Modelling and Forecasting of Milk Production in Chhattisgarh and India

Cereals are the most important kharif season crop in Odisha. The present study was carried out to forecast the production of kharif cereals in Odisha by using the forecast values of area and yield of kharif cereals obtained from the selected best fit Autoregressive Integrated Moving Average (ARIMA) model. The data from 1970-71 to 2010-11 are considered as training set data and used for model building and from 2011-12 to 2015-16 are considered as testing set data and used for cross-validation of the selected model on the basis of the absolute percentage error. The ARIMA models are fitted to the stationary data which may be the original data or the differenced data. The different ARIMA models are evaluated on the basis of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) at various lags. The possible ARIMA models are selected on the basis of significant coefficient of autoregressive and moving average components by using the training set data. The best fitted models are then selected on the basis of residual diagnostics test and model fit statistics. The ARIMA model found to be best fitted for area under kharif cereals and yield of kharif cereals are ARIMA (1,1,0) without constant and ARIMA (0,1,2) without constant respectively which are successfully cross-validated with the testing set data. The respective best fit ARIMA model has been used to forecast the area and yield of kharif cereals for the years 2016-17, 2017-18 and 2018-19. The forecast values of area shows a decrease, whereas, the forecast values of yield shows an increase. The decrease in area might have been the result of limited availability of area for cereals due to shifting towards non-food grain crops. The forecast values of production of kharif cereals obtained from the forecast values of area and yield of kharif cereals shows an increase which is due to the increase in forecast values of yield. Since there is limited scope for area expansion, the future production of kharif cereals can only be increased by increasing the yield to achieve the goal of food security for the growing population.

Section: Introductionmentioning

confidence: 99%

Using Autoregressive Integrated Moving Average (ARIMA) Technique to Forecast the Production of Kharif Cereals in Odisha (India)

Dash

Mangaraju

Mishra

et al. 2020

“…with the target, where the expense and return for mustard development is considered by Dhekale et. al (2017), Vishawajith et al [21] and Mishra et.al [10].…”

Section: Original Research Articlementioning

confidence: 97%

“…Mustard is best changed in accordance with create in ready, all around drained, loamy soils. Creators are asked to create mustard under consent to guarantee a market and a selling cost [10][11][12][13]. The 3 sorts of mustard are: yellow, earthy colored and oriental.…”

Section: Original Research Articlementioning

confidence: 99%

Study of Cost and Profit Strategies for the Mustard Crop in Madhya Pradesh

Shukla

Gupta

2020

“India occupies fourth position in the World which share the 5.06 million hectares area, and the production is 5.83 million tones and 1,152kg per hectare productivity. The mustard oil seed produce in the states of India are Gujarat, Andhra Pradesh, M.P., Maharashtra, Karnataka, U.P., and Rajasthan. Around 85% of the oilseed crop is from the seven states of India. In India Madhya Pradesh contributes 40 percent production, in the national economy and its occupies 6th position, and also contributes 0.31million hectare area, 0.21 million tonnes production. Mustard is the most remunerative and dominant oilseed crop and more than 20% percent of this crop area lies in the Neemuch district of M.P. The most popular district of the state for the mustard is Neemuch. In this study, the profit is found from the cultivation of the mustard. This crop contains the area per hector, which is higher for the existing yield level with the level of the sample farms.

“…Dhekale et al [7] used time series model in tea production. Mishra et al [8] used arima modelling in milk production of India.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Rabi Pulse Production in Odisha (India) by Using Autoregressive Integrated Moving Average (ARIMA) Technique

Dash

Mangaraju

Suman

et al. 2020

The present study was carried out to forecast the production of rabi pulse in Odisha by using the forecast values of area and yield of rabi pulses obtained from the selected best fit Autoregressive Integrated Moving Average (ARIMA) model. The data from 1971-72 to 2010-11 are considered as training set data and used for model building and from 2011-12 to 2015-16 are considered as testing set data and used for cross-validation of the selected model on the basis of the absolute percentage error. The ARIMA models are fitted to the stationary data which may be the original data and/or the differenced data. The different ARIMA models are judged on the basis of Autocorrelation Function (ACF) and Partial autocorrelation Function (PACF) at various lags. The possible ARIMA models are selected on the basis of significant coefficient of autoregressive and moving average components. The best fitted models are selected on the basis of residual diagnostics test and model fit statistics. The ARIMA model found to be best fitted for area under rabi pulse and yield of rabi pulse are ARIMA (2,0,0) with constant and ARIMA (0,1,1) without constant respectively which are successfully cross-validated with the testing set data. The excellent fit ARIMA model has been used to forecast the area and yield of rabi pulse for the years 2016-17, 2017-18 and 2018-19. The forecast value of area shows an increase, where as, the forecast values of yield shows a decrease. The forecast values of production of rabi pulse obtained from the forecast values of area and yield of rabi pulse shows an increase which is due to the increase in forecast value of area. Thus emphasis must be laid on increasing the future yield of rabi pulse so as to achieve sufficient increase in production of rabi pulses which could ensure nutritional security to more extent.