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<records>

  <record>
    <language>eng</language>
          <publisher>Enviro Research Publishers</publisher>
        <journalTitle>Current Agriculture Research Journal</journalTitle>
          <issn>2347-4688</issn>
              <eissn>2321-9971</eissn>
        <publicationDate>2026-01-10</publicationDate>
    
        <volume>13</volume>
        <issue>3</issue>

 
    <startPage>711</startPage>
    <endPage>717</endPage>

         <doi></doi>
        <publisherRecordId>26136</publisherRecordId>
    <documentType>article</documentType>
    <title language="eng">Dynamics of Tea Production in Assam: A Time Series Forecasting Approach</title>

    <authors>
	 


      <author>
       <name>Nandita Debnath</name>

 
		
	<affiliationId>1</affiliationId>
      </author>
    

	 


      <author>
       <name>Giribabu Mahasamudram</name>


		
	<affiliationId>1</affiliationId>
      </author>

    

	 


      <author>
       <name>Dipankar Saha</name>

		
	<affiliationId>1</affiliationId>
      </author>
    

	


	


	
    </authors>
    
	    <affiliationsList>
	    
		
		<affiliationName affiliationId="1">Department of Economics, Mizoram University, Tanhril, Aizawl, Mizoram, India,</affiliationName>
    

		
		
		
		
		
	  </affiliationsList>






    <abstract language="eng">Assam is the largest state in India which produces tea production, and it produces more than half of the tea production in the Nation. This paper analyses the nature of the tea industry in Assam through the time series forecasting. Data on annual tea production (2001-2024) was collected from the Tea Board of India. The trend in the historical series is high. In order to examine this trend, the Box Jenkins methodology developed an Auto-Regressive Integrated Moving Average (ARIMA) model. The Diagnostic analysis exhibited an ARIMA (2,1,2) specification as the best suited. The model was fitted to give a forecast of the future tea production. The projections maintain the trend which has been increasing and this means that the production of tea in Assam will continue to grow in the next several years.</abstract>

    <fullTextUrl format="html">https://www.agriculturejournal.org/volume13number3/dynamics-of-tea-production-in-assam-a-time-series-forecasting-approach/</fullTextUrl>



      <keywords language="eng">
        <keyword>ARIMA; Exponential Smoothing; Stationarity Tests; Tea Production; Time Series Forecasting</keyword>
      </keywords>

  </record>
</records>