DRM Webinar II: Governing and managing disaster risk in the agriculture sector Assessing risks and impacts from extreme events/natural hazards on the agriculture sector
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Over the past decade, economic damages resulting from natural hazards have amounted to USD 1.5 trillion caused by geophysical hazards such as earthquakes, tsunamis and landslides, as well as hydro-meteorological hazards, including storms, floods, droughts and wild fires. Climate-related disasters, in particular, are increasing worldwide and expected to intensify with climate change. They disproportionately affect food insecure, poor people – over 75 percent of whom derive their livelihoods from agriculture. Agricultural livelihoods can only be protected from multiple hazards if adequate disaster risk reduction and management efforts are strengthened within and across sectors, anchored in the context-specific needs of local livelihoods systems. This series of three webinars on Disaster Risk Reduction and Management (DRR/M) in agriculture is organized to: 1. Discuss the new opportunities and pressing challenges in reducing and managing disaster risk in agriculture; 2. Learn and share experiences about disaster risk reduction and management good practices based on concrete examples from the field; discuss how to create evidence and conditions for upscaling of good practices; and 3. Exchange experiences and knowledge with partners around resilience to natural hazards and climate-related disasters. This webinar covered: • Monitoring risk in agriculture - the Agriculture Stress Index System • Damage and loss from disasters on agriculture and food security - recent data and the new SFDRR monitoring mechanism - indicator C2
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DRM Webinar II: Governing and managing disaster risk in the agriculture sector Assessing risks and impacts from extreme events/natural hazards on the agriculture sector
- 1. KORE - Knowledge Sharing Platform on Resilience KORE@fao.org
- 2. INFORMED INFORMATION FOR NUTRITION FOOD SECURITY AND RESILIENCE FOR DECISION MAKING
- 4. THE IMPACT OF DISASTERS ON AGRICULTURE Assessing risks and impacts from extreme events/natural hazards on the agriculture sector with focus on drought Moderator Stephan Baas, Strategic Advisor on Resilience, FAO Tuesday, 30 May 2016: 15.00 – 16.30 CEST
- 5. ASSESSING DISASTER IMPACTS FROM NATURAL HAZARDS IN AGRICULTURE natural hazard–induced disasters occur more often and with higher magnitude 90 % of these disasters are weather-related high dependence of the agriculture sector on climate.
- 6. THE INFORMATION GAP Actual cost of disasters to the agriculture sector? Drawing conclusions available data? and information? Documentation of disaster impacts on the AG sectors?
- 7. FAO’S WORK IN ADDRESSING THE INFORMATION GAP
- 8. THE IMPACT OF DISASTERS ON AGRICULTURE Recent data and the new SFDRR monitoring mechanism - indicator C-2 Speaker I Niccolo Lombardi, Expert in Disaster Impacts and DRR, FAO
- 9. 0 50 100 150 200 250 300 350 400 450 500 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 Geophysical Meteorological Hydrological Climatological Source: FAO (2017), based on EM-DAT CRED Geophysical Number of disasters triggered by natural hazards worldwide, 1980 - 2016 BACKGROUND Increasing disasters, increasing impacts
- 10. Source: FAO (2017), based on EM-DAT CRED Economic damage of disasters triggered by natural hazards worldwide, 1980 - 2016 BACKGROUND Increasing disasters, increasing impacts $0 B $50 B $100 B $150 B $200 B $250 B 1980198219841986198819901992199419961998200020022004200620082010201220142016 Damage (weather and climate related) Damage (geophysical)
- 11. Review of 74 PDNAs of disaster events in 53 developing countries b/n 2006-2016 Analysis of crop and livestock production losses caused by natural hazards and disasters affecting at least 100,000 people, or 10% of the population. BACKGROUND Increasing disasters, increasing impacts
- 12. 23%16% 31% D&LLossesDamage IMPACT OF DISASTERS ON AGRICULTURE Share of D&L absorbed by agriculture (2006-2016) Source: FAO (2017), based on 74 PDNAs conducted between 2006 and 2016
- 13. Source: FAO (2017), based on 74 PDNAs conducted between 2006 and 2016 IMPACT OF DISASTERS ON AGRICULTURE Varying sectoral vulnerabilities to disasters 14% 65% 20% 1% Crops Livestock Fisheries and Aquaculture 86% 9% 4% 1% 6% 44%38% 11% 1% Forestry 5% 64% 31%
- 14. IMPACT OF DISASTERS ON AGRICULTURE Focus on drought oMore than 80% of the impact of drought is on agriculture oDrought caused 19% of total crop and livestock losses between 2005 and 2014, in developing countries oUnder-reporting on the impact of drought, especially on small scale events such as dry spells
- 15. IMPACT OF DISASTERS ON AGRICULTURE Focus on drought in Africa Drought losses as a percentage of potential production oBetween 2004 and 2014, drought has led on average to a loss of 3 to 4 percent of potential agricultural production in Africa - peaks of 10 and even 20 percent in certain cases oBetween 1980 and 2014, droughts affected over 363 million people in Africa, of whom 203 million in Eastern Africa o2015-2016 El Niño affected more than 60 million people worldwide, with strong impact in Eastern and Southern Africa
- 16. Standardiz ed procedures and methodolog ies Strengthene d country capacities Better informed DRR planning Comprehens ive D&L databases Key issues: o Sub-national level data o Disaster thresholds oCausal relationship o Complementarity with existing systems and methods. FILLING THE KNOWLEDGE GAP Steps required to improve data quality and quantity
- 17. FAO’s METHODOLOGY TO MEASURE DISASTER IMPACT SFDRR Global Targets Target C
- 18. Indicator C-2: Direct agricultural loss attributed to disasters will be computed using the FAO methodology, as requested by member countries. C-2 is calculated as the sum of five sub-indicators: C2(C): Direct crop loss C2(L): Direct livestock loss (and apiculture) C2(FO): Direct forestry loss C2(AQ): Direct aquaculture loss C2(FI): Direct fisheries loss FAO’s METHODOLOGY TO MEASURE DISASTER IMPACT SFDRR Indicator C-2
- 19. FAO’s METHODOLOGY TO MEASURE DISASTER IMPACT SDG targets Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure to climate- related extreme events and other economic, social and environmental shocks and disasters SFDRR Indicator C-2 will be used, among others, also to monitor SDG Target 1.5
- 20. Damage Losses Crops Livestock Fisheries Aquaculture Forestry Production Value of destroyed stored production and inputs, dead animals, and fully damaged perennial trees Value of lost production Assets Replacement or repair value of destroyed machinery, equipment, tools FAO’s METHODOLOGY TO MEASURE DISASTER IMPACT Components
- 21. • FAO’s technical guidance and formulas to compute Indicator C2 are included in the draft Collection of Technical Notes on Data and Methodology for monitoring SFDRR. • The draft was consolidated by UNISDR with inputs from technical agencies. It is now being shared with countries for consultation. Source: UNISDR, draft of 26 April 2017 FAO’s METHODOLOGY TO MEASURE DISASTER IMPACT Technical Guidance and Computation Methods
- 22. o Typhoon Haiyan (Yolanda) hit central Philippines in Nov 2013 o Winds registered at over 300 km per hour (Figure) - strongest wind speed recorded in the country for the landfall of a cyclone o Storm surges reached up to 5.3 meters in height, causing devastation and loss of lives in affected coastal provinces o At least 6,300 deaths recorded (Nov ‘13), estimated 16 million people affected, over 1.1 million houses damaged/destroyed, overall damage to public infrastructure and agricultural land across 41 provinces TESTING THE METHODOLOGY Typhoon Haiyan, Philippines 2013 (Case Study Application)
- 23. $ 522 M $ 902 M Key Results o Total D&L in Agriculture: USD 1.4 billion – in line with government assessment o Most affected sub-sectors: crops, followed by fisheries and livestock. o Losses are almost 80 percent higher than damages TESTING THE METHODOLOGY Typhoon Haiyan, Philippines 2013 (Case Study Application)
- 24. DATA COLLECTION Harmonize and systematize assessment methods FAO’s support to countries DATA SOURCES WAY FORWARD Towards an integrated disaster impact information system D&L ASSESSMENT DATA REPORTING Harmonize and systematize data collection Improve access to data Facilitate data reporting
- 25. o Cooperation/coordination with UNISDR on monitoring indicator C-2 o Further testing and validation of the methodology on different hazards and regions – 2011 drought in Ethiopia o Development of a systematic, harmonized data collection and reporting process on D&L in agriculture o Country capacity enhancement to (a) monitor disaster impacts; and (b) make use of data for DRR/M planning o Development of a global information system on damage and loss in agriculture, linked to existing national and international disaster loss databases (e.g. EM-DAT CRED, Desinventar) – Start from questionnaire o How much damage and loss can be avoided/reduced by adequate DRR/M investment? (ongoing FAO work on resilience) WAY FORWARD Towards an integrated disaster impact information system
- 26. THANK YOU!
- 27. THE IMPACT OF DISASTERS ON AGRICULTURE From the Global Agriculture Drought Monitoring to Country Level using Geospatial Information Speaker II Oscar Rojas, Natural Resources Officer (Agrometeorology), FAO ASI S In collaboration with: http://www.fao.org/climatechange/asis/en/
- 28. Agricultur a 58% Industria 8% Electricid ad 25% Agua potable 2% Emergen cia 7% CENTROAMÉRICA: PERDIDAS OCASIONADAS POR LA SEQUÍA 2001 LOSSES DUE TO DROUGHT ARE CONCENTRATED ON THE AGRICULTURAL SECTOR (58%) AGRO RONDA EL 60% EN SEQUÍAS Philippine s 1991
- 29. OBJECTIVE Limitation using rainfall data: o Currently weather stations are sparse and provide discontinuous data o Rainfall estimates have a bias and show deviations in different regions of Africa (Dinku et al. 2007, Lim and Ho 2000). What is ASIS? o Is a expert system for agricultural drought monitoring based on 10-day satellite data of vegetation and land surface temperature from METOP-AVHRR sensor at 1 km.
- 30. ELECTROMAGNETIC ENERGY RECORDED BY THE SENSOR RE D NI R Water stress
- 31. Source: Kogan, F. 1995. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bulletin of the American Meteorological Society vol.76, No. 5 655-668 pp. 0 0,5 1 J F M A M J J A S O N D NDVI Hodh El Gharbi, Mauritania Weather Ecosystem
- 32. Vegetation Health Index (VHI) VHI = a*VCI + (1-a)* TCI low VHI high VHI Temperature condition index (TCI)Vegetation condition index (VCI) AGRICULTURAL STRESS INDEX SYSTEM IS BASED ON THE VEGETATION HEALTH INDEX (VHI) (Kogan et al. 1995)
- 33. VHI temporal average value agric Crop area Administrative unit ASIS ASSESS THE SEVERITY (INTENSITY, DURATION AND SPATIAL EXTENT) OF THE AGRICULTURAL DROUGHT 0- 10 10-20 20-25 25-35 >35 Percentage of the agriculture areas with VHI below 35 % of crop area affected by drought
- 35. SOS and EOS of the first season, as derived from the long term NDVI averages of SPOT-VGT (roi GLD, 21 km resolution). TEMPORAL AGGREGATION Defining SOS (start of growing season) & EOS (end of growing season)
- 36. 1989
- 37. http://www.fao.org/giews/earthobservation NEAR REAL TIME MONITORING AT GLOBAL LEVEL
- 39. Global ASIS FAO HQ Input data Country/regional ASIS External inputs a and b VHI= a VCI + b TCI Weighted VHI from SOS to EOS Calculation % area with wVHI<35 (Quick look map) Calculation wVHI using ASI as a weighted factor for each Drought Category (Quick look map) % area with wVHI in each Drought Category ROI (lat, long) of VCI, TCI, SOS, EOS, POS Quick look maps of each Drought Category Export to Excel % of each Drought Category by administrative unit Cumulative Weighted VHI (cwVHI) from SOS to EOS Introduction of threshold of critical cwVHI and probability calculation Probability of deficit as defined by threshold (Quick look map) National database and National early warning system (NEWS)
- 41. VHI temporal average value agricCrop area Administrative unit % of crop area affected by drought 0- 10 10-20 20-25 25-35 >35 Percentage of the agriculture areas with VHI below 35
- 42. 0 0.2 0.4 0.6 0.8 1 1.2 0 25 35 45 55 65 75 85 100 Indice VHI Drought categories Abnormal dry No drought Extreme drought Severe drought Moderate drought Exceptional drought Indicator Drought category VHI pixel ASI* 1 Exceptional Drought <35 % 0.75-0.99 Extreme Drought 36-45 % 0.50-0.74 Severe Drought 46-55 % 0.25-0.49 Moderate Drought 56-65 % 0.01-0.24 Abnormal dry 66-75 % 0 No Drought >75 % * Percentage of pixels in each drought categoy DROUGHT CATEGORIES
- 43. PROBABILISTIC FORECAST OF AGRICULTURAL DROUGHT
- 44. CALIBRATED ASIS FOR NICARAGUA First crop season (Primera) Second crop season (Postrera) Third crop season (Apante) Land used study (rice, maize and beans)
- 45. PERCENTAGE OF AREA STAPLE CROPS AFFECTED BY DROUGHT AT COUNTRY LEVEL
- 46. 0 10 20 30 40 50 60 70 80 90 100 ASI Estelí, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Granada, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Jinotega, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Nueva Segovia, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Región Autónoma Caribe Sur 0 10 20 30 40 50 60 70 80 90 100 ASI Managua, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Chinandega, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Rivas, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Madriz, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Chontales, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI León, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Región Autónoma Caribe Norte 0 10 20 30 40 50 60 70 80 90 100 ASI Carazo, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Río San Juan, Nicaragua 0 10 20 30 40 50 60 70 80 90 100 ASI Masaya, Nicaragua
- 47. TRIGGER FOR A INDEXED CROP INSURANCES BASED ON GEOSPATIAL DATA (1985-2014) 40% 60% Fuente: INETER, 2017Fuente: INETER, 2017
- 48. HISTORICAL PROBABILITY OF OCCURRENCE OF >50% OF GRAIN AREA AFFECTED BY DROUGHT DURING PRIMERA, POSTRERA AND APANTE First crop season (Primera) Third crop season (Apante) Second crop season (Postrera) Probability
- 51. UNDERSTANDING THE DROUGHT IMPACT OF EL NIÑO ON THE GLOBAL AGRICULTURAL AREAS An assessment using FAO’s Agricultural Stress Index (ASI) El Niño observed from sattelite. The red areas of the tropical coasts of South America indicate the pool of warm water. Source: NOAA
- 52. ASIS´S CONTRIBUTION 1. Automatic-system fed by pre- processed imagery from VITO that guarantee the sustainability of the system 2. Temporal-spatial integration, normally not take into consideration for most of the systems on agricultural monitoring based on remote sensing data 3. Unique time series (>30 years) a 1 km resolution that guarantee the long term memory of the pixel of having an extreme drought event
- 53. THANK YOU!
- 54. Assessing risks and impacts from extreme events/natural hazards on the agriculture sector Comments ? Questions? Please write them in the chat box
- 55. THANK YOU! Give us your feedback Click on the link in the chat box KORE - Knowledge Sharing Platform on Resilience KORE@fao.org
- 56. Stay tuned for the final DRM webinar: Returns from investments in disaster risk reduction technologies and practices in agriculture KORE - Knowledge Sharing Platform on Resilience KORE@fao.org
Editor's Notes
- Good morning to all and thank you for joining us, My name is Stephan Baas and I am Strategic Advisor on Resilience at the Food and Agriculture Organization of the United Nations. I would like to welcome all participants to this 10th webinar of a series on resilience that is an initiative of KORE, the knowledge sharing platform on resilience of the INFORMED programme, funded by the European Union (EU) and implemented by FAO’s strategic programme on resilience. This series is co-hosted by the FAO Strategic programme on resilience, the European Union Directorate General for International Cooperation and Development (DG-DEVCO) and the Learn4dev network. We would like to thank our colleagues from the EU DEVCO C1 (Rural Development, Food Security, Nutrition) and 03 (Knowledge management) for their kind support. The webinar will last around one and a half hours and will be recorded. A link will be shared after the event so that all are able to see this presentation again. Following the presentation, participants will be able to provide comments and ask your questions using the ‘chat box’ in the left side of the screen. Participants’ microphones are turned off in order to avoid any disrupting background noise. The event will have a twitter coverage under the hashtags: #ks4resilience and #UNFAO (both hashtags will be shown on screen, action: Frederique)
- Today, we will host our second webinar from the series on Disaster Risk Reduction and Management in Agriculture. Two weeks ago, we discussed the the role of the agricultural sector in disaster risk governance. Today, in our second webinar, the focus is on understanding disaster risk and impacts from extreme events on the agriculture sector. In this context we will look in detail at challenges and opportunities of monitoring drought risk. The Sendai framework for disaster risk reduction stresses the need to better understand the impacts of these disasters and to “systematically evaluate, record, share and publicly account for disaster losses and understand the economic, social, health, education, environmental and cultural heritage impacts, as appropriate, in the context of event-specific hazard-exposure and vulnerability.
- Alerting is that records of the past 20 years show that natural hazard–induced disasters occur more often and with higher magnitude. About 90 per cent of these disasters are weather-related. For agriculture, the increase of weather and climate related extreme events is a great concern, given the high dependence of the agriculture sector on climate.
- We know that disaster caused about 1.4 trillion in economic damages between 2005 and 2014. Yet, the actual cost of disasters to the agriculture sector is not well known. Both at global and national level, the documentation of disaster impacts on the AG sectors is limited. This means in other words that systematic and coherent recording of damage and loss data caused by disasters on crops, livestock, fisheries/aquaculture and forestry is missing. Another challenge is that the available data and information does often not allow to draw clear conclusions, because disaster impact assessment methodologies are highly heterogeneous.
- In 2015, FAO started to address these information gaps and launched a new global report on the Impact of Disasters on Agriculture and Food Security. Thereafter, as part of the new SFDRR monitoring system (under construction) FAO has been mandated by member states to systematically monitor a global SFDRR indicator on Losses in agriculture attributed to disasters (C2). Since then, and in order to further harmonize and systematize data collection and reporting on disaster impacts, FAO is working with member countries, experts and relevant stakeholders to fine-tune a methodology to consistently assess the extent of damage and losses in agriculture and its subsectors , which will help evaluate progress towards SFDRR and SDG resilience targets.
- Our first speaker, Mr. Niccolo Lombardi is an expert in disaster impact assessment and disaster risk reduction. To set the scene for this seminar about damage and loss in agriculture he will present in a minute our most recent findings on disaster impacts on agriculture, he will elaborate specifically on drought impacts and the importance of systematic information on disaster impact for evidence-based decision making in agriculture. He will also describe the FAO methodology for monitoring indicator C2 - direct agricultural loss attributed to disasters. But we also want to talk today about ways how damage and loss can be avoided or limited. To illustrate that we will share one example focusing on drought monitoring, which is crucial for timely and accurate early warning that triggers early action and emergency responses, as/when they may be needed. You may later on share/add other examples or lessons on other types of hazards. We chose drought as example since droughts have the highest impacts specifically on agriculture, but also bc it provides specific scope for early action a since people have more lead time to act in case of drought as compared to fast-onset disasters such as earthquakes. However drought monitoring its also specifically complex as it often goes parallel with water scarcity, and depends also on the type of crops and stage in the cropping cycle. Yet national weather observation capacities and agro-meteorological station networks in many countries are often not sufficient. To fill this gap, the use of high-end technologies became more prominent in the recent past as also called for in the Sendai Framework, namely by the call to promote and enhance (…) access to and the sharing and use of non-sensitive data and information (…) communications and geospatial and space-based technologies and related services; maintain and strengthen in situ and remotely-sensed earth and climate observations (…) to support national measures for successful disaster risk communication (…); Our second speaker today is Mr. Oscar Rojas. who is a FAO expert in Agro-meteorology will introduce the Agriculture Stress Index System, or ASIS a FAO tool that won the ‘Geospatial World Excellence Award 2016’. After the two presentations we look forward to receive your questions, comments as basis for our discussion.
- Between 2004 and 2015 droughts have been frequent and severe in many African countries (Figure 4). There were 84 reported drought occurrences in 30 countries, which have led on average to a loss of 3 to 4 percent from potential agricultural production; this number can go up to 10 and even 20 percent in certain cases. Agricultural losses from drought, expressed percentage of potential production are calculated based on FAOSTAT production data for droughts affecting over 100 000 people or 10 percent of the national population.
- Targets: (a) Substantially reduce global disaster mortality by 2030, aiming to lower average per 100,000 global mortality rate in the decade 2020-2030 compared to the period 2005-2015. (b) Substantially reduce the number of affected people globally by 2030, aiming to lower average global figure per 100,000 in the decade 2020 -2030 compared to the period 2005-2015. (c) Reduce direct disaster economic loss in relation to global gross domestic product (GDP) by 2030. (d) Substantially reduce disaster damage to critical infrastructure and disruption of basic services, among them health and educational facilities, including through developing their resilience by 2030. (e) Substantially increase the number of countries with national and local disaster risk reduction strategies by 2020. (f) Substantially enhance international cooperation to developing countries through adequate and sustainable support to complement their national actions for implementation of this Framework by 2030. (g) Substantially increase the availability of and access to multi-hazard early warning systems and disaster risk information and assessments to the people by 2030.
- Indicator 1.5.1 = Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population (Based on SFDRR indicators for targets A and B) Indicator 1.5.2 = Direct economic loss attributed to disasters in relation to global GDP (Based on SFDRR indicator C-1)
- The FAO methodology for measuring SFDRR indicator C2 builds on the key elements of the PDNA methodology and allows measuring the value of direct damage and losses attributed to disasters in the crops, livestock, fisheries, aquaculture and forestry sectors, in a systematic manner and for all types of disasters. Damage and loss Damage is defined as the replacement/repair cost of totally or partially destroyed physical assets and stocks in the disaster-affected area Loss refers to changes in economic flows arising from the disaster (i.e. declines in output in crops, livestock, fisheries, aquaculture and forestry) Production and assets Each sub-sector is sub-divided into two main sub-components, namely production and assets. The production sub-component measures both damage and loss from disaster on production inputs and outputs, while the assets sub-component measures damage on facilities, machinery, tools, and key infrastructure related to agricultural production. Damage Replacement/repair cost of fully/partially damaged assets at pre-disaster price (crops, livestock, fisheries, aquaculture, forestry). Pre-disaster value of destroyed stored products and inputs (crops, livestock, fisheries, aquaculture, forestry). Value of fully damaged perennial trees/ dead animals (perennial crops, livestock) Losses Difference between expected and actual value of production (crops, livestock, forestry, aquaculture production and fisheries capture) in disaster year For perennial crops and forestry: Pre-disaster value of fully destroyed standing crops and trees and Discounted expected value of crop production in fully damaged harvested area until full recovery For livestock and aquaculture: Discounted foregone value of products from dead livestock until full recovery Temporary costs incurred towards the maintaining of post-disaster agricultural and farming/fishing activities
- MINIMUM AND DESIRED DATA REQUIREMENTS: Data to be collected for each disaster [Minimum Requirement]: If a proper economic valuation of direct loss (compliant with SFDRR) is available, indicators C-2, C2-C, C2-L, C2-Fo, C2-Fi and C2-Ia it can be reported directly. C-2C: Loss in crops damaged or destroyed by disasters C-2L: Loss in livestock dead or affected by disasters C-2Fo: Loss in of hectares of forests affected/destroyed by disasters C-2A: Loss in of hectares of Aquaculture production area affected C-2Fi: Loss in Fisheries production area affected C-2Ia: Loss in associated damaged/destroyed machinery and facilities. In the case of fishing sector this will include vessels C-2Ib: Pre-disaster value of Stock (stored inputs such as Seeds, fertiliser, feed, fodder, etc., and stored production such as crops, livestock produce, fishes, logs, etc.) The following physical damage indicators will be required, and will be accepted in lieu of the corresponding estimated economic loss. C-2Ca: Number of Hectares of crops damaged or destroyed by disasters C-2La: Number of livestock dead or affected by disasters C-2Foa: Number of hectares of forests affected/destroyed by disasters C-2Aa: Number of hectares of Aquaculture production area affected C-2Fia: Number of hectares of Fisheries production area affected C-2Iaa: Number of associated damaged/destroyed machinery and facilities. In the case of fishing sector this will include vessels. Note that for sub-indicator C-2I damaged/destroyed machinery and facilities, which are clearly Productive Assets, the following annotation applies, and the data collection will follow the same pattern, definitions and methods: Productive assets would be disaggregated by economic sector, including services, according to standard international classifications. Countries would report against those economic sectors relevant to their economies. This would be described in the associated metadata. Agricultural productive assets will be reported in C-2 and will not be duplicated in C-3. The classification and related metadata mechanism will allow this distinction. Metadata mechanism will also allow the standard definition of the different types of crops, livestock, forests, aquaculture and fisheries activities. Initial metadata will be assembled by UNISDR based on an international standard such as FAO classification. To be Included based upon A/71/644: C-2d: Losses to apiculture Definition of Metadata Describing Assets and Infrastructure elements [Minimum Requirement]: For each type of productive asset that is reported: Code Description of type of asset Group or Economic Sector/Activity in ISIC or adopted FAO/UNISDR classification Measurement Units (m2, mts, Hectare, Km, Tonne, etc.) Value per unit [Series per Year 2005… 2030] % of value for equipment, furniture, materials, product (if applicable) % of value for associated physical infrastructure (if applicable) Average number of workers per facility or infrastructure unit Formula to calculate economic value Please see ANNEX I for more information and examples of proposed Metadata schema. Recommended disaggregation: ALL: by Hazard ALL: by Geography ALL: by totally destroyed (lost, dead, destroyed) or damaged (affected) C-2C: by types of cultivated crops in the affected areas C-2L: by types of livestock C-2Fo: by types of forest C-2A: by types of aquaculture activities in affected areas C-2Fi: by types of fishing activities in the affected areas C-2I: by Sector (Crops, livestock, forest, aquaculture, fisheries) by Types of damaged machinery and facilities
- Thank you Niccolo. Now I would like give the floor to Oscar.
- I would like to invite participants to comment and ask questions, typing their input in the chat box in the bottom right corner (the chat box window can be expanded)
- “As indicated earlier, this presentation has been recorded and the link will be shared if you are registered to the INFORMED mailing list. If you are not registered, send us an email to the address you see appearing in the chat box: KORE@fao.org Finally, please make sure to give us your quick feedback through the survey - the link is being given in the chat box. Please click on this link right away and let us know your thoughts, this is very useful to improve our webinars. Please join us for our next webinar in the last week of June when we will be discussing the third part of this series on Disaster Risk Reduction and Management, focusing on returns from investments in disaster risk reduction technologies and practices in agriculture. You can find any information on KORE, the Knowledge Sharing Platform on Resilience! (link being given in the chat box)