scholarly journals The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining

2021 ◽  
Author(s):  
Yan Guo ◽  
Xiaonan Hu ◽  
Zepeng Wang ◽  
Wei Tang ◽  
Deyu Liu ◽  
...  
Keyword(s):  
Price Change ◽  
Agricultural Products ◽  
Agricultural Product ◽  
Association Mining ◽  
Price Forecasting ◽  
Time And Space ◽  
Product Price ◽  
Industry Chain ◽  
Price Prediction ◽  
Butterfly Effect

With the advent of the era of big data, data mining methods show their powerful information mining ability in various fields, seeking the association information hidden in the data, which is convenient for people to make scientific decisions. This paper analyses the butterfly effect in the agricultural product industry chain from the perspective of producer and consumer by using multidimensional time and space theory and proposes a new price forecasting method. We consider that the price change of agricultural products is not only affected by the balance of market supply and demand but also by the factors of time and space. Taking the pig industry chain of Sichuan Province as an example, this paper explores and excavates the data from 2010 to 2020 in the time dimension. Interestingly, we found that the price changes in pork in the market are generally highly correlated with the prices of slaughtered pigs, piglets a few weeks ago and the prices of multiple feed a few months ago. Based on the precise time-space factors, we improved the price forecasting model, greatly improved the accuracy of price prediction, and proved the effectiveness of multidimensional spatiotemporal association mining. The research in this paper is helpful to establish a brand-new agricultural product price prediction theory, which is of great significance to the development of the agricultural economy and global poverty alleviation.

Operational decisions and game analysis in the agricultural supply chain: invest or not?

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Qinyi Zhang ◽  
Wen Cao ◽  
Zhichao Zhang
Keyword(s):  
Supply Chain ◽  
Agricultural Products ◽  
Price Sensitivity ◽  
Agricultural Product ◽  
Sales Volume ◽  
Product Price ◽  
Content Type ◽  
Game Analysis ◽  
Preservation Technology ◽  
Agricultural Supply

PurposeWith the rapid growth of the economy, people have increasingly higher living standards, and although people simply pursued material wealth in the past, they now pay more attention to material quality and safety and environmental protection. This paper discusses the lack of motivation for investing in fresh-keeping technology for agricultural products by individual members of an agricultural supply chain composed of a supplier and a retailer by means of mathematical models and data simulations and discuss the optimal price-invest strategies under different sales models.Design/methodology/approachFirst, based on the model of no investment by both sides (NN), this paper considers three models: supplier only (MN), retailer only (NR) and cooperative investment (MR). Then, the authors analyze the influence of consumer price sensitivity and freshness sensitivity on the investment motivation of agricultural products under four models. Subsequently, the paper makes a sensitivity analysis of the optimal strategies under several models, and makes a game analysis of the suppliers and retailers of agricultural products. Finally, we conduct an empirical analysis through specific values.FindingsThe results show that (a) when the two sides cooperate, the amount of investment is largest, the freshness of the agricultural products is highest, and the sales volume is greatest; however, when both sides do not invest, the freshness of agricultural products and sales volume are lowest. (b) The price and freshness sensitivity of the consumer have an impact on investment decisions. Greater freshness sensitivity corresponds to a higher investment, higher agricultural product price, greater sales volume, and greater supply chain member income and overall income; however, greater price sensitivity corresponds to a lower investment, lower agricultural product price, lower sales volume, fewer supply chain members and lower overall income. (c) The investment game between the supplier and retailer is not only related to the sensitivity to price and freshness but also to the coordination coefficients of interest. At the same time, the market position of agricultural products should be considered when making decisions. The market share of agricultural products will affect the final game equilibrium and then affect the final benefit of the supply chain and individual members.Practical implicationsThese results provide managerial insights for enterprises preparing to invest in agricultural products preservation technology.Originality/valueAt present, the main problem is that member enterprises of agricultural supply chains operate based on their own benefits and are resistant to investing alone to improve the freshness of agricultural products. Instead, they would prefer that other members invest so that they may reap the benefits at no cost. Therefore, the enterprises in each node of the agricultural product supply chain are not motivated enough to invest, and competition and game states are observed among them, and such behavior is definitely not conducive to improving the freshness of agricultural products. However, the current research on agricultural products is more about price, quality and greenness, etc., and there are few studies on agricultural investment. Through the establishment of the model, this paper is expected to provide theoretical suggestions for the supply chain enterprises that plan to invest in agricultural products preservation technology.

Agricultural product price forecasting methods: research advances and trend

2020 ◽  
Vol 122 (7) ◽  
pp. 2121-2138 ◽  
Author(s):  
Luyao Wang ◽  
Jianying Feng ◽  
Xiaojie Sui ◽  
Xiaoquan Chu ◽  
Weisong Mu
Keyword(s):  
Future Research ◽  
Agricultural Product ◽  
Seasonal Adjustment ◽  
Price Forecasting ◽  
Product Price ◽  
Research Directions ◽  
Content Type ◽  
Main Research ◽  
Forecasting Methods ◽  
The Future

PurposeThe purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.Design/methodology/approachThis paper reviews the main research methods and their application of forecasting of agricultural product prices, summarizes the application examples of common forecasting methods, and prospects the future research directions.Findings1) It is the trend to use hybrid models to predict agricultural products prices in the future research; 2) the application of the prediction model based on price influencing factors should be further expanded in the future research; 3) the performance of the model should be evaluated based on DS rather than just error-based metrics in the future research; 4) seasonal adjustment models can be applied to the difficult seasonal forecasting tasks in the agriculture product prices in the future research; 5) hybrid optimization algorithm can be used to improve the prediction performance of the model in the future research.Originality/valueThe methods from this paper can provide reference for researchers, and the research trends proposed at the end of this paper can provide solutions or new research directions for relevant researchers.

scholarly journals Price Forecasting and Span Commercialization Opportunities for Mexican Agricultural Products

Agronomy ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 826
Author(s):  
Wilfrido Jacobo Paredes-Garcia ◽  
Rosalia Virginia Ocampo-Velázquez ◽  
Irineo Torres-Pacheco ◽  
Christopher Alexis Cedillo-Jiménez
Keyword(s):  
Fruits And Vegetables ◽  
Moving Average ◽  
Agricultural Products ◽  
Future Price ◽  
Price Forecasting ◽  
Product Price ◽  
Product Characterization ◽  
Price Estimation ◽  
Auto Regressive ◽  
Hass Avocado

Decision-making based on data analysis leads to knowing market trends and anticipating risks and opportunities. These allow farmers to improve their production plan as well as their chances to get an economic success. The aim of this work was to develop a methodology for price forecasting of fruits and vegetables using Queretaro state, MX as a case study. The daily prices of several fruits and vegetables were extracted, from January 2009 to February 2019, from the National System of Market Information. Then, these prices were used to compute the weekly average price of each product and their span commercialization in Q4 and over the median of historical data. Moreover, product characterization was performed to propose a methodology for future price forecasting of multiple agricultural products within the same mathematical model and it resulted in the identification of 18 products that fit the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model. Finally, future price estimation and validation was performed to explain the product price fluctuations between weeks and it was found that the relative error for most of products modeled was less than 10%, e.g., Hass avocado (7.01%) and Saladette tomato (8.09%). The results suggest the feasibility for the implementation of systems to provide information for better decisions by Mexican farmers.

scholarly journals Is Futurization the Culprit for the Violent Fluctuation in China’s Apple Spot Price?

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 342
Author(s):  
Lin Xie ◽  
Jiahua Liao ◽  
Haiting Chen ◽  
Xuefei Yan ◽  
Xinyan Hu
Keyword(s):  
Price Volatility ◽  
Futures Market ◽  
Agricultural Products ◽  
Spot Price ◽  
Agricultural Product ◽  
Interrupted Time Series Analysis ◽  
Product Price ◽  
Price Fluctuation ◽  
Spot Prices ◽  
The Impact

China aims to utilize the futures market to stabilize agricultural product price fluctuation by quantifying the effects of risk transfer and price discovery. However, the role of futurization has been questioned and even posited as the cause of drastic fluctuations in spot market prices. This research aims to clarify the impact of futurization on the price fluctuation of agricultural products and to provide policy reference for the development of the agricultural futures market through the research. Here, we examine the spot price data for apples and use Interrupted time-series analysis (ITSA) and GARCH models to estimate the impact of apple futures on the volatility of spot prices. Our findings demonstrate that the launch of China’s apple futures did not increase the volatility of apple spot prices; that is, futurization was not the cause of skyrocketing apple spot prices. In the long term, our results suggest that futures will help reduce the volatility of apple spot prices and that the introduction of futures will ultimately reduce the price volatility of agricultural products.

scholarly journals Developing an Agricultural Product Price Prediction Model using HADT Algorithm

2019 ◽  
Vol 9 (1S4) ◽  
pp. 569-575
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Decision Tree ◽  
Prediction Model ◽  
Predictive Analytics ◽  
Agricultural Product ◽  
Agricultural Crop ◽  
Product Price ◽  
Price Prediction ◽  
Model Based

Big Data Predictive Analytics and Data mining are emerging recent research field to analyse the agricultural crop price. The applications and techniques of data mining as well as Big Data using agriculture data is considered in this paper. In particular, the farmers are more concern about estimating that how much profit they are about to expect for the chosen crop. As with many other sectors the amount of agriculture data are increasing on a daily source. In this work, agriculture crop price dataset of Virudhunagar District, Tamilnadu, India is considered and for the price prediction model based on data mining decision tree techniques. The main goal is to establish the new predictive model based on Hybrid Association rule-based Decision Tree algorithm (HADT). The outcome for the suggested HADT forecast model is heartening and precise to predict agricultural product prices than other current decision tree models.

scholarly journals Agricultural Product Price Forecasting using ARIMA Model

2020 ◽  
Vol 8 (5) ◽  
pp. 5203-5207
Keyword(s):  
Arima Model ◽  
Market Price ◽  
Green Gram ◽  
Agricultural Product ◽  
Future Market ◽  
Price Forecasting ◽  
Product Price ◽  
Price Movement ◽  
The Future ◽  
Actual Price

The study consider the comparison of actual market price of green gram and the forecasted price of green gram to identify how close the forecasting tools help to identify the market price of agro product in future. This analysis will help to identify the expected fluctuation from the expected price and actual price of a commodity that will support the farmers and middle to decide the price of a particular commodity. In this study the green gram price of Odisha (India) is taken into consideration for analysis. The study will also explore the best method to identify the future market price. For the analysis weekly price of the product is considered and seasonal ARIMA (1, 1, 1) (1, 0, 1), S=4 model is used as it was found to be the best model for forecasting. It is expected that this analysis will benefit the farmers in deciding the price of the products and support in rescheduling their crop as per the price movement in future.

scholarly journals ESTIMATION OF PRICES IN AGRICULTURAL COMMODITY USING MACHINE LEARNING

2021 ◽  
pp. 59-65
Author(s):  
CH.Bhanu Kumar ◽  
Sri.M.Anil
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Annual Rainfall ◽  
Agricultural Product ◽  
Agricultural Crops ◽  
Agricultural Commodity ◽  
Product Price ◽  
Price Prediction ◽  
Difficult Time ◽  
Agricultural Industries

India is a country where agriculture and agricultural industries provide the majority of the country’s income and economy. Farmers have traditionally had a difficult time predicting prices for agricultural crops. Farmers are currently losing a lot of money owing to price fluctuations caused by climatic change and other price influencing factors. Farmers are unable to obtain the price they desire for their produce. The goal of this project is to develop a decision-making assistance model for agricultural product price prediction. This technique can be used as a guide when deciding what a farmer should plant, taking into account factors such as annual rainfall, WPI, and so forth. The system provides a 12-month forecast in detail. Decision tree regression, a machine learning regression technique, is the methodology we employ in the system. KEY WORDS: Price Prediction, Machine Learning, WPI, Decision Tree Regression.

scholarly journals A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 359
Author(s):  
Kai Ye ◽  
Yangheran Piao ◽  
Kun Zhao ◽  
Xiaohui Cui
Keyword(s):  
Decision Making ◽  
Essential Role ◽  
Online Discussion ◽  
Professional Community ◽  
Experimental Results ◽  
Agricultural Products ◽  
Price Prediction ◽  
Price Data ◽  
Rich Information ◽  
Lstm Network

Forecasting the prices of hogs has always been a popular field of research. Such information has played an essential role in decision-making for farmers, consumers, corporations, and governments. It is hard to predict hog prices because too many factors can influence them. Some of the factors are easy to quantify, but some are not. Capturing the characteristics behind the price data is also tricky considering their non-linear and non-stationary nature. To address these difficulties, we propose Heterogeneous Graph-enhanced LSTM (HGLTSM), which is a method that predicts weekly hog price. In this paper, we first extract the historical prices of necessary agricultural products in recent years. Then, we utilize discussions from the online professional community to build heterogeneous graphs. These graphs have rich information of both discussions and the engaged users. Finally, we construct HGLSTM to make the prediction. The experimental results demonstrate that forum discussions are beneficial to hog price prediction. Moreover, our method exhibits a better performance than existing methods.

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