The presentation narrates the possible prediction of climate change over the geographic location of Tamil Nadu state and its most predominant impact on agriculture. Furthermore, it also deals with the crop yield prediction and possible mitigation of adverse impacts.

1. Awareness on Impact of Climate
Change in Agriculture, A
Comparative Study of
Chidambaram Agricultural Area by
Using Educational Global Climate
Model Software & Weather
Research and Forecasting Model
Atun RoyChoudhury
M.E. Scholar (Env Engg) & ResearchAsst
Annamalai University (Ramky)

2. Over view
Introduction
Study Area
Basic Study
Advanced Study
Elaborative Description of the Instruments
Working module
Modeling & Prediction
Results & Discussion
Conclusion & Mitigation
Future Research
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3. Brief Overview
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4. Introduction
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5. Climate Change Cycle
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6. Share of GHG by Different Sectors
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7. Share of GHG from Agricultural Sector
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8. Study Area
Chidambaram, a semiarid region is subjected to high climate variability and
sensitivity.
The town nearly lies at 11.3982° N, 79.6954° E latitude and longitude, with a
geographical area of 4.8 km2.
In Chidambaram 1376 people comprising 5 per cent were involved in agricultural
sector activities.
In spite of the environmental confrontation primary agricultural sector consists of
local and regional marketing, with paddy being the primary traded product,
accompanied by cereals, black gram, pulses and sugarcane.
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9. Preliminary Study
The study area has been explored at local scale, by means of interviewing
several farmers.
A sophisticated weather station established by Annamalai University was
utilized to explore the weather phenomenon at micro scale.
Abundant elementary instruments were utilized to gather interpretable
meteorological data namely,
• “Rain Gauge”
• “SoilThermometer”
• “Sunshine Recorder”
• “WindVane”
• “Anemometer”
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10. AU Weather Station and Instruments
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11. Advanced Study
In order of broad scale understanding, advanced meteorological Meta data
have been collected from the National atmospheric research lab, Tirupati for
the period of 3 months (i.e. October to December 2016), which relates the
study to the national scale.
The data has been interpreted using High Performance Computing (HPC
7000). The features are listed below.
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Sl No. Feature Value
1 Core 408
2 RAM 2448 GB
3 Data Exchange Rate 40 GB/S
4 Storage 21.024 TB
5 Computing Power 10 TF
6 Avg. Efficiency 89.43%

12. Datelining of Instruments
Surface Flux Tower (10m)
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Relative
Humidity
& Temp.
Sensor Thermoc
ouple
Anemo
meter

13. Continuation
Lightening Detector (Visala Alarm)
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Optical Sensor
Magnetic Sensor
Electrical Field
Sensor

14. Continuation
Microwave Radio meter
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Microwave
sensorDevice
Rainfall
IndicatorDevice

15. Miscellaneous instruments
Electric Field Mills
• Thunder storm studies
Net Radio Meter
• Measure incoming and
outgoing radiation
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16. The Overview of Incorporated Instruments
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17. Working Module
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18. EdGCM Modelling
EdGCM is an incorporated software module, which efficiently performs the
process such as-
• monitoring
• analyzing
• prediction
It’s a complete climate change prediction platform, based on the real time data
availability .
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19. WRF Model
The system is compatible with a high performance super
computer in Linux operating system.
The data source of WRF model is of 2 types namely,
• Static Data
• Grib Data
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20. CYP Simulation Model
CYP stands for “Crop Yield Predictor”.
It’s a multi disciplinary restricted-access approach software toolkit which
predicts the yield of crop for next climate year, based on the micro scale
phenomenon.
Input data needs to be very precise in order to get an accurate real-time
values.
Feed data:
• Location and Rainfall
• Soil information
• Irrigation efficiency
• Crop selection & irrigation schedule
• Runoff & Soil water
• Climate review input
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21. Analysis Report
A complex study was performed by HPC supercomputers through
Weather research and forecast model which yields the following
outputs-
• Surface temperature
• Rainfall data
• Meteograms
• Tephigrams
• Mixing rate of CO
• Relative Humidity & PM10 analysis
• Sea level Pressure
• Wind speed
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22. Surface temperature & Rainfall data
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23. Meteograms for Puducherry
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24. Tephigrams for Puducherry
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25. Mixing rate of CO, RH & PM10 analysis
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26. Relative humidity, Sea level Pressure, Wind speed
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27. Change in various meteorological parameters with
respect to height
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28. Temperature Variation Pattern over Time
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29. Change in Precipitation Pattern
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30. Change in Wind Velocity with Respect to Time
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31. CO2 emissions during 2015- 2016 concerning
with various other parameters
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32. Global Warming During 2015-2016
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33. Prediction of Wheat Production with CYP Model
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34. Prediction of Evaporation losses
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35. Prediction of Surface Run-off
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36. Impact on Crop Production
Tamil Nadu posses 22% of its annual gross income from the basic agricultural
and associated activities.
A susceptible rise of 15.28% during 2006-’07, faced a subsequent loss of
4.46% in 2007-‘08 due to the ill effects of natural calamities.
The total crop of Annamalai Agricultural sector is approximately 258
hectares.
It’s irrigated and managed by “sewage farming” technique, by means of
partially treated wastewater collected from several residential buildings
under university premises.
The above analysis predicts that the Rainfed crops are the worst sufferer due
to climate change susceptibility.
Based on the precipitation pattern the analysis also indicates that the Tamil
Nadu and Southern Peninsula is going to be the worst case scenario
compares to northern parts of India.
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37. Categorization of Impacts
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38. Schematic Generalized Impact Representation
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39. Global Scenario of Crop Yield Variability
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40. Change in Crop Yield in Last One Decade
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41. Change in Crop Yield in Last Decade
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Crop varieties 2005-2006 (kg/ha) 2015-2016 (kg/ha) Ultimate change
(%)
Paddy 2887 2792 -3.3
Maize 1047 1334 +27.4
Wheat 1483 1461 -1.49
Ragi 432 518 +19.9
Pulse 268 261 -2.7

42. Mitigation Measures
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43. Climate Smart Agriculture
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44. Adaptation Measures
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45. Stage of the Project
Pilot Scale Mitigation Measure implementation ongoing.
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46. Acknowledgements
I sincerely thank NARL for financially supporting the project.
I would like to convey my sincere gratitude to Pondicherry Engineering
College for giving me an opportunity to present my research in front of the
jury.
I’m also grateful to Dr. Kathiresan, Former Dean, Director & Syndicate
Member, Centre of Advanced Study in Marine Biology, Annamalai University
for sharing his pearls of wisdom with us during the course of this research.
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47. Conclusion
The calibrated model predicts that the subsequent temperature rise of about
2.00C will result in major declination of mass production.
The experimental values indicate that predominant pessimistic change takes
place in the case of paddy, wheat & pulse.
Above study reveals a potential temperature rise of 5.6oF within 2060 &
uncertainty in precipitation is over various climatic zones.
Making possible of interlinking rivers from the northern to the southern
India region to solve the problems of drought.
A practical remedial strategy of raising mangrove forest is suggested to offer
protection against natural calamities.
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48. Incorporation of
Crop Data
Weather
Prediction
and
Modelling
Comparative
Analysis
Mitigation
measures
InitialWeather Data
Collection
(AU Campus)
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49. Publications
Atun et al. (2016) Awareness on Impact of Climate Change in
Agriculture, A Study of Chidambaram Agricultural Area by Using
Educational Global Climate Model Software. IJSR, 5(11), 689-693.
Atun et al. (2017) Awareness on Impact of Climate Change in
Agriculture, A Comparative Study of Chidambaram Agricultural Area by
Using Educational Global Climate Model Software & Weather Research
and Forecasting Model. IRJES, 6(4), 24-36.
Atun et al. (2017) Mangrove based sequestration system renovates
with algal remediation. Journal of Civil Engineering, KSCE, Springer
(Accepted).
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50. Future Research
In order to improve the understanding of all the
meteorological parameters effect on crop yield, individual
parameters will be monitored and analyzed.
Water and nutrition management also could be undertaken
into study, for better understanding of the growth pattern of
crops.
Influence of pests and bugs also will be taken into account.
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51. References
• Ashalatha K.V., Gopinath M., and Bhat A. R. S. (2013). Impact of Climate Change on Rainfed Agriculture in India: A Case Study
of Dharwad. International Journal of Environmental Science and Development, 3(4), 368-371.
• Climate change effect on crop production (2016 November 04). Retrieved from
http://www.slideshare.net/AtunRoyChoudhury/effect-of-climate-change-on-crop-production
• Upadhyaya S.D. and Agrawal K.K. (2014). Rainfed agriculture in Central India: strategies for combating climate change.
JNKVV, 48 (1), 1-13.
• Contemporary global climate change (2016 January 21). Retrieved from http://www.insightsonindia.com/2015/01/21/1
• Casarejos F., Rocha M.N.F.J.E., Silva W.R.D. and Barreto Jr. J.T. (2016). Corporate Sustainability Strategies: A Case Study in
Brazil Focused on High Consumers of Electricity. Multidisciplinary Digital Publishing Institute open access journals
(Sustainability), 1-20.
• Phan R.S., Weber F. and Santamouris M. (2015). The Mitigative Potential of Urban Environments and Their Microclimates.
Multidisciplinary Digital Publishing Institute open access journals (Buildings), 5, 783-801.
• Raymond C.L., Peterson D.L. and Rochefort R.M. (2013). The North Cascadia Adaptation Partnership: A Science-Management
Collaboration for Responding to Climate Change. Multidisciplinary Digital Publishing Institute open access journals
(Sustainability), 5, 136-159.
• Tamilnadu climate change action plan (2013 October). Retrieved from docslide.us/documents/tamil-nadu-climate-change-
action-plan.html
• Quick start guide (2016 November 04). Retrieved from http://edgcm.columbia.edu
• Tamilnadu state action plan on climate change, Agriculture and allied sector (2013 October). Retrieved from
www.environment.tn.nic.in/doc/pdf/Chapter 5.pdf
• Weather forecast (2016 November 01). Retrieved from
http://forecast.narl.gov.in/weather/pdf/new/20161101/meteograms.html
• Salifu A.N. (2012). Analysis of Information Needs of Agricultural Extension Agents in Rural Ghana. GIMPA Journal of
Leadership, Management, and Administration, 1-24.
• M.C.M Silva, “Public Space and Flood Management,” journal of university of Barcelona, pp. 0-51.
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52. Special Thanks to Dr. Amit P. Kesarkar, Scientist, NARL,
Head-WRG
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