Applications of yield monitoring

systems and agricultural statistics

in agricultural (re)insurance

18 October 2018

Ernst Bedacht

Image: used under license from shutterstock.com

Agenda

Introduction

1. Munich Re

Parametric Insurance Solutions

2. Weather

3. Yield

4. NDVI

5. Crop Models

Conclusions

06 October 2017 2

Added value within the groupDiversified structure – more security

Reinsurance Primary insurance

Asset Management

Munich Re (Group)1

06 October 2017Company Presentation Munich Re1 This listing is incomplete and provides no precise indication of shareholdings.

Munich Re (Group)

3

Munich ReWorld market leader in agricultural (re)insurance (Indemnity Based Agricultural

Insurance, Parametric Insurance, Crop Revenue Insurance)

6 October 20174

Goal:

Provide a product that helps the farmers in adverse conditions, is objective/transparent and gives the insurer

the ability to assess the inherent risk.

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Challenges for Parametric Products

Product Requirements Challenges

Basis RiskMinimizing the basis risk by being as close to the farmers

risk as possibleBasis risk can never be avoided in parametric products

TransparencyThe parametric product (index calculation) needs to be

well documented and understood by the InsuredComplexity vs. Transparency

Data AvailabilityReliable data source to ensure permanent delivery and

have adequate fallback procedures in place

Satellites products might depend on clouds

Technical problems may take long to solve

Data history Homogeneous data history in order to assess the risk

From our experience, 10 years of data provides

reasonable information, but the more the better, also

depending on the loss entry probability.

Extreme scenariosHow can rare events (e.g. Natcat) influence the parametric

cover?Hard to assess if not observed

TriggersImportant to understand the intention of the cover as

triggers should ideally reflect the agronomical risk

Difficult task requiring data analysis and agronomical

background

Weather3

Potential perils: drought, excessive rainfall

(1) Rain gauge‐only products that build only on observations using different interpolation methods, e.g.

Global Precipitation Climatology Centre (GPCC) monthly precipitation, the Climatic Research Unit (CRU)

monthly precipitation, Climate Prediction Center (CPC) unified gauge‐based analysis of daily

precipitation

(2) Reanalysis products, i.e. products from data assimilation schemes and numerical weather predictions

or atmospheric models that use a combination of atmospheric variables from various sources as inputs,

e.g. the Climate Forecast System Reanalysis (CFSR) reanalysis or products from National Centers for

Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) and European

Centre for Medium‐Range Weather Forecasts (ECMWF)

(3) Satellite‐only products that use infra‐red (IR), microwave (MV) or IR‐MV combined information, e.g.

CHIRP, CMORPH_RAW, CHOMPS

(4) Satellite‐gauge products that combine gauge‐only and satellite‐only products through different bias

correction or blending procedures, e.g. TRMM, CMORPH, PERSIANN, and CHIRPS

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RainfallData Set Classification

8

Data used: CHIRPS

Description

CHIRPS: Climate Hazards Group InfraRed Precipitation with Station

data

Early research focused on combining models of terrain-induced

precipitation enhancement with interpolated station data

New resources of satellite observations such as gridded satellite-

based precipitation estimates from NASA and NOAA have been

leveraged to build high resolution (0.05°) gridded precipitation

climatologies. When applied to satellite-based precipitation fields,

these improved climatologies can remove systematic bias

Horizontal resolution: 0,05°x 0,05°

Available from 1981 – now

Latency: Final CHIRPS (all station data) is available sometime in the

third week of the following month

http://chg.geog.ucsb.edu/data/chirps/

Rainfall CHIRPSWeather index based crop protection on County Level in China

9

Basic Concept

Description

Divide the growing season of each crop in three 6-8 weeks risk

periods, starting with the planting date

Protect drought in all three periods and excessive rainfall just in the

third period (harvest)

Calculate the county wide rainfall per period for all years available

Either: Estimate reasonable rainfall triggers for drought and

excessive rainfall per period.

Or: Set target rate on line for each period (drought and excessive

rainfall) and calculate the corresponding triggers

Pros

Concept is applicable in all

relevant growing areas of the

world

Satellite rainfall data helps a

lot in areas where the weather

station density is low

Works good for larger scale

(countywide) drought or

excessive rainfall events

Independent and public data

source

Cons

Basis risk of weather index for

crop shortfall

Latency of the data (data is

available sometime in the

third week of the following

month)

Does not work for single

fields/farms

Rainfall CHIRPSWeather index based crop protection on County Level in China

10

Rainfall and Temperature Ukraine

Perils covered

• Drought and Heat stress

• Crops: Corn, Winter Wheat

Data used

• Interpolated Daily Meteorological data (temp, rainfall)

• MeteoGroup, based on weather station data

• Historical data: 1991-2016

• Resolution: 25x25km

Weather Index

• Heat Stress Period:

No of days with Tmax > Critical Temperature

• Drought Period (31/41 days):

40% of long-term average rainfall

Yield2

Potential perils: all

Data Sets

Public available data (based on surveys or crop cutting experiments)

Examples: USDA (USA), IBGE (Brazil), SIAP (Mexico), …

Important considerations:

Basis Risk for the single farmer (due to size of areas)

Quality and availability of data

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Area Yield

2016: Huge shift from weather based crop insurance (WBCIS) to area yield crop insurance (PMFBY =

Pradhan Mantri Fasal Bima Yojana)

Market Premium ~ 3bn US$

Rate paid by farmer (loanee and non-loanee) is not higher than 2%, rest is subsidized by state and

government

For some crops (e.g. Rice) on a very high resolution (Gram panchayat, ~250.000 in India) or on block

level (~ 5.500 in India)

Perils covered:

- Prevented Sowing – due to deficit rainfall/adverse weather

- Standing Crop - due to Drought, Dry Spell, Flood, Inundation, Pest & Diseases, Landslides, Natural

Fire and Lightening, Storm, Hailstorm, Cyclone, Typhoon, Tempest, Hurricane & Tornado

- Post Harvest Losses - maximum up to 2 weeks in cut and spread condition due to cyclonic and

unseasonal rains

- Localized Calamities - Hailstorm, Landslide & Inundation affecting individual farms 06 October 2017 13

Area YieldExample India

Every state of India manages the PMFBY

State is divided in several clusters which consist of some districts

The primary insurers bid for each cluster and the cheapest gets the cluster

Most important pitfalls:

Potential trends in yield data

Change in spatial dimension of areas aggregated for pricing and settlement (Block and Gram

panchayat)

Challenges with Crop Cutting Experiment process

06 October 2017 14

Area YieldExample India

Normalized Difference Vegetation

Index (NDVI)

4

Parameter: NDVI

Potential perils: Lack of Biomass production (e.g. drought)

Data Sets

Satellites providing NDVI values

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Dataset Details Temporal Coverage Temporal Resolution LatencySpatial

Resolution

MODIS

moderate-resolution imaging spectroradiometer (MODIS) is built

by Santa Barbara Remote Sensing that was launched into Earth

orbit by NASA in 1999 on board the Terra Satellite, and in 2002 on

board the Aqua satellite.

2000 - present Both daily few days 250m

LANDSAT

Landsat, a joint initiative between the U.S. Geological Survey

(USGS) and NASA, represents the world's longest continuously

acquired collection of space-based moderate-resolution land

remote sensing data.

1972 - present 16-days up to 7 weeks 30m

SENTINEL

Sentinel-2 is an Earth observation mission developed by ESA as

part of the Copernicus Programme to perform terrestrial

observations (Sentinel 2A & 2B)

2015 - present 5-days few days 20m

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Data used: NDVI

Description

NDVI (MODIS): MODIS 8-day composite developed and provided by

the NASA/GSFC/GIMMS group for the USDA/FAS/IPAD Global

Agricultural Monitoring project https://glam1.gsfc.nasa.gov/

Use relevant gridcells and Images for the relevant riskperiod

Define Index (e.g. average NDVI of the gridcells over the riskperiod

per year)

Define trigger value for the NDVI and Payoutfunction

Horizontal resolution: 0,25°x 0,25° (is only available aggregated)

Available from 2002 – now

Latency: few days

https://glam1.gsfc.nasa.gov/

NDVI ModisExample Australia

Crop Models5

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Modelled Yield-index

Statistical Crop Model

Process-based Crop

Model

Yield data

Weather data

Remote sensing data https://www.gaf.de/

https://www.pik-potsdam.de/

Crop Yield Estimation ProjectOutlook

Conclusions6

Unfortunately,

always comes with basic risk

the smaller the farmers, the larger the basis risk

But,

Transparent and objective

Quick loss payments possible

Systemic risks like drought or excessive rainfall can be covered on an aggregated scale (catastrophic

risks)

from a worldwide perspective, no clear preference regarding preferred parameters

data availability, quality and resolution will improve over time, which will lead to more tailored products

06 October 2017 21

Parametric productsExperience and Conclusions

Thank you for your attention

Image: used under license from shutterstock.com

Ernst Bedacht

ebedacht@munichre.com

www.munichre.com/agro