There are a number of reasons why self-reported land areas may be subjected to measurement error. First, farmers may knowingly over- or under-state their landholdings for strategic reasons if they perceive information may be used for a certain purpose such as property taxes or access to a program. Second, there are also a number of factors that can lead to unintentional errors in reporting. There is a natural tendency to round off numbers and provide approximations of land areas, which leads to lumping of the data. Geography, particularly parcel slope, can also change the way farmers interpret the land (Keita and Carfagna, 2009). Slope-related effects on area measurement are rooted in the fact that the actual area should be the horizontal projection of the parcel, as opposed to the parcel area itself since plants and trees grow vertically rather than perpendicular to the slope and therefore require for their growth a vertical cylinder of soil (Keita, Carfagna and Mu’Ammar, 2010; Muwanga-Zake, 1985). The difference between actual area and projection appear to be particularly important for slopes greater than 10 degrees (Fremont and Benson, 2011).

Survey design itself can lead to measurement error as well. When farmers are required to report in a single unit, enumerators may be forced to convert the self-reported area from local units if farmers are unfamiliar with the required unit. This can lead to inconsistent conversions across enumerators or data quality issues if the required unit is not used. In some cases, enumerators are advised on common conversion factors, but only for standard units (as opposed to local units) and for common fractions (quarter, half and three-quarters) or they are given conversion estimates in paces for square areas. Alternatively, farmers can report local units and conversion factors can be applied prior to analysis to standardize the units. This use of local units can severely limit reliability of self-reported areas because conversion factors may be unreliable or location specific and acquiring conversion factors for individual zones of the country is difficult to accomplish with accuracy.

This range of factors—intentional or unintentional misreporting, rounding, topography and the existence of local units—opens the door for systematic measurement error in land data based on self-reported areas. It is the presence of these errors that led to a search for alternative measure and particularly the use of GPS. GPS is costly in terms of the units themselves—although this cost continues to decrease and is becoming negligible—and the cost of labor to visit plots and physically walk the perimeter. The question becomes whether these costs are justified by the benefit of more precise land measure.

Despite the great potential of GPS technology, GPS-based coordinates are subject to known types of measurement error rooted in satellite position, signal propagation, and receivers. Approximate contributions of these factors to the overall position error range from 0.5 to 4 meters (Hofmann-Wellenhof, Lichtenegger and Wasle, 2008). On a large plot this may not be substantial, but on a smaller plot, the errors may be significant. The number of satellites, in particular, can cause the distribution of position error to be elliptical, rather than spherical (van Diggelen, 2007). Although position estimates are subject to a certain level of inaccuracy and may be distributed in a non-spherical manner, in theory the error associated with area measurement should be random— that is, the factors that cause non-spherical position error are largely macro level factors that are unlikely to change in the short period of time required to pace the perimeter of a plot, rendering the position error distribution consistent at all points along the perimeter.<sup>4</sup> A study by Bogaert, Delince and Kay (2005) using simulated coordinates and European Geostationary Navigation Overlay Service (EGNOS) augmentation concluded the position error can be reasonably assumed to be

> 4 One factor that could possibly change the distribution of the position error around the plot is multipath, or signal reflection. Multipath is typically caused by large buildings, however, and given the rural setting analyzed here, this type of error is assumed negligible.

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