# **1. Introduction**

Between 2008 and 2018, disasters accounted for declines in crop and livestock production totaling USD 108.5 billion in low- and lower-middle income countries (LMICs), with the impact expected to further exacerbate with climate change (FAO, 2021). This toll is not only significant from an aggregate economic perspective, but its consequences are particularly dire in the poorest countries where over two-thirds of the population directly depend on agriculture for their livelihoods. Recent estimates by the FAO (2021) suggest that as much as 6.9 trillion kilocalories, equivalent to the amount it would take to end undernourishment among half of the undernourished population in low-income countries,<sup>2</sup> are lost to disasters diminishing crop and livestock production in the developing world.

With the impacts large in magnitude, widespread throughout LMICs, and likely to exacerbate, the accurate measurement of disaster-induced damages and losses<sup>3</sup> has been identified as a key component for economic development<sup>4</sup> and enshrined in Sustainable Development Goal 1.5.2 and Sendai indicator C. A large share of the disaster impact in LMICs accrues in agriculture and particularly due to reductions in pre-harvest crop production (FAO, 2018). Therefore, their accurate measurement at a micro-level is paramount to track progress toward the SDG and Sendai goals and inform strong disaster risk reduction strategies.

In a recent effort to provide a standardized methodology for measuring damages and losses in agriculture, Conforti, Markova and Tochkov (2020) propose the use of survey data to measure preharvest losses in crop production. However, there is yet scant evidence applying the method for micro-level loss measurement and a need for refinement to make full use of a variety of data sources including agricultural household survey data and geospatial data (FAO, 2018). This paper conducts a thorough descriptive assessment of the suitability of existing survey data to measure pre-harvest production losses of annual crops in LMICs at the micro-level. For this, it draws on a diverse set of data sources, rich plot-level data on harvest, inputs and different losses proxies along with household- and community-level data from the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA), geospatial data, as well as experimental data from crop cut and survey experiments, in three SSA countries.

The paper’s approach is guided by a set of six distinct questions that we attempt to empirically test and mold into concrete recommendations for survey design and measurement. These questions fit into two broad issues for the measurement of disaster crop losses, the **quantification** of losses (i.e.

> 2 Authors’ calculations based on (FAO et al., 2020; Our World In Data, no date; World Bank, no date)

> 3 Throughout this paper, we follow the definitions employed in (FAO, 2021, pp. 198–200):

> “Disaster: A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental loss and impacts (UNDRR Terminology).”

> “Damage: The monetary value of total or partial destruction of physical assets and infrastructure in disaster-affected areas, expressed as replacement and/or repair costs.” In agriculture, this comprises, for example, the destruction of stored agricultural inputs or assets used in the production process.

> _“Loss: The change in economic flows occurring as a result of a disaster.”_ In the context of this paper, the focus will be on losses due to declines in crop production.

> 4 Specifically, accurate measurement of damages and losses matters for: 1. disaster risk reduction; 2. poverty eradication; 3. food security; 4. climate change action; and 5. sustainable development (FAO, 2020).

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