Application of Extended Range &Seasonal

Forecasting in Agriculture

Dr.N.Chattopadhyay

Deputy Director General of Meteorology

Agricultural Meteorology Division, India Meteorological

Department,

nabansu.nc@gmail.com

Major forcing for production of Major crops in Different States of India

Intraseasonal

Variability of

Monsoon

rainfall

Extreme Weather:

Drought, Flood etc.

Monsoon

Onset

Cut of dates

for Sowing

Pests&

Diseases

Heat/Cold Wave

More variable R/F

Increased Extremes Weather Events

Erratic Onset, advance and retrieval of Monsoon

Shift in Active/break cycles

Intensity and frequency of Monsoon lows/depressions

Components of Variability in Weather & ClimateHailstorm in Maharashtra and North and Central India in 2014 & 2015

Multi-decadal changes in Break days

during Monsoon

PERIOD

NUMBER OF BREAK DAYS DURING

JULY AUGUST

01-10 11-20 21-31 1-10 11-20 21-31

1888-1917 46 49 53 43 84 26

1918-1947 14 36 21 55 54 25

1948-1977 22 44 64 21 33 41

1978-2003 23 32 39 6 14 37

Data of past 50 years show that number of Break days are more

in July as compared to August

Extreme Events (Cyclone, Cold Wave, Hailstorm)Advisories for cyclonic storm “PHAILIN” and “HUD HUD” on standing crops

• Drain out the excess water from

the rice fields

• Completely drain out non-paddy

crops

• Harvest non-harvested Matured

crops and keep on aerated safe

place

• Strait up the lodged crops

• Harvest groundnut.anf hang in

bunches at aerated safe place.

• Pop the sugarcane crops again

• Harvest already matured rice,

groundnut, sesame, green gram,

black gram and also vegetables

immediately. Keep harvested

Advisories for Cold Wave

Use hail net for orchard

crops to protect from

mechanical damage. Also

provide mechanical

support to young fruit

plants and vegetables to

prevent the crops from

lodging due to strong

winds. Keep already

harvested crops at safe

Advisories for Hailstorm

Linear trend(oC/decade) in mean and diurnal

temperature (Long term analysis different seasons

over India based on 27 stations (dots) Dot size is

related to trend

Monsoons - a relatively regular Phenomenon interspersed with large extremes

Negative extremes tend to bunch together in bi-decadal blocks marking them out as stressful epochs.

1899 1920 1965 1987

Annual Ep anomalies

(mm/day) between 1961 &

1992 wrt to the 1961-92

mean for three stations &

for four seasons. Dashed

lines show best-fit linear

trend

Regionally averaged annual Ep

anomalies(mm/day) for the period

1961-92 wrt the 1976-90 mean for

different seasons over India.

Number of stations is ten between

1961-75 and 1991-92 and 19

between 1976 &1990 . Dashed

lines show best-fit linear trend

1.0º C

Increase in Surface Temperature(IPCC 2007)

Number of GCM experiments & P/PE ratio

For the monsoon season, all six GCMs agree that

P/PE ratio becomes more favourable over north-

eastern India.

Five out of six agree that this ratio increases, apart

from the extreme south, over the rest of the country.

Changes in this ratio are less favourable in the post-

monsoon season and in the extreme south of the

countryChattopadhyay & Hulme,

1997

Generation of WRF-ARW Forecast with WRFDA Assimilation

Seed Management,Irrigation,

Fertilizer Application,Pesticide spraying,

etc...

SowingTransplanting

VegetativeGrain formationHarvesting, etc...

In Farm operations

In different stages of crops

Weather forecast

Applications

Tactical Decisions

Onset of Monsoon and Cut-off dates of major kharif crops

Seasonal Forecast

Extended Range Forecast System

(based on CFS model) on 27th MAY

2015 Initial condition

Monthly Forecast

Deterministic Precipitation Forecast (percent departure) for JJAS - 2015

• Normal rains are recognized to have in all

parts of the country except Punjab,

Haryana, CHD & Delhi, Uttarakhand and

West UP where deficit rainfall is

expected.

• For the country as a whole, the forecast

of precipitation is expected about 98% of

long period average (LPA).

Probabilistic Precipitation Forecast for JJAS - 2015

•Signals of below normal

precipitation are prevailing

over Punjab, Haryana, CHD

& Delhi, Uttarakhand, West

UP, Rayalseema, Telangana,

Orissa, and Marathwada.

There is absence of any clear

signals for the remaining

parts of the country.

•At all India level, probability

for below normal rainfall is

high.

Marathwada & Vidarbha

June July August Sept.

5th June – 2nd July 17th July-13th August

NorVidarbha

(Buldana,Chandrapur,Yeot

mal: 3 districts: Worst

affected)

Bt. Cotton

American/Desi cotton

Intercropping

cotton:sorghum:pigeonpea:so

rghum

Short duration pigeonpea

Intercropping of sorghum with

pigeonpea

In eastern parts of Vidarbha,

Rice

direct sowing of early

maturing and mid late

maturing rice varieties by wet

seeding method

Marathwada

(Aurangabad,Beed,Jalna,

Osnamabad,Parbhani,Hin

goli,Nanded,Latur: 8

districts Worst affected)Cotton, soybean,red/black gram,

sorghum,sunflower,sugarcane

Intercropping of

cotton+pigeon pea

pigeon pea+ sunflower or bajra

Short duration varieties of

soybean.

Madhya Maharashtra

Sunflower, soybean, cotton,

hybrid jowar, hybrid pearlmillet,

redgram and seasame

Intercropping of -

Pearlmillet+redgram

Sunflower+ redgram

Soybean+ redgram

guar + redgram

States affected during

early season

States affected during

entire season

The southwest monsoon 2014 was delayed from its normal date of onset.

The spatial and temporal variability of the monsoon impacted the productivity and production of

major crops in the country.

Some of the crops could be sown within the sowing window in many regions.

In regions like Marathwada, West Uttar Pradesh, Haryana, Interior Karnataka, Gujarat,

Rayalaseema, Telengana alternate contingent crops were grown due to delayed or deficient rain.

States affected during

mid- season

Agromet advisories based on Medium Range weather forecast for 5 days

does not allow sufficient lead time for arrangement of farm inputs under

extreme weather condition.

Agromet advisories based on Extended Range Forecast system (ERFS)

provide adequate lead time to the farmers as well as to different users that

need relatively longer time for input management under stressed situation

during occurrence of extreme weather events, e.g. to help –

the planners to take necessary measures in terms of preparing

contingency plans on

• selection of crops,

• selection of varieties,

• conservation of soil moisture,

• release of irrigation water from dams etc.,

the seed companies to mobilise seeds of appropriate crops or varieties

to affected regions,

the other input providing companies to mobilise various inputs.

Need for Agromet advisories based on

Seasonal Weather Forecast

crops and farming system

• Choice of crops and crop varieties, type of tillage, depth and density of sowing / planting.• Choice of farming systems, e.g., single or multi cropping or inter cropping;

Time of operation

• Time of farm operations e.g., land preparation, tillage, sowing / planting / transplanting,thinning, weeding, irrigation, harvesting, application of insecticides, herbicides,fungicides, and fertilizers.

Water management

• Whether to adopt water conserving practices and which type to adopt;

• How much water to store and when to apply irrigation; and

• Mode of irrigation (flooding or sprinkler);

Input management

• When to use inputs, i.e. fertilizers, insecticides, herbicides and fungicides to apply;

• How much inputs to apply;

• Mode of application of various inputs;

Weather based farm operations

Need for Agromet advisories based on Seasonal Weather Forecast

June 2015Vidarbha:

Normal crops: Bt. cotton and green gram.

Alternate crops: Red gram, hybrid sunflower, sesame, castor,

bajra and intercropping of bajra + red gram instead of Bt.

Cotton and green gram.

Chhattisgarh:

Normal crops: maize.

Alternate crops: Maize variety JM 216 instead of regular

varieties of maize, pearl millet and finger millet varieties Dapoli-

1, HR- 374, RAU-8, PR- 202 instead of regular varieties of

finger millet.

East Madhya Pradesh:

Normal crops: maize , soybean.

Alternate crops: Early maturing varieties of maize (JM-421,

JM 216, JM 12), sesame, black gram, green gram and niger

instead of regular varieties of maize and soybean varieties JS-

9560, JS-9305, JS-335, JS 80-21, JS 97-42 and JS 94-60

instead of regular varieties of soybean.

West Madhya Pradesh:

Normal crops: maize and sorghum, soybean.

Alternate crops: Soybean varieties JS- 9560, JS- 9305

instead of regular varieties of soybean, black gram (JU-2, JU-

3, JU-86, T-9, JBG-623, LBG684, TAU-1, Berkha) instead of

regular varieties of maize, sorghum varieties JJ1041, JJ1022

instead of regular varieties of sorghum.

Madhya Maharashtra:

Normal crops: rice, pearl millet and

finger millet.

Alternate crops: Rice varieties Indrayani,

LK- 248, Phule Radha, Phule Samrudhi,

Pavana instead of low land rice, pearl

millet varieties Shanti, Shraddha, Saburi

instead of regular varieties of pearl millet

and finger millet varieties Dapoli-1, HR-

374, RAU-8, PR- 202 instead of regular

varieties of finger millet.

July 2015Deficient rainfall: Punjab, West Rajasthan, Gujarat

State, Kokan & Goa, Madhya Maharashtra,

Marathwada, Karnataka and Tamil Nadu.

The regions like Gujarat, Konkan and Goa, Madhya

Maharashtra, Marathwada and Interior Karnataka may

experience moisture stress leading to withering of

already sown crops. Due to likely deficient rainfall, further

sowing of regular varieties of major crops or resowing of

already sown areas may not be possible within the

sowing window.

For saving already sown crops under moisture stress

situation in Gujarat State, Madhya Maharashtra,

Marathwada and Interior Karnataka –

Arrangement of life saving irrigation as well as

mulching may be made to save the already sown.

Thinning to maintain plant population

Hoeing for conservation of soil moisture.

Sowing of the alternate crops

South Interior Karnataka:

Normal crop: Paddy.

Alternate crops: Pearl millet

varieties ICTP 8203 and ICMV

221, pigeon pea varieties Maruti,

TS 3 R, red gram varieties BRG-

1, BRG2, TTB-7, paddy varieties

BR-2655, Tunga.

August 2015Deficient rainfall : Kokan & Goa, Madhya

Maharashtra, Marathwada, coastal Karnataka, East

Madhya Pradesh and Telangana .

In view of continuous deficient rainfall situation

during most of the period of July 2015, probable

measures as well as sowing of alternate crops

after receipt of sufficient rain were suggested for

North Interior Karnataka, Telangana, Marathwada

and Rayalaseema.

North Interior Karnataka:

Normal crops: Ragi, maize-hybrid, bajra, pigeon pea, ground

nut

Alternate crops:

Finger millet :Indaf- 5, PR-202, GPU-28 and HR-911

Maize: Ganga, Deccan, Vijaya NAC Composite)

Sunflower: KBSH-42 and KBSH-44 up to 15th August

Horse gram: GPM-6, PHG-9, KBH-1,

Foxtail millet: PSC-1, RS-118

Pearl millet + pigeonpea (2:1) instead of regular varieties.

Sowing of Cotton crop and short duration pigeon pea (TS 3R)

in the districts of Raichur, Kalaburgi and Yadgir.

Marathwada:

Bt cotton (short duration),

Pearl millet: Shradha, Saburi, AIMP-92901

Sunflower: Morden, SS-56, LSFH-35, BSH-1,

Castor:VI-9, Aruna , DCS-9 (Jyothi, GCH-4, 5, 6

and DCH-117 / 32

Intercropping of pearl millet + pigeonpea in 3:3 or

4:2 row proportion.

Guar and coriander in light soil.

Experimental Seasonal Forecast (June to

September) 2015

Popularisation and

feed back Mechanism

of agromet advisories

Major Rabi Crops

NorthWest India

EastIndia

North East India

Central India West India South Peninsula

Wheat Mustard Wheat Wheat WheatSorghum (Jowar/Great

Millet)

Barley (Jau)Wheat

Indian rapeseed and

mustard (yellow

sarson)

Bengal Gram

(Gram/Chick

Pea/Kabuli/Chana)

Safflower (kusum/kardi)

Maize (Makka)

Bengal Gram

(Gram/Chick

Pea/Kabuli/Chan

a)

Paddy (Dhan) Paddy (Dhan) Cotton (Kapas)Sorghum (Jowar/Great

Millet) Indian rapeseed and

mustard (yellow sarson)

Lentil (Masur) Potato

Indian rapeseed and

mustard (yellow

sarson)

GarlicIndian rapeseed and

mustard (yellow sarson)Paddy (Dhan)

Peas (field peas/

garden

peas/matar)

Tea Maize (Makka)

Bengal Gram

(Gram/Chick

Pea/Kabuli/Chana)

Maize (Makka) Sesame

(Gingelly/Til)/Sesamum

Sunflower

(suryamukhi)Brinjal Niger (Ramtil)

Green Gram (Moong

Bean/ Moong)

Pigeon pea (red

gram/arhar/tur) Sunflower (suryamukhi)

Cabbage Tomato

Green Gram

(Moong Bean/

Moong)

Peas (field peas/

garden peas/matar)Sunflower (suryamukhi)

Horse Gram (kulthi/kultha)

Cauliflower Barley (Jau) Jute

Indian rapeseed and

mustard (yellow

sarson)

FingerMillet

(Ragi/Mandika)

NORTHEAST

MONSOON

Rainfall Forecast (Oct-Dec)

Advisories based on ERFS Forecast

South & North Coastal Andhra Pradesh:

Harvest already matured maize, groundnut and pulses

Keep harvested produce in safe places.

Arrange for propping in sugarcane and banana crops to prevent lodging

due to high winds.

Arrange for extensive drainage facilities to remove excess water from rice

fields and maintain 5-7 cm water. Provide adequate drainage channels in

the fields of standing crops.

Postpone irrigation, intercultural operations and application of fertilizers

and plant protection measures in standing crops.

Odisha:

Harvest already matured rice, groundnut, sesame, green gram, black gram and also vegetables immediately. Keep

harvested produce at safe places.

Postpone sowing of rabi crops like groundnut, sunflower, maize, sesame, green gram, black gram and niger.

Arrange for propping in sugarcane and banana crops to prevent lodging due to high winds.

Postpone irrigation, intercultural operation, application of fertilisers and plant protection measures in standing

crops.

IC = 8 Oct 2014

South & North Coastal Andhra Pradesh:

Rice: Drain out excess water from the field as early as possible.

Apply booster dose of urea @25 kg and potash @ 10-15 kg per acre or spray

multi K (13-0-45) @ 10 g /litre of water and spray StreptoCycline @ 0.1 g /litre

where BLB noticed (varieties: BPT-5204, MTU-1001, MTU-075).

Maize: Drain out excess water from the field and lift the lodged crop and undertake

earthing up whereever possible.

Cotton: Drain out excess water from the field. Spray with Copper oxy

chloride@ 3g+ Streptocycline @ 0.1 g /litre of water and spray 19:19:19 @ 10 g

/litre of water 4 days after Fungicide spray.

Sugarcane: Drain out water, lift the cane and undertake propping .

Odisha

Rice

Spray 1% Gibberellic acid at flowering stage to prevent pollen drop.

Drain out excess water by providing drainage channel at an interval of every

three feet distance in medium duration rice at flowering stage, if lodged.

Drain out water and apply N @ 8 kg/ac for quick recovery in late rice or spray 2%

urea in the afternoon at flowering stage for better yield.

Heavy rainfall may aggravate the infestation of cut worm in late rice. Go for plant

protection measures by spraying Chloropyriphos @ 2 ml per liter water.

Advisories based on ERFS Forecast

IC = 8 Oct 2014

In Jharkhand, drain out excess water from rice fields.

In Chhattisgarh, drain out excess water from standing crop fields.

In Bihar, remove excess water from standing crop fields. Provide mechanical support for affected sugarcane due to

high winds.

In East Uttar Pradesh, remove excess water from standing crop fields. Provide mechanical support for affected

sugarcane due to high winds.

In East Madhya Pradesh, drain out excess water from fields of soybean, green gram and black gram

Advisories based on ERFS Forecast

Transplanting of samba rice and thaladi rice

in the Cauvery Delta Zone of Tamil Nadu.

As rain/thundershowers would likely occur

at many places over Tamilnadu,

Postpone irrigation, intercultural

operations and application of fertilizers

and plant protection measures in

standing crops.

Provide adequate drainage in standing

crop fields to remove excess water.

In Madhya Maharashtra, sowing of rabi

safflower ,wheat, irrigated gram and

planting of pre-seasonal sugarcane.

IC = 18 Oct 2014

Advisories based on ERFS Forecast

IC = 26 Oct 2014In view of likely occurrence of heavy rainfall over north

Coastal Andhra Pradesh and south coastal Odisha in

association with the Deep depression over central Bay of

Bengal, the following agromet advisories are suggested;

In North Coastal Andhra Pradesh

Postpone sowing of pulses and rice.

Undertake propping in sugarcane and provide

mechanical support to banana to prevent lodging

due to high winds.

In South Coastal Odisha

Harvest already matured medium duration rice,

groundnut and vegetables immediately. Keep

harvested produce at safe places.

Postpone sowing of rabi crops like mustard,

sunflower, sesame, green gram, black gram and

niger.

Undertake propping in sugarcane and provide

mechanical support to banana crops to prevent

lodging due to high winds.

WINTER

RABI CROPS

Rabi crops are agricultural

crops that are sown in the

winter and harvested in

spring.

Rabi crops are grown

between mid November to

te month of April.

Major Rabi crops grown in

India are wheat, sesame,

peas, mustard and barley.

Major Rabi Crops

NorthWest India

EastIndia

North East India

Central India West India South Peninsula

Wheat Mustard Wheat Wheat WheatSorghum (Jowar/Great

Millet)

Barley (Jau)Wheat

Indian rapeseed and

mustard (yellow

sarson)

Bengal Gram

(Gram/Chick

Pea/Kabuli/Chana)

Safflower (kusum/kardi)

Maize (Makka)

Bengal Gram

(Gram/Chick

Pea/Kabuli/Chan

a)

Paddy (Dhan) Paddy (Dhan) Cotton (Kapas)Sorghum (Jowar/Great

Millet) Indian rapeseed and

mustard (yellow sarson)

Lentil (Masur) Potato

Indian rapeseed and

mustard (yellow

sarson)

GarlicIndian rapeseed and

mustard (yellow sarson)Paddy (Dhan)

Peas (field peas/

garden

peas/matar)

Tea Maize (Makka)

Bengal Gram

(Gram/Chick

Pea/Kabuli/Chana)

Maize (Makka) Sesame

(Gingelly/Til)/Sesamum

Sunflower

(suryamukhi)Brinjal Niger (Ramtil)

Green Gram (Moong

Bean/ Moong)

Pigeon pea (red

gram/arhar/tur) Sunflower (suryamukhi)

Cabbage Tomato

Green Gram

(Moong Bean/

Moong)

Peas (field peas/

garden peas/matar)Sunflower (suryamukhi)

Horse Gram (kulthi/kultha)

Cauliflower Barley (Jau) Jute

Indian rapeseed and

mustard (yellow

sarson)

FingerMillet

(Ragi/Mandika)

Wheat

• It needs low temperature for growth with ideal temperature

between 10 – 150C during sowing and 21-260C during

harvesting.

• It thrives well in 75-100 cm of rainfall.

• Germination may occur between 4° and 37°C, optimal

temperature being from 12° to 25°C.

• Leaf photosynthesis is negatively affected as leaf

temperature rises above 25°C

• Optimum temperature for tillering is 32-340C.

• 27-290C is optimum for floral initiation.

• The temperature of the irrigation water should not be less

than 210C or more than 310C.

• Temperatures above 30°C during floret formation cause

complete sterility .

• At 45°C leaf photosynthesis may be halved.

• Sowing: Karnataka,

Maharashtra, Andhra Pradesh,

Madhya Pradesh and West

Bengal (September-October);

Bihar, Uttar Pradesh, Punjab,

Haryana and Rajasthan

(October-November); Himachal

Pradesh and Jammu &

Kashmir.(Nov.-Dec.)

• Harvesting : in Karnataka,

Andhra Pradesh, M.P., and in

West Bengal(Jan.- Feb );

Punjab, Haryana, U.P. and

Rajasthan (March-April)and

Himachal Pradeshi(April-May )

Maize/Mustard

The predominant maize growing states are Andhra Pradesh,

Karnataka, Rajasthan , Maharashtra, Bihar, Uttar Pradesh, Madhya

Pradesh, Himachal Pradesh. Apart from these states maize is also

grown in Jammu and Kashmir and North-Eastern states.

It is sown before winter rains.50 to 100 cm of rainfall is required. It

cannot tolerate frost.

The range of temperature for the growth of maize is from 9-460C with

the optimum around 340C. Beyond 400C root growth is again

severely affected.

Karnataka,

• September-October to February-March.

• 25 to 40 cm of rainfall.

• The vegetative development is optimum at 300C and

increase in temp. range and/or low temp. increase

the duration of vegetative phase.

• On an average, when the temp. is 250C the

germination is better.

Sesame/Barley

• Period: 26 Feb-10 Mar

• This is drought tolerant crop. Adequate moisture

requirement for early growth and germination.

• Ideal temp: 25 to 270C

• Cannot stand heavy rainfall and high humidity and low

temperatures.

• A temp. regime around 27 0C is optimum for its vegetative

and reproductive growth.

• The minimum soil temperature for satisfactory

germination is over 200C

• 130C or lower to induce flowering,

• Optimum temperature for germination 13-17 0C and the

maximum is 300C.

• The minimum and maximum temperatures for growth are 40C and 38 0C, respectively while the optimum is 25 to 20 0C.

• It can not tolerate frost at any stage of growth.

• Incidence of frost at flowering is highly detrimental for

yield.

Peas/Onion

• Karnataka, Rajasthan, Madhya Pradesh, West Bengal,

Punjab, Assam, Haryana, UP, Uttarakhand, Bihar, Odisha.

• Sowing of peas is taken during first week of October in

Madhya Pradesh and Uttarakhand.

• It requires cold and dry climate.

• Pea seed can germinate even at a minimum temperature of

50 C.

• The optimum temperature for germination is about 220 C.

• The temperature for good growth is between 100 C to 180 C.

• Ideal rainfall 500 mm

• Onion produce bulbs more rapidly at temp 210C to 260C.

• In rabi, 10-15 irrigations are given at bulb formation.

• Irrigation is necessary and moisture stress at this stage

results in low yield.

• Early Vegetative stage: 13-240C

• Vegetative : 16-210C

• Maturity: 30-350C

Maximum Temperatures (0C) Forecast

Jan-Feb

02.01.2015 12.01.2015 17.01.2015 22.01.2015

27.01.2015 01.02.2015 06.02.2015 11.02.2015

16.02.2015 21.02.2015 27.02.2015

Minimum Temperatures (0C) Forecast

Jan-Feb02.01.2015 12.01.2015 17.01.2015 22.01.2015

27.01.2015 01.02.2015 06.02.2015 11.02.2015

16.02.2015 21.02.2015 27.02.2015

Advisories based on ERFS Forecast In view of the expected low minimum temperatures over Jammu & Kashmir,

Himachal Pradesh (15-20/0-4), Uttar Pradesh (15-20/10-15), Punjab (15-20/4-6),

Haryana (15-20/4-8) & Delhi and parts of Rajasthan (25-30/6-10), apply light and

frequent irrigation in standing crops and arrange smoking around the crop fields

during nights.

Provide one supplemental irrigation to red gram in Andhra Pradesh (25-30/15-20)

and protective irrigation to wheat and sorghum in Karnataka (25-30/15-20).

Spray Potassium Nitrate @ 10 g/litre of to water in Bengal gram in Andhra

Pradesh (25-30/15-20) to avoid moisture stress is noticed

Gap filling in boro rice after 10-15 days after transplanting in Assam (20-25/10-15)

Apply irrigation to mustard/rapeseed at flowering stage and cole crops in

Meghalaya (20-25/10-15) and Manipur (20-25/10-15) and to vegetables in Mizoram

20-25/10-15) and Nagaland (20-25/10-15).

02.01.2015

02.01.2015

Maximum Temp.(0C)

Minimum Temp.(0C)

Advisories based on ERFS Forecast

Apply light and frequent irrigation in standing crops and arrange for

smoking around the crop fields during night hours to protect the crops

from cold injury in Telangana (30-35/10-15), Interior Karnataka (25-

35/10-15), Odisha (25-35/10-15), Madhya Pradesh (25-30/5-10),

Chhattisgarh (25-30/5-10), Madhya Maharashtra (30-35/5-10) and

Vidarbha (30-35/5-10).

Protect black grapes from cold injury by applying drip irrigation for

half an hour during morning hours in North Madhya Maharashtra (30-

35/5-10).

In Vidarbha (30-35/5-10) and Uttar Pradesh (15-20/5-10), apply light and

frequent irrigation to orange and sweet lime, apply mulch with crop

residue to protect the plants from cold. Arrange for smoke around the

orchards during night hours.

In Bihar (20-25/5-10) apply irrigation in timely sown wheat crop is at

tillering stage and late sown wheat crop is at CRI stage In Jharkhand

(20-25/5-10), apply irrigation in wheat, arhar, mustard, field pea and

potato. Nursery sowing of. medium duration improved varieties like

MTU 1010, IR 36, 64 and Naveen of summer rice.

12.01.2015

12.01.2015

Maximum Temp.(0C)

Minimum Temp.(0C)

Advisories based on ERFS Forecast

In Telangana (30-35/14-16) and Odisha (30-35/12-16),

as dry weather is prevailing, apply irrigation in rice

nurseries.

Mulching with crop residue and dried leaves in

coconut, banana and nutmeg fields in Kerala (30-

35/14-20) to reduce evapotranspiration

Apply irrigation in banana and maize in Tamil Nadu

(30-35/14-20).

Apply irrigation to rabi maize, lentil, rapeseed &

mustard in Assam and Odisha (30-35/12-16).

In Jharkhand (35-40/12-20), apply irrigation in wheat,

arhar, mustard, field pea and potato.

27.02.2015

27.02.2015

Maximum Temp.(0C)

Minimum Temp.(0C)

Forecasting Agriculture output using Space, Agrometeorology and Land based

observations

Aims at providing multiple pre-harvest production forecasts of crops at National/State/ District level

Crops:

• Kharif : Jute,

Rice, Cotton.

Sugarcane

• Rabi :

Rapeseed-

Mustard, Wheat,

Winter potato,

Sorghum and

Rabi RiceRice

kharif

Mustard Potato Wheat Rice

rabi

Jute

Date of issue of Forecast

S.N.

Crop Date of Issue

Vegetative stage(F1)

Mid season stage (F2)

Pre harvest stage (F3)

I Kharif 2014

Jute - - 16/07/2014

Rice 28/08/2014 28/09/2014 -

Rice (Tamilnadu) - - 28/12/2014

Cotton - 29/10/2014 29/11/2014

Sugarcane - - 29/11/2014

II Rabi 2014-15

Rapeseed-Mustard

30/12/2014 30/01/2015 27/02/2015

Wheat 30/01/2015 27/02/2015 30/03/2015

Potato 30/01/2015 27/02/2015 -

Sorghum - 25/01/2015 -

Summer/RabiRice

- - 28/03/2015

Sugarcane

Decision Support System based on seasonal forecast

and Crop models

Integrated, interdisciplinary crop performance forecasting systems,

linked with appropriate decision and discussion support tools, could

substantially improve operational decision making in agricultural

management.

Recent developments in connecting numerical weather prediction

models and general circulation models with quantitative crop growth

models offer the potential for development of integrated systems

Simulation analyses conducted on specific production scenarios are

especially useful in improving decisions.

Improved management of crop production system with an

interdisciplinary approach, is beneficial in the development of

targeted seasonal forecast systems.

Application of seasonal forecast systems in agricultural production

thus offers considerable benefits in improving overall operational

management of agricultural production.

IndiaModel/Method for seasonal forecasting

Crop model

Reference

The relationship between the El-Niño/Southern Oscillation (ENSO) and

variations in Indian summer monsoon rainfall is widely recognized (see, e.g., the

review by Webster et al. 1998).

The predictability of climate and yield variability associated with ENSO at farm-

scale suggests a potential to improve agricultural production decisions to either

reduce the negative impacts of adverse conditions or to take advantage of

favourable conditions.

Successful farm-level application of ENSO based climatic forecast for managing

risk have been reported elsewhere (Meinke et al. 1996; Messina et al. 1999;

Phillips et al. 2001).

Crop simulation models- GCM-based climate forecasts were linked with crop models

for yield prediction

Approaches allowing smallholder farmers in India to benefit from seasonal climate forecasting

Ramasamy Selvaraju, Holger Meinke and James Hansen

Western Africa (Ghana)Model/Method for seasonal forecasting

Climate model which has a resolution of 200 km. Probabilistic

forecasts are furthermore based on ensembles of individual forecasts,

which have been calculated using different atmospheric initial

conditions from several different climate models. Seasonal

forecasting is based on the notion that slowly varying SST modulates

the weather. Downscaling technique based on statistical specification

for seasonal f/c with SVD (Singular value decomposition) of cross-

covariance matrix analysis is used for model output.

Crop model

PNUTGRO crop model of the DSSAT software system

Reference

DMI, Ministry of Transport

Application of seasonal climate forecasts for

improved management of crops in Western Africa

J. H. Christensen1, et. al

West Africa

Characteristic Choice in CLIMAG

Type of downscaling - Statistical

Type of statistical model - Stepwise regression

Predictands - Amount of rainfall in wet season, onset and end dates

of wet season

Predictors - Sea surface temperatures (principal components) and

measures of the Southern Oscillation Index (SOI)

Area of prediction- Mali – where possible three latitudinal

agroclimatic zones

Lags - Data up to and including February is used to predict the

following wet season.

Reference

The CLIMAG methodology for seasonal Forecasting in West Africa:

Description and comparison with existing Methodologies

Jean Palutikof1, Daouda Zan Diarra2 and Tom Holt1

FloridaModel/Method for seasonal forecasting

Study of the effects of ENSO phases on crop yield. This phenomenon is known best as an

increase or decrease in the sea surface temperature in the eastern equatorial Pacific Ocean.

Remarkably, the temperature of this region of the ocean has significant influence on climate,

and therefore crop production.

A study of total production value and its three components --- yield, area harvested, and price -

--demonstrated the impact of climate on crop yield (Hansen et al 1998). Researchers included

data for a 35-year period, from 1960-1995. The study examined ENSO phases, quarterly SST,

and their possible influence on the production of six crops in four southeastern states (peanut,

tomato, cotton, tobacco, corn and soybean in Alabama, Florida, Georgia and South Carolina).

states were significantly affected.

Information available through the AgClimate website (www.agclimate.org)

To understand yield risk for specific crops in Alabama, Florida, and Georgia AgClimate was

developed by the Southeast Climate Consortium (SECC) in partnership with the Cooperative

State Extension Service

Using Seasonal Climate Variability Forecasts: Crop Yield Risk

Clyde W. Fraisse, Joel O. Paz, and Charles M. Brown

England (Wales)

Model/Method for seasonal forecasting

How far might medium-term weather forecasts improve nitrogen

fertiliser use and benefit arable farming in the England and Wales?

A.G. Dailey a, J.U. Smith b, A.P. Whitmore

Reference

A weather generator was used to produce series of weekly values for

rain, evapotranspiration (ET) and the weekly mean of daily mean

temperature (T) required by SUNDIAL.

Two sets of weather data were generated. The first series represents

expected weather, which is the weather used to optimise SUNDIAL at

the time a decision on fertiliser application needs to be made; the

second series, which deviates from the first, represents realised

weather.

The weather generator for a particular location is based on a

cumulative density function (CDF) of weekly amounts of rain for

each of the 134-week periods of the year.

AustraliaModel/Method for seasonal forecasting

SOUTHERN OSCILLATION INDEX. The effect of SOI on the rainfall is studied and the relation

between NDVI and rainfall is used to find out the crop production/yield.

Crop model

NORMALIZED DIFFERENCE VEGETATION INDEX

Seasonal Weather Forecasting and Satellite Image Analysis for

Agricultural Supply Chain Management

Reference

Michael Ferrari, PhD

VP, Applied Technology & Research

Weather Trends International, Inc.

1495 Valley Center Parkway -Suite 300

Bethlehem, PA 18017 USA

office: 610.807.3582 |mobile

484.542.0111

e: mferrari@wxtrends.com

South Australia

Model/Method for seasonal forecasting

Phase 1 – Long-lead ENSO forecasting

Mapping of the sequence of atmospheric (pressure, winds) and oceanographic variables (sea surface

temperatures) leading into ENSO events is done. This includes the transitions to strong and weak El Niño,

from El Niño to neutral, and from El Niño to La Niña. From this work, new indices to track changes in ENSO

State, i.e. an El Niño Prediction Index (EPI), ENSO Transition Index (ETI) and a more broad-scale measure of

the Southern Oscillation (MeanSOI) were determined .

Better long-lead seasonal and crop forecasts for southern Australia. Principal investigator

Dr David Stephens, Research Officer, Department of Agriculture, Western Australia

Reference

Phase 2 – Understanding regional factors that contribute to climate extremes

Spatially averaged rainfall for the south-western and south-eastern Australian grain belt has been correlated

spatially with gridded atmospheric pressure and sea surface temperature data from the region around

Australia. In future months, we will combine these factors to see if a regionally-based forecasting system can

improve on the broad-brush global-scale ENSO Sequence System described above

West Australia

Model/Method for seasonal forecasting

Crop model

Reference

The Southern Oscillation Index (SOI) is a key indicator of ENSO

MUDAS (Model of an Uncertain Dryland Agricultural System).

MUDAS represents seasonal uncertainty in the farming system with eleven

discrete weather-year states, each with an associated probability of occurrence.

Classification of MUDAS weather-year states is based on amount of summer and

early autumn rain, Nature and duration of sowing opportunities, Post-sowing

Weather conditions, probabilities, etc.

An assessment of the value of seasonal forecasting technology

for Western Australian farmers†

Elizabeth Petersena and Rob Fraserb

BrazilModel/Method for seasonal forecasting

Crop model

Reference

Monthly mean climate forecasts system is used. A stochastic weather generator was used to

disaggregate monthly mean rainfall from the European Centre for Medium-range Weather Forecast

(ECMWF) seasonal forecast model (known as System 3) into daily sequences of rainfall. The

stochastic model generates daily rainfall based on 16-years of daily observed rainfall. The

disaggregated daily rainfall was used as input data to a process-based crop model – GLAM

(General Large Area Model) to predict maize crop yield. Preliminary results show promising

usefulness of monthly mean rainfall forecasts produced by ECMWF coupled model for producing

maize yield predictions for Rio Grande do Sul five months in advance.

Crop yield predictions using seasonal climate forecasts, Simone M.S. Costa e Caio A. S. Coelho

New York, USAModel/Method for seasonal forecasting

Crop model

Reference

Stochastic daily weather time series models (‘‘weather generators’’) a stochastic weather

generator that disaggregates monthly rainfall by adjusting input parameters or by constraining

output to match target rainfall totals, and demonstrates its use with a maize crop simulation

model at three locations. It was developed with the dual purpose of generating stochastic

realizations of synthetic weather with realistic interannual variability, and supporting

stochastic disaggregation of historic or predicted monthly climate statistics, for crop

simulation applications. Precipitation occurrence is modelled by a two-state, second-order

hybrid Markov chain that simulates precipitation occurrence with a first-order chain if the

previous day was wet, or a second-order chain if the previous day was dry. If the Markov

model simulates occurrence of precipitation in a given day, the amount is sampled from a

probability mixture of two exponential distributions , also known as a hyperexponential

distribution.

Simulation model

Realizations of Daily Weather in Forecast Seasonal Climate

D. S. WILKS

The weather generation game: a review of stochastic weather models

D.S. Wilks and R.L. Wilby

Thank you……..