University Research projects (URP) of Agro Climate Research Centre

Completed

S.No Title of the Project Project PI / Co-PI period

1. Quantifying Crop Weather relationship of 2014 - V. Geethalakshmi selected food crops under current and future 2016 climate scenarios – Network trial

2. Integration of remote sensing data for yield 2003 - Dr. S. Panneerselvam, prediction and climate related studies in FASAL 2005 Dr. V. Geethalakshmi, scheme - Yield forecasting for rice, maize and & Dr. S. Sivasamy Groundnut in Western zone of Tamil Nadu

using space, Agrometeorology and land based observation

3. Computation of Water Budgeting for blocks/ 2015 - Dr. S. Kokilavani Taluks of Western agro climatic zone of Tamil 2018 Nadu

4. Revalidation of efficient cropping zonation for 2016 - Dr. S. Kokilavani, major food crops in Tamil Nadu 2018 Dr. Ga. Dheebakaran

5. Effect of climate change on shift in rainfall 2016 - Dr. Ga. Dheebakaran events of Tamil Nadu at block level 2018 Dr. S. Kokilavani

6. Effect of elevated temperature on nutri millets 2016 - Dr. S. Panneerselvam, Thenai, Samai, Kuthraivali and pulses 2019 Dr. N. Chandrasekaran, Dr. N. Sritharan

Ongoing

S.No Title of the Project Project PI / Co-PI period 1. Impact of microclimate modification 2019 - Dr. N. Maragatham, ACRC on the performance of cross bred 2021 Dr. Thirunavukkarasu, VAS animals 2. Crop Simulation Model studies on the 2019 - Dr. NK Sathyamoorthy, ACRC impact of climate variability on millets 2021 Dr. S. Kokilavani,ACRC Dr. SP. Ramanathan, ACRC 3. Developing TNAU‟s village level 2019 - Dr. Ga. Dheebakaran, ACRC medium range forecast with higher 2021 Dr. KP. Ragunath, RS & GIS accuracy. 4. Developing hybrid weather forecast by 2019 - Dr. Ga. Dheebakaran, ACRC integrating the numerical and 2021 Dr. SP. Ramanathan, ACRC astrometeorological forecast Dr. S. Kokilavani, ACRC 5. Piloting pulse produce support system 2019 - Dr. Ga. Dheebakaran, ACRC through ICT enabled services 2021 Dr. C. Uma Maheswari, TRRI Dr. B. Arthirani, ARS, KVPT Dr. K. M. Sivakumar, CARDS Dr. L. Rajendran, CPPS 6. Enhancing the predictability of location 2019 - Dr. S. Kokilavani, ACRC specific seasonal rainfall for Tamil 2021 Dr. V. Geethalakshmi, DCM Nadu Dr. N.K.Sathyamoorthy, ACRC 7. Climate Smart Organic Farming in 2019 - Dr. SP. Ramanathan, ACRC Rice 2021 Dr. E. Somasundaram, SOA Dr. K. Ganesan, SOA Dr. S. Kokilavani, ACRC Dr. C. Uma Maheswari, TRRI Dr. M. Raju, TRRI Dr. E. Subramanian, MDU Dr. P. Kannan, MDU Dr. V.M.Sankaran, Tirur Dr. S. Malathi, Tirur Dr. S.R. Srirangasami, ASD Dr. KG. Sabarinathan, ASD

Externally Funded Research projects(EFRP) of Agro Climate Research Centre

Completed

S.No Title of the Sponsor Project Grant PI / Co-PI Project Name period Lakh Rs Capturing the Benefits of ACIAR, 1999 – 40.00 V. Geethalakshmi 1. seasonal Climate Australia 2002 Forecast in Agrl. Management 2. Effect of Weather on ICAR 2003 - 5.60 V.Geethalakshmi Downy mildew 2005 3. Economic Evaluation of GOI 2004 – 5.00 V.Geethalakshmi Medium range weather 2007 forecast 4. Resilience of Tsunami TDH- 2005 – 28.00 V. Geethalakshmi devastated coastal area of Germany 2008 N. Thavaprakaash Nagapattinam district – (IP) A. Bhaskaran with reference to agriculture 5. Impacts of climate ICAR 2007 93.00 V.Geethalakshmi change on regional crops of TN 6. Establishment of NADP 2007 - 15.60 R. Jagannathan Automatic Weather 2011 V. Geethalakshmi Stations in Tamil Nadu N.K Sathyamoorthy 7. Starting of experimental GOI 2007 – 36.00 V. Geethalakshmi Agromet Advisory 2011 services with the farmers 8. Resilience of agricultural TDH- 2008 - 14.65 V.Geethalakshmi lands and increased food Germany 2009 security – (IP) 9. Assessment of impacts of UNFCCC 2008 – 3.00 V. Geethalakshmi climate change on major under 2009 irrigated and rain fed MoEFCC crops in India 10. Climate change and Ministry of 2008 – 250.00 V.Geethalakshmi persistent droughts: Foreign 2011 R. Jagannathan Impacts, vulnerability Affairs, A. Lakshmanan and adaptation in rice Norway growing sub-divisions in India (ClimaRice- I) 11. Impacts, Adaptation & ICAR 2009 – 122.19 C.R. Ranganathan Vulnerability of Indian 2012 V. Geethalakshmi Agriculture to Climate A. Lakshmanan Change – ICAR Climate change project 12. Application of extended GOI 2009 – 18.90 V. Geethalakshmi range forecast for climate 2014 risk management on crops in coastal and western agro ecosystems of TN. 13. Sustaining rice Ministry of 2010– 160.00 V. Geethalakshmi production in a changing Foreign 2013 A. Lakshmanan climate: Testing climate Affairs, uncertainties & validating Norway selected adaptation techniques on farmers field – ClimaRice II 14. Fund for infra structural DST, GOI 2010 - 59.00 V. Geethalakshmi development (FIST) of 2015 ACRC, TNAU 15. NADP Phase II: GoI - NADP 2011- 576.35 SP. Ramanathan Expansion of Automatic 2014 Ga. Dheebakaran Weather Station network S. Kokilavani in 73 blocks of Tamil S.Panneerselvam, Nadu V. Geethalakshmi 16. NADP Phase III: GoI - NADP 2011- 717.60 N. Maragatham Expansion of automatic 2014 N.K. Sathyamoorthy weather station network in 88 blocks of Tamil Nadu 17. Formulation of Weather Agrl. Ins. 2011- 20.00 V. Geethalakshmi based Crop Insurance Co. of India, 2014 K. Sathymoorthi Index for selected major New Delhi N. Maragatham crops of Peninsular India: Application of Crop S. Enayathullah weather model as a tool Shah 18. Integrated Agromet IMD, GoI 2012 – 11.00 V. Geethalakshmi Advisory Services 2013

19. Integrated Assessment of DFID, UK 2012 - 70.00 V. Geethalakshmi Climate change impacts 2014 P. Paramasivam on Principal crops and R. Balasubramanian farm household incomes K. Mahendran in Southern India (AgMIP) D. Suresh kumar A. Lakshmanan R. Krishnan 20. Climate Change: DST-CCP 2012- 65.87 V. Geethalakshmi assessing impacts and SPLICE 2014 A. Lakshmanan developing adaptation New Delhi J.S. Kennedy strategies for agriculture S.K. Natarajan in Tamil Nadu V. Davamani 21. Can Seasonal Climate AUSAID, 2012 - 31.00 V. Geethalakshmi Forecasts improve food Australia 2015 A. Lakshmanan security in Indian Ocean N. Maragatham Rim Countries in a R. Karthikeyan variable and changing climate (CSIRO) K. Sathyamoorthi P. Shanthi S. Marimuthu S. Ramesh 22. NADP – AAS: GoI - NADP 2013- 350.00 SP. Ramanathan Development of Agro 2018 Ga. Dheebakaran Advisory Services using S. Kokilavani Automatic Weather S.Panneerselvam, Station data at block level V. Geethalakshmi in TN N. Maragatham 23. Adaptation to climate Ministry of 2012 – 310.27 V. Geethalakshmi change:An integrated Foreign 2017 A. Lakshmanan science-stakeholder Affairs, R. Thamizhvendan approach to develop Norway K. Annadurai Adaptation framework for Water and Agriculture S. Avudaithai sectors in Andhra Pradesh S.K. Natarajan and Tamil Nadu states of Mohamed Yassin India - ClimaAdapt S. Marimuthu 24. Evolving climate resilient DFID, UK 2015 – 72.52 V.Geethalakshmi farming systems in South 2017 A.Lakshmanan India through Integrated P.Paramasivam modeling, adaptation and S.Kokilavani stakeholders participation S.Jeyaprakash (AgMIP – II) Sravanakumar D.Suresh kumar 25. Organizing Farmers' GoI - IMD 2016 - 0.35 Ga. Dheebakaran awareness programme by 2017 S.Panneerselvam Coimbatore centreAgrometerological Field Unit (AMFU) of IMD 26. Organizing Tamil Nadu GoI - IMD 2016 - 1.41 Ga. Dheebakaran, State Level Meeting of 2017 S.Panneerselvam Stakeholders on Agromet Advisory Services by Coimbatore centreAgrometerological Field Unit (AMFU) of IMD 27. NATCOM: Mapping UNFCCC 2017– 23.00 V. Geethalaksmi climate change under 2018 N.K. Sathymoorthi vulnerability to MoEF&CC, J. Prabhakaran strengthen food security GoI with climate smart adaptation and mitigation options in Tamil Nadu

Ongoing

S.No Title of the Project Sponsor Project Grant Lakh PI / Co-PI Name period Rs 1. GraminKrishiMausamSe IMD, 1998 - Yearly Ga.Dheebakaran wa GoI 2020 10.00 SP. Ramanathan to12.00 S.Panneerselvam N.K.Sathyamoorthy V. Geethalakshmi 2. FASAL - Yield IMD, 2011 – 45.00 V. Geethalaksmi forecasting for rice, GoI 2020 Ga. Dheebakaran maize and Groundnut in Western zone of Tamil M. Rajavel Nadu using space, agro- meteorology and land N.K Sathymoorthi based observation (FASAL) 3. Building Resilience to DST- 2018 – 241.20 V. Geethalakshmi Climate Change and CCP 2021 A. Lakshmanan Improving Food Security SPLICE, S. Pannerselvam Through Climate Smart New V. Balasubramani Solutions (BRIFS) Delhi P. Meenakshisundaram Ga. Dheebakaran S. Kokilavani S. Senthilnathan S. Umeshkanna

Research highlights of ACRC during 2015 - 2019

Theme 1: Weather forecasting and Agro Advisory

2014 – 15

1.1 EFRP–GoI - UGC: Revalidating Pre Monsoon Sowing Week with Higher Resolution for changing climate of Tamil Nadu. (Adopted in CSM 2015, Scientist: Dr. Ga. Dheebakaran) Decadal analysis for identifying the shift in NEM onset had clearly indicated that the onset week was shifted over these periods from 1951 to 2010. The shift has both temporal and spatial variation as like rainfall quantity. The shift observed in monsoon onset was one or two weeks on either side. The NEM onset was two weeks earlier in southern districts of Tamil Nadu and one week earlier in north western and western parts of Tamil Nadu. Whereas, the onset was become delayed a week at Western Ghats, North eastern and coastal regions of Tamil Nadu. In general comparing 1950, current monsoon onset was become early towards inlands and delayed near sea. The differences in the onset have been observed over these periods in Tamil Nadu and are more pronounced by an earlier onset in southern districts and delayed in coastal part of Tamil Nadu where North East Monsoon is the case.

2015 -16

1.2 EFRP – IMD –GKMS – Weather based agro advisories to farmers Scientists: Dr. Ga.Dheebakaran& Dr. S.Panneerselvam) The farmers receiving weather based Agro Advisory service (AAS) from AMFU centers of TNAU earn additional income (Rs. 10000 – 15000 per ha) than the non AAS farmers. The increased income is due to timely planning of agricultural operations, lesser cost of cultivation, reduced risk in loss of inputs and increase in yield. Popularization of weather based AAS facility, mKisan portal have to be taken up to increase the beneficiaries.

1.3 Adhoc research: Astrometeorology (Scientist: Dr. Ga. Dheebakaran) Methodology for hourly astromet weather forecast using planet ephemeris have been developed from five years of Adhoc research. The important finding was planet activeness chart. Planets are not active always for a location‟s weather. Earth and planets have both self-rotation and revolving around sun. This alters the planet activeness by every fraction of time. After identification of activeness chart, the hourly astromet rainfall forecast accuracy had been increasedfrom 50 to 80 per cent. This activeness chart could be patented after verifying for other parameters.Moist planets viz., Neptune, Saturn and Venus at their active azimuth and hot planets viz., Sun, Mars and Uranus at their negative range to a particular location had good influence on the rainfall of that location.

2016 - 17

1.4 EFRP – IMD –GKMS – Weather based agro advisories to farmers Scientists: Dr. Ga.Dheebakaran& Dr. S.Panneerselvam) Weather based agro advisory helps responsive farmers to get 14 - 20 per cent yield increase and reduce 20 – 24 per cent in cost of cultivation.

1.5 M.Sc. Student Thesis – Astrometeorology in rainfall forecasting (Mr. Arul Prasad & Chairman: Dr. Ga. Dheebakaran): Astrometeorological forecast has higher Forecast Accuracy and Critical Success Indices than other agencies forecast in daily rainfall forecast. The Critical Success Indices of all the forecasting agencies was very less during winter and Summer than NEM.

1.6 MSc. Student Thesis – Astrometeorology in rainfall forecasting (Mr. C. Balamurali& Chairman: Dr. Ga. Dheebakaran): Astrometeorological rainfall forecast studies inferred that planets have good influence on the rainfall. Among the planets, the Saturn and Neptune at 61 – 90 and 271 – 300 degrees azimuth had higher rainfall influencing capability. The nearer planets (Sun, Moon, Mercury, Venus) had influenced more of low intensity rainfall events and the far away planets (Neptune, Uranus, Saturn and Jupiter) had influence on high intensity rainfall events. Irrespective of 36 two-planet combinations, the 0 - 30 degrees aspects had more rainfall events than other aspects.

2017 – 18

1.7 EFRP – IMD –GKMS – Weather based agro advisories to farmers Scientists: Dr. Ga.Dheebakaran& Dr. S.Panneerselvam) Accuracy of TNAU‟s block level rainfall forecast is 86, 70, 78, 98 and 81 per cent for the summer, south west monsoon, north east monsoon, winter and annual, respectively, which are higher than IMD‟s district level forecast.

1.10 M.Sc. Student Thesis – Numerical weather forecasting accuracy (Ms. M. Megala and Chairman: Ga. Dheebakaran): In Weather Research Forecast(WRF)Model, the Kessler scheme produced highly usable forecast in the both input data resolution (0.25 and 0.5) than other schemes with same resolutions input data. The usability of forecast (correct + usable) obtained from WRF model with 0.25 degree resolution cum Kessler scheme microphysics options were between 76 to 89 per cent, whereas it was 64 to 77 in WSM3 class scheme and 60 to 80 in WSM6 class schemes. Rainfall forecast usability structure of microphysics schemes with 0.25o and 0.50o data resolution

Kessler scheme in predicting rainfall with 0.25o and 0.50o input data resolution

WSM3 class scheme in predicting rainfall with 0.25o& 0.50o input data resolution

WSM6 class scheme in predicting rainfall with 0.25o& 0.50o input data resolution

1.11M.Sc. Student Thesis – Astrometeorology on wind speed (Ms. K. Rathika& Chairman: Dr. Ga. Dheebakaran): In Astrometeorological wind speed forecast studies inferred that, among the planets, Neptune and Mercury had higher number of wind speed influencing capability. Irrespective of planet, the azimuth range of 61 – 120 degree and 240 – 300 degree azimuth had good influence on the wind speeds in the study locations. Active state of Mercury, Venus, Moon, Mars, Jupiter and Uranus and ruling state of Saturn have increased the wind speed of a location. Irrespective of 36 two-planet combinations, the 0- 30 degrees aspects had more wind speed events than other aspects. S.N Planet Angle Azimuth range (Degrees) of high frequency wind speed events

Calm Light Light Gentle Moderate Strong Extreme air air breeze breeze breeze breeze (<2 (2.1 - 6 (6.1 - (12.1 - (19.1 - 30 (19.1 - (>45 km) km) 12 km) 19 km) km) 30 km) km) 61 - 90 15.8 18.6 17.7 10.3 25.0

91 - 120 8.1 14.6 25.0 40.2 46.8 46.8 1 Sun 241 - 270 21.1 16.1 13.1 13.0 9.8 271 - 300 31.6 16.4 25.0 331 - 360 33.3 31 - 60 8.4 33.3 61 - 90 16.0 19.1 20.2 12.2 25.0 2 Mercury 91 - 120 7.7 13.1 21.9 36.8 40.8 51.8 241 - 270 18.0 14.4 12.5 15.0 12.2 271 - 300 34.3 17.7 61 - 90 14.9 16.9 16.4 9.6 75.0 91 - 120 8.6 15.9 24.4 39.9 49.4 43.0 3 Venus 241 - 270 19.3 16.3 13.8 13.1 9.0 21.7 271 - 300 27.3 15.2 10.0 61 - 90 16.5 18.2 18.1 14.1 14.6 58.5 91 - 120 19.4 17.7 18.2 21.7 21.5 27.2 4 Moon 241 - 270 18.1 18.9 17.9 15.1 15.0 271 - 300 18.7 16.9 17.7 22.7 23.6 33.9 61 - 90 19.0 17.3 15.7 11.2 50.0 91 - 120 11.3 16.8 21.6 22.1 24.3 63.0 25.0 5 Mars 241 - 270 21.3 16.7 14.0 15.2 14.4 25.0 271 - 300 19.0 17.2 16.0 20.6 25.6 16.7 61 - 90 50.0 6 Jupiter 91 - 120 31.1 29.2 30.2 41.5 45.2 57.9 241 - 270 27.9 30.4 29.1 19.1 15.1 15.7 25.0 61 - 90 28.2 34.4 33.4 20.5 13.9 27.9 25.0 7 Saturn 241 - 270 25.0 271 - 300 37.1 31.2 32.6 43.4 52.3 52.9 25.0 31 - 60 25.0 8 Uranus 91 - 120 26.2 22.5 24.4 34.3 41.4 41.5 25.0 241 - 270 25.2 24.7 23.8 20.8 15.5 24.2 61 - 90 37.9 34.8 35.2 59.0 40.3 25.4 75.0 9 Neptune 271 - 300 35.5 34.4 34.1 34.9 33.1 30.1 25.0

2018 - 19

1.8 EFRP – GoI – GKMS – Weather based agro advisories to farmers (Scientists: Dr. Ga. Dheebakaran & Dr. S. Panneerselvam) In GKMS scheme, analysis of IMD‟s district level rainfall forecast indicated that, inclusion of Western Ghats in Coimbatore gives more false alarms to other plains of coimbatore district. The correctness of 2018-19 rainfall forecast was <50% in all districts.Need separate forecast from IMD for Coimbatore plains and Valparai. Whereas verification and error structure analysis of IMD‟s rainfall forecast indicated higher usability of forecast Kovilpatti (55 – 75%) and Pechiparai (45 – 60%). The forecast of other parameters viz., minimum temperature, maximum temperature, wind speed, relative humidity and cloud cover were perfectly (>80%) matched with actual. Weather based agro advisories issued by GKMS-AMFU scheme has increased the income of Agro Advisory Service adoptive farmers to the tune of Rs. 9500/- per acre maize (Coimbatore-AMFU), Rs. 2400/- acre maize and Rs. 7000 – 8000/- per acre Chillies (Kovilpatti- AMFU)

Usefulness of GKMS – Weather based Agro Advisory Service

Error structure of weather forecast from IMD

1.12EFRP – GoI – NADP: Automated Agro Advisory “Web cum Mobile App”(Scientists: Dr. V. Geethalakshmi, Dr. S. Panneerselvam, Dr. Ga.Dheebakaran, Dr. N.Maragatham, Dr. S.Kokilavani, Dr. SP. Ramanathan Dr. R. Jagannathan, Dr. T.N.Balasubramanan,):  Weather during the cropping season contributes more than 50 per cent of crop yield. Response farming with timely weather based agro adviosries helps the resource poor farmers to plan in advance on crop selection, intercultural, harvest and post harvest operations will increase the yield in addition to reduce the weather based risks on input loss.  In this context, Weather based Automated Agro Advisory Web cum Mobile App has been successfully devloped under Government of Tamil Nadu sponsored NADP scheme titled “Development of Agro Advisory Services using Automatic Weather Station data at block level in Tamil Nadu” at Agro Climate Research Centre, Directorate of Crop Management, TNAU, Coimbatore.  “TNAU-AAS web cum Mobile App” automatically generates block level weather forecast for next 6 days, develops weather scenario for the every block of Tamil Nadu using past weather data observed from AWS installed in 385 blocks of Tamil Nadu under Tamil Nadu Agricultural Weather Network (TAWN) and for the block level forecasted weather data.  It picks up weather scenario based agro advisory from the data base containing adviosry for six crop stages of 108 major agricultural and horticultural crops and sends to the registered farmer mobile as SMS. The weather specific, crop specific, stage specific agro adviosries includes both management and plant protection.  Registration requires farmer‟s active mobile number, crop name and date of sowing. From the date of sowing, automatically calculate the stages and generate advisory specific to the crop stage. The registration of the farmer needs to be approved by the Block level Agricultural officer and the block level officer needs to be approved by the Admin after due verification.  Registration is restricted as “One crop to One mobile number”, but one farmer can register more than one active mobile numbers. He can change the crop only after the closure of the crop or after two months of previous registration.  Android App of this Software “TNAU-AAS” can be downloded from Google Play store, which facilitate easy registration, changing of crops, weather condition of farmer‟s location and continuous history of advisories given.  The “TNAU - AAS Web cum Mobile App” is first of its kind in India developed with recent ICT tools. Multi fold safety is maintained to secure the database.  Admin has the option to add new crops and advisories and to generates reports of individual farmer wise, block wise, district wise, crop wise advisories diseminated.  Farmers and extension officals trained for this “TNAU - AAS Web cum Mobile App” opined that it will be highly useful and need of the hours.

 Usefulness of “TNAU - AAS web cum Mobile App” o Fully automated, reduce the work load of extension functionaries o Lab to land transfer of technologies become direct and easy. o No holidays, no need of technocrats except simple monitoring o Surely reduce the crop failure risk of climate dependent farming o AAS is Viable option to do weather based precision farming o Expected about 45 lakhs beneficiaries may get SMS every year. o Total number of SMS required is very less as it is specific to farmer o Can send special advisory / extreme events warning SMS

Work flow of TNAU - AAS App TNAU - AAS web portal

Weather based AAS Mobile App TNAU – AAS Mobile App

Theme 2: Basic and applied meteorology

2015 -16

2.1 Adhoc research –TNAU Weather soft: Weather database cum weather analyzing tool (Scientist: Dr. Ga. Dheebakaran, Dr. S. Paneerselvam& Dr.S. Kokilavani) Released as Technology 2016)

Success of agricultural technologies depends on weather prevailed during the application of that technology. Now, all the scientists are correlating their results with weather to assess and improve the technologies. The weather calculations for multiple locations are cumbersome and need proficiency in methodology. Hence, a simple and user-friendly weather data base cum analysis tool named “TNAU Weather soft 2016” has been developed for the use of agricultural scientist and students. The features of the TNAU Weather soft are

 Weather database cum weather analyzing tool, developed for weather correlation studies

 Useful to scientist working in management and plant protection studies

 Store and retrieve multiple locations data in an organized way

 Windows Desktop application developed in VB .Net with MS Access database

 Very simple and user friendly and basic Windows working knowledge is enough

 Create new station and work with huge data (even for 100 years)

 Simply import input data and export output as excel format.

 Check for error, missing values and list out.

 Work with any specific range of available data

 About 21 weather parameters can be stored and retrieved

 Automatically calculate Standard weekly, monthly & yearly values.

 Mean values for daily, standard weekly, monthly & annual for both individual and multiple Parameter

 List out date-wise extreme events and possibility of occurrence of any specified event in particular day

 Modules for initial, conditional probability (any value), GDD and Heat units

2.2 Adhoc research: TNAU Energy Soft: Energy efficient technology identifying tool (Scientist: Dr. Ga. Dheebakaran, Dr. P. Devasenapathy& Dr.D. Jegadeeswari) Released as Technology during 2016) Climate change scientists are aimed to finding the opportunities to reduce the factors causing climate change, in turn to protect the lives in earth. One of the major climate change cause is the excess use of energy, particularly non-renewable energy sources such as fossil fuels. India is an agrarian country and agriculture is one of major energy utilizing sector. Hence, it is necessary to identify and promote the energy efficient agricultural technologies without affecting the productivity. Adopting lesser energy consuming technologies and avoiding more non-renewable energy source using technologies, is itself a climate change mitigation technology. A simple and user-friendly method of energy calculation is needed to facilitate our scientist and to produce energy efficient technologies which would sustain in future. In this context, the “TNAU Energy Soft 2016” is developed at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore, to facilitate scientific community with database for energy details of different inputs and outputs, agricultural operations for different crops and simplified energy efficient calculation.

 Simple modeling tool, written in VB .Net programme with MS Access database.  Windows desktop application and user friendly.  Calculates and produces report for the total energy utilized, source wise, category wise, type wise energy utilized.  In addition, it could compare six treatments at a stretch and give comparison report.  Like economic analyses viz., benefit cost ratio and net profit with this TNAU energy soft 2016, we could calculate Energy efficiency and net energy benefit.  This software will helpful for the scientist to identify the energy efficient agricultural technologies towards the climate change mitigation and adoption.  This will also helps to identify the better input source combination of a technology without doing repeated and expensive field trials.

2.3 URP – Rainfall analysis (Scientist:Dr. Ga. Dheebakaran & Dr.S. Kokilavani)  The rainfall shift analysis indicated that, all the zones have increasing trend in quantity of rainfall except hilly and high rainfall zone during 1950 – 2010.  In all the zone, NEM was shown increasing trend in quantity of RF. SWM was observed with sharp decrease in quantity of RF at western, hilly & high RF zone where the SWM have good influence.  Per cent of decrease in rainy days was higher than rain day and with increase in rainfall quantity resulted in more intensity of rainfall.  Rate of change in number of rainy days was lesser in NEM than other season. Rate of change in number of rainy day was lesser in north western zone than all other and it was more in high rainfall and hilly zone.  It is clear that, except hilly & high rainfall zone, the quantity of annual rainfall is in increasing trend due to increase in quantity of RF during NEM.  The decrease in rainy days is an alarming one, which will be resulted in poor distribution of rainfall and reduction in LGP.  The increasing intensity of rainfall may cause soil erosion in turn affect the land productivity.  The cropping system should be altered accordingly and more emphasis on soil moisture conservation research is needed to cope up with the shift.

2.5 M.Sc. Student Thesis: (Ms. N. Velunachiyar and Chairman: S. Panneerselvam) Nutrient enrichment by rainfall and throughfall study indicated that both the rainfall and throughfall are potential source of plant nutrients and the amount of nutrients delivery varies with type of vegetation. Nutrient contribution from rainfall during NEM was 24.43 kg/haof nitrate nitrogen, 22.42 kg/ha of Ammoniacal nitrogen and 0.30 kg/haof sulphate. Among the tree species, throughfall from Azadirachtaindicahad supplemented higher nitrate N (28.67 kg/ha) and K (2.6 kg/ha). The Peltophorum pterocarpumrecorded thehighest ammoniacal nitrogen (41.92 kg/ha). Throughfall from Casuarina equisetifoliaprovide Ca, which resulted in higher pH and EC. Throughfall from

Peltophorum pterocarpumsupplementedP2O5 and Na. It is quite interesting to note that the amount of nitrogen delivered by rainfall and throughfall were ranged between 33 and 68 kg/ha which is sufficient for the most of rainfed crops.

2016 - 17

2.4 URP: Crop Efficient Zonation for Cereals and Millets (Scientist: Dr. S. Kokilavani and Dr. Ga. Dheebakaran)

District wise Efficient Cropping Zone (ECZ) for major cereals and millets viz., rice, maize, sorghum and cumbu in Tamil Nadu were identified by using past 30 years data (1981 – 2010). The Most Efficient Cropping Zone has higher yield and crop area, hence periodical technology up gradation is sufficient to sustain the same. In SpreadEfficient Cropping Zone, technology intervention has to be done to increase the yield, where the area under the crop is high with low crop productivity. In Yield Efficient Cropping Zone, extension activities may be initiated to increase the area, where there is good yield potential with minimum spread. In Inefficient Cropping Zone, alternate suitable cropping system may be promoted, where both the area and yield is low.

2017 - 18

2.6 URP: Crop Efficient Zonation for pulses and oil seeds (Scientist: Dr. S. Kokilavani and Dr. Ga. Dheebakaran) District wise Efficient Cropping Zone (ECZ) for major pulses and oil seeds viz., black gram, green gram, ground nut and gingelly in Tamil Nadu were identified by using past 30 years data (1981 – 2010). The Most Efficient Cropping Zone has higher yield and crop area, hence periodical technology up gradation is sufficient to sustain the same. In Spread Efficient Cropping Zone, technology intervention has to be done to increase the yield, where the area under the crop is high with low crop productivity. In Yield Efficient Cropping Zone, extension activities may be initiated to increase the area, where there is good yield potential with minimum spread. In Inefficient Cropping Zone, alternate suitable cropping system may be promoted, where both the area and yield is low.

2.7 Adhoc research: Drought Assessment for GoTN (Scientist - Ga. Dheebakaran) According to Moisture Adequacy Index (MAI) analysis, there was mild to severe drought in Erode, Namakkal, Pudukottai, Ramnad, Tuticorin in both SWM and NEM of 2017, where as other districts had mild to moderate drought during SWM and then recovered during NEM 2017.

2.8 Ph.D. Thesis Research – Microclimate modifications in Groundnut+Maize system (Dr. Arunkumar& Chairman Dr. N. Maragatham) Micrometeorological studies under groundnut intercropping system inferred that the groundnut equivalent yield and WUE were increased by 51, 48 and 22 per cent in intercropping with maize, red gram and castor than sole crop, respectively.

2018 - 19

2.9 AIT Final year (2014-18) Students Project: TNAU – MAI calculator(Ms. V Swathi, Ms. K. Aparna, Guide: Dr. Ga. Dheebakaran and Dr. S. Kokilavani)

Simple, user friendly, web based “TNAU Moisture Adequacy Index Calculator” is developed at Agro Climate Research Centre, Directorate of Crop Management, TNAU, Coimbatore for agricultural drought assessment using Weekly MAI calculation formula of Thornthwaite and Mather (1955). Moisture Adequacy Index (MAI) is one of the important indices for agricultural drought assessment. This calculator will be highly useful for Department of Agriculture and State Disaster Management officials, Scientist and Students to calculate weekly drought scenarios for any length of period during a year. In this application, simply typing the weekly rainfall data and PET of a district/block will give the MAI. If the current PET values are not readily available for the district, the MAI calculator is designed to use the PET normal of respective district, stored in Database. The web application was written in web based PHP programme with a database of MySql.

3 Adhoc research: Drought Assessment for GoTN (Scientist - Ga. Dheebakaran) According to Moisture Adequacy Index (MAI) analysis, there was moderate drought in Dharmapuri, Karur and Krishnagiri, in both SWM and NEM of 2018. The Namakkal, Perambalur, Pudhukkottai, Salem, Vellore, Trippur and Virudhunagar had moderate drought during SWM and mild drought during NEM. Where as other districts had mild to moderate drought during SWM and then recovered during NEM 2018.

2.10M.Sc. Student Thesis research – Microclimate modification in SRI (Mr. C. Navinkumar and Chairman: Mr. Tavaprakash) Microclimate modification with variety, spacing and method of planting influences the growth and yield of organic rice varieties. Among the varieties Co52 with SRI planting perform better. Favourable micro-meteorological parameters such as warmer soil temperature with lesser canopy temperature and leaf temperature; more photosynthetic rate, transpiration rate, stomatal conductance in Rice CO-52 variety planted in SRI method enhanced better growth characters (LAI and DMP), yield attributes (productive tillers/m2, total number of grains/panicle and filled grains/panicle) in turn increased the yield and productivity of rice.

Effect of spacing and method of planting on Effect of spacing, variety and method of Soil temp. (oC) during 1400 hrs at flowering planting on Canopy temp. (oC) at stage in organic rice panicle initiation stage in organic rice 2.11Ph.D. Student Thesis research – Climate variability on Chillies productivity (Ms. Kowshika and Guide: Dr. S. Panneerselvam) Climate variability and chillies productivity studies inferred that Sivaganga, Ramanathapuram, Thoothukudi, Tiruppur, Karur and Ariyalur were low productivity regions for Chillies. Whereas. Vellore, Kancheepuram Tiruvannamalai, Viluppuram, Cuddalore, Namakkal, Thanjavur, Thiruvarur, Nagapattinam, Pudukkottai, Theni, Madurai were fallen under moderate productive region. Districts such as Erode, Krishnagiri, Coimbatore, Thiruchirappalli, Dharmapuri Dindigul, Tirunelveli, Virudhunagar districts managed to be in high productive regions for Chillies. In comparing the average productivity index of Tamil Nadu with rainfall deviation, it could be conclude that the excess and normal years gave moderate to high productivity, but deficit rainfall years resulted in low to moderate productivity. The positive nature of correlation also proves that rainfall deviation and productivity index are linearly correlated with a positive relationship. Further, the regression model indicated that there was a considerable amount of variation in productivity due to rainfall deviation.

Productivity index of chilli crop during Spatial distribution of TNAU Chilli 2014-15 hybrid CO1 yield (kg/ha) under irrigated condition Theme 3: Climate change and crop weather modeling

2014 -15

3.1 EFRP – CSIRO – Seasonal Climate Forecast (Scientist: Dr. V. Geethalakshmi) Forecast developed through multi-category probabilistic method and compared against the actual observation to investigate the usefulness of seasonal climate forecasts in enhancing food security by reducing agricultural production risks associated with climate variability and climate change and to develop a blueprint for the use of improved seasonal climate information in the case study regions. Northern and western parts of Tamil Nadu has comparatively better predictability

RPSS – Rank Probability Skill Score: Zero indicates no skill when compared to the reference forecast and 1 represents a perfect forecast. Values of 0.2 and above would suggest modest skill. Skill of prediction varies spatially, mainly due to orography.

3.2 EFRP – DFID – UK - AGMIP II – Climate resilient farming system for South India through integrated modeling (Scientist: Dr. V. Geethalakshmi)

RCP 8.5 mid century - 20 GCMs indicate increase in both maximum and minimum temperatures to the tune of 2.2 to 3 0C. In Tamil Nadu, rainfall is expected to increase during the Northeast monsoon (October – December), The models agree on warmer future conditions

Regional Climate Change Projections across 20 GCMs in the Mid-century RCP 8.5

Current (black line and stars) and future (box-and-whiskers) monthly and seasonal mean temperature and Rainfall for Coimbatore, in the 2050s under RCP8.5. The green line represents projected mean changes in mid century.

Future farm yields were higher from 3 to 17 per cent with an average gain of about 10 percent. The increase is attributed to expected increase in rainfall in the future time period. 3.3 EFRP-IMD–FASAL- Strategic yield forecasting under Climate Change for Rice, Maize and Ground yield (Scientist: Dr. V. Geethalakshmi& Dr. N.K. Sathyamoorthy)

Impact of HQ0 on rice (ADT 43) The simulated yield of rice for HQ0

projections for control (without CO2) has showed a decreasing trend for yield. In baseline, 1971-1980 decade the yield was 3,153 kg ha-1 and for the decade 2091-2100, the predicted yield was 1716 kg ha-1.

The CO2enriched simulation yield for HQ0 showed a decreasing trend initially till 2041- 2050 decade, a narrow period of increase was projected in 2051-2060 (3066 kg ha-1).However, the highest per hectare yield was recorded only during 1971-1980 decade (3176 kg ha-1)

in case of HQ0. The range of increase in yield was higher in CO2 enriched simulation compared to controlled simulation Impact of HQ0 on Groundnut (TMV 7) The simulated yield of groundnut for HQ0 projections for control (without

CO2enrichment) has showed decreasing trend for yield. In HQ0, the yield predicted was 941 kgha-1 for 1971-1980 and 372 -1 kgha for 2091-2100.

The CO2 enriched simulation yield showed a decreasing trend but the range of increase in

yield was higher in CO2 enriched simulation compared to controlled simulation. Impact of HQ0 over Maize (NK6240) The simulated yield of maize for HQ0 projections for control (without

CO2enrichment) has showed decreasing trend for yield. In HQ0 for 1971-1980 decade the yield was 3303 kg ha-1 while 2091-2100 decade, the predicted yield was -1 2,134 kg ha . The CO2enriched simulation yield also showed decreasing trend. The CO2 enriched simulation yield showed a decreasing trend but the range of increase in yield

was higher in CO2 enriched simulation compared to controlled simulation. 3.4 EFRP – Norway – ClimaAdapt - (Scientist: Dr. V. Geethalakshmi): Climate change (elevated temp.) and its effect on rice productivity

Increase in temperature resulted in increased plant height whereas total number of tillers/m2 and dry matter production gives a negative result with increased temp.Crop yield was drastically reduced with the increase in temperature due to increased spikelet sterility % and reduction in number of productive tillers/ m2. In case of quality parameters, starch content in the plant reduced with elevated temp. whereas chalkiness of the grain increased with increase in temperature.CERES – Rice model predicted that under elevated temperature grain yield was found reduced. Under planting system, SRI method got affected compared to conventional planting with increase in temperature. Among hybrid and variety, effect of increase in temperature was found more on hybrid compared to the variety.

Rice productivity was negatively impacted for elevated temperatures. The yield reduction ranged from 4-6%, 12-15%, 22-25%, 37-40% and 53-56% for 1°C, 2°C, 3°C, 4°C and 5°C, respectively. 2015 -16

3.3 EFRP-IMD–FASAL- Strategic yield forecasting (Scientist: Dr. V. Geethalakshmi& Dr. N.K. Sathyamoorthy)

Difference between the average rice grain yield predicted at Aduthurai, Bhavanisagar, Trichy and Vaigaidam by DSSAT crop weather model and actual district average were ranged from 8 to 20 per cent during Kharif 2015 and 4 to 12 per cent during rabi 2015. Hence, the validated DSSAT model could be used for future projections in Aduthurai, Bhavanisagar, Trichy and Vaigaidam.Yield of rice, maize and groundnut crop could be predicted two weeks before harvest with 70 % accuracy and at flowering stage with 65 % accuracy. Crop simulation model could be used as a tool to forecast the productivity of crops well before harvest and the yield predictions could be used for policy decisions by the State and Central Government. Genetic coefficients for five rice cultivars and two maize cultivars were derived for DSSAT model, which could be used by scientist for future research.

Genetic coefficients for rice cultivars in DSSAT (CERES-rice) model

Cultivar P1 P2R P5 P2O G1 G2 G3 G4 ADT38 330 130.0 300.0 11.0 53.0 0.0160 1.00 1.13 CORH2 590 160 337 12.5 59.5 0.0245 1.00 1.00 ADT43 357 60.5 448.1 11.9 50.8 0.0210 0.38 1.03 CO50 640.7 160.4 328.9 11.9 68.0 0.0200 1.00 1.10

Genetic coefficients for MAIZE cultivars in DSSAT (CERES-Maize )model

Cultivar P1 P2 P5 G2 G3 PHINT

COHM (5) 330 0.520 860 769 8.5 38.8

CO6 450 2.000 580 600 16.5 50.0

3.5 EFRP – Norway – ClimaAdapt(Scientist: Dr. V. Geethalakshmi): The major adaptation & mitigation technologies being promoted to include demand based irrigation scheduling in Ponnaniyar basin, System of Rice Intensification, summer ploughing, application of bio inoculants, promoting climate resilient cultivars / cropping pattern, shifting the sowing window, integrated farming, Integrated Pest Management and Alternate livelihood options, Paddy cultivars suitable for summer paddy cultivation Rice varieties with temperature tolerance viz., CO 51, ASD 16, ADT45, CORH 3 have been identified. In Ponnaniyar and Kalingarayan basins, the conveyance efficiency of irrigation water have been improved from 30 to 50% due to the implementation of best management practices gained from the training programs.SRI technology as Climate change adaptation technology: SRI is the best system for the environment and the society as it saves water, reduces cost on inputs, reduces greenhouse gas emission and increases yield. Lower amount of input requirement and high productivity per unit area are the major reasons for improving the SRI‟s performances in a sustainable way. In the rabi season, WUE was 7.40 kg of grain per mm of water for SRI which was 4.77 for conventional.

Yield parameters of different methods of irrigation in Kalingarayan & Ponnaiyar basin

Alternate agro based livelihood ventures like mushroom cultivation, bee keeping, sericulture, value addition of millets, vermi compost preparation have been introduced by ClimaAdapt and now becoming popular in most of the project villages. This has Water used by SRI and conventional ensured livelihood security to rural poor. plots from Nursery until maturity stages of rice, Rabi 2014 3.6 EFRP – DFID – UK - AGMIP II – Climate resilient farming system for South India through integrated modeling (Scientist: Dr. V. Geethalakshmi) Minimum temperature of Trichywitness a warming of 0.047°C /annum, Maximum temperature did not show any considerable trend over the years between 1980 – 2010.Both Maximum and minimum temperatures is projected to have same magnitude of increase during the mid centuryby 2.2°C by RCP 4.5 and 3.0°C by RCP 8.5.Rainfall is anticipated to increase by 5- 8 % through RCP 4.5 and 15 to +28 % through RCP 8.5 in the md century time scale.

 Influence of ENSO on SWM Rainfall of Tamil Nadu El-Nino: No relationship in all El-Nino years whereas weak years SOI had positive correlation with southern, central and northwestern parts of TN. In strong years the positive correlation extends to eastern and northeastern parts of TN covering most parts of eastern coast.

La-Nina: All La-Nina years had positive correlation over most parts of western Ghats and northern coastal area. Weak years had positive correlation over southern parts of western Ghats and central TN. Strong years had the positive relationship covering the whole western Ghats and eastern coast.

 Influence of ENSO on NEM Rainfall of Tamil Nadu

El Nino: In all the years, only southern TN showed some correlation, while all other parts had weak correlation. Weak years had good negative relationship over entire western Ghats, east coast and northern pockets of TN. Strong years exhibits positive correlation with northern and central TN including eastern coast.

La-Nina: In all years northern and central TN showed negative relationship while in weak years the negative relationship extends to the total TN except few parts of western and northern TN. In strong years except Cauvery basin all other parts showed positive relationship.  Impact of El Niño/Southern Oscillation on Hydrology and Rice Productivity

Major share of rainfall is received during northeast monsoon (44.44%) followed by southwest Monsoon seasons (36.18%) and the total flow gradually increased from March to September.

El Niño years received more rainfall (with high inter annual Water balance of Cauvery basin as influenced by rainfall variability of 809.3 mm to El-Niño, La Niña and Normal conditions 2366 mm), which resulted in high soil water recharge including percolation and soil water availability in the surface layers.

The mean rice productivity was shifted up in El Niño and Normal years indicating the possibility of getting more rice yields with less crop production risk compared to Impact of ENSO on rice productivity La Niña years.

 Climate change and its impact on hydrology - Cauvery basin  Future climate predictions indicated increase in rainfall ranges between 7 and 21% towards mid century (2040 -2069) while 10 and 33% increase in end century (2070- 2099) compared to baseline (1971-2005)  In the mid century, the predicted increase in annual Potential Evapotranspiration (PET) varying from 3 to 4.5 % whereas it is 8.4 to 9.3% for end century scenario  Annual water yield is expected to increase by 14 to 21 % during mid century and is projected to increase further by 20 to 27 % towards end century  The annual soil water storage is also predicted to increase by 5 to 14 % and 7 to 18 % in the mid and end century respectively

 Changes (%) in irrigation water requirement for paddy during mid and end century compared to the baseline In the mid century, the predicted irrigation water requirement changes ranged from 4 to 8.1 % and the changes are expected between 4.5 % and 14.7% in the end century.

Seasonal and annual changes expected in PET (%) in Cauvery basin

The increasing trend in PET is the main cause for the increase in water demand

 Sensitivity analysis for change in temperature, rainfall and CO2 concentration on maize productivity

Sensitivity analysis gives a clear indication on the impact of combination of effects of change in temperature and precipitation on crops with and with out CO2 fertilization. This would help in designing appropriate adaptation strategies

Screening of rice cultivars for elevated temperature

ADT 40, Vellai samba and Karthigai samba recorded highest spikelet fertility, exhibited higher tolerance to elevated temperature (43 0C). These varieties could be used in breeding for developing heat tolerant cultivars

Rainfall projection during mid century through climate models  Models predicted that the annual rainfall quantity during midcentury is increased from current situation  The increment also higher at RCP 8.5 than RCP 4.5

3.7 EFRP – CSIRO, Australia – Seasonal climate forecast to improve food productivity (Scientist: Dr. V. Geethalakshmi) Information on seasonal climate forecast (SCF) has the potential to increase the economic advantage in agriculture production by reducing agricultural risks associated with climate.It gives an opportunity for farmers to modify their strategic farming decisions to minimize adverse weather events impact and to find opportunities within favorable events. SCF is utilized for on-farm (crop and variety choice, land allocation among different crops, land configuration) and off-farm decisions (input sourcing/distribution, stock maintenance, subsidy and yield forecasting by agriculture department).

3.8 M.Sc. Thesis Research – ENSO on Tamil Nadu monsoons (Ms. M. Venkateswari& Chairman Dr. V. Geethalakshmi)

40

30

20

10

0

-10 Rainfall (%Rainfalldeviation)

-20 Elnino ( % deviation from overall mean) Lanina (% deviation from overall mean) Elnino (% dev from the netural phase mean) Lanina (% dev from the netural phase mean)

-30

Karur

Theni

Erode

Salem

Nilgiris

Vellore

Ariyalur

Chennai

Dindigul

Madurai

Tiruppur

Namakkal

Sivaganga

Thanjavur

Cuddalore

Tirunelveli

Thiruvarur

Krishnakiri

Thiruvallur

Villupuram

Thothukudi

Perambalur

Coimbatore

Dharmapuri

Pudukkottai

Viruthunagar

Nagapattinam

Tiruchirappalli

Kanniyakumari

Kancheepuram Tiruvannamalai

Ramanathapuram

El Niño Southern Oscillation (ENSO) had impacted the seasonal rainfall patterns over Tamil Nadu. El Niño episode recorded 11 to 30 per cent higher rainfall than normal year‟s North East Monsoon rainfall and opposite was the condition with La Niña. In most of the El Niño years (68%), sowing rain occurred during 1st week of September, while in 40 per cent of the La Niña years sowing rain was delayed by one week and received during 2nd week of September. Cessation was earlier in most of the La Niña years compared to El Niño and neutral years. Compared to neutral years, there was increased Length of Growing Period (LGP), more wet spell and lesser dry spell weeks observed under El Niño condition while the decrease in LGP, lesser wet spell and more dry spell were observed under La Niña conditions.

3.9 M.Sc. Thesis Research – Spatial response to climate change adaptation option for rainfed maize (Mr. R. Gowtham& Chairman Dr. S. Panneerselvam) Maximum temperature over Tamil Nadu are expected to increase up to 3.7°C and 4.7°C by the end of mid century under RCP 4.5 and RCP 8.5 pathways. Minimum temperature over Tamil Nadu are expected to increase up to 4.9°C and 5.7°C by the end of mid century as projected through stabilization and overshoot emission pathways. The rate of increase in minimum temperature is higher than that of maximum temperature. Among the monsoon seasons, SWM is projected to have a higher increase in both maximum and minimum temperatures than NEM over Tamil Nadu. Annual rainfall over Tamil Nadu is expected to vary between a decrease of -17.1 per cent to an increase of 34.6 per cent and - 33.0 per cent to 45.1 per cent as projected through RCP 4.5 and RCP 8.5 scenarios. Rainfall during the SWM is expected to have wider variation in future than NEM. In future, the rainfed maize productivity is expected to decline to a maximum of 30 per cent from the current yield levels due to climate change. Among the two climate scenarios, the magnitude of decline in yield was more in RCP 8.5 (30.7 % spread across Tamil Nadu) over RCP 4.5 (10.6 % of Tamil Nadu) for more than 20 per cent. Figure

3.10 M.Sc. Thesis Research – Soil amendments and methane emission from rice production (Mr. N. Kowshika& Chairman Dr. N. Maragatham) Application of fly ash and silica solubilizing bacteria had higher rice production (6697 kg/ha) compared to other treatments studied. The application of fly ash with Silica

Solubilizing Bacteria had reduced the N2O emissions (20.4%) compared to control. methane reduction was higher in Fly ash application (26.2%). The population of methanogens lowest in case of fly ash application.Methanotrophs had higher population growths in fly ash amendment application.

2016 – 17

3.11 EFRP-IMD–FASAL– Strategic yield forecasting(Scientist: Dr. V. Geethalakshmi& Dr. N.K. Sathyamoorthy) The adaptation packages in rice to overcome the climate change impact is 15th July planting + 25 per cent additional Nitrogen. In rice, APSIM simulation with this package indicated a yield increase with the mean change of 6.8 to 9.7 per cent and DSSAT simulation showed an increase with the mean of 7.5 to 12.8 per cent. The adaptation package in maize issowing on 15th August with 25 per cent supplemental nitrogen fertilizer, showed an increase in maize yield with the mean of 9.9 to 10.5 per cent. Crop simulation model forecasted the actual yield of rabi rice with higher accuracy (1.5% to 15.6 %) than the statistical forecast (2.1 to 30%). Crop simulation model forecasted the actual yield of rabi maize with higher accuracy (1.9% to 10.8 %) than the statistical forecast (23.6 to 24.2%).

3.12 M.Sc. Thesis Research – WUE under varied climate condition using AquaCrop model (Mr. V. Guhan& Chairman Dr. V. Geethalakshmi) The study proved the capability of AquaCrop in predicting tomato yield and biomass as well as water use efficiency. WUE was reduced in excess rainfall condition by 11.4 and 33.8 per cent and under the deficit rainfall year the yield reduction was 1.7 and 14.1 per cent during Kharif and Rabi respectively.Irrigating the crop in synchronises with the water stress during both Kharif and Rabi season under varied climatic conditions (excess, normal and deficit condition) improved the WUE efficiency considerably with increase in fruit yield. With optimised schedule of the loss in yield was minimised and the reduction was 0.3, 2.1, 2.7 and 9.8 per cent at the 10, 25, 35 and 50 per cent of deficit irrigation. The WUE enhanced in the range of 5.1 to 14.1 per cent during Rabi season. Optimised irrigation schedule of 50 mm at planting, the rest of irrigation with 30 mm at 3, 7, 14, 21, 35, 42, 49, 63, 84, 91, 98, 105, 112 under 25 per cent deficit irrigation enhanced the yield by 9.3 per cent. Tomato productivity is expected to decline in the range of 12.6 to 19.4 per cent with the mean decrease of 13 per cent by mid of the century from the current yield levels due to climate change.

Deviation of tomato yield under Current and RCP 8.5 future scenario

3.13 M.Sc. Thesis research – Temperature and relative humidity on broiler production (Mr. M. Monisha and Chairman Dr. N. Maragatham) The temperature fluctuations were distinctly observed between seasons in inside of the poultry houses. The temperature descended as the height increased. The Thermal Humidity Index (THI) was maximum during summer. The THI was minimum in winter season compared to summer season. During summer 2017, the mortality level was high since the heat production was greater than heat dissipation. Lower environmental temperature enhanced the feed consumption. High temperature and RH influenced the performance of broilers by reducing feed intake, feed efficiency, nutrient utilization and feed conversion ratio (FCR) Temperature increases the Water: Feed intake ratio also increases. In winter Water: Feed intake ratio was ranged from 1.60 to 2.31. Similarly, during summer it ranged from 3.05 to 3.61 for the experimental period. Figure

2017 - 18

3.14 URP – Elevated temperature on green gram (Scientist: Dr. S. Panneerselvam)

Elevated temp. on root nodules/plant Elevated temp. on Chlorophyll index

Elevated temp. on phenophases Elevated temp. on yield attributes

 Nodule formation affected by the elevated temp. invariably in exposure of both elevated temperature at early stages. The size of root nodules are less with +4oC followed by +2oC than ambient.  Phenophases had decreased if exposed to elevated temp.  Higher yield reduction were observed in plants exposed to elevated temperatures (+4oC & +2oC) at flowering and pod development. Green gram showed a yield reduction of 79.4 and Effect of elevated temp. on 61.1% under +4oC and +2oC elevated Greengram pod temperatures throughout crop period, than ambient temperature, respectively.

3.15 EFRP-IMD–FASAL-Strategic yield forecast(Scientist: Dr. V. Geethalakshmi& Dr. Ga. Dheebakaran)

Verification of Statistical and Crop Simulation Model Kharif and rabirice 2017 yield forecast (% deviation from actual yield) Statistical model Crop Simulation Model District Kharif Rabi Kharif Rabi F2 F3 F2 F3 F2 F3 F2 F3 Coimbatore 7.2 19.1 20.8 -5.8 0.4 15.2 16.6 -12.4 Erode 22.2 23.3 7.9 -1.2 20.1 15.9 4.4 -8.2 Vellore 16.8 9.6 17.8 4.1 14.3 9.2 10.9 1.3 Dharmapuri 14.6 22.7 24.5 23.8 8.0 14.8 18.9 18.6 Krishnagiri 10.4 22.1 24.0 23.2 5.5 8.5 20.6 17.6 Namakkal 18.5 20.5 25.3 17.8 15.8 14.3 17.9 11.7 Salem 15.1 15.4 23.0 -1.0 12.7 12.6 17.3 -4.2 Thiruppur 1.3 25.5 15.8 -8.1 -0.7 16.3 11.4 -11.6 Thiruvallur 22.9 21.5 23.1 24.1 18.3 19.6 14.6 19.2

Verification of Statistical Model– Kharif Maize and groundnut - 2017 yield forecast Statistical model Crop Simulation Model District Maize Groundnut Maize Groundnut F2 F3 F2 F3 F2 F3 F2 F3 Coimbatore 7.6 8.0 17.7 20.5 0.5 4.3 6.6 8.4 Erode 13.4 12.7 19.5 14.6 12.7 3.1 14.6 11.9 Vellore 21.0 23.4 16.1 21.6 10.3 7.4 13.5 16.3 Tirunelveli 25.8 26.3 14.2 14.9 13.6 9.9 5.8 11.6 Tuticorin -3.8 24.7 -1.2 2.9 -19.3 1.1 -6.1 -10.8

Among the methods, the crop simulation model yield forecast has higher accuracy in all the crops (Rice, Maize and Ground nut) than statistical methods. The crop simulation model based yield forecast‟s deviation was well within the range of 15 per cent and could be used for yield prediction and policy decisions.

3.16 Ph.D. Thesis research – Climate change on rice yield (Dr. K. Ajith and Chairman Dr. V. Geethalakshmi) Studies on the impact of climate change on rice yield for 21st century indicated that, in Cauvery delta zone, for RCP 4.5 scenario, irrespective of models and varieties, the decline in rice productivity was consistent from near (-22.4 %) century to end century (-33.5 %). Figure

3.17 Ph.D. Thesis research – Carbon sequestration potential of Coconut plantation (Dr. Lincy Davis and Chairman Dr. S. Panneerselvam) The carbon sequestration potential of coconut palms was assessed using allometric equation developed by CPCRI, Kasaragod. The total carbon sequestration potential of a dwarf palm is 7 t/ha (< 10 years), 14.9 t/ha (> 10 years), where as for the tall palm it was 11.2 t/ha (<10 years) and 36.4 t/ha (>10 years). The organically managed tall type palms more than 10 years has carbon sequestration potential of 50.7 t/ha. It is found that mean above ground (stem) carbon stock was high during summer season with 13.7 t ha-1. The mean below ground (surface soil) carbon stock was high during NEM season with 21.3 t/ha whereas mean below ground (sub surface soil) carbon stock was high during winter and NEM season with 19.5 and 19.4 t/ha respectively. It was found that the total carbon and carbon dioxide sequestration potential of coconut plantation in western zone of Tamil Nadu was assessed as 67,79,270 tons and 2,48,54,834 tons respectively.As per the century model, the building up of soil passive carbon pool at Pollachi area showed a slight declining trend from 2000 to 2050. However, the coconut plantation has the potential to increase the carbon content in the passive pool; thereby it can minimize the Green House

Gases, which cause global warming. It can also sequester more CO2 in lignin accumulated form. The carbon sequestration by coconut ecosystem can further be studied to provide financial incentives to farmers.

3.18 Ph.D. Thesis research – Impact of climate change on rice productivity in Cauvery Delta Zone (Dr. Shajeesh john and Chairman Dr. S. Panneerselvam) Projected changes in future climate over two regions during 21st century revealed significant increase of maximum and minimum temperature at 1 per cent. In Cauvery delta zone, the increase in maximum temperature from base year (1971-2005) at the end of century was estimated up to 4.2 0C irrespective of models and scenarios used while the increase of minimum temperature during the same period was 3.90C. At the end of 21st century, deficit rainfall was projected for Cauvery delta (-98mm) when compared to base year rainfall.

Impact of climate change on rice yield for 21st century indicated that, In Cauvery delta zone, for RCP 4.5 scenario, irrespective of models and varieties, the decline in rice productivity was consistent from near (-22.4 %) century to end century (-33.5 %) whereas for RCP 8.5 scenario, highest decline was during mid-century (-31% ) followed by end century (-30.5% ).

Genetic coefficient of pulse varieties for CROPGRO crop-weather model

CROP Blackgram Redgram VAR# Identification code or number for a specific BG0001 BG0002 PP0001 cultivar. VAR- Name of cultivar CO 6 VBG6 CO (RG) 7 NAME ECO# Code for the ecotype to which this cultivar CP0411 CP0411 PP0001 belongs CSDL Critical Short Day Length below which 11.90 7.140 12.94 reproductive development progresses with no day length effect (short day plants) (hour) PPSEN Slope of the relative response of 0.394 0.700 0.100 development to photoperiod with time (positive for short day plants) (1/hour) EM-FL Time between plant emergence and flower 15.23 13.90 37.50 appearance (R1)(photo thermal days) FL-SH Time between first flower and first pod (R3) 3.000 2.50 10.40 (photo thermal days) FL-SD Time between first flower and first seed (R5) 7.046 6.00 16.50 (photo thermal days) SD-PM Time between first seed (R5) and physio- 21.84 15.03 19.02 logical maturity (R7) (photo thermal days) FL-LF Time between first flower (R1) and end of 7.073 4.00 20.87 leaf expansion (photo thermal days) LFMAX Maximum leaf photosynthesis rate at 30 C, 90.09 8.94 50.10 2 350 vpm CO2, and high light (mg CO2/m ) SLAVR Specific leaf area of cultivar under standard 565.8 320.0 400.0 growth conditions (cm2/g) SIZLF Maximum size of full leaf (3 leaflets) (cm2) 300.0 300.0 301.4 XFRT Maximum fraction of daily growth that is 0.980 1.000 0.900 partitioned to seed + shell TPSD Maximum weight per seed (g) 0.037 .0295 0.240 SFDUR Seed filling duration for pod cohort at 2.788 5.099 39.600 standard growth conditions (photo thermal days) SDPDV Average seed per pod under standard 3.702 2.000 2.000 growing conditions (#/pod) PODUR Time required for cultivar to reach final pod 1.478 3.500 10.300 load under optimal conditions (photo thermal days) THRSH Threshing percentage. The maximum ratio of 82.00 82.00 76.20 (seed/(seed+shell)) at maturity. SDPRO Fraction protein in seeds (g(protein)/g(seed)) 0.300 0.300 0.224 SDLIP Fraction oil in seeds (g(oil)/g(seed)) 0.065 0.065 0.015

3.19 M.Sc. Thesis Research – Elevated temperature on Mungbean growth and yield (Mr. K.P. Jeevanand& Chairman Dr. S. Panneerselvam) The results indicated that, the plants grew taller under elevated temperature. The plants grown at 4oC elevated temperature throughout crop period had highest plant height at all stages. Morphological characteristics like leaf area, dry matter production and number of nodules were found reduced under elevated temperatures. With the elevation in temperature, the physiological parameters like photosynthetic rate and stomatal conductance reduced in plants. The transpiration rate and leaf temperature increased with increase in temperature whereas the chlorophyll index decreased. The yield traits showed higher reduction when plants were exposed to 4oC elevated temperatures at flowering stage and pod development stage followed by 2oC elevated temperature respectively. The +4oC elevation throughout crop period and +2oC elevation throughout crop showed 60 per cent and 50 per cent of seed yield reduction over control respectively. It is concluded that under elevated temperature, the grain yield would get adversely affected especially when exposed during flowering stage and pod development stage than during vegetative stage.

3.20 M.Sc. Thesis research – Methane footprint of rice genotypes (Mr. S. Deepakraju and Chairman Dr. A. Lakshmanan)

Study on methane emission from rice varieties inferred that the methane emission rates varied significantly between the genotypes tested, and the flux values ranged between 8.10 mg m-2 hr-1 and 12.1 mg m-2 hr-1 among the short, medium and long duration genotypes. The Genotypes recorded higher grain yield, such as CO 51, ASD 16, CO 50

and CB05022 emitted less methane (0.23 - 0.29 kg CO2 equivalents/kg of grain), while

certain land races like Norungan (0.41kgCO2 equivalents/kg of grain) recorded lower grain yield but emitted high methane. The morphological differences in intercellular gas spaces between genotypes resulted in varying levels of methane emission among genotypes.

KODAI CB13132 BPT5204 Figure 4.33 Microtome section images of root aerenchyma of different rice varieties Rice varieties and lacunae transport of methane through aerenchyma cells

Sl. No Rice Genotype *Lacunae transport of methane through 2 aerenchyma (mg/m /hour) 1 NORUNGAN 9.10

2 KODAI 9.30

3 BPT 5204 10.20

4 CB 15144 9.20

5 CB 13132 9.80

2018 – 19

3.21 URP – Elevated temperature on Tenai (Scientist: Dr. S. Panneerselvam)  Plants exposed to elevated temperature of +40C throughout cropping period recorded the lowest plant height in all the stages of crop growth.. Maximum height was recorded in ambient temperature

• Plants under ambient temperature recorded the highest yield per plant • Lowest yield was obtained in plants exposed to +4oC followed by +2oC throughout the crop growth period. • Similar trend was observed in panicle weight and test seed weight.

3.22 EFRP-IMD–FASAL-Strategic yield forecast(Scientist: Dr. V. Geethalakshmi& Dr. Ga. Dheebakaran)  The crop yield forecast developed under FASAL scheme with crop simulation model perform better (- 6 to 10.8) than Statisitical model (-24 to 24). Between F2 and F3 forecast, the F3 forecast gave more accurate estimation of rice, maize and groundnut yield. Hence, the CSM models could be used for yield estimation.

• Forecast at flowering stage (F2) had higher accuracy than F3 (pre harvest) • Forecast from CSM showed higher accuracy than statistical model

• Forecast at flowering stage (F2) had higher accuracy than F3 (pre harvest) • Forecast from CSM showed higher accuracy than statistical model

• Unlike rice forecast at F3 (pre harvest) had higher accuracy than at flowering stage (F2) • Forecast from CSM showed higher accuracy than statistical model

• Forecast at flowering stage (F2) had higher accuracy than F3 (pre harvest) • Forecast from CSM showed higher accuracy than statistical model

3.23 Ph.D. Thesis research – Sustaining pulse production under changing climate of Tamil Nadu (Dr. C. Pradeepa and Chairman Dr. S. Panneerselvam) Climate change and pulse productivity study infered that the major pulse crops such as blackgram and redgram are expected to be highly vulnerable for the future temperature rise in Tamil Nadu. However, this effect would be counteracted to certain extent by the simultaneous increase in the carbon dioxide. Even though the yield is expected to sustain, the climate variability would adversely affect the yield of blackgram and redgram. Selection of suitable cultivar and a perfect sowing time would sustain the productivity and make the crop more profitable.

3.24 Ph.D. Thesis Research – Climate Change impact assessment of groundnut under CMIP5 projections (Mr. S. Arul Prasath& Chairman Dr. N. Maragatham) Genetic co-efficient for CO6 groundnut variety was developed

Code Description of parameter coefficients of development aspects Value CSDL Critical Short-Day Length below which reproductive development 11.84 progresses with no day length effect (for short day plants) (hours) PPSEN Slope of the relative response of development to photoperiod with time 0.00 (positive for short day plants) (1/hours) EM-FL Time between plant emergence and flower appearance (R1) 27.0 FL-SH Time between first flower and first pod (R3) (photothermal days) 11.7 FL-SD Time between first flower and first seed (R5) (photothermal days) 15.4 SD-PM Time between first seed (R5) and physiological maturity(R7) 84.71 (photothermal days) FL-LF Time between first flower (R1) and end of leaf expansion (photothermal 78.00 days) LFMAX Maximum Leaf photosynthesis rate at 30 0C, 350 vpm CO2 and high 1.39 light (mgCO2/m2-S) SLAVR Specific leaf area of cultivar under standard growth condition (cm2/g) 273. SIZLF Maximum size of full leaf (three leaflets) (cm2) 15.0 XFRT Maximum fraction of daily growth that is partitioned to seed + shell 0.53 WTPSD Maximum weight per seed (g) 1.000 SFDUR Seed filling duration for pod cohort at standard growth conditions 38.0 (photothermal days) SDPDV Average seed per pod under standard growing conditions (#/pod) 1.85 PODUR Time required for cultivar to reach final pod load under optimal 15.0 conditions (photothermal days) THRSH The maximum ratio of (seed/(seed+shell)) at maturity.Causes seed to 79.1 stop growing as their dry weights increase until shells are filled in a cohort. (Threshing percentage). SDPRO Fraction protein in seeds (g(protein)/g(seed)) .270 SDLIP Fraction oil in seeds (g(oil)/g(seed)) .510

In Tamil Nadu, seven major groundnut growing districts were identified with low productivity index, sixteen district comes under moderate productivity index and nine district has high productivity index. The rainfall deviation showed a liner influence on groundnut growth rate.

Ground nut productivity indxe during 200 - 2016

Theme 4: Remote sensing and others

2016 – 17

4.1 M.Sc. Thesis research – Methane footprint of rice genotypes (Mr. N.S. Sudarmanian and Chairman Dr. S. Palanivelan)

In flooded rice field of Tiruchirapalli district, the methane emissions from Samba paddy were ranged between 0.43 and 0.49 kg/ha/day and the seasonal methane emission was ranged between 40.8 and 48.5 kg/ha. The rate of methane emission based on IPCC factor was ranged from 37.4 to 45.74 kg/ha for a period of 87 to 121 days of flooding. The total methane emission from Tiruchirapalli district was 1.57Gg during Samba season 2015-16. The estimated LST T factor was ranged from 0.8810 to 1.0527. The rate of LST based methane emission was ranged from 35.36 to 48.17 Kg/ha in Tiruchirapalli district with a total methane emission of 1.5706 Gg during the season. The agreement between observed values with IPCC and LST T factor based methane emission was 94 per cent and 91 per cent respectively. The multi date Sentinel 1A Synthetic Aperture Radar data can be used for estimating rice area, Start of Season (SOS) and days of agronomic flooding at regional scale. IPCC factor and MODIS derived LST T factor along with remote sensing based rice area and SOS can be used as a tool for precise and near real time assessment of methane emission at regional or country level.

Figure 4.49 LST T factor based Methane Figure 4.50 IPCC factor based Methane Emission (Kg/ha) from rice fields of Emission (Kg/ha) from rice fields of Tiruchirapalli District Tiruchirapalli District 2017 – 18

4.2 Ph.D. Thesis Research – Rice yield prediction using satellite based observation and crop simulation model (Mr. K. Ajith& Chairman Dr. V. Geethalakshmi) Remote sensing forecast deviation was < ±10 % in five out of ten blocks, where as the crop simulation model yield forecast deviation was < ±10 % in six blocks only. The crop simulation model yield forecasts had better accuracy compared to remote sensing yield forecast. Comparison of remote sensing Vs crop simulation model for rice yield prediction During 2015-„16 nine blocks had deviation below ± 10 per cent in simulation model method while twelve blocks had deviation below ± 10 per cent in integration method. During 2016-„17 in crop simulation model method ten blocks showed

deviation less than ±10 percent while in integration method the number of blocks with PBIAS values below ±10 percent increased to twelve blocks The study shows that yield prediction with CERES-rice model integrated with MODIS-LAI gave higher

accuracy in all the varieties compared Comparison of crop simulation model Vs crop to yield prediction by crop simulation simulation model integrated with MODIS-LAI model alone. for yield prediction averaged for 3 varieties (2017-18)

Results clearly indicate that assimilation of satellite products in crop simulation models can provide rice yield estimates with higher accuracy compared to remote sensing and crop simulation techniques when used alone. Crop simulation models have dynamic simulation process, which can bring out the interactions between plant, soil and environment. Remote sensing products are capable of providing updates of contingencies in crop production over large areas at a later stage. An integration of these two methods would be more effective in predicting rice yields.