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


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


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


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


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


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


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


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


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.


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


AWiFS NDVI scaled MODIS LSWI NDVI Stratum LSWI Stratum

NDVI+LSWI Stratum

CCE Point Generation Steps (Ex. Kalburgi District, Karnataka)

Proposed CCE Points


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


Remote Sensing Strata based onLSWI (MODIS) & NDVI (AWiFS)

Yield Quality Checking: Use of Satellite Data


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


Yield Mapping(Rabi Sorghum of Karmala Taluk of Solapur district, Maharashtra)

Input Parameters

NDVI NDWI Land Capability Rainfall LST


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, …..).

Documents

High Resolution Yield Estimation for Crop Insurance