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Uniting remote sensing, crop modelling and economics for agricultural risk management

Nature Reviews Earth & Environment volume 2, pages 140–159 (2021)Cite this article

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Abstract

The increasing availability of satellite data at higher spatial, temporal and spectral resolutions is enabling new applications in agriculture and economic development, including agricultural insurance. Yet, effectively using satellite data in this context requires blending technical knowledge about their capabilities and limitations with an understanding of their influence on the value of risk-reduction programmes. In this Review, we discuss how approaches to estimate agricultural losses for index insurance have evolved from costly field-sampling-based campaigns towards lower-cost techniques using weather and satellite data. We identify advances in remote sensing and crop modelling for assessing agricultural conditions, but reliably and cheaply assessing production losses remains challenging in complex landscapes. We illustrate how an economic framework can be used to gauge and enhance the value of insurance based on earth-observation data, emphasizing that even as yield-estimation techniques improve, the value of an index insurance contract for the insured depends largely on how well it captures the losses when people suffer most. Strategically improving the collection and accessibility of reliable ground-reference data on crop types and production would facilitate this task. Audits to account for inevitable misestimation complement efforts to detect and protect against large losses.

Key points

  • In many developing regions, adverse weather can lead to food insecurity, reduced investments or distressed asset sales that ensnare people in a cycle of poverty.

  • Tools to manage risk — such as well-designed insurance — can help people avoid the most severe possible consequences of bad weather and build confidence to invest in additional income-generating opportunities.

  • In recent decades, governments and researchers across the globe have trialled approaches to inexpensively assess agricultural losses. Index-based insurance offers promise, but detecting losses cheaply and accurately remains challenging.

  • Recent advances in crop modelling and remote sensing can improve index-based approaches by strengthening the link between indices and actual losses, as well as reducing programme costs.

  • We provide an economic framework to evaluate indices, suggesting how the remote sensing and modelling communities can contribute to enhancing index insurance quality through better detection of adverse conditions.

  • Promising opportunities to enhance index insurance programmes include inexpensively addressing heterogeneous conditions on the ground, such as employing audits, optimizing insurance zones, using new sensors or increasing contract flexibility.

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Fig. 1: The benefits of effective risk management before and after a shock.
Fig. 2: Insurable and uninsurable production risks under index insurance.
Fig. 3: Timeline of selected index insurance programmes.
Fig. 4: Timeline of selected sensors, measurement missions and gridded products used for evaluating indicators of agricultural conditions.
Fig. 5: Strength of various remotely sensed data used to indicate crop health, productivity and environmental stressors.
Fig. 6: Illustrating the effects of cloud cover on image quality.
Fig. 7: Illustrating index insurance quality evaluation.

Data availability

The authors declare that the data supporting the findings of this study are available within the article and its supplementary information files.

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Acknowledgements

This work has benefited from research conducted under the auspices of the United States Agency for International Development (USAID) Feed the Future Innovation Lab for Markets, Risk and Resilience (grant no. 7200AA19LE00004), which M.R.C. directs and from which M.R.C., E.B. and A.H. have previously received funds. The contents are the responsibility of the authors and do not necessarily reflect the views of the USAID or the United States Government.

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Authors and Affiliations

  1. Department of Agricultural and Applied Economics, Virginia Tech, Blacksburg, VA, USA

    Elinor Benami

  2. Department of Bioproducts and Biosystems Engineering, University of Minnesota, St Paul, MN, USA

    Zhenong Jin

  3. Agricultural and Resource Economics Department and Innovation Lab for Markets, Risk, and Resilience, University of California, Davis, Davis, CA, USA

    Michael R. Carter

  4. International Center for Tropical Agriculture (CIAT), Nairobi, Kenya

    Aniruddha Ghosh

  5. Department of Environmental Science and Policy, University of California, Davis, Davis, CA, USA

    Robert J. Hijmans & Benson Kenduiywo

  6. Department of Economics, University of San Francisco, San Francisco, CA, USA

    Andrew Hobbs

  7. Department of Earth System Science, Stanford University, Stanford, CA, USA

    David B. Lobell

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Contributions

E.B. and Z.J. jointly designed, wrote and edited the full manuscript prior to submission, with substantial input from M.R.C., D.B.L. and R.J.H. A.H. edited the manuscript prior to submission and reviewed the code for the case study for accuracy. A.H. and B.K. helped E.B. and Z.J. research data for Fig. 3 and A.G. for Figs 4,5.

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Nature Reviews Earth & Environment thanks K. Takahashi, A. Vrieling, L.M. Robles and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Glossary

Loss-adjusted insurance

Insurance that requires the assessment of claims to issue payment.

Moral hazard

A hazard that occurs when an insured individual takes actions that increase risk and make insurance payouts more likely.

Adverse selection

A situation that insurers are prone to when only the riskiest subset of the population purchases insurance, such that pay-offs occur more frequently than they would if every person purchases insurance.

Insurance zone

An area covered by a single index value.

Premium

The amount of money paid to have insurance coverage.

Crop cuts

Preharvest crop yield estimates derived from visiting and physically harvesting and weighing a sample of production from a selection of fields.

Design risk

The failure of the index to accurately capture average losses in the insurance zone.

False negatives

The cases when no payout is triggered, despite some insured individuals experiencing losses; arises from design or idiosyncratic risk.

False positives

The cases when the insurance index signals a loss and issues a payout, even though some insured individuals did not experience a loss.

Basis risk

The risk that index insurance payments do not cover the losses experienced by an individual. Basis risk is the sum of the design risk and the idiosyncratic risk.

Expected utility

A measure of anticipated future economic well-being that increases with expected income and, for a risk-averse person, decreases with the variance of income.

Certainty equivalent

The amount of money that, if received for sure, would make a person indifferent between the sure money and a set of risky income prospects.

Crop mask

A map that characterizes the extent and type of crops over a region, often derived from satellite imagery classification.

Idiosyncratic risk

Risk that is specific to an individual and is uncorrelated with losses experienced by neighbours or others in the insurance zone.

Trigger

The level of the index at which payouts begin to occur (for example, 90 mm of rainfall during planting season).

Risk averse

Exhibiting the preference to give up some money in expectation in order to reduce variability.

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Benami, E., Jin, Z., Carter, M.R. et al. Uniting remote sensing, crop modelling and economics for agricultural risk management. Nat Rev Earth Environ 2, 140–159 (2021). https://doi.org/10.1038/s43017-020-00122-y

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