Improved drought detection to support crop insurance models: powerpoint

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Proba-V Symposium

Oostende

29-31 May 2018

Improved drought detection to

support crop insurance models

Kees de Bie

Ben Maathuis

Anton Vrieling

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Higher basis risk

Lower

basis

risk

More

accurate / Less

error-propagation

Accumulated response to Agronomic Drought

Climatological Drought

INTRO: why NDVI and what is insured ?

Agronomic Drought

Response to Agronomic Drought

•Insures risks associated to “exposure to drought” •A specified money amount

(credit taken) is insured, NOT the value of suffered yield-loss!!

•Is area-specific (1x1km) •Is growing season specific •Is crop a-specific

•Is fully scaleable

•Suits only rainfed systems

Looking at

responses

Looking at

inputs

Never insure the output of a business model (=yield) !!

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The key-problem to quantify NDVI-anomalies at pixel level and by dekad:



we always have

insufficient annual-repeats

to get robust frequency distribution estimates !

ANOMALY

mapping is mostly based

on comparing the present reading

with the ‘long-duration’ average

Advised is to use ALL available

annual repeats BY dekad and BY

pixel.

Based on absolute deviation from

the long term average, calculated

for 1999-2010.

Insurance schemes require quantification of probabilities and not: poor, normal and good !!

Insurance companies deal in MONEY

Farmers will argue regarding COSTS and PAY-OUTS

With too few repeats, results are

often generalized, by using the

terms: poor, normal and good.

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All these ‘anomalies’ refer to differences around the average. Insurance schemes require probabilities at the low-extreme !!

ANOMALY

mapping is mostly based

on comparing the present reading

with the ‘long-duration’ average

The key-problem to quantify NDVI-anomalies at pixel level and by dekad:



we always have

insufficient annual-repeats

to get robust frequency distribution estimates !

How to calculate anomalies

BY dekad and BY pixel ??

(5)

The key-problem to quantify NDVI-anomalies at pixel level and by dekad:



we always have

insufficient annual-repeats

to get robust frequency distribution estimates !



AND

we have very few (or no) data at the frequency distribution

extremes

!

Any “relative” difference operator can show very unpredictable and sharply fluctuating results …(https://land.copernicus.eu/global/products/vci)

Based on 10 year data, the 20%

left-tail threshold is derived

from 2 readings only!

And the 5% left-tail is derived

from ……. none!

Do NOT assume,

for a pixel and

dekad, that it

has a normal

NDVI frequency

distribution

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The key-problem to quantify NDVI-anomalies at pixel level and by dekad:



we always have

insufficient annual-repeats

to get robust frequency distribution estimates !



AND

we have very few (or no) data at the frequency distribution

extremes

!

Do NOT assume,

for a pixel and

dekad, that it

has a normal

NDVI frequency

distribution

To address all specified issues, we need to use population statistics instead of sample statistics.

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How do insurances

perceive DROUGHT



Is there ever a drought in the Sahara ?  NO, the Sahara is perpetually dry

Is Tigray suffering from more droughts then Oromia ?



_{NO, the only differences relate to drought severity, otherwise, Tigray is just dryer!!}

So: drought is a

negative anomaly

(deviation) from what is

normal

.

Anomalies can be measured through pre-defined percentile thresholds (by dekad):

15% 50% 5%

0%

100%

tr

ig

ge

r

ex

it

ND

VI

or a Normal Distribution ? Mean Mean Mode Mode Median Median Ri gh t sk ew ed di str ibu tio n HIGH Le ft sk ew ed di str ibu tio n LOW HIGH LOW

ND

VI

The key-problem to quantify NDVI-anomalies at pixel level and by dekad:



we always have

insufficient annual-repeats

to get robust frequency distribution estimates !



AND

we have very few (or no) data at the frequency distribution

extremes

!

Use of the 5 and 15 percentiles as thresholds, matches a Pure Premium rate of 10%

Pay-Out

probability

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Insurance schemes require quantification of probabilities and not: poor, normal and good !!

Insurance companies deal in MONEY

Farmers will argue regarding COSTS and PAY-OUTS

All these ‘anomalies’ refer to differences around the average. Insurance schemes require probabilities at the low-extreme !!

Any “relative” difference operator can show very unpredictable and sharply fluctuating results …(https://land.copernicus.eu/global/products/vci)

To address all specified issues, we need to use population statistics instead of sample statistics.

Use of the 5 and 15 percentiles as thresholds, matches a Pure Premium rate of 10%

Once solved (space-time cube): • we do not require a long-duration

average of 30-years

• we do have enough degrees of freedom (pixel x annual repeats) to derive robust %-estimates for trigger and exit thresholds.

The key-problem to quantify NDVI-anomalies at pixel level and by dekad:

Actuaries: we cannot create

sound insurance models, based

on ≤ 30 historical records (annual

repeats) ?

- How do we define/generate a

POPULATION (a group of pixels),

from which to extract the

required percentile statistics ?

- Is using a 30 years window

representative for present-day

situations ?

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Medium NDVI-Values by dekad of all 18 annual-repeats

(the reference to assess anomalies)

ETHIOPIA: from a

space-time cube to

population statistics !

Solution:

ZONATION

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Results of unsupervised classification: • Pixels  Zones (= Classes, Clusters) • Time  Legend (with NDVI-info)

We used the ISODATA tool:

About 2,000,000 pixels were grouped into 160 clusters; each with 2,500 to 12,500 pixels.

Medium NDVI-Values by dekad of all 18 annual-repeats

(the reference to assess anomalies)

ETHIOPIA: from a

space-time cube to

population statistics !

Solution:

ZONATION

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A small part of the generated legend (NDVI-Information by zone/class/cluster)

Differences between

zones relate to

differences in:

• growing seasons,

• land use and mix of

crops grown,

• cropping calendars,

• soils and terrain

• land cover, species

composition, and

abundance (density)

• weather and climate

• farming systems,

• etc.

Shown legend-items

depict various actual

gradients !

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Dekadal assessments during a growing season

Pay-out starts once the actual NDVI-value drops below the preset Trigger-value.

Pay-out is 100% if the actual NDVI-value drops below the preset Exit-value.

actual NDVI-values

for 1 pixel Zonal thresholdsby dekad

The pay-out amount will be the averaged value (%) for the specific season

x

insured amount.

Indemnity amounts (0-100%)

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… with full drought assessment by pixel by season: 2015 (

Early+Long

)

These maps show where ‘land’ was exposed in 2015

to agronomic drought *

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… with full drought assessment by pixel by season: 2015 (

Late+Long

)

2nd_{major drought}

in ONE year!!

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… with full drought assessment by pixel by season: 2016 (

Early+Long

)

2015 “after-drought” effects

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… with full drought assessment by pixel by season: 2016 (

Late+Long

)

This drought (that lasted well into 2017) caused lots

of nomadic displacements ! These maps show

where ‘land’ was exposed in 2016

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Farmer claimed yield-losses % 0 0-20 20-40 40-60 60-80 = Payout % 0 43% 6% 3% 0% 0% 0-20 20% 12% 0% 0% 0% 20-40 1% 4% 7% 0% 0% 40-60 0% 0% 1% 2% 0% 60-80 0% 0% 0% 0% 1%

Frequency counts of pay-outs (%)

versus farmer claimed yield-losses (%)

[n=190; North Wollo zone]

Only 3+1 = 4% of the farmers claims

deviated between 20-40% from our

pay-out estimates.

Most remain happy (some got money,

while not needed), and for the

remaining 1% a payout was expected

(but not received).

The acceptable accuracy

(< 20% deviation) for the surveyed

area and farmers is thus 96% !

Field validation of Insurance accuracy: Basis risk

Density plot

[many 0-0 combinations; R2_=60%]

Modified from: Fetene Zerihun Minale, 2017

Two additional, independent studies, provided

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… and the

highest

pay-out by pixel by season in 18 years (

Early+Long

)

The maps show where agronomic

drought was severe and potential impacts

high *

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… and the

highest

pay-out by pixel by season in 18 years (

Late+Long

)

The maps show where agronomic

drought was severe and potential impacts

high *

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Not all pixels in a zone experienced a drought, while some experienced several droughts;

outliers based on past facts: Red+Green (

Early+Long

)

The maps show where customer

satisfaction would have been

high (red) or low (green)*

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Not all pixels in a zone experienced a drought, while some experienced several droughts;

outliers based on past facts: Red+Green (

Late+Long

)

The maps show where customer

satisfaction would have been

high (red) or low (green)*

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Solved the “space-time cube”

• We can now focus on using only long-duration data that: • represents the present-day climatic status (moving window) • represents the present land conditions, land use, and farming

systems (that follows developments/trends/changes)

• We have now tested logic, concepts and tools to group pixels into clusters (classes), which allows us to derive robust trigger- and exit-threshold estimates.

• During that process, we did re-obtained (lost!) logic on ‘how to act like an actuary, representing the insurance world’

• The product proved fully scaleable, low cost, practical and accurate • Basis-risks, moral hazards, and fraud-opportunities are all minimized • Fields never need to be visited once their location is known (1km2_grid)