High Resolution Yield Estimation for Crop Insurance
Transcript of High Resolution Yield Estimation for Crop Insurance
Mahalanobis National Crop Forecast Centre
Shibendu S. RayMahalanobis National Crop Forecast Centre
Dept. of Agric., Coop. & Farmers’ Welfare, MoA&FW, Govt. of India, New Delhi – 110 012, [email protected]; [email protected]
High Resolution Yield Estimation for Crop Insurance
Workshop on Emerging Technologies and Methods in Earth Observation for Agricultural Monitoring, Dates: February 13-15, 2018, Washington, USA
Mahalanobis National Crop Forecast Centre
About MNCFC
• Mahalanobis National Crop Forecast Centrewas established under Ministry of Agriculture &Farmers Welfare with support from Indian SpaceResearch Organization (ISRO). Centre wasinaugurated on 23rd April, 2012.
• Named after the great statistician, P. C.Mahalanobis
• Mandate: Use geospatial technology foragricultural assessment.
• Collaboration: 20 State Agriculture Dept., 12 StateHorticulture Dept., 16 State Remote SensingCentres, 3 ISRO Centres, IMD, ICAR, StateAgricultural Universities …
• 5 National Programmes: FASAL, NADAMS,CHAMAN, KISAN, Rice-Fallow
www.ncfc.gov.in
Mahalanobis National Crop Forecast Centre
Crop ForecastingFASAL (Forecasting Agricultural output usingSpace, Agrometeorlogy & Land basedobservations) Project: Since 2007
Multiple Crop production forecasts of 8major crops (Rice, Wheat, Cotton, Sugarcane,Mustard, Sorghum, Pulses & Jute)
Satellite data of optical and Microwave(National and International): One of thelargest users of Indian Satellite data
Yield Models (Empirical, Semi-physical, CropGrowth Simulation)
Forecasts National/ State/ District level: Pre-sowing to pre-harvest
Used as one of the inputs for Government’sFinal Estimates
>90 partner organisations (DACFW, MNCFC, 3ISRO centres, 19 SDAs, 18 SRSACs, 46 AMFUs,IEG, IMD,)
Multidate AWiFS NDVI Product used in FASAL Project
Smartphone based Field Data Collection
Mahalanobis National Crop Forecast Centre
Horticultural Crop Estimation
CHAMAN (Coordinated HorticulturalAssessment & Management usinggeoinfromatics) Project: Since 2014
Production estimation for 7 majorhorticultural crops (Potato, Onion, Tomato,Chilli, Mango, Banana, Citrus)
Satellite Data: Resourcesat 2 (AWiFS, LISS III,LISS IV), Cartosat, Sentinel-2 & Landsat-8
Yield Models: Operational for Potato,Developmental stage for other crops
Estimates at State/ District level; ForOrchards – Maps on Bhuvan
Used as one of the inputs for Government’sFinal Estimates
Partner organisations (DACFW, MNCFC, ISROcentres, State Horticulture Departments,State Remote Sensing Centres, IMD, Agrl.Univ, IEG)
Mango Orchard Inventory – Sitapur District, Uttar PradeshLISS IV + Cartosat Data
Mahalanobis National Crop Forecast Centre
Drought Assessment
Rainfall Deviation (Jun-Sep, 2017)
Vegetation Condition Index (Sep, 2017)
Moisture Adequacy Index (Jun-Sep, 2017)
As per New Drought Manual 2016, RemoteSensing Index is one of the 4 impactindicators (crop, satellite, soil moisture &hydrology) to be used for droughtdeclaration
Remote Sensing Index: NDVI/NDWIDeviation or VCI (Vegetation ConditionIndex)
Use of Drought Manual has been madeMandatory for Drought Declaration
High Demand for Satellite based VegetationIndex Data by User Departments
Satellite based soil moisture can be input foranother Impact Indicator: MoistureAdequacy Index
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Agricultural Development Planning
Post Rice-Rabi Fallow, Suitable for Crop
Site Suitability for HorticulturalExpansion in North Eastern States inJhumlands (shifting cultivation)
GIS based Plans for Infrastructure (Coldstorage) development
Site suitability mapping for growingpulses in Post-kharif Rice Fallow Lands
Potential Assessment for growth ofMicro-irrigation
Proposed Cold Storage in Bihar
Suitable sites for Grapes in Khawbung block, Mizoram
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Rice, r2 = 0.74
Wheat, r2 = 38
Wheat, r2 = 0.58
Rice, r2 = 0.27
Simulation
Model
Semi-Physical Model
Accuracy Assessment of Yield Estimates
SAR Biomass Model
Yield Estimation: Under FASAL ProjectI. District level Agro-meteorological models
(Correlation weighted step-wise regression)
II. Crop Simulation Models (DSSAT)
III. Empirical Yield Models using Remote Sensing Indices(NDVI, VCI, Biomass)
IV. Semi-Physical Models for Sugarcane, Wheat andR&M
V. Crop Cutting Experiments using RS based SamplingPlan
Mahalanobis National Crop Forecast Centre
Pradhan Mantri Fasal Bima Yojana
(the new Crop Insurance programme of India)
• Yield Index Insurance (Crop Cutting Experiments)
• Area based approach
• Compulsory for Loanee farmers, voluntary for non-loanee farmers
• Coverage of Crops: Food crops (Cereals, millets, pulses), Oilseeds, Annual Commercial/Horticultural crops
• Coverage of Risks: Prevented sowing, Non-preventable risks (Widespread calamities) of standing crops, Post-harvest losses, Localized calamities
• Uniform premium for farmers (2.0% for kharif, 1.5% for Rabi, 5% for Annual crops)
• Use of Technology:
• Smartphone based yield data collection
• IT Portal for Insurance Database and Management
• Use of Satellite Remote Sensing
Mahalanobis National Crop Forecast Centre
Issues
1. Crop Cutting Experiment (CCE) is the sole method of yield estimation in the country.
2. Number of CCEs to be done, under PMFBY, 4 per Insurance Units (where IU is Village
Panchayat for a major)
3. Number of Village Panchayats in Country : ~2,40,000. So number of CCEs to be conducted
very high.
4. Selection of plots is done on random number basis, hence may not account for the in-season
crop variability
5. CCEs have to be mandatorily conducted using smartphone. Difficult for village level officials.
6. If Crop is multi-picking, the number of observations multiply. Picking numbers are also not
fixed: depend upon region, variety, irrigated/unirrigated.
7. Many technologies (satellite, weather, modelling, farmers’ survey) are promising, but not
foolproof at village or farm level.
Mahalanobis National Crop Forecast Centre
Satellite Remote Sensing Role
1. CCE Planning/Optimization- Smart Sampling
2. Yield Discrepancy/Quality Checking
3. Yield Estimation
Mahalanobis National Crop Forecast Centre11
Smart Sampling
Mahalanobis National Crop Forecast Centre
AWiFS NDVI scaled MODIS LSWI NDVI Stratum LSWI Stratum
NDVI+LSWI Stratum
CCE Point Generation Steps (Ex. Kalburgi District, Karnataka)
Proposed CCE Points
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Approaches for Yield Quality Checking• Statistical Analysis of Yield
• Test of Normality (Mean, Median, Mode, Standard Deviation, Std.Err.,
Skewness, Kurtosis, Q-Q Plots, Shapiro-Wilk(p value)
• Yield Trends
• Weather:
• Rainfall
• Soil moisture (Satellite and modelled)
• Satellite based Indices
• Normalized Difference Vegetation Index
• Normalized Difference Wetness Index
• Vegetation Condition index
• Other Collateral Information
• Market arrivals & Prices
• Supervised Experiments of NSSO
• Any Government Report
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Remote Sensing Strata based onLSWI (MODIS) & NDVI (AWiFS)
Yield Quality Checking: Use of Satellite Data
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Comparison of Remote Sensing and Yield Classes
Same Category: 30%Within 1 Category Difference: 74%Within 2 Category Difference: 94%
Yield
Stratum
Number of CCE PointsMatching
Level
No. of CCE
points%Remote sensing (NDVI+NDWI) Stratum
TotalA B C D
Y1 914 712 1156 389 3171 Both same 6679 30
Y2 950 878 1635 573 4036 1 Cat Diff 9709 44
Y3 933 1059 2204 947 5143 2 Cat Diff 4330 20
Y4 976 1668 4406 2683 9733 3 Cat Diff 1365 6
Total 3773 4317 9401 4592 22083 Total 22083 100
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Yield Mapping(Rabi Sorghum of Karmala Taluk of Solapur district, Maharashtra)
Input Parameters
NDVI NDWI Land Capability Rainfall LST
Mahalanobis National Crop Forecast Centre
Need
• Large number of pilot studies to identify optimum yield proxies for better yield
estimation at village and farm level.
• More research on SAR, as limited availability of cloud free optical data during
Kharif season.
• Need to integrate satellite remote sensing with other inputs (weather, soil &
crop management), models and technologies (IoT, Cloud Computing, UAV,
Machine Learning, …..).