using the LSMS-ISA data match the macro-statistics reported in Table 1 reasonably closely in four countries (see grayed column).<sup>10</sup> Organic fertilizer is used by an estimated 29 percent of households in our data. This figure likely represents a very low bound since the LSMS-ISA surveys do not include all possible organic nutrient sources used by farmers, like crop residues, which may be an important source of nutrients (Berazneva, Lee, Place 2014). The large degree of heterogeneity in percentages across countries, to some extent, mimics the slight differences in what is considered an organic fertilizer in the survey questionnaires, with Ethiopia and Niger having more options than the other countries. In Nigeria, we only observe if a household applies “composite manure” which may be the reason for the low percentage of households there. The rates of application in organic fertilizer are inconsistently observed (not shown). Of those countries with continuous application rate data, the 3 percent of Nigerians who apply organic fertilizer claim to do so at an average rate of nearly 650 kilograms per hectare; 20 percent of Tanzanians at over 1100 kilograms per hectare; and 13 percent of Ugandans at around 550 kilograms per hectare.

Table 7: Average household-level organic and inorganic fertilizer use trends

||Organic<br>% of<br>cultivating<br>households|% of<br>cultivating<br>households|Use (kg/<br>all ho<br>(includ<br>|ha) across<br>useholds<br>es zeros)<br>|In<br>|organic<br>only<br>|Use (kg/ha<br>fertilizer usi<br>(excludes<br>|) across<br>ng househ<br>zeros)<br>|olds<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||using|using|mean<br>total|mean<br>nutrients|median<br>total|mean<br>total|mean<br>N|mean<br>P|mean<br>K|mean<br>nutrients|
|Ethiopia|66.4|55.5|45.0|25.2|60.0|81.0|23.0|22.5|-|45.5|
|Malawi|17.6|77.3|146.0|56.3|148.9|188.8|53.1|19.4|0.4|72.8|
|Niger|55.1|17.0|4.5|1.7|3.5|26.3|7.6|2.6|-|10.3|
|Nigeria|3.4|41.4|128.2|64.3|227.9|310.1|93.9|30.8|30.8|155.5|
|Tanzania|20.3|16.9|16.2|7.7|61.0|95.6|32.0|7.0|6.6|45.6|
|Uganda|12.6|3.2|1.2|0.7|9.3|37.5|11.5|8.3|1.0|20.7|
|_Average _|_29.2_|_35.2_|_56.9_|_26.0_|_85.1_|_123.2_|_36.9_|_15.1_|_9.7_|_58.4_|

Note: All summary statistics are weighted at the household level. Household level fertilizer values are calculated by taking the total inorganic fertilizer used overall the total land area cultivated. Nutrient values represent the actual nutrient content in all applied fertilizers. Extreme high values are winsorized at the 99 percent level. When unreasonably high application rates remain after winsorizing, additional replacements are made consistently across all countries (see text for details). The grayed column represents the values best compared with macro-statistics from Table 1. The “average” row includes simple (unweighted) averages across the statistics reported at the country level.

# <u>Improved seed varieties and commercial seed purchases</u>

The LSMS-ISA surveys are not designed with the purpose of estimating overall improved seed variety adoption or current main season usage. Ethiopia and Niger are the only countries where any seed applied to a plot is categorized as improved or traditional no matter if it was purchased, saved, or given for free. In Malawi, we are able to make the distinction only for maize, tobacco, groundnuts, and rice, despite the existence of other improved crop varieties on the market (e.g., for cassava, beans, cowpeas, soybeans, pigeon peas). In Tanzania and Uganda, we only observe the seed type when it was purchased in this season, which likely under-estimates total improved

> 10 There are many possible reasons for the mismatches in the remaining two countries: differences in land area definition used between the two estimates (cultivated land versus land available for cultivation, including temporary fallow; differences in time frame used (calendar year versus main agricultural season); differences in included sample (smallholders versus all of agricultural production); etc. This paper does not concern itself with rectifying these differences in methodology.

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