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‘Ceylon tea’ industry plays a vital role in relation to the economic sustainability in

Sri Lanka. Low productivity of tea growing areas in the country has been attributed

to climatic changes, ageing of tea fields, low replanting rate, land degradation,

worker shortage and high input cost. Climatic variability has direct and indirect

impacts on tea production in Sri Lanka.

Tea crop is highly sensitive for water adequacy and affect for plant growth and

development. Optimum rainfall required for tea varied from 350 ± 20 mm/month in

Up Country Wet Zone.(Amarathunga et al,2007) Photosynthesis rate is decreased

by 50%, when water is reduced by 10%. Lack of sunshine at high rainfall is affected

for the low yield. High rainfall is reduced the shoot growth of the tea plant due to

water logging condition of the soil. Tea plants are very sensitive for monthly rainfall

variations. Amount of rainfall in previous month was affected to productivity of

crop on next month. Climatic variations is a critical factor for yield variability of

upcountry tea industry.

Kelani Valley Plantations PLC(KVPL) is one of leading plantation company in Sri

Lanka. KVPL Upcountry tea saturated 8 estates in Hatton Region and 6 estates in

Nuwaraeliya Region.

Introduction

To identify impact of rainfall variation on productivity of upcountry tea lands

managed by Kelani Valley Plantations PLC in Sri Lanka

Objective

Results and Discussion

Conclusion

Vagaries in climate is a critical factor for yield variability of the upcountry tea

industry. Maintaining good agricultural practices (GAP) and choosing tea cultivars

more adaptable to varying climatic condition will mitigate the effects of climatic

variation on productivity of tea lands.

References

Liu, C. L., Zhang, Q., Singh, V.P and Cui, Y.(2011). Copula-based evaluations of

drought variations in Guangdong, South China. Natural Hazards, 59, 1533-1546

Poudel, S., and Shaw, R., (2016) Relationships between Climate Variability and

Crop Yield in a Mountainous Environment : A Case study in Lamjung District,

Nepal. Climate. 4,13

Preprah, K., (2014) Rainfall and Temperature Correlation with Crop Yield: The

Case of Asunafo Forest, Ghana. International Journal of Science and Research

(IJSR), 3, 784-789

Wijeratne, M.A., Anandacoomaraswamy, a., Amarathunga, M.KS.L.D., Rathnasiri,

J., Basnayake B.R.S.B., Kalva, N.,(2017) Assessement of impact of climatic

change on productivity of tea(Camelia Sinenssis L.) plantations in Sri Lanka.

Journal National Science foundation Sri Lanka. 35(2), 119-126

Acknowledgement

I wish to offer my deepest gratitude to Dr. Prasad Dharmasena, Director of NIPM,

Mr. Roshan Rajadurai, Managing Director of Kelani Valley Plantations PLC, Mr.

Maruday Kandasamy, Chief Clerk of General Manger Office, Mrs. Niluka

Amerasena and Ms. B.N Gayathri, Staff Members of General Mangers’office.

Manawasinghe, K.S.1, Abeysinghe, D.C.1, Weerakoon, A.2, Thennakoon, T.M.N.S.3

Department of Plantation Management, Faculty of Agriculture and Plantation Management, Wayamba University of Sri Lanka, Makandura, Gonawila (NWP), 60170, Sri Lanka1

Kelani Valley Plantations PLC, Dickoya, Hatton2

Faculty of Science, University of Peradeniya, Peradeniya, Sri Lanka3

Effects of Climatic Variation on Yield of Upcountry Tea: A Case Study based on Upcountry Tea Estates of Kelani Valley Plantations PLC in Sri Lanka

Methodology

Study Site

The Study was conducted in upcountry tea estates in Kelani Valley Plantations

PLC. Tea estates data of rainfall and yield from 2004 to 2015 was used.

Selected Indicators for the Study

Pearsons’Correlation Coefficient

Pearson’s Correlation Coefficient was used to determine the correlation between the

rainfall and the tea yield. (Poudel et al, 2016)

Where,

n = Number of Pairs of Scores

Ʃxy = Sum of the products of paired scores

Ʃx = Sum of x scores

Ʃy = Sum of y scores

Ʃx = Sum of squared x scores

Ʃy = Sum of squared y scores

Methodology Cont.

Standard Precipitation Index

Standard Precipitation index was used to identify Climatic condition within a year in

the regions(Table 1) (Liu et al.2011).

SPI = X1 –X

S

Where,

X1 – rainfall

X - mean value

S - Standard deviation

Table 1 :- SPI values and Climatic events

Precipitation Trend

Total annual rainfall was recorded the mean of 3044 mm in Hatton Region and 2606 mm in Nuwaraeliya Region, Standard deviation of 455mm in Hatton Region and

322mm in Nuwaraeliya Region. It reveals an ascending trend lines of annual rainfall from 2004 to 2015 in both regions, However the trend line in Nuwraeliya region

indicates slightly increasing.(Figure 1)

Figure 1:- Annual Precipitation trends in KVPL upcountry estates in Hatton and

Nuwaraeliya regions from 2004 to 2015

The maximum rainfall was recorded in month of June and the minimum rainfall was recorded in month of January (Figure 2). Standard deviation 121mm in Nuwaraeliya

region and 129mm in Hatton region.

Standardized Precipitation Index

Table 2 :- Variations of Climatic conditions across tea growing areas in Upcountry KVPL estates

FOE – Frequency of events, POC – Percentage of Occurrence

Monthly rainfall was highly fluctuated within the year. There were not any extremely dry or severely dry months both Hatton and Nuwaeaeliya tea growing regions.

extremely wet month was recorded in Hatton region and Severely wet month was recorded in Nuwaraeliya region (Table 2).

Rainfall- Crop Yield Relationship

Pearsons’Correlation Coefficient

There was weak Positive correlation between annual rainfall and yield, in Hatton Region (r = +0.12 ) and Nuwaraeliya Region ( r = +0.15).

Strong positive correlation is shown between previous month rainfall and following month yield, in Hatton Region ( r= +0.62) and Nuwaraeliya Region(+0.70). Tea plants

are very sensitive for monthly rainfall variation.

Climatic events Hatton Region Nuwaraeliya Region

FOE POC (%) FOE POC (%)

Extremely Wet 1 8.33 - -

Severely Wet - - 1 8.33

Moderately Wet - - 1 8.33

Mild Wet 1 8.33 4 33.33

Normal Wet 3 24.99 - -

Normal Dry 4 33.33 2 16.66

Mild Dry 1 8.33 1 8.33

Moderately Dry 2 16.66 3 24.99

Severely Dry - - - -

Extremely Dry - - - -

y = 15.655x + 2504.1R² = 0.0307

y = 82.461x + 2508.7R² = 0.4258

0

500

1000

1500

2000

2500

3000

3500

4000

4500

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Nuwaraeliya Hatton Linear (Nuwaraeliya) Linear (Hatton)

0.00

100.00

200.00

300.00

400.00

500.00

600.00

700.00

Nuwaraeliya Hatton

Figure 2 :- Monthly Precipitation trends in KVPL upcountry estates in Hatton and

Nuwaraeliya regions from 2013 to 2015

SPI Value Category

2.0+ Extremely Wet

1.5 to 1.99 Severely Wet

1.0 to 1.49 Moderately Wet

0.5 to 0.99 Mild Wet

0 to 0.49 Normal wet

-0.1 to -0.49 Normal dry

-0.5 to -0.99 Mild dry

-1.0 to -1.49 Moderately dry

-1.5 to -1.99 Severely dry

-2.0+ Extremely dry

SPI = X1–XS