Globally, precipitation trend analysis in different space and time has great impact on crop-planning activities. To get accurate unbiased results a long-term climate analysis of a particular area required in large variability in both spatially, temporally. For sustainable crop production long term weather analysis act as vital role in alternation of existing cropping patterns. This study aimed at analysing the trend of rainfall events in Prakasam district of Andhra state of India the data consists of annual precipitation time series from 1991-2019. Initially study concerns with analysis of data base using descriptive statistics, later trend change was detected by using non parametric tests. The results indicate an increased trend in June and monsoon season, with a decreased trend in July and winter season at 5% level of significance.

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Rice is the most important crop of Odisha occupying 41.24% of net sown area in Kharif season and contributing 65.85 % of total food grain production of Odisha state and this is being cultivated in various types environmental and ecological condition. Assessment of rice phenology is prime for management and yield prediction. In view of characterizing rice ecology in East and South Eastern Plateau from 2008 – 2018 to know the time series analysis , remote sensing tools were used . MODIS can0 acquire data over a wide area with high spatial and temporal resolutions easily providing regional scale information .In order to study the seasonal /annual as well as spatial variability of kharif rice vigour and wetness spectral vegetation indices like NDVI(Normalised Difference Vegetation Index),LSWI(Land surface water index) derived from 15 day composite 250 m data were analysed at block level for Odisha state. For studying the start of season variability, SASI index was used. The season maximum NDVI, LSWI were computed for the year 2008-2018 for kharif rice in East and Southern eastern coastal plain zone of Odisha and graphs were generated which shows the variability of the kharif rice vigour and wetness.