are grown.<sup>6</sup> In these situations, aggregating all of the production from the previous completed agricultural season becomes a taxing mental exercise.

Also, inherent in human behavior is the inclination to round off values. For instance, recent research has illustrated the persistence of rounding in plot area estimates and the ensuing effects on data quality (Carletto et al., 2015; Forthcoming). The same problem is likely present in the estimation of production. Rounding of production estimates may pose a bigger problem on plots with lower production, such as smaller plots, as the rounding error relative to true production is likely greater on these plots. Suppose, for the sake of example, that farmer A, with true production of 1.5 bags, reports that he produced 2 bags. Farmer B has a true production of 11.5 bags, but he reports 12 bags of production. Farmer A overstates his production by 33.3 percent, while farmer B only overstates by 4.3 percent.

Further, depending on the context and farmer characteristics, intentional bias may be at play. Farmers may be inclined to understate production if they perceive an incentive to do so. Perceived incentives may come in the form of eligibility for various assistance programs or a possible threat of taxation, for example. Social desirability bias may lead farmers to overstate their production in order to appear successful.

Setting the challenges with human behavior aside, aggregating farmer-reported production data for analysis poses several challenges, as summarized by Oseni et al. (2017). First, farmers frequently utilize non-standard measurement units for the quantification of production. These units vary across countries, and often exhibit within-country variation across space and time. Even if the questionnaire instrument permits the use of non-standard measurement units for recording production quantities, for standard analyses, these values must either be monetized or converted into kilogram (kg) equivalent terms. In the case of monetization, if the common measurement units used for production quantification differ from the common units associated with crop sales, the valuation of production will be a challenge without converting crop production and sales into kgequivalent terms. In the case of kg-equivalent conversion, one would need to rely on crop-unitspecific conversion factors (possibly differentiated by region), which are often either unavailable to the researcher or available but not documented adequately and/or suspected to be out of date/in need of further validation.

A factor that further mediates the success of the kg-equivalent conversion is the issue of crop condition and state, and in the case of cereals, the ability to express production in kg-grainequivalent terms. Put differently, a farmer that reports two 100 kg bags of green maize that is on the cob would have drastically different production than a farmer that reports two 100 kg bags of maize in dried grain form. If the questionnaire instrument does not allow for the distinction of crop condition and state, the researcher would need to make assumptions regarding the crop conditions

> 6 Extended-harvest refers to harvest on an ongoing basis.

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