Hostname: page-component-77f85d65b8-7lfxl Total loading time: 0 Render date: 2026-04-17T19:16:45.220Z Has data issue: false hasContentIssue false
  • English
  • Français

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: rishi.journal@gmail.com
Get access Rights & Permissions [Opens in a new window]

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.

Information

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Barua, D. N. (1969). Seasonal dormancy in tea (Camellia sinensis L.). Nature 224: 514.CrossRefGoogle Scholar
Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D., Sierra, J., Bertuzzi, P., Burger, P., Bussiere, F., Cadiboche, Y. M., Cellier, P., Debaeke, P., Gaudillère, J. P., Hénault, C., Maraux, F., Seguin, B. and Sinoquet, H. (2003). An overview of the crop model STICS. European Journal of Agronomy 18: 309–332.CrossRefGoogle Scholar
Brisson, N., Mary, B., Ripoche, D., Jeuffroy, M. H., Ruget, F., Gate, P., Devienne-Barret, F., Antonioletti, R., Durr, C., Nicoullaud, B., Richard, G., Beaudoin, N., Recous, S., Tayot, X., Plenet, D., Cellier, P., Machet, J. M., Meynard, J. M. and Delécolle, R. (1998). STICS: A generic model for the simulation of crops and their water and nitrogen balance. I. Theory and parameterization applied to wheat and corn. Agronomie 18: 311–346.CrossRefGoogle Scholar
Brisson, N., Ruget, F., Gate, P., Lorgeou, J., Nicoullaud, B., Tayot, X., Plenet, D., Jeuffroy, M. H., Bouthier, A., Ripoche, D., Mary, B. and Justes, E. (2002). STICS: A generic model for the simulation of crops and their water and nitrogen balance. II. Assessment by comparing with experimental reality for wheat and corn. Agronomie 22: 69–93.CrossRefGoogle Scholar
Cannell, M. G. R., Harvey, F. J., Smith, R. I. and Deans, J. D. (1990). Genetic improvement of tea. Project Report Number TO1057cl. Edinburgh, UK: Institute of Terrestrial Ecology.Google Scholar
Dang, M. Van. (2007). Quantitative and qualitative soil quality assessments of tea enterprises in Northern Vietnam. African Journal of Agricultural Research 2 (9): 455–462.Google Scholar
Das, S. C. and Barua, D. N. (1987). Mechanism of tea dormancy: Effect of temperature on growth and dormancy of tea plant in North-East India. Two and a Bud 34: 36–41.Google Scholar
De Costa, W. A. J. M., Mohotti, A. J. and Wijeratne, M. A. (2007). Ecophysiology of tea. Brazilian Journal of Plant Physiology 19 (4): 299–332.CrossRefGoogle Scholar
Dogo, Y. W., Owuor, P. O. and Wanyoko, J. K. (1994). High rates of nitrogen on tea at high altitudes. VII. The effects of rates and frequency of applications on soil chemical properties in Mount Kenya area. Tea 15: 17–26.Google Scholar
Dutta, R., Stein, A., Smaling, E. M. A., Bhagat, R. M. and Hazarika, M. (2010). Effects of plant age and environmental and management factors on tea yield in Northeast India. Agronomy Journal 102: 1290–1301.CrossRefGoogle Scholar
Fei, Q. P. and Ripley, E. A. (1988). Simulation of Spring Wheat Yields in the Saaskatoon Crop District 1960 to 1984 Using the CERES Wheat Model. SRC Publication no. E-906-46-B-85. Saskatoon, SK, Canada: Saskatschewan Research Council.Google Scholar
Fordham, R. (1970). Factors Affecting Tea Yields in Malawi. PhD thesis, University of Bristol, UK.Google Scholar
Francis, W., Ng'etich, W., Omolo, J. and Mamati, G. (2002). Genotype × environment interactions for tea yields. Euphytica 127: 289–296.Google Scholar
Hadfield, W. (1976). The effect of high temperature on some aspects of physiology and cultivation of the tea bush (Camellia sinensis) in N.E. India. In Light as an Ecological Factor, 477–495 (Ed Evans, G. C.). London: Backwell Scientific.Google Scholar
Herd, E. M. and Squire, G. R. (1976). Observations on the winter dormancy in tea (Camellia sinensis L.) in Malawi. Journal of Horticultural Science 51: 267–279.CrossRefGoogle Scholar
Jackson, M. L. (1973). Soil Chemical Analysis. New Delhi, India: Prentice-Hall.Google Scholar
Jégo, G., Pattey, E., Bourgeois, G., Morrison, M. J., Drury, C. F., Tremblay, N. and Tremblay, G. (2010). Calibration and performance evaluation of soybean and spring wheat cultivars using the STICS crop model in Eastern Canada. Food Crops Research 117: 183–196.Google Scholar
Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Bachelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J. and Ritchie, J. T. (2003). The DSSAT cropping system model. European Journal of Agronomy 18: 235–265.CrossRefGoogle Scholar
Matthews, R. B. and Stephens, W. (1998a). The role of photoperiod in determining seasonal yield variations in tea. Experimental Agriculture 34: 323–340.CrossRefGoogle Scholar
Matthews, R. B. and Stephens, W. (1998b). CUPPA-Tea: A simulation model describing seasonal yield variation and potential production of tea. 1. Shoot development and extension. Experimental Agriculture 34: 345–367.CrossRefGoogle Scholar
Matthews, R. B. and Stephens, W. (1998c). CUPPA-Tea: A simulation model describing seasonal yield variation and potential production of tea. 2. Biomass production and water use. Experimental Agriculture 34: 369–389.CrossRefGoogle Scholar
Morita, A., Ohta, M. and Yoneyama, T. (1998). Uptake, transport and assimilation of 15Nnitrate and 15N-ammonium in tea (Camellia sinensis L.) plants. Soil Science and Plant Nutrition 44: 647–654.CrossRefGoogle Scholar
Otter-Nacke, S., Godwin, D. C. and Ritchie, J. T. (1986). Testing and validating the CERES wheat model in diverse environments. AgRISTARS Technical Report, YM-15-00407, JSC-20244. Houston, TX: Johnson Space Center.Google Scholar
Panda, R. K., Stephens, W. and Matthews, R. (2003). Modeling the influence of irrigation on the potential yield of tea (Camellia sinensis) in Northeast India. Experimental Agriculture 39: 181–198.CrossRefGoogle Scholar
Ritchie, J. T. and Otter, S. (1984). Description and Performance of CERES-Wheat, a User-Oriented Wheat Yield Model, 159–175. Temple, TX: USDA-ARS-SR Grassland Soil and Water Research Laboratory.Google Scholar
Ruan, J., Ma, L. and Shi, Y. (2006). Aluminium in tea plantations: Mobility in soils and plants, and influence of nitrogen fertilization. Environmental Geochemistry and Health 28: 519–528.CrossRefGoogle ScholarPubMed
Sinclair, T. R. and Seligman, N. (2000). Criteria for publishing papers on crop modeling. Field Crops Research 68: 165–172.CrossRefGoogle Scholar
Spiertz, J. H. J., Struik, P. C. and Van Laar, H. H. (Eds). (2007). Scale and Complexity in Plant Systems Research: Gene–Plant–Crop Relations. Wageningen UR Frontis Series 21. Dordrecht, The Netherlands: Springer, 332 pp.CrossRefGoogle Scholar
Squire, G. R. (1979). Weather, physiology and seasonality of tea (Camellia sinensis) yields in Malawi. Experimental Agriculture 15: 321–330.CrossRefGoogle Scholar
Stephens, W. and Carr, M. K. V. (1990). Seasonal and clonal differences in shoot extension rates and numbers in tea (Camellia sinensis L.). Experimental Agriculture 26: 83–98.CrossRefGoogle Scholar
Stephens, W. and Carr, M. K. V. (1994). Responses of tea (Camellia sinensis L.) to irrigation and fertilizer. IV. Shoot population density, size and mass. Experimental Agriculture 30: 189–205.CrossRefGoogle Scholar
Stockle, C. O., Martin, S. and Campbell, G. S. (1994). CropSyst, a cropping systems model: Water/nitrogen budgets and crop yield. Agricultural System 46: 335–359.CrossRefGoogle Scholar
Tandon, H. L. S. (1993). Methods of Analysis of Soil, Plants, Waters and Fertilizers. New Delhi, India: Fertilizer Development and Consultation Organization.Google Scholar
Tanton, T. W. (1981). Growth and yield of the tea bush. Experimental Agriculture, 17: 323–331.CrossRefGoogle Scholar
Tea Board of India (2007). Production of tea in India. Tea Statistics Annual Report. Kolkata, India: Tea Board of India.Google Scholar
Tea Board of India (2009). Growers and areas under tea. Tea Statistics Annual Report. Kolkata, India: Tea Board of India.Google Scholar
Walkley, A. J. and Black, I. A. (1934). Estimation of organic carbon by the chromic acid titration method. Soil Science 37: 29–38.CrossRefGoogle Scholar
Williams, J. R., Jones, C. A. and Dyke, P. T. (1984). A modelling approach to determining the relationship between erosion and soil productivity, 1984. Transactions of the ASAE 27: 129–144.CrossRefGoogle Scholar