Rohit Sharma, J. Chaudhary, Sanjeev Kumar, Ranjit Rewar, Surinder Kumar
{"title":"Forecasting of milk productionof crossbred dairy cattle by AutoregressiveIntegrated Moving Average (ARIMA) model","authors":"Rohit Sharma, J. Chaudhary, Sanjeev Kumar, Ranjit Rewar, Surinder Kumar","doi":"10.33785/ijds.2022.v75i04.011","DOIUrl":null,"url":null,"abstract":"The objective of this study was to forecast the milk production in crossbred dairy cattle. In this study secondary data was used, collected from Livestock Farm of CVSc. & A.H., CAU, Aizawl, Mizoram, from year 2010 to 2019. The main focus of our study was based on forecasting through ARIMA model. To perform exploratory information examination, box-plot was used while stationarity of data was checked with Augmented Dicker-fuller test, Autocorrelation Function (ACF) and Partial autocorrelation function (PACF). Model fit checking and forecasting of milk was done through software package R. The results indicated that ARIMA (1, 0, 0) was the most suitable model for forecasting of milk for our dataset. Milk production is expected to be 1910.20 litres by 2022 with 95% confidence interval.","PeriodicalId":45524,"journal":{"name":"INDIAN JOURNAL OF DAIRY SCIENCE","volume":" ","pages":""},"PeriodicalIF":0.2000,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"INDIAN JOURNAL OF DAIRY SCIENCE","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33785/ijds.2022.v75i04.011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The objective of this study was to forecast the milk production in crossbred dairy cattle. In this study secondary data was used, collected from Livestock Farm of CVSc. & A.H., CAU, Aizawl, Mizoram, from year 2010 to 2019. The main focus of our study was based on forecasting through ARIMA model. To perform exploratory information examination, box-plot was used while stationarity of data was checked with Augmented Dicker-fuller test, Autocorrelation Function (ACF) and Partial autocorrelation function (PACF). Model fit checking and forecasting of milk was done through software package R. The results indicated that ARIMA (1, 0, 0) was the most suitable model for forecasting of milk for our dataset. Milk production is expected to be 1910.20 litres by 2022 with 95% confidence interval.