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ANALYSIS OF FACTORS THAT DETERMINE TEA PRODUCTIVITY IN NORTHEASTERN INDIA: A COMBINED STATISTICAL AND MODELLING APPROACH

Published online by Cambridge University Press:  09 September 2011

RISHIRAJ DUTTA ,
ERIC M. A. SMALING ,
RAJIV MOHAN BHAGAT ,
VALENTYNE A. TOLPEKIN  and
ALFRED STEIN
RISHIRAJ DUTTA*
Affiliation:
Faculty of ITC, University of Twente, P.O. Box: 217, 7500 AE, Enschede, The Netherlands
ERIC M. A. SMALING
Affiliation:
Faculty of ITC, University of Twente, P.O. Box: 217, 7500 AE, Enschede, The Netherlands
RAJIV MOHAN BHAGAT
Affiliation:
Tea Research Association, Jorhat 785001, Assam, India
VALENTYNE A. TOLPEKIN
Affiliation:
Faculty of ITC, University of Twente, P.O. Box: 217, 7500 AE, Enschede, The Netherlands
ALFRED STEIN
Affiliation:
Faculty of ITC, University of Twente, P.O. Box: 217, 7500 AE, Enschede, The Netherlands
*
‡Corresponding author. E-mail: [email protected]
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Summary

This study analyses the factors affecting tea productivity in Northeast India using a combined statistical and modelling approach. The effects of a number of genotypic, environmental and management factors on tea yield are quantified and modelled, using a three-year (2007–2009) field trial in Assam, Northeast India. Simulations of the potential tea yield are obtained using the Cranfield University Plantation Productivity Analysis (CUPPA) Tea model to find out how well the predicted and observed values for tea production match. This combined approach shows that plantation age has a significant negative (R2 = 0.77) effect on tea yield. Monthly rainfall had a significant positive effect on monthly yields (R2 = 0.43). Rainfall was more strongly associated with tea yield when rainfall in month x was related to the tea yield in month x + 1 (R2 = 0.49). When repeating the analysis for a hypothetical situation that the fields are fully planted, the correlation between monthly rainfall in month x and tea yield for month x + 1 increases (R2 = 0.58). Adjusted yields show a higher correlation than actual yields. The results obtained show a close correspondence between predicted and observed yields, indicating that the model could be used on contrasting soil types, genotypes and also on daily, weekly and monthly weather data. It can be further calibrated and validated for Northeast Indian conditions if more required input parameters are collected in a series of plantations. Tea research might benefit from developing new versions of the CUPPA Tea model for the major clonal tea cultivars, with a more flexible module for fertiliser application as is currently the case.

Type
Research Article
Information
Experimental Agriculture , Volume 48 , Issue 1 , January 2012 , pp. 64 - 84
Copyright
Copyright © Cambridge University Press 2011

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