Agricultural Risk Transfer E-Book

Roman Marco Hohl

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Finance Editions
• Sprache: Englisch

Seitenzahl: 932

Veröffentlichungsjahr: 2018

Beschreibung

Gain a holistic view of agricultural (re)insurance and capital market risk transfer Increasing agricultural production and food security remain key challenges for mankind. In order to meet global food demand, the Food and Agriculture Organisation estimates that production has to increase by 50% by 2050 and requires large investments. Agricultural insurance and financial instruments have been an integral part to advancing productivity and are becoming more important in increasingly globalized and specialized agricultural supply chains in the wake of potentially more frequent and severe natural disasters in today's key producing markets. Underwriting, pricing and transferring agricultural risks is complex and requires a solid understanding of the production system, exposure, perils and the most suitable products, which vastly differ among developed and developing markets. In the last decade, new insurance schemes in emerging agricultural markets have greatly contributed to the large growth of the industry from a premium volume of US$10.1 billion (2006) to US$30.7 billion (2017). This growth is bound to continue as insurance penetration and exposure increase and new schemes are being developed. Agricultural (re)insurance has become a cornerstone of sovereign disaster risk financing frameworks. Agricultural Risk Transfer introduces the main concepts of agricultural (re)insurance and capital market risk transfer that are discussed through industry case studies. It also discusses best industry practices for all main insurance products for crop, livestock, aquaculture and forestry risks including risk assessment, underwriting, pricing, modelling and loss adjustment. * Describes agricultural production risks and risk management approaches * Covers risk transfer of production and financial risks through insurance and financial instruments * Introduces modelling concepts for the main perils and key data sources that support risk transfer through indemnity- and index-based products * Describes risk pricing and underwriting approaches for crop, livestock, aquaculture and forestry exposure in developed and developing agricultural systems * Become familiar with risk transfer concepts to reinsurance and capital markets * Get to know the current market landscape and main risk transfer products for individual producers, agribusinesses and governments through theory and comprehensive industry case studies Through Agricultural Risk Transfer, you'll gain a holistic view of agricultural (re)insurance and capital market solutions which will support better underwriting, more structured product development and improved risk transfer.

Leseprobe

Table of Contents

Cover

Foreword

Introduction

OVERVIEW OF CHAPTERS

Acknowledgements

Special Thanks

CHAPTER 1: Agricultural Markets and Risk Management

1.1 INTRODUCTION

1.2 TRENDS AND CHALLENGES IN THE AGRICULTURAL SECTOR

1.3 RISK MANAGEMENT IN AGRICULTURE

BIBLIOGRAPHY

NOTES

CHAPTER 2: Concepts of Insurance

2.1 INTRODUCTION

2.2 GLOBAL INSURANCE MARKET

2.3 KEY CONCEPTS OF INSURANCE

2.4 ACTUARIAL INSURANCE PRICING

CATASTROPHE RISK MODELLING

BIBLIOGRAPHY

NOTES

CHAPTER 3: Agricultural Perils and Risk Modelling Concepts

3.1 INTRODUCTION

3.2 OVERVIEW OF MAIN PERILS FOR AGRICULTURE

3.3 DROUGHT

3.4 FLOOD

3.5 HAIL

3.6 FROST

3.7 SNOW

3.8 CYCLONES

3.9 WILDFIRES

3.10 EPIDEMIC LIVESTOCK DISEASES

3.11 EPIDEMIC AQUACULTURE DISEASES

BIBLIOGRAPHY

NOTES

CHAPTER 4: Agricultural Data and Proxies

4.1 INTRODUCTION

4.2 CLIMATE DATA

4.3 SATELLITE DATA

4.4 CROP YIELD DATA

4.5 DISASTER AND CALAMITY DATA

4.6 LIVESTOCK AND AQUACULTURE DATA

4.7 FORESTRY DATA

BIBLIOGRAPHY

NOTES

CHAPTER 5: Agricultural Insurance

5.1 INTRODUCTION

5.2 GLOBAL AGRICULTURAL INSURANCE MARKETS

5.3 BENEFITS OF AGRICULTURAL INSURANCE

5.4 CHALLENGES OF AGRICULTURAL INSURANCE

5.5 AGRICULTURAL INSURANCE SYSTEMS

5.6 FORMS OF AGRICULTURAL INSURANCE OPERATIONS

BIBLIOGRAPHY

NOTES

CHAPTER 6: Crop Insurance

6.1 INTRODUCTION

6.2 SECTOR TRENDS

6.3 OVERVIEW OF MAIN CROP INSURANCE PRODUCTS

6.4 NAMED-PERIL CROP INSURANCE

6.5 MULTI-PERIL CROP INSURANCE

6.6 REVENUE INSURANCE

6.7 INCOME INSURANCE

6.8 AREA-YIELD INDEX INSURANCE

6.9 WEATHER INDEX INSURANCE

6.10 SATELLITE-BASED INDEX INSURANCE

6.11 MODEL-BASED INDEX INSURANCE

BIBLIOGRAPHY

NOTES

CHAPTER 7: Livestock Insurance

7.1 INTRODUCTION

7.2 SECTOR TRENDS

7.3 OVERVIEW OF RISK TRANSFER SOLUTIONS

7.4 STANDARD LIVESTOCK INSURANCE

7.5 EXTENDED LIVESTOCK INSURANCE

7.6 LIVESTOCK INDEX INSURANCE

BIBLIOGRAPHY

NOTES

CHAPTER 8: Aquaculture Insurance

8.1 INTRODUCTION

8.2 SECTOR TRENDS

8.3 OVERVIEW OF RISK TRANSFER SOLUTIONS

8.4 ALL-RISK STOCK MORTALITY AND NAMED-PERIL AQUACULTURE INSURANCE

8.5 AQUACULTURE INDEX INSURANCE

BIBLIOGRAPHY

NOTES

CHAPTER 9: Forest Insurance

9.1 INTRODUCTION

9.2 SECTOR TRENDS

9.3 OVERVIEW OF RISK TRANSFER SOLUTIONS

9.4 PLANTATION INSURANCE

9.5 CARBON REVERSAL INSURANCE

9.6 FIREFIGHTING COST INSURANCE

9.7 INDEX-BASED FOREST INSURANCE

BIBLIOGRAPHY

NOTES

CHAPTER 10: Risk Transfer to Reinsurance Markets

10.1 INTRODUCTION

10.2 GLOBAL REINSURANCE MARKET

10.3 KEY CONCEPTS OF REINSURANCE

10.4 FORMS OF REINSURANCE

10.5 BASICS OF REINSURANCE PRICING

BIBLIOGRAPHY

NOTES

CHAPTER 11: Risk Transfer to Capital Markets

11.1 INTRODUCTION

11.2 GLOBAL INSURANCE-LINKED SECURITIES MARKETS

11.3 OVERVIEW OF KEY PRODUCTS

BIBLIOGRAPHY

NOTES

Acronyms and Abbreviations

ORGANISATIONS AND INSTITUTIONS

CONCEPTS AND PRODUCTS

Index

End User License Agreement

List of Tables

Chapter 1

TABLE 1.1 Overview of the main risk types for an individual crop farmer and a grain processor with informal and formal risk management options.

Chapter 2

TABLE 2.1 Overview of the largest 10 insurance markets in 2016 including agriculture.

TABLE 2.2 Examples of distribution functions used to model loss frequency and severity in (re)insurance.

TABLE 2.3 Historical recast of gross loss ratios and post-SRA loss ratios from the AIR MPCI model (columns 2–5 at 2016 insurance terms and conditions) and observed gross loss ratios (column 6) for the US crop insurance industry, 2006–2015.

Chapter 3

TABLE 3.1 Overview of El Niño and La Niña events classified into different intensities based on the Oceanic Niño Index, with numbers in brackets indicating the number of events, 1951–2018.

TABLE 3.2 Overview of the 10 costliest drought disasters ranked by total damage, 1980–2017.

TABLE 3.3 Overview of the main insurance plans (in alphabetical order), a description of the coverage, premium volume and loss ratios incurred in 2012.

TABLE 3.4 Common drought indices per type (M = Meteorological Index, A = Agricultural Index, RS = Remote Sensing Index) and inputs (P = precipitation, T = temperature, SM = soil moisture, ET = evapotranspiration, SF = streamflow, NIR = near infrared, SWIR = short wave infrared).

TABLE 3.5 Overview of the 10 costliest flood disasters ranked by total damage, 1980–2017.

TABLE 3.6 Lethal minimum temperature for some main crop types and three growth phases.

TABLE 3.7 Overview of the 10 costliest tropical cyclone and extratropical cyclone disasters, 1980–2017.

TABLE 3.8 Overview of recent severe winter storms with large losses to forests.

TABLE 3.9 Overview of some of the most severe wildfires that caused large losses to forests.

TABLE 3.10 Overview of some well-documented epidemic livestock disease outbreaks ranked by total damage.

TABLE 3.11 Overview of diseases and infections of the OIE but excluding diseases for equine, lagomorphs (hares and rabbits), bees and amphibians.

TABLE 3.12 Overview of some well-documented epidemic aquatic disease outbreaks.

Chapter 4

TABLE 4.1 Overview of livestock indicators from surveys that are most relevant for risk transfer products.

Chapter 5

TABLE 5.1 Overview of the main agricultural insurance lines, sub-classes, insured perils and products.

TABLE 5.2 Top 10 agricultural insurance markets ranked by premium in 2017 in comparison with 2006.

TABLE 5.3 Insurance premium per main product and line of business in 2017, including key markets and size (in brackets).

TABLE 5.4 Crop insurance terms for selected crop types and provinces (2008 and 2016) under the National Agricultural Insurance Program in China.

Chapter 6

TABLE 6.1 Overview of the main types of crop insurance products.

TABLE 6.2 Crop classification according to general hail vulnerability, with weights relative to the base rate of vulnerability class 1.

TABLE 6.3 Quality downgrading of apples following hail damage.

TABLE 6.4 Corn yield loss from frost in function of leaf area damage and resulting yield losses in the USA.

TABLE 6.5 Example of rate relativity factors for different coverage levels in the USA.

TABLE 6.6 Overview of the main methods for pricing revenue insurance products.

TABLE 6.7 Risk premium rates for the revenue protection (RP) and the revenue protection with harvest price exclusion (RP-HPE) products, with yield distributions following a logistic and a normal function and yield-price correlation coefficients of −0.75 (as observed) and −0.30.

TABLE 6.8 Overview of the most common weather indices used for agricultural crops.

Chapter 7

TABLE 7.1 Overview of the main types of livestock insurance products.

TABLE 7.2 Mortality ratios as a function of animal age and herd size for calves and beef cattle, dairy cattle, pigs and poultry in the USA.

Chapter 8

TABLE 8.1 Annual production in function of aquaculture systems (ponds and cages), hydraulic detention time and density for different farmed fish species.

TABLE 8.2 Overview of premium rates for different perils for different fish species in offshore and onshore production systems.

Chapter 9

TABLE 9.1 Overview of the main types of forest insurance products.

Chapter 10

TABLE 10.1 Overview of the largest seven international and three national reinsurers in agriculture, including gross written premium and total gross written premium (life- and non-life reinsurance), ranked by the agriculture reinsurance premium volume in 2016.

TABLE 10.2 Overview of the basic mechanism of quota share, surplus, excess of loss, aggregate excess of loss and stop-loss reinsurance treaties.

TABLE 10.3 Example of a crop insurance portfolio with 16 years of experience (2002–2017) including number of districts and crop types insured, sum insured, premium volume, average premium rate and losses. The As-If Analysis includes adjustments for premium volumes and translates the 2017 premium volume through the as-if loss ratios into weighted losses (Loss Today).

TABLE 10.4 Risk premium rates for four stop-loss layers based on the portfolio used in Figure 10.4 based on the burning cost approach as well as four distributions.

List of Illustrations

Chapter 1

FIGURE 1.1 Layering of risks in function of loss probability/severity with typical risk management approaches.

Chapter 2

FIGURE 2.1 Above: Modelled (recast) MPCI loss ratios from the AIR Multiple Peril Crop Insurance (MPCI) Model. Bellow: Observed MPCI loss ratios as reported by RMA for the drought year 2012 at the resolution of a county. Note that a loss ratio of 4.5 signifies 450% loss ratio..

Chapter 3

FIGURE 3.1 Left: Number of annual natural disasters relevant for agriculture, 1980–2016. Right: Economic losses relevant for agriculture, 1980–2016..

FIGURE 3.2 Left: Yield for maize, barley, sorghum, soybeans and wheat in the USA, 1961–2014. Right: Premium volume (right axis, columns), loss ratio (right axis, solid line) and insured area (left axis, dotted line) of the US Federal Crop Insurance Programs, 1981–2017..

FIGURE 3.3 Wheat yields (left axis) and wheat area harvested (right axis) in Ukraine, 1991–2014..

FIGURE 3.4 Left: Modelled three-second wind gusts of Super Typhoon Haiyan with different intensities (grey scales), 3–11 November 2013 over Asia. Right: Premium volumes (right axis, columns), loss ratios (right axis, solid line) and area insured (left axis, dotted line) for rice insured by the Philippine Crop Insurance Corporation (PCIC), 1981–2016..

Chapter 4

FIGURE 4.1 Monthly difference in temperature between the weather stations of Charlotte (NC, USA) and Greensboro Piedmont Triad International Airport (NC, USA) which lies 160 km apart for January 1985 to June 2001. The instrument in Charlotte was relocated in 1998 from an area near concrete to a low grassy area which lead to a discontinuity of 2°F. The same discontinuity was detected from other weather stations compared to Charlotte.

FIGURE 4.2 Left: Simulated winter wheat yield with EPIC and observed winter wheat yield (EUROSTAT) for NUTS2 resolution for 276 regions in Europe in 2007 including 130 data points and a correlation coefficient of 0.8 and Right: same as Left but for 214 data points and for 1997–2007 and a correlation coefficient of 0.89.

FIGURE 4.3 Top Left: Annual potato yield in the district of Sikar (Rajasthan, India), 1998–2010 with missing values in 2000, 2001 and 2004. Top Right: Rice yield in the district of Umaria (Madhya Pradesh, India), 1998–2012 with a missing value in 2009. Bottom Left: Cotton yield in the district of Patiala (Punjab, India) for the Kharif season, 1998–2011 with yields jumping after 2–4 seasons abruptly to a next level. Bottom Right: Sugarcane yield in the district of Rajnandgaon (Chhattisgarh, India), 1998–2011 with an erroneous value in 1999 where probably sugarcane biomass instead of yield was reported.

FIGURE 4.4 Top Left: Corn yield for 9 counties in Illinois, USA (thin lines) and the average county yield (solid line), 1968–2015 showing a positive trend over time (increasing yields); Top Right: Winter wheat yields for 9 counties in Oklahoma, USA (thin line) and the average county yield (solid line), 1968–2015 revealing a negative trend over time (decreasing yields); Bottom Left: Average district rice yields [t / ha] in the Kharif season in Uttar Pradesh, India, 1998–2010 and Bottom Right: Linear trends in district rice yields [kg/ha] in the Kharif season in Uttar Pradesh, India, 1998–2010 with the level of the trend (shading).

FIGURE 4.5 Piecewise linear trends in corn yields in Adams County (Illinois, USA), 1968–2015 with Left: 1 knot in the year 2000 and an average yield of 144.3 bushels/acre (2000–2015) and Right: 1 knot with a breaking point in the year 2000 (dotted line) and an average yield of 148.2 bushels/acre (2000–2015).

FIGURE 4.6 Left: Observed and linearly de-trended corn yields in Adams County (Illinois, USA), 1968–2015 with an average yield of 118 bushels/acre (observed) and a higher yield of 158 bushels/acre (detrended) due to a positive trend and Right: Observed and linearly de-trended winter wheat yields in Blaine County (Oklahoma, USA), 1968–2015 with an average yield of 40.7 bushels/acre (observed) and a lower yield of 30.7 bushels/acre (detrended) due to a negative trend.

FIGURE 4.7 Probability density functions for linearly de-trended corn yields in Adams county (Illinois, USA), 1968–2015 with Top Left: Normal function with all parameters determined from the observations; Top Right: Beta function with the shape parameters determined from the observations, the minimum yield set as zero and the maximum yield as 10% above the highest observed yield; Bottom Left: Non-parametric Kernel function and Bottom Right: the three probability density functions in comparison.

Chapter 5

FIGURE 5.1 Left: Agricultural insurance premium in emerging and mature markets, 2006–2017. Right: Agricultural insurance premium per main geography (continents).

Chapter 6

FIGURE 6.1 Development of global area planted (left), global production (middle) and global yield (right) of corn, wheat, rice and soybeans, 1960–2016.

FIGURE 6.2 Left: Relative yield reduction for corn at the 7–10 leaf and 11–17 leaf growth stage from stand reduction due to hail, with each a plant density of the original stand (S) of 300/acre and 400/acre. Right: Relative corn yield in function of defoliation extents at different corn growth stages.

FIGURE 6.3 Left: Corn yield of 15 fields of different farms in Lyon County (Iowa, USA) that are irrigated or rainfed and average county corn yield (1988–1997). Right: Number of years of available yields of the 15 fields and risk premium rates at 80% coverage based on historical yields (observations) and modelled through a beta distribution based on observed yields.

FIGURE 6.4 Example of 20 realisations of futures prices using the Geometric Brownian Motion for corn futures prices in Illinois (USA), with a starting point of US$3.96/bushel and an implied volatility of 17.23% for 1 March to 15 October (228 days).

FIGURE 6.5 Left: Base corn prices (averages of February) and harvest corn prices (averages of October) from the December futures contracts (CBOT contract CZ17), 1990–2015. Right: Detrended corn yield for Adams County (Illinois, USA, solid line) and corn price differentials from harvest and base prices (dotted line), 1990–2015.

FIGURE 6.6 Left: Production of barley, canola, oats, wheat and flaxseed in five Canadian provinces, 1980–2017. Right: Production of the same main crops as Left (solid line) and EBIT for the grain handling divisions of United Grain Growers, 1988–2000, Agricore United, 2001–2006 and Viterra, 2007–2011 (columns).

FIGURE 6.7 Left: Observed and linearly detrended corn yields in Adams County (Illinois, USA), 1968–2015. Right: Risk premium rates for corn in Adams County (Illinois, USA) based on detrended and observed yield data for coverage levels of 75% and 85% and guaranteed yield based on the last five years, all years and projected yields, from the linear trend with the most suitable approach (in bold).

FIGURE 6.8 Risk premium rates derived for linearly detrended corn yields in Adams County (Illinois, USA), 1968–2015, using a normal probability density function (Normal), a beta probability density function (Beta) and a non-parametric kernel function (Kernel) for Left: coverage level 75% and Right: coverage level 85%.

FIGURE 6.9 Left: Cumulative GDDs for corn in Adams County (Illinois, USA), 2006–2015 (1 May–30 September) and the mean cumulative GDDs. Right: Cumulative GDDs for different crop types and growth phases summarised in emergence (EM), vegetative phases (V1, V2, V3), anthesis (ANT), seed filling phases (SF1, SF2) and maturity (MAT) in the Midwest of the USA.

FIGURE 6.10 Example of a term sheet as used in Rajasthan (India) for corn in the Kharif 2012 season. The WII protects against severity of deficit rainfall (deficit rainfall cover with three phases), the frequency of deficit rainfall events over the growing season (number of consecutive dry days, one phase) and the frequency of excessive rainfall events (number of consecutive days of cumulative rainfall with three phases), with an

either or

payment for deficit rainfall and excessive rainfall as experiencing both deficit and excessive rainfall over the same growth period was judged to be extremely rare. The sum insured reveals the total maximum payout for the three covers. The reference weather station and the area insured are specified in individualised agreements between the insurer and the policyholder and in case of loanee farmers attaches automatically to the agricultural loan. A Thesil is a sub-unit of a district.

FIGURE 6.11 Left: Observed and linearly detrended corn yields in Adams County (Illinois, USA), 1968–2015. Right: Linear detrended corn yield (left axis, solid line) in Adams County (Illinois, USA), with cumulative rainfall (right axis, columns) and cumulative number of heat days (right axis, columns) for growth periods defined in each year through GDDs.

Chapter 7

FIGURE 7.1 Left: Indexed (1961) global number of livestock for the main livestock types, 1961–2016. Right: Global meat, egg and milk production, 1961–2016..

FIGURE 7.2 Left: Frame scores for steers (solid line) for score of 1 (A = low), 5 (B = medium) and 9 (C = high) and frame scores for heifers (dotted line) for a score of 1 (D = low), 5 (E = medium) and 9 (F = high), with the expected finish weight [kg], e.g. a steer with a frame score of 1 (A) is expected to reach 400 kg and a steer with a frame score of 9 (C) 667 kg. Right: Typical weekly weight gain for a guilt (young female pig), a barrow (castrated male pig) and a boar (intact male pig).

FIGURE 7.3 Left: Indexed (1961) numbers of main livestock types in Mongolia (left axis, lines), total livestock deaths (right axis, columns) due to dzud and drought and dzud in Mongolia, 1961–2016. Right: Premium volume (left axis, columns) and loss ratios (right axis, line) of the IBLIP in Mongolia, 2006–2016. Note that the year (e.g. 2009) designates the winter period (2009–2010).

FIGURE 7.4 Left: Livestock gross margin (LGM) insurance in Iowa (USA) for cattle (contract 0803) calf finishing (807) for an insurance period of January to November with expected monthly gross margins (EGM) and actual monthly gross margins (AGM) and insurance payouts (grey areas), 2006–2017. Right: LGM in Illinois (USA) for milk from dairy cattle (contract 0847) including expected monthly prices (ECP) and

actual monthly price

s (

ACP

) of corn, soybean meal (SM) and milk for an insurance period of January to November, 2009–2017.

Chapter 8

FIGURE 8.1 Left: Global wild catch and aquaculture production (excluding aquatic plants), 1960–2014. Right: Global aquaculture production (excluding aquatic plants) for crustaceans (includes shrimp, lobsters, crabs), diadromous fish (eels, salmon, shads, sturgeon), freshwater fish (carp, barbels, tilapias), marine fish (cod, flounders, tunas), aquatic animal products (pearl, shells), other aquatic animals (amphibians, urchins, turtles) and molluscs, 1960–2016.

Chapter 9

FIGURE 9.1 Left: Indexed (1990) forest land (FAO definition) by geographical region, 1990–2015. Right: Indexed production (1961) of the main forestry products, 1961–2016.

FIGURE 9.2 Left: Yield curves in function of stand age for (i) Douglas fir (solid lines) with <500 trees/acre and no thinning at site index 50 (A), 70 (B) and 90 (C = most productive), and (ii) naturally regenerated stands in a fir ecosystem (dashed lines) at site index 50 (D) and 70 (E) and 90 (F = most productive) for inland forests in the Northwest of the USA. Right: Yield curves in function of stand age for a Douglas fir stand with <500 trees/acre with no thinning (A), thinning from below to 250 trees at age 20 (B), thinning at age 20 from below to 100 trees at age 50 (C), thinning at age 20 from above to 100 trees at age 50 (D) and thinning at age 20 and to 100 trees at age 50 so that mean diameters at breast height stays the same (E) for inland forests in the Northwest of the USA.

FIGURE 9.3 Carbon stock in function of tree age for

P. radiata

(PR) in two locations in New Zealand and other main tree species as well as indigenous forests for Left: the first rotation, and Right: harvested residues of the previous rotation (above-ground wood and in roots) and carbon stock in the second (or later) rotation with <10 years after the previous forest was cleared or harvested.

FIGURE 9.4 Left: Area burnt (left axis, solid line), number of fires (right axis, dashed line) and firefighting costs of the Oregon Department of Forestry (right axis, columns) in the state of Oregon (USA), 1985–2016, with the insurance cover of US$25 million in excess of US$20 million (dotted lines). Right: Number of fires (left axis, solid line) and area burnt (right axis columns) in the province of Alberta (Canada), 1990–2015, with the triggers of the insurance cover in terms of number of fires and area burnt (dotted lines).

Chapter 10

FIGURE 10.1 Schematic overview of a quota share reinsurance contract with an example of agricultural insurance Policy 1, how sum insured (SI), premium and losses are shared between the insurer (60% retention) and the reinsurer (40% cession).

FIGURE 10.2 Schematic overview of a surplus reinsurance contract with an example of agricultural insurance Policy 1 and Policy 20, how sum insured (SI), premium and losses are shared between the insurer (1 Band) and the reinsurer (2 Bands).

FIGURE 10.3 Schematic overview of a stop-loss reinsurance contract including a stop-loss layer expressed in terms of GNPI and in monetary terms.

FIGURE 10.4 Example of an experience-based stop-loss pricing with Left: Pricing input data including premiums and losses adjusted at the 2017 portfolio (as-if analysis) and Right: Empirical losses and losses simulated through different PDFs.

Chapter 11

FIGURE 11.1 Left: Alternative capital per product type, 2002–2016, with half-yearly figures for 2017. Right: Issued and outstanding cat bonds and cumulative cat bond issuance for property exposure, 2008–2017.

FIGURE 11.2 Left: Banana production in Australia (left axis, dotted line), 1961–2017, banana production in Queensland (right axis, solid line) and detrended production (right axis, dashed line), 1984–2017, and cyclone intensity (left axis, columns) for cat 3 cyclone Winifred (A), cat 4 cyclone Larry (B) and cat 5 cyclone Yasi (C). Right: Historical cyclone tracks over Northeast Australia with intensities of cat 1 and cat 2 (dotted), cat 3 and cat 4 (grey) and cat 5 (black) and a circle of a radius of 60 miles centred over Innisfail in the heart of the banana growing industry in Northern Queensland, 1970–2016.

Guide

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Table of Contents

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Agricultural Risk Transfer

From Insurance to Reinsurance to Capital Markets

ROMAN MARCO HOHL

This edition first published 2019

© 2019 Roman Hohl

Registered office

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Library of Congress Cataloging-in-Publication Data

Names: Hohl, Roman, author.

Title: Agricultural risk transfer : from insurance to reinsurance to capital markets / Roman Marco Hohl.

Description: Chichester, West Sussex, United Kingdom : John Wiley & Sons, [2019] | Series: Wiley finance series | Includes bibliographical references and index. |

Identifiers: LCCN 2018041509 (print) | LCCN 2018042300 (ebook) | ISBN 9781119345640 (Adobe PDF) | ISBN 9781119345657 (ePub) | ISBN 9781119345633 (hardcover)

Subjects: LCSH: Agricultural insurance. | Agriculture–Risk management. | Agriculture–Finance.

Classification: LCC HG9966 (ebook) | LCC HG9966 .H64 2019 (print) | DDC 368.1/21–dc23

LC record available at https://lccn.loc.gov/2018041509

Cover Design: Wiley

Top Image: © WHYFRAME/Shutterstock,

Bottom Image: © Valentin Valkov/Shutterstock

To Mark, Sayaka and Makiko

Foreword

Agriculture is an important industry segment of the global economy in terms of GDP contribution and employment, particularly in many low- and middle-income countries. At a time when population growth drives further demand for food and agricultural raw materials, and the producing sector faces supply challenges and uncertainties from potentially more natural disasters related to climate change, risk management is of ever-growing importance. As experienced in 2007/2008, and to a lesser extent in 2010, production shortfalls in key exporting countries cause immediate responses on global commodity markets and trigger food security crises for many net importers.

The agricultural insurance sector has been adapting to the increasing need for risk transfer and reached a premium volume of close to US$31 billion in 2017 – a threefold increase since 2006. While the large growth in our business allows for a better geographical diversification for global reinsurers and capital markets, it means at the same time higher exposure for specialised local insurers.

Agricultural Risk Transfer comes at the right time, when our industry is optimising covers in well-established markets and expanding in new territories where insurance penetration is low, demand is increasing but product development is challenging and requires high level of expertise. Underwriting agricultural risks has and will continue to be highly complex. Pure actuarial risk pricing methods that rely on limited historical claims data and loss proxies are not sufficient any more to quantify future loss expectations and they require catastrophe risk modelling skills. While learning by doing has largely been the main approach for all stakeholders in our industry, the systematic methods provided in this book form a common standard for risk transfer of crop, livestock, forestry and aquaculture assets in developed as well as in emerging markets.

As the theory is illustrated through comprehensive case studies from our industry, Agricultural Risk Transfer is of equal interest for (re)insurance underwriters, loss adjustors, risk assessors, corporate risk managers, actuaries, catastrophe modellers and fund managers as well as development agencies, government entities, funding and financing institutions, the agricultural supply chain and students in disciplines that relate to our industry. Agricultural Risk Transfer closes the knowledge gap between insurers and reinsurers as well as between academics and our industry. It therefore greatly supports the efforts of our professional associations to exchange experience and best practices among stakeholders in the agricultural risk transfer industry.

Arnaud de Beaucaron

President

International Association of Agricultural Production

Insurers (AIAG)

Paris, France

Dr Thomas Zacharias

President

National Crop Insurance Services (NCIS)

Overland Park, Kansas, USA

Introduction

This book is written for practitioners and researchers in agricultural development, risk transfer and risk management and aims to close the knowledge gap between mature and developing agricultural markets as well as between practitioners and academia. Agricultural Risk Transfer brings together the most important concepts and methods, from the fundamentals to specific products and modelling techniques, and demonstrates their applications through case studies from the industry.

This book has been a nearly two-year project, one that started on the suggestion of the (re)insurance industry to discuss the current risk transfer theory with practical examples. Based on the encouragement from (re)insurers, international development agencies and those in academia, and given that agricultural insurance is one of the fastest growing specialty lines, I started developing an outline for Agricultural Risk Transfer.

OVERVIEW OF CHAPTERS

While all chapters can be read alone, some references are made throughout the book to some of the technical chapters that define key concepts and practices. At the end of each chapter, a list of books and reports is provided for the interested reader.

Each chapter contains examples from the industry and numerical examples to highlight the main risk transfer products, which are intended to illustrate the concepts and methodologies and therefore do not suggest any particular price for the products described. To represent the growth of agricultural risk transfer by country and main types of products, I developed a detailed database for each country (2006–2017) based on market reports and in-depth discussions with leading (re)insurers and brokers.

Chapter 1 provides an overview of current trends and future constraints in the agricultural sector, which are discussed in more detail in subsequent chapters for crops, livestock, aquaculture and forestry. This chapter further provides an overview of risk transfer concepts and discusses the role of risk management at farm level, for the agricultural supply chain and for government entities. As risk transfer addresses mostly market and production risks, a special emphasis is put on these risks.

Chapter 2 presents the key concepts of insurance and actuarial as well as catastrophe risk pricing, with references as to how these concepts are applied in agriculture. This chapter forms the basis for Chapter 5 (Agricultural Insurance) and links to Chapter 10 (Risk Transfer to Reinsurance).

Chapter 3 discusses the main perils for agriculture, including drought, flood, hail, frost, snow, cyclones, wildfires and epidemic livestock and aquatic diseases. The chapter includes the main risk modelling concepts, and for most perils, a list of open source data and model outputs is provided.

Chapter 4 discusses the sources and statistical techniques to address agricultural data and proxies, including climate data, satellite data for vegetation monitoring and wildfire detection, crop yield data, and livestock/aquaculture as well as forestry datasets. These concepts form the basis of most of the following chapters.

Chapter 5 reveals specific concepts as well as benefits and challenges of agricultural insurance, and it includes an overview of different products and key markets. Agricultural insurance systems and the role of the government is discussed specifically.

Chapter 6 discusses all main crop insurance products, including named-peril covers, multi-peril insurance, revenue and income covers as well as index-based insurance through area-yield, weather-, satellite- and model-driven indices.

Chapter 7 is dedicated to livestock insurance and presents standard insurance covers and extensions to business interruption and epidemic livestock diseases. Revenue insurance and index-based structures are discussed separately.

Chapter 8 introduces insurance solutions for aquaculture risks and discusses indemnity-based as well as index-based products, with a view on the main insurance structures, pricing and loss adjustment.

Chapter 9 deals with forestry insurance, with a perspective on underwriting, pricing and loss adjustment for standard coverage and extensions to carbon sequestration and firefighting expenses for governments.

Chapter 10 reveals how agricultural risks are transferred to reinsurance markets and presents the concepts of the main reinsurance structures and particularities for agriculture.

Chapter 11 shows risk transfer concepts of capital markets which are gaining in importance and includes a discussion on how insurance-linked securities are or can be used for agriculture exposure.

Many colleagues and friends from the industry and academia have helped in reviewing the book's chapters and case studies – all remaining errors are my sole responsibility. I would appreciate if readers could report errors or inconsistencies to [email protected] or www.agriculturalrisktransfer.com.

Acknowledgements

I would like to sincerely thank the reviewers for their very helpful comments, including Prof Robert Finger (Swiss Federal Institute of Technology, Switzerland), Prof Barry Barnet (University of Kentucky, USA), Prof Yui Leong (National University of Singapore, Singapore), Dr Auguste Boissonnade (Risk Management Solutions, USA), Michael Owen (Guy Carpenter, Singapore), Claudio Busarello (Swiss Re, Switzerland), Randall Reese (Allianz ART, USA), Charles Stutley (World Bank, UK), Phil Cottle (Forest Re, UK), Dan Fairweather (Willis, UK) and Dr Hervé Castella (Partner Re, Switzerland).

I am equally grateful for the contributions and reviews of different case studies from colleagues and friends in the industry and academia, including Chen Peng (PICC, China), Zhao Wei (AXA Corporate Solutions, China), Prof Long (Ministry of Agriculture, China), Dr Chutatong Charumilind (Thai General Insurance Association), MK Poddar (Agriculture Insurance Company of India), Harini Kannan and Yu Deng (Swiss Re, Singapore), Sonia Rawal (Allianz Re, Singapore), Ulziibold Yadamsuren (National University of Mongolia), SVRK Prabhakar (Institute for Global Environmental Strategies, Japan), Mani Upadhyay (AFSC, Canada), Joseph Bradonisio (Guy Carpenter, Canada), Dr Oscar Vergara (AIR Worldwide, USA), StefanoNicolini (Beach Group, USA), Gift Livata (Microensure, Malawi), Daisy Sabao (World Bank, Mozambique), Shadreck Mapfumo (IFC, South Africa), Roman Shynkarenko (Allianz, Australia), Chis Coe (Aon Benfield, UK), Dr Tom Osborne (Ironshore, UK), Julian Roberts (Willis, UK), Hansueli Lusti (Swiss Hail Insurance Company, Switzerland), Dr Olena Sosenko (Agricultural Reinsurance Specialist, Switzerland), Reto J. Schneider (Allianz Re, Switzerland), Maximilian Strobl and Dr Lambert Muhr (Munich Re, Germany), Luis Pulido (Hannover Re, Germany), Dr Juraj Balkovic (International Institute of Applied Systems Analysis, Austria), as well as Dr Marc Wueest, Dr Hans Feyen, Peter Welten, Dr Petra Winter and Lovemore Forichi (all Swiss Re, Switzerland).

Special Thanks

Special thanks go to Reto Zihlmann, a master student in agronomy at the Swiss Federal Institute of Technology, who helped me to generate most of the plots. I am also very grateful to Arnaud de Beaucaron (president of AIAG) and Dr Tom Zacharias (president of NCIS) for writing the foreword of Agricultural Risk Transfer and for all the encouragement. I wish to express my appreciation to my colleagues from the International Finance Corporation (World Bank Group), particularly Vijay Kalovakanda and Utako Saoshiro, for the understanding they showed over my limited ability to participate in field missions during the time writing the book. My sincere gratitude goes to Wiley as the publisher of Agricultural Risk Transfer and specifically, Emily Paul (project editor), Gemma Valler (commissioning editor), Gladys Ganaden (editorial executive), and Sharmila Srinivasan (production editor) for the great support and input to the manuscript.

Last but not least, special thanks go to my wife and children, who have lived with the book for two years, and to our golden retriever, who had shorter walks than usual at times.

CHAPTER 1Agricultural Markets and Risk Management

1.1 INTRODUCTION

Agriculture has always been a core human activity, and over the past century it has made enormous progress in increasing the production of food and agricultural raw materials. Much of the growth is due to specialisation, verticalisation, expansion in land use and water resources, the improvements in farming techniques and risk management. At the same time, food production has become globalised, is dominated by a few producing countries, and has managed to keep pace with population growth and increasing demand.

The large growth in production, verticalisation and industrialisation has led to increased stress on natural resources and a higher vulnerability to unexpected shocks, including natural disasters and epidemic diseases that impact local and global markets. Climate change, including more extreme weather events, and future economic developments are major factors that drive supply and demand for agricultural products and food security. Risk management, including risk transfer, has been an integral part of advancing agricultural production in coping, mitigating and transferring production risks. The (re)insurance industry and capital markets have been developing products to satisfy the growing need of farmers, agribusinesses and governments to transfer risks.

This chapter provides a brief introduction of the main trends that drives demand and supply in agriculture, while trends in the individual sectors are discussed in subsequent chapters. Key risks and risk management options are discussed for producers, agribusinesses and governments.

1.2 TRENDS AND CHALLENGES IN THE AGRICULTURAL SECTOR

At the change of the millennium, there was a reasonably high level of confidence that projected food demand could be met by improved crop production. In more recent years, the consensus is that future food production will struggle to keep up with growing demand. Part of the change in viewing future global food security is that (i) grain prices were initially assumed to decrease in future decades, (ii) rates of economic development in the most populated countries have exceeded initial projections, (iii) the demand for grain, energy and livestock products has increased more rapidly through higher than anticipated increases in purchasing power, (iv) increases in grain yields have been slowing, and (v) climate change is perceived to have larger impacts on most agricultural activities. The global 2017 World Economic Forum (WEF) risk survey revealed that (i) extreme weather events ranked as the likeliest of the 10 most likely risks and ranked as number 2 of the 10 risks with the largest impact and (ii) food security was ranked seventh among the 10 risks with the largest impact.1

Generally, a more sustainable approach to agriculture is needed to use land, water and input supplies more efficiently (conservation agriculture) and to increase farm incomes and food security while adapting to climate change through mixed crop–livestock systems and sustainable livestock production (climate-smart agriculture).2 Producing more with fewer resources, reducing greenhouse gas (GHG) emissions (global warming) and enhancing the livelihoods of smallholders in low- and middle-income countries remain key challenges for the agricultural sector. Increasing investments that are backed by safety nets of more specialised and verticalised agriculture (risk transfer) is essential to increasing production.

RISING DEMAND

Recent projections on demand and supply conclude that the agricultural sector will need to produce almost 50% more food, feed and biofuel by 2050 compared with 2012.3 This means global markets will need to produce on average one third more, while sub-Saharan Africa and South Asia will need to double production. There is a consensus that the additional food will need to come predominately from yield increases since expansion of arable land is challenging as it is not readily available due to a lack of infrastructure in remote locations and a concentration of available land in only a few countries.

A key driver of demand is a growing human population that is likely to reach 9.73 billion in 2050 and 11.2 billion in 2100. Demand is undergoing structural changes in that increasingly affluent middle classes in low- and middle-income countries can afford to change their dietary pattern towards more resource-intensive dairy and meat products. As the global demand for livestock products is projected to increase by 70% by 2050 relative to 2010, production of feed from grains and cereals has to increase substantially to satisfy demand for meat and dairy products.4 Additionally, the demand for biofuels, which use the same grains and oilseeds as livestock feed, is projected to continue growing and has increased the competition between food and non-food uses of biomass and created an interlinkage between food, feed and energy markets.

After peaks in 2008 and 2011, food prices have stabilised, but price volatility seems to have increased since 2000. Future food price levels are difficult to estimate and depend on how production systems will respond to resource constraints and climate change. On average, imports are 0–20% of domestic food supply, with some large agricultural economies exporting 50% of their domestic production while many African and Asian countries are among net food importers.

CHALLENGED SUPPLIES

While productivity in all agricultural sectors and key markets has significantly improved over the past 50 years, intensification and industrialisation put increased stress on natural resources, while the industry is going through structural changes. In a number of countries, faulty and distortionary government policy incentives led agriculture production to be highly inflexible to market demand. Global free trade and stringent domestic agricultural policies have added to the vulnerability of individual agricultural sectors and producers. A growing number of interrelated and longer-term trends that are likely to include more frequent natural disasters (climate change), rural transformation, stresses on natural resources and financial shocks in the global economy are difficult to estimate, but all have the potential to severely impact all agricultural sectors.

Structural Changes

The agricultural sector has undergone large structural changes, particularly in high-income countries where farming's share of gross domestic product (GDP) has decreased and where the industrial and service sectors have become multiple times larger. Under such changes, agriculture has become more efficient, specialised and verticalised, as well as more capital-intensive and better integrated into the wider economy. Consolidation of smaller farms into large operations has gained efficiency while entire supply chains have been developed and integrated. Although evidence is still limited, the same transformational processes seem to appear in agricultural sectors of low- and middle-income countries. As agricultural production bears large risks and low productivity, agriculture results in low income, most of any young rural population preferring to work in other sectors in cities, which leads to a lack of resources in agriculture, aging of farmers and rural–urban migration.

Productivity

The production of most main crop types has increased by more than 300% (1961–2016) as a function of greater arable land, higher yields and advanced production technology (Section 6.2). However, production of main crop types is concentrated in a few countries that dominate global markets, and while yields of key staple crops have doubled in the past 50 years, they have been stagnating since the 1990s at annual growth rates of 1%. While the area equipped for irrigation has increased at annual rates of 1.6% (1961–2009), it is projected to grow at 0.1% in future decades due to competition for water from other sectors.

Industrial-scale livestock production led the doubling of the global livestock population in 2016 compared with 1961. As grain and oilseeds are important components in livestock feed, a larger part in the increase of crop production is explained by the needs of the livestock industry. Increased livestock mobility, global trading and large differences in biosecurity plans of high- and low-income countries have resulted in a higher overall vulnerability to large-scale outbreaks of epidemic diseases (Section 7.2). Future increases in livestock production are thought to come from larger herds rather than from higher per-animal productivity, which in turn requires larger quantities of grains and oilseeds for feed.

Between 1960 and 2016, the production of aquatic animals increased 50 times based on the adaptation of new production methods and the expansion of aquaculture areas (Section 8.2). Aquaculture provided only 7% of fish for human consumption in 1974, which grew to 44% in 2014. However, intensified production has led to overuse of antibiotics in fish feed, polluted waste waters and environmental degradation. Growth rates in aquaculture production are expected to slow due to constraints in water availability and accessibility of high-quality broodstock.

Driven mainly by commercial agriculture in tropical environments, global forest land decreased by 3% between 1990 and 2015, while over the same time, forest plantations increased in size (Section 9.2). With strong demand for forest conversions from population growth and crop production, the global forest area is likely to continue to decrease.

Availability of Natural Resources and Investments

Agriculture production is highly water intensive and accounts for 70% of global water withdrawals. While the efficiency of irrigation has increased, water allocations to agriculture are shifting towards other industries and growing urban centres. Adaptation of production techniques is necessary to increase the efficiency of water usage, such as drop irrigation and alternate wetting and drying, which can reduce water use in rice cultivation by 25% without affecting yields. Today, over 33% of the global arable land is moderately to highly degraded, with particularly high levels in dryland production systems.

Investments in agriculture have increased over the past 15 years and low- and middle-income countries now invest, with US$190 billion annually, about the same as high-income countries. Government-driven investments into research and development rapidly reduced after the green revolution in the 1970s but are now growing, particularly in low- and middle-income countries. Agricultural trades closely follow global economic trends, with rapid increases since 2000 and a drop during the 2008–2009 financial crisis and a recovery thereafter, agriculture being one of the most protected sectors through import tariffs.5 The use of biotechnology, including genetically modified organisms, which is thought to support production increases through higher-yielding crop species, remains controversial in Europe and Asia.

Supply Chains

Inefficient supply chains in harvesting, storing, transporting, processing, packaging and marketing agricultural products and changing consumer attitudes have led to food waste in the range of 33%, which is a particularly severe problem in low- and middle-income countries. Improving supply chain efficiency and linking local food production systems to growing cities are thought to be key measures to reduce food losses and wastage.

Conflicts and Poverty

Civil conflicts have increased since the 2000s and are the cause of large-scale migration, which undermines agricultural development and can lead to humanitarian crises. Countries with the highest levels of undernourishment tend to have experienced conflicts, and the prevalence of hunger rises exponentially with the degree of fragility.6 Poverty is closely linked to agricultural productivity as both are highly concentrated in rural areas. Population increases, growing income inequalities, resource stress and impacts of climate change are likely to aggravate poverty and food security in the next decades.

Climate Change

The agricultural sector contributes 21% of total global GHG emissions and if energy usage is included (e.g. fuel for tractors) the share of agriculture activities increases to 26%. With intensification of production, agriculture-related GHG emissions have nearly doubled in the past 50 years and projections foresee a further increase. Climate change is seen as a significant hunger-risk multiplier and projections anticipate that by 2050, an additional 120 million people, particularly in sub-Saharan Africa, will be at risk of undernourishment.

Climatological Disasters

Global warming is likely to change the frequency and severity of climatological and meteorological disasters with potentially more frequent and intensive events. Climate change through increasing temperatures can lead to an intensification of certain plant pests and diseases and these spreading to larger areas. This will make agricultural production more volatile and requires adaptation strategies in the most affected regions and an increase in humanitarian assistance. Through increasingly globalised markets, production shocks from severe weather events in major producing markets are immediately reflected in commodity prices, which can rapidly develop into food security crises such as the events of 2007–2008 and 2011. Many low- and middle-income countries are likely to continue to rely on grain imports for food security and are at the mercy of international markets and export bans in the case of low domestic supply of a key production country. For example, following a severe drought in 2010, the Russian government ordered a ban on grain exports, which increased global wheat prices significantly and caused grain shortages for large net importers such as Egypt.

Impacts on Crop Production

The latest Intergovernmental Panel on Climate Change (IPCC) report states that (i) crop production in low-latitude countries will be negatively affected by climate change with high confidence while impacts in northern latitudes are more uncertain, (ii) climate change will increase the inter-annual variability of crop yields in many regions with medium confidence, and (iii) agronomic adaptation can improve yields by 15–18% with moderate confidence. Rainfed smallholder production systems in highland areas and the tropics, which produce 60% of global agricultural output on 80% of the global arable land, will be most severely impacted through more volatile rainfall and temperature patterns.7 Most studies of climate change impacts on crop yields show that crop yield variability will generally increase in the future (2030); however, this varies per crop type and by geography.8 Potentially more frequent and severe extreme weather events increase yield variability and the volatility of staple food prices.9 Past climate trends display yield volatilities of 20–24% and could increase to 43–53% in 2020–2040.10

Impacts on Livestock, Aquaculture and Forestry

Depending on the region, climate change has large impacts on livestock production through lower quantity and quality of feed, increased heat stress and limited water availability, potentially more frequent and extreme climate events (e.g. severe winters in Mongolia, El Niño-associated flooding in east Africa and droughts in southern Africa) and faster spread of certain livestock diseases. Poor livestock households in Africa and South Asia, and pastoralists in drylands in Africa and the Middle East, are most severely impacted by climate change due to limited water and forage availability, with a potential for political conflicts. Temperature increases in low-latitude regions are likely to cause local extinction of some fish species, while rising sea levels will threaten coastal aquaculture systems in river deltas and estuaries. Warming temperatures could prolong the wildfire season through heatwaves and fewer snowcaps in winter.

1.3 RISK MANAGEMENT IN AGRICULTURE

Risk management has been an integral part of agricultural industrialisation, which has led to significant production growth that is necessary to satisfy growing demand for food and agricultural raw materials. Sources of risk in agriculture are numerous and diverse and the sector is exposed to random (idiosyncratic) and highly systemic (co-variate) risks, which can impact an individual producer, a larger region, the wider supply chain, an entire country or global commodity markets. Production and market risk are some of the largest risks in the agricultural sector and are addressed through constantly evolving risk management approaches.

RISK MANAGEMENT STRATEGIES

Risks in agriculture are diverse and often interconnected and require different strategies to cope with the risk, mitigate the risk or transfer the risk, depending on its magnitude. Considering the risk and the impact on the economy and the wider society, government agencies and the private sector collaborate to develop adequate risk strategies. Holistic risk management approaches include a set of complex relations between the original sources of risk, the available strategies and interrelated tools from governments and markets.11 The holistic framework supports a system where public policy enables market solutions and risk is managed at different levels, including (i) frequent and limited losses are part of the normal business environment and are managed at farm level, (ii) larger and infrequent risks that are beyond farm-based risk management are addressed by market mechanisms (e.g. financial and insurance products), and (iii) very large and rare risks that can lead to market failure require government intervention.12

Risk Strategies

Agricultural risk management strategies can be divided into (i) mitigation to limit the adverse impact of a disaster, including production diversification (e.g. growing different crop types), income diversification and management measures (e.g. soil drainage, mulching, optimal planting schedules, weather forecasts), (ii) transfer of the financial consequences to a third party through informal, formal and/or semi-formal approaches, (iii) coping to manage financial consequences in, for example, complementing farm income by other activities, contract farming, and (iv) prevention, through irrigation, flood water management, drainage and crop protection.

Risk strategies can further be distinguished as (i) informal approaches, which are ex-ante strategies and include diversification of income sources, risk-adopted agricultural production strategies (e.g. buffer stock accumulation, irrigation) and risk avoidance, (ii) formal approaches provided by governments (e.g. infrastructure development, establishment of social schemes and/or cash transfer schemes) or markets (e.g. financial products and insurance), and (iii) semi-formal approaches, including informal risk sharing and mutualisation. Risk strategies largely depend on the type of risk, the impact in terms of area affected and the available response measures and risk mitigation and transfer mechanisms that are in place (see Figure 1.1).

FIGURE 1.1 Layering of risks in function of loss probability/severity with typical risk management approaches.

Source: Adapted from World Bank (2016) and OECD (2009).

Risks are often classified according to severity on three levels, including (i) micro-level risks, where random (idiosyncratic) risks affect individual producers, (ii) meso-level risks, where systemic (covariate) risks affect larger communities and the agricultural supply chain, and (iii) macro-level risks, where systemic and highly systemic risks impact an entire country and can have global consequences (see Figure 1.1).

Risk Layering

Risk layering is a core analytical concept to develop a risk financing strategy to protect against events of different frequencies and severities; it includes different mechanisms to address needs for funds before or after a disaster. Risk layering assigns monetary levels at which risks can be retained, pooled or transferred through different levels of the agricultural sector while assuring that financial resources are optimised. Optimal risk layering contains probabilistic analyses where frequent low-consequence events and rare catastrophe-type events are assessed in terms of loss potential to develop disaster risk management strategies for each layer, which is particularly important in the wake of climate change.13

Risk Transfer

Risk transfer is one of the key risk strategies in agriculture and shifts identified risks or responsibilities from their source to a third party through mechanisms such as (re)insurance, capital market instruments and legislation. In a narrower sense, risk transfer instruments include financial derivatives, insurance and insurance-linked securities.14

Financial derivatives derive a value from one or more underlying assets, securities, prices or indices and differ according to (i) the type of the underlying value (e.g. equity, interest rate, exchange rate, commodity or credit), (ii) the structure of the derivative contract, (e.g. forward, swap, option), and (iii) the market in which they are offered. Insurance transactions are financial agreements that transfer losses against a cost (premium) and where insurers pool risks over different lines of businesses and geographical areas to absorb risks while maximising revenue from premiums and minimising the risk of payouts. While financial derivatives focus on transfer of market risk, insurance instruments cover production risks and some elements of market risks. Insurance-linked securities present an alternative to reinsurance with transfer of insurance risk to capital markets.

MARKET RISK MANAGEMENT

High price volatility is one of the main causes of volatile farm revenues and delayed or defaulted loan reimbursements and payments of input supplies. For low-income countries with large agricultural sectors and exports of a few leading commodities, commodity price volatilities have a large impact on export earnings, fiscal revenues and creditworthiness.

The international community and governments have tried to manage commodity price risks by stabilising price volatility through market interventions, including compensatory mechanisms (e.g. stabilisation funds, stockpiles, buffer stock), international commodity agreements and marketing boards. As set prices were often based on political bargains, market fundamentals were not accurately reflected and led to a failure of most stabilisation schemes and to the development of market-based commodity risk management mechanisms.15 The main price risk management approaches for agriculture include financial instruments and contract farming.

Commodity Price Management Instruments