# 1. Introduction

For many developing countries, boosting agricultural productivity and incomes of small food producers are key policy objectives in the effort to alleviate poverty and improve food security. The international community has also embraced those goals, making them prominent targets in the Sustainable Development Goals (SDG) monitoring agenda under SDG 2, Zero Hunger.<sup>1</sup> High-quality, reliable farm output and input data are indispensable both for measuring progress towards the SDGs and for effective policy and program design and analysis.

Household and farm surveys are the most important source of agricultural data, rivalled in some countries by administrative data. In all measurement, survey-based or otherwise, there is some amount of measurement error. In surveys, the necessary reliance on imperfect respondent recall is a common source of error. Measurement in agricultural surveys in low-income countries is particularly difficult because agricultural operations are complex and seasonally variable, respondents are often illiterate, familiarity with standard units of measures is the exception rather than the norm, and a host of other factors. When data are of poor quality, and particularly when they are affected by systematic biases, policies based on that information may turn out to be less effective, if not misguided.

Methodological research shows that survey design matters and that conscious efforts to adopt improved design and survey administration choices can contribute significantly to minimizing measurement error, including recall error,<sup>2</sup> and improve the quality of agricultural survey data in developing countries (Carletto et al., 2015). This paper contributes to these efforts by focusing on the length of the recall period and how it affects the reliability of agricultural survey data. The length of the recall period is thought to be associated with recall decay, in which survey respondents forget details of events or forget events entirely, leading to reporting inaccuracies (Sudman and Bradburn, 1973). In agricultural surveys, recall length also often differs across the various activities recorded because farming activities, such as planting, plot maintenance, or harvest, take place at different times during the agricultural year. Depending on the timing of survey administration, respondents may be asked to recall the details of events sometimes many months in the past and the recall period may vary substantially across households or farms in the same sample if the fieldwork is completed over several months. In addition, some countries have two or more cropping seasons in one agricultural year and agricultural activities can be very different in both extent and nature depending on the season, which can make it even harder to recall events correctly.

Survey designers and planners have some control over the length of the recall period by choosing the number and timing of field visits to farms. This choice, however, has clear cost implications. Visiting households or farms multiple times in order to shorten the length of the recall period is more expensive and logistically demanding than collecting information only once during the agricultural year. In practice, surveys differ widely with respect to the number and timing of visits.<sup>3</sup> Survey designers may also choose to rely on more

> 1 Especially relevant are indicators 2.3.1 _Productivity of small-scale food producers_ and 2.3.2 _Income of small-scale food producers._

> 2 See, for example, Kasprzyk (2005), Zezza et al. (2016), Gaddis et al. (2019) and Kilic et al. (2018).

> 3 For example, the Tanzania National Panel Survey relies on one visit per household per year to collect information about Tanzania’s two cropping seasons. Data collection begins after the main season’s harvest and is rolled out over the course of 12 months. The AGRIS model, developed as part of the Global Strategy for Agricultural and Rural Statistics and published at the end of 2017 (Global Strategy to improve Agricultural and Rural Statistics, 2017), proposes a core module administered once per year

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