them suitable for phone surveys but these should be considered carefully in light of the objectives of a given survey. One approach is asking questions at a more aggregated level of observation, such as the farm or the household instead of the plot or the individual. In this approach, instead of enumerating a plot roster and eliciting inputs and outputs at the plot-level, questions are asked in aggregate, e.g. _“What is the total area of land, summing all parcels, you operate?”,_ This approach was used in some of the LSMSsupported HFPS. A similar kind of aggregation was done in the IPA’s RECOVR surveys (Innovations for Poverty Action, 2020) but with respect to household composition and demographic characteristics. This kind of aggregation may save time, though some aggregation questions, such as total area of land summing all parcels, may require respondents to do computations which increases respondent burden and introduces potential for error. Also, aggregation of individual- or plot-level outcomes at the household-level necessarily entails a loss of information.

Another strategy to simplify complex questions, such as those involving land or harvest quantifications, is posing such questions in relative terms, as compared to a previous period. This makes responses easier because farmers only need to roughly compare two periods, without the need of an exact quantification. For example, IFPRI’s vegetable value chain phone surveys in Ethiopia ask questions like _“Do you use more or less fertilizer currently than normally?”_ with responses _“1. The same; 2. Lower; 3. Higher”_ (Hirvonen et al., 2021b; Minten et al., 2020) _._ This simplification strategy is useful for before-and-after comparisons when no baseline quantification exists for comparison, as was the case in many of the COVID-19 phone surveys. However, responses may be biased positively or negatively depending on the sources of bias at play (for example, social desirability bias or perceived benefits to indicating poor outcomes). Moreover, the loss of information is significant, so that this strategy is not suitable for many types of agricultural surveys. For instance, it would not be feasible for a large-scale crop production survey seeking to generate national statistics.

A third approach is to retain some detailed and disaggregated data collection but reduce the response burden by asking fewer questions or covering only a sub-sample of crops, plots, or individuals. IFPRI’s vegetable value chain phone surveys in Ethiopia asked detailed questions focusing on the most important vegetable household’s grow (Hirvonen et al., 2021b; Minten et al., 2020). The LSMS-supported HFPS followed the same approach, focusing on the ‘main crop’ in the reference agricultural season. In contrast, the World Bank’s Africa Gender Innovation Lab used phone calls to collect input data for one selected parcel in a survey of rural agricultural households in Western Uganda (Sharma et al., 2021). In the context of their survey experiments, Arthi et al. (2018), Gaddis et al. (2021), Kilic et al. (2021) successfully collected plot-level input and output data over the phone, focusing on one specific topic (labor inputs and extendedharvest crop production). In all three settings, the plot-level data collected through phone interview was deemed reliable and even superior to the standard end-of-season labor module administer in a one-off in-person visit. These examples illustrate that it is possible to retain a certain level of complexity if focusing on a narrow set of issues or a sub-sample of plots, crops, or individuals, thus allowing for short phone interviews. Sub-sampling has implications for the representativeness of the data so this approach may not be suitable to replace an extensive agricultural sample survey in full but may be used to complement inperson data collection.

# _Survey timing and seasonality_

In addition to questionnaire design, survey timing is a critical design choice in agricultural data collection, particularly because of the highly seasonal nature of agricultural production and the varying frequency 15