However, it is important to note that this paper is primarily concerned with the _measurement_ of disaster crop losses as opposed to identifying their causal drivers. In as much as our identification strategy for _measuring_ losses is thus unaffected by these factors, they do not pose a major challenge for this study.

On the methodological side, a strand of literature relevant for this paper is concerned with estimating and forecasting the climate change impacts on agricultural yields. For this, three approaches are common (Challinor _et al._ , 2014; Liu _et al._ , 2016; Zhao _et al._ , 2017). The first two approaches use process-based crop models in conjunction with meteorological models and can be split into grid-based models (e.g. Rosenzweig _et al._ , 2014; Webber _et al._ , 2018) and less coarse but more geographically scattered site-based models (e.g. Asseng _et al._ , 2013).<sup>6</sup> These models are also becoming increasingly popular as in-season, real-time monitoring tools for early crop loss prediction and swift relief (e.g. the World Food Program’s LEAP tool employed in Ethiopia and documented in Drechsler and Soer (2016)).

As discussed previously, crop models vary in their data requirements but usually require a rich set of sensor data at high frequency and sufficient spatial resolution. They are thus demanding for accurate, large-scale, and micro-level measurement in LMICs where smallholder agriculture with small and irregular plot sizes, intercropping, and large idiosyncratic variations in yields are the norm. Despite recent advancements in high resolution yield estimation (e.g. Burke and Lobell, 2017), survey data may thus provide complementary information. Survey data are typically the data source for a third approach in the climate change and crop yield literature that employs statistical modeling based on observed harvest data at varying levels of aggregation (e.g. Lobell, Schlenker and Costa-Roberts, 2011; Imran, Zurita‐Milla and Stein, 2013; Liu _et al._ , 2016).

Survey data may be a valuable source of complementary information for several reasons.

- 1) They can provide information down to the plot-crop level and thus possibly higher microlevel accuracy than most grid-based models to date. This opens up opportunities for better understanding of micro-level dynamics that can inform disaster risk reduction policies.

- 2) They provide high data density. Relative to site-based models, this reduces the need for strong assumptions about the generalizability of the sample for larger aggregates (though it may not eliminate it entirely) (van Wart _et al._ , 2013; van Bussel _et al._ , 2015).

- 3) They provide an approach that may be more suitable to the peculiarities of smallholder farming in LMICs (see above).

- 4) They are able to pick up non-meteorological sources of crop losses such as pests and diseases (Liu _et al._ , 2016).

- 5) Their information can be effortlessly integrated with other information collected in surveys that make understanding, for example, behavioral components of crop management

> 6 In comparison to gridded models that rely on remote-sensed data of sufficiently high resolution, site-based models typically use sensor data collected at several distinct experimental sites, e.g. farms or even single plots. While the former can thus cover a larger geographic area and may be more representative for aggregate measurement, the latter has the advantage of more fine-grained measurement based on data observed “on the ground”.

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