2021
DOI: 10.1007/s12355-021-01004-3
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Modeling and Forecasting of Sugarcane Production in India

Abstract: Sugarcane plays an essential role in the economy of the India. During 2018, 79.9% of total sugarcane production of India was used in the manufacture of white sugar, 11.29% was used for jaggery production, and 8.80% was used as seed and feed materials. 840.16 Mt sugarcane was exported in the year 2019. Prediction of production level is basic to effective decision-making for policymakers. The objective of this study is thus to find the suitable models of forecasting for sugarcane production. India and major suga… Show more

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Cited by 29 publications
(14 citation statements)
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“…The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/v14071367/s1, Table S1: Comparison of Application, assumptions, and limitations of forecasting models and Table S2: Forecast values of FMD outbreak episodes during January 2021 to December 2023 [30,[59][60][61][62][63][64][65][66].…”
Section: Supplementary Materialsmentioning
confidence: 99%
“…The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/v14071367/s1, Table S1: Comparison of Application, assumptions, and limitations of forecasting models and Table S2: Forecast values of FMD outbreak episodes during January 2021 to December 2023 [30,[59][60][61][62][63][64][65][66].…”
Section: Supplementary Materialsmentioning
confidence: 99%
“…In non-stationary data, ARMA( p , q ) model is known as the ARIMA( p , d , q ) models if the d-order difference operation is performed to make the data stationary. In the kind of equations in the ARIMA ( p, d, q ) models, p represents the degree of the AR model, q represents the degree of the MA model, and d represents the number of differences needed to stabilize the data ( Yonar et al, 2020 ; Ray et al, 2021 ; Mishra et al, 2021a , Mishra et al, 2021b ).…”
Section: Methodsmentioning
confidence: 99%
“…In a study by Vishwajith et al (2018) on forecasting mung production, ARIMA(4,1,4) was the best-fitting model over ARIMAX and generalized autoregressive conditional heteroscedasticity (GARCH) models. Mishra et al, 2021a , Mishra et al, 2021b considered ARIMA models for forecasting of sugarcane production by major states for 2025.…”
Section: Introductionmentioning
confidence: 99%
“…The Chinese health system per 100,000 citizens has 3.6 beds in intensive care units, but with the slight increase in confirmed cases, China can expand the construction of mobile ICUs. They used the ARIMA model and the results were very accurate [15]. That's concluded.…”
Section: Introductionmentioning
confidence: 93%