intercropping<sup>27</sup> ; (3) logarithmic transformation of kilograms of maize seed planted; (4) a binary variable identifying whether any inorganic fertilizer was applied on the plot<sup>28</sup> ; (5) logarithmic transformation of total household member days of season-specific labor input on the plot<sup>29</sup> ; (6) a binary variable identifying whether there was any hired labor input on the plot; (7) logarithmic transformation of total hired days of season-specific labor input on the plot<sup>30</sup> ; (8) percent seasonal (May-June) rainfall deviation from plot location-specific long-term average rainfall<sup>31</sup> ; (9) logarithmic transformation of GPS-based distance between plot and dwelling in kilometers; (10) enumerator-assessed percent damage in the crop cut sub-plot<sup>32</sup> ; and (11) a binary variable identifying whether any cover crops were on the plot prior to planting.

_H_ is a vector of household characteristics, including (1) wealth index, (2) agricultural implement and machinery index, (3) logarithmic transformation of household size, and (4) dependency ratio. M is a vector of plot manager characteristics, including (1) a binary variable identifying whether the respondent is also the plot manager, (2) a binary variable identifying whether the plot manager received agricultural extension services on topics relevant to crop production and marketing in the last 12 months, (3) a binary variable identifying whether the plot manager is female, and (4) logarithmic transformations of plot manager age and years of education.

Specific to Equation 2 and Equation 3, _T_ is our time fixed effect, i.e. binary variable identifying the second round of plot observations in the panel regressions. Further, _θ_ in Equation 2 is the fixed effect that accounts for time-invariant unobserved heterogeneity at the _household-level,_ while _γ_ in Equation 3 is the fixed effect that accounts for time-invariant unobserved heterogeneity at the _parcel-level_ . The sample for Equation 3 includes plots in both rounds only if the plot selected for crop cutting in MAPS II happened to be on the same _parcel_ as the plot selected for crop cutting in MAPS I.<sup>33</sup> Further, the vector _O_ includes the rest of the objectively-measured plot-level covariates, including (1) soil fertility index, (2) genetic heterogeneity of the maize on the plot, based on DNA fingerprinting of seed samples obtained from the combined harvest tied to the 4x4m crop cut sub-

> 27 This is calculated as the ratio between the quantity of maize seed planted under intercropping and the counterfactual quantity of maize seed that would have been planted had the plot been cultivated pure stand, as reported by the farmer. The pure stand maize plots are assigned a value of 1.

> 28 The rare occurrence of inorganic fertilizer use prevents us from using the logarithmic transformation of the quantity of inorganic fertilizer applied.

> 29 This is calculated as the sum of all household member-specific labor inputs reported by the farmer at the plot-level. 30 The plots without any hired labor are assigned a value of 0.

> 31 The plot location-specific dekadal time series rainfall data are sourced from the CHIRPS database. The plot locationspecific long-term average for the period of May-June is computed over the period of 1981-2015 for MAPS I, and 1981-2016 for MAPS II.

> 32 In MAPS I, this is calculated as the average percent damage across both 4x4 and 2x2 sub-plots.

> 33 MAPS I parcel rosters were carried forward for updating in MAPS II to identify the MAPS I parcels that were still owned and/or cultivated by the households during the first rainy season of 2016. This information, together with the known nesting of the maize plots with the parcels in each round, was used to determine whether the selected plot in MAPS II happened to be on the same parcel as the selected plot in MAPS I.

19