Forecasting of Common Paddy Prices in India

Darekar, A. S.; Reddy, A Amarender A.

doi:10.2139/ssrn.3064080

Cited by 16 publications

(10 citation statements)

References 10 publications

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“…Haryana and Punjab markets showed the highest seasonal price index variation as compared to others. The seasonal price index for all the markets in July is low due to the surge in arrivals of wheat to the respective markets after crop harvesting (Horo et al 2016, Darekar and Reddy 2018, Cariappa et al 2020.…”

Section: Resultsmentioning

confidence: 99%

Price dynamics in Indian wholesale wheat markets: Insights and implications

et al. 2021

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Wheat is an integral part of food as well as nutrition security holding a significant share in the consumption basket. Hence, any extreme deviations in the staple food price will affect the prospects of the economy. In the context, we analysed the price dynamics and price volatility in Indian wholesale wheat markets using compound annual growth rate, instability indices, and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model. The study sourced monthly data (July 2002 to June 2019) from the AGMARKNET portal for 15 states. Research findings indicated that the wholesale price, as well as divergence between maximum and minimum price, was highest in the case of Kerala implying the non-production region. Monthly price indices (highest as well as lowest in Haryana respectively during December: 109.90 and July: 91.48) exhibited a clear-cut seasonality attributed to the wheat arrivals post-crop harvest. Barring Maharashtra, the growth and variation in seasonal price indices were positive and relatively low respectively across markets. Corroborating the price trend, Kerala exhibited the highest volatility as evident by the GARCH model estimates, followed by Gujarat, Haryana and Chhattisgarh. Inter alia, volatility in wheat prices is directly linked to regional production. The study advocates for effective dissemination of market information such as price forecast, demand and supply to counter the price volatility as well as decision making for profitable agri-business.

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Section: Resultsmentioning

confidence: 99%

Price dynamics in Indian wholesale wheat markets: Insights and implications

et al. 2021

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“… In order to anticipate trial information and assess the accuracy of the classifier model, classification techniques are used. [25].…”

Section: Machine Learning Algorithms: 631 Hybrid Association Rule-bas...mentioning

confidence: 99%

A Survey of Data Analytical Techniques on Commodity Price Prediction for Smart Agricultural System

Vikranth

Nethravathi

2022

IJCSBE

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Purpose: Food or agricultural products are one of the most basic needs of people. The population of India and the rest of the world is growing at an exponential rate, as is the demand for food commodities. As a result, there should be a proper and convenient way to increase food production, as well as the introduction of efficient technologies in all aspects of the agriculture sector. Commodity prices are an important factor in agriculture because they determine the former's economic status and wealth. The farmer's income and profit are determined by the current and future price of the commodity. Farmers are losing a lot of money because they don't know what the price of their products will be in the future. As a result, there should be a proper approach that provides future information about agricultural products, allowing farmers to make decisions ahead of time before cultivating any product. Design/Methodology/Approach: Developing a theoretical concept based on model building using the secondary sources and focus group interaction method and analysis of the model using the ABCD listing framework. Findings/Result: A method known as price prediction uses historical and current data from a database to estimate future agricultural commodity prices. This paper was primarily concerned with identifying the appropriate data analysis techniques for implementing price prediction systems, particularly for agricultural products. Also conducts a survey on various predictive analytics approaches related to agricultural datasets. Finally, we used our own suggested model to implement a price prediction system with the help of a smart agricultural system. Paper Type: Conceptual Research.

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“…Time series forecasting is a major challenge in many real world applications especially in agricultural commodities. Numerous studies have been carried out in agricultural commodities based on forecasting such as on cocoa bean (Assis et al 2010;Fatimah & Roslan 1986), soybean oil (Gallagher 1987;Johnson 2014;Kenyon et al 1993;Kumar et al 2017), corn (Darekar & Reddy 2017a;Johnson 2014;Kenyon et al 1993), grains (Darekar & Reddy 2017b;Marchezan & Souza 2010) and cottons (Darekar & Reddy 2017c). According to Zabid and Abidin (2015), in the world's oil and fats market, palm oil is one of the important agricultural commodities where both Indonesia and Malaysia are the top producing countries.…”

Section: Literature Reviewmentioning

confidence: 99%

Crude Palm Oil Price Forecasting in Malaysia: An Econometric Approach

Khalid¹,

Hamidi

Thinagar

et al. 2018

JEM

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This paper aims to forecast the performance of crude palm oil price (CPO) in Malaysia by comparing several econometric forecasting techniques, namely Autoregressive Distributed Lag (ARDL), Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Integrated Moving Average with exogenous inputs (ARIMAX). Using monthly time series data spanning from 2008 to 2017, the main results revealed that ARIMAX model is the most accurate and the most efficient model as compared to ARDL and ARIMA in forecasting the crude palm oil price. The results also show that the spot price of palm oil is highly influenced by stock of palm oil, crude petroleum oil price and soybean oil price. The empirical findings provide some insights for decision making and policy implementations, including the formulation of strategies to help the industry in dealing with the price changes and thus enable the Malaysian palm oil industry to continue dominating the international market.

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Forecasting of Common Paddy Prices in India

Cited by 16 publications

References 10 publications

Price dynamics in Indian wholesale wheat markets: Insights and implications

Price dynamics in Indian wholesale wheat markets: Insights and implications

A Survey of Data Analytical Techniques on Commodity Price Prediction for Smart Agricultural System

Crude Palm Oil Price Forecasting in Malaysia: An Econometric Approach

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