the size of crop losses) and their **attribution** to specific disasters. For the quantification of losses, the paper assesses the suitability of proxies based on (i) land area and crop damage, (ii) expert assessments, (iii) past harvest data or data from similar but unaffected plots, and (iv) farmers’ postplanting expectations to inform a micro-level crop production loss estimate. Subsequently, it (v) studies the reliability of self-reports for linking losses to specific disasters and (vi) compares them to geospatial data on disaster exposure.

Based on these questions, the paper makes a contribution in three ways. First, it operationalizes the methodology proposed in Conforti, Markova and Tochkov (2020) for use with state-of-the-art, crop production survey data at the plot level based on the format of the LSMS-ISA questionnaire. Second, the paper provides an in-depth investigation into the feasibility of measuring pre-harvest crop production losses due to disasters at scale and yet a highly disaggregated level and provides concrete recommendations for survey design. As such, it informs a new generation of survey data that facilitates the integration of a diverse set of data sources for measurement and analysis. Lastly, the paper identifies crucial remaining knowledge gaps and delineates relevant avenues for future research. Importantly, the methodology explored lends itself primarily to small- to medium-sized disasters and those with slow onset (e.g. droughts). For large scale disasters and swift policy action requiring an immediate appraisal of damages and losses, post-disaster needs assessments (PDNAs) and aggregate-level analyses may present complementary vehicles that are more readily deployable.

The remainder of this paper is structured as follows. Section 2 contextualizes the measurement of disaster crop losses in the literature and depicts the key approaches and challenges associated with their accurate quantification and attribution to disasters. Section 3 introduces the data sources the paper draws on and the key questions that motivate its empirical strategy. Section 4 presents the paper’s analysis for each of these questions while Section 5 closes with our headline findings and highlights key takeaways as well as open questions.

# **2. Crop Production Losses Measurement in the Literature – Approaches and Challenges**

To accurately measure crop production losses due to disasters, this paper distinguishes between two issues, first, the quantification of crop production losses, that is determining their magnitude, and second, attributing losses to specific disasters.

The task of measuring crop production losses relates to a large body of research broadly concerned with crop yield prediction and the explanation of geographical and intertemporal variation in yields. Part of this is an extensive agronomic literature measuring yield potential, that is the optimum achievable yield in a certain location without limitations from pests, diseases, nutrition, or suboptimal crop management apart from uncontrollable factors such as temperature or precipitation in rainfed systems (Lobell, Cassman and Field, 2009; van Ittersum _et al._ , 2013; 3