Group04 Proposal
Water For Life: Effect Of Rainfall On India's Crop Productivity
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Contents
Introduction
Climate change has a serious impact on the availability of various resources on the earth especially water, which sustains life on this planet. One occupation that has experienced a direct impact due to these increasing climate fluctuations is Agriculture. Especially in a country like India where water, the most critical agricultural input is scarce. Nearly 55% of the total cultivated areas in India do not have irrigation facilities. And hence Indian farmers are highly dependent on rainfall as rainfall is the fundamental driver of water availability for agriculture. Changes in precipitation affect the quality and quantity of the agriculture produce in direct proportions of the rainfall received. In this study, we intend to analyse India’s rainfall pattern for past few years using various exploratory techniques, primarily focusing on apt visualizations to reveal the undiscovered truth. Over 80% of the annual rainfall is received in the four rainy months of June to September. There is great regional and temporal variation in the distribution of rainfall and although the monsoons affect most parts of India, the amount of rainfall varies from heavy to scanty on different parts. The primary motive of our analysis is to scrutinize the effect of irregularities in the rainfall pattern on agricultural productivity in India. Through our analysis, we would like to derive meaningful insights that foster our understanding about the most affected regions and crops because of the variability in climactic change so that actionable recommendations could be sought from the final developed application.
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Inspiration
In India, average food consumption at present is 550 g per capita per day. With the growing population, the imminent challenge is to increase food production in order to feed the population that will reach 1.30 Billion by the year 2020. Farmers will have to produce 50% of more grains to meet the current growing demand. India is home to 17% of the world population, but only 4% of the world water resources. India has experienced 23 large scale droughts starting from 1891 to 2009.
A new study suggests that India will see more erratic whether events in the coming years bringing more drought and more storms. Additionally, with the increase in global warming, the longevity of heat waves across India is all set to increase with warmer night temperatures. The days are getting hotter and this trend is only expected to continue. With every 1 degree rise in temperature, wheat yields are predicted to fall by 5-10%. India could experience a 40% decline in in its agricultural productivity by the year 2080. Climate change is posing a massive threat to agriculture and food security in India.
All these factors make it vitally important to study historical data to understand future better. This analysis is an honest endeavour in gaining deeper knowledge into the impact of increasingly changing weather trends so that we can be prepared to mitigate the risk of these uncontrollable factors and seek remedies that would help sustain such drastic natural phenomenon.
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Key Objectives
As stated above, the objective of this project is to analyze the rainfall pattern changes in India and finding out how these rainfall variations in turn affect India’s overall agricultural production.
A Glance At The State-Wise Rainfall Patterns Across India for Years 2008 and 2015
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About The Dataset
| Data | Format | Source |
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| Monthly & Annual Rainfall Precipitation For 34 Indian Meteorological Subdivisions | xls | http://www.tropmet.res.in/~lip/Publication/RR-pdf/RR-138.pdf |
| District-wise & Season-wise Crop Production | xls | https://data.gov.in/catalog/district-wise-season-wise-crop-production-statistics |
| Shapefile For 36 Indian Meteorological Subdivisions | GIS (shp, shx) | https://groups.google.com/forum/#!topic/datameet/12L5jtjUKhI |
Key Milestones
Key milestones (tentative) are listed below:
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Expected Challenges
1. Data collection – We are still in the process of data collection since we are collecting data from various sources (Indian Government websites). We will be integrating it together before analysis. This would be an arduous task considering the nature, format & quality of the different datasets that we have collected thus far. 2. Steep learning curve – There is a steep learning curve involved in learning and working with R-Shiny along with figuring out the relevant visualizations. 3. Correlation between rainfall pattern and agricultural crop productivity – Even though it is apparent that changes in the rainfall pattern over past few years has affected agriculture industry in India and increased farmers' worries significantly, discovering direct correlation between rainfall pattern and crop productivity from the collected data would be challenging.
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