Table 32: Decomposition of binary inorganic fertilizer use decision at plot level, <u>part two</u>

||Fi<br>|xed effects m<br>|odel<br>|||Househol<br>|d “demeaned<br>|” model<br>||
|---|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|P|Sig.|Coef.|Std. Err.|P|Sig.|Shapley|
|Sex of plot manager (1=female)|-0.0627898|0.023196|0.007|***|-0.08155|0.016154|0|***|0.51|
|Size of plot (hectares)|0.0367664|0.004143|0|***|0.040718|0.002883|0|***|4.86|
|Irrigated (1=yes)|0.2523292|0.046333|0|***|0.244164|0.032542|0|***|0.72|
|Cash crop grown on plot (1=yes)|-0.0040303|0.008979|0.654||-0.0978|0.00584|0|***|19.97|
|Plot owned by hh (1=yes)|0.0128825|0.01236|0.297||0.03394|0.008619|0|***|0.37|
|Number of crops on plot|0.0222392|0.004572|0|***|-0.00483|0.003134|0.123||3.1|
|Type of plot|||||0.192109|0.003733|0|***|69.67|
|Non-maize plot|omitted|||||||||
|Pure stand maize plot|0.3868727|0.008902|0|***||||||
|Intercropped maize plot|0.2905062|0.011408|0|***||||||
|HH-reported soil quality|||||0.027676|0.006066|0|***|0.51|
|Good|omitted|||||||||
|Fair|0.0273239|0.011684|0.019|**||||||
|Poor|0.0345945|0.019959|0.083|*||||||
|HH-reported erosion on plot (1=yes)|-0.0011885|0.012425|0.924||0.006294|0.008683|0.469||0.18|
|HH-reported slope of plot|||||0.001605|0.006332|0.8||0.11|
|Flat/valley|omitted|||||||||
|Slight slope|0.0156812|0.011015|0.155|||||||
|Steepslope/hilly|-0.020706|0.022819|0.364|||||||

_Notes: *** p<0.01, ** p<0.05, * p<0.1. n=26,440 plots across 14,102 households in only Uganda, Malawi, and Tanzania. Overall R_<sup>_2_</sup> _for the fixed effects model=0.214. Overall R_<sup>_2_</sup> _for the demeaned model=0.164. Average number of plots per household is 1.9. 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._

# **6. Main findings and conclusions**

While not designed as agronomic surveys, the newly collected and nationally-representative LSMS-ISA data sets in six major Sub-Saharan African countries allow us the unprecedented and hugely valuable opportunity to update understandings of the modern agricultural input landscape in the region, particularly following decades of structural change, external shocks, and a range of policy environments that may have influenced input adoption patterns in the 21<sup>st</sup> century. Overall, we find that input use across SSA is far more complex than stylized prior prevailing beliefs and often-quoted macro-scale statistics might suggest. Using mostly comparable covariates across the represented countries, we uncover a rich story of agricultural input use in African agriculture. Given the large volume of information presented in this paper, we summarize key descriptive results in ten important and/or surprising findings that may help to guide policy choices, serve as an empirical check on conventional wisdom about modern input use in SSA, and motivate a new wave of research to further our understanding.

**_1. Modern input use may be relatively low in aggregate, but is not uniformly low across these six countries, especially for inorganic fertilizer and agro-chemicals._**

Many SSA smallholders still use rudimentary technologies on the farm and eschew the use of modern inputs. Nevertheless, modern agricultural input use has picked up to a significant level in some regions within some countries, especially in the case of inorganic fertilizer and agrochemical use. In 3 of 6 countries with large-scale, nationally representative household farm input use data, average inorganic fertilizer use rates are well above the widely quoted 13 kg/ha statistic, with a simple cross-country average nutrient application rate of 26 kg/ha (equivalent to 57 kg/ha total fertilizer). <mark>Over three-quarters of all cultivating households in Malawi, half in</mark> 49