<mark>Other inputs, recorded at the plot level, include land input (cultivated plot and farm area in hectares); labor inputs including labor days spent on the plot by family members, total labor days spent on the plot including by hired labor and free labor from non-family members, hired labor value; use of organic and inorganic fertilizer inputs, inorganic fertilizer quantity in nitrogen equivalent, inorganic fertilizer value. We also include information on the number of seasonal crops grown on the plot, which was the “main crop” (i.e. the crop type with the highest value of production on the plot) and the share of output attribute to the “main crop” type. Agricultural asset indices are computed using a principal component analysis (PCA), quantifying asset ownership in single dimensions drawn from inventories of household agricultural assets (either from agriculture or household questionnaire). A regression method is used to predict factor scores</mark> (Rencher, 2002).

<mark>Variables on the production environment and practices include crop shocks; irrigation status of the plot; use of erosion protection techniques; use of a tractor on the plot and plot ownership modalities, including plot ownership and possession of a formal title for the plot; livestock ownership; perennial crop production; number of plots under management (cultivated or not); and number of plots left fallow by the household in the current agricultural season; beginning and end dates of the current agricultural season.</mark> <mark>We draw on the household questionnaire to create variables describing plot manager characteristics (the plot manager is the household member mainly responsible for the cultivation of the plot), which include plot manager age, education, and gender.</mark> <mark>All variables described above are based on farmer self-reports. In addition, we include key geospatial variables, which are provided as part of the publicly available data files. These include agro-ecological zones, distance of the household from the nearest market and population center as well distance of the plot from the household (in kilometers), plot slope, elevation, a wetness index and soil quality indicators. The soil quality indicators contain binary variables for nutrient availability, nutrient retention, rooting conditions, oxygen availability, excess salts, toxicity, and workability. These variables are equal to one if there is no or a slight constraint to the soil in each dimension. To summarize these variables into a single dimension, we compute a soil fertility index using a principal component analysis (PCA), which also employs a regression method to predict factor scores</mark> (Rencher, 2002) <mark>. All the geospatial variables outlined above are obtained from remote sensing products and integrated via the geocoordinates of the interviews (without offset). They are provided alongside the raw survey data, and their integration was therefore conducted prior to the creation of the HP. More information, along with their origin, can be found in the surveys’ online documentation (see Table 1).</mark> <mark>A few crucial processing steps, undertaken while creating the HP, must be highlighted:</mark>

- A fraction of plot areas is self-reported, and not measured with handheld GPS devices. Due to the importance of that variable to calculate key agricultural variables such as yields, and known inaccuracies of self-reported plot areas, we impute missing GPS area values. We employ a predictive mean matching method with five nearest neighbors, using self-reported areas along with administrative divisions to obtain linear predictions which serve as our distance variable (Little, 1988).

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