Agricultural risk management strategies and climate change

Sums insured under multi-peril agricultural insurance in the past 10 years have grown substantially. This highlights the importance this type of insurance has as an instrument for managing the risks faced by farms and for sustaining the livelihoods of farmers. This has, however, gone hand in hand with rising loss rates that have remained at high levels for a number of consecutive years. This is a wake-up call about the sustainability of the insurance scheme. Events like droughts, floods, frost, and hailstorms are occurring more often than expected and more often than recorded in the past. 2024 provided something of a breather in this regard, but all signs point to a future in which the frequency of extreme events will be rising as a consequence of climate change. This has put all the actors involved in the scheme on guard, from farmers, who are calling for more support for multi-peril agricultural insurance, to insurance companies and the government, who are pushing for reforms designed to cope with the higher loss rates separately from any additional support provided.

The effects of climate change have made managing risk in agriculture into a necessity. Multi-peril agricultural insurance should be viewed as one more tool to that end, and refining that tool will be both essential and unavoidable. With all this in mind, in this article we consider the extent to which climate change will affect risk management in agriculture, options for adjusting to this situation, and the role of multi-peril agricultural insurance.

Risk management

Risk is a concept that pairs the existence of a threat or peril capable of causing harm with a system’s vulnerability to the impact of that peril, which will depend on its characteristics and nature. Our ability to exert an influence on those aspects is what we mean when speaking about risk management. Vulnerability to a peril depends on exposure, e.g., the location of crops on soils with differing water retention capacities and potential susceptibility to drought; system sensitivity, e.g., phenological conditions when the peril occurs or the drought resistance of different crop varieties, and adaptability, the ability to reduce vulnerability and adjust to potential perils. Managing risk entails acting on systemic vulnerability by means of either ex ante (taken before an adverse event) or ex post (taken after the event) strategies. Proactive strategies include:

• Prevention measures: Avoiding the impact of a threat (e.g., changing crops, not planting vulnerable varieties, changing sowing dates, changing conduction systems, etc.).
• Mitigation measures: Reducing or minimising adverse effects (e.g., crop diversification, soil or water management).
• Transfer measures: Transferring risk to a third party, such as an insurance company, by paying a premium.
  
  

The reactive strategy referred to is acceptance, i.e., assuming losses (for instance, using savings or by selling assets). These strategies are not mutually exclusive, and choosing among them depends on the scope and level of risk. Risks can therefore be stratified and segmented by level and scope. This helps in identifying management options and in assuring that the risk management system is coherent overall. Risk levels can be classified as:

• Normal risk (lower levels): Addressed mainly through prevention and mitigation measures (management at the farm level), privately financed.
• Transferable risk (middle levels): Requires prevention, mitigation, and transfer measures (crop, livestock, or income insurance) financed publicly and/or privately.
• Catastrophic risk (high levels): Entails prevention, mitigation, transfer, and acceptance measures (crisis management, reactive support), financed publicly.
  
  

High scope coupled with low risk levels can be tackled by means of prevention and mitigation measures taken by farmers themselves; however, an increased level of risk, and with it the potential for losses, makes taking out insurance more necessary. At the same time, the more prevention and mitigation measures that are taken, the smaller the losses that will occur, lessening the need for and the required effectiveness of multi-peril agricultural insurance.

Effects of climate change on agriculture

All climate models suggest that the frequency, severity, and duration of extreme events like heat waves and droughts are increasing and that at the same time temperatures are climbing and precipitation is decreasing and becoming subject to higher variability. Moreover, in the Mediterranean region, indications are that interannual variability is on the rise, which will result in warmer and drier average conditions with more frequent periods of drought and heat waves as well as episodes of hail and frost outside the normal seasons for these events, abnormal temperatures, and rainfall starting earlier or later than expected.

A recent study by CEIGRAM (CEIGRAM, 2023) that looked at trends in and the incidence of the extreme events most relevant to farm and agricultural insurance for different farm operations corroborated that tendency, this time based on time series of observed climate data (temperature and precipitation). That is, the change is not a model-based projection but is borne out by observations recorded to date. An analysis linking singular extreme events to sensitive times for the main crops showed a tendency towards an increase in the frequency and/or duration of events associated with maximum temperatures and drought and a decrease in events associated with minimum temperatures and frosts. The trend has been growing more pronounced in recent decades. The impact of this trend on multi-peril agricultural insurance has been complex, and the analysis was hampered by the fact that the time series available for insurance data did not go back as far as the time series of climate data, but it nonetheless detected signs of a link between the occurrence of extreme events and damage to crops. Other studies more local in scope have made similar findings. For example, an analysis of the impact of climate change scenarios on grain yields in Castilla y León (CEIGRAM, 2020) found a decrease in precipitation and more drought, with an increase in conditions producing yields below the viability threshold and a two-fold increase in the number of years with abnormally low yields, though spatial variability was high.

Figure 1. Maps of changes in precipitation (mm) and maximum temperature (°C) in the region of Castilla y León in autumn in 1990-2019 (the present) and in 2020-2049 (the near future) under the RCP8.5 GHG emissions scenario.
Source: CEIGRAM.

In any case, climate change in Spain will bring about harsher conditions for crop and livestock farming, making unavoidable the implementation of prevention and mitigation strategies designed to adapt to highly adverse average and extreme climate conditions not just affecting crop yields but also increasing the risk of the emergence of new pests and diseases that we will have to learn to combat and control in an environmentally and economically sustainable manner.

Adaptation to climate change

Climate change will increase the likelihood that threats will emerge and the potential for adverse events to occur, and hence if our aim is to keep the scale of the risks at an acceptable level, the vulnerability of the farming sector will have to be reduced by decreasing exposure and susceptibility to these adverse events or by increasing adaptation. This heightens the importance of prevention and mitigation strategies designed to reduce the negative impacts of adverse events if transferring risks through insurance is to be assumable.

Making optimal crop choices (woody, herbaceous, perennial, etc.), introducing new crops that are more resistant to the new conditions, and in some cases moving out of areas that are now under cultivation will help protect against adverse effects. Yet in this scenario it is mitigation strategies that will be more important. Improved soil management, crop diversification, adding legumes to rotations, switching to more resistant varieties with different cold temperature requirements or shorter cycles, changing growing methods (sowing dates or soil enhancement), including improvements in irrigation efficiency and employing practices like planting cover crops to enhance soil structure and organic matter content, or improving grazing management, are all measures that have to be implemented taking local farming conditions into account. The difficulty lies in the fact that optimal solutions are local and are usually different for each crop and production system, and this calls for major data collection and analysis efforts, namely, research, to identify them.

Figure 2. Minimum summer temperature trends for Northeastern Spain based on Spain02 temperature time series from 1950 to 2015 (Herrera et al., 2016) plotted on a 0.1° (~10 km) grid. Downward trends are shown in blue and upward trends in red; non-significant trends are in grey.
Source: CEIGRAM.

A series of papers have confirmed the importance of making adaptations in crop and livestock production and have identified strategies to be implemented (Medina Martín, 2015). Using an interdisciplinary approach, CEIGRAM has, since its inception in 2007, focused on designing tools, research, and policies to support the farming sector meet the challenges posed by climate change and to help the sector adapt to climate change (https://ceigram.upm.es/proyectos-investigacion/(A new window will open))(se abrirá nueva ventana). For instance, the problem affecting winter cereal crops in Castilla y León in the near term was seen to be mainly in autumn, with less rainfall and higher temperatures, and changing sowing dates and switching over to shorter cycles have been suggested. A study carried out on fruit crops in Huesca and Lérida pointed to a direct statistical relationship between minimum summer temperature and hailstorm intensity (CEIGRAM, 2019a). A map was drawn up plotting minimum temperature trends by zone and marking zones where a higher risk of hailstorms was to be expected and where investing in hail netting made sense. Frost risk to vineyards has also been researched in depth, analysing climate trends and such varied factors as the characteristic variety, conduction systems, and growing methods in each designation of origin, assessing the effects on loss rates in each wine growing region (CEIGRAM, 2020a). Characterising local and traditional grape varieties (CEIGRAM, 2017) is one of the mitigation and adaptation strategies currently used in order to identify adapted genotypes more tolerant to abiotic stress and extreme climate conditions. In line with this, traditional stone and seed fruit trees have been characterised to identify genetic improvements to adapt them to climate change (CEIGRAM, 2021), e.g., flowering in the absence of chilling conditions, biotic pollination, and biodiversity of pollination animals. Research has also been carried out on the optimal date for transplanting tomatoes for medium and long-term industrial use with a view to reducing the risk of becoming parched (CEIGRAM, 2023a).

Figure 3. Projected changes in the last frost date (in days) in spring in different wine designations of origin (Rioja, La Mancha-Ciudad Real, Utiel-Requena) in 1990-2019 (the present) and in 2020-2049 (the near future) under the RCP4.5 and RCP8.5 GHG emissions scenario. Negative numbers indicate time brought forward in days, positive numbers indicate time set back in days.
Source: CEIGRAM.

Other ways to adapt to climate change are enhanced soil management using different methods to promote more efficient water use and to increase the nutrients in agroecosystems (CEIGRAM, 2017). For example, research on using canopies to cover woody plants in the context of climate change is under way (CEIGRAM, 2024a), and using cover crops in annual rotations instead of leaving land fallow in between crop plantings has been shown to improve soil properties, enhance effective nutrient use, and control weeds (CEIGRAM, 2015). When conducting research aimed at optimising crop farming, especially when it is also aimed at simultaneously reducing crop losses due to climate change impacts, it is essential to focus efforts on areas like drainage basins or sub-basins that form coherent hydrological units. This enables more effective integration of water management, ecological flows, and land-use planning, in that way contributing to more sustainable adaptation based on natural system processes (CEIGRAM, 2023b).

New technologies and adaptation

New technologies are a basic tool in developing and supporting the process of transitioning and adapting the agricultural sector to meet the challenge of climate change. It has already been pointed out that in many regions around the world agriculture is particularly vulnerable to climate change. Ability to manage climate risk among different regions, production systems, and actors is uneven (FAO, 2016). Since climate change manifests through increased variability and the intensification of extreme events on different time scales, one of the key adaptation strategies is to improve the ability to anticipate and respond to this sort of risk (IPCC, 2022).

Precision agriculture has become a very interesting option for managing soil-plant-microclimate variability on farms. Precision irrigation helps with risk management decision-making for crops like grapes and olives through the use of sensors and multispectral imaging and has proved to be a useful tool for increasing the efficiency of water use and for anticipating high water demand events (CEIGRAM, 2017a). Agrometeorological indices like SPEI (Standardized Precipitation-Evapotranspiration Index, a temperature and precipitation-based drought index) and NDVI (Normalized Difference Vegetation Index, a satellite imaging-based vegetation activity index) hold out interest because they can be used to diagnose, model, and forecast impacts like the impact of drought on cereal crops. SPEI is useful because of its different time scales and its ability to detect droughts based on temperature trends and high spatial variability. This has its uses in the context of climate change, though as a purely agricultural index it has limitations. Soil indices like WCI (weighted coenotic index) also come into play. It is important to develop predictive models capable of integrating recorded observations and future scenarios.

Developing this ability calls for increasing “climate knowledge” on the part of all the actors involved in the decision-making process, from crop and livestock farmers to those in charge of public policy and representatives of the private sector. This knowledge is not restricted to understanding climate phenomena, it should also extend to the ability to interpret forecasts, assess potential impacts, and implement suitable management measures. This could help lessen the adverse effects of climate change on agrifood production and at the same time assist in benefiting from opportunities that are emerging in certain contexts.

In this scenario, multi-peril agricultural insurance plays a central role as a risk mitigation tool, both at the individual level (by safeguarding farm income from losses due to adverse weather phenomena), and at the macroeconomic level (by dampening the financial impact on governments and on society). Its effectiveness is boosted when it is integrated into a broader risk management strategy based on technology tools, reliable data, and advance planning.

One of the most salient advances in this area has been widespread inclusion of climate forecasts in agricultural decision-making (Hansen et al., 2011). Though still facing challenges associated with reliability and the reporting system, statistical models based on relationships between local variables and large-scale climate patterns (like the Madden-Julian Oscillation, the Walker Circulation, and sea surface temperature anomalies) enable more proactive planning. As long as these relationships stay reasonably stable, statistical forecasts will remain a valuable tool (Goddard and Dilley, 2005).

Nevertheless, advances have also been made towards more complex predictive models based on physical processes and coupled atmosphere-ocean models that hold out greater potential to integrate the changes produced by climate change on different time scales. The emergence of big data, artificial intelligence (like machine learning), and fuzzy logic methods and greater data availability thanks to both remote sensing and on-site sensors have appreciably improved the predictive ability of these models (Kamilaris & Prenafeta-Boldú, 2018). However, for this information actually to contribute to enhanced adaptation, it is essential to adjust the language, formats, and reporting channels so that it can be effectively transmitted to end users.

At the same time, many adaptation strategies need to be context-driven and to take into account local practices and traditional knowledge (Meuwissen et al., 2019). For example, in certain regions transhumance continues to be an effective response to climate variability that can be complemented by modern technology tools.

Other emerging fields, like nanotechnology and biotechnology, are also calling forth new capabilities for analysing multi-factor interactions (CO₂ concentrations, temperature, precipitation, pests, diseases, air pollution, etc.) and their combined impact on agricultural systems. To be able to take informed decisions, it is essential to be able to have robust baseline estimates for current impacts as a basis for precisely assessing the costs and benefits of the different adaptation options (CEIGRAM, 2023b). This includes better risk threshold definition and understanding how future impacts may vary, not just in magnitude but also in direction, in the face of different climate scenarios.

The effectiveness of available technical solutions will ultimately depend on their being actually put into practice by farmers. It is therefore vital to promote participatory studies that involve stakeholders in structured research to assess both implementation rates and any conditioning socioeconomic, cultural, and technical barriers. In this respect digital tools for analysing the status and recommendations of actions, for instance, a tool for calculating the carbon footprint in the wine growing sector (CEIGRAM, 2022), may assist in decision-making and in proposing measures aimed at mitigating the impact of climate change on the agrifood sector. Research of this kind can also be helpful in realistically evaluating the costs and benefits of adaptation strategies, taking into consideration both market values and other non-monetary factors. In addition, this makes it possible to explore the viability of options aimed at lowering greenhouse gas emissions and increasing resilience without losing sight of key limitations like water, energy, fertiliser, and pesticide availability.

Advances in precision agriculture and improvements in estimating crop yields based on sensors, satellite imaging, and predictive models have thus proved essential (Wolfert et al., 2017). These tools make it possible to optimise the use of farming inputs, raise farm efficiency, and improve the capability to respond to adverse events (CEIGRAM, 2023c).

At the same time, climate risk management must be approached by accepting that there are multiple sources of uncertainty both in models and in future changes in climate variables. Still, this uncertainty must not keep us from taking action. Instead, our approach to uncertainty must be that it is an inherent feature of the system. Scientists are increasingly developing better ways to pass on incomplete knowledge, and decision-makers need to learn to place value on imperfect, fuzzy knowledge as a useful tool that is better than doing nothing.

In this context producers and systems with greater flexibility will be the ones that are best adapted and will be able to benefit the most from changing circumstances. Adaptation strategies should therefore focus on designing resilient agricultural systems capable of remaining operational in the face of a wide range of possible future scenarios. That resilience should be viewed holistically and should encompass not just technical and agricultural elements but also social, economic, and institutional structures (Moser & Ekstrom, 2010). Adaptation needs to stop being viewed as a set of farm-scale measures and instead needs to be conceived as systemic transformation.

One of the areas in the field of climate science that has experienced the most growth is studying damage caused by extreme events. That field combines statistical analysis of climate data time series and advanced computational models to evaluate the extent to which anthropogenic climate change has altered the probability and severity of specific extreme phenomena. These attribution studies are particularly relevant to the design and sustainability of multi-peril agricultural insurance by helping to answer key questions concerning the causes and repetition of adverse events.

In short, as climate change gains traction, growing climate uncertainty and the exacerbation of extreme events together place the farming way of life, already fragile, in jeopardy. To tackle this challenge, integrating technological innovation, big data analytics, and robust insurance systems makes up a basic strategy for safeguarding food security and long-term agricultural sector sustainability.

References

CEIGRAM (2015). Mejora del manejo de los cultivos cubierta para aumentar la sostenibilidad de sistemas de cultivo en regadío [Improved management of cover crops to increase the sustainability of irrigated farming systems].

CEIGRAM (2017). Estudio del potencial enológico de variedades de vid minoritarias en la Denominación de Origen Méntrida (PEVIMEN 2017) [Wine-making potential of minority grape varieties in the Méntrida Designation of Origin (PEVIMEN 2017)].

CEIGRAM (2017a). Riego de precisión en olivar y viñedo. Evaluación de sensores en suelo-planta atmosfera e imágenes multiespectrales en estrategias de riego deficitario (SENSOLEOVID) [Precision irrigation in olive and grape farming. Evaluation of soil-plant-atmosphere sensors and multispectral imaging in deficit irrigation (SENSOLEOVID)].

CEIGRAM (2017b). SolACE – Solutions for improving Agroecosystem and Crop Efficiency for water and nutrient use.

CEIGRAM (2019). Estudio sobre seguros agrarios y su tarificación en relación con la meteorología extrema (STORM-SOS). Fase I: Estudio del riesgo de pedrisco para frutales en Huesca y Lérida. [Multi-peril agricultural insurance and rate setting for extreme weather (STORM-SOS). Phase I: Hail risk for fruit trees in Huesca and Lérida].

CEIGRAM (2020). Estudio sobre seguros agrarios y su tarificación en relación con la meteorología extrema (STORM-SOS). Fase II: Estudio del riesgo de sequía para cereales de invierno en Castilla y León. [Multi-peril agricultural insurance and rate setting for extreme weather (STORM-SOS). Phase II: Drought risk for winter cereal crops in Castilla y León].

CEIGRAM (2020a). Estudio sobre siniestralidad del riesgo de helada en el viñedo de vinificación: evaluación retrospectiva teniendo en cuenta la reestructuración del sector desde 1995 [Losses to wine grapes due to frost risk: a retrospective assessment considering sector restructuring since 1995].

CEIGRAM (2021). Caracterización de las variedades tradicionales de las especies de pepita de interés comercial y agroecológico, y de sus necesidades de mejora en relación con su adaptación al CC [Characterisation of traditional varieties of commercial stone fruit species of agroecological interest and their needs for enhancement to adapt to climate change].

CEIGRAM (2022). Sistema inteligente de diagnóstico de la huella de carbono y la mitigación del cambio climático en el sector vitivinícola (VID-EXPERT) [Smart carbon footprint diagnostic and climate change mitigation systems in the wine growing sector (VID-EXPERT)].

CEIGRAM (2023a). Estudio sobre el Sistema de Seguros Agrarios Combinados en la actualidad. Retos y perspectivas de futuro [The Multi-peril Agricultural Insurance Scheme today. Challenges and future outlook].

CEIGRAM (2023b). Evaluando la demanda de AGUA para el uso aGRario para la ADAptación al CC a nivel de subcuenca (AGUAGRADA) [Assessing drainage sub-basin scale demand for water for agricultural use to adapt to climate change (AGUAGRADA)].

CEIGRAM (2023c). Soluciones tecnológicas 4.0 de predicción del rendimiento vitícola a largo plazo y prevención de daños derivados de la influencia del CC. BIGPREDIDATA [Technology 4.0 solutions for predicting long-term wine grape yields and preventing damage due to climate change (BIGPREDIDATA)].

CEIGRAM (2024). Agroclimatic risk assessment and designing insurance in tomato cultivation in Malta.

CEIGRAM (2024a). Viñedos con CUBIertas vegetales ante el Cambio climático (CUBIC) [Vineyard cover crops and climate change (CUBIC)].

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Kamilaris, A., Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016(A new window will open).

Medina Martín, F. (2015). Impactos, vulnerabilidad y adaptación al CC en el sector agrario: Aproximación al conocimiento y prácticas de gestión en España [Impacts, vulnerability, and adaptation to climate change in the farming sector: knowledge approach and management practices in Spain]. Oficina Española de CC [Spanish Climate Change Office]. Ministerio de Agricultura, Alimentación y Medio Ambiente [Ministry of Agriculture, Food, and the Environment], Madrid. https://www.miteco.gob.es/content/dam/miteco/es/cambio-climatico/temas/impactos-vulnerabilidad-y-adaptacion/impactos_vulnerabilidad_adaptacion_cambio_climatico_sector_agrario__tcm30-178448.pdf(A new window will open) (in Spanish).

Meuwissen, M. P. M., Feindt, P. H., Spiegel, A., et al. (2019). A framework to assess the resilience of farming systems. Agricultural Systems, 176, 102656. https://doi.org/10.1016/j.agsy.2019.102656(A new window will open).

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Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023(A new window will open).