Tamil Nadu Agricultural University & Agrimet Division

India Meteorological Department

Gramin Krishi Mausam Sewa (GKMS) Agro Meteorological Field Unit (AMFU) TN - Western Zone -

ANNUAL REPORT 2018 - 2019

Agro Climate Research Centre Directorate of Crop Management Agricultural University Coimbatore Ð 641 003

GKMS Ð AMFU - COIMBATORE - TN - WESTERN ZONE ANNUAL REPORT 2018 - 19

INDEX

I. General 1 1. Project details 1 2. About project área - Western zone 3 2.1 Coimbatore 8 2.2 district 10 2.3 district 14 3 Objectives 15 4 Communication details 16 II Status of agromet observatory 17 III Details of agromet advisory service 19 IV Feedback from Farmers 24 V Verification of weather forecasting 32 5.1 Rainfall 32 5.2 Maximum , Minimum Temperature and Wind Speed 39 VI Economic impact of the AAS bulletin 45 6.1 Usefuleness of agro advisory 45 6.2. Specific instances of benefit / losses due to AAS Ð Case study 46 VII Problems and suggestions for improvement of AAS unit 48 VIII Salient achievements 49 8.1 District wise Moisture Adequacy Index (MAI) of Tamil Nadu Ð 49 2018 8.2 Downscaling and verification of ERFS forecast to western zone 53 Ð An outcome from ERFS training to Technical officer IX Weather and AAS related messages on News papers 67 X Utilization Certificate for the year 2018 Ð 19 XI Adminsitrative sanction orders

Tamil Nadu Agricultural University & Agrimet Division Meteorological Department

Gramin Krishi Mausam Sewa (GKMS) Agro Meteorological Field Unit (AMFU) TN - Western Zone - Coimbatore Agro Climate Research Centre Coimbatore Ð 641 003

ANNUAL REPORT 2018 - 19

I. GENERAL 1. Project Details

1. Title of the project : Gramin Krishi Mausam Sewa

2. Funding and Nodal GOI Ð Ministry of Earth Science agency India Meteorological Department, New Delhi

3. DST Sanction : Lr.No.NMRF/17/91-90 (vi) dt.24.02.92 of the Deputy Secretary to number and date Govt. of India, Ministry of Science and Technology, New Delhi.

4. IMD Sanction order : No. ASC/TN-19/04/HQ-2007 dt. 25.01.2019, Budget sanction order for the current year for 2018 Ð19 from the Meteorologist-A, Agromet Division, IMD, 2018 -19 New Delhi.

5. University Admin. : No. DR/P7/DCM/ACRC,CBE/GoI-IMD/Agromet Field Unit/ ASO/ Sanction order for the 2019 dt. 12.02.2019, O/o Director of Research, TNAU, Coimbatore Ð year 2018 -19 641 003

6. Year of start : Experimental Agromet Advisory Services: June, 1993 Integrated Agromet Advisory Services: April, 2008 - 12 Gramin Krishi Mausem Seva - 2013

7. Agro Climatic Zone : Western Zone of Tamil Nadu

8. AAS Unit : Agro Climate Research Centre, TNAU, Coimbatore -3.

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9. Project Period : 01.04.2013 Ð 31.03.2020

10. Technical Officer : Dr. Ga. Dheebakaran Ph.D., Assistant Professor (Agronomy) Mobile: +91 94439 35107, Email: [email protected]

11. Nodal Officer Dr. SP. Ramanathan Ph.D., Professor and Head Mobile: +91 9442284759, Office: 0422 6611305 Email: [email protected]

12. Observer Mr. R. Selvaraju Mobile: +91 9487876226

Fig. 1 AMFU Coimbatore Unit and Agro Advisory service coverage area

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13. About project area The Agro-Meteorological Field Units (AMFUs) of Western Zone is geographically situated at 11¡N latitude, 77¡E longitude with an altitude of 426.7 m above MSL. AMFU Ð western zone at Coimbatore covers three district viz., Coimbatore, Erode and Tiruppur districts in western zone of Tamil Nadu (Fig. 1). The latitude range is between 10o and 12o North and longitudinal range is between 76o 30’ and 80o. It has been grouped under Western Agro Climatic Zone of Tamil Nadu and the climatologically classified as Semi-Arid. The land Use Classification of AAS western zone districts is depicted in Table 1. The weather normal and actual weather prevailed during Mar. 2018 to Feb. 2019 are detailed in Table 2 & 3 and depicted in Fig. 2 to11. The irrigation sources, area under irrigation and general cropping pattern of Western zone districts are detailed in Table 4 and 5.

Table 1 Land Use Classification of AAS Western Zone (ha)

Particulars Coimbatore Tiruppur Erode

Total Geographical area 367098 519559 572264

Forest 6647 48168 1425

Barren & uncultivable area 4798 2542 6269

Land under non agricultural purpose 76643 51531 53353

Cultural Waste 8357 3936 1731

Permanent pastures& other grazing lands 77 126 101

Misc. tree crops & not incl.in net area sown 3462 1768 952

Current Fallow 30013 98292 58711

Other fallow 63502 102623 47072

Net Area Sown (ha) 172409 175156 179460

Gross Area Sown (ha) 176811 183248 181463

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Table 2. Seasonwise Normal and actual rainfall quantity (mm) and rainy days (Nos. ) of Western zone during 2018 -19

Rainfall District Coimbatore Coimbatore Erode Tiruppur mm (Incl.) (Excl. Valparai) Season Criteria Nor Actual Nor Actual Nor Actual Nor Actual mal mal mal mal Summer Rain days 50 27 25 10 40 7 32 2018 Rainy days 39 11 19 9 22 6 21 Quantity 184 338 150 323 159 247 130 258 SWM Rain days 104 69 45 19 45 8 42 2018 Rainy days 72 21 32 14 20 7 10 Quantity 618 1297 193 367 253 286 187 84 NEM Rain days 49 43 19 20 37 18 36 2018 Rainy days 30 21 16 15 26 16 21 Quantity 365 327 327 161 348 228 296 211 Winter Rain days 5 5 0 2 1 1 0 2019 Rainy days 0 2 0 1 0 1 0 Quantity 28 2 26 0 11 2 13 0 Total Rain days 208 144 89 51 123 33 110 Rainy days 141 54 67 39 68 30 52 Quantity 1195 1874 696 840 771 763 626 533

Table 3 Seasonwise Normal and actual weather of Western Zone during 2018 -19 Max. Min. Cloud RH RH Wind Wind Season Criteria temp temp 0830 1730 Speed direction (oC) (oC) (octa) (%) (%) (kmph) (Degree) Lower 29 20 1 30 15 3 20 Summer 2017 Higher 39 27 7 90 95 25 360 Average 35 24 5 73 45 12 165 Lower 25 21 3 54 38 3 50 SWM 2017 Higher 36 27 8 96 95 27 320 Average 32 23 6 80 66 18 198 Lower 25 19 1 52 30 0 0 NEM 2017 Higher 34 24 8 96 95 15 360 Average 31 22 5 81 53 8 95 Lower 29 16 0 50 18 0 20 Winter 2018 Higher 37 24 7 91 83 17 200 Average 33 20 4 77 34 10 96 Lower 25 16 0 30 15 0 0 Total Higher 39 27 8 96 95 27 360 Average 33 22 5 78 52 12 147 Normal Average 32 22 5 75 55 12

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5

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Table 4 Desired cropping pattern of Western Zone

Canal/Tank irrigated Well irrigated Rainfed

• Ð pulses / ground nut / • Tapioca / Turmeric Cotton / • Tapioca Groundnut- gingelly Turmeric groundnut - vegetables - pulses horsegram • Sugarcane - ratoon sugar- cane / gingelly / sunflower. • Sorghum -maize / pulses • Rice Ð rice. Banana / sugarcane

Table 5 Irrigation sources and area irrigated of AAS Western Zone (ha)

Particulars Coimbatore Erode Tiruppur

Canal numbers (Nos.) 29 10 18

Canal length (km) 245 690 364

Tube / other wells (Nos.) 20426 9908 14986

Open wells (Nos.) 49637 75459 79230

Reservoirs (Nos.) 7 7 5

Tanks (Nos.) 48 698 42

Net Area irrigated by different sources (ha)

Canal 15597 36337 26530

Tank 0 42 900

Tube wells 28658 29762 15176

Open wells 70739 54176 69453

Other sources 0 767 0

Total Net Area irrigated (ha) 114994 121084 112059

Gross Area irrigated (ha) 116849 129020 113667

Source: Department of Economics and Statistics, -600 006

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2.1 Coimbatore is situated in the extreme west of Tamil Nadu, near the state of (Fig. 12). It is surrounded by mountains on the west, with reserve forests and the (Nilgiri Biosphere Reserve) on the northern side. The district is bounded by Palghat district of Kerala on the west and by Idukki district of Kerala in the South. Coimbatore shares its borders with Tirupur in the East and Nilgiris in the North. A small portion of shares the border near Puliampatti in the North East. The district has an area of 7,649 square kilometers. The headquarters of the district is Coimbatore city. The district of Coimbatore is situated between 10o 36’ and 11 o 58’ North Latitudes and 76 o 49’ and 77 o 58’ East Longitudes. As of 2011, Coimbatore district had a population of 3,458,045 with a sex-ratio of 1,000 females for every 1,000 males. Block wise number of revenue villages in Coimbatore district details are given Table 6. In the rain shadow region of the , Coimbatore enjoys a very pleasant climate all the year round, aided by the fresh breeze that flows through the 25 kms long . The rich black soil of the region has contributed to Coimbatore's flourishing agriculture industry and, it is in fact the successful growth of cotton that served as a foundation for the establishment of its famous textile industry. The eastern side of the district, including the city is predominantly dry. The entire western and northern part of the district borders the Western Ghats with the Nilgiri biosphere as well as the Anaimalai and Munnar ranges. Because of its close proximity to the Western Ghats, the district is rich in fauna. The soil classification of Coimbatore districts are given in Table 7. Many lakes and ponds were constructed near the river in ancient times. The major rivers flowing through the district are Bhavani, Noyyal, Amaravathi,Kousika River and Aliyar. The Siruvani is the main source of drinking water for Coimbatore city and is known for its tasty water. Waterfalls in Coimbatore District include Chinnakallar Falls, , Sengupathi Falls, , Thirumoorthy Falls and Vaideki Falls. A well known ancient river called Kousika river. Its starts from Kurudi Hill, Coimbatore and travel via Kovilpalaym, Vagarayampalaym, Thekkalur and joined in at Tiruppur. The city of Coimbatore has nine lakes. , Kuruchi Lake, Valankulam Lake, Krishnampatti Lake, Muthannan Lake and Seevagasintamani Lake are important of them. In most of the urban ecosystems, these wetlands are the major life-supporting component with high concentrations of birds, mammals, reptiles, amphibians, fish and invertebrate species. Average annual rainfall (1951-2010) of Coimbatore is 695.8 mm. The mean maximum and minimum temperatures for Coimbatore city during summer and winter vary between 35 ¡C to 18 ¡C.

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Fig. 12 Coimbatore District Block map

Table 6 Coimbatore district block wise revenue villages

SN Block Rev. Villages SN Block Rev. Villages

1. Anaimalai 19 7. 21

2. Karamadai 17 8. Kinathukadavu 34

3. Madhukkarai 9 9. Periyanaickenpalayam 9

4. (North) 39 10. Pollach (South) 26

5. Sarcarsamakkulam 8 11. Sultanpet 20

6. 17 12. Thondamuthur 10

Total 229

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Table 7 Coimbatore district Soil Classification

2.2 Erode district The district of Erode is surrounded by land from all sides and does not have a sea coast of its own (Fig. 13). The district is having its borders as and district to the East, district to the South, Coimbatore district, to the West and of in north. The district`s region can be portrayed as a long undulating plain sloping towards the River in the south-east.

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Fig. 13 Erode District Block map

Table 8 Erode district block wise revenue villages

SN Block Rev. Villages SN Block Rev. Villages

1. Ammapet 20 8. Andiyur 14

2. Bhavani 15 9. 15

3. 22 10. Erode 11

4. Gopichettipalayam 21 11. 10

5. 23 12. 15

6. 29 13. Satyamangalam 15

7. Talavadi 10 14. Thoockanaickenpalayam 10

Total 343

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Fig. 14 Block map

Table 10 Tiruppur district block wise revenue villages

SN Block Revenue Villages SN Block Revenue Villages

1 31 8 16

2 Gudimangalam 23 9 15

3 Kundadam 24 10 Madathukulam 11

4 Mulanur 12 11 Palladam 10

5 Pongalur 16 12 Tiruppur 21

6 Udumalpettai 38 13 37

7 9 Total 273

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Table 9 Erode district soil classifications

The three major tributaries of Kaveri River are , and Noyyal River which drain the long stretch of mountains in the north. A part of the eastern boundary of Erode district is formed by Kaveri River, entering the district from Salem and flowing in the southern direction. The district forms the meeting point of Western Ghats and separated by Bhavani River. The district of Erode is situated between 10 degrees 36 minutes and 11 degrees 58 minutes to the North Latitudes and 76o 49’ and 77o 58’ to the East Longitudes. Block wise number of revenue villages in Erode district details are given Table 8. Erode district’s major soil are given in Table 9. The climate is mostly dry and characterized by good rainfall. Unlike nearby Coimbatore district, Erode District has dry weather throughout the year except during the monsoons. The Palghat Gap in Western Ghats, which has a moderating effect on the climate of Coimbatore district, does not help in bringing down the dry climate in this area. The cool moist wind that gushes out of the west coast through Palghat gap loses its coolness and becomes dry by the time it crosses Coimbatore district and reaches Erode. Generally the first two months of the year are pleasant, but in March the temperature begins to rise, which persists till the end of May. The highest temperatures are normally recorded during May. The scanty showers during this period do not provide much relief from the oppressive heat. However, there is an improvement in the climate during the JuneÐAugust period. During the pre-monsoon period, the temperature reverses its trend. By September the sky gets heavily overcast, although the rains pour down. The northeast monsoon sets in vigorously only during OctoberÐNovember, and by December the rains disappear, rendering the climate clear and cold.

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Tiruppur Tiruppur district is recently bifurcated from part Coimbatore and Erode districts. It lies on the western part of Tamil Nadu bordering the Western Ghats (Fig. 14) and hence the district enjoys a moderate climate. The district is surrounded by Coimbatore district in the west, Erode district to the North and northeast and in the east, in the south east and idukki district of Kerala state in the South. The district has an area of 516.12 square kilometers. Block wise number of revenue villages in Trippur district details are given Table 10. The southern and south western part of the district enjoys maximum rainfall, due to the surrounding of Western Ghats. The rest of the district lies in the rain shadow region of the Western Ghats and experiences salubrious climate most parts of the year, except the extreme east part of the district. The mean maximum and minimum temperatures for Tiruppur city during summer and winter vary between 35¡C to 18¡C. The average annual rainfall in the plains is around 700 mm with the North East and the South West monsoons contributing to 47% and 28% respectively to the total rainfall. The major rivers flowing through the district are Noyyal and Amaravathi. The Amaravati river is the main source of irrigation in the district. , which created Amaravathi , is located at Amaravathi nagar. Thirumurthy dam which is created by the PAP project is situated in this district. Both Amaravathi dam and Thirumurthy dam are the prime source of irrigation in the district, whereas Uppaar dam is another dam which receives water from seasonal rains. According to 2011 census, Tiruppur district had a population of 2,479,052 with a sex-ratio of 989 females for every 1,000 males, much above the national average of 929.

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14. OBJECTIVES 1. Preparation of weather based advisories for management practices in agriculture after due consultation with subject matter specialists of concerned disciplines and disseminate the same to the end users up to village level using all possible modes of communication. 2. Liaise with Indian Council of Agricultural Research (ICAR), District Agriculture/Horticulture/Animal Husbandry Offices, Krishi Vigyan Kendras and other agencies to render Agromet Advisory Service in a holistic manner. 3. Arrange to provide necessary feedback to IMD for development of:

• Relevant Agromet Advisories for the stress crops/livestock

• Regional / locale-specific agromet predictive models including pests and disease forewarning models and expert system;

• Crop-weather relationships etc. 4. To receive weather forecast, prepare and disseminate weather based agromet advisory bulletins (bi lingual) for different districts /sub district / blocks of the Agro Climatic Zones. 5. To disseminate SMS advisories from KISAAN portal and other firms under PPP mode and upload in the website of Agromet Divisions and to print media. 6. To organize awareness activities to popularize Agromet Advisory Services and be willing to undertake economic assessment studies under grant-in aid program. 7. To provide appropriate space for conventional Agromet observatory / AWS / ARG / any other instruments, whenever requirement arises. 8. To provide security and maintain Agromet observatory / Automatic Weather Station (AWS).etc 9. To record agromet observations on weather from conventional Agromet observatory and AWS, soil moisture and for estimation of evapotranspiration (ET) and transmit to IMD. 10. To prepare data base for weather and crop observations and transmit to IMD. 11. To prepare local climatological information and agricultural data from districts and transmit to IMD. 12. To make available experimental field data for calibration and validations of Agromet models and to identify weather sensitivity of crops, animals, P&D and management practices.

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D. COMMUNICATION DETAILS

(i) Land Line : 0422 6611519, 6611305 (ii) Mobile: 9442284759 (Nodal Officer), 09443935107 (Technical officer) (iii) Email ID : [email protected], [email protected] (iv) Nearest railway station: Coimbatore Junction - 8 Km away from TNAU campus (v) Nearest Airport Ð Coimbatore Ð 22 Km away from the Campus (vi) Nearest AIR : AIR, Coimbatore Ð 12 Km away from the campus (vii) Nearest Doordharsan: Doordharsan, Coimbatore

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II. STATUS OF AGROMET OBSERVATORY

1. Date of installation of the observatory 1951 /Automatic Weather Station

2. Owner of the observatory (IMD, Tamil Nadu Agricultural University, Coimbatore University, any other agency)

3. List Instruments presently available in i. Air temperature (Maximum and Minimum) working condition. ii. Relative Humidity iii. Grass minimum temperature iv. Soil temperature (Surface and 5,10, 15, 20, 30 and 60 cm depth) v. Wind direction vi. Wind speed at different heights (2,4,8 & 10 feet) vii. Bright sun shine hours viii. Total global radiation ix. Evaporation x. Rainfall (Intensity and quantity) xi. Dew deposit at different heights xii. Water temperature xiii. Evapotranspiration xiv. Cloud amount xv. Weather

4. Is there arrangement of spare Yes thermometer and Rainguage in the event of their breakage or theft?

5. Instruments to be replaced/repaired Mercury in the Fortin’s Barometer is contaminated; indicating the type of defect. instrument is not in working condition and therefore it needs to be replaced.

6. Number of years of data recorded 1951-2018 up to date (68 years) available (also in soft format).

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7. Whether the observatory is periodically Yes inspected, maintained and calibrated by IMD. (If yes, please indicate latest date of inspection by IMD)

8. Details of soil moisture observation Yes. Hourly with Automatic Weather Station. taken, if any (please provide frequency of observation, exposure conditions of site etc.)

9. Is the observer of the observatory under Observatory is under direct control of Technical your direct control or belonging to officer and IMD trained observer. another non-IMD observatory (whose data you are utilizing or supplying) been trained by IMD?

10. Is there a standby trained observer for Yes. One PUSM experienced in weather observation taking observations in the event of the is available in addition to the observer. regular observer going on leave due to sickness etc.? If not, what is the arrangement for taking observation during such period?

11. Weather observation taken and Yes recorded regularly? If not, the duration and cause of break in observation.

12. What is the arrangement of security of Observatory is completely fenced with iron barbed the observatory? wire and chain link to prevent entry of animals and human beings. Security arrangement is made by Tamil Nadu Agricultural University

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III. DETAILS OF AGROMET ADVISORY SERVICES a. Mode of sending : Real time data observed at Automatic weather station weather data installed by IMD is being sent directly through SIM mode. Manual data observed at Principal observatory of TNAU is being sent daily to IMD server and gov.in through mail. Hard copy is being sent once in a month. b. Receipt of : Weather forecast for next five days is being received on weather forecast every Tuesday and Friday from RMC through E-mail. c. No. of contact : This year, with an addition of 121 farmers, we have 554 farmers contact farmers in western zone. The farmers, who adopt crop diversity in their farm and who are progressive have been selected for this mega scientific project. In addition, mass contact of farmers is being made through AIR, Television and dailies. In addition to that one AAS farmer group has been farmed at Village Erode district. d. Gathering : The farmers are being contacted over telephone for the information from information on stages of crops, prevalence of pest and the farmers disease, problem experienced by the farmers if any, on every week and documented. Field visits are made regularly to ascertain the real situation prevailing in the farm by random selection. e. Information on : The expert committee members include agronomist, expert committee entomologist, pathologist, Veterinary plant physiologist, soil scientist, economist and extension specialist. Based on the discussion agro-advisory bulletin is being prepared and sent to farmers through Multimedia. f. Mode of : All India Radio, Coimbatore, communication News papers (Dinamalar, ), Pothigai Television of AAS bulletin and TNAU website http://www.tnau.ac.in/

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Offices to which AAS Bulletin is given besides the farming community

1. The Station Director, All India radio, Coimbatore

2. Deputy Director General of Meteorology (Agrimet), IMD, Shivajinagar, Pune - 411005 and RMC, Chennai.

3. The Editor, Dinamalar, Sundarapuram, Coimbatore Ð 24.

4. The Director, Development Board, Regional office, 31/1, Saderlla street, T.Nagar, Chennai - 600 017.

5. The Director, NCMRWF, Mausam Bhavan Complex, Lodi road, New Delhi - 110 003.

6. Th. M. Raghuman, Chief Reporter, Valarum Thamilagam, 2 / 80, N.P.New Ittery, Pothanur Ð 23.

7. CCIR, Regional Station, Maruthamalai Road, Coimbatore Ð 641003.

8. The Director of Agriculture, Attn. IAP.JDA (Cereals), Chepauk, Chennai-5.

9. Dr.L.S.Rathore, Director, NCMRWF (DST), Mausam Bhavan Complex, Lodi Road, New Delhi Ð 110 003.

10 The Editor, The Hindu, Coimbatore

11. The Editor, Dinakaran, Coimbatore

12. The Editor, Dinamani, Coimbatore

13. Mr. Arun, Nokia service, Coimbatore

14. The Editor, farm radio, Coimbatore

15. Programme Coordinator, MYRADA Krishi Vigyan Kendra, 272, Perumal Nagar, Puduvalliampalayam, Ð 638 453 Ð Taluk, Erode District

16. The Programme Coordinator, Sri Avinashilingam Krishi Vigyan Kendra Vivekanandapuran, , Coimbatoe - 641113.

Details on receipt of forecast, collection of information, development of agro advisory, use of agromet products, advisory products and farmers registration detailed are depicted in Fig 15 Ð 26.

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Fig. 15 Method of weather based agro advisory development and dissemination

Fig. 16 Source of Agro Advisory

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Fig. 17 Use of advanced technologies in preparation of agro advisory

Fig. 18 Experts in agromet advisories preparations

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Agro Advisory Service by Coimbatore Ð AMFU during 1.4.2018 Ð 31.03.2019

1. Number of bulletin sent : 102 2. Total Advisory sent 507 3. Number of SMS sent : 102 (86 alone disseminated from mKisan) 4. Sector wise advisory given from Coimbatore Ð AMFU Sector Advisory SMS Nos. % Nos. % Agriculture 278 54.8 49 48 Horticulture 108 21.3 27 26 Cattle 76 15.0 16 16 Poultry 38 7.5 6 6 Extreme events 7 1.4 4 4 Total 507 100 102 100

5. Major crop/Animal covered with advisory given from Coimbatore Ð AMFU Sector Advisory SMS Nos. % Nos. % Paddy 21 4 8 8 Maize 54 11 8 8 Millets 42 8 7 7 Groundnut 20 4 6 6 Sugarcane 58 11 10 10 Cotton 21 4 4 4 General 62 12 6 6 Total Agri 278 55 49 48 Vegetables 32 6 6 6 Turmeric 36 7 7 7 Banana 24 5 8 8 Grapes 12 2 5 5 Jasmine 4 1 1 1 Total Horti 108 21 27 26 Cattle Production 42 8 10 10 Cattle Protection 34 7 6 6 Total Cattle 76 15 16 16 Poultry Production 18 4 4 4 Poultry Protection 20 4 2 2 Total Poultry 38 7 6 6 Extreme Events 7 1 4 4

6. Details of special programme conducted 1. Nil

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IV FEEDBACK FROM FARMERS (2017-18) a. Number of farmers contacted : 100 farmers b Receipt of agro advisory bulletin : i. Below 2 years - 23 % (Category wise by the farmers of ii. 2 - 3 years - 32 % the scheme including revised iii. Above 3 years - 45 % farmers list based on the years of participation) c. Types of crops grown by the : Rice, Sorghum, Maize, Banana, Sugarcane, selected farmers Cotton, Grapes, Coconut, Sunflower, Turmeric, Onion, Chilies, Vegetable crops, Small millets, Tapioca, Pulses d The advantages gained by the i.: Awareness about the weather: 100 % receipt of AAS bulletin ii. Planning agricultural operation: 78 % e. The Agricultural decisions made : i. Land preparation : 65 % based on weather forecasting ii. Planting : 78 % iii. Weeding : 58 % iv. Irrigation : 75 % v. Spraying : 64 % vi. Fertilizer application : 70 % vii. Harvesting : 72 % viii. Post harvest : 60 % f. Percentage of farmers used the : Around 80 per cent of the farmers were contacted AAS bulletin for planning their and are making use of the agro advisory services agricultural operation bulletin for planning their farm activities and doing farm related operations.

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Fig. 19 Centre wise agro advisory sent by TN-Centers from initial and during 2017 Ð 18

Fig. 20 Centre wise advisory SMS sent by TN-Centers from initial and during 2017 Ð 18

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Fig. 21 Farmers registered (%) for SMS advisory in Tamil Nadu up to Mar. ‘19 (10.9%)

Fig. 22 Farmers registered (Nos.) for SMS advisory in Tamil Nadu up to Mar.‘19 (15.1 lakh)

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Fig. 23 Districtwise farmers registered for mKisan SMS in Tamil Nadu up to Mar. 19 Coimbatore - 47,398 Nos., Erode - 40,594 Nos. and Trippur Ð 17851 Nos..

Fig. 24 Blockwise farmers registered for SMS advisory in mKisan portal Ð Coimbatore

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Fig. 25 Blockwise farmers registered for SMS advisory in mKisan portal Ð Erode

Fig. 26 Blockwise farmers registered for SMS advisory in mKisan portal Ð Tripur

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Fig. 27 Sector wise advisory and SMS sent by Fig. 28 Sector wise Advisory and SMS sent by Coimbatore during 2018 - 19 Tamil Nadu centers during 2018 Ð 19

Fig. 29 Crop wise advisory and SMS sent during 2018 - 19

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Table 12 Usefulness of agro advisory

Awarness criteria & field operation 2014 -15 2015 -16 2016 -17 2017-18 2018-19

No. of farmers contacted 75 250 150 150 100 Awareness about the weather 98 98 98 98 100 Planning 70 73 74 77 78 Land prep. 55 60 62 65 65 Planting 65 74 73 77 78 Weeding 50 51 51 60 58 Irrigation 75 75 77 71 75 Spraying 60 64 64 63 64 Fertilizer appln. 75 77 75 74 70 Harvest 60 68 64 69 72 Post harvest 56 60 60 61 60

Fig. 30 Usefulness of advisory sent by Coimbatore AMFU during 2014 to 2019

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Specific comments made by the farmers: Ø Accuracy of forecast was decreasing. Ø Need separate forecast for area near Western Ghats (ex.Valparai, Anaimalai) and plains. The present common forecast of Coimbatore district is giving false alarm to plains. Ø Farmers are also in need still more précised block level forecast instead of district forecast as the receive many false alarms.

Usefulness of the advisory bulletins enumerating from the selected cases when advisories were helpful or otherwise Ø The awareness on the weather forecast has been increased among the farmers (Fig. 30 & Table 12). Ø Increase in usage of AAS bulletin was observed when compared to 2017-18 and farmers were shared advisories to neighbours. Ø Based on weather forecast farmers are planning accordingly for their weather sensitive operations such as land preparation, sowing, weeding, fertilization, harvesting and post-harvest operations. Ø One way the weather based advisory helps the farmers to improve the use efficiency of the inputs and on the other hand it reduces the risk on loss of inputs and outputs. Ø The responsive farmer is obtaining more yield than non-responsive farmers. Ø The responsive farmer has reduced cost of cultivation due to saving in labour, and inputs. Ø Advisory on sowing rains helps the farmers to get good crop stand and establishment. And also farmer kept ready with the inputs for the sowing. Ø The usage of AAS is more for planting than other farm operations Ø The weather-based advisory on pest and disease helps the farmer to go for defensive spray, and helps the farmer from loss due to most vulnerable pest and diseases. Ø Information on wind speed and wind direction helped in saving from lodging of very sensitive crops such as banana and sugarcane though not fully, partly. Ø Information on lightning and thunderstorm helps to keep away the farmers and animals from thunders and lightning effect. Ø The information on hotness and wind helps the poultry growers to take necessary protective measures.

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V. VERIFICATION OF WEATHER FORECASTING The weather forecast received from RMC, Chennai and the actual observed data of TNAU agromet observatory and automatic weather stations installed at Tiruppur and Erode district were compared to assess the validity of weather forecasting from March, 2018 to February, 2019. Total number of days forecast received from March 2018 to Feb 2019: 365 Total number of Agro advisory bulletins prepared and circulated during this period: 102 5.1. Rainfall Season wise analysis of rainfall data of western zone districts was done using the following statistical tools and the results were presented in Fig 31, 32 and table 13, 14 and 15. The usability comparison between 2016-17, 2017-18 and 2018-19were presented in Fig.33 and comparison between IMD district and TNAU’s block level were presented in Fig.34. 5.2. Maximum, Minimum Temperature and Wind speed Season wise analysis of maximum temperature, minimum temperature and wind speed data during 2018-19 in western zone districts were done using the following statistical tools and the results were presented in table 16, 17 and 18, Fig. 35 and 36.

Rainfall Temperature (max & min) Wind Speed i. Correct - Upto 2 % a. Correlation a. Correlation ii. Usable - Upto 20 % b. RMSE b. RMSE iii. Not Usable - >20 % c. Error structure c. Error structure iv. RMSE i. Correct: 1 0C i. Correct: Diff ≤ 3 m/s v. Ratio Score ii. Usable: 2 0C ii. Usable:3 m/s < Diff ≤ 6 m/s vi. H.K. Score iii. Not usable: > 2 0C iii. Unusable:Diff > 6 m/s The results from the study indicated that in Coimbatore district, the usability of forecast was high during CWP followed by HWP, where as it were unusable during SWM and NEM of Coimbatore district. This is mainly due to inclusion of Western Ghats and Valparai regions and same has been already represented to the Regional Meteorological Centre, IMD, Chennai to provide separate forecast for plains and Western Ghats of Coimbatore district. The forecast at Erode was usable during CWP, HWP and NEM, where as it was unusable during SWM. Similarly in Trippur district, the forecast was usable in all the seasons except SWM.The maximum temperature and minimum temperature forecast during 2018-19 had high usability (>80% & >90%) in all season, whereas the wind speed forecast had lesser usability of 55, 32, 68 and 52 per cent for summer, SWM, NEM and winter, respectively.

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A. Error structure

Figure 31. Comparison of season wise rainfall forecast and observed in western zone

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Figure 32. Season wise error structure of rainfall forecast and observed in western zone

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Table 13. Statistical analysis on predicted and observed rainfall in Coimbatore district

HWP SWM NEM CWP Season Total 2017 2017 2017 2018 Total days n 92.0 122.0 92.0 59.0 365.0 Missing days M 0.0 0.0 0.0 0.0 0.0 Observed mm 337.7 1207.5 326.8 2.4 1874.4 Forecast mm 1149.0 1716.5 1121.0 53.0 4039.5 Rain observed and Forecasted YY 50.0 102.0 44.0 2.0 198.0 Rain observed but not forecasted YN 0.0 2.0 5.0 3.0 10.0 Rain not observed but forecasted NY 27.0 17.0 24.0 9.0 77.0 Rain not observed and not NN 15.0 1.0 19.0 45.0 80.0 forecasted Number of matching cases YY + NN 65.0 103.0 63.0 47.0 278.0 Number of forecast days N 92.0 122.0 92.0 59.0 365.0 (Total days less missing days) Ratio Score of rainfall RS 70.7 84.4 68.5 79.7 75.8 Hanssen & Kuipers index H.K. score 0.4 0.0 0.3 0.2 0.2 Root Mean Square Error RMSE 3.1 3.7 3.2 1.0 2.8 Correct 23.1 2.9 30.2 95.7 38.0 (N1/N)*100 Error structure for rainfall Usable 6.2 12.6 3.2 0.0 5.5 (for matching cases) (N2/N)*100 Unusable 70.8 84.5 66.7 4.3 56.5 (N3/N)*100 Correlation of rainfall R 0.4 -0.1 0.3 0.0 0.2

35

Table 14. Statistical analysis on predicted and observed rainfall in Erode district

HWP SWM NEM CWP Season Total 2017 2017 2017 2018 Total days n 92.0 122.0 92.0 59.0 365.0 Missing days M 0.0 0.0 0.0 0.0 0.0 Observed mm 246.9 286.2 228.2 1.8 763.1 Forecast mm 695.0 927.5 638.0 34.0 2294.5 Rain observed and Forecasted YY 39.0 42.0 32.0 0.0 113.0 Rain observed but not forecasted YN 1.0 3.0 5.0 1.0 10.0 Rain not observed but forecasted NY 28.0 66.0 17.0 7.0 118.0 Rain not observed and not NN 24.0 11.0 38.0 51.0 124.0 forecasted Number of matching cases YY + NN 63.0 53.0 70.0 51.0 237.0 Number of forecast days N 92.0 122.0 92.0 59.0 365.0 (Total days less missing days) Ratio Score of rainfall RS 68.5 43.4 76.1 86.4 68.6 Hanssen & Kuipers index H.K. score 0.4 0.1 0.6 -0.1 0.2 Root Mean Square Error RMSE 2.7 2.6 2.5 0.8 2.1 Correct 38.1 26.4 55.7 100.0 55.1 (N1/N)*100 Error structure for rainfall Usable 6.4 17.0 7.1 0.0 7.6 (for matching cases) (N2/N)*100 Unusable 55.6 56.6 37.1 0.0 37.3 (N3/N)*100 Correlation of rainfall R 0.1 0.4 0.3 0.0 0.2

36

Table 15. Statistical analysis on predicted and observed rainfall in Tiruppur district

HWP SWM NEM CWP Season Total 2017 2017 2017 2018 Total days n 92.0 122.0 92.0 59.0 365.0 Missing days M 0.0 0.0 0.0 0.0 0.0 Observed mm 257.8 84.2 210.7 3.0 555.6 Forecast mm 449.0 885.0 1008.0 53.0 2395.0 Rain observed and forecasted YY 31.0 35.0 35.0 1.0 102.0 Rain observed but not forecasted YN 1.0 7.0 1.0 1.0 10.0 Rain not observed but forecasted NY 32.0 69.0 25.0 9.0 135.0 Rain not observed and not NN 28.0 11.0 31.0 48.0 118.0 forecasted Number of matching cases YY + NN 59.0 46.0 66.0 49.0 220.0 Number of forecast days N 92.0 122.0 92.0 59.0 365.0 (Total days less missing days) Ratio Score of rainfall RS 64.1 37.7 71.7 83.1 64.2 Hanssen & Kuipers index H.K. score 0.4 0.0 0.5 0.3 0.3 Root Mean Square Error RMSE 2.3 2.7 3.1 1.0 2.2 Correct 49.2 23.9 47.0 98.0 54.5 (N1/N)*100 Error structure for rainfall Usable 11.9 15.2 4.6 0.0 7.9 (for matching cases) (N2/N)*100 Unusable 39.0 60.9 48.5 2.0 37.6 (N3/N)*100 Correlation of rainfall R 0.1 0.2 0.4 0.3 0.2 During this year (2018-19) the usability of district level rainfall forecast given by IMD was lesser than the previous year (2017-18) invariably in all districts.

37

Forecast usability comparison between 2016-17, 2017-18 and 2018-19

a

b

c Fig. 33 a,b,c Rainfall forecast usability comparison between 2016-17, 2017-18 and 2018-19 in wester zone Ð Issued by IMD at district level

38

5.2. Maximum, Minimum Temperature and Wind speed Season wise analysis of maximum temperature, minimum temperature and wind speed data during 2018-19 in western zone districts were done using the following statistical tools and the results were presented in table 16, 17 and 18, Fig. 35 and 36.

Maximum and Minimum Temperature Wind Speed d. Correlation d. Correlation e. RMSE e. RMSE f. Error structure f. Error structure iv. Correct: 1 0C iv. Correct: Diff ≤ 3 m/s v. Usable: 2 0C v. Usable: 3 m/s < Diff ≤ 6 m/s vi. Not usable: > 2 0C vi. Unusable: Diff > 6 m/s

The maximum temperature and minimum temperature forecast during 2018-19 had high usability (>80% & >90%) in all season, whereas the wind speed forecast had lesser usability of 55, 32, 68 and 52 per cent for summer, SWM, NEM and winter, respectively.

39

Table 16. Statistical analysis on predicted and observed maximum temperature in Western zone

HWP SWM NEM CWP Season Average 2017 2017 2017 2018 Observed High oC 39.0 36.0 34.0 37.0 36.5 Forecasted High oC 39.0 35.0 34.0 37.0 36.3 Observed Low 29.0 25.0 25.0 29.0 27.0 Forecasted Low 31.0 28.0 29.0 29.0 29.3 Observed Average 35.2 31.7 31.2 32.6 32.7 Forecasted Average 36.0 31.9 31.4 32.3 32.9 Missing days M 0.0 0.0 0.0 0.0 0.0 Number of forecast days N 92.0 122.0 92.0 59.0 91.3 (Total days less missing days) Absolute values of the difference N1 55.0 75.0 66.0 30.0 56.5 (obs -fcst) <=1.0 Absolute values of the difference N11 37.0 47.0 26.0 29.0 34.8 (obs -fcst) >1.0 Absolute values of the difference N3 12.0 23.0 11.0 10.0 14.0 (obs -fcst) >2 Absolute values of the difference N2 (N11-N3) 25.0 24.0 15.0 19.0 20.8 (obs-fcst) >1 & <=2, Root Mean Square Error RMSE 2.0 2.0 1.7 1.8 1.9 Correct 59.8 61.5 71.7 50.9 61.0 (Diff ≤ 1oC) Error structure for maximum Usable 27.2 19.7 16.3 32.2 23.8 temperature (Diff 1 - 2oC) Unusable 13.0 18.9 12.0 17.0 15.2 (Diff >2oC) Correlation of maximum R 0.5 0.4 0.2 0.7 0.4 temperature

40

Fig. 35 Comparison of season wise forecast and observed values of maximum, minimum temperature and wind speed in western zone during 2018-19

41

Fig. 36 Comparison of season wise error structure of forecast and observed values of maximum, minimum temperature and wind speed in western zone

42

Table 16 Statistical analysis on predicted and observed minimum temperature in Western zone

HWP SWM NEM CWP Season Average 2017 2017 2017 2018 Observed High oC 27.0 27.0 24.0 24.0 27.0 Forecasted High oC 26.0 25.0 24.0 24.0 26.0 Observed Low oC 20.0 21.0 19.0 19.0 20.0 Forecasted Low oC 21.0 21.0 19.0 19.0 21.0 Observed Average oC 23.6 22.8 21.8 21.8 23.6 Forecasted Average oC 24.2 22.8 21.6 21.6 24.2 Missing days M 0.0 0.0 0.0 0.0 0.0 Number of forecast days N 92.0 122.0 92.0 92.0 92.0 (Total days less missing days) Absolute values of the difference N1 67.0 106.0 68.0 68.0 67.0 (obs -fcst) <=1.0 Absolute values of the difference N11 25.0 16.0 24.0 24.0 25.0 (obs -fcst) >1.0 Absolute values of the difference N3 11.0 2.0 8.0 8.0 11.0 (obs -fcst) >2 Absolute values of the difference N2 (N11-N3) 14.0 14.0 16.0 16.0 14.0 (obs-fcst) >1 & <=2, Root Mean Square Error RMSE 1.7 1.2 1.4 1.4 1.7 Correct 72.8 86.9 73.9 73.9 72.8 (Diff ≤ 1oC) Error structure for maximum Usable 15.2 11.5 17.4 17.4 15.2 temperature (Diff 1 - 2oC) Unusable 12.0 1.6 8.7 8.7 12.0 (Diff >2oC) Correlation of maximum R 0.5 0.2 0.2 0.2 0.5 temperature

43

Table 17 Statistical analysis on predicted and observed wind speed in Western zone

HWP SWM NEM CWP Season Average 2017 2017 2017 2018 Observed High km/hour 25.0 27.0 15.0 17.0 25.0 Forecasted High km/hour 20.0 24.0 12.0 8.0 20.0 Observed Low km/hour 3.0 3.0 0.0 0.0 3.0 Forecasted Low km/hour 4.0 6.0 4.0 4.0 4.0 Observed Average km/hour 11.6 17.9 7.6 10.1 11.6 Forecasted Average km/hour 10.8 13.1 6.9 7.4 10.8 Missing days M 0.0 0.0 0.0 0.0 0.0 Number of forecast days N 92.0 122.0 92.0 59.0 92.0 (Total days less missing days) Absolute values of the difference N1 31.0 19.0 32.0 14.0 31.0 (obs -fcst) <=1.0 Absolute values of the difference N11 41.0 83.0 29.0 28.0 41.0 (obs -fcst) >1.0 Absolute values of the difference N3 61.0 103.0 60.0 45.0 61.0 (obs -fcst) >2 Absolute values of the difference N2 (N11-N3) 20.0 20.0 31.0 17.0 20.0 (obs-fcst) >1 & <=2, Root Mean Square Error RMSE 1.8 2.4 1.7 1.9 1.8 Correct 33.7 15.6 34.8 23.7 33.7 (N1/N)*100 Error structure for maximum Usable 21.7 16.4 33.7 28.8 21.7 temperature (N2/N)*100 Unusable 44.6 68.0 31.5 47.5 44.6 (N3/N)*100 Correlation of maximum R 0.0 0.3 -0.1 0.2 0.0 temperature

44 VI. ECONOMIC IMPACT OF THE AAS BULLETIN 6.1 Usefulness of the advisory bulletins enumerating from the selected cases when advisories were helpful or otherwise Ø The awareness on the weather forecast has been increased among the farmers. Ø Increase in usage of AAS bulletin was observed when compared to previous years and farmers were shared advisories to neighbours. Ø Based on weather forecast farmers are planning accordingly for their weather sensitive operations such as land preparation, sowing, weeding, top-dressing of fertilizers, harvesting, post-harvest operations and storage. Ø One way the weather based advisory helps the farmers to improve the use efficiency of the inputs and on the other hand it reduces the risk on loss of inputs and outputs. Ø The response farmer is obtaining more yield than non-responsive farmers. Ø The responsive farmer has reduced cost of cultivation due to saving in labour, chemicals and other inputs. Ø Advisory on sowing rains helps the farmers to get good crop stand and establishment. And also farmer kept ready with the inputs for the sowing. Ø The usage of AAS is more for planting than other farm operations Ø The weather-based advisory on pest and disease helps the farmer to go for defensive spray, and helps the farmer from loss due to most vulnerable pest and diseases. Ø Information on wind speed and wind direction helped in saving from lodging of very sensitive crops such as banana and sugarcane though not fully, partly. Ø Information on lightning and thunderstorm helps to keep away the farmers and animals from thunders and lightning effect.

Ø The information on hotness and wind helps the poultry growers to take necessary protective measures.

45 6.2 Specific instances of benefit / losses due to AAS with cultural practices modified as per advisories. Economic benefit of Agro Advisory - Partial busdgeting (Table 18 and 19) Crop: Maize Period: July Ð November 2018 Village: Anthiyur Farmers: 5 AAS and 2 Non AAS farmers Table 18 Partial budget calculations

Date Forecast Actual Advisory Added Non AAS Loss cost to farmers Rs. AAS farmers 1-5 42mm 30mm By utilizing Added irrigation 300 Jul.18 anticipated rainfall, due to delayed land preparation and sowing sowing maize crop in irrigated condition. 6 - 9 6mm 0mm Spray Atrazine @ 1000 Additional 1000 Jul.18 250g/acre within 2 weeding cost days after sowing. with manual labour 12-16 13mm 5mm Complete the gap 300 Poor Jul.18 filling by utilizing germination. And rainfall and provide no gap filling. irrigation based on Lesser population soil moisture. 26 - 30 37mm 8 mm By utilizing 1500 Missing weeding 300 Jul.18 anticipated rainfall, and fertilizer do weeding and application apply first dose of during rain and fertilizer applied additional irrigation 01 Ð 04 34 mm 30 mm Postpone the Done pesticide 1000 Jul. 18 pesticide spray spray. Washout. 08-11 10mm 3mm Drizzling alone No irrigation.

46 Aug.18 expected. Provide Crop growth irrigation based on affected soil moisture 27-31 39 mm 19 mm By utilizing the rain Early fertilizer 300 Aug.18 apply 2nd dose of application fertilizer without rain cost additional irrigation 10 Ð 14 90 mm 20 mm Rain anticipated. Applied irrigation 300 15 Ð 18 95 mm 36 mm Stop irrigation. Post Sep.18 pone harvest 19 - 23 12 mm 1 mm Complete the Missed harvest. 1000 Sep.17 harvest using dry Suffered in period rainfall during 24 Ð 30, Sep. 18 Total 2800 4200

Yield and income obtained: AAS farmer: 2930 kgs x Rs. 15 = 43950/- Non AAS farmer: 2390 kgs x Rs. 15 = 35850/- Added return = 8100/-

Table 19 . Partial budgeting on maize cultivation between AAS and non-AAS farmers

Loss (Rs/ha) Gains (Rs/ha) Added cost: Application of Rs. 2800 Added return: Additional income 8100/- fertilizer, hand weeding and from increased yield plant protection spray. Reduced return: Nil Reduced Cost: No loss of seed, Rs. 4200/- fertilizer and weedicide. Sub total Rs. 2800 Rs. 12300 Net Benefit Rs. 12300-2800 = Rs. 9500 /acre

47

VII. PROBLEMS AND SUGGESTIONS FOR IMPROVEMENT OF AAS UNIT 1. Farmers are more interested on quantity of rainfall than rainfall occurrence. Most of the time the occurrence of rainfall could be predicted well but the quantity of rainfall does not match. This leads to problems in issuing recommendation on appropriate management practices. 2. Forecast on rainfall during south west monsoon has to be improved. 3. Forecast on wind speed has to be improved 4. Forecasted rainfall for Coimbatore district and actual rainfall are varied highly due to inclusion of rainfall at Valparai, Hence, seaparate forecast is required for Western Ghats areas in western zone.

48

VIII SALIENT ACHIEVEMENTS District wise Moisture Adequacy Index (MAI) of Tamil Nadu Ð 2018 Moisture Adequacy Index (MAI) is the calculation of weekly water balance and it is equal to ratio of actual evapotranspiration (AET) to the potential evapotranspiration (PET). MAI is the important factor in crop planning and it can be worked out on the basis of weekly/monthly average rainfall. Drought has high negative impact on agricultural production, which will alter the economy of the state or a country. MAI (severity) at different growth stages are integrated into a single index value to identify drought impact on the crop. To assess the agricultural drought, it is necessary to measure the extent to which rainfall and soil moisture is falling short of the water requirement of crops during the cropping season. Agricultural droughts during different seasons (years) were classified into four groups based on average MAI during the season. Drought classification based on MAI

Drought severity MAI No drought MAI > 0.75 Mild drought MAI <0.75 and >0.50 Moderate drought MAI <0.50 and >0.25 Severe drought MAI <0.25

49

Table 20 Season wise MAI and drought severity during 2018

Winter 2018 Summer 2018 SWM 2018 NEM 2018 District MAI Drought MAI Drought MAI Drought MAI Drought Ariyalur 49.9 Moderate 27.5 Moderate 32.5 Moderate 86.5 No Chennai 43.7 Moderate 7.5 Severe 59.1 Mild 86.2 No Coimbatore 46.8 Moderate 60.9 Mild 94.4 No 86.9 No Cuddalore 50.4 Mild 14.3 Severe 41.3 Moderate 94.9 No Dharmapuri 41.2 Moderate 38.6 Moderate 27.2 Moderate 37.9 Moderate Dindigul 50.0 Mild 54.6 Mild 51.5 Mild 86.4 No Erode 45.6 Moderate 47.0 Moderate 46.9 Moderate 76.7 No Kanchipuram 43.8 Moderate 13.2 Severe 53.0 Mild 86.8 No 51.9 Mild 68.1 Mild 90.7 No 85.9 No Karur 46.1 Moderate 30.5 Moderate 30.6 Moderate 43.2 Moderate Krishnagiri 41.8 Moderate 37.1 Moderate 33.1 Moderate 42.1 Moderate 44.5 Moderate 38.1 Moderate 54.5 Mild 82.3 No 59.2 Mild 13.0 Severe 29.8 Moderate 95.9 No Namakkal 46.8 Moderate 30.0 Moderate 47.1 Moderate 51.1 Mild Nilgiris 50.0 Mild 67.1 Mild 99.6 No 90.1 No Perambalur 47.4 Moderate 23.0 Severe 30.8 Moderate 61.7 Mild Pudukkottai 43.7 Moderate 26.7 Moderate 48.4 Moderate 73.8 Mild Ramanad 43.7 Moderate 28.0 Moderate 19.5 Severe 92.7 No Salem 44.2 Moderate 39.7 Moderate 51.5 Mild 50.7 Mild Sivagangai 43.7 Moderate 54.2 Mild 65.3 Mild 83.5 No 48.5 Moderate 19.3 Severe 25.7 Moderate 80.8 No 53.0 Mild 73.1 Mild 92.7 No 83.0 No Thiruvallur 45.4 Moderate 13.6 Severe 50.7 Mild 92.7 No T.malai 52.4 Mild 22.6 Severe 34.1 Moderate 78.7 No 51.1 Mild 18.6 Severe 46.6 Moderate 83.7 No 42.8 Moderate 35.6 Moderate 34.5 Moderate 87.5 No Tiruchirapalli 44.0 Moderate 28.2 Moderate 33.9 Moderate 89.1 No Tirunelveli 49.5 Moderate 62.9 Mild 43.5 Moderate 88.1 No Tiruppur 45.4 Moderate 44.1 Moderate 29.6 Moderate 51.8 Mild Vellore 44.8 Moderate 27.2 Moderate 40.7 Moderate 56.7 Mild Villupuram 46.2 Moderate 13.7 Severe 35.8 Moderate 90.9 No Virudhunagar 50.4 Mild 52.3 Mild 54.2 Mild 72.7 Mild

50

MAI based agricultural drought intensity in MAI based agricultural drought intensity in Tamil Nadu during Winter 2018 Tamil Nadu during Summer 2018

MAI based agricultural drought intensity in MAI based agricultural drought intensity in Tamil Nadu during SWM 2018 Tamil Nadu during NEM 2018

51

52

M.Sc. Student Thesis Research

Downscaling and Validation of ERFS forecast for Western Zone districts (Out come of Training to Technical officer)

53

54

Downscaling and Validation of ERFS forecast for Western Zone districts

Weather is a dominant factor for the success of agriculture in a region and favourable weather throughout the cropping period is essential for maximizes the productivity. Timely weather forecast minimize the weather risk on crop production, reduces the input loss and increases crop productivity. Different types of weather forecast, varying in spatial, temporal, purpose and accuracy are being issued by many agencies for the farming community. Extended Range of Forecast Service (ERFS) is a forecasting type, issued for more than 10 days to a month or season and highly useful for planning of cropping season, crop, variety and other weather risk management options. Also, useful for the policy maker to assess the food production and post harvest options.

The forecast of the active or break cycle of monsoon generally known as the Extended Range Forecasts (ERF). In the agriculture sector, if accurate outlook of monsoon conditions are provided in the extended range, farmers are very much benefited. Forecasts of precipitation on the intermediate timescale are critical for the optimized farm planning such that planting and harvesting. Pattanaik and Das (2015) stated that a skillful extended range forecast can also be very helpful for reservoir operation in reducing floods. The ERF in the tropics are very difficult and the most challenging tasks in atmospheric sciences. It connects the gap between medium-range weather forecasting and seasonal or long range forecasting.

Since 2009, IMD has been issuing experimental extended range forecast by using available products from statistical and dynamical models outputs from various centers located in India and abroad. The spatial scale of the ERFS is still wider for the farm level decision making and need to be downscaled to district level for increasing the accuracy of the forecast. Accuracy of the weather forecast is determined by the skill of forecast. The accurate and skill full forecast can able to help the farmers in developing prevention and management measures against the climatic risk and also these forecasts help the policy makers in decision making process. Verification of a forecast must be done before integrating the forecast in farm planning to avoid false alarms. Forecast verification for medium range forecast had many studies, whereas the Extended Range of Forecast is a newer one and need to be verified for effective use and further improvement.

In this context, the study titled “Downscaling monthly and seasonal ERFS products and integration with DSSAT crop simulation model for climate risk management” was taken up at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore as a Masters Degree in Agrometeorology student’s thesis research under the advisory committee including Dr. Ga. Dheebakaran, Technical officer, Coimbatore Ð AMFU. The objectives are… 55

1. Downscaling and validating the extended range of weather forecast issued for Tamil Nadu state by India Meteorological Department to district level. 2. Integrating the downscaled extended range of forecast with DSSAT model for the yield prediction of rice, maize and groundnut of Western Agro Climate Zone of Tamil Nadu.

Methodology

• Extended Range of forecast of Tamil Nadu region for the period from 1986 to 2015 (30 years) isssued during the ERFS training at IIT, Bhubaneswar was used for the study • The district level observed weather data of Tamil Nadu Western Zone districts viz., Coimbatore, Erode and Trippur were collected from Agro Climate Research Centre data base for the same period. • DSSAT weatherman, Disaggregation tools, MATlab programmes issued during the ERFS training at IIT, Bhubaneswar was used for the study. • About 400 weather scenarios were developed for observed weather and ERFS forecast compared incorporated weather, analysed for best fit and selected 200 scenarios.. • The observed and ERFS forecast weather scenario files had been used in DSSAT to simulate yield of three crops viz., rice, maize and ground nut for the three weatern zone districts. • The DSSAT simulated yield with observed weather and ERFS forast incorporated weather were compared with the actual yield obtained from Department of statistics, Chennai, Tamil Nadu.

The results obtained from the study is presented here under.

56

Results

Performance of disaggregated monthly observed Data - Coimbatore

Monthly rainfall Frequency - Coimbatore Rainfall Intensity - Coimbatore

Total Monthly Rainfall - Coimbatore Seasonal Rainfall - Coimbatore

Monthly rainfall frequency: Disaggregated monthly observed rainfall data for the 200 realizations was correlated decently with the rainfall frequency of June (r value of 0.69), July (0.65), August (0.78) and September (0.72)

Rainfall intensity: The r value for June, July, August and September were 0.76, 0.59, 0.85 and 0.69, respectively, high during August and low during July.

Total monthly rainfall: The performance of the disaggregated data were found good correlation for Coimbatore region with 0.99 r value

Seasonal rainfall: The seasonal wise correlation was more in intensity 0.79 and little lower in frequency 0.69

57

Performance of disaggregated monthly Observed Data - Erode

Month wise rainfall Frequency - Erode Rainfall Intensity - Erode

Total Monthly Rainfall - Erode Seasonal Rainfall - Erode

Month wise rainfall frequency: The monthly frequency has high correlation for the month of June (0.93) and July (0.92) but for August (0.57), the correlation was average and below average in September (0.36)

Rainfall intensity: In intensity, the correlation was poor for June and September. For August correlation was moderate and high for the July .

Total monthly rainfall: In total, the correlation was high for all the months with the r value more than 0.99

Seasonal rainfall: The correlation between the seasonal frequency and total was high as 0.93 and 0.99 whereas the intensity had very low correlation

58

Performance of disaggregated monthly Observed Data - Tiruppur

Month wise rainfall Frequency - Tiruppur Rainfall Intensity - Tiruppur

Total Monthly Rainfall - Tiruppur Seasonal Rainfall - Tiruppur

Month wise rainfall frequency: The disaggregation correlation for the frequency was better in the Tiruppur district for June and July with r values 0.77 and 0.91, respectively. Correlation was above average in August (0.61) and poor in the month of September (0.21)

Rainfall intensity: Correlation for the intensity was in the range of 0.59 and 0.51 for July and September, respectively June month reported 0.41 whereas august reported 0.21.

Total monthly rainfall: In total rainfall, the correlation between the observed and disaggregated was high with more than 0.99 r value.

Seasonal rainfall: The seasonal wise performance of disaggregated data was very good in frequency but poor in intensity calibration. The correlation value was very high for the seasonal frequency and low for the intensity seasonal total had very high correlation.

59

Performance of disaggregated monthly ERFS Data - Coimbatore

Monthly rainfall Frequency - Coimbatore Rainfall Intensity - Coimbatore

Total Monthly Rainfall - Coimbatore Seasonal Rainfall - Coimbatore

Monthly rainfall frequency: The correlation of the disaggreegated ERFS forecast rainfall frequency to the observed monthly frequency of Coimbatore was in negative correlation

Rainfall intensity: Disaggreegated ERFS forecast intensity of rainfall were in positively correlation for all the months. August (0.3) and June (0.28) had the better correlation when compared to July and September months, which were in the range of 0.6 to 0.1

Total monthly rainfall: In total rainfall, correlation for all the months except August was in negative correlation, where August had 0.09 r value

Seasonal rainfall: In season wise analysis, the correlation was -0.3 for the seasonal frequency and 0.23 for seasonal intensity and seasonal total also low correlation

60

Performance of disaggregated monthly ERFS Data - Erode

Month wise rainfall Frequency - Erode Rainfall Intensity - Erode

Total Monthly Rainfall - Erode Seasonal Rainfall - Erode

Month wise rainfall frequency: The disaggreegated ERFS forecast frequency of rainfall of the Erode district had high correlation for June and July months with the r values 0.93 and 0.92, respectively. During August and September the correlation was reduced into 0.57 and 0.37, respectively

Rainfall intensity: The correlation was negative for June month in the case of rainfall intensity. July month had very good correlation having r value of 0.88. August and September showed average correlation value of 0.46 and 0.26, respectively.

Total monthly rainfall: The correlation for monthly total rainfall had a good correlation to the forecasted values in all the months

Seasonal rainfall: Seasonal wise frequency and total rainfall had the good correlation towards the forecasted where in seasonal intensity showed the correlation of 0.27

61

Performance of disaggregated monthly Observed Data - Tiruppur

Month wise rainfall Frequency - Tiruppur Rainfall Intensity - Tiruppur

Total Monthly Rainfall - Tiruppur Seasonal Rainfall - Tiruppur

Month wise rainfall frequency: Tiruppur had average correlation in month wise rainfall frequency. June month showed a better correlation of 0.44 when compared to other months.

Rainfall intensity: The correlation values for the months July, August and September were 0.37, 0.27 and 0.11, respectively. All the months have showed poor correlation for the disagreegated ERFS and observed rainfall intensity and August and September months are negatively correlated with the forecasted intensity

Total monthly rainfall: The monthly total rainfall also showed poor correlation towards the forecast. June month had the better correlation (0.37) when compared to the other months followed by July (0.34) and August (0.03). September month showed a negative correlation.

Seasonal rainfall: The season wise frequencies were also comparatively poor while considering Coimbatore and Erode districts. Correlation between the observed seasonal frequency and disaggregated forecasted frequency was 0.33 and for monthly rainfall was 0.30. The intensity of rainfall was negatively correlated to the forecasted rainfall intensity.

62 Performance of observed weather and ERFS forecast on rice, maize and ground yield of Western Zone Districts Ð A comparison Deviation from Deviation Deviation Deviation yield simulated between yield Yield simulated Yield simulated between yield between yield Yield with simulated with with with simulated with simulated with Actual Yield simulated disaggregated disaggregated District/Crop disaggregated disaggregated observed disaggregated (kg/ha) with observed observed observed observed ERFS forecast weather and ERFS weather weather weather and weather and weather (kg/ha) (kg/ha) actual yield and Actual actual observed Actual yield (%) yield (% weather (%) (%) 1 2 3 4 5 6 (3-2)/2*100 7 (4-3)/3*100 8 (4-2)/2*100 9 (5-2)/2*100 Rice Coimbatore 3980 2743 2771 2555 -26.6 1.9 -26.0 -31.4 Erode 3999 2266 2247 2947 -42.3 -0.4 -42.6 -14.7 Tiruppur 4100 2450 2398 2303 -37.2 -1.6 -39.3 -41.2 Maize Coimbatore 3297 4628 4181 4356 95.6 -3.5 82.7 92.3 Erode 4283 2947 2468 2398 -14.7 -15.7 -29.2 -29.7 Tiruppur 7375 3861 3255 4497 -36.4 -13.6 -46.6 -26.3 Ground nut Coimbatore 1326 1753 1602 1507 44.5 -6.7 31.9 26.2 Erode 1526 1615 1445 1244 9.9 -9.8 -2.1 -16.6 Tiruppur 3566 1851 1884 1579 14.3 4.4 15.5 -1.2

Rice ¥ Average per cent deviation between yield using observed weather and disaggregated weather was -1.6 per cent. ¥ The average per cent deviation was -31.4 per cent for Coimbatore. ¥ The average per cent deviation between yield using observed weather and disaggregated weather since 1986 to 2015 was 0.4 per cent for Erode. ¥ Between the actual yield and forecasted yield, the average Per cent deviation was - 44.2 per cent for Erode. ¥ Mean per cent deviation between yield using observed weather and disaggregated weather since 2009 to 2015 was -1.6 Per cent and for the ERF and actual yield is - 41.2 per cent. Maize ¥ The average deviation between the yields of ERF forecasted weather data and actual yield was very high as 92.3 per cent for Coimbatore. ¥ The average Per cent deviation between the yields of observed weather and the disaggregated one was found to be -3.5 per cent ¥ average deviation between the yields of ERF forecasted weather data and actual yield was very high as -29.5 per cent for Erode ¥ The average per cent deviation between the yields of observed weather and the disaggregated one was found to be -15.7per cent ¥ The average deviation between the yields of ERF forecasted weather data and actual yield was very high as -26.3 per cent for Tiruppur Groundnut ¥ Average deviation between ERF weather and actual yield was of 26.2 per cent as well as observed and disaggregated weather, average deviation between the observed and disaggregated weather yields were -6.7 per cent for Coimbatore. ¥ For Erode an average deviation between ERF weather and actual yield was of -16.6 per cent and average deviation between the observed and disaggregated weather yields are -2.1 per cent. ¥ For Tiruppur average deviation between ERF weather and actual yield was of -1.2 per cent. The mean deviation between the observed and disaggregated weather yields are 4.4 per cent

CONCLUSION ¥ The performance of ERF forecast in the Western zone districts had an average skill and need to be improved by using various downscaling tools ¥ The downscaled and validated forecasts could be used to predict the yields of various crops by using DSSAT crop simulation model ¥ The model predicted the yields under observed weather were in correct and usable ranges and the model over predicted the yields when used the ERF forecasted weather data ¥ The model and the ERF forecast are to be improved further for higher resolution by validation and calibration would give more accuracy in agro advisories

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Weather and AAS related messages in News papers

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Audit Utilization Certificate 2018 Ð 19

Adminsitrative orders 2018 - 19

Tamilnadu Agricultural University Mail - Transferred GIA on 1... https://mail.google.com/mail/u/0?ik=f29df3909f&view=pt&sea...

Ga Dheebakaran

Transferred GIA on 19.02.2019 1 message

Awadhesh Prasad Wed, Feb 20, 2019 at 5:27 PM To: [email protected], [email protected], FASAL FAIZABAD , Rohit Sharma , Veena_sharma29 , [email protected], ajith aju , Ajith Pillai , [email protected], "Dr.P.Sivaprasad, N.O. Vellayani" , Girija Devi , aas kau , [email protected], [email protected], "Dr.Pasupalk" , "aksethy.ouat" , [email protected], [email protected], "aas.bhawanipatna" , [email protected], sajeeb biswasi , Chakradhar Sahoo , [email protected], ">" , itishree das , NIRANJAN SENAPATI , AAS Malkangiri , [email protected], [email protected], Comptroller Uhf , [email protected], Parminder Baweja , Monika Thakur , [email protected], [email protected], AMFU Ranchi , Sanjiv Kumar Gupta , [email protected], [email protected], "Dept. of Livestock Production and Management MVC" , [email protected], meteorology TNAU , [email protected], subrah_arul , [email protected], Senthil Kumar , Mano Suman , [email protected], [email protected], [email protected], [email protected], Head Agrimet , anil kumar , "Dr.M.L.Khicher AAS UNit, Hisar" , [email protected], Narendra Kumar Pareek , Sagarka Bhimjibhai Khimarabhai , [email protected], [email protected], ADR of Natural Resource & Plant Health Management - S S Kukal , Additional Director Communications , [email protected], [email protected], [email protected], GOPAL SAKARKAR , sonu patil Cc: kksingh2022 , Anand Sharma , shiv attri , Ranjeet Singh , kripan ghosh , "baxla.asc" , rbala_india , Priyanka Singh

Sir/Madam, It is to inform that Grant-in-aid had been transferred to your university account on 19.02.2019. The details of AMFUs are given below.

S.No. Name of AMFUs Amounts 1 Roorkee 473525 2 Rajouri 1312921 3 Velleyani 662318 4 Bhubneswar 590864 5 Bhawanipatnam 771337 6 Ranital 1145338

7 G.udaigiri 804836 8 Kirie 780131 9 solan 865613

1 of 2 20/02/19, 7:02 pm Tamilnadu Agricultural University Mail - Transferred GIA on 1... https://mail.google.com/mail/u/0?ik=f29df3909f&view=pt&sea...

10 Ranchi 794808 11 Chennai 1105328 12 coimbatore 1007300 13 Kalimela 702928 14 Bikaner 888059 15 Hisar 781633 16 Ludhiana 925890 17 Adhuthurai 923128 18 Akola 812022

Kindly send confirmation. with best regards

A.Prasad ASC, IMD New Delhi

2 of 2 20/02/19, 7:02 pm