recall length. Similarly, African Union’s (AU) Comprehensive Africa Agriculture Development Programme (CAADP) was conceived in 2003 to achieve economic growth, eliminate hunger, and reduce poverty through concerted investments in African agriculture. CAADP adopted the Malabo Declaration in 2014 in which AU member states resolved, among other things, to double agricultural productivity and pledged to allocate at least 10 percent of public expenditure towards agriculture. The CAADP results framework monitors progress towards its goals through a set of priority indicators and guides member countries’ strategic investment decisions. CAADP target 2.1 – Increased agriculture production and productivity<sup>14</sup> is comprised of five priority indicators, focusing on growth in agricultural production volume and value added as well as land and labor productivity. For instance, priority indicator 2.1.5 measures yield for the five AU priority commodities, of which maize is one; indicator 2.1.2 tracks the level of agricultural production.

The results of our analysis show that recall error can lead to unreliable measurement of all these indicators at an economically significant level. This is especially true when it comes to monitoring their evolution over time. For illustration, maize yields in Africa (reflected in CAADP 2.1.5) have grown at an average annual rate of 0.8 percent between 2000 and 2017, according to FAOSTAT. Maize production (reflected partially in CAADP 2.1.2) has grown by an average of 3.9 percent per year during the same period. In comparison, our analysis indicates that an additional month in recall length can lead to a change of between 2 and 5 percent in reported maize yields and 2 and 7 percent in the reported maize harvest quantity. Further, an annual growth rate of around 7 percent would be required to achieve SDG target 3.2 of doubling labor productivity within about 10 years. We find an additional month of recall length is associated with a 2 to 3 percent increase in reported output per unit of labor. What this shows is that the recall effect is likely to introduce an amount of variability that makes tracking progress towards achieving SDG targets and CAADP goals challenging and sensitive to the survey design choices that affect the length of recall. Policy and investment priorities based on these indicators risk being ineffective or misguided. Improving data quality is critical to minimize this risk.

The results lend support to ongoing efforts to mainstream more objective ‘gold standard’ measures of key variables into the design and implementation of surveys. The use of GPS devices to measure the area of agricultural plots is already considered best practice and incorporated into many surveys concerned with agricultural productivity and land use. In the field of production measurement, the practice of crop cutting is used in many national production estimations and other surveys, and our findings offer strong reasons for doing so systematically. Finally, when it comes to accurately measuring labor and fertilizer input, as well as counting all plots used in agricultural production, the use of diaries appears worth exploring.

One strategy to improve agricultural data reliability by shortening recall periods and recall length variation in household and farm surveys is to field two visits, one in the post-planting and one in the post-harvest period, rather than just one. While this strategy has cost implications, as field teams need to visit farms twice rather just once, it has the additional advantage of providing countries with more frequent and timely data on planting in the ongoing agricultural season. Another avenue to explore is the use of higher-

> 14 CAADP Indicator 2.1. _Increased agriculture production and productivity_ contains the following sub-indicators: 2.1.1 _Agriculture value added_ ; 2.1.2 _Agriculture production index_ ; 2.1.3 _Agriculture value added per agricultural worker_ ; 2.1.4 _Agriculture value added per hectare of arable land_ ; 2.1.5 _Yields for the five AU priority commodities_ (cassava, yams, maize, meat, and cow milk).

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