s):  
Bhabani Sankar Rout ◽  
Nupur Moni Das ◽  
K. Chandrasekhara Rao
Keyword(s):  
Price Discovery ◽  
Spot Market ◽  
Futures Market ◽  
The Other ◽  
Volatility Spillover ◽  
Agricultural Commodities ◽  
Commodity Derivatives ◽  
Derivative Market ◽  
Negative Shock ◽  
Lengthy Discussion

The present work has been designed to intensely investigate the capability of the commodity futures market in achieving the aim of price discovery. Further, the downside of the cash and futures market and transfer of the risk to other markets has also been studied using VaR, and Bivariate EGARCH. The findings of the work point that the metal commodity derivative market helps in the efficient discovery of price in the spot market except for nickel. But, in the case of the agricultural commodities, the spot is found to be leading and thus there is no price discovery except turmeric. On the other hand, the volatility spillover is bidirectional for both agri and metal commodities except copper, where volatility spills only from futures to spot. Further, the effect of negative shock informational bias differs from commodity to commodity, irrespective of metal or agriculture.

scholarly journals Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea

Agriculture ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 513
Author(s):  
Tserenpurev Chuluunsaikhan ◽  
Ga-Ae Ryu ◽  
Kwan-Hee Yoo ◽  
HyungChul Rah ◽  
Aziz Nasridinov
Keyword(s):  
Deep Learning ◽  
Topic Modeling ◽  
Prediction Models ◽  
Strong Relationship ◽  
Retail Price ◽  
Business Strategies ◽  
Agricultural Commodities ◽  
Statistical Machine Learning ◽  
News Stories ◽  
South Korean

Knowing the prices of agricultural commodities in advance can provide governments, farmers, and consumers with various advantages, including a clearer understanding of the market, planning business strategies, and adjusting personal finances. Thus, there have been many efforts to predict the future prices of agricultural commodities in the past. For example, researchers have attempted to predict prices by extracting price quotes, using sentiment analysis algorithms, through statistical information from news stories, and by other means. In this paper, we propose a methodology that predicts the daily retail price of pork in the South Korean domestic market based on news articles by incorporating deep learning and topic modeling techniques. To do this, we utilized news articles and retail price data from 2010 to 2019. We initially applied a topic modeling technique to obtain relevant keywords that can express price fluctuations. Based on these keywords, we constructed prediction models using statistical, machine learning, and deep learning methods. The experimental results show that there is a strong relationship between the meaning of news articles and the price of pork.

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