Everyone in India is aware that agriculture and associated industries are the primary sources of food and the major employment sector. The agriculture sector contributed 17.2 per cent; industry contributed 18.5 per cent while the service sector had a contribution of 64.5 per cent of the GDP according to 2008-09 estimates (Das et al., 2011). Among several factors that govern agricultural production, the weather appears to be the most critical factor as the farmers have no control over it and its inter and intra seasonal variabilities are difficult to predict with greater reliability and confidence.
So, the farmers believe that the weather in the coming year will be different from what they were aware in the past. Contingency crop planning strategies were evolved to address the problem with the delayed start of the farming season through the adoption of short duration crops/varieties and mid-season corrections (Singh and Ramana Rao, 1988) were advocated as a short term measure to cope with aberrant weather conditions after initial establishment of the crop.
During the recent years, the inter-seasonal and intra-seasonal variability in weather is believed to outsmart the abilities of climatologists and statisticians in defining the limits of which these variabilities are likely to be observed. This has to lead to the Weather Experts to realize and convince themselves that the global climate is changing due to enhanced human activities detrimental to the natural environment.
Rapid industrialization, increased use of chemicals, deforestation, burning of fossil fuels, etc., are contributing to increased concentrations of the so called green house gases in the atmosphere, which will, in turn, contribute to global warming, changes in general circulation in the earth’s atmosphere leading to climate change and / or climate variability.
The meteorologists are now required not only to predict weather on short term basis but also climate on long term basis. Until the last two decades of 20th century, there was no sound scientific basis for believing that climate predictions might be possible. According to Cane and Arkin (2000), some of the year to year variations in climate are the result of random sequences of events, just as a series of coin flips will occasionally produce a long run of either heads/tails.
In tropical countries like India, a region may experience a dry spell because no storms happen to pass that way for a time. Prediction of such stochastic events is not possible. Climatologists, now see, however, that many climatic variations are part of the large scale, slowly evolving patterns.
Many critical agricultural decisions from farm to policy level must be made several months before those weather conditions are experienced. Hansen and James (2000) observed that recent advances in atmospheric and oceanic research, much of it focusing on El Niño -Southern Oscillation and its teleconnections made it possible to forecast climate with useful skill with lead times of several months.
Such predictions offer the potential for farmers and other agricultural decision makers to predict crop responses to expected climate and modify decisions to decrease unwanted impacts or take advantages of expected favorable conditions. According to Wilby and Wigley (2000), even if global climate models in the future are run to high resolution, there will remain the need to ‘downscale’ the results from such models to individual sites or localities or regions for impact studies.
Downscaling methodologies are still under development, and more work needs to be done in inter-comparing these methodologies and quantifying the accuracy of methods. Not long ago, the term El Niño was seldom seen or heard outside ivied wall of academia and research laboratories.
In the last two decades, it has been brought to the attention of every one, in every country and is probably here to stay. The ENSO (El Niño – Southern Oscillation Index) is a pervasive climate phenomenon which has been found to be associated with regional climatic variations throughout the world. These are three phases: Warm-El Niño, Cold-La Niña and other (non-El Niño or La Niña), generally referred to as neutral.
The term El Niño (Spanish for “the Christ Child”) was originally used by fishermen along the coasts of Ecuador and Peru to refer to a warm ocean current that typically appears around Christmas time and lasts for several months. Fish are less abundant during these warm intervals, so fishermen often take a break to repair their equipment and spend time with their families..read more
The district wise monthly rainfall data for all the districts of Andhra Pradesh recorded during the years 1971-2009, as available in the database at CRIDA, Hyderabad were used in the present study. The rainfall totals for the summer (March to May), southwest monsoon (June- September), Rabi (October-December) and winter (January-February) seasons were computed year wise for all the districts and state as well..read more
The year wise total food grain production in the state for the years 1981 to 2007 is shown in Figure 7. It is generally observed that the total food grain production in the state was
- less than 12 million tons during the period up to 1987
- between 11 to about 13 million tons during the years 1988 to 1995 and
- between 10 to 19 million tons after the year 1996 onwards, Therefore, there was an increasing trend in the total food grain production in the state during the study period considered..read more
Reliable climate predictions may not be available immediately and it might take some more time and until then, the necessity arises to identify some of the global parameters like El Niño, which can be used as a signal to climate variability at least during some of the years, even if not for all the years..read more
- Central Institute of Dryland Agriculture