Role of Technology in PMFBY

Mahalanobis National Crop Forecast Centre (MNCFC)

Department of Agriculture, Cooperation & Farmers’ Welfare

Ministry of Agriculture & Farmers’ Welfare, New Delhi

Web: www.ncfc.gov.in, Email: shibendu.ncfc@nic.in

Shibendu Shankar Ray

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MNCFC: An Introduction

Established in 23rd April, 2012

Attached office of Department of Agriculture, Cooperation & Farmers’ Welfare

With Technical Support & Human Resources Support from ISRO

Mandate: Use of Geospatial Technology for Agricultural Assessment

Major Programmes: FASAL, NADAMS, CHAMAN, KISAN; Rice-Fallow

www.ncfc.gov.in

Indian Earth Observation Sensors for Agriculture Monitoring

AWiFS: 56m (5 day)

LISS-III: 24m (24 day)

LISS-IV: 6 m

INSAT CC, 1

km ( daily)

1995/96*/97 2003

IRS-P6 (Resourcesat-1)

LISS 3 – 24/140Km

LISS 4 - 5.8m/27Km

AWiFS - 56m/740Km

Resourcesat-2

LISS 3 – 24m/140Km

LISS 4 - 5.8m/70Km

AWiFS - 56m/740Km

2011

IRS-1C/P3/1D LISS-3 (24m) WiFS (188m) *P3 (WiFS: 188m)

1999 2003

INSAT-3A

VHRR – 2 Km(VIS); 8

Km(IR & WV)

CCD – 1 Km

1988/91/94

IRS-1A/1B/P2

LISS-I/II (72/36m)

2008

IMS

HySI

(506m), Mx

(70m)

INSAT-2E VHRR, CCD (1 Km)

RISAT-1

2012 April 26

With Resourcesat 2A the frequency of Data Availability improves 2 times

Resourcesat-2A

LISS 3 –624m/140Km

LISS 4 - 5.8m/70Km

AWiFS - 56m/740Km

2016

• Use of Mobile Phone Technology to improve Yield-data Quality and Timeliness

• Use of Innovative Technologies to Rationalize CCEs

• Use of Innovative Technologies for Direct Yield Estimation

• Use of Technology to remove area discrepancy in coverage

• Determination of extent of loss for on Account payout

Role of Technology in PMFBY

(as given in Operational Guidelines)

Five Types of Technology 1. IT/ICT 2. Remote Sensing 3. GIS (Geographical Information System) 4. Smartphone 5. UAS/UAV/Drone

Android App for CCE Data Collection

More Than 1 lakh CCEs Data Collected using this App

Uniqueness: • Only Approved Users • Large number of parameters to provide an excellent database for crop information

(management practice, varieties, conditions etc.) • All data are geocoded, time-stamped, with photograph

District wise CCE Distribution : Odisha (up to 16th Nov)

Total - 7661

Remote Sensing Stratum wise CCE Distribution

Remote Sensing Strata based on LSWI (MODIS) & NDVI (AWiFS)

Same Category – 30% Within 1 Cat Diff – 45% >=2 Cat Diff - 24%

Improvement in CCE Planning

Selection of suitable remote sensing indices

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Index Kurukshetra Seoni

Yield (t/ha) Efficiency % Yield (t/ha) Efficiency %

NDVI 3.43 1.05 1.46 1.37

Biomass 3.46 1.04 1.45 1.03

LAI 3.42 1.14 1.51 1.37

LSWI 3.48 1.07 1.40 1.53

NDVI+LAI 3.45 1.07 1.53 1.49

LAI+LSWI 3.41 1.17 1.41 1.62

NDVI+LSWI 3.46 1.22 1.45 1.85

LAI+NDVI+LSWI 3.46 1.20 1.49 1.31

Stratification Efficiency with Different Indices

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Comparison between NDVI+LSWI Map and Yield Map

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0.00

0.10

0.20

0.30

0.40

0.50

0.60

0

500

1000

1500

2000

2500

3000

ND

VI

& L

SW

I

Yie

ld (

kg

/h

a)

Blocks

Yield (Kg/ha) NDVI LSWI

Block wise Rice yield of Seoni District (MP)

(Correlation Between Block level Yield & NDVI – 0.96; Block level Yield & LSWI – 0.65)

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Summary of block wise LSWI Groups Count Sum Average Variance

Lakhnadu 49 10.4 0.21 0.0023

Seoni Tehasil 34 9.7 0.28 0.0033

Barghat 33 11.5 0.35 0.0025

Ghansaur 29 6.7 0.23 0.0037

Kurai 16 4.8 0.30 0.0025

Kewlari 13 4.1 0.32 0.0013

ANOVA

Source of Variation SS df MS F P-value F crit

Between Groups 0.45 5 0.091 33.6 1.07E-23 2.3

Within Groups 0.45 168 0.003

Total 0.91 173

Summary of block wise Yield Groups Count Sum Average Variance

Lakhnadu 45 23153 514 89013

Seoni Tehasil 34 65409 1923 1559536

Barghat 33 82603 2503 1266351

Ghansaur 29 25712 886 365378

Kurai 16 43522 2720 1784747

Kewlari 13 11378 875 155580

ANOVA

Source of

Variation SS df MS F P-value F crit

Between Groups 122757874 6 20459646 24.74468 9.66E-21 2.15

Within Groups 134773289 163 826830

Total 257531163 169

Other Inferences from Yield Data Analysis

• The Analysis of Variance Showed the variance of yield and remote sensing indices within the block is less compared to between the blocks

• There is strong auto-correlation of yield values spatially (Range 0.65 degree)

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Optimum Number of CCE points

• From large number of CCE points, smaller sets were selected randomly.

• Mean, standard deviation and standard error of each set were assessed.

• The optimum number of CCE is the minimum number, where the mean yield is not significantly different from the original and standard error is below a threshold value (5% for District and 10% for block)

• It was found around 30 CCEs are optimum for district level assessment and 15 CCEs for block level assessment (PMFBY Guidelines: District 24 and Block 16)

District level Block level

Yield Estimation from Remote Sensing based Indices

State Range

R2 SEE F value

Gujarat 0.40-0.78 1.69-10.04 4.72-24.84

Haryana 0.33-0.68 4.02-11.02 4.53-19.28

Karnataka 0.26-0.85 3.45-9.90 2.53-39.91

Maharashtra 0.12-0.66 8.08-22.29 1.23-18.13

Uttar Pradesh 0.35-0.85 1.21-04.55 4.51-52.61

• Yield Estimation from VCI (Vegetation Condition Index) : District level yield models for Sugarcane and Cotton

Sugarcane

Rice, r2 = 0.74 Wheat, r2 = 38

Wheat, r2 = 0.58

Semi-Physical Model Simulation Model

• Yield modelling using field data and multiple approaches

Biomass estimation from Microwave Data

Rice, r2 = 0.27

State Range

R2 SEE F value

Haryana 0.30-0.78 38.36-91.65 3.8-33.1

Maharashtra 0.22-0.71 32.13-122.32 2.6-13.7

Gujarat 0.48-0.80 68.55-97.38 6.47-20.13

Punjab 0.15-0.49 73.0-93.8 1.68-8.6

Karnataka 0.20-0.69 41.38-113.95 2.3-20.0

Cotton

NDVI Stratum LSWI Stratum

NDVI+LSWI Stratum

CCE Points Generation Steps in Kalburgi (Gulbarga) district

Dadri Block, Bhiwani District, Haryana

Landsat -8 (09-March-2016), 30 m data

Sentinel-2 (06-Feb-2016), 10 m Data

Landsat -8 (22-Feb-2016), 30 m data

Mustard

Wheat

Mustard

Wheat

Mustard

Wheat

Mustard

Wheat

Classified Map

Satellite Data for Area Discrepancy

14 Oct -2014 9 Oct -2015

Cotton in Parbhani

Sorghum in Aurangbad

24 Dec 2015 10 Dec 2014

Prevented Sowing?

Agricultural Loss Assessment

Flood Affected Area

MODIS NDWI October 2016 MODIS NDVI October 2016

Agricultural Condition - October 2016 Vegetation Condition Index (NDVI)

October 2016

Vegetation Condition Index (NDWI)

October 2016

Rainfall Deviation

June to October 2016

Agricultural Drought Assessment

Other Products for Crop Insurance

Drought Frequency

(May be used for computation of Premium, Indemnity etc.)

Phenology/Crop Calendar

(May be used for Seasonality)

UAS/UAV/Drone in Agriculture

• Platform: Fixed Wing/ Rotary Wing Aircrafts with different degrees of Autonomy

• Cameras: Digital Colour, Multispectral • Flying Height: Around 50 – 200 m

• Image Resolution: Few cm to sub-meter

• Organizations in India: NECTAR, DRDO, ISRO,

DTU/IARI, Quidich, Precision Hawk, Amigo Optima, techbaaz, etc.

• Clearances Needed: • For Flying: DGCA, MHA, MoD, Local Authority • For Data Use: SoI

• Adv.: As and When (?), High Resolution • Disadv.: Low coverage, Complex Analysis

(Source: NECTAR)

Conclusion

1. Smartphone based CCE data collection, will not only ensure execution of CCE, but also will improve the timeliness and quality of CCE. It will also create a wealth of information for many R&D activities.

2. CCE Planning using remote sensing will improve the representativeness of CCEs and also reduce the number of CCEs required.

3. Satellite based remote sensing data can provide assessments of crop damage (at least qualitative) and may help in on account payment.

4. Preventive sowing (or sowing failures) and area discrepancy can be observed using high resolution data, but needs to be integrated with digitized and geo-referenced cadastral maps.

5. Remote Sensing based indices have good correlation with crop yield and may, in future, act as a parameter for index based insurance. However, many more pilot studies are needed to authenticate it.

6. UAVs can provide very high resolution pictures for sample locations, especially for localized risks. However, there is a need for a single-window clearance system for UAV data collection and use.

7. There is a strong need for high resolution satellite data with high temporal frequency. A constellation of small satellites (combination of microwave and optical) is the need of the hour.

8. There is need for collaborative (research institutes, state governments, insurance companies) efforts in exploring the role of technology for PMFBY.

Acknowledgment

Thank You

The results presented here have been derived from the KISAN (C[K}rop Insurance using Space technology And geoiNFormatics) Project of DAC&FW, MoAFW