application of seasonal climate forecast for sustainable agricultural production in telangana...
TRANSCRIPT
Application of Seasonal Climate Forecast for Sustainable
Agricultural Production in Telangana Sub-division of Andhra
Pradesh, India
K.K. Singh, D. Raji Reddy1, Sunil Kaushik, L.S. Rathore and James W. Hansen2
National Centre for Medium Range Weather Forecasting (NCMRWF), New Delhi, India.
1Acharya N. G. Ranga Agricultural University, Hyderabad, India
2 International Research Institute for Climate Prediction, NewYork USA
OUTLINES Background
Objectives
Methods
Analyses and interpretation of results
Conclusions
Background Predictability of climate fluctuations at seasonal
time scale offers opportunity to improve agricultural risk management. Study shows significant correlation between observed and predicted rainfall for monsoon season over Telangana sub-division.
Rainfed agricultural scenario of Telangana sub-division is dominated by the monsoon climate. Main concerns are:
(i)Large variations in the dates of commencement of rainy season.
(ii) Variations in total seasonal rainfall received.
(iii) Prolonged dry spells within the rainy season.
(iv) High intensity rainfall due to cyclones, depressions, etc., resulting in flood damage to the crop.
(v) Variations in the cessation date of the rainy season.
Predominant cropping systems- Rice, Maize and Sorghum/Castor based. Yield of these crops greatly vary in time with variation in rainfall (quantity and distribution).
The existing network of 107 Agromet. Advisory Service (AAS) Units of National Centre for Medium Range Weather Forecasting (NCMRWF), is already working towards the dissemination of farm weather advisories in India. This network can be used towards achieving the common goal of developing crop management strategies based on seasonal climate forecasts.
Agromet Advisory Service (AAS) Agromet Advisory Service (AAS) NetworkNetwork
Total Units: 107(only 83 are displayed)
Objectives Maximize crop yield through application of
seasonal climate forecast in agriculture for selected locations.
Generate seasonal rainfall hindcast for selected locations.
Select sowing window for selected crops.
Determining plant population density.
Contingent planning- Find alternative option when monsoon delay.
Methodology
Criterion for location selectionTarget locations identified on the basis of:
Availability of data on rainfall, temperature and crop yield on district basis
Relation between climate variability and crop performance.
Access to cooperating farmers and their degree of interest.
Proximity of target locations with existing AAS units.
Districts maps of study sites in Andhra Pradesh India.
Rainfall and district crop yield relation
(a) Rice- Karimnagar district
-800
-600
-400
-200
0
200
400
600
800
1967 1972 1977 1982 1987 1992 1997
Rain
fall(m
m)
an
d Y
ield
(kg
\ha)
devia
tio
n
-800
-600
-400
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0
200
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600
800Rain
Yield
(b) Maize - Karimnagar district
-800
-600
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0
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400
600
800
1967 1972 1977 1982 1987 1992 1997
Ra
infa
ll (
mm
) V
s. Y
ield
(k
g\h
a)
de
via
tio
n
-800
-600
-400
-200
0
200
400
600
800
Fig: Comparison between rainfall (%) deviations Vs. yield (%) deviation
(c) Sorghum - Mahabubnagar district
-400
-300
-200
-100
0
100
200
300
400
1965 1970 1975 1980 1985 1990 1995
Year
Ra
infa
ll (
mm
) V
s. Y
ield
(k
g\h
a)
de
via
tio
n
-400
-300
-200
-100
0
100
200
300
400
Fig: Comparison between rainfall (%) deviations Vs. yield (%) deviation
Description of key sites
North Telangana agroclimatic zone:
Assured rainfall region
Southwest Monsoon Rainfall: 780-950 mm (900-1050 mm annual).
Site/District: Jagtiyal AAS unit in Karimnagar district
Predominant soil: Medium to deep black soils and red sandy soil (Chalkas)
The source of irrigation: Well and canal (Sri Ram Sagar project)
Cropping system: Double cropped area- Rice-rice and Maize-groundnut.
South Telangana agroclimatic zone:
Low rainfall region
Southwest Monsoon Rainfall: 550-700mm (750-870 mm annual)
Site/District: Paleam AAS unit in Mahabubnagar district
The predominant soil types: Dubba (sandy) and red chalka (sandy loam) soil with low water holding capacity
Cropping systems: Single cropped area-Sorghum–Fallow and Castor-Fallow.
The district is drought prone and agriculture is mainly rainfed.
Data requirementWeather
District wise monthly rainfall and crop yield for years: 1967-97 at Karimnagar,
1965-1997 at Mahabubnagar
Daily weather data from
RARS Jagtiyal in Karimnagar district for 1989-2002,
Rajendranagar (proxy station for Palem) in Mahabubnagar district for 1971-2002.
South-west monsoon rainfall
0
200
400
600
800
1000
1200
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
Year
Rai
nfa
ll (
mm
)
South-west monsoon rainfall
0
200
400
600
800
1000
1973
1976
1979
1982
1985
1988
1991
1994
1997
Year
Rai
nfa
ll (
mm
)Figure 2: Southwest monsoon rainfall at Jagtiyal (1989-1998)
Southwest monsoon rainfall at Rajendranagar (1971-1998)
Average rainfall
0
50
100
150
200
250
300
June July August September October
Month
Rai
nfal
l (m
m)
Average Rainfall
0
20
40
60
80
100
120
140
160
180
June July August September October
Month
Rai
nfal
l (m
m)
Average rainfall during monsoon for Rajendranagar
Figure 3: Average rainfall during monsoon for Jagtiyal
0
10
20
30
40
50
60
70
80
90
100
CV
(%)
EG SRK VSK WG GNT KRS NLR ATP CUD CHT KRL NLG MHB HYD MDK ADB NZB KHM KRM WGL
1963-82 1983-02
Coefficient of variation (%) in rainfall in different districts of Andhra Pradesh for the month of July
0
50
100
150
200
250
300
350
ADB KHM NZB KRM WGL MHB NLG MDK HYD KRS GNT NLR SKK VSK EG WG ATP CHT CUD KRL
1963-92 1993-02
District–wise decadal rainfall (mm) variation for the month of July
Table 1: Summary of monthly average weather parameters of Jagtiyal
(1989-1998)
Month Solar Radiation (MJ m-2d-1)
Maximum Temperature(OC)
Minimum Temperature (OC)
Rainfall (mm)
January 17.5 29.6 14.7 17.4
February 20.5 32.3 16.2 5.4
March 21.9 36.2 19.9 11.6
April 23.1 39.0 23.1 15.3
May 23.5 41.4 25.8 38.5
June 17.8 36.7 25.3 191.8
July 13.4 32.2 24.0 260.4
August 13.9 30.9 23.1 220.5
September 16.9 32.2 22.8 138.1
October 17.8 32.4 20.8 109.4
November 16.9 30.6 17.0 14.7
December 16.0 28.8 13.4 3.6
Table 2: Summary of monthly average weather parameters of Rajendranagar (1971-1998)
Month Solar Radiation (MJ m-2d-1)
Maximum Temperature(OC)
Minimum Temperature (OC)
Rainfall (mm)
January 20.2 28.9 13.7 5.2
February 23.2 31.9 16.5 4.7
March 24.8 35.9 19.7 13.9
April 25.5 38.0 23.3 19.4
May 25.1 39.1 25.4 33.6
June 19.1 34.5 23.9 107.6
July 16.3 31.1 22.8 158.2
August 15.9 29.9 22.3 161.2
September 18.0 30.7 22.1 129.7
October 19.3 30.6 19.7 91.0
November 18.9 28.9 15.9 26.7
December 18.8 27.8 12.8 3.7
Soil Karimnagar district
Medium to deep black soils (vertisols) with clay sub soils and red sandy soil (Chalkas)
Profile depth: 90 cm.
Mahabubnagar district
Dubba (sandy) and red chalka (sandy loam) soil with low water holding capacity,
Profile depth- 80cm.
Soil characteristics at Jagtiyal
Description of soil parameter
0-10 cm
10-20cm
20-40cm
40-72cm
72-90cm
Clay, % 60.0 60.0 55.0 55.0 30.0
Silt % 10.0 10.0 05.0 15.0 05.0
Coarse fraction, % 12.6 12.6 11.4 16.6 06.8
Bulk density, g cm-3 1.07 1.07 1.41 1.37 1.52
Lower limit, cm3 cm-3
(Soil moisture).150 .150 .140 .160 .150
Drained cm3 cm-3 (Soil moisture)
.290 .290 .280 .280 .280
Saturation, cm3 cm-3
(Soil moisture).400 .400 .380 .390 .370
Soil characteristics at Rajendranagar
Description of Soil parameter
0-10 cm
10-22cm
22-52cm
52-82cm
Clay, % 28.4 28.4 30.5 47.5
Silt % 35.4 35.4 31.5 30.5
Bulk density, g cm-3 1.61 1.61 1.62 1.64
Lower limit, cm3 cm-3
(Soil moisture).080 .090 .125 .150
Drained cm3 cm-3 (Soil moisture)
.220 .220 .245 .230
Saturation, cm3 cm-3
(Soil moisture).310 .310 .315 .290
Crop
Karimnagar district
Rice (irrigated) cv. Sambhamasuri- 145-155 days,cv. IR-64- 115-120 days
Maize (rainfed) cv. Proagro- 120-130 days
Mahabubnagar districtSorghum (rainfed)
cv. CSH-5- 90-105 days
Reasearch farm experiment data were collected to workout genetic coffeicient of crop cultivar under study.
Genetic coefficients used in the CERES-Rice model
Name Description Genetic coffecients
IR-64 Sambhamasuri
P1 Time period during juvenile stage. 200.0 540.0
P2O Critical photoperiod. 140.0 170.0
P2R Extent to which phasic development leading to panicle initiation is delayed for increase in photoperiod above P20.
350.0 400.0
P5 Time period start of grain filling to PM
12.0 12.0
G1 Potential spikelet number coefficient at anthesis.
100.0 100.0
(G2) Single grain weight- under non stress conditions
0.0220 0.0220
(G3) Tillering coefficient 1.00 1.00
(G4) Temperature tolerance coefficient. 1.00 1.00
Name Description Genetic coeff. for Proagro
P1 Thermal time from seedling emergence to the end of the juvenile phase
310.0
P2 Extent to which development is delayed for each hour increase in photoperiod above critical photoperiod
0.520
P5 Thermal time from silking to PM 900.0
G2 Maximum number of kernels per plant.
600.0
G3 Kernel filling rate- non stress condition
7.90
PHINT Phylochron interval 38.90
Genetic coefficients used in the CERES-Maize model
Name Description Genetic coeff. for CSH-5
P1 Thermal time from seedling emergence to the end of the juvenile phase
415.0
P20 Critical photoperiod 13.50
P2R Extent to which phasic development leading to panicle initiation above critical photoperiod.
40.5
P5 Thermal time from beginning of grain filling to PM
525.0
G1 Scaler for relative leaf size. 10.0
G2 Scaler for partitioning of assimilates to the panicle (head).
5.5
PHINT Phylochron interval 49.00
Genetic coefficients used in the CERES-Sorghum model
Management strategiesCrop managements practices considered are similar as followed by the farmers’ in the study region. Rice: The planting date considered for simulation of crop was 26 July.
Cultivar Sambhamasuri IR-64
Plant population 130 plants/m2 130 plants/m2
Row spacing 15 cm 15 cm
Planting depth 5 cm 5 cm
N-fertilizer (3 split doses of 40 kg/ha)
28 July,27 Aug.,01 Oct. 28 July,27 Aug., 01 Oct.
Irrigation The field was kept always with 2 cm of water.
Maize:
Planting window: between 02 June to 20 July with lowermost soil water as 90% and uppermost soil water as 100%. Plant population (at emergence)- 8 plants/m2 Row spacing- 35 cm, Planting depth- 6 cm. Nitrogen fertilizer (Urea): 40 kg/ha as basal dose, 40 kg/ha after 25 DAS, 40 kg/ha after 55 DAS.
Sorghum:
Planting window: between 1 June to 15 August with lowermost soil water as 70% and uppermost soil water as 100%. Plant population (at emergence)- 18 plants/m2 Row spacing- 45 cm, Planting depth- 5 cmNitrogen fertilizer (Urea): 40 kg/ha as basal dose 40 kg/ha at 30 DAS.
GCM Predictor selection and rainfall hindcasts
Seasonal forecast fields for rainfall were taken from the GCMs viz: ECHAM, GSFC, CCM, COLA, NCEP
Domain- 66E-90E and 5N-30N PC analysis. Each PC pattern represents a predictor field with high spatial resolution and spatial coherence, yet without the risk of over-fitting the empirical model.
MOS downscaling technique was applied on PCs fields and historical observed precipitation data at selected location to generate rainfall hindcasts for the years 1989-1998 at Jagtial and 1971-1998 for Rajendranagar.
Correlation is drawn between the observed and hind-cast rainfall.
Stochastic dissaggregation of monthly rainfall.
We used a stochastic weather generator to generate synthetic daily weather sequences to input crop model from monthly rainfall hindcast such that the monthly climatic means exactly match specified targets, [Hansen and Mavromatis, 2001].
For each hindcast year we generated 10 stochastic realizations of daily weather.
Crop simulation and CERES models
Crop yields were simulated using CERES models for crops along with management options under study.
The CERES models for Rice, Sorghum and Maize crops, used in the present study are available in DSSATv3.5 (Hoogenboom et al., 1999).
Analyses and interpretation of results
Hindcast of rainfall
We used time series data on PCs for all five GCMs to estimate MOS downscaled rainfall hindcast for the years 1989-1998 at Jagtial and 1971-1998 at Rajendranager. ECHAM was found to give a better forecast.
0
100
200
300
400
500
600
700
800
0 100 200 300 400 500 600 700 800
Observed rainfall (mm)
Hind
cast
rain
fall
(mm
)
r=0.5689
June - Sept.
0
100
200
300
400
500
600
0 100 200 300 400 500 600
Observed rainfall (mm)
Hind
cast
rain
fall
(mm
)r = 0.5887
Aug.-Sept.
Figure 4: Scatter plot between observed and hindcast rainfall using ECHAM model for Rajendranagar
ECHAM COLA CCM NCEP GSCF
June -0.20 -0.49 -0.30 -0.06 0.03
July 0.04 0.17 0.12 -0.02 -0.09
Aug 0.45 0.34 -0.20 0.13 -0.05
Sept 0.28 0.27 0.21 0.12 0.13
Jun-Jul -0.36 -0.06 0.03 -0.03 -0.02
Jul-Aug 0.49 0.44 -0.07 0.13 0.00
Aug-Sept 0.59 0.47 0.15 0.24 0.16
Jun-Aug 0.43 0.35 -0.06 0.12 0.02
Jul-Sept 0.61 0.53 0.19 0.20 0.15
Jun-Sept 0.57 0.47 0.24 0.19 0.16
Table 8: Correlation coefficients between observed and hindcast rainfall using different climate models for Rajendranagar
At Jagtiyal COLA modal gives the better correlation for the season, whereas for the individual month (July, August, and September.) the ECHAM model gives better correlation (figure 5 and table-9).
0
200
400
600
800
1000
1200
0 200 400 600 800 1000 1200
Observed rainfall (mm)
Hin
dca
st r
ain
fall
(m
m) June - Sept.
r = 0.4400
Figure 5: Scatter plot between observed and hindcast rainfall using COLA model for Jagtiyal
ECHAM COLA CCM NCEP GSCF
June 0.23 -0.39 -0.19 -0.32 -0.81
July -0.38 -0.20 -0.19 0.16 -0.15
Aug -0.12 -0.09 -0.32 -0.16 -0.24
Sept 0.23 0.01 -0.30 -0.20 -0.17
Jun-Jul 0.00 0.15 0.03 0.08 -0.42
Jul-Aug -0.16 0.18 0.04 0.12 0.13
Aug-Sept 0.02 0.25 -0.25 -0.06 -0.36
Jun-Aug 0.13 0.28 0.00 0.09 -0.28
Jul-Sept 0.20 0.35 0.13 0.21 0.01
Jun-Sept 0.24 0.44 0.09 0.17 -0.31
Table 9: Correlation coefficients between observed and hindcast rainfall using different climate models for Jagtiyal
Optimum transplanting time for rice
Simulation results of grain yield of rice cv. IR-64 and Sambhamasuri for 12 different dates of transplanting with observed weather revealed that the simulated rice yield is higher for cv. IR-64, when transplanted on 26 July and for cv. Sambhamasuri, transplantedon 19 July (figure 6).
(a) Rice cv. IR-64
610062006300640065006600670068006900
7-J
un
14
-Ju
n
21
-Ju
n
28
-Ju
n
5-J
ul
12
-Ju
l
19
-Ju
l
26
-Ju
l
3-A
ug
10
-Au
g
17
-Au
g
24
-Au
g
Dates
Yie
ld (
kg
/ha
)
(b) Rice cv. Sambhamasuri
72007400760078008000820084008600880090009200
7-J
un
14-J
un
21-J
un
28-J
un
5-J
ul
12-J
ul
19-J
ul
26-J
ul
3-A
ug
10-A
ug
17-A
ug
24-A
ug
Dates
Yie
ld (
kg
/ha)
Figure 6: Grain yield simulated for different dates of transplanting for (a) IR-64 and (b) cv. Sambhamasuri
Crop yield simulated with observed and hindcast weather
Rice:
Comparison of rice yield with observed and hindcast weather at Jagtiyal for (a) cv. IR-64 and (b) cv. Sambhamasuri
(a) Rice cv. IR-64
4500
5000
5500
6000
6500
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
Years
Yie
ld (
kg
/ha)
CCMCOLAECHAMGFCSNCEPOBSERVED
(b) Rice cv. Sambhamasuri
6000
6500
7000
7500
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
Years
Yie
ld (
kg/h
a)
CCMCOLAECHAMGFCSNCEPOBSERVED
Maize: Comparison of the grain yield of maize cv. Proagro simulated by the model with the hindcast and observed weather data.
Maize cv. Proagro
3000
4000
5000
6000
7000
8000
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
Years
Yie
ld (
kg/h
a)
CCMCOLAECHAMGFCSNCEPOBSERVED
Sorghum: Yield simulated with ECHAM hindcast shows the close resemblances to the observed yield data in some years and having the same trend.
Sorghum cv. CSH-5
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
Years
Yie
ld (
kg/h
a)
CCMCOLAECHAMGFSCNCEPOBSERVED
Farmers perception
•Farmers’ awareness programmes were conducted periodically at both locations during 2003 monsoon season (plate).• They were informed about the efforts to generate Seasonal Climate Forecast (SCF) for Indian region by leading international centres viz. IRI and its limitation. • Farmers are receving farm advisiory based on weekly medium range weather forecast (MRWF).•Interaction with the farmers brought out their following needs about weather and climate forecast.
(i) Start of rainy season (i.e. monsoon onset)(ii) End of rainy seasons(iii) Break in monsoon(iv) Extreme weather events(v) Preferred monthly / fortnightly forecast
Farmers perception …
Suggested to increase the lead-time with 10-15 days.They felt the need to integrate the seasonal/long range climate
forecast with agro-advisory services. Integration will help to select right crop and right variety based on
SCF and mid-season corrections like intercultural operation, supplement irrigation etc. using medium range forecast.
Low rainfall zone-farmers are interested in correct forecast of sowing rains that is very critical.
High rainfall zones-farmers are interested in knowing the quantum of rainfall required to get the tanks filled for release for paddy transplantation.
Interpretation of seasonal/medium range weather forecast
IRI provides probabilistic seasonal forecast every month for all the regions of the world. IMD issues Long Range Forecast of All India Monsoon rainfall. NCMRWF is generating Extended Range Prediction (ERP) in experimental mode, besides MRWF on weekly basis for all the agroclimatic zones in the country.
Crop management options for different weather forecast situations.
If the rainfall forecast is normal, the options available are timely sowing of maize and sorghum; and raising of rice nurseries of long duration varieties.
If the forecast is the deficit rainfall with delayed onset of monsoon, no scope to grow sorghum beyond June 30 and one should resort to alternate crops; and to raise rice nurseries of medium to short duration varieties.
Interpretation of seasonal/medium range weather forecast
Integration of monthly/seasonal forecast with medium range weather forecast is given.
For timely sown crop of sorghum and maize conditioned on normal seasonal forecast,
(i) If medium range weather forecast is dry spell- thinning and intercultural operation are suggested
(ii) If medium range weather forecast is wet spell- top dressing of nitrogen is suggested.
Early season drought based on medium range prediction- late fillings of tanks, and hence management practices for transplanting of aged seedlings of rice are suggested.
Integration of MRWF and SCF/LRF will help to change management decisions to minimize the risk.
Conclusions
Database related to crop, soil and weather for two locations in Telangana sub-division were developed. Based on relation between crop performance and climate variability during monsoon season, two locations Jagtiyal (Karimnagar district) for rice and maize and Palem (Mahabubnagar district) for sorghum were selected. Genetic coefficients for rice cv. IR-64 and Sambhamasuri, maize cv. Proagro in Karimnagar district and sorghum cv. CSH-5 in Mahabubnagar district were worked out. ECHAM model generated better rainfall hindcast at seasonal/sub-seasonal scale for Rajendranagar. For Jagtiyal COLA model gives better correlation between hindcast and observed at seasonal scale whereas for individual months ECHAM does better hindcast.
Conclusions Contd.Correlation between simulated rice yield with observed and ECHAM hindcast weather was –0.16 for Rice cv. IR-64 and –0.48 for cv. Sambham.; 0.43 for Sorghum cv. CSH-5, and -0.56 for Maize cv. Proagro. Awareness was created amongst the farmers, researchers and planners about utility and limitations of seasonal climate forecast for application in agriculture through group meeting. Farmers preferred fortnightly forecast to monthly instead of seasonal forecast for better decision-making in agriculture operation and desired for integration of ERP along with existing AAS. A National workshop on the “Seasonal climate prediction for sustainable agriculture” was organized involving planners, farmers, researchers and extension workers to deliberate on improvement in seasonal climate prediction and its limitations, and to develop a mechanism to reach out to the farmers for better management of agricultural activities and optimal use of resources.
Future line of work
In view of the farmers perception with regard to forecast requirement the study needs to be continued.
Present study is based on point yield simulations but yield simulations are required spatially for identifying the constraints for yields.
Application of seasonal climate forecast as an integral part of Agro-Advisory Services for better agricultural planning and management needs extensive studies.
Fine-tuning of the GCMs to suit farmers’ needs for sustaining the agricultural productivity in rainfed areas needs further studies.
NCMRWF
107 AAS UNITS
DISTRICT AGRICULTURE OFFICES OF STATE
GOVERNMENTS
PREPARATION OF DISTRICT WISE MEDIUM RANGE WEATHER FORECAST
PREPARATION OF AGRO-CLIMATIC
ZONE LEVEL AGRO-ADVISORIES
PREPARATION OF DISTRICT LEVEL
AGRO-ADVISORIES
FARMERS(THROUGH MEDIA, EXTENSION
SERVICES, PERSONAL CONTACT)
District-wise Agro-met data
Agro-climate level agro-met data
Feedback analysis
10 July
15 June
Maize cob size in year 2003 in Karimnagar district
AcknowledgementThis project has been supported by grants from the START and Packerd Foundation. The project team would like to gratefully acknowledge START, IRI and the David and Lucile Packard Foundation.
