Weather plays an important role in agricultural production. It plays a major role before and during the cropping season and if the same is provided well in advance, it results in inspiring the farmers to organize and activate their own resources in the best possible way to increase the crop production.

Climate plays important role in production of coffee. Adequate quantum and timely receipt of blossom rainfall for flowering and subsequent backing showers influence the berry set and yield of coffee. Harvesting of Arabica coffee in Kerala State with humid tropical climate in India is done by December-January and harvesting of Robusta coffee is taken up during January-February. In this paper, attempt was made to develop agrometeorological models to forecast the yield of these two varieties coffee by utilising monthly climate variables from January to December.

Climate projections have confirmed the need to adapt to a changing climate, but have been less beneficial in guiding how to effectively adapt. The reason is the uncertainty cascade, from assumptions about future emissions of greenhouse gases to what that means for the climate to real decisions on a local scale. Each of the steps in the process contains uncertainty and these uncertainties from various levels of the assessment accumulate. This cascade of uncertainty should be critically analyzed to inform decision makers about the certain range of future changes.

On the basis of past 115 years (1901-2015) rainfall data of five districts of south Gujarat, the MannKendall trend, Sen’s slope and regression slope showed that annual and monsoon rainfall at Valsad, Dang and Surat shows the increasing trend while, that of Navsari and Bharuch districts are declining. The monsoon season (summer monsoon) rainfall variability of Valsad, Dang, Surat, Navsari and Bharuch districts was recorded is 30.1%, 30.9%, 35.9%. 33.3% and 38.6%.

Annual and seasonal variability and trends in low cloud cover over India were analyzed for the period 1961-2010. Taking all period into account, there is a general decrease in mean low cloud cover over most regions of India, but an increase in the Indo-Gangetic plains and northeast India. Long term mean low cloud cover over India has inter-annual variations with highest cloud cover (39.4%) in monsoon and lowest cloud cover (10.5%) in winter season.

Among the extreme weather events hailstorm in recent past caused significant crop damage across the country. In 2014 and 2015 unseasonal rains and hailstorms during March and April damaged rabi crops as well as horticultural crops extensively in many parts of the country.

Ultraviolet (UV) radiations from the Sun in the spectral range 100-280 nm react with the stratospheric atmosphere, and oxygen molecules (O2) and atoms (O) combine to produce ozone (O3). Since there are destruction processes also, the equilibrium amount is small, only a few percent of the atmosphere. However, it serves a very vital, useful purpose as it absorbs UV in the spectral range 280-320 nm (termed as UVB), which is very dangerous for terrestrial life and is a cause of skin cancer, etc.

Diwali is one of the major and most important festivals celebrated all over India which falls in the period late October to early November every year. It is associated with burning of firecrackers especially during the night of Diwali day that leads to degradation of air quality that lasts for a longer duration of time. Firecrackers on burning releases huge amount of trace gases such as NOx, CO, SO2 and O3 and huge amount of aerosols and particulate matter.

Trend analysis of hydro-climatic variables provide useful information for effective planning, designing and management of water resources and agricultural production. Trends in observed stream flow at upstream and midstream gauging stations (GS), Wellawaya, Thanamalwila & rainfall and temperature in the Kirindi Oya river basin were assessed using the Mann-Kendall, Modified Mann-Kendall and Sen’s slope. Average rainfalls for the two catchments and for the entire basin were computed using ‘Thessen polygon’ method.

The impact of projected climate change on groundnut (cv. Robut 33-1 and GG-2) yield have been studied for Anand station of middle Gujarat Agro-climatic region using PRECIS output of AR 2 Rscenario and base line data. Yield simulation study was performed by PNUTGRO (DSSAT v4.5) model. The field experiment data on groundnut cv. Robut 33-1 and GG-2 during the years 2008 to 2011 have been used to calibrate and validate the model. The weather condition as projected by AR 2R scenario (2070-2100) showed that there will be 13.7% higher rainfall as compared to base line (1961-90).