was solicited for all maize plots cultivated by the household during the respective season, inclusive of the plot on which crop cut sub-plots were laid.

# **4.4.3. Objective Soil Fertility Measurement (MAPS I Only)**

Analysis of soil fertility was done in partnership with the World Agroforestry Center (ICRAF). Plot level soil samples were collected from each plot selected for crop cutting following a protocol carefully designed to maximize the representativeness of the samples while maintaining feasibility of implementation. From each plot, four samples were collected from the top-soil (0-20cm depth) and combined to create one composite top-soil sample. Additionally, a single sub-soil sample (2050cm depth) was collected from the center of the plot. After being processed at the ICRAF Kampala office, the samples were shipped to ICRAF Nairobi office, where approximately 10 percent were subject to conventional wet chemistry testing and all samples were subject to spectral soil analysis. A portion of this 10 percent sample was used to calibrate the prediction models, while the remainder was used to verify the predictions made onto the spectral data.<sup>20</sup> The final results from the soil analysis include key indicators of soil fertility such as pH, texture analysis (% sand, % clay, % silt), cation exchange capacity, and the concentration of multiple elements and macroand micronutrients, including carbon, nitrogen, and potassium.<sup>21</sup>

# **4.5. Descriptive Statistics**

Before moving to the empirical strategy used to examine the existence of the IR, descriptive statistics are used to illustrate the deviations between yield measures based on farmer-reported maize production vis-a-vis crop-cutting and remote sensing. Though not reported, the average GPS-based plot area was 0.14 hectares in MAPS I (with a maximum value of 1.37 and a standard deviation of 0.16) and 0.18 hectares in MAPS II (with a maximum value of 2.56 and a standard deviation of 0.23).<sup>22 , 23</sup> The plot-level averages for maize production (kilograms) and yield adjustment factors as the average ratio between shelled and unshelled (on the cob, dry after additional drying, prior to shelling) crop cut sub-plot harvests obtained in MAPS II, and multiply all kg-equivalent production estimates within the “cob” domain in MAPS I and MAPS II with its corresponding EA-specific adjustment factor. 20 For details, see Shepherd and Walsh (2002).

> 21 During the post-planting interview and prior to visiting the plot for GPS-based area measurement and laying crop cut sub-plots, each plot manager was also asked a multitude of questions about the soil attributes, specifically color, texture, type, and overall quality, of the plot selected for crop cutting. We do not provide further information on these variables as we only rely on objectively-measured soil quality index.

22 The round-specific descriptive statistics, including GPS-based plot area, are reported in Table A1. Where available, means are reported for the comparable UNPS 2015/16 sample. Due to high missingness rates of GPS-based area measurement, and area measurement at the parcel level rather than plot level, plot area and yield figures for UNPS are not comparable to those in MAPS I or II, and are, therefore, not reported.

23 In MAPS I and II, the GPS-based area measure was obtained in each household only for the randomly selected maize plot subject to crop cutting. Farmer-reported area was, however, solicited for the complete set of maize plots that may have been cultivated by the household during the reference agricultural season. Following Kilic et al. (2017a, 2017b), we estimated a simple imputation model of GPS-based plot areas pooled across MAPS I and II as a function of farmer-reported plot area, a dichotomous variable identifying 2016 round observations, and EA fixed effects. The 15