and intensity with which agricultural activities of interest are performed. Some outcomes in agriculture (labor inputs, harvesting of some crops) that are dynamic over the agricultural season would benefit from more frequent data collection, some happen only during certain periods, while others are static within a given crop growing season and may well be collected only at one point during the season. Centered around the main crop growing season, agricultural surveys often visit farms at the end of the season. This approach can lead to respondents having to remember activities and outcomes many months in the past, and recall decay has been found to affect data quality in agricultural surveys (Beegle et al., 2012a; Wollburg et al., 2020). Increasing the frequency of data collection has been shown empirically to improve data quality in several domains of interest (Arthi et al., 2018; Gaddis et al., 2021; Kilic et al., 2021; Knippenberg et al., 2019).

Phone surveys offer the flexibility and cost advantage to conduct surveys at the appropriate time and more frequently. Agricultural labor inputs are used frequently and often in a manner that is difficult to track. Arthi et al. (2018) in Tanzania and Gaddis et al. (2021) in Ghana show experimentally that weekly phone surveys improve the quality of labor data relative to the standard end of agricultural season inperson modules. Weekly phone surveys have also shown to yield better harvest data quality for extended harvest crops, like tomatoes, roots and tubers (Ceballos et al., 2020; Kilic et al., 2021), which are often harvested over the course of the season rendering recall difficult. The Georgia Survey of Agricultural Holdings fielded an AGRIS production survey over the phone relying on quarterly data collection to shorten the recall period (50x2030 Initiative, 2020). Higher frequency data collection over the phone can also be suitable for agricultural input and output prices (Hermosilla, 2017; Hoogeveen et al., 2014), though community- rather than household-level data collection is likely appropriate. Recall bias has also been documented in the livestock sector, with Zezza et al. (2016) providing evidence on recall bias in the measurement of milk off-take in Niger. The Global Strategy for Improving Agricultural and Rural Statistics successfully used phone surveys in the collection of livestock productivity data, also in Niger (Bako, 2018).

There are other agricultural variables to which similar reasoning applies, and which would likely benefit from higher frequency data collection by phone, though this has not been validated empirically. These include non-labor inputs such as fertilizers and pesticides as well as weather shocks such as droughts, flooding, extreme heat, or insect infestations, and the damage these shocks cause (Knippenberg et al., 2019). Some agricultural variables are generally static over a crop growing season, such as access to irrigation, land ownership, stocks of large ruminants, and more frequent data collection is unlikely to improve the quality of these variables, diminishing the possible benefits of collecting such information via phone.

Food consumption (expenditure) data could in principle benefit from more frequent data collection, particularly to temper the excess variation that may be associated with collecting data over a short time interval (Gibson and Alimi, 2020). Full-fledged consumption modules are however too time intensive to be suitable for implementation over the phone (Abate et al., 2021). More synthetic measures of consumption like dietary diversity indicators or food frequency questions can be adapted and collected 16