Due to the small sample sizes and evidence of inconsistent matching of suitability classification relative to the MAPS plot-level soil sample classification, results are not reported in Table 4 for highly suitable (S1) or marginally suitable (S3) categories for AFSIS. Analysis on these categories, available in annex tables for all tables from this point forward, revealed highly-sensitive results contrary to expectations, including those expectations rooted in agronomic science, and, depending on the specification, implied potential yields far greater than agronomically feasible (for S1 plots). These unattainable potential yields, which are a function of low technical efficiency scores, support the descriptive finding that the geospatial data fails to sufficiently and consistently distinguish between maize-specific soil suitability relative to ground-based measures.<sup>17</sup> Coefficients in the overall specification are generally in line with those reported for moderately suitable (S2) plots, both for MAPS- and AFSIS-based specifications.

While AFSIS results for S1 and S3 are not reported here due to the sensitivity and inconsistency of findings (see Annex Table A3 for full reporting), there are takeaways from the comparison of MAPS S1 and S3 categories. Most apparent is the insignificant effect of cultivation pattern on S3 plots. In this category, neither the binary indicator on pure stand cultivation nor the intercropping seed rate is significant, suggesting that production on S3 plots is unchanged with cultivation pattern. On S1 plots, however, returns to cultivating pure stand maize are positive and significant, and higher in magnitude than the returns to pure stand cultivation on S2 plots. Related to seeding, the S3 specification exhibits a negative and significant coefficient on quantity of seed planted. A negative coefficient on seed application is contrary to expectations. One potential explanation could be over-seeding on these lower-suitability plots in an attempt to encourage greater production, while serving only to crowd out successful plants.

Across all specifications reported, technical inefficiency is largely unexplained by observable factors. While not statistically significant, coefficients on the technical inefficiency predictors are generally in the expected direction (Table 4). For example, the manager’s completion of primary school and count of agricultural assets both exhibit negative (but insignificant) coefficients in the overall specification, thereby hinting at a reduction in technical inefficiency. In the MAPS S2 specification, manager education has a positive (and insignificant) coefficient, but its value very near zero. The coefficient on manager’s receipt of agricultural extension services is positive (but insignificant in all but MAPS S3 specifications), suggesting that use of extension services is correlated with increased technical inefficiency. It is conceivable, however, that this relationship is driven by those with lower technical efficiency self-selecting into the use of extension services. Only in MAPS S3 do we see statistically significant coefficients on household or manager characteristics: that on the household dependency ratio which suggests that technical inefficiency

> 17 The technical efficiency scores derived from the AFSIS-based suitability classifications are sensitive to the variables in the technical efficiency model. When both the MAPS- and AFSIS-based stochastic frontier models are implemented without any controls in the technical efficiency specification, MAPS technical efficiency scores remain fairly stable while AFSIS technical efficiency scores vary widely for S1 and S3 classes.

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