expert consultation on: establishing an information …step 1: the improvement of the methodology...

Expert consultation on: Establishing an information system on damage and losses from disasters in crops, livestock, fisheries, aquaculture and forestry Rome, 09-10 June 2016

Table of Contents

i

Contents

Background _________________________________________________________________ 1

High-level Introduction ________________________________________________________ 3

FAO work on Agriculture Damage and Losses and the need for an improved information

system _____________________________________________________________________ 5

Methodologies for disaster assessment in agriculture ________________________________ 7

Country experiences _________________________________________________________ 11

Group discussions – Towards an action plan ______________________________________ 16

Data sources, baseline data and post–disaster data ________________________________ 18

Spelling out the causal link between stressors and disaster impacts on crops, livestock,

fisheries/aquaculture, forestry __________________________________________________ 23

Panel Discussion, Q&A, Conclusions ____________________________________________ 27

The road ahead _____________________________________________________________ 31

Closing Remarks ____________________________________________________________ 34

List of participants ___________________________________________________________ 35

References ________________________________________________________________ 37

Acronyms _________________________________________________________________ 38

Contact Information __________________________________________________________ 39

Organization Information ______________________________________________________ 39

Appendix: Agenda of the meeting ________________________________________________ i

Background

1

Background

Well defined, systematically assessed and statistically standardized information on the impact

of disasters on crops, livestock, fisheries and aquaculture, and forestry will support policy-

makers and other stakeholders to make evidence-based programmes and plans on disaster

preparedness, response and recovery actions.

The two-day expert consultation on establishing an information system on damage and losses

from disasters in the agriculture sector and its subsectors held at FAO Headquarters on 09 and

10 June 2016 brought together experts from international, regional and national organizations,

research institutes, and academia aimed at determining components and actions for

establishing an information system on damage and losses from disasters in the agriculture

sector and its sub-sectors.

The present report aims at summarizing and capturing the key points of the expert consultation

on damages and losses in agricultural sectors (DLA) and follows the structure of the

consultation: The high-level introduction sets the objectives Damage and Losses in Agriculture

Initiative and relevant international frameworks. Session 1 presents and discusses FAO’s work

on Agriculture Damage and Losses and the need for an improved information system. Session

2 presents and discusses internationally acknowledged and current methodologies for disaster

assessment in agriculture. Session 3 presents country level experiences from applied disaster

assessment in agriculture. Three group discussion aimed at capturing and strengthening the

components and aspects to be considered when establishing in an information system on

damage on losses in agriculture. Session 4 presents the rich spectrum of data sources, need

for baseline and post-disaster data and discusses sources and aspects to be considered when

establishing in an information system on damage on losses in agriculture. Session 5 presents

and discusses the causal links between stressors and disaster impacts on agriculture and

discusses causalities to be captured in an information system on damage on losses in

agriculture. Session 6 presents the deepening and complementary discussion of panel and the

general expert consultation. The report concludes with a specific potential way forward and

general outlook.

The expert

consultation on

establishing an

information

system on

damage and

losses from

disasters in the

agriculture sector

and its subsectors

is a key milestone

in strengthening

country capacity

to monitor a

number of major

global initiatives,

like the

Sustainable

Development

Goal targets.

Background

2

The expert consultation under FAO Strategic Programme - Resilience demonstrates how

international collaboration not only boosts our own efforts but also strengthens trust and seeks

for synergies between FAO and it’s partners at international, regional and national levels.

High-level Introduction

3


Role of the Information System on DL in Agriculture

The SFDRR, the Paris Agreement and the SDGs suggest better monitoring and

assessments to better address challenges.

FAO is set to develop an information system to monitor damage and losses caused by

natural disasters on the agricultural sector and its subsectors.

Step 1: the improvement of the methodology for assessing damage and losses.

Step 2: support member countries to collect and report relevant, standardized data on the

damage and loss caused by disasters on agricultural sectors.

Objectives of the consultation

Illustrate, discuss and provide inputs on the current FAO work.

Foster exchanges, establish partnership with stakeholders on approaches, methods

and tools for assessing impact of disasters on agriculture.

Specify components of an action plan to develop information system on the impact of

disasters in agriculture, livestock, fisheries, aquaculture, and forestry.

Sendai Framework for DRR

National and Local Strategies ready by 2020 based on priorities for action

DRR is not a cost but investment for development and governance subject.

Issues reach from humanitarian interventions to development work and counter

measures reach from early-warning to prevention.

Build back better – livelihoods instead of rebuilding vulnerability (1:7 ROI).

Indicators needed to measure 7 SFDRR Targets: reduced outcomes; increased inputs

Minimum level: Monitor the implementation of SFDRR with proxy data.

Medium level: Show evidences for leaders that DRR needs governance policy.

Ideal level: Background data sets that can support risk figures and solutions.

Session Chair:

Dominique

Burgeon – FAO

Kostas Stamoulis –

FAO, Assistant

Director-General a.i.

Kimio Takeya –

JICA, Distinguished

Technical Advisor

to the President

Youssef Nassef –

UNFCCC,

Adaptation

Programme

Coordinator

Mannava

Sivakumar – IPCC,

Acting Secretary


4

United Nations Framework Convention on Climate Change

Needed to maximize the outcomes across initiatives, including the Paris Agreement,

and NAPs.

SDGs connect different issues under one super-framework.

In many LDCs, NAPs give importance to agriculture and food security. However, the

data available are scarce; therefore, planning and monitoring exercises often rely on

multi-criteria analyses and subjective judgment.

FAO’s initiative links agricultural data to the different frameworks and their targets:

UNFCC, SFDRR, and FAO’s DLA.

Readiness for preemptive planning: invest in prevention versus contingency planning.

5th Assessment Report of Intergov. Panel on Climate Change

IPCC’s Working Group II (Impacts, Adaptation and Vulnerability) inform in the 5th

Assessment Report’s Chapter 7 on food security and food production systems,

including the disruptive effects of climate change on normal functioning of societies

including agricultural production (e.g. cereals, oilseeds, livestock, fishery, forestry).

FAO’s initiative on damage and losses is timely.

IPCC’s 5th Assessment Report in Chapter 6 is also about maintaining information

systems. Information systems can assist in estimating economic costs of climate

change which can be billions dollars in agriculture in developing as well as in

developed countries.

Next to natural effects, anthropogenic effects need attention.

FAO’s upcoming information system on damage and losses in agriculture shall

address the needs of poor countries.

FAO work on Agriculture Damage and Losses and the need for an improved information system

5


Governments, GFDRR, UN, and EC require for effective DRR policy and practice

sector-specific damage and losses data to design measures specific to the crop,

livestock, fisheries and forestry subsectors.

Statistics on damage or losses rarely disaggregate the impact on individual sectors,

and reporting is not systematic and consistent across countries

FAO’s PDNA Analysis: Damage and Losses in Agriculture Subsectors:

Crops 58% by floods; Livestock 85% by droughts; Fisheries 69% by tsunamis;

Forestry 89% by storms.

Periodic FAO reporting on The Impact of Disasters on Agriculture and Food Security

Collaboration of FAO with UNISDR and the OEIWG on monitoring agricultural damage

and losses under the SFDRR – especially indicator C-2 on Direct Agricultural Loss

due to Disasters.

Liaison of FAO with UNFCCC’s Warsaw International Mechanism for LD associated

with CC on issues related to agriculture sectors.

FAO-UNISDR Joint SDG Indicator proposal: “Agricultural loss due to disasters (hazard

events)”.

Session Chair:

Dominique

Burgeon – FAO

Introducing FAO

study and report:

“Impact of disasters

on agriculture and

food security”

achievements and

challenges

(Stephan Baas –

FAO)


6

Discussion:

Zapata (ECLAC): A number of effects were added to the PDNA guidelines, in

addition to damage and losses. The analysis of effects focuses on cross-cutting

issues such as governance, gender, livelihoods, and environment. There is an

ongoing World Bank’s initiative aiming to train governments on PDNA methodology;

therefore, the level of knowledge of damage and losses assessment in countries will

increase.

K. Warner (UNFCCC): The approach has a lot of potential. Decision-makers need

this information to guide their actions and investments. We should wonder if a

money metric is enough, or if we need to go deeper and understand what is behind

monetary impacts.

M. Sivakumar (IPCC): Systematic data collection at local level is challenging, mainly

because officers are not aware of standards in many cases. This capacity has to be

built.

R. Stefanski (WMO): Implementing standardized procedures at local level is

challenging.

K. Takeya (JICA): PDNAs only handle extreme events where people die. However,

in rural areas the death toll might be lower, while the impact on agriculture might be

significant. At the same time, we need to better understand smaller scale events

which still have a great impact, for instance on productivity.

Furthermore, we should not forget that some disasters may also bring benefits; for

example, floods in the Mekong river basin increase bring benefits to small scale

fisheries. The 2008 report of the Mekong River Commission showed that the

benefits of floods in the basin are more than 20 times bigger than the damage.

D. Ehrlich (EU-JRC): Different datasets were used in the FAO study. Is FAO already

collecting data at country level?

C. Arcilla (National Institute of Geological Sciences, Philippines): Technology can

help reducing the impact of disasters; this was the case after Typhoon Haiyan.

G. Macfadyen (Poseidon Cons.): The datasets used in the study are different in

terms of granularity. For example, EM-DAT does not disaggregate data at local

level, nor by sector.

Sawano (ICHARM): How is bad management assessed? For example, how do we

consider when there is over-cropping?

Methodologies for disaster assessment in agriculture

7


This session presents the relevant methodologies, concepts, frameworks, and

standards for assessment of damage and losses from disasters in agricultural sectors.

Post Disaster Needs Assessment in Agriculture: An Overview.

Neil Marsland, FAO

The analysis of disaster effects in Post Disaster Needs Assessment:

Context analysis;

Disaster effects: infrastructure and assets; production of and access to goods and

services;

Governance processes;

Increased risks and vulnerabilities.

In 2014-16, the World Bank has rolled out a global initiative for training on PDNA at

country level.

Damage refers to impact on assets, while losses are measured 3 to 5 years into the

future.

In Armenia, work is ongoing to establish a methodology for reporting not only on large-

scale events but to include also small events;

A key challenge of the PDNA process is the collection of secondary data. There is a

need for improved data archiving.

Fisheries and forestry are not yet well covered in PDNAs.

It is important to ensure that post-disaster assessment work is really needed and cost-

effective. For instance, the costs of a PDNA recently conducted in Serbia turned out to

be higher than the funds received in compensation by the government.

Another key challenge that needs to be addressed is to ensure follow-up and

monitoring of actions taken on the basis of PDNA outcomes/recommendations.

In Syria, PDNA is being tested for assessing the impact of protracted crises. Analysis

at the household level is also part of the assessment (random sample survey with

5,000 households for analyzing impacts on livelihoods).

Session Chair:

Ricardo Zapata –

ECLAC

Post Disaster

Needs Assessment

in Agriculture: An

Overview (Neil

Marsland – FAO;

Daniele Barelli –

FAO)

Methodology to

estimate disaster

impact on

agriculture under

the SFDRR

(Sylvain Ponserre –

UNISDR)

Methodology to

establish causal

relations between

extreme events and

impact on

agriculture (Robert

Stefanski – WMO)


8

Methodology to estimate disaster impact on agriculture under the SFDRR.

Sylvain Ponserre, UNISDR

SFDRR focuses on small and large scale disasters, as well as frequent and infrequent

ones.

SFDRR’s global target C is “Reduce direct disaster economic loss in relation to global

gross domestic product (GDP) by 2030”. To measure progress towards this target, a

composite indicator is proposed to measure economic loss in different sectors,

measured as asset losses in terms of recovery cost. The baseline is 2005-2015.

Proposed indicator C2 is “direct agricultural loss due to hazardous events”. The

methodology to measure this indicators is based on data on number of damaged

hectares and number of 4-legged livestock heads lost. Damaged hectares are

multiplied by a direct cost per hectare and a damage ratio fixed at 25 percent. The

cost per hectare is estimated using producer prices of all crops cultivated in each

country, weighted by harvested areas. A similar approach is used for livestock.

This approach can be refined by having more details on physical characteristics

(crops, livestock etc.).

These indicators are linked to the SDG targets 1.5 and 11.5.

Methodology to establish causal relations between extreme events and impact on

agriculture.

Robert Stefanski, WMO

WMO works on cataloguing extreme events (weather-related). The goal is to agree on

unique identifiers and indices to identify disasters. So far, 70 types of disasters were

identified. A GLobal IDEntifier Number (GLIDE) is proposed to classify each event

(including information on location, duration, magnitude, recovery, endpoint, etc.).

An increase in extreme events was observed since 1970s; droughts have particularly

increased since the 1980s, and storms especially in the last two decades. Wildfires

are growing in the most recent period.

Many countries have already established damage and losses databases, which also

use GLIDE to classify events.

WMO works closely with FAO on drought under the Integrated Drought Management

Programme (IDMP). Work include reviewing definitions and phases of drought (IDMP

handbook on drought indices), as well as supporting the development and

implementation of drought management policies in countries. Only for droughts, there

are about 35 indicators dealing with temperatures and precipitation.


9

WMO also works on an Integrated Flood Management Programme together with

FAO and others.

Discussion

Y. Nassef (UNFCCC): We should focus on the best ways to exchange existing data, keeping in mind confidentiality and national security issues attached to sharing data on national vulnerability.

C. Arcilla (National Institute of Geological Sciences, Philippines): In the Philippines, the data from the disaster risk reduction and management program NOAH were housed in the University. The impartiality of the academic institution proved very useful to encourage collaboration between different government departments and other actors.

J. Teran (OCHA): OCHA has developed an open platform for data sharing (UNISDR, WFP and more than 240 organizations are using it). This platform is very useful to share data for coordination purposes.

R. Stefanski (WMO): data sharing is incremental. As we start, agencies may

become less reluctant.

R. Zapata (ECLAC): A clear message from the presentations is that we need to move towards harmonizing terminology and methods.

G. MadFadyen (Poseidon Cons.): Is the fisheries sector monitored under the SFDRR? Is UNISDR working on forecasting or ex-post assessment?

S. Ponserre (UNISDR): Fisheries losses are calculated as part of indicator C3

(facilities). The GAR also includes forecasting analyses. However, the

methodology presented is based on ex-post assessments done by countries.

J. Conforti (FAO): it is important not to forget who is affected and when; this changes significantly the economic and social impact.

D. Verdonk (WAP): It is important to link SFDRR’s indicator C to indicator B7 on livelihoods of affected people. It is also important to take into account the loss due to animals that are weakened, not only dead animals.

P. Conforti (FAO): the composite index proposed for target C may create issues related to weighting and compensation. For instance: what weight should we assign to each sector?

R. Zapata (ECLAC): It is not clear how the data will be validated, and how damage and losses will be distinguished in the UNISDR methodology. There are also different livelihood implications: one cow lost by a peasant farmer has a different impact than one cow lost by a large agricultural company.

K. Takeya (JICA): PDNA only covers extreme events, and Philippines is the only country applying PDNA at local level. The purpose of the PDNA is to estimate recovery needs, but PDNAs are often focused only on the affected area without covering underlying risks in other areas.


10

S. Baas (FAO): It is important to underline that this initiative does not aim to

replace PDNA, but just to improve it. Does the classification of WMO help in

terms of defining impact?

K. Warner (UNFCCC): I this session we had 3 examples of what is being done,

from which we can derive lessons on what FAO should do. What is the most

needful thing to be done – data to be collected – given the current perspective

in agriculture?

C. Arcilla (National Institute of Geological Sciences, Philippines): the

government of Philippines has built a wall in the sea to prevent future

occurrences of storm surges after Haiyan. However, now we can predict

typhoons and get prepared. How do you balance the 1 in 100 years’ disaster

with everyday livelihoods?

K. Takeya (JICA): We need a combination of structural and non-structural measures protecting assets and flows, respectively.

G. MacFadyen (Poseidon Cons.): very few PDNAs comply with the PDNA methodology, it changes a lot from country to country. Do the PDNAs put the ECLAC methodology into practice? We should be careful not to overload member countries in terms of data that need to be collected and sent to FAO.

S. Baas (FAO): The approach is bottom-up. Data is collected at national level,

and we should build on what already exists in each country.

N. Marsland (FAO): we have to strengthen the link between impacts and DRR, i.e. what are the costs saved after implementing DRR? We can still use the PDNAs for monitoring small disasters at the local level. It is true that not all PDNAs use homogeneous methodology; however, this reflects variable capacities to collect and process the information.

R. Zapata (ECLAC): Cost-benefit analysis of DRR is also essential.

K. Takeya (JICA): in Sendai there is a macro-economic model looking at how to

reduce the impact on GDP (consistent with the 7 subsequent targets highlighted

in the morning). PDNA focuses on the short run. We need something broader:

we need underlying risks datasets to convince political leaders about investment

priorities.

Country experiences

11

Country experiences This session reviews the practical experiences of countries in assessing damage and losses

from different types of disasters in agricultural sectors. The session also presents lessons

learned and recommendations for improvement of damage and losses assessment.

Flood disasters

Robert Brakenridge – Univ. Colorado, Dartmouth Flood Observatory

Dartmouth Flood Observatory analyzed 44,000 floods occurred between 1985 and

2015. Remote sensing data (near real time, using MODIS and LandSat 8) are used to

measure floods as they occur, monitoring change in surface water every day, and the

information is stored. GIS maps are also used to analyze the information.

Historical maps are needed to understand if an event is new or if it is happening every

year.

The advantage of MODIS is that it covers large areas, with high time frequency. Water

pixels are classified and colored to show historical water patterns. Highly frequent

floods allow understanding what to prepare for.

Same maps with long time series can be used to predict the expected extent of flood

via probability of flood – based on frequency criteria. Hence classify events as x-years

probability events. This allows building a quantifiable flood prediction map. The

probability maps can be used to define insurance contracts (public or private), that is,

to set thresholds above which insurance companies are willing to pay.

P. Conforti (FAO): We would need to overlay these maps with land use maps.

S. Baas (FAO): We would need information also on the height of the flood.

For measuring height of floods, passive microwaves satellite radiometry can be used;

when rivers rise you can see a global network of satellite river gauging sites (from

1998), developed in collaboration with JRC (T. De Groeve).

Wind disasters

Carlo Arcilla – National Institute of Geological Sciences, The Phlippines

Philippines is hardly and frequently hit by typhoons, on average 20 per year,

especially in September and October.

There was a problem with terminology in the aftermath of Typhoon Haiyan. The

typhoon caused the highest storm water surge ever recorded in the Philippines;

however, people were used to the word tsunami (different type of disaster) and they

Session Chair:

Stephan Baas –

FAO

Flood Disasters

(Robert

Brakenridge – Univ.

Colorado,

Dartmouth Flood

Observatory)

Wind Disasters

(Carlo Arcilla –

National Institute of

Geological

Sciences,

Philippines)

Drought Disasters

(Tewodros

Yeshiwork – Save

the Children,

Ethiopia)

Wildfire Disasters

(Larsgunnar

Marklund – FAO;

Peter Moore –

FAO)

Flood Risk

Assessment on

Agricultural

Damage of Rice-

crops and

Government of

Japan's Damage

Data Management

(Hisaya Sawano –

ICHARM)

Country experiences

12

did not pay enough attention to the storm surge, which caused many deaths and

extensive damage.

There is a need for high resolution hazard maps. The NOAH project in the Philippines

use a technology called Lidar. Data from Lidar provides historical information on

floods, landslides and storm surges, and safe areas.

The NOAH project started in 2011. It was able to prevent many deaths, e.g. in 2014

floods in Manila, no one died thanks to the prediction.

We also have a project on app-based information sharing on disasters. By using

mobiles, people can be alerted fast enough. In fact, this technology is reducing the

death toll significantly.

An important impact of typhoons and floods is debris flows, which bring huge stones

and cause severe damage and losses, including in agriculture.

Drought Disasters

Tewodros Yeshiwork, Save the Children, Ethiopia

Ethiopia is highly vulnerable to rainfall deficits and drought. El Nino is worsening the

situation.

The first drought management policy was used in 1993, and it was always linked to

food security since then. In 1995, a disaster prevention and preparedness commission

was established.

In 2008, a Disaster Risk Management Centre was set up, including analysis,

mitigation, prevention and recovery. Early warning is a major component, aimed at

triggering timely and appropriate responses.

There is a regular monitoring system at district level, with seasonal assessments

(twice a year) to evaluate the outcome of main/short rainy seasons and the impact on

food security. Livelihood baseline data at household level are analyzed in combination

with seasonal data to assess intervention needs, and considering livelihood zones.

Emergency Nutrition Assessments and Disaster Area Assessments are carried out.

Woreda Disaster Risk Profiling is an initiative funded by the World Bank to examine

the root causes of disaster at district level, and developing contingency plans.

Livelihoods, Early Assessment and Protection (LEAP) system was established since

2005.

The need for an information system on damage and losses in agriculture is there.

S. Baas (FAO): Ethiopia is a good example where FAO could perhaps roll-out the pilot

at country level.

Country experiences

13

Wildfire disasters

Peter Moore (FAO)

Wildfires are a relatively new area. They are increasing at rapid pace: 400 percent to

700 percent increases since 1970s, mainly in Australia and parts of Canada, but also

in Tanzania (MODIS images show 10 to 14 percent of Tanzania burning every year).

How do we assess impact?

Low impacts, large fires: in remote forest areas or where no assets and population

exist. With a natural cycle of regeneration there is no major problem. The same is for

small fires that do not affect people and activities.

High impacts, large fires: these occur in areas where there are people and productive

activities. For example, in 2000 a huge fire caused extensive damage to production

forests in Ethiopia. In Canada, forest fires may cause a decline of 3-4% of GDP next

year. Part of the damages can be insured easily as a monetary value can be easily

identified.

High impact, small fires: these fires, although small, may cause significant disruption

of natural forest cycle of regeneration and renewal of particular habitat or ecosystem.

However, it is difficult to assign a monetary value to ecological impacts. We need to

better document damage and losses caused by these types of wildfires.

Also, there is a need for information on diseases outbreaks related to the disruption of

forests.

FAO has expertise in remote sensing and forest resource assessment; in the last 5-

year review of forests (2015), burned areas were also assessed. The Memorandum of

Understanding recently signed between FAO and Google could help enhancing this

stream of work.

S. Baas: we could have a case-study on wildfires in the next report.

Country experiences

14

Flood Risk Assessment on Agricultural Damage of Rice-crops and Government of

Japan's Damage Data Management

Hisaya Sawano – ICHARM

Assessment of flood impact on rice crops. The relation between stressors and damage

(hazard-damage relation) and expected damages is poorly studied.

At ICHARM, we have analyzed risk indicators in order to build risk curves, considering

crop production stages. The damage curves for rice crops link flood depth with relative

damage and vegetative stage. To do so, we have used geo-referenced data, including

“social data”

Applied in Japan, where there is a special post-disaster survey for agriculture within 7

days from the disaster, with report in 20 days based on crop-specific data (Emergency

Disaster Damage Survey of Farm Products).

The source of data is very important. Damage calculated through satellite data and

models are much higher than those reported; data are also very different when

comparing satellite and models.

ICHARM aims to apply global climate models to estimate the impact of climate

change, linking information at all levels.

Country experiences

15

Discussion

S. Ramasamy (FAO), discussion opener: The goal is to identify data that can

help quantifying disaster impacts for prevention. We should keep in mind the

diagram presented this morning, which goes from data sources to the building

of baseline/primary data and eventually the assessment of damage and losses.

El Idrissi (FAO): A key question is how do we integrate different dataset? We

invest a lot of resources in prevention data (risk data, e.g. locust).

S. Baas (FAO): The entry point is on collecting data in post-disaster settings.

K. Warner (UNFCCC): it would be interesting to know more about salt water

intrusion after cyclone Nargis. How can it be measured using remote sensing?

R. Brakenridge (DFO): Saltwater intrusion cannot be seen with satellite imagery.

For that we need information from the field.

S. Ponserre (UNISDR): Regarding wildfires, we want to use TEEB methodology

to quantify the value of forest fire damages.

Y. Nassef (UNFCCC): The focus is on the supply side; but in your document you

look at food security, the demand side. You need to think conceptually to a 4 th

step which is the calibration of information to demands of policy makers, based

on feedbacks.

C. Arcilla (National Institute of Geological Sciences, Philippines): We could

consider height of plans to understand what is on the ground. Question to

Sawano: how did you quantify the damage starting from the imagery?

Sawano (ICHARM): We got damage data from the government, although

accuracy we know is an issue. Sometimes collecting field data is challenging; in

Japan, for example, data collection becomes a tax issue.

K. Takeya (JICA): We think that FAO should propose an indicator relevant for

agriculture. In particular, indicator C2 and also indicator D13 need to be supplied

by FAO. You should think to make a proposal practically. This is the same

indicator that the SDGs need to use.

S. Baas (FAO): Thank you. We have proposals for the Informal Consultation of

the OEIWG on 20 and 21 June in Geneva.

P. Conforti: (FAO) Hazard data should be overlaid with land use maps globally.

Do you think it is possible? (to Sawano)

Sawano (ICHARM): for our activity we choose representative crops, but covering

all crops would be very hard.

C. Arcilla (National Institute of Geological Sciences, Philippines): Drone data

could be used for that purpose.

Group discussions – Towards an action plan

16


Group 1: Methodological challenges for assessing damage and losses in agriculture

What measurement, for whom? We need to show the magnitude of disaster

impacts on agriculture to show the cost-effectiveness of investment in

prevention.

Targets are policy makers (decision makers) at national level. They need

information and evidence to inform and monitor their actions.

It is crucial to go beyond direct damage. Indirect impacts are huge. For drought

they are larger than direct. In a second phase, it would be relevant to also

analyze environmental impacts, as well as household perspectives to identify

more specific needs.

How do we establish the information system? We need to harmonize existing

methodologies, including the use of remote sensing. Quality of the data can be

variable, it is important to have other means to do quality checks.

We need to be careful about sensitive data – not to expose people.

Group 2. Data gaps and possible solutions

There is agreement on the importance of baseline information. We need to

measure (affected) surfaces. This is happening in some countries, but there is

no harmonization. Data should be collected possibly at national level and in a

more timely fashion.

Crops: we need acreage and areas. Losses are in yields at subnational level.

We also need to consider typologies of producers.

Livestock: number of heads and purposes need to be considered. Baseline

information of expected production and damage as departure from it. Distinguish

nomadic from fixed raising.

Fisheries: importance of infrastructure, vessels.

Aquaculture: areas and reduction in yields.

Forestry: basic information in area covered. Consider timber and environmental

services.

Methodological

challenges for

assessing

damage and

losses in

agriculture;

Data gaps and

possible

solutions;

From sub–

sectoral and

country

specificities to

global standards.


17

Group 3. From sub–sectoral and country specificities to global standards.

To move across levels, we need to:

Define minimum requirements: specific variables (similar to the ones highlighted

in Group 2)

Establish common measurement methods

Different disaggregation levels (global, regional, local)

Standardize, the collection and aggregation procedures

Re-processing national data to generate regional and global figures

ICTs to feed data collection. Some countries are ahead of the game – need to

collect examples.

Need partnership among international organizations, and private-public

partnerships;

Need a matrix of impact curves

UNEP GAP Adaptation report.

R. Brakenridge (DFO): USDA is starting to collect data on losses in agriculture

due to floods using remote sensing.

D. Ehrlich (EU-JRC): Big data are also important. How are they standardized?

P. Moore (FAO): South Africa has an archive of 30 years of data on satellite

imagery.

Data sources, baseline data and post–disaster data

18

Data sources, baseline data and post–disaster data This session focuses on the most important data sources, methods and techniques of

data collection (ante-disaster, post-disaster), for identification and assessment of

damage and losses.

Geophysical and Meteorological Stressors

Selvaraju Ramasamy - FAO

Challenge is identifying causal links between risks and outcomes, between stressors

and disaster impacts.

Each disaster can be determined by a combination of factors. A number of stressor

indicators need to be considered, e.g. for drought, floods, storms, pest and diseases,

earthquakes and volcanic eruptions.

Also, the stressors trigger direct impact and also other secondary slow onset events

(e.g., salinization, erosion), which have significant impact on agriculture sectors.

There are many methods to link stressors and outcomes: these include, among

others, book-keeping, statistics, analogues, summary models (combination of

statistical relations), and detailed eco-physiological models. Extreme events are

difficult to capture in eco-physiological models.

Case study on drought (Gujarat, India): based on FAO methodology, yield reduction

factors were identified for different types of crops at each growth stage, depending on

the number of dry spell weeks.

Case study on typhoons (Philippines): based on FAO methodology, rice yield

reduction factors were identified at each growth stage, depending on wind intensity

and period of exposure to strong winds (i.e. more or less than 12 hours).

Case study on floods (Philippines): based on FAO methodology, rice yield reduction

factors were identified at each growth stage, depending on the number of days of

submergence.

The study of the relationship between stressors and impacts is challenging because of

the multiple elements to be considered, including the types of stressors, the level of

exposure, sensitivity and vulnerability, the varying thresholds depending on location

and time.

Several data sources and tools are available; the challenge is access, harmonization

and application.

Session Chair:

Daniele Ehrlich –

EU-JRC

Geophysical and

meteorological

stressors (Selvaraju

Ramasamy – FAO)

Earth observation

data (Luca Dell'Oro

– UNITAR,

UNOSAT)

Insurance data

(Massimo Pera –

FAO)

DesInventar:

Disaster

Information

Management

System (Sylvain

Ponserre –

UNISDR, Risk

Knowledge)

EM-DAT:

International

Disaster Database

(Regina Below –

CRED)

Hazard and Impact

Modelling (Erin

Hughey – PDC)


19

Earth Observation Data

Luca Dell’Oro – UNITAR, UNOSAT

UNOSAT has the capability to forecast and follow climatic hazards, even 6 days in

advance. All is based on satellite imagery (Foresight Report).

There are methodologies to extract information from satellite imagery, e.g. overlaying

vector baseline data with UNOSAT satellite images in a GIS environment. By doing so,

it is possible to conduct disaster impact assessment.

Several examples from UNOSAT work show that it is possible to estimate population

affected and infrastructure damage to support humanitarian operations. Also, satellite

images can be used to assess trees damaged (e.g. Fantala cyclone), as well as land

use change (e.g. in Myanmar, from mangrove forests to paddy fields). In the case of

drought, NDVI anomalies and number of hectares affected by droughts can be

identified; changes in production can be computed if baseline data is available,

although baseline data is missing in many countries.

Examples of technologies used include, among others:

MODIS-based flood monitoring;

SPOT sensor to identify coffee plantations in Yemen;

NDVI analysis for drought monitoring, especially agro-meteorological drought;

Flood-FINDER to predict floods with hydrological maps, rainfall data and pre-

acquired satellite maps;

IGAD-ICPAC Live Map for monitoring El Nino-related floods;

UN-ASIGN crowd-source photos mobile app, which can be used to share geo-

tagged photos with UNOSAT. Relevant ones are then shared with OCHA to

support humanitarian interventions. Crowd-source images can help validate

satellite imagery.

Insurance data

Massimo Pera – FAO

The two main re-insurance companies working on disasters are Swiss Re. and Munich

Re.: together they account for 40 percent of premiums. The databases of these two

companies cover direct damages to property and assets; in few cases they include

indirect losses such as lost revenues.

Re-insurance companies have significant amounts of data on disaster impacts;

however, not all these data can be shared.


20

The NATCAT database collects all types of data on disasters (press, meteorological

services, insurance available information, academic research, donors, UN agencies).

This data is used for modelling D&L. The dataset is based on peril families, events in

each family and then sub-perils. This dataset is to fill data gaps (e.g. non-insured

markets), re-insurance companies develop textual descriptions from which they do

quantitative assessments – using modelling approaches (instead of payout values) for

estimating economic damages.

All the events since 1980s are recorded, and major events since 1950s. The following

steps are followed to feed the database:

1. Data collection and maintenance, generally done by externals; 2. Loss estimation (using information on insured losses and estimation

procedures to fill data gaps); 3. CPI adjustment, GDP normalization, GCP normalization; normalization is

used to account for inflation and changes in wealth over time. 4. Analytics

What data can be available for FAO? Insurance companies can provide aggregated

data, updated every year. However, it is not possible to disaggregate by sector.

Rather, the modeling and normalization can be used in the building of risk curves.

K. Warner: the variety of data used by these companies cannot be considered fully

reliable, even if important market decisions are made based on this data.

R. Mechler: Insurance data on claimed losses are not disclosed and they are usually

very rough.

DesInventar: Disaster Information Management System

Sylvain Ponserre - UNISDR

DesInventar is a flexible tool developed by UNISDR to collect data and information on

disasters. The tool can accommodate the computation of the new indicators required

by the SFDRR and SDGs.

Currently, DesInventar includes more than 300 000 records on disaster impacts from

90 countries, subdivided into 16 quantitative indicators and 12 qualitative indicators.

For agriculture, there are two indicators on (1) damaged hectares; and (2) livestock

units lost. At present, the database recorded 221 million hectares of crops damaged,

and 32 million lost cattle. Estimated losses amount to USD 377 billion between 1980

to 2013.

DesInventar allows comparative analysis across countries. Additional effort needs to

be done to disaggregate the data. Trainings are being conducted at country level to

improve the reporting on DesInventar indicators.


21

EM-DAT: International Disaster Database

Regina Below, CRED

EM-DAT CRED was created in 1988 by the Louvain University. It collects disaster data

from all over the world from 1900. It focuses mainly on human and economic losses at

global level.

Disasters are increasing in magnitude and frequency. In post-disaster conditions, data

collection is extremely challenging: there are many data, generally poorly organized

and scarcely reliable. There are no international best practices on how to collect

disaster data.

EM-DAT has four criteria for inclusion of disasters, i.e.: (1) 10 or more people dead; (2)

100 or more people affected; (3) declaration of a state of emergency; and (4) call for

international assistance. At least one of these four criteria has to be filled; therefore,

not all disasters are recorded, but rather the “most relevant” ones.

EM-DAT uses several sources of information; however, the most relevant sources are

prioritized (e.g. UN sources). Three main steps for data entry: (1) define the type of

disaster and its description (a coding system is used to classify the events); (2)

compile country level information; (3) validate the data using the most reliable figures.

This process can take time; it is not real time data. Efforts are being made to geo-

reference all the data.

Regarding agriculture, sector-specific data is collected, but final values are

aggregated.

Some key limitations: (1) small-scale disasters are hardly recorded; and (2) there are

issues with the homogeneity and comparability of data.

The Foundation for Effective Post-Impact Decision-Making

Erin Hughey – Pacific Disaster Center

The Center was established in 1992 after hurricane Iniki. Geo-data is combined with

information on exposure modelling on the ground available at sub-national and

national level. Information is disseminated through social media and other media for

early warning and other purposes.

The starting point is socioeconomic risk and vulnerability assessments, which are then

combined with data on stressors. The Center uses models to link risks with impacts for

quick ex-ante and ex-post assessments. Models are calibrated on assessment of

post-disaster impact. A vulnerability assessment exercise is conducted every year.

Vulnerability maps can be overlaid with the stressor maps.


22

An example of dissemination tool is EMOPS, an internet-based decision support

system. Almost every piece of data is brought together in this tool, which shows

impacts and how impacts translate into needs. EMOPS generates alerts of various

types (SMS, emails etc.).

Discussion

P. Moore (FAO): Can we link remote sensing data with crowdsourcing?

S. Ponserre (UNISDR): Yes, crowdsourcing and remote sensing data can be

linked. The main challenge remains data validation.

L. Dell’Oro (UNITAR/UNOSAT): You can model disaster through remote

sensing, but FAO seems to need more.

M. Sivakumar (IPCC): Regarding the example on Nepal done by Hughey: do

you have contacts with the government or other institutions in the country?

E. Hughey (PDC): Yes, we have contacts with the government, and we provided

our data.

D. Ehrlich (EU-JRC): Does any of the data produced by PDC feed into a database?

E. Hughey (PDC): the data is archived but not into disaster databases; we are ready to collaborate on this.

R. Zapata (ECLAC): The stressors presented by Selvaraju are only natural. How

about anthropogenic stressors? All these datasets are good systems to collect

information. But still need to collect better information at country level.

S. Ramasamy (FAO): Anthropogenic stressors are also very important, although

they were outside the scope of my presentation.

R. Mechler (IIASA): Harmonization is required, although we cannot prevent

people from doing what they need. It is also important to work on vulnerability

and resilience.

P. Conforti (FAO): do we want to have more detailed data than what was shown

by DesInventar? We do need more details on agriculture.


fisheries/aquaculture, forestry

23

Spelling out the causal link between stressors and disaster impacts on crops, livestock, fisheries/aquaculture, forestry This session highlights the key variables of each sub-sector specific in the assessment

of damage and losses from particular disasters.

Crop Production

Wilson Hugo - FAO

Impact on crops depends on a number of elements. Some stressors can be good for

some crops and bad for others. It is important to analyze the interaction between

different stressors, and their cascading effects. For instance, high temperature

combined with humidity leads to increase in mycotoxins.

Temperature is a key stressor: too high and too low temperature may generate

negative impacts on crops. Excessive variability can also provoke damage, e.g. it can

break dormancy in young plants. The impact depends on the season and the growth

stage. Horticultural crops show even more variable impacts.

Radiation (UV light) is another possible stressor.

In the case of earthquake, the negative impact on crops may be due to limited access

to cultivated areas by farmers.

R. Zapata (ECLAC): We need to also consider technology. How about GMOs? Do

they have an impact? Planting periods are changing with climate change.

R. Stefanski (WMO): do you know of any study on impact caused by ozone layer?

Reply: None that I am aware of.

Livestock

Vincent Briac, J. Pinto & Ahmed El Idrissi, FAO

As shown by the FAO study on The Impact of Disasters on Agriculture and Food

Security, livestock is mostly affected by drought.

Disaster impacts on the sub-sector largely vary between nomadic and stable

operations; and among farming systems (e.g. more or less intensive; large vs small

scale).

Session Chair:

Mannava

Sivakumar –

IPCC

Crop production

(Wilson Hugo –

FAO)

Livestock (Ahmed

El Idrissi – FAO)

Fisheries and

aquaculture

(Florence Poulain

– FAO; Graeme

Macfadyen –

Poseidon Cons.)

Forestry (Peter

Moore – FAO;

Erik Valinger –

Swedish Univ. of

Agric. Sciences)



24

There is also a cross-impact between livestock and crops, i.e. crop losses may reduce

the availability of animal feed.

When assessing disaster impacts on livestock, it is important to consider that animals

have traditional and cultural values in many countries. Furthermore, animals are not

only used for consumption: they are assets (e.g. used as dowry payments), and they

are used for draft power.

In addition to natural disasters, animal diseases have a strong impact on the livestock

sector. They cause decline in production efficiency by about 33 percent globally. For

instance, FMD caused USD 21 billion in damage and losses, considering also the

additional vaccination costs. FAO has experience on assessing the impact of

transboundary animal diseases, e.g. via SAMs. However, data is very poor, especially

baseline data.

H. Sawano (ICHARM): UNISDR wants to assess damage of animals based on full

values. Reply: a generalization is needed on animals, but I have no specific

suggestion on this.

Fisheries and aquaculture

Florence Poulain – FAO; Graeme MacFadyen – Poseidon Consulting

When assessing the impact of disasters on fisheries and aquaculture, it is important to

also consider the processing stage. Women are mainly involved in processing

activities.

Fisheries are particularly relevant for SIDSs, which are vulnerable to hazards.

FAO hosts the FISH-STAT database, which collects several types of data on fisheries

and aquaculture also through a questionnaire on the Code of Conduct for Responsible

Fisheries. Questions on damage and losses could be included in this questionnaire,

and some assessments could be included in country profiles.

There are many sources of baseline data for fisheries. For example, data on f ish

stocks are available from regional fishing bodies – FAO has the data.

FAO is using satellite data in the context of the BlueBridge project. Aquaculture plants

are detected via satellite imagery. These images could be used in assessments.

Each type of disaster has different impacts on the sector. Floods may be good for

fishing in some cases, while they have negative impacts on aquaculture. Storms can

provoke damage in harbors and to vessels and other equipment, thereby limiting the

possibility to go fishing. Droughts affect floodplain fisheries and aquaculture plants.

Biological disasters can also have high impacts on the sector.

Most impacts do not feature in annual production data – while GDP reduction can be

very significant.



25

Using simplistic methods such as the one proposed by UNISDR would imply losing a

wealth of data and information that should instead be collected. In particular, indirect

losses should be estimated.

Forestry

P. Moore – FAO; Erik Valinger – Swedish University of Agricultural Sciences

Large increase in the damage over the last 15 years, not only in developed countries

but also in developing where the record is poorer (there’s no information). Increasing

risks from increased anthropic pressure.

Storm damage (eg) impacts both the assets – environmental damage etc – but also

the “asset” that is the stock of wood.

One key variable – the forest land – is easily computed from remote sensing.

Baselines are available in most developed countries – not quite the same in

developing.

Wildfire damage:

Reaction to events may overshadow the underlying risks. The latter need to be

addressed.



26

Discussion

R. Zapata (ECLAC): We should focus on identifying key damage and losses

variables, including what data is being collected in FAOSTAT, what are the key

stressors to be looked at, etc.

G. MacFadyen (Poseidon Cons.): Agricultural sectors are very complex and

specific. As a way forward, FAO could expand data collection and reporting at

country level. Modelling would be questionable because it requires several

assumptions.

K. Warner (UNFCCC): There are many data available on current and historical

disaster impacts. Is it possible to establish correlations between hazards and

production in order to better understand what could happen in the future?

P. Conforti (FAO): I would be skeptical, it is not obvious to use past observations

to predict the future.

R. Mechler (IIASA): It is not possible to predict the future, but it is important to

project the future; this is IPCC’s business and it has proven useful.

Y. Nassef (UNFCCC): We need to focus on demand. Do countries really feel the

need to collect these data?

D. Barelli (FAO): Capacities and needs vary largely from country to country. In

Malawi, FAO worked with local teams and created capacity on damage and

losses data collection. Overall, there is poor capacity in developing countries,

but maybe also little interest in collecting this kind of information.

R. Brakenridge (DFO): Valuing ecosystem services is crucial to analyze

cascading impact, from environmental degradation to increasing disasters and

impacts, e.g. from mangrove clearing to increasing storm damage.

P. Conforti (FAO): Valuing ecosystems is more a research type of effort, which

has a strong subjective element.

S. Ponserre (UNISDR): The TEEB methodology will be used to calculate

environmental losses for SFDRR monitoring.

Panel Discussion, Q&A, Conclusions

27

Panel Discussion, Q&A, Conclusions Chair: Dominique Burgeon - FAO

Based on previous discussions and the results of the small group discussion on data gaps

methodological challenges and specificities by sector/country/disaster type, experts in the panel

will propose concrete steps and milestones to move forward with the establishment of an

information system on damage and losses in agriculture, fisheries and forestry.

Panel discussion

D. Burgeon (FAO): The institutionalization process is very important. Since its foundation 70

years ago, FAO has very good relationships with National Statistical Offices and Ministries of

Agriculture of member countries for data collection and dissemination. FAO can leverage this

experience and expertise for building the information system on damage and losses, working in

close collaboration with all relevant stakeholders.

D. Burgeon to D. Ehrlich (EU-JRC): what could be the collaboration between FAO and JRC in

this field?

D. Ehrlich (JRC): We have worked at European level collecting satellite imagery. We have also

collected data at country level in EU. It is important to come up with homogeneous ways of

collecting data. We are collaborating with FAO, and we look forward to collaborating also in this

field, e.g. through the development of common standards and guidelines.

D. Burgeon to R. Mechler (IIASA): We know IIASA works also on linking disaster risk reduction

and climate change adaptation. How do you see our collaboration in this field?

R. Mechler (IIASA): IIASA is an international institution with 46 members. Data on damage and

losses in agriculture could be very useful for “forensic” investigations. In fact, we need to

understand and study disasters also to prevent the next event to happen. It should motivate

action at policy level but also at project level for climate change adaptation.

D. Burgeon to S. Ponserre (UNISDR): How do you see UNISDR and FAO enhancing

collaboration?

S. Ponserre (UNISDR): It is very important to work together. DesInventar could become a unique

tool for all the UN agencies. We need resources to make this possible, and we also need new

Chair:

Dominique

Burgeon – FAO

Panel discussion

Daniele Ehrlich –

EU, JRC

Reinhard Mechler

– IIASA

Sylvain Ponserre

– UNISDR

Mannava

Sivakumar –

IPCC

Kimio Takeya –

Japan, JICA

Javier Teran –

OCHA

Koko Warner –

UNFCCC

Ricardo Zapata –

ECLAC

The road ahead

(Piero Conforti –

FAO)


28

technologies, such as crowdsourcing. We could put together a new set of tools for agriculture

together, to present at the UNGA in February 2017.

D. Burgeon to M. Sivakumar (IPCC): Based on your experience in IPCC reports, could you tell

us what are the existing gaps?

M. Sivakumar (IPCC): In the 5th assessment report on IPCC there is an important chapter on

agriculture. At COP21, we were asked to provide 3 Special Reports by 2018: (1) a report on the

impact of global warming of 1.5°C above pre-industrial levels; (2) a report on climate change,

desertification, land degradation, sustainable land management, food security, and greenhouse

gas fluxes in terrestrial ecosystems; and (3) a report on climate change and oceans and the

cryosphere. Contributions from FAO into these special reports would be very important. In this

sense, the information system would be very important for IPCC’s special reports. We also need

FAO to fill the gaps in the current 6th Assessment Report. There need to be exchanges and

partnerships between all relevant stakeholders, including NGOs and private companies,

especially industries linked to agriculture (e.g. pesticides, machinery producers). Mobile

technologies could be very helpful for data collection, we need to translate this into mobile

Applications.

D. Burgeon to K. Takeya (JICA): You have highlighted the need for FAO to be involved in a

number of indicator related to the SFDRR. Could you please elaborate on that and perhaps on

strengthening indicator monitoring while overcoming the trap of becoming too complicated

K. Takeya (JICA): The challenge of SFDRR is that it now covers also man-made disasters. The

purpose of SFDRR indicators is monitoring the implementation. I really strongly believe that the

most important Agency to contribute to agriculture-related indicators (including C-2 and D-13)

under the SFDRR is FAO. We also need to simplify: more than 160 indicators are currently

proposed by the OEIWG; even in Japan we can only report on 50% of these. We need to use

proxy data, we do not need too detailed scientific data. The focus should be not only on

monitoring the SFDRR implementation, but also on showing evidence to policy makers for

investing in DRR. We do not have to forget that political leaders will have to convince their

electors about investments. There are existing risks and future risks (underlying risks); the latter

will be increasing, and we need to show how we reduce them.


29

D. Burgeon: In FAO, we are working on a global study that focuses on measuring the returns

from investments into DRR good practices. The goal of this study is to provide evidence to policy

makers on the cost-effectiveness of DRR measures.

D. Burgeon to J. Teran (OCHA): the recent World Humanitarian Summit was organized by

OCHA: one of key messages was that humanitarian needs have increased by 600%; it was clear

from the summit that we need to focus on resilience. How do you see our work contributing to

OCHA?

J. Teran (OCHA): OCHA is developing a platform that combines several variables and indices

(e.g. INFORM) from countries, as well as the correlation among these variables (e.g. IDPs

presence and fatalities) on a spatial basis. The platform is called HDX, and it uses data from

UNISDR, UNOSAT, UNHCR. It would be good to also connect with FAOSTAT.

D. Burgeon to K. Warren (UNFCCC): We know that climate change is key when assessing

disaster impacts. In the Paris Agreement there is a section on loss and damage: how do you see

the role of this initiative contributing to inform policy level decision?

K. Warren (UNFCCC): As a way forward, I suggest to focus on context, challenges and possible

solutions. Data availability does not seem to be an issue; the important thing is to measure what

is most important for decision-makers. Some possible reflections while moving forward: a

dashboard approach would be helpful for the FAO information system, along the lines of the

platform presented by OCHA. The dashboard approach is useful and should focus only on a

few indicators, e.g., production, livelihoods, food availability. Focus on what decision makers

really need: for example the graphs showed yesterday (S. Baas presentation) on disaster

impacts on agriculture by region. Decision makers have a holistic view and want to know where

to invest their money.

D. Burgeon: As FAO we have direct contact with ministers of agriculture, livestock etc.; hence

we need to respond in a simple and effective way to their needs.

D. Burgeon to R. Zapata (ECLAC): You have been pioneering the methodologies for damage

and losses and personally involved in many PDNAs. What is your views on the gaps that needs

to be addressed and how can we move forward?

R. Zapata (ECLAC): Let me link to something that Regina Below said today: keep it simple. We

must be demand driven. How do we use all the abundance of data and platforms in a way that


30

really supports decision-makers? Let’s focus on terminology as well: we need to speak the same

language.

Y. Nassef (UNFCCC): I agree that it is very important to focus on the “clients”; at the same time,

however, we do not have to forget what the humanity needs. In some cases, decision makers

may not perceive these needs as they often focus on short-term decisions, but these issues may

be still very relevant for humanity.

C. Arcilla (National Institute of Geological Sciences, Philippines): We should focus on making it

simple and use existing data available in FAO, e.g. analyze the correlation between FAO data

and disasters. Countries are reluctant to invest resources to build solid baseline data.

K. Takeya (JICA): I agree that simplicity is important; however, we also need disaggregated data

to make good decisions.

D. Barelli (FAO): Baseline data remains a key challenge of this exercise. It is missing in most

countries.

The road ahead

31

The road ahead P. Conforti, FAO

There are two major sets of conclusions/recommendations that can be drawn from the

expert consultation.

1. Suggestions on how the information system on damage and losses in

agriculture should look like (desirable features); and

2. Suggestions on how to proceed for building the information system.

1. Desirable feature of an information system; contributing to building a vision

of a functioning information system.

The Sendai Framework for Disaster Risk Reduction (SFDRR) and the Sustainable Development

Goals (SDGs) should be used as overarching frameworks for this effort. Monitoring is the

minimum level of achievement envisaged in the Sendai framework. At the same time, we need

to focus on convincing decision makers to invest in prevention. We need to do so by looking at

the future and showing that investment in prevention pays off. The SDGs bring things together.

They are an ambitious agenda that contrasts with the reality of lack of progress due to increasing

vulnerability and exposure. FAO can contribute to the SDG process by supplying technical

insights for the monitoring.

The information system should be demand-driven, calibrated on the demands of policy makers

and stakeholders. It is important to find an agreement on the different clients, but at the same

time we should not forget what humanity needs. We need to consider how measurement affects

decision-making. Hence, we cannot exaggerate in simplicity, as perhaps the key is being able to

provide details. For instance, disaggregated information on the value of damage and losses in

crops, livestock, fisheries, forestry can tell decision makers what to do. Money is a useful way to

aggregate and compare.

It is crucial to agree on terminology. For example, it was highlighted the need to include disruption

of governance and gender aspects into the definition of damage and losses. Also, a damage and

losses classification system should be created, along the lines of the one proposed by WMO.

We need to enhance partnerships with sister and other organizations, including interoperability

for sharing and accessibility.

The road ahead

32

We should go beyond direct damage. We are starting from damage and losses in crops,

livestock, fisheries and forestry. Assessing damage and losses in these areas is already quite

complex – we have seen how specific the impacts on the subsectors and the interaction among

different risks are. At the same time, we will have to include aspects related to food security,

livelihoods and poverty, the so called “indirect impacts”, which are significant (e.g., in the case

of drought, they are larger than direct impacts). We also need to consider environmental damage,

household perspective and prioritization of needs.

We should start from ex-post assessments in order to enhance the ability to project future risks.

These two aspects are related: if post-disaster data is available, projecting future risks is also

possible. In order to enhance the understanding of current and future disaster impacts, we

should work on risk curves (i.e. functional relations between risks and damage). In the case of

crops, for example, we can analyze stressors and impacts by growth stage, based on the

knowledge available in FAO. In other words, this means that we need to analyze the causal link

between risks and damage and losses.

2. How to achieve this vision -- what do we start doing tomorrow?

The general feeling is that there is room for improving the type of information that is currently

available; we miss important details in the estimates made by some of the existing methods and

tools. This is the case especially if we are ambitious as we should be, and want to look at impact

of disasters on livelihoods, food security and poverty.

Several data sources are already available, including among others Insurance data, EM-DAT

CRED, DESINVENTAR, the data from the Pacific Disaster Centre. Many of these have

information and calculations, which should be better clarified in the context of agriculture.

PDNAs are an important part of the picture. They are a good vehicle for collecting data, and also

a process that can benefit from more detailed efforts. In particular, we need to go beyond the

approach of reconstruction and focus on longer-term, i.e. prevention and preparedness.

Risk curves can be obtained with different methods, such as case studies (i.e. piling up evidence

on ex-post assessments); modeling; overlaying maps on outcomes and consequences.

Furthermore, many methods were presented on how to link stressors and outcomes, including:

book-keeping, statistics, analogues, summary models (combination of statistical relations), and

detailed eco-physiological models.

The road ahead

33

Satellite imagery is promising, but images need to be turned into data. We have seen examples

of successful institutional set-ups (e.g. Ethiopia, the Philippines), which are based on a sound

combination of images and data. At the same time, images may not be relevant for all types of

disasters, subsectors, and impacts. In particular, images seem less usable to assess

consequences, as data on production (yields) by specific item (crops, livestock fisheries,

aquaculture, and forestry products) cannot be obtained through images. Surveys are still

required.

Data on consequences require baseline information, as damage and losses are deviations from

expected paths. We know these are poor in several disaster-prone countries, but there are also

excellent country examples that can serve as examples. Baseline data is also important to

disentangle disaster impacts from other contributing factors. In some cases, for instance, pre-

existing mismanagement can lead to higher disaster damages. This is important to know for

prioritizing actions.

As shown by UNITAR, avenues to pursue are observations such as the NDVI and its deviations;

very interesting is also the possibility to do crowdsourcing of images for validating the information

provided by satellite imagery. This seems promising.

We should focus on few variables, to avoid overloading countries involved in the monitoring. For

example, we have seen more than 20 indicators of stressors related to temperature and

precipitation. We need to prioritize and choose. Key variables needed include, among others:

yields, areas, prices, infrastructures. These data should be (1) timely (seasonal, allowing short

run adjustments based on evolution); (2) Granular – enough small scale to allow qualification by

district/area; and (3) Typified: we need at least some rough information on production system

(large scale, small scale, nomadic etc.). In order to measure impacts on livelihoods, we will need

to have spatial information on rural livelihoods.

In cases when data are scarce, an alternative that can be considered is to obtain damage data

from (re-) insurance companies. They have information on insurable risks, although it is not

broken down by sector. Rather, elements of the technology of the insurances for modeling and

normalization can be used in the building of risk curves.

As a general approach, the development of a dashboard with 3 or 4 key indicators that most

countries care about was suggested.

Closing Remarks

34

Closing Remarks K. Stamoulis - FAO, Assistant Director-General a.i.

Two intensive days of presentations and discussions on methodologies and practical

experiences on disaster impact assessment in agriculture have certainly contributed to achieve

the objectives of the expert consultation. This meeting was particularly important in view of

establishing strong partnerships and collaboration with key experts and institutions in this field.

This work will be able to inform the implementation and monitoring of the SDG targets, the Sendai

Framework for Disaster Risk Reduction (SFDRR) and the follow up to the Paris agreement as

well as of the commitments of the World Humanitarian Summit (WHS). We acknowledge and

welcome the suggestion of the experts especially about FAO having to play a crucial role in

providing technical support in monitoring agriculture related indicators under the Sendai

Framework. We are ready to support UN partners in this important task.

At the country level, it was clear from discussions that the effectiveness of this initiative depends

heavily on harmonizing existing data and strengthening information on the impact of disasters

on agricultural sectors. It was highlighted the need for evidence-based decision-making by

governments and investments in disaster prevention and preparedness and adaptation to

climate change.

We should think of this moment as an important beginning, considering the challenging and

exciting next steps identified in the panel discussion and summarized into the proposed action

plan. All these efforts would equip us in better governing and monitoring crisis and disaster risks,

reducing community vulnerability, and better preparing for and responding to crises and shocks.

List of participants

35

List of participants The consultation included experts, practitioners, researchers and other relevant stakeholders

involved in the assessment of damages and losses from natural disasters from relevant

international, regional and national organizations, research institutes and academia.

ARCILLA National Institute of Geological Sciences University of the Philippines

Diliman, Quezon City, The Philippines

AYELE Expert Save the Children, Ethiopia

BELOW Centre for Research on the Epidemiology of Disasters (CRED), Université

catholique de Louvain

BRAKENRIDGE Director, Dartmouth Flood Observatory "Senior Research Scientist

CSDMS, INSTAAR, University of Colorado

DELL'ORO Analyst and capacity development expert. UNITAR’s Operational Satellite

Applications Programme (UNOSAT) UN Operational Satellite Geneva

EHRLICH Scientific Officer. European Commission Joint Research Centre

HUGHEY "PhD Director of Disaster Services Pacific Disaster Center Hawaii

MACFADYEN Director Poseidon (fisheries and aquaculture consultants)

MECHLER Deputy Program Director Risk and Resilience International Institute for Applied

Systems Analysis (IIASA) Laxenburg, Austria

WARNER UNFCCC, Adaptation Programme Coordinator

NASSEF Coordinator of the Adaptation Programme. UNFCCC Adaptation

Programme

PONSERRE Information specialist UNISDR Risk Knowledge Section

SAWANO Chief Researcher. ICHARM, International Centre for Water Hazard and Risk

Management under the auspices of UNESCO Japan

SIVAKUMAR Acting Secretary. World Meteorological Organization (WMO).

Intergovernmental Panel on Climate Change.

STEFANSKI Chief World Meteorological Organization. Agricultural Meteorology Division

TAKEYA Distinguished Technical Advisor to the President JICA

TERAN CASTRO Statistician Humanitarian Data Exchange United Nations, Office for the

Coordination of Humanitarian Affair (OCHA) New York, USA

VERDONK Head of Programmes World Animal Protection Netherlands Programmes

ZAPATA-MARTI Senior Advisor EU Part. PDNA/PCNA Coordination Support Office

List of participants

36

Participating and contributing staff from FAO

Stamoulis, Kostas

Burgeon, Dominique

Baas, Stephan

Shukri, Ahmed

Ramasamy, Selvaraju

Lombardi, Niccolò

Fujisawa, Mariko

Hideki, Kanamaru

Gennari, Pietro

Conforti, Piero

Caprazli, Kafkas

Raisaro, Claude

Edge, James

Glori, Dana

Thomas, Sylvaine

Miccinilli, Elisa

Marsland, Neil

Barelli, Daniele

Moore, Peter F.

Marklund, Larsgunnar

Sathyapala, Shiroma

Cedergren, Jonas

Briac, Vincent

El Idrissi, Ahmed

Pinto, Julio

Wilson, Hugo

Cressman, Keith

Pera, Massimo

Hernandez, Emilio

Poulain, Florence

Taconet, Marc

Ghosh, Kakoli

Korolija, Gordana

Whimpenny, Paul

Chang, Kaison

Calpe, Conception

Mustafa, Shirley

Thoenes, Peter

References

37

References

The Impact of Disasters on Agriculture and Food Security

http://www.fao.org/3/a-i5128e.pdf

Notes on an Information System on Damage and Losses from Disasters in

Agriculture. A Strategic Programme 5 – Resilience Initiative


Increasing resilience of agricultural livelihoods


FAO Position Paper – The World Humanitarian Summit


FAO Website on Resilience

http://www.fao.org/resilience

FAO Website on Emergencies

http://www.fao.org/emergencies

FAO Website on Statistics

http://www.fao.org/statistics /

Website for Participants of

Expert consultation on: Establishing an information system on damage and

losses from disasters in crops, livestock, fisheries, aquaculture and forestry

http://www.fao.org/economic/ess/ess-events/infosystem





http://www.fao.org/resilience

http://www.fao.org/emergencies/

http://www.fao.org/statistics

http://www.fao.org/economic/ess/ess-events/infosystem

Acronyms

38

Acronyms

CC Climate Change

CRED Centre for Research on the Epidemiology of Disasters

DesInventar Disaster Information Management System

DL Damage and Losses

DLA Damage and Losses in Agricultural Sectors

DRR Disaster Risk Reduction

EC JRC Joint Research Centre’s Science Hub - European Commission

EM-DAT Emergency Events Database (The Intern. Disaster Database)

FAO Food and Agriculture Organization of the United Nations

ICHARM The International Centre for Water Hazard and Risk Management

IPPC Intergovernmental Panel on Climate Change

JICA Japan International Cooperation Agency

LDCs Least Developed Countries

NAPs National Adaptation Plans

PDC Pacific Disaster Center

PDNA Post-disaster Needs Assessment

RRR Recovery, Rehabilitation, and Reconstruction

SDGs Sustainable Development Goals

SFDRR Sendai Framework for Disaster Risk Reduction

UNFCCC UN Framework Convention on Climate Change

UNISDR The United Nations Office for Disaster Reduction

UNITAR United Nations Institute for Training and Research

UNOSAT United Nations Operational Satellite Applications Programme

WMO World Meteorological Organization

Contact Information

39

Contact Information

Shukri Ahmed

Senior Economist

Tel (+39) 06 570 53737

[email protected]

Stephan Baas

Natural Resources Officer

Tel (+39) 06 570 55051

[email protected]

Piero Conforti

Senior Statistician

Tel (+39) 06570 53664

[email protected]

Kafkas Caprazli

Statistician

Tel (+39) 06570 54916

[email protected]

Selvaraju Ramasamy

Environment Officer

Tel (+39) 06570 56832

[email protected]

Niccolò Lombardi

Expert in DRR a. Statistics

Tel (+39) 06 570 54170

[email protected]

Organization Information FAO of the UN

Viale delle Terme di Caralla, 00153 Rome – Italy

Tel (+39) 06570 51

http://www.fao.org/economic/ess/ess-events/infosystem/

Appendix: Agenda of the meeting

i



ii


iii


iv

expert consultation on: establishing an information …step 1: the improvement of the methodology...

Documents