This document discusses the impacts of El Niño and La Niña on the Indian agriculture. It begins by introducing El Niño and La Niña, and their effects on the South West Monsoon and Indian temperatures. It then examines how decreases in rainfall during El Niño years negatively impact agriculture in various regions of India. Specific data on rainfall deficits in states like Gujarat and Haryana during El Niño years compared to normal years is presented. Accurately predicting El Niño and La Niña events is crucial for agriculture and water management planning in India.

1. Impact of El Niño and La-Nina on Indian
Agriculture
Doctoral Seminar
On

2.  Introduction
 Effect on South West Monsoon
 Effect on Temperature
 Effect on Agriculture
 Conclusion
 Future Thrust
Contents

3. Introductio
n

4. 4
Eastern Pacificcentral PacificWestern Pacific

5. 5
Figure:1 El Niño &La Nina circulation
(Source: Bureau of Meteorology, Australia)

6. Difference between EL Nino & La Nina
Feature El-Nino La- Nina
Meaning El Nino is a Spanish term which represents “little boy” La Nina is a Spanish term which represents ‘little girl’.
Temperature at
Sea Surface
Temperature at sea surface is warmer than normal sea-surface
temperatures. El Nino is a warming of the Pacific Ocean between South
America and the Date Line, centred directly on the Equator, and
typically extending several degrees of latitude to either side of the
equator.
Temperature at sea surface is cooler than normal sea-
surface temperatures. La Nina exists when cooler than
usual ocean temperatures occur on the equator between
South America and the Date Line.
Pressure It accompanies high air surface pressure in the western Pacific accompanies low air surface pressure in the eastern
Pacific
Trade winds El Niño occurs when tropical Pacific Ocean trade winds die out and
ocean temperatures become unusually warm
La Nina, which occurs when the trade winds blow
unusually hard and the sea temperature become colder
than normal
seasons Winters are warmer and drier than average in the Northwest of pacific,
and wetter in Southwest of pacific and experience reduced snowfalls.
Winters are wetter and cause above-average precipitation
across the Northwest of pacific and drier and below
average precipitation in South west of pacific.
Coriolis force El Nino results in a decrease in the earth’s rotation rate (very minimal) ,
an increase in the length of day, and therefore a decrease in the strength
of the Coriolis force
La Nino results in increase in the earth’s rotation rate,
decrease in the length of day, and therefore a increase in
the strength of the Coriolis force.
Ocean waters in
Pacific
Warm water approaches the coasts of South America which results in
reduced upwelling of nutrient-rich deep water impacting impacts on the
fish populations.
Cold water causes increased upwelling of deep cold
ocean waters numbers of drought occurrence, with more
nutrient-filled eastern Pacific waters.
cyclone Comparatively less compared to La Niña as wind speed is low La Nina had a greater tendency to trigger intense tropical
cyclones as wind direction changes pilling up water
between Indonesia and nearby areas as winds from Africa
6

7. 7
• Changes in the normal patterns of trade wind circulation.
• Winds move westward, carrying warm surface water to Indonesia and
Australia and allowing cooler water to upwell along the South American
coast.
• Warmer water causes heat and moisture to rise from the ocean off
Ecuador and Peru, resulting in more frequent storms and torrential rainfall
over these normally arid countries.
Why El Niño occurs?

8. 8
• Occur due to increases in the strength of the normal patterns of
trade wind circulation.
• Under normal conditions, these winds move westward, carrying
warm surface water to Indonesia and Australia and allowing
cooler water to upwell along the South American coast.
• Periodically these trade winds are strengthened, increasing the
amount of cooler water toward the coast of South America and
reducing water temperatures.
Why La Niña occurs?

9. • Better predictions for extreme climate episodes like floods and droughts could
save the India’s billions of rupees in damage costs.
• Predicting the life cycle and strength of a Pacific warm or cold episode is critical
in helping water, energy and transportation managers and farmers plan avoid or
mitigate potential losses.
• Advances in improved climate predictions will also result in significantly
enhanced economic opportunities, particularly for the national agriculture,
fishing, forestry and energy sectors, as well as social benefits.
Why is predicting El Niño and La Niña so important?
9

10. The Oceanic Niño Index (ONI) has become the de-facto standard that National
Oceanic and Atmospheric Administration (NOAA) uses for identifying El Niño (warm) and
La Niña (cool) events in the tropical Pacific.
Weak El Niño: Episode when the peak Oceanic Niño Index (ONI) is greater than or equal
to 0.5°C and less than or equal to 0.9°C.
Moderate El Niño: Episode when the peak Oceanic Niño Index (ONI) is greater than or
equal to 1.0°C and less than or equal to 1.4°C.
Strong El Niño: Episode when the peak Oceanic Niño Index (ONI) is greater than or
equal to 1.5°C.
Oceanic Niño Index
10

11. 11
Figure 2: Sea surface temperature during March 2015.
National Oceanic and Atmospheric Administration (NOAA), Washington Annon., (2015)

12. (Pidwiry, 2010)
Figure 3: Global climatological effects of the El Niño

13. EffectonSouthWestMonsoon

14. • India’s climate is dominated by monsoons.
• Monsoons are strong, often violent winds that change
direction with the season.
• The term technically describes seasonal reversals of wind
direction caused by temperature differences between the land
and sea breeze, creating zones of high and low pressure over
land in different seasons.
14

15. Being a tropical monsoon country
there are two monsoon seasons.
 South–West (summer) monsoon has warm
winds blowing from Indian Ocean. Its span
is June to September, with 75 % of the
annual rainfall in India. It varies from 10
cm in western Rajasthan to over 900 cm in
Meghalaya.
 North-East (winter) monsoon is
characterized by a dry continental air
mass blowing from the vast Siberian high
pressure area from December to March.
The rainfall includes snowfall during
winter monsoon which is of the order of
1000 km2 in India. This is also known as
Retreating monsoon.
South Monsoon
Winter Monsoon 15

16. Figure 4: Normal onset and withdrawal of South West Monsoon. (Source: IMD) 16

17. Southwest monsoon rainfall and agriculture in India
South west monsoon accounts 75 % of country's total annual rainfall
Impacts over 1.7 billion people of the Indian subcontinent and is crucial
for the agriculture-dominated economy of India.
60 % of area as rainfed.
50 % of agricultural production.
 India is expected to be the first to suffer, with weaker monsoon rains,
undermining the nation’s fragile food supply.
17

18. Sr.
No.
Districts
Normal southwest monsoon
rainfall (range)
No. of years with below normal rainfall during:
June July August Sept.
Coastal Andhra Pradesh
1 Srikakulam 710 5 2 4 2
2 Visakhapatnam 598 4 5 2 3
3 East Godavari 694 4 5 2 5
4 West Godavari 743 4 4 4 4
5 Krishna 673 4 5 5 5
6 Guntur 533 5 5 5 5
7 Nellore 345 2 5 3 5
Rayalaseema
8 Chittoor 393 3 5 3 5
9 Cuddapah 394 3 5 3 4
10 Anantapur 334 2 5 3 3
11 Kurnool 442 1 4 4 4
Telangana
12 Mahaboobnagar 555 2 4 4 4
13 Nalgonda 551 4 4 4 5
14 Hyderabad 663 2 4 3 4
15 Medak 778 2 4 4 3
16 Warangal 832 5 3 4 5
17 Khammam 867 4 4 3 5
18 Karimnagar 789 5 1 3 3
19 Nizamabad 934 3 3 3 4
20 Adilabad 915 4 3 4 4
Table 1: Normal southwest monsoon rainfall and number of years with below normal rainfall during years
with El Niño on an all-India basis in different districts of Andhra Pradesh (1960–89).
Victor et al., (1995)CRIDA, Hyderabad 18
Deficit rainfall years during 1960-89: 1965, 1972, 1986, 1987.

19. Sr.
No.
Rainfall sub-division
Rainfall
All years Warm ENSO Cold ENSO
1 Punjab 530 401 (-24%) 598 (13%)
2 East Uttar Pradesh 884 758 (-14%) 982 (11%)
3 Gangetic West Bengal 1167 1108 (-5%) 1188 (2%)
4 West Rajasthan 285 219 (-23%) 315 (11%)
5 Gujarat 860 743 (-14%) 943 (10%)
6 West Madhya Pradesh 924 816 (-12%) 1026 (11%)
7 Tamil Nadu 320 291 (-9%) 345 (8%)
8 Total 710 619 (-14%) 771 (9%)
Table 2: Average SMR (June–September) in major sub-divisions of India during
all years, warm and cold ENSO years for the period from 1950 to 1999.
Selvaraju, (2003)
Note: The values in the parentheses are the percentage departure from average.
Tamil Nadu Agricultural University, Coimbatore
19

20. Table 3: Years of El Niño are classified into early, normal and delayed Monsoon Onset
over Kerala (MOK) for the period 1901- 1998.
Shankar et al., (2011)
Sr.
No.
Monsoon Onset over
Kerala
(MOK)
El Niño La Niña
Indian Ocean Dipole
(IOD)
1 Early -
1933, 1949 1961
2 Normal
1902, 1911, 1914,
1963, 1965, 1982,
1986, 1987, 1991
1909, 1910, 1917,
1928 1938, 1950,
1964, 1973, 1975,
1988
1902, 1909, 1910,
1917, 1919, 1926,
1928, 1945, 1946,
1950, 1963, 1974,
1975, 1980, 1982,
1985, 1989, 1991,
1992
3 Delayed
1905, 1923, 1930,
1940, 1972, 1997
1903, 1906, 1942 1905, 1906, 1923,
1930, 1935, 1942,
1958, 1967, 1968,
1972, 1983, 1997
4 Total 15 15 33
(Note: Normal MOK is considered as the period from 25 May to 7 June)
National Institute Oceanography, Goa 20

21. 21
Sr.
No.
Station
Monsoon season (June-September) Annual (January-December)
El Niño years Non-El Niño years P C El Niño years Non-El Niño years P C
1 Bharuch 729 797.2 -9 741.2 832.6 -11
2 Navsari 1272.2 1441.9 -12 1304.2 1498.7 -13
3 Pariya 2205.5 1562.4 41 2232.3 1611.1 39
4 Vyara 1116.7 1212.1 -8 1128.9 1256.3 -10
5 Tanchha 851.7 753.3 13 879.1 783.3 12
South Gujarat 1235.0 1153.4 5.0 1257.1 1196.4 3.4
7 Arnej 610.8 705.4 -13 633.5 735.9 -14
8 Anand 810.2 838.1 -3 851.7 882.5 -3
9 Godhra 696.2 933.6 -25 730 968.5 -25
10 Mahuva 506.2 478.6 6 516.8 511.7 1
11 Nawagam 890 675.1 32 908.2 717.7 27
12 Viramgam 546.5 640.7 -15 553.1 663.8 -17
13 Dabhoi 704.5 666.9 6 713.8 689.7 3
14 Umbhrat 1152.9 1144.5 1 1161.2 1180.5 -2
Middle Gujarat 739.7 760.4 -1.4 758.5 793.8 -3.8
15 Khedbrahma 1066.4 598.9 78 1075.2 620.3 73
16 Ladol 574.4 568.1 1 578.1 613.5 -6
17 Sanand 862 533.2 62 883 557.9 58
18 SKnagar 574.1 568.8 1 610 609.9 0
19 Radhanpur 481.5 479.8 0 486.5 507.3 -4
20 Vijapur 1036.7 521.5 99 1048.3 610.5 72
North Gujarat 765.9 545.1 40.2 780.2 586.6 32.2
21 Amreli 608.3 622.9 -2 638 677.2 -6
22 Jamnagar 723.5 619.5 17 725.3 646.2 12
23 Rajkot 454.1 530.4 -14 480.4 563.5 -15
24 Mangrol 950.2 768.7 24 969 786.2 23
Saurashtra 709.1 660.8 6.2 727.3 700.5 2.6
25 Bhachau 484 471.8 3 496.6 497.7 0
26 Bhuj 253.2 334 -24 270.4 386.9 -30
Kutch 368.6 402.9 -10.5 383.5 442.3 -15
Table 4: Comparison of seasonal and annual rainfall (mm) at different locations of Gujarat during El Niño years to non
El Niño years (1978-2011).
Patel et al., (2014)Anand, Gujarat

22. Table:5 Percent change in district-wise average annual rainfall (mm) during El
Nino years compared to normal rainfall years in Haryana (1981-2010)
Sr. No. Districts
Rainfall (mm)
El Nino years Normal years % change
1 Ambala 897.56 834.3 7.6
2 Panchkula 1007.50 1109.0 -9.2
3 Yamunanagar 894.50 952.2 -6.1
4 Kurukshetra 417.61 645.6 -35.3
5 Kaithal 406.21 551.2 -26.3
6 Karnal 382.84 759.4 -49.7
7 Panipat 490.89 534.7 -8.2
8 Sonepat 523.86 616.4 -15.0
9 Rohtak 413.76 576.2 -28.2
10 Jhajjar 315.53 592.0 -46.7
11 Faridabad 440.00 595.4 -26.1
12 Gurgaon 443.04 732.7 -39.5
13 Rewari 344.04 569.6 -39.6
14 Mahendragarh 335.14 430.6 -22.2
15 Bhiwani 331.46 410.9 -19.3
16 Jind 393.62 487.3 -19.2
17 Hisar 300.54 452.0 -33.5
18 Fatehabad 251.89 346.4 -27.3
19 Sirsa 230.26 499.6 -53.9
22
Diwan et al.,(2015)

23. Sr.
No.
Districts
Southwest monsoon
(JUN-SEPT)
Winter
(OCT-MAY)
El Nino
years
Non El Nino
years
P C
El Nino
years
Non El Nino years P C
1 Bilaspur 753.7 910.7 -17.2 360.8 286.4 26.0
2 Chamba 610.4 568.7 7.3 603.7 569.5 6.0
3 Hamirpur 1175.8 1000.5 17.5 324.4 280.0 15.9
4 Kangra 738.7 808.2 -8.6 248.3 174.4 42.4
5 Kullu 634.6 753.5 -15.8 336.0 232.8 44.3
6 Sirmaur 769.4 779.0 -1.2 179.5 138.4 29.7
7 Solan 691.8 973.8 -29.0 227.5 199.4 14.1
8 Shimla 696.6 782.9 -11.0 237.7 185.2 28.4
9 Una 826.9 882.6 -7.4 255.7 185.0 38.2
10 Mandi 924.6 1194.8 -22.6 371.8 378.9 -1.9
Table 6: Per cent change (PC) in average seasonal rainfall (mm) during El Niño years
compared to non El Niño years in selected districts of Himachal Pradesh (1971-2009).
Prasad et al., (2014)CSK HPKV, Palampur 23

24. Sr.
No.
Global El Niño and
Indian Drought
Since 1950s ( 64 years) Since 1980s (34 years) Since 2000s (14 years)
1 Global El Niño
23
1951, 1953, 1957, 1958, 1963,
1965, 1968, 1969, 1972, 1976,
1977, 1982, 1983, 1986, 1987,
1991, 1992, 1994, 1997, 2002,
2004, 2006, 2009
12
1982, 1983, 1986, 1987,
1991, 1992, 1994, 1997,
2002, 2004, 2006, 2009
4
2002, 2004, 2006, 2009
2 Indian Droughts
14
1951, 1965, 1966, 1968, 1972,
1974, 1979, 1982, 1986, 1987,
1991, 2002, 2004, 2009
7
1982, 1986, 1987, 1991,
2002, 2004, 2009
3
2002, 2004, 2009
3
Drought and El
Niño
11
1951, 1965, 1968, 1972, 1982,
1986, 1987, 1991, 2002, 2004,
2009
7
1982, 1986, 1987, 1991,
2002, 2004, 2009
3
2002, 2004, 2009
4
El Niño but not
Drought
12
1953, 1957, 1958, 1963, 1969,
1976, 1977, 1983, 1992, 1994,
1997, 2006
5
1983, 1992, 1994,1997,
2006
1
2006
5
Drought but not
El Niño
3
1966, 1974, 1979
None None
Table 7: Summary of the Global El Niño and Indian Drought Years since 1950s.
Saini and Gulati (2014)Indian Council for Research on International Economic Relations, New Delhi 24

25. Figure 5: Indian monsoon since 1901.
Saini and Gulati, (2014)Indian Council for Research on International Economic Relations, New Delhi
25

26. 26
Effects on Temperature

27. Sr. No. Stations El Niño Normal Difference
1 Anantapur 27.9 27.6 0.3
2 Tirupati 27.7 28.1 -0.4
3 Kurnool 26.0 26.4 -0.4
4 Aorgyavaram 25.4 25.3 0.1
Rayalaseema 26.8 26.9 -0.1
6 Lam 28.7 28.5 0.2
7 Rajahmundry 27.7 27.9 -0.2
8 Marutera 26.6 26.8 -0.2
9 Ongole 29.5 29.6 -0.1
10 Ankapalle 27.9 27.7 0.2
11 Machilipatanam 28.2 28.2 0.0
12 Rentichintala 29.4 29.3 0.1
13 Kovur 29.3 29.3 0.0
14 Gannavaram 28.5 28.6 -0.1
Coastal AP 28.4 28.4 0.0
16 Hayathnagar 25.7 25.8 -0.1
17 Rajendranagar 26.2 26.1 0.1
18 Jagital 27.0 27.0 0.0
19 Palem 25.9 26.4 -0.5
20 Patancheru 25.9 25.7 0.2
21 Rudur 26.4 26.3 0.1
22 Medak 26.6 26.5 0.1
23 Hanmakonda 28.3 28.3 0
Telengana 26.5 26.5 0.0
Table 8: Mean annual temperature (°C) during El Niño years compared to normal temperature at
some selected locations in Andhra Pradesh
CRIDA, Hyderabad Rao et al., (2011) 27

28. Table 9: Mean annual temperature (°C) during El Niño years compared to normal
temperature at some selected locations in Haryana
Stations El Nino years Normal years Deviation
Ambala 23.1 21.2 1.9
Hisar 28.2 22.4 5.8
Karnal 23.8 22.1 1.7
Gurgaon 23.8 24.1 -0.3
Narnaul 23.6 23.8 -0.2
28

29. Effects of El Niño & La Nina on
Agriculture

30. Sr.
No.
Particular SMR NINO1+2
NINO3
NINO3.4 NINO4
MAM JJA SON
1 SMR - -0.26 -0.13 -0.48 -0.51 -0.53 -0.48
2
Total
foodgrains
0.71 -0.35 -0.22 -0.50 -0.50 -0.50 -0.42
3
Kharif
foodgrains
0.80 -0.26 -0.14 -0.52 -0.57 -0.54 -0.45
4
Rabi
foodgrains
0.41 -0.22 -0.11 -0.27 -0.24 0.20 -0.11
5 Total cereals 0.72 -0.35 -0.21 -0.50 -0.50 -0.49 -0.41
6 Total pulses 0.57 -0.22 -0.13 -0.40 -0.39 -0.46 -0.40
7 Rice 0.66 -0.28 -0.22 -0.40 -0.39 -0.46 -0.40
8 Wheat 0.49 -0.39 -0.26 -0.42 -0.36 -0.36 -0.28
9 Sorghum 0.02 -0.70 0.19 -0.17 -0.23 -0.21 -0.16
10 Chickpea 0.49 -0.20 -0.18 -0.27 -0.18 -0.24 -0.16
Table 10: Correlations of normalized Summer Monsoon Rainfall (SMR), foodgrain production and Pacific
SST anomaly (JJA) over different sectors.
Note: For NINO3 region, correlations were worked out for three seasons for the period from 1950 to 1999
Selvaraju, (2003)TNAU, Coimbatore 30

31. Sr.
No.
Country SOI
Sr.
No.
Country SOI
1 Brazil +0.2499 11 Chile -0.1730
2 Colombia -0.0489 12 Costa Rica +0.0242
3 Ecuador +0.1925 13 El Salvador +0.1035
4 Mexico +0.3558 14 Peru -0.0271
5 India +0.1524 15 Indonesia +0.0157
6 Malaysia -0.0613 16 Philippines +0.1988
7 Thailand -0.0095 17 South Africa +0.4450
8 Australia +0.4066 18 Canada +0.3745
9 Italy +0.1077 19 Japan +0.1465
10 United Kingdom -0.0028 20 United States +0.1049
31
Table 11: Correlations of National Gross Domestic Product (GDP) Growth
with El Niño SOI
Laosuthi and Selover, (2007)Rome

32. 32
Sr.
No.
Country SOI
Sr.
No.
Country SOI
1 Brazil -0.2554 11 Chile +0.3436
2 Colombia -0.2618 12 Costa Rica -0.2342
3 Ecuador -0.0750 13 El Salvador -0.1064
4 Mexico -0.2200 14 Peru +0.0131
5 India -0.1071 15 Indonesia -0.0629
6 Malaysia +0.0731 16 Philippines +0.1178
7 Thailand +0.0516 17 South Africa -0.2809
8 Australia +0.1022 18 Canada -0.0063
9 Italy -0.0639 19 Japan +0.0866
10 United Kingdom +0.1302 20 United States -0.0294
Table 12: Correlations of National Consumer Price Index (CPI) Inflation
with El Niño SOI
Laosuthi and Selover, (2007)Rome

33. Figure 6. Year-wise total food grain production (‘000 tones) in Andhra Pradesh.
Rao et al., (2011)CRIDA, Hyderabad
33

34. Sr.
No.
Districts
Production Yield
El Niño Non El Niño P C El Niño Non El Niño P C
1 Anantapura 354.8 474.2 -25.2 585.6 638.1 -8.2
2 Chittoor 150.5 206.5 -27.1 736.8 818.1 -9.9
3 Y.S.R. Kadapa 68.0 104.4 -34.8 458.6 505.3 -9.2
4 Kurnool 127.4 157.7 -19.2 643.0 809.7 -20.6
Rayalaseema 700.8 942.8 -25.7 609.5 747.0 -18.4
5 Srikakulam 30.8 33.9 -9.3 852.9 895.0 -4.7
6 Vizianagaram 50.0 59.8 -16.4 819.8 940.3 -12.8
7 Visakhapatanam 17.5 23.3 -25.0 934.6 1167.9 -20.0
8 East Godavari 3.5 3.6 -3.0 802.2 1019.0 -21.3
9 West Godavari 1.9 2.4 -20.4 801.6 1414.6 -43.3
10 Krishna 7.8 6.5 20.6 1191.0 963.4 23.6
11 Guntur 2.3 2.5 -7.7 957.4 1065.0 -10.1
12 Prakasam 10.9 13.4 -18.7 743.3 923.7 -19.5
13 S.P.S. Nellore 8.0 7.8 3.3 1402.8 1655.7 -15.3
Coastal AP 132.7 153.2 -13.4 860.6 947.0 -9.1
14 Adilabad 0.4 0.4 15.3 511.6 628.5 -18.6
15 Nizamabad 1.0 1.2 -18.1 540.6 971.5 -44.4
16 Karimnagar 8.3 9.9 -16.9 664.6 583.0 14.0
17 Warangal 20.0 24.4 -18.1 794.7 942.3 -15.7
18 Khammam 5.0 4.5 10.8 705.9 664.5 6.2
19 Medak 1.3 1.9 -33.7 604.9 853.8 -29.2
20 Rangareddi 1.3 1.7 -24.7 557.3 867.4 -35.7
21 Mahaboobnagar 42.6 58.4 -27.1 476.9 631.4 -24.5
22 Nalgonda 20.6 26.4 -22.0 647.9 742.4 -12.7
Telangana 100.4 128.8 -22.1 554.5 638.1 -13.1
AP State 931.8 1320.4 -29.4 613.2 817.4 -25.0
Table 13: Per cent change in production (1000 tons) and yield (kg/ha) of kharif groundnut during El
Niño years compared to normal years from 1981- 2006 in Andhra Pradesh.
Rao et al., (2011)CRIDA, Hyderabad 34

35. Sr.
No.
Districts
Production Yield
El Niño Non El Niño P C El Niño Non El Niño P C
1 Anantapura 63.7 80.0 -20.4 2277.6 2348.4 -3.0
2 Chittoor 55.9 85.6 -34.7 1978.0 2174.0 -9.0
3 Y.S.R. Kadapa 101.3 125.8 -19.4 2449.0 2583.6 -5.2
4 Kurnool 159.7 173.6 -8.0 2551.3 2519.9 1.2
Rayalaseema 380.7 464.9 -18.1 2376.0 2434.3 -2.4
6 Srikakulam 343.0 373.9 -8.3 1926.6 1883.7 2.3
7 Vizianagaram 197.5 237.7 -16.9 1752.6 1901.3 -7.8
8 Visakhapatanam 109.1 147.0 -25.8 1256.4 1470.1 -14.5
9 East Godavari 514.1 556.2 -7.6 2197.6 2216.2 -0.8
10 West Godavari 638.4 621.8 2.7 2473.0 2319.1 6.6
11 Krishna 679.3 734.1 -7.5 2457.8 2579.8 -4.7
12 Guntur 768.1 854.4 -10.1 2787.2 2974.2 -6.3
13 Prakasam 149.6 200.8 -25.5 2471.4 2600.7 -5.0
14 S.P.S. Nellore 125.5 144.6 -13.3 2602.1 2574.2 1.1
Coastal AP 3524.7 3870.6 -8.9 2303.1 2348.9 -2.0
15 Adilabad 80.4 117.2 -31.4 1428.3 1821.1 -21.6
16 Nizamabad 215.4 278.0 -22.5 2136.7 2405.7 -11.2
17 Karimnagar 294.6 348.7 -16.4 2562.8 2719.7 -5.8
18 Warangal 246.4 298.9 -17.6 2252.8 2434.4 -7.5
19 Khammam 283.5 325.6 -12.9 2170.5 2341.2 -7.3
20 Medak 119.9 155.9 -23.1 1845.2 2000.1 -7.7
21 Rangareddi 49.3 61.4 -19.7 2020.9 2081.8 -2.9
22 Mahaboobnagar 128.3 155.5 -17.4 1969.3 1947.3 1.1
23 Nalgonda 360.3 425.1 -15.2 2633.3 2778.7 -5.2
Telangana 1775.3 2166.3 -18.0 2212.7 2379.9 -7.0
AP State 5680.7 6501.9 -12.6 2278.6 2364.9 -3.6
Table 14: Per cent change in production (‘000 tons) and yield of kharif rice during El Niño years
compared to normal years in Andhra Pradesh (1981 to 2007).
Rao et al., (2011)CRIDA, Hyderabad 35

36. Sr. No. Districts
Production Yield
El Niño Non El Niño PC El Niño Non El Niño PC
1 Anantapura 85.7 134.5 -36.2 529.7 816.7 -35.1
2 Chittoor 19.4 69.1 -71.9 607.7 1088.9 -66.0
3 Y.S.R. Kadapa 44.0 90.2 -51.2 549.2 1062.3 -48.3
4 Kurnool 269.0 332.5 -19.1 788.2 1000.1 -21.2
Rayalaseema 379.4 626.2 -39.4 616.7 970.2 -36.4
5 Srikakulam 40.6 80.7 -49.7 534.2 807.8 -33.9
6 Vizianagaram 33.4 76.6 -56.4 460.5 916.7 -49.8
7 Visakhapatanam 71.7 118.5 -39.5 599.5 890.0 -32.6
8 East Godavari 16.1 125.6 -87.2 171.6 973.0 -82.4
9 West Godavari 49.0 77.8 -37.1 2313.4 2127.4 8.7
10 Krishna 107.8 188.2 -42.7 627.7 1132.7 -44.6
11 Guntur 130.7 261.4 -50.0 650.2 1245.7 -47.8
12 Prakasam 108.1 241.0 -55.1 535.9 1053.6 -49.1
13 S.P.S. Nellore 39.2 53.9 -27.2 838.1 1239.5 -32.4
Coastal AP 596.8 1223.7 -51.2 593.9 1082.4 -45.1
14 Adilabad 131.0 225.4 -41.9 476.5 776.9 -38.7
15 Nizamabad 134.6 264.3 -49.1 1167.2 1878.8 -37.9
16 Karimnagar 262.6 417.5 -44.3 1481.1 2235.4 -33.7
17 Warangal 120.4 229.6 -47.5 743.5 1228.1 -39.5
18 Khammam 80.5 173.5 -53.6 546.1 997.1 -45.2
19 Medak 219.2 303.7 -27.8 785.3 1023.6 -23.3
20 Rangareddi 123.2 140.2 -12.1 745.6 783.8 -4.9
21 Mahaboobnagar 200.6 295.4 -32.1 532.1 735.4 -27.6
22 Nalgonda 13.5 139.0 -90.3 74.3 681.9 -89.1
Telangana 1285.6 2242.6 -42.7 684.0 1076.2 -36.4
Table 15: District-wise production (‘000 tons) and yield (kg/ha) of other foodgrains (excluding Rice) in
Andhra Pradesh (1981–2007).
Rao et al., (2011)CRIDA, Hyderabad 36

37. Sr.
No.
Deficit Rainfall
Years
Monsoon Rainfall
(% Departure from LPA)
Decline in Production (%)
1 1972-73 -24 9.76
2 1974-75 -12 11.29
3 1979-80 -19 27.02
4 1982-83 -14 13.01
5 1986-87 -13 5.40
6 1987-88 -19 12.26
7 2002-03 -19 29.69
8 2009-10 -22 11.33
Table 16: Impact of Deficit Rainfall on All-India Rice Production during El Niño years.
Aijaz, (2013)New Delhi 37
LPA- Long Period Average

38. Figure 7. Productivity of rice crop (Kg ha-1) as influenced by El Nino.
Bhuvaneswari, et al., (2013)TNAU, Coimbatore
38

39. Sr.
No.
Name of the district
El Niño category
Weak Moderate Strong Combined
1 Ahmedabad 5.9 -5.2 -9.0 -2.0
2 Vadodara -64.4 -1.2 -5.3 -59.4
3 Bharuch -26.5 40.0 -31.9 -12.7
4 Kheda 3.6 -15.2 -34.6 -14.3
5 Surat -5.0 15.0 -26.9 -7.9
6 Panchmahals -4.2 13.6 -26.4 -7.8
Table 17: Anomalies (%) in paddy yields during El Niño years compared to non El Niño years in major paddy
growing districts.
Patel et al., (2014)Anand, Gujarat
Table 16. Anomalies (%) in groundnut yields during El Niño years compared to non El Niño years in major
groundnut growing districts.
Sr.
No.
Name of the district
El Niño category
Weak Moderate Strong Combined
1 Bhavanagar -0.6 33.0 5.8 32.4
2 Jamnagar -0.2 -44.2 -19.3 -17.3
3 Junagadh -22.8 56.8 -41.0 -10.5
4 Kutch 13.0 -19.2 -10.4 -2.8
5 Rajkot 6.9 -23.2 -16.3 -8.3
6 Surendranagar -0.05 17.6 -16.0 -1.5
39

40. Sr.
No.
Name of the district
El Niño category
Weak Moderate Strong Combined
1 Vadodara 24.2 -0.7 -7.1 7.3
2 Kheda -47.7 12.9 -50.1 -34.3
3 Panchmahal -94.3 -14.8 -69.8 -67.0
4 Sabarkantha 20.1 -29.0 -6.4 -0.8
Table 18: Anomalies (%) in maize yields during El Niño years compared to non El Niño years in
major maize growing districts.
Patel et al., (2014)Anand, Gujarat
Sr.
No.
Name of the district
El Niño category
Weak Moderate Strong Combined
1 Ahmedabad -0.01 2.80 -20.89 -6.71
2 Vadodara -0.36 4.31 -21.11 -6.58
3 Bharuch -0.04 3.67 -20.73 -6.47
4 Panchmahal 6.26 -16.17 -15.67 -6.75
5 Rajkot 0.05 3.30 -20.62 -6.47
6 Surendranagar 4.22 10.28 -10.77 0.35
7 Banaskantha 11.91 -17.19 -11.59 -3.23
Table 18. Anomalies (%) in cotton yields during El Niño years compared to non El Niño years in
major cotton growing districts.
40

41. Sr. No. Name of the district
El Niño category
Weak Moderate Strong Combined
1 Valsad 0.05 -0.31 -20.48 -7.28
2 Surat -0.85 5.47 -20.63 -6.34
3 Bharuch 12.40 -23.49 -3.43 -1.63
Table 19: Anomalies (%) in sugarcane yields during El Niño years compared to non
El Niño years in major sugarcane growing districts.
Anand, Gujarat Patel et al., (2014)
41
Table 20. Anomalies (%) in bajra yields during El Niño years compared to non El Niño years in major
bajra growing districts.
Sr. No. Name of the district
El Niño category
Weak Moderate Strong Combined
1 Banaskantha -45.9 -21.8 -65.4 -65.4
2 Kheda 6.5 27.3 -12.8 4.6
3 Jamnagar -25.0 36.9 -47.6 -18.4
4 Kutch -26.5 13.5 -44.5 -23.4

42. Sr. No. Name of the district
El Niño category
Weak Moderate Strong Combined
1 Ahmedabad -18.8 -9.2 -43.8 -25.3
2 Banaskantha -16.1 -12.7 -42.7 -24.7
3 Vadodara -17.3 -13.6 -40.9 -24.8
4 Bharuch -17.2 -13.1 -41.2 -24.7
5 Gandhinagar -17.7 -13.5 -42.1 -25.3
6 Kheda -20.9 -9.9 -46.1 -27.2
7 Mehsana -17.7 -13.0 -42.0 -25.2
8 Panchmahal -17.1 -13.1 -41.8 -24.9
9 Sabarkantha -17.3 -13.3 -41.9 -25.1
10 Bhavnagar -15.0 -15.0 -39.0 -23.5
11 Junagadh -17.6 -13.4 -41.5 -25.0
12 Rajkot -17.7 -14.1 -41.3 -25.2
Table 20: Anomalies (%) in wheat yields during El Niño years compared to non El Niño years in major
wheat growing districts.
Anand, Gujarat Patel et al., (2014)
42

43. Sr.
No.
Districts
Production Yield
El Nino Non El Nino P C El Nino Non El Nino P C
1 Bilaspur 41 46.4 -11.6 1558.1 1791.2 -13
2 Chamba 62.8 64.4 -2.4 2268.5 2358.9 -3.8
3 Hamirpur 49.2 57.6 -14.5 1523.7 1762 -13.5
4 Kangra 83.3 97 -14.1 1462.4 1690.3 13.5
5 Kinnaur 0.713 0.866 -17.7 1842.9 2082.5 -11.5
6 Kullu 31.2 40.9 -23.7 1982.8 2468.2 -19.7
7 Lahual & Spiti 0.055 0.066 -15.9 1379.8 1666.3 -17.2
8 Mandi 105.2 116.5 -9.7 2273.3 2472.2 -8
9 Shimla 32.9 40 -17.9 1915 2194.1 -12.7
10 Sirmaur 54.8 64.7 -15.3 2275.3 2618.1 -13.1
11 Solan 43.8 51.4 -15.6 1791.9 2111.9 -15.2
12 Una 51.6 51.8 -0.3 1689.3 1770.5 -14.6
Himachal Pradesh 556.8 631.6 -11.9 1830.3 2082.2 -12.1
Table 21: Per cent change in average production (‘000 tones) and (kg/ha) of maize during El Niño years
compared to non El Niño years in Himachal Pradesh (1981-2009).
Prasad et al., (2014)CSK HPKV, Palampur 43

44. Sr.
No.
Districts
Production Yield
El Niño Non El Niño P C El Niño Non El Niño P C
1 Bilaspur 2.65 3.02 -12.5 1152.7 1321.7 -12.8
2 Chamba 4.09 4 2.3 1376.9 1383.2 -0.5
3 Hamirpur 3.44 3.88 -11.3 1076.1 1239.6 -13.2
4 Kangra 46.08 50.20 -8.2 1230.7 1343.4 -8.4
5 Kinnaur 0.0337 0.0339 -0.7 1249.7 1330.9 -6.1
6 Kullu 2.90 2.86 1.1 1344.0 1327.1 1.3
7 Mandi 27.04 27.84 -2.9 1219.3 1269.9 -4.0
8 Shimla 3.59 3.91 -8.3 1111.5 1136.8 -2.2
9 Sirmaur 7.27 8.09 -10.1 1409.9 1541.3 -8.5
10 Solan 7.05 6.48 8.7 1630.3 1586.0 2.8
11 Una 3.50 3.47 0.8 1631.0 1638.5 -0.5
Himachal Pradesh 107.6 113.8 -5.4 1202.7 1259.9 -4.5
Table 22: Per cent change in average production (‘000 tonnes) and Yield (kg/ha) of rice during
El Nino years compared to non El Niño years in Himachal Pradesh (1981-2009).
Prasad et al., (2014)CSK HPKV, Palampur 44

45. Sr.
No.
Districts
Production Yield
El Niño Non El Niño P C El Niño Non El Niño P C
1 Bilaspur 38.9 31.3 24.0 1444.2 1160.7 24.4
2 Chamba 25.3 24.4 4.0 1290.3 1586.0 -18.6
3 Hamirpur 44.8 34.7 29.3 1279.7 991.9 29.0
4 Kangra 131.1 119.7 9.5 1426.1 1310.9 8.8
5 Kinnaur 0.716 0.754 -5.1 1282.9 1324.0 -3.1
6 Kullu 36.3 38.2 -5.2 1691.1 1691.8 0.0
7 Lahual & Spiti 0.154 0.163 -5.6 1091.9 984.4 10.9
8 Mandi 94.4 82.0 15.2 1417.6 1238.1 14.5
9 Shimla 27.1 27.5 -1.2 1185.6 1121.1 5.8
10 Sirmaur 41.7 37.4 11.4 1499.3 1331.5 12.6
11 Solan 33.6 30.0 12.1 1414.7 1249.0 13.3
12 Una 47.6 44.3 7.5 1465.6 1417.7 3.4
Himachal Pradesh 521.7 470.4 10.9 1374.1 1283.9 7.0
Table 23: Per cent change in average production (‘000 tonnes) and Yield kg/ha) of wheat during El
Niño years compared to non El Niño years in Himachal Pradesh (1981- 2009).
Prasad et al., (2014)CSK HPKV, Palampur 45

46. Sr.
No.
Districts
Production Yield
El Niño Non El Niño P C El Niño Non El Niño P C
1 Bilaspur 0.446 0.408 9.2 1258.2 1165.6 7.9
2 Chamba 4.3 4.7 -8.4 1087.3 1136.1 -4.3
3 Hamirpur 0.174 0.184 -5.6 1259.9 1165.3 8.1
4 Kangra 3.417 3.420 -0.1 1183.5 1133.3 4.4
5 Kinnaur 2.1 1.9 8.3 1486.4 1333.3 11.5
6 Kullu 5.5 5.0 9.4 1528.8 1399.8 9.2
7 Lahual & Spiti 0.556 0.560 -0.7 931.0 884.7 5.2
8 Mandi 6.2 5.4 14.2 1429.5 1248.2 14.5
9 Shimla 6.6 6.1 8.8 1267.5 1117.7 13.4
10 Sirmaur 2.8 2.7 1.8 1052.8 981.4 7.3
11 Solan 2.0 1.5 29.9 1100.5 851.7 29.2
12 Una 0.015 0.042 -64.3 635.0 961.0 -33.9
Himachal Pradesh 34.0 32.0 6.3 1185.0 1114.8 6.3
Table 24: Per cent change in average production (‘000 tonnes) and Yield (kg/ha) of barley
during El Niño years compared to non El Niño years in Himachal Pradesh (1981-2009).
CSK HPKV, Palampur Prasad et al., (2014)
46

47. Sr.
No.
Crops
Production Yield
El Niño Non El Niño P C El Niño Non El Niño P C
1 Sesame 1.7 1.9 -10.5 310.6 347.2 -10.5
2
Rapeseed
& mustard
3.3 2.9 13.8 372 344.4 8.0
3 Linseed 0.9 0.97 -10.0 275.8 272.9 1.1
Table 25: Per cent change in average production (‘000 tonnes) and Yield
(kg/ha) of oilseed crops during El Niño years compared to Non El Niño
years in Himachal Pradesh (1981-2007).
Prasad et al., (2014)
CSK HPKV, Palampur
47

48. Conclusion
 An improved understanding of the relationship between El Nino events and the southwest monsoon
will be helpful in the development of long-range forecast.
 In India, an alteration in the spatial and temporal variability of rainfall induced by El Niño and its
intensity, sensitivity of crops to El Niño episodes is not uniform across locations.
 In Haryana, the southwest monsoon rainfall or annual rainfall is likely to decrease with a possibility
of increased winter rain in some districts.
 The inter-annual variations of monsoon revealed that El Nino play a significant role in altering the
Monsoon Onset over Kerala.
 A drought in summer monsoon generally leads to a large reduction in foodgrain production and has
large intraseasonal variability of rainfall and hence the day to day variation of rainfall can have
significant impact on kharif foodgrain yield of the country.
48

49. Future thrust
 Cropping pattern and input use i.e. quicker maturing crop variety and rainwater
conservation would all help to bolster agricultural production in low rainfall El Nino years.
 High quality seeds of alternative crops should be distributed among farmers in drought
affected areas.
 Use harvested rain water or ground water from bore wells to provide lifesaving/supplement
irrigation in critical stages.
 Prepare alternative crop plans and provide financial and technical assistance to farmers.
 Crop contingency plans should be prepared for all districts in the country.
 Need to study historical relationship between El Nino and Agriculture commodities and
economy.
49

50. Thank you