~ 143 ~ Journal of Pharmacognosy and Phytochemistry 2020; 9(4): 143-151 E-ISSN: 2278-4136 P-ISSN: 2349-8234 www.phytojournal.com JPP 2020; 9(4): 143-151 Received: 07-05-2020 Accepted: 09-06-2020 Moumita Baishya Assistant Professor, Agricultural Statistics, GIET University, Gunupur, Rayagada, Odisha, India Ravi Ranjan Kumar Ph.D. Scholar, Department of Agricultural Statistics, Institute of Agriculture, Visva-Bharati University, Sriniketan, West Bengal, India Corresponding Author: Ravi Ranjan Kumar Ph.D. Scholar, Department of Agricultural Statistics, Institute of Agriculture, Visva-Bharati University, Sriniketan, West Bengal, India Mushroom price forecasting in the major producing states Moumita Baishya and Ravi Ranjan Kumar Abstract The present study is an attempt to forecast the prices of mushroom in the markets of Punjab, Jammu and Kashmir, Himachal Pradesh, and India. The time-series data on the monthly price of mushroom required for the study was collected from the AGMARKNET website for the period January 2008 to December 2019. The seasonal ARIMA model was used for the modeling of price using the Box-Jenkins technique and best-fitted models were selected based on lowest RMSE, AIC, BIC and MAPE values. The models ARIMA (2,1,2)(1,1,1)[12], ARIMA (1,0,4)(1,1,2)[12], ARIMA (1,1,1)(2,1,1)[12], and ARIMA (3,1,1)(1,1,2)[12] were found to be the best fitted model for Punjab, Jammu and Kashmir, Himachal Pradesh and India respectively. After model models validations, the mean absolute percentage error values were close to the value of fitted models. The results revealed that forecasted wholesale prices of mushroom were higher in the markets from September to November 2020-21. The best-identified models were used for predicting the future prices of 24 months (January 2020 to December 2021). The analysis was done in “R” statistical software. Keywords: ARIMA, SARIMA, MAPE, forecasting, price, mushroom, time series Introduction Agriculture is the backbone of our country. The Green revolution provided the required food sufficiency but nutritional sufficiency still needs to be achieved. The scarcity of land and water resources for agriculture along with climate change is aggravating the prevailing situations. In answer to above, all challenges mushrooms playing a vital role. The mushroom cultivation as an eco-friendly alternative for agro-waste recycling and provide better nutrition for the vast vegetarian population. In the present diet-conscious era, mushrooms are increasingly considered as a future vegetable owing to its medicinal and nutritional properties and the consumer demand for mushrooms markedly expanded in recent years. Mushrooms are considered as a potential substitute of muscle protein on account of their high digestibility (Pavel, 2009) [12] . The mushroom cultivation also strengthens the livelihood of farmers by generating constant farm income and employment opportunities. The recent production data (official data of ICAR-DMR (2017), Solan) showing that the share of button mushroom in India is maximum amounting to 73% followed by oyster mushroom which contributes about 16%. There are two main types of mushroom growers in India, those who are growing white button mushroom round the year under controlled conditions and seasonal growers who are growing button mushrooms during the winter seasons in north-western part of India. In India, the total production of mushrooms is 0.15 million tonnes (ACRIPM-2018). The present study is aimed to forecast the wholesale prices of mushroom for the markets of Punjab, Jammu, and Kashmir, Himachal Pradesh, and India. As the price of mushrooms keeps changing from time to time, it creates risks to producers, traders and consumers involved in the production, marketing and consumption of mushroom. Thus, it is important to forecast the mushroom prices. Price forecasts are critical to market participants who make production and marketing decisions, and to policymakers who administer commodity programs and asses the market impacts of domestic or international events (Sharma, 2015) [13] . Literature Review Gupta et al (2019) [8] studied on the price behaviour of pigeon pea in the Kawardha market by using of ARIMA model. Dhakre and Bhattacharya (2014) [17] worked on the price behaviour of potato and their forecasting in the Agra market using the ARIMA models. Gupta et al (2018) [9] forecasting of arrivals and prices of major pulse in Chhattisgarh. Darekar et al. (2016) validated that the ARIMA model forecasted onion prices in Kolhapur and Yeola markets respectively. Khin et al. (2008) [10] forecasted natural rubber prices in the world market. Burark and Sharma (2012) [3] confirmed the suitability of ARIMA models in agricultural price