Application of Extended Range &Seasonal Forecasting in ......Application of Extended Range &Seasonal...
Transcript of Application of Extended Range &Seasonal Forecasting in ......Application of Extended Range &Seasonal...
Application of Extended Range &Seasonal
Forecasting in Agriculture
Dr.N.Chattopadhyay
Deputy Director General of Meteorology
Agricultural Meteorology Division, India Meteorological
Department,
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
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……..