Crop Forecasts in India and the Role of Weather Predictions

Transcription

1 Crop Forecasts in India and the Role of Weather Predictions Crop Condition of August, 2015 Shibendu S. Ray Mahalanobis National Crop Forecast Centre Ministry of Agriculture & Farmers Welfare, Government of India, New Delhi

2 Conventional Crop Estimation in India Area Statistics Land Record States or temporarily settled states: 17 Major States, 4 UTs: 86% of reporting area, Timely Reporting Scheme (TRS), 20% villages are selected at random for complete area remuneration. States where area statistics are collected on the basis of sample surveys. Establishment of an Agency for Reporting of Agricultural Statistics (EARAS). 9% of reporting area. Sample surveys of 20% villages/ investigator zones. Hilly districts of Assam, rest of the states in NER, & other UTs. Area statistics based on impressionistic approach. 5% of the reporting area. Yield Estimates Crop Cutting Experiments (CCE) under scientifically designed General Crop Estimation Surveys (GCES). Around 950 thousand CCEs. Stratified multi-stage random sampling: Tehsil / Taluk > Revenue Village> Survey Number / Field> Experimental Plot (Specified size / shape) CCEs for a crop in a major district Advance Estimates September (1st ), January (2nd ), March/April (3rd ), June/July (4th), January (Final)

3 Major Crops of India * Million bales S.N. Group Crop Type Crop Season Avg. Area (M Ha) Avg. Prod. (M Ton) Status (RS Assessment) 1 Food grains Cereals Rice Kharif + Rabi Wheat Rabi Coarse Cereals Maize Kharif + Rabi Sorghum Kharif + Rabi Bajra Kharif Pulses Tur (Arhar) Kharif Gram Rabi Oilseeds Groundnut Kharif R & M Rabi Soybean Kharif Fibre Crops Cotton Kharif * 1 13 Jute Kharif * 1 14 Sugarcane Sugarcane Long Durn

4 46 Years of use of Space Technology in Crop Forecasting NASA ISRO JEP 1988 CAPE MoA FASAL FASAL 2012 MNCFC Pilot Coconut Root Wilt study in Kerala Experimental Studies on Crop Discrimination Area & production Estimates of major crops at State level. National Wheat, FASAL-Odisha District-State- National forecasts using multiple approaches for multiple forecasts Institutionalisatio n of Space Technologies developed by ISRO

5 Forecasting Agricultural out put using Space, Agrometeorology and Land based observations 5

6 Crop Forecasting: Salient Features Crops (8) Rice (Kharif & Rabi), Jute, Cotton, Sugarcane, Wheat, Rapeseed & Mustard, Sorghum(Rabi), Rabi Pulses Data Multidate Microwave (RISAT-1 C band SAR) and Optical (Resourcesat-2 AWiFS & LISS III) Data Approach Ground truth using Smartphone, Acreage using satellite data, Yield using weather and remote sensing based models States Covered All those states, which together contribute >90% of the Crop s area in the Country Periodicity (All forecasts are issued pre-harvest) Paddy, Wheat & Mustard 3 Forecasts Sugarcane, Cotton 2 Forecasts Jute, Sorghum & Pulses- 1 Forecast Software FASALSoft, developed by ISRO

7 Geospatial Technology for Field Data Collection Groundtruth Crop Cutting Expt Sampling plan based on RS data Smartphones/Tablets Android based App. by NRSC Bhuvan Geoportal State Agrl. Dept. Officials >12000 GT points, >1200 CCEs

8 Crop Yield Forecasts Meteorological Sub Division Level Yield Models: SAC, MNCFC District Level Yield Models: IMD, SAUs Semi Physical Remote Sensing Models: Rice, Wheat Empirical Models (Agromet + Remote Sensing) Simulation Models (Assimilation of RS data) Agromet + Remote Sensing+ Field Observation

9 Yield Forecasts by IMD 1. Empirical: Models District Level Correlation Weighted Regression Models Variables: Rainfall, Temperature and Relative Humidity 2. Simulation 1. DSSAT-CERES 2. Calibrated for major varieties 3. One run per district and forecast at State/District level 4. Actual Data upto Forecast Date and rest average weather data 9

10 National Agricultural Drought Assessment & Monitoring System (NADAMS) Operational Drought assessment during Kharif using Remote Sensing (Methodology developed by ISRO) Periodic District/Sub-District level drought assessment for 14 Agriculturally Dominant states of India (5 at Sub District level) Satellite based indices, Rainfall data, Ground information on Sowing progression and Irrigation Statistics are used for drought assessment Drought Warning (Normal, Watch & Alert) is given in June July & August, while Drought Declaration (Mild, Moderate & Severe) in September & October

11 Drought Assessment: Inputs 1. Remote Sensing based indices: Normalized Difference Vegetation Index (NDVI) Normalized Difference Water Index (NDWI) Vegetation Condition Index (VCI) NDVI from NOAA Data (1 km) NDWI from MODIS Data (500 m) 2. Area Favourable for Crop Sowing (using Satellite based Index and Soil Moisture Index) 3. District level Rainfall Deviation 4. Irrigation percentage NDVI from AWiFS Data (56 m) District level Irrigation% Rainfall Data from IMD Soil Moisture Index 28 August 2015

12 Drought Assessment: September,

13 Vegetation Condition

14 Numbers of Districts affected Category (upto Sept end) Normal Mild Moderate

15 Usage by States- Extract from Andhra Pradesh Drought Memorandum (Feb, 2015)

16 Horticultural Crops CHAMAN (Coordinated Horticulture Assessment & Management using geoinformatics) Project: Launched in September, 2014 Area assessment and production forecasting of major horticultural crops Vegetables: Potato, Onion & Tomato Fruits: Mango, Citrus & Banana Spices: Chilli Geospatial technologies will be used for generating horticultural development plans for i) Site Suitability ii) iii) iv) Post-Harvest Infrastructure Crop Intensification Orchard Rejuvenation v) Aqua-horticulture

17 Space Technology for Crop Insurance Exploring the role of Remote Sensing technology to supplement the yield assessment through Crop Cutting Experiments (CCE) Launched in September, 2014 under National Crop Insurance Programme (NCIP) Current season remote sensing data based CCE Planning Smartphone use for CCE Data Collection & CCE database on Bhuvan Portal Multi-level yield modeling using Remote Sensing and Crop Simulation Model KISAN Project Launched in Oct, 2015 Proposed use of UAV/Drones Selection of CCE sites using NDVI map Overlaying of CCE sites in Google Map for better identification in the field

18 Impact of Disaster on Agriculture Rice-Flooded Area Assessment, post-phailin Cyclone in Odisha State, October, 2013 Impact Assessment of Heavy Rainfall and Hailstorm in Northern India during Feb-Mar, 2015

19 IMD Weather Forecasts (In relation to Crop Assessments) All India Summer Monsoon: 1 st Stage National Level; 2 nd Stage 4 Homogenous zones Met Sub-division wise 5 Day Rainfall (Categories) NWP: District level 5 Day Forecasts (RF, Temp, RH, Cloud, Wind) Nowcast: 3 Hour Extreme Weathers ERFS (under FASAL project): Ensemble Technique (9 models), Monthly & Seasonal Forecasts of Precipitation, Met Sub-Division level

20 Weather Data Requirements for Agriculture Crop Forecasting Agro-meteorological parameters at higher spatial scale, Extended range forecasts (for crop simulation models) Drought Assessment Rainfall data at Taluk level, Extended Range forecasts, Derived products (MAI, SPI, PDSI, AAI, Soil Moisture), Dry & Wet Spells Farmers Advisory Higher accuracy of Short term & Medium term forecasts, with value additions at block level; Extreme Weather Events

21 Summary India has established successful operational programmes for Crop Forecasting and Drought Assessment These activities are highly dependent upon weather data and forecasts Though there has been tremendous improvement in weather forecasting in India, what is further needed is higher accuracy at higher spatial scale, extended range forecasts and derived products.

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