Presentation on theme: "Application of Seasonal Climate Forecast for Sustainable Agricultural Production in Telangana Sub-division of Andhra Pradesh, India K.K. Singh, D. Raji."— Presentation transcript:
Application of Seasonal Climate Forecast for Sustainable Agricultural Production in Telangana Sub-division of Andhra Pradesh, India K.K. Singh, D. Raji Reddy 1, Sunil Kaushik, L.S. Rathore and James W. Hansen 2 National Centre for Medium Range Weather Forecasting (NCMRWF), New Delhi, India. 1 Acharya N. G. Ranga Agricultural University, Hyderabad, India 2 International Research Institute for Climate Prediction, NewYork USA email@example.com
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) Network 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 selection Target 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 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 requirement Weather 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.
Figure 2: Southwest monsoon rainfall at Jagtiyal (1989-1998) Southwest monsoon rainfall at Rajendranagar (1971-1998)
Average rainfall during monsoon for Rajendranagar Figure 3: Average rainfall during monsoon for Jagtiyal
Coefficient of variation (%) in rainfall in different districts of Andhra Pradesh for the month of July
District–wise decadal rainfall (mm) variation for the month of July
Table 1: Summary of monthly average weather parameters of Jagtiyal (1989-1998) MonthSolar Radiation (MJ m -2 d -1 ) Maximum Temperature ( O C) Minimum Temperature ( O C) Rainfall (mm) January17.529.614.717.4 February20.532.316.25.4 March21.936.219.911.6 April23.139.023.115.3 May23.541.425.838.5 June17.836.725.3191.8 July13.432.224.0260.4 August13.930.923.1220.5 September16.932.222.8138.1 October17.832.420.8109.4 November16.930.617.014.7 December16.028.813.43.6
Table 2: Summary of monthly average weather parameters of Rajendranagar (1971-1998) MonthSolar Radiation (MJ m -2 d -1 ) Maximum Temperature ( O C) Minimum Temperature ( O C) Rainfall (mm) January20.228.913.75.2 February23.231.916.54.7 March24.835.919.713.9 April25.538.023.319.4 May184.108.40.2063.6 June19.134.523.9107.6 July16.331.122.8158.2 August15.929.922.3161.2 September18.030.722.1129.7 October19.330.619.791.0 November18.928.915.926.7 December18.827.812.83.7
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 10-20 cm 20-40 cm 40-72 cm 72-90 cm Clay, %60.0 55.0 30.0 Silt %10.0 05.015.005.0 Coarse fraction, %12.6 11.416.606.8 Bulk density, g cm -3 1.07 1.411.371.52 Lower limit, cm 3 cm -3 (Soil moisture).220.127.116.11 Drained cm 3 cm -3 (Soil moisture).290.280 Saturation, cm 3 cm -3 (Soil moisture).400.380.390.370
Soil characteristics at Rajendranagar Description of Soil parameter 0-10 cm 10-22 cm 22-52 cm 52-82 cm Clay, %28.4 30.547.5 Silt %35.4 31.530.5 Bulk density, g cm -3 1.61 1.621.64 Lower limit, cm 3 cm -3 (Soil moisture).080.090.125.150 Drained cm 3 cm -3 (Soil moisture).220.245.230 Saturation, cm 3 cm -3 (Soil moisture).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 district Sorghum (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 NameDescriptionGenetic coffecients IR-64Sambhamasuri P1Time period during juvenile stage.200.0540.0 P2OCritical photoperiod.140.0170.0 P2RExtent to which phasic development leading to panicle initiation is delayed for increase in photoperiod above P20. 350.0400.0 P5Time period start of grain filling to PM 12.0 G1Potential spikelet number coefficient at anthesis. 100.0 (G2)Single grain weight- under non stress conditions 0.0220 (G3)Tillering coefficient1.00 (G4)Temperature tolerance coefficient.1.00
NameDescriptionGenetic coeff. for Proagro P1Thermal time from seedling emergence to the end of the juvenile phase 310.0 P2Extent to which development is delayed for each hour increase in photoperiod above critical photoperiod 0.520 P5Thermal time from silking to PM900.0 G2Maximum number of kernels per plant. 600.0 G3Kernel filling rate- non stress condition 7.90 PHINTPhylochron interval38.90 Genetic coefficients used in the CERES-Maize model
NameDescriptionGenetic coeff. for CSH-5 P1Thermal time from seedling emergence to the end of the juvenile phase 415.0 P20Critical photoperiod13.50 P2RExtent to which phasic development leading to panicle initiation above critical photoperiod. 40.5 P5Thermal time from beginning of grain filling to PM 525.0 G1Scaler for relative leaf size.10.0 G2Scaler for partitioning of assimilates to the panicle (head). 5.5 PHINTPhylochron interval49.00 Genetic coefficients used in the CERES-Sorghum model
Management strategies Crop 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. CultivarSambhamasuriIR-64 Plant population130 plants/m2 Row spacing15 cm Planting depth5 cm N-fertilizer (3 split doses of 40 kg/ha) 28 July,27 Aug.,01 Oct. IrrigationThe 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/m 2 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 cm Nitrogen 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. Figure 4: Scatter plot between observed and hindcast rainfall using ECHAM model for Rajendranagar
ECHAMCOLACCMNCEPGSCF June-0.20-0.49-0.30-0.060.03 July0.040.170.12-0.02-0.09 Aug0.450.34-0.200.13-0.05 Sept0.280.218.104.22.168 Jun-Jul-0.36-0.060.03-0.03-0.02 Jul-Aug0.490.44-0.070.130.00 Aug-Sept0.590.422.214.171.124 Jun-Aug0.430.35-0.060.120.02 Jul-Sept0.610.5126.96.36.199 Jun-Sept0.570.4188.8.131.52 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). Figure 5: Scatter plot between observed and hindcast rainfall using COLA model for Jagtiyal
ECHAMCOLACCMNCEPGSCF 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, transplanted on 19 July (figure 6).
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
Maize: Comparison of the grain yield of maize cv. Proagro simulated by the model with the hindcast and observed weather data.
Sorghum: Yield simulated with ECHAM hindcast shows the close resemblances to the observed yield data in some years and having the same trend.
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
Acknowledgement This 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.