Table 31 shows the regression and R<sup>2</sup> decomposition results using the set of plot-level variables consistently observed across all six countries. Perhaps not surprisingly, most of the variation comes from what is grown on the plot—maize alone, maize interspersed with any other crop, another type of plot all together, and/or a cash crop included on the plot—which matches well with findings in Tables 19, 20, and 21. Indeed, plots that are mainly maize continue to emerge as those more likely to receive inorganic fertilizer applications. This appears to be in contrast with the household level results presented in Table 30 where households that cultivate maize anywhere on the farm are less likely to use inorganic fertilizer. That relationship, however, likely picks up on livelihood strategies in general, given that well over 70 percent of our (unweighted) full sample cultivates at least some maize. Results are largely the same when (i) dropping Niger from the sample given very low incidence of maize plots and (ii) dropping Uganda from the sample and adding the distance from the plot to the household (a variable not observed in Uganda). Otherwise, plot size relative to other plots cultivated by the household has a positive and statistically significant relationship explaining almost 10 percent of R<sup>2</sup> . This result does not necessarily run contrary to that described in Figure 2 which explores application rates rather than the propensity to apply any fertilizer.

Table 31: Decomposition of binary inorganic fertilizer use decision at plot level, <u>part one</u>

||Fi<br>|xed effects<br>|model<br> <br>||Househol<br>|d “demeaned<br>|” model<br>||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|P<br>Sig.|Coef.|Std. Err.|P|Sig.|Shapley|
|Sex of plot manager (1=female)|-0.0248184|0.013225|0.061<br>*|-0.0292|0.010567|0.006|***|0.38|
|Size of plot (hectares)|0.0478397|0.003172|0<br>***|0.048181|0.00249|0|***|9.73|
|Irrigated (1=yes)|-0.0153352|0.014509|0.291|-0.01022|0.011578|0.378||0.07|
|Cash crop grown on plot (1=yes)|-0.0763105|0.005549|0<br>***|-0.10189|0.004346|0|***|22.54|
|Plot owned by hh (1=yes)|-0.0541668|0.007275|0<br>***|-0.04467|0.005704|0|***|1.21|
|Number of crops on plot|0.0134958|0.002822|0<br>***|0.000118|0.002204|0.957||1.44|
|Type of plot||||0.132505|0.002737|0|***|64.63|
|Non-maize plot|omitted||||||||
|Pure stand maize plot|0.2343585|0.005694|0<br>***||||||
|Intercropped maizeplot|0.1898541|0.007696|0<br>***||||||

_Notes: *** p<0.01, ** p<0.05, * p<0.1. n=57,665 plots across 21,258 households in all six LSMS-ISA countries. Overall R_<sup>_2_</sup> _for the fixed effects model=0.085. Overall R_<sup>_2_</sup> _for the demeaned model=0.072. Average number of plots per household is 2.7. Plot level weights are not used. This table is created using the “rego” user-written command in Stata. Household level weights are not used (meaning households from Malawi are over-weighted in these results). See text for more details on the decomposition methodology and interpretation._ We estimate one final regression model with the subset of three countries where householdreported characteristics of the plot—namely soil qualities, the incidence of erosion, and slope— are observed (Malawi, Tanzania, and Uganda). Results in Table 32 show that while householdreported soil quality is a statistically significant predictor of the binary inorganic fertilizer use decision, it explains less than one percent of R<sup>2</sup> , with the plot type and cash crop variables remaining most important. The erosion status and slope of the plot are not important predictors of within-household fertilizer allocation decisions. These findings substantiate our original claims taken from evidence in Table 22 and Table 23 that understanding of the soil conditions necessary for efficient fertilizer response may be lacking, implying the need for improved extension programs around soil fertility.

48