possible. This facilitates causal investigations into variation in crop losses or the microlevel impacts of disasters (Hallegatte and Rozenberg, 2017; Markhvida _et al._ , 2020).

Importantly, this paper does not study nor does it propose the use of survey data as a substitute for the other approaches presented. Rather, it highlights how survey data may yield additional insights and ultimately contribute to the development of more accurate, integrated approaches suitable for the context of LMICs. For example, high-quality survey data may be useful to train remote-sensing algorithms (Jain _et al._ , 2016) and survey data, geospatial data, and/or crop model data can be combined in statistical models to improve accuracy and analyze causal relationships (Imran, Zurita‐Milla and Stein, 2013; Hill and Porter, 2017).

Next to the issue of determining the magnitude of crop losses (quantification), accurately attributing losses to disasters is an essential challenge for this paper to study. As the most common disasters in our study region are rainfall-related (FAO, 2018, 2021), this task poses some challenges akin to those covered in the literature on agricultural index insurance (Miranda and Farrin, 2012; Cole _et al._ , 2013; Karlan _et al._ , 2014; Tadesse, Shiferaw and Erenstein, 2015).

Self-reported insurance claims may be hard to verify without significant administrative effort and may thus be subject to moral hazard and adverse selection (Tadesse, Shiferaw and Erenstein, 2015). To overcome issues of information asymmetry and reduce administrative costs, insurance contracts that tie payouts to a local index proxying for disaster impact intensity, e.g. a rainfall gauge placed at a representative location for a small geographic area, have received considerable attention in the literature. However, while “measuring” shock exposure through an index provides an objective proxy, it leaves farmers with a certain “basis risk”, the possibility that the index does not capture idiosyncratic deviations at the farm or even plot level.

Basis risk is a substantial impediment to the uptake of index insurance (Giné, Townsend and Vickery, 2008; Miranda and Farrin, 2012; Cole _et al._ , 2013; Karlan _et al._ , 2014; Jensen, Mude and Barrett, 2018) and of particular concern in heterogenous regions with many micro-climates (Barnett, Barrett and Skees, 2008). The extent of basis risk furthermore depends on shock intensity with more severe shocks reducing the idiosyncrasy in incidence (Barnett, Barrett and Skees, 2008; Hazell and Hess, 2010; Jensen, Barrett and Mude, 2016). Consequently, most insurance payouts have been found to constitute large, infrequent payouts in response to severe disasters (Giné, Townsend and Vickery, 2007). Yet, idiosyncratic risks have been found to be substantial in the case of smallholder livestock farming and inadequately covered by index insurance (Jensen, Barrett and Mude, 2016). This suggests that crop losses could be quite localized and require finegrained measurement to accurately attribute losses to disasters. Similarly, a number of studies have found large variation in drought exposure between different measures. Notably, farmers’ selfreported shock exposure commonly deviates from measured data collected from weather stations or satellite data (Meze-Hausken, 2004; Sohnesen, 2020). Determining the degree of idiosyncrasy in farmers’ self-reported shock exposure and disentangling variation due to differences in perception from differences in actual exposure is thus also a relevant issue for this paper.

Lastly, a number of factors commonly studied in the behavioral literature may matter for the measurement of crop production losses through survey data (Schilbach, Schofield and 6