In this study an attempt was made to analyse the fruit arrival and price patterns in a wholesale fruit market (Haldwani Mandi) for trend and seasonality in order to develop forecasting models for the fruit arrival process so as to rationalize an important input to fruit mandi system design. Historical time series data on monthly arrivals and average monthly prices was collected from the Haldwani mandi records for the period January, 1990 to April, 2009. Arrivals and prices of five fruits (Mango, Banana, Apple, Peach and Orange) were considered. A program in MATLAB 7.0 was developed for trend and seasonal analysis. For forecasting, these trend models were extended and seasonal index were applied for each month. Forecasting models were developed on the basis of first 204 months (January 1990 to December 2006) data using time series analysis technique. Forecasts were generated for the next 36 months (January 2007 to December 2009). These forecasts were compared with the actual arrivals for January 2007 to April 2009. Results of the present investigation have shown that arrival of Apple was observed in the month of July, Banana arrived maximum in October, whereas Mango, Orange and Peach recorded maximum arrival in the months of July, April and May respectively. Seasonal indices corresponding to these peak arrivals were 626.66, 136.29, 876.06, 371.17 and 439.89 respectively. In the case of Apple, arrivals and prices were inversely correlated; i.e., prices were low during peak arrival. However, in the case of banana, there was very little correlation; one major reason for this seems to be that the prices of Banana did not vary throughout the year. There was a increasing trend in the case of prices. In the trend analysis, it was observed that the arrival of Apple and Orange had a decreasing trend for the years under analysis, whereas Mango and Banana had an increasing trend for this period. The monthly arrival of Peach almost remained the same. Future forecasts were developed for the monthly arrival and average monthly prices of these selected commodities. Maximum error for each forecast was calculated on the basis of peak arrival/ prices in a year and maximum error was obtained as 26.89% in forecasting the arrival of Apple.
Table of Contents
1. INTRODUCTION
2. REVIEW OF LITERATURE
3. MATERIALS AND METHODS
3. 1 Haldwani Mandi and its Operations
3.2 Data Collection
3.3 Data Analysis
3.4 Programming for computation of trend
3.5 Programming for computation of seasonal variations
4. RESULTS AND DISCUSSION
4.1 Trend Analysis
4.2 Seasonal variations in arrivals and prices
4.3 Future Forecasts:
5. SUMMARY AND CONCLUSIONS
Research Objectives and Core Themes
The primary objective of this research is to analyze historical fruit arrival and price patterns within the Haldwani Mandi market to develop predictive mathematical models. By applying time series analysis, the study aims to capture the time-dependent structure of monthly fruit arrivals and average prices, ultimately providing a tool to generate accurate future forecasts that support rational decision-making and efficient design of Mandi infrastructure.
- Development of MATLAB 7.0 programs for trend computation and seasonal analysis.
- Application of the Least Square Method for analyzing long-term trends in market data.
- Utilization of the Ratio to Trend Method to calculate monthly seasonal indices.
- Comparative validation of forecasted arrival and price data against actual recorded values.
- Identification of seasonality, periodicity, and arrival patterns for specific fruit varieties.
Excerpt from the Book
1. INTRODUCTION
The fruit trade in India is conducted through a network of wholesale markets called Mandies. These Mandies represent a complex interaction amongst seller (grower), buyer (trader) and the regulatory agency (government). Therefore, for a properly managed post production system fruit Mandies are the nodal points through which all the primary production of the concerned production catchment must pass. Thus a suitably designed modern Mandi could provide a unique opportunity for processing of fruits, which may involve receiving, cleaning and grading, drying, storage and disposal. Such a system is also expected to afford substantial reduction in primary losses of the total fruit production. However, for a rational and logical design of the Mandi system, including the various processing facilities, the characteristics of inflow i.e. fruit arrival pattern must be known, or be capable of being forecasted.
These arrival patterns are strongly time variant. One of the widely applied techniques to describe the time dependent stochastic processes is time series analysis. Since, the Mandi arrivals and prices form an ordered sequence of observations; they also represent a time series. Therefore, their time dependence could also be established using time series analysis techniques. Once the mathematical model to describe the time dependent structure of the series is developed it could be extended to generate future forecasts and help in Mandi design. The present study is a step in this direction.
Summary of Chapters
1. INTRODUCTION: Outlines the significance of wholesale fruit markets (Mandies) in India and defines the study's goal to model and forecast arrival patterns using time series analysis.
2. REVIEW OF LITERATURE: Provides a comprehensive overview of existing forecasting techniques, including econometric approaches and time series models, citing various studies on market arrivals and pricing.
3. MATERIALS AND METHODS: Describes the data collection process at Haldwani Mandi and details the algorithms and MATLAB implementation for trend and seasonal analysis.
4. RESULTS AND DISCUSSION: Presents the findings of the trend analysis, seasonal indices, and the performance evaluation of the forecasting models against actual data.
5. SUMMARY AND CONCLUSIONS: Consolidates the research findings, highlighting the predictive success of the developed models and identifying specific seasonal peaks and correlation trends for the studied fruits.
Keywords
Fruit Trade, Haldwani Mandi, Matlab Programming, Time Series Analysis, Market Arrivals, Price Prediction, Seasonal Variations, Trend Analysis, Forecasting Models, Agriculture, Least Square Method, Ratio to Trend, Statistical Modeling, Market Infrastructure, Economic Analysis
Frequently Asked Questions
What is the primary focus of this work?
The work focuses on modeling and forecasting monthly fruit arrivals and average prices in the Haldwani Mandi to assist in the efficient planning and design of market infrastructure.
Which fruits are analyzed in the study?
The study analyzes five specific fruits: Apple, Banana, Mango, Orange, and Peach.
What is the central research question?
The research asks how historical time series data can be used to describe the time-dependent structure of fruit arrivals and prices to generate reliable future forecasts.
Which scientific methods are employed?
The researchers utilized time series analysis, specifically the Least Square Method for trend computation and the Ratio to Trend Method for calculating seasonal indices, implemented via MATLAB 7.0.
What does the main body of the work cover?
It covers data collection methodology, the algorithmic development of mathematical models for trend and seasonality, and a discussion of the empirical results comparing forecasted values to actual market data.
Which keywords best characterize this research?
Key terms include Time Series Analysis, Mandi, Fruit Trade, Forecasting, MATLAB, and Seasonal Variations.
How were the models validated?
The models were validated by using the first 204 months of data for training and the subsequent 28 months for testing the accuracy of the forecasts.
Why is the Haldwani Mandi significant to this research?
It serves as the case study location due to its proximity to the university and its role as a key agricultural node in the Kumaon region of Uttarakhand.
- Quote paper
- Ram Krishna Pandey (Author), Abhinav Mishra (Author), Rupesh Tomer (Author), 2009, Fruit Trade in India. Matlab Programming for Prediction of monthly Arrival and Prices of various Fruits, Munich, GRIN Verlag, https://www.grin.com/document/295348
