<div>Background: The increasing demand of swine meat, both for local consumption and for export,can be taken as an opportunity for increased revenue and foreign exchange. Since north east India is considered as the hub of pork market in India, an attempt is made to study the current trend of export of swine meat from India and forecasting the same for the next decade.&nbsp; 
 
Methods: Six different ARIMA models were fitted to the time- series data pertaining to the period 2003 to 2019 for the export of annual swine meat (fresh, chilled or frozen). The forecasting of future annual meat export value in leading 10 years is done using the best model with higher accuracy.&nbsp; 
 
Result: The most fitted model (1,0,1) out of other models (1,1,0), (2,0,2), (1, 2, 1), (1,1,1) and (1,1,2) suggests the positive trend of annual swine meat export from India. It is anticipated to grow at 6.54 units from 2020 to 2029 as compared to 1.45 units from the last decade (2010-2019). The commercialisation of piggery sector can be a vital source for sustainable livelihood, for doubling the farmer&rsquo;s income and for an enterprise building.</div>

​Understanding the Potential of Livestock Market with Special Reference to the Export of Swine Meat from India: A Study of Time-series Analysis using ARIMA-based Forecasting Method

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<div>Background: Reliable rainfall forecast could be helpful to farmers as major decisions regarding selection of crops and sowing time are based on the rainfall. The univariate time series ARIMA model requires only past data for model formulation and to simulate stochastic processes. The current study was aimed to obtain the probability distribution of monthly rainfall using the method of moments and to forecast rainfall using the ARIMA model. 
 
Methods: The method of moments was used to determine the parameters of distributions and the chi-square test was used as a goodness of fit test to obtain the best fit distribution for monthly rainfall of Navsari, Gujarat utilizing 36 years of rainfall data. Auto regressive moving average (ARIMA) model, popular owing to its simplicity and ability to simulate various stochastic processes was used in the study.&nbsp; 
 
Result: It was revealed that the Weibull distribution was the best fit distribution for June and September, whereas Gumbel was the best fit distribution for July. For simulating monthly rainfall, the seasonal ARIMA model (0,0,1) (0,1,1)12 was found to be the appropriate model based on its performance. The model had the least root mean square value and also the residuals were found to have no correlation.&nbsp;</div>

​Understanding the Potential of Livestock Market with Special Reference to the Export of Swine Meat from India: A Study of Time-series Analysis using ARIMA-based Forecasting Method

​Rainfall Probability Distribution and Forecasting Monthly Rainfall of Navsari using ARIMA Model

Understanding the Potential of Livestock Market with Special Reference to the Export of Swine Meat from India: A Study of Time-series Analysis using ARIMA-based Forecasting Method

Rainfall Probability Distribution and Forecasting Monthly Rainfall of Navsari using ARIMA Model