Meteorological and Crop Yield Data of Tea at Monthly, seasonal and Annual scales

Published: 13-03-2020| Version 2 | DOI: 10.17632/rcgvn92yxx.2
Edwin Raj Esack,
KV Ramesh,
Rajagopal Raj Kumar


Meteorological data recorded by the UPASI Tea Research Institute and its Regional Centres are used (UPASI Annual Reports 1981-2015), which is the longest period continuous data across the region is currently available. The rain gauges are provided by the India Meteorological Department (IMD, Chennai), and the Department personnel inspect these gauges and undertake quality control of the data. Reference evapotranspiration (ETo) and cloudiness are calculated according to Penman-Monteith (Allen et al. 1998) and Black (1956) methods, respectively, by using SPEI and EcoHydrology Packages of R Statistics. Soil temperature at 830h and 1430h and soil moisture were derived from daily values input to the Java Newhall Simulation Model (jNSM) Version 1.6.1 (NRCS 2016). All meteorological records were subjected to a visual inspection of reasonableness, completeness and any obvious discontinuities. After quality control of data, the daily values aggregated into monthly, seasonal and annual series. The crop production data at different temporal scales across the regions for the agricultural years 1981-2015 have been collected from the various sources viz., Annual Reports of Tea Board (1981-2005), J Thomas Tea Statistics and Theillai, UPASI monthly advisory circular (2010-2015) publications. The productivity of tea is referred as the ratio of the area harvested and the dry weight of the yield.


Steps to reproduce

As many non-climatic factors influence the crop production rapidly with time, a low-pass filter (Handler 1984) have been used to separate the long-term changes from the short-term inter-annual variations about the trend (Press et al. 1987). The low-frequency (long-term trend) values are got by using a Hamming-type filter followed by applying five points Gaussian moving average. But, the person who reads should consider that it does not exactly remove the technology renovation trend over the years, but it is the best way to exclude the effect of other factors on historical yield change but the climate effects. In order to avoid the spurious effect in the models, stationarity of the data was checked before performing the analysis. Outliers, random walk, drift, trend, or changing variance in the time series is removed by transforming the data using the Augmented Dickey-Fuller (ADF) test and the KPSS test.