metadata: authors: 0: author: Amankwah,Akuffo
1: author: Ambel,Alemayehu A.
2: author: Gourlay,Sydney
3: author: Kilic,Talip
4: author: Markhof,Yannick Valentin
5: author: Wollburg,Philip Randolph
count: Africa,World
docna: 0: docna: Fertilizer Price Shocks in Smallholder
            Agriculture : Cross-Country Evidence from High-Frequency
            Phone Surveys in Sub-Saharan Africa
docty: Policy Research Working Paper
abstracts: cdata!: Since 2020, many countries in
            Sub-Saharan Africa have experienced disruptions to
            agricultural activities due to the adverse effects of
            multiple global crises. Notably, the Russian invasion of
            Ukraine caused a surge in inorganic fertilizer prices, which
            had potentially significant impacts on Sub-Saharan
            Africa&#x2019;s agriculture sector given that most
            countries in the region are net importers of inorganic
            fertilizers and the Russian Federation is the
            world&#x2019;s largest exporter. Using high-frequency
            longitudinal phone survey data spanning four years from six
            Sub-Saharan African countries, this paper examines the
            dynamics of smallholder agriculture against the backdrop of
            these crises, with particular focus on prices, availability,
            and use of inorganic fertilizer, as well as the strategies
            employed by farmers to cope with high fertilizer prices and
            other accessibility constraints. The results show that
            inorganic fertilizer prices have increased in the region
            since 2020, forcing smallholder farmers to adopt coping
            mechanisms that are less productivity-enhancing, making them
            even more susceptible to future crises. Specifically,
            farming households reduced the quantity of inorganic
            fertilizer applied, by applying it at lower rates or to a
            smaller area. In some cases, households sold assets or
            borrowed money to cope with the high prices of inorganic
            fertilizers. This calls for policies to help smallholder
            farmers in the region to build strong support systems to be
            more resilient and better able to cope with the adverse
            effects of rising inorganic fertilizer prices during
            polycrises and related shocks.
display_title: Fertilizer Price Shocks in
            Smallholder Agriculture : Cross-Country Evidence from
            High-Frequency Phone Surveys in Sub-Saharan Africa
listing_relative_url: /research/2024/07/34362440/fertilizer-price-shocks-smallholder-agriculture-cross-country-evidence-high-frequency-phone-surveys-sub-saharan-africa
topicv3: Economic Shocks and Vulnerability m1326506 702,Crop Production m1326627 174
publishtoextweb_dt: 2024-07-09T00:00:00Z
dois: 10.1596/1813-9450-10842
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 50
author_keys: 0: author_key: 1390066
1: author_key: 1042144
2: author_key: 1192808
3: author_key: 886702
4: author_key: 1974294
5: author_key: 1464872
model_extractions: Policy Research Working Paper 10842 Fertilizer Price Shocks in Smallholder Agriculture Cross-Country Evidence from High-Frequency Phone Surveys in Sub-Saharan Africa _Akuffo Amankwah Alemayehu Ambel Sydney Gourlay Talip Kilic Yannick Markhof Philip Wollburg_ Development Economics Development Data Group July 2024
Policy Research Working Paper 10842

# **Abstract**

Since 2020, many countries in Sub-Saharan Africa have experienced disruptions to agricultural activities due to the adverse effects of multiple global crises. Notably, the Russian invasion of Ukraine caused a surge in inorganic fertilizer prices, which had potentially significant impacts on Sub-Saharan Africa’s agriculture sector given that most countries in the region are net importers of inorganic fertilizers and the Russian Federation is the world’s largest exporter. Using high-frequency longitudinal phone survey data spanning four years from six Sub-Saharan African countries, this paper examines the dynamics of smallholder agriculture against the backdrop of these crises, with particular focus on prices, availability, and use of inorganic fertilizer, as well as the strategies employed by farmers to cope with high fertilizer prices and other accessibility constraints. The results show that inorganic fertilizer prices have increased in the region since 2020, forcing smallholder farmers to adopt coping mechanisms that are less productivity-enhancing, making them even more susceptible to future crises. Specifically, farming households reduced the quantity of inorganic fertilizer applied, by applying it at lower rates or to a smaller area. In some cases, households sold assets or borrowed money to cope with the high prices of inorganic fertilizers. This calls for policies to help smallholder farmers in the region to build strong support systems to be more resilient and better able to cope with the adverse effects of rising inorganic fertilizer prices during polycrises and related shocks.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aamankwah@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Fertilizer Price Shocks in Smallholder Agriculture: Cross-Country Evidence from High-Frequency Phone Surveys in Sub-Saharan Africa**

Akuffo Amankwah<sup>‡</sup> , Alemayehu Ambel<sup>+</sup> , Sydney Gourlay<sup>#</sup> , Talip Kilic<sup>*</sup> , Yannick Markhof<sup>±</sup> and Philip Wollburg<sup>#</sup>

- Corresponding Author – Economist, Living Standards Measurement Study, Development Data Group, World Bank Group, Washington DC, USA; aamankwah@worldbank.org;

- + Senior Economist, Living Standards Measurement Study, Development Data Group, World Bank Group, Washington DC, USA.

- # Senior Economist, Living Standards Measurement Study, Development Data Group, World Bank Group, Rome, Italy.

- Senior Program Manager, Development Data Group, World Bank Group, Washington DC, USA.

- ± UNU-MERIT, United Nations University, Maastricht, Netherlands, and consultant at World Bank Group.

**JEL Codes:** O12; O13; Q12; Q18.

**Keywords:** Inorganic fertilizer; coping strategies, price shocks; household surveys; Sub-Saharan Africa.

**<mark>WB Thematic Area:</mark>** <mark>Crop production; economic shocks and vulnerability.</mark>
# **1. Introduction**

Since 2020, many countries in Sub-Saharan Africa have faced repeated disruptions to agricultural activities through the culmination of a number of crises. Most notably, the COVID-19 pandemic posed a serious risk to public health and led to widespread disruption of public life, transportation, and trade starting in March 2020. In parallel, some countries, such as Burkina Faso, Ethiopia and Nigeria, continue to be exposed to recurrent domestic conflicts and insecurity. The Russian invasion of Ukraine in February 2022 raised concerns about adverse effects on access to and prices of inorganic fertilizers - in view of many Sub-Saharan African countries being net importers of inorganic fertilizer and the Russian Federation being the world’s largest exporter (Simoes and Hidalgo, 2011). These developments have been further compounded by rising inflation, tightening fiscal constraints, and growing macroeconomic imbalances across the region (IMF 2023). Understanding smallholder farmers' exposure to these crises and their coping strategies is fundamental for any policy response and the subject of this paper.

Crises aside, despite evidence of the role of inorganic fertilizer in increasing agricultural yields, smallholder farming systems in Sub-Saharan Africa have historically utilized relatively low rates of inorganic fertilizer vis-à-vis agronomic recommendations and farming systems in other regions (FAO, 2022). Minot and Benson (2009) point to an average fertilizer application rate of 13kg/ha for the region, with Sheahan and Barrett (2017) unveiling heterogeneity in application rates across countries, ranging from 1.2kg/ha (Uganda) to 146kg/ha (Malawi) among the six countries included in their study. The incidence of fertilizer application also varies significantly by country, with about 3, 17, and 77 percent of cultivating households applying any inorganic fertilizer in Uganda, Tanzania, and Malawi, respectively (Sheahan and Barrett, 2017).

The literature points to multiple barriers to fertilizer adoption, including imperfect markets, lack of access to credit, and limited or variable profitability often linked to soil health (Croppenstedt, Demeke, and Meschi, 2003; Marenya and Barrett, 2009; Xu et al., 2009). Duflo, Kremer, and Robinson (2008) find, for example, an annual rate of return of nearly 70 percent for maize on average when the most profitable level of fertilizer was applied in field trials in Kenya, though other rates of fertilizer application were found to be non-profitable (even with gains in yield as high as 63 percent on average). This has also attracted the attention of governments and their development partners. The onslaught of crises and the varied channels in which they may impact farmers and fertilizer application, through restricted market access, limited supply, and 2
price shocks, among others, threaten to dampen already-low rates of fertilizer use with potentially significant impacts on agricultural production and productivity.

In this paper, we leverage high-frequency phone survey data to shed light on dynamics in African smallholder agriculture against the backdrop of these crises, particularly as they relate to the availability, use, and price of inorganic fertilizer. Our data come from the longitudinal highfrequency phone surveys (HFPS) implemented since April 2020. The LSMS-HFPS regularly fielded a rotating set of questions on agricultural practices and outcomes, which form the basis for this paper. This phone survey data is also complemented with data from the face-to-face longitudinal household surveys that have been conducted under the LSMS-Integrated Surveys on Agriculture (LSMS-ISA) initiative and that have served as the sampling frames for the LSMSHFPS. Together, the data span the period from 2018 to 2024. In total, we rely on 34 waves of survey data across six countries, Burkina Faso, Ethiopia, Malawi, Nigeria, Tanzania and Uganda.

Using these data, we unpack the farmer experience with inorganic fertilizer over time, including not only incidence of use, but also the strategies employed by households in the face of price shocks and other constraints. We find that while the incidence of application remains relatively stable over most of the crisis period, a significant share of households reported using less than the amount that they needed, driving many households to apply fertilizer to a smaller share of their cultivated area or apply at a lower intensity. Some farmers tried to minimize further reduction by borrowing money or selling assets to buy the input, increasing their vulnerability to future shocks.

The remainder of the paper is organized as follows. Section 2 discusses the data utilized and the methodology deployed. Section 3 presents the results, and Section 4 discusses the importance and policy implications of these results. Section 5 concludes.

# **2. Data and Methods**

# _2.1. High-Frequency Phone Surveys_

This paper uses data from a series of high-frequency phone surveys (HFPS) conducted in six SSA countries. The HFPS data used here have been collected primarily by national statistics offices,<sup>1</sup>

> 1 The Ethiopia HFPS was implemented by a private survey firm, not the national statistics office.

3
with support from the World Bank’s Living Standards Measurement Study (LSMS) team.<sup>2</sup> These six countries are part of the LSMS-Integrated Surveys on Agriculture (LSMS-ISA) project that fields longitudinal, multi-topic household surveys with focus on agriculture. Thus, the households included in the HFPS were also interviewed as part of the LSMS-ISA panel survey conducted in these countries. A uniform methodology was adopted in sampling, weighting, and implementing the HFPS across the countries, making cross-country comparison feasible. In each country, the most recent face-to-face survey prior to the start of the pandemic formed the frame for the HFPS. The HFPS was designed to be nationally representative, with rural/urban stratification.

The HFPS collects information on various indicators, including demographic information, shocks and coping strategies, economic sentiments, employment and business operation, and agriculture, among others. The current study uses data primarily from the agriculture and price modules. Given the season-specific requirements of agriculture data, the HFPS rounds that collected agriculture information were implemented either at the end of the main agriculture season or split across the post-planting and post-harvest periods in the respective countries. The agriculture module collects information on crops planted and area; harvest; fertilizer access, use and challenges with access and coping strategies; among others. While the HFPS began after the onset of the COVID-19 pandemic, the timing of implementation and number of rounds completed to date varies across countries. Table A1 gives the sample distribution across countries and the rounds of the phone survey data used for the analysis. Figures A1-A6 visualize the timeline of data collection.

# _2.2. Variable Creation_

The information collected through the HFPS that is most relevant to the objectives of this study broadly falls into three categories, described below. Not all information was collected across all rounds of the HFPS such that data availability varies by country and over time. Unless explicitly specified otherwise, all information collected was with respect to the latest agricultural season.<sup>3</sup> First, we collected information on agricultural engagement, which comprises information on the incidence of crop farming among all households, the share of crop-farming households that

> 2 This survey is part of the World Bank’s effort to support the collection of monthly high-frequency phone surveys to monitor the impact of the COVID-19 pandemic and external shocks on households. <u>https://www.worldbank.org/en/programs/lsms/brief/lsms-launches-high-frequency-phone-surveys</u> 3 For interviews conducted after planting but before harvesting, this was the ongoing agricultural season. For interviews conducted after harvesting, this referred to the recently completed agricultural season.

4
could not conduct their agricultural activities as usual in the reference agricultural season, and the reasons for disruption to agricultural activities.

Second, we collected detailed information on extensive (whether the household used any inorganic fertilizer) and intensive margin fertilizer use (how much inorganic fertilizer was used across all plots of the household; and how much was purchased). Depending on country and survey round, we also collected information on the different types of fertilizer applied. For intensive margin fertilizer use, respondents reported the total quantity of fertilizer used across all plots of the households in a unit of their choice. Wherever a quantity was reported in non-standard units, we use standardized conversion factors to express the quantity in kilograms. For those households that used no inorganic fertilizer, we collect information on their reasons. For those who used any fertilizer, we further ask whether households were able to acquire the full amount of fertilizer they desired. Those that responded that they could not acquire as much fertilizer as desired were asked how much more fertilizer (in kilograms or a unit of their choice) they would have liked to acquire. They were also asked about the reasons for the fertilizer shortfall and their coping strategies for having had less fertilizer than desired. We normalize all quantities reported (fertilizer quantity per hectare, purchased fertilizer quantity per hectare, fertilizer shortfall per hectare) by the total selfreported area under cultivation and winsorize at the 2.5 and 97.5 percentiles within each country.

Third, we collected information on fertilizer prices from two sources depending on the survey round. The first source of fertilizer price information comes from a dedicated prices module which, among others, collected information on the current price of inorganic fertilizer for all households reporting that fertilizer was available for sale in their community or a nearby location.<sup>4</sup> This question was not specific to any fertilizer type. The second source is the HFPS agriculture module which asked all respondents in crop-farming households to report the current price of inorganic fertilizer by fertilizer type.<sup>5</sup> Respondents reported on prices for either the types of fertilizer they used or, if they did not use any, for any type of fertilizer they could report on. Prices were reported in local currency units (LCU) for a certain amount of fertilizer and unit. We standardize all prices by expressing them in LCU per kilogram. If a household reported prices for

> 4 Respondents did have the option to answer that they did not know the price.

> 5 In Ethiopia (round 19) and Uganda (round 15), only those that used any inorganic fertilizer were asked to report on the price.

5
multiple types of fertilizer, we take the average price across all fertilizer types.<sup>6</sup> We winsorized the fertilizer price per kilogram at both the 5<sup>th</sup> and 95<sup>th</sup> percentiles within each country. In addition to quantitative data on fertilizer prices, we collected self-reported data on the change in fertilizer prices compared to the previous agricultural season and, for those reporting higher prices than last season, the strategies they used to cope with high fertilizer prices.

# _2.3. Estimation_

Our analysis is mostly descriptive, focusing on the estimation of national-level means by survey wave. To examine the correlates of fertilizer use and shortfall, we estimate linear probability models using OLS. When examining trends over time, we fit a linear trend for each country by regressing the dependent variable on a discrete variable taking the value of 1 for the first round we collected data on the outcome in that country, a value of 2 for the second data point, and so on. Throughout our analysis, we weight our estimates using the HFPS survey weights that are designed to correct for selection bias in the HFPS sample compared to a nationally representative sample (Ambel, McGee, and Tsegay 2021; Zezza et al. 2023).

# **3. Results**

# _3.1. Agricultural Engagement_

To provide context, we first show the share of households engaged in crop farming over time (Figure 1). For five of our study countries – Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda – we observe this before the COVID-19 pandemic and then at various points during and after it, from 2020 to 2024. In the pre-COVID period, between 56 percent (Ethiopia) and 80 percent (Malawi) of households engaged in crop farming. The first data points during the COVID-19 pandemic suggest there is a significant increase in the household-level incidence of crop farming, as pandemic lockdowns limited other income generating activities, of 5.2 points in Burkina Faso, 13.5 points in Ethiopia, 9.3 points in Malawi, and 8.4 points in Uganda (Figure 1).

The initial increase at the onset of the pandemic leveled off during the course of 2020 but remained above pre-COVID levels until 2023 in Ethiopia, Malawi, and Nigeria. In Malawi and

> 6 We do so to maximize longitudinal coverage as fertilizer prices as reported in the price module and as reported in the agriculture module were collected across distinct survey rounds. To lend confidence to this approach, we compared the most common fertilizer types, compound and nitrogen-based fertilizers, using the type-specific price data. The median difference in prices across the two types was approximately zero.

6
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<!-- Start of picture text --> Burkina Faso Ethiopia Tanzania Uganda<br>Reduced availability of hired labor<br>Unable to acquire / transport seeds<br>Unable to acquire / transport fertilizer<br>Unable to acquire / transport other inputs<br>Unable to sell / transport outputs<br>Ill / need to care for ill family member<br>Delayed planting / not yet planting season<br>Climate<br>Pests<br>Insecurity<br>0 5 1 0 5 1 0 5 1 0 5 1<br>Percent of crop-farming households<br><!-- End of picture text -->
specifically in Ethiopia and Uganda, did not decrease during the period of the COVID-19 pandemic and the Russian invasion of Ukraine.<sup>7</sup> Over our study period there are statistically significant time trends in Burkina Faso, Malawi, and Uganda. In Burkina Faso and Malawi, the incidence of fertilizer use fell whereas in Uganda, there was a slight increase (though the magnitude remains low relative to the other countries included (see Table A6)). In Ethiopia, the linear time trend is negative but not statistically significant. Separate regression analysis, presented in Table A7, highlights the relationship of fertilizer use with household wealth and remoteness. In all countries for which a household wealth index can be constructed, which excludes Tanzania, the relationship between fertilizer use and household wealth quintile is positive (with the exception of Nigeria), suggesting that poorer households are less likely to use fertilizer relative to wealthier households, without controlling for other potential correlates. When explanatory variables are added that proxy the degree of connectedness of a household, for the countries where these data are available (Ethiopia, Malawi, and Nigeria), the direction of the relationship between household wealth and fertilizer use holds and there is evidence that remoteness it correlated with a lower likelihood of fertilizer use in Ethiopia and Nigeria.

> 7 This could be due to the limitation of the incidence indicator to capture the full impact of price and availability of fertilizer in the market. Discussion of coping mechanisms, in the subsequent sub-section, suggests that many farming households reduced the quantity of fertilizer use.

9
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<!-- Start of picture text --> Burkina Faso Ethiopia<br>Did not need ete lo + — 1<br>Too expensive/ could not afford BFA-(02/24) ARS,: = F ETH (ori23)© { oe—<br>Not available -|AED ® 9-4irs BFA (0224) FAAET @ wi03) 1<br>BFA(02/24) .<br>Unable to travel / transport 3, 5.1e ETHHe(02/24)<br>Malawi Nigeria<br>Did not need aoe, ‘eo faa il<br>;<br>Too expensive/ could not afford newin (OBI24) po(GA (06/23)<br>Not available - == | | | | “o | | | p | | | |<br>Unable to travel / transport Mya| @(06:21)<br>Tanzania Uganda<br>. ‘TZA (11/23) UGA (0723<br>Did not need os real<br>Too expensive/ could not afford esTZA (11/23) ayUGA oursarn (20/23)<br>Not available TZAhe (11/23) aUGA (01723) UGA (10/23)<br>Unable to travel / transport | isa)¢ IGA (10123)<br>0 2 4 6 8 0 2 4 6 8<br>Percent of HHs not using fertilizer<br><!-- End of picture text -->
<!-- Start of picture text --> 1<br>9<br>8<br>3<br>2 a ex<br>o<br>g<br>=DD 6 —e— BFA<br>zc<br>2 5 &; ~@ ETH<br>A i —— MW<br>= 4 in Sloe —-TM- UGA<br>5 nn 4b<br>g \<br>sg 3 N<br>” \<br>2 \ N<br>i<br>A<br>0<br>a|<br>pre-COVID Jan20 Jan21 Jan22 Jan23 Jan24<br>Date<br><!-- End of picture text -->
<!-- Start of picture text --> Burkina Faso Ethiopia<br>Too expensive / could not afford<br>Not available<br>Tanzania Uganda<br>Too expensive / could not afford<br>Not available<br>0 5 1 0 5 1<br><!-- End of picture text --> Percent of households who could not access desired amount
<!-- Start of picture text --> Burkina Faso Ethiopia<br>Only fertilized part of cultivated area<br>Used lower rate of fertilizer<br>Cultivated a smaller area<br>Supplemented with organic fertilizer<br>Practiced legume intercropping<br>Malawi Nigeria<br>Only fertilized part of cultivated area es<br>Used lower rate of fertilizer es<br>Cultivated a smaller area es<br>Supplemented with organic fertilizer es<br>Practiced legume intercropping<br>Tanzania Uganda<br>Only fertilized part of cultivated area<br>Used lower rate of fertilizer<br>Cultivated a smaller area<br>Supplemented with organic fertilizer<br>Practiced legume intercropping<br>0 2 4 6 8 0 2 4 6 8<br>Percent of households with any shortfall<br><!-- End of picture text -->
<!-- Start of picture text --> 1<br>® ~<br>. 9 ae ~<br>* “ ~ ~ ~<br>8 : YS ~»<br>mo]22) I ~ N N ne<br>3 ‘e<br>26<br>2 -e- BFA<br>55 > ETH<br>& -—§- UGA<br>SA<br>fo} mM.<br>83 "<br>”n<br>2<br>|<br>0<br>a s<br>pre-COVID Jan20 Jan21 Jan22 Jan23 Jan24<br>Date<br><!-- End of picture text -->
<!-- Start of picture text --> Burkina Faso Ethiopia Malawi<br>650 65 1400<br>g @ 60 ° S 1350<br>8 Fy ? g / 8 1300 \<br>a d \ a / a \<br>8<a 50> iF,/ =8a 6 / 8=o 1250 \ Pp<br>[Ys \ ing / in &<br>500 45 é 1200<br>CT as st. ua 0. a rr a: a a: cs: | GC«aac eu<br>pre-COVID Jan20 Jan21 Jan22 Jan23 Jan24 pre-COVID Jan20. Jan21 Jan22 Jan23 Jan24 pre-COVID Jan20. Jan21 Jan22 Jan23 Jan24<br>Date Date Date<br>Tanzania Uganda<br>re 12000<br>2 1750 z<br>77<br>3g 10000<br>&8 1700- , &3 r<br>8 3 8000 !<br>5 1650 5 /<br>1600 a es ee es | 6000- rr re a a |<br>pre-COVID Jan20 Jan21 | Jan22 Jan23 Jan24 pre-COVIDJan20 Jan21 Jan22 Jan23 Jan24<br>Date Date<br><!-- End of picture text -->
<!-- Start of picture text --> Burkina Faso Ethiopia Tanzania<br>Used lower rate of fertilizer<br>Cultivated a smaller area<br>Borrowed money<br>Sold productive assets<br>Assistance from family/friends<br>Sharecropped/rented out land<br>0 5 1 0 5 1 0 5 1<br>Percent<br><!-- End of picture text -->
purchased (kg) is negative across the same set of countries, but only significant in Tanzania (see Table A21).

# **4. Discussion and Policy Implications**

During the 2020-2024 period multiple factors were in play, and farmers faced unusually high prices as a result. Using nationally representative, longitudinal, and cross-country microdata from household surveys in six Sub-Saharan African countries, we unpack the implications of the price shocks on farmers, and the heterogeneous strategies employed by households with respect to inorganic fertilizer use.

The share of farming households utilizing inorganic fertilizer does not appear to have been harshly dampened during this period, with statistically significant negative time trends in Burkina Faso and Malawi but with moderate magnitudes, a finding potentially contrary to expectation. Though incidence of use was relatively stable, those that did not apply fertilizer were on the poorer end of the wealth distribution and reported affordability as a key barrier to adoption, potentially hinting at longer-run issues of input affordability beyond this crisis period.

While we do not find large changes in fertilizer use at the extensive margin, important insights emerge with respect to the intensive margin. A significant share of fertilizer-using households, up to 70 percent in Malawi and more than 45 percent in Burkina Faso, Ethiopia, Malawi, Nigeria, and Tanzania, reported a shortfall in the quantity of fertilizer acquired. These shortfalls were predominantly due to affordability as opposed to availability-related constraints, and tended to affect poorer households more than those at the upper end of the wealth distribution. In light of price shocks and related fertilizer shortfalls, farming households employed a variety of coping strategies. Some farmers reduced the intensity with which they applied fertilizer (i.e., reduced the application rate) while others reduced the area in which it was applied. Some households, in the face of price shocks, sold assets or borrowed money from friends and relatives to purchase the input. These strategies threaten to reduce agricultural productivity, while also indebting households and/or depleting their savings.

Many farming households, in the recent period of global crises and observed shocks to fertilizer prices, have been pushed to take measures that could dampen productivity and/or increase their vulnerability to future shocks. While many different interventions were implemented in different countries during the crisis period (Amaglobeli et al., 2023), most of the policy responses 18
were targeted at reducing the impact of food price increases. Consideration should be made also of the potential shock to food prices stemming from reduced application of inorganic fertilizer and related reductions in local food production. Input subsidy programs have the potential to address issues fertilizer affordability, though studies have shown that the cost of input subsidy programs can outweigh their benefits (Jayne and Rashid, 2013) and that increasing fertilizer use alone may not be profitable due to associated expenses in acquiring the input as well as lack of complementary inputs such as improved seeds, irrigation and credit (Liverpool-Tasie, Omonona and Sanou, 2015).

This study has some limitations. Though the experience of farmers in six countries is represented, and represented at various points in time, the findings would be strengthened with the inclusion of additional country coverage as well as additional data points for each country. Data on the agricultural experience is not as well represented or as harmonized across countries immediately after the onset of the COVID-19 pandemic as it is in later years, which limits our ability to analyze precise time trends at the onset of that crisis. Additionally, given the mode of implementation of the surveys, through mobile phone, the sample may be biased towards farmers that are less remote and/or less poor. Though the use of survey weights addresses this concern in part, the findings may be seen as a lower bound as households without mobile phones, and whom price shocks may be expected to impact most severely, are excluded from the HFPS sample.

# **5. Conclusions**

This study examined recent trends in inorganic fertilizer use by smallholder farmers in six SubSaharan African countries over the 2018 – 2024 period in view of price increases following multiple global and local crises such as the COVID-19 pandemic, the Russian invasion of Ukraine and, in some countries, conflict and insecurity. Using high-frequency longitudinal phone survey data from these countries implemented around the same period, in conjunction with face-to-face surveys conducted on the same sample of households before the crisis period, this study documents the dynamics of incidence and use of inorganic fertilizer and farmer’s coping strategies.

The results highlight the varied responses by farmers to the price shocks prevalent in this period. While the share of households using fertilizer remained relatively stable in most of the studied countries, in early 2023 more than 45 percent of fertilizer-using households in Burkina Faso, Ethiopia, Malawi, Nigeria, and Tanzania reported shortfalls in the quantity of fertilizer acquired, reaching nearly 70 percent in Malawi. These fertilizer shortfalls were sizable, with 19
average shortfalls being more than 55 percent of the total desired quantity in Ethiopia, Tanzania, and Uganda. The findings point towards affordability as the primary constraint, as opposed to availability of supply, driving some households to use fertilizer at lower rates or on smaller areas, and in some cases to borrow money or sell assets to cope with high prices. These coping strategies are not only likely to reduce agricultural productivity and production, but also they have the potential to increase the vulnerability of farming households.

# **References**

- Amaglobeli, D., Gu, M., Hanedar, E., Hong, G., & Thévenot, C. 2023. “Policy Responses to High Energy and Food Prices”, IMF Working Paper 23/74.

- <mark>Ambel, A., McGee, K. and Tsegay, A., 2021. Reducing Bias in Phone Survey Samples: Effectiveness of Reweighting Techniques Using Face-to-Face Surveys as Frames. World Bank Policy Research Working Paper Series No. 9676. W</mark> ashington, D.C. : World Bank Group. <u><mark>https://doi.org/10.1596/1813-9450-9676</mark></u>

- Croppenstedt, A., Demeke, M., & Meschi, M. M. 2003. “Technology adoption in the presence of constraints: the case of fertilizer demand in Ethiopia”, _Review of Development Economics_ , 7(1), 58-70.

- Davis, B., Di Giuseppe, S., & Zezza, A. 2017. “Are African households (not) leaving agriculture? Patterns of households’ income sources in rural Sub-Saharan Africa”, _Food Policy_ , 67, 153-174. https://doi.org/10.1016/j.foodpol.2016.09.018.

- Duflo, E., Kremer, M., & Robinson, J. 2008. How high are rates of return to fertilizer? Evidence from field experiments in Kenya. _American Economic Review_ , 98(2), 482-488. DOI: 10.1257/aer.98.2.482

- FAO. 2022. Inorganic fertilizers – 1990–2020. FAOSTAT Analytical Brief, no. 47. Rome. <u>https://doi.org/10.4060/cc0947en</u>

- IMF. 2023. “Regional Economic Outlook. Sub-Saharan Africa: The Big Funding Squeeze. April 2023.” Regional Economic Outlook. Washington, D.C.: International Monetary Fund. <u>https://www.imf.org/en/Publications/REO/SSA/Issues/2023/04/14/regional-economicoutlook-for-sub-saharan-africa-april-2023.</u>

- Jayne, T.S., & Rashid, S. 2013. “Input subsidy programs in sub-Saharan Africa: A synthesis of recent evidence”, _Agriculture Economics_ , volume 44, p. 547 – 562.

- Liverpool-Tasie, L. S. O.,  Omonona, B.T., & Sanou, A. 2015. _Is increasing inorganic fertilizer use in Sub-Saharan Africa a profitable proposition? Evidence from Nigeria._ World Bank Policy Research Working Paper, No. 7201, LSMS Washington, D.C. : World Bank Group

- Marenya, P. P., & Barrett, C. B. 2009. Soil quality and fertilizer use rates among smallholder farmers in western Kenya. _Agricultural Economics_ , 40(5), 561-572.

- Minot, N. and Benson, T., 2009. Fertilizer subsidies in Africa, are vouchers the answer?. IFPRI Issue Brief 60. International Food Policy Research Institute, Washington DC.

- Ricker-Gilbert, J., 2020. Inorganic Fertiliser Use Among Smallholder Farmers in Sub-Saharan Africa: Implications for Input Subsidy Policies. In: Gomez y Paloma, S., Riesgo, L., Louhichi, K. (eds) The Role of Smallholder Farms in Food and Nutrition Security. Springer, Cham. https://doi.org/10.1007/978-3-030-42148-9_5

20
Sheahan, M., & Barrett, C. B. 2017. Ten striking facts about agricultural input use in Sub-Saharan Africa. _Food Policy_ , 67, 12-25. https://doi.org/10.1016/j.foodpol.2016.09.010

- Sheahan, M., & Barrett, C. B. 2014. _Understanding the agricultural input landscape in SubSaharan Africa. Recent plot, household, and community-level evidence_ . World Bank Policy Research Working Paper No. 7014. Washington, DC.

- Simoes, A. J. G., & Hidalgo, A. H. 2011. “The Economic Complexity Observatory: An Analytical Tool for Understanding the Dynamics of Economic Development.” In _Scalable Integration of Analytics and Visualization: Papers from the 2011 AAAI Workshop_ .

- Xu, Z., Guan, Z., Jayne, T. S., & Black, R. 2009. Factors influencing the profitability of fertilizer use on maize in Zambia. _Agricultural Economics_ , 40(4), 437-446.

- Zezza, A., McGee, K., Wollburg, P., Assefa, T. and Gourlay, S., 2023. From necessity to opportunity: lessons for integrating phone and in-person data collection. _European Review of Agricultural Economics_ , 50(4), pp.1364-1400.

21
**Table A1. HFPS fieldwork rounds and** **<u>sample distribution by country</u>**

|Country|Surveyround|Fieldwork end|Sample size|HHs farmingcrops|
|---|---|---|---|---|
||EHCVM 2018-19||1,968|886|
||1|Jul-20|1,965|1,124|
|Burkina Faso|2|Aug-20|1,860|1,130|
||3|Oct-20|2,013|1,152|
||11|Jul-21|1,924|1,047|
||16|Jan-23|1,668|889|
||21|Jan-24|1,832|840|
||ESS 2018-19||3,247|670|
||3|Jul-20|3,058|1,058|
||4|Aug-20|2,835|947|
||5|Sep-20|2,770|863|
|Ethiopia|6|Oct-20|2,703|834|
||9|Jan-21|2,077|649|
||ESPS 2021-22||2,876||
||14|Jan-23|764|556|
||19|Feb-24|2,566|903|
||IHPS 2019||1,726|1,143|
||1|Jun-20|1,729|1,356|
|Malawi|7|Feb-21|1,560|1,292|
||11|Jun-21|1,541|1,223|
||17|Apr-23|1,317|1,039|
||18|Jun-23|1,345|1,078|
|Nii|GHS 2018-19||3,267|2,155|
|gera|21|Jun-23|2,429|1,785|
|Tanzania|9|Nov-23|1,988|1,320|
||UNPS 2019-20||2,224|1,752|
||1|Jun-20|2,225|1,887|
||2|Aug-20|2,197|1,877|
||3|Oct-20|2,144|1,752|
|Uganda|4|Nov-20|2,135|1,715|
||7|Nov-21|1,948|1,655|
||11|Jan-23|1,665|1,284|
||13|Aug-23|1,765|1,421|
||15|Oct-23|1,729|1,344|
|Total|34 rounds||71,060<br>(16,857 distinct HHs)||

22
**Table A2: Share of households farming crops.**

||Mean|Std. Err.|_N_|
|---|---|---|---|
|**Burkina Faso**||||
|pre-COVID|69.4|1.4|1,968|
|2020/07|71.3|1.4|1,965|
|2020/08|74.6|1.4|1,860|
|2020/10|71.0|1.4|2,013|
|2021/07|69.8|1.4|1,924|
|2023/01|63.7|1.7|1,668|
|2024/01|52.8|1.8|1,832|
|**Ethiopia**||||
|pre-COVID|56.9|1.5|3,247|
|2020/07|70.4|1.2|3,058|
|2020/08|69.0|1.4|2,835|
|2020/09|68.2|1.4|2,770|
|2020/10|67.7|1.4|2,703|
|2021/01|70.0|1.6|2,077|
|2023/01|66.5|4.5|764|
|2024/02|59.7|2.6|2,566|
|**Malawi **||||
|pre-COVID|80.3|1.4|1,726|
|2020/06|89.6|0.9|1,729|
|2021/02|90.8|0.9|1,560|
|2021/06|85.7|1.3|1,541|
|2023/04|87.8|1.4|1,317|
|2023/06|88.9|1.1|1,345|
|**Nigeria**||||
|pre-COVID|69.2|1.5|3,267|
|2023/06|75.2|1.4|2,429|
|**Tanzania**||||
|2023/11|66.2|1.6|1,988|
|**Uganda**||||
|pre-COVID|70.2|1.5|2,224|
|2020/06|78.6|1.3|2,225|
|2020/08|78.5|1.3|2,197|
|2020/10|75.6|1.4|2,144|
|2020/11|75.9|1.4|2,135|
|2021/11|78.8|1.4|1,948|
|2023/01|71.4|1.7|1,665|
|2023/08|72.9|1.6|1,765|
|2023/10<br>Note: Share of house|72.3<br>holds farming|1.6<br>crops. All mean|1,729<br>s in percent.|

23
**Table A3: Share of crop-farming households with disruption to their agricultural activity.**

||Pooled|BFA|ETH|TZA|UGA|
|---|---|---|---|---|---|
|Disruption to ag activity|9.9|6.3|23.3|1.6|9.0|
||(0.88)|(1.19)|(2.96)|(0.41)|(1.11)|
|_N_|4,407|840|903|1,320|1,344|

Note: Share of crop-farming households with disruption to their agricultural activity in most recent round. Standard error in parentheses. Survey timing: BFA (01/24), ETH (02/24), TZA (11/23), UGA (10/23).

24
# **Table A4: Reasons for disruption to normal agricultural activity**

||BFA|ETH|TZA|UGA|
|---|---|---|---|---|
|Lack hired labor|6.6||37.0|3.7|
||(6.36)||(14.15)|(1.69)|
|Lack of seeds|0.0|25.0|12.0|13.2|
|||(6.36)|(8.65)|(5.09)|
|Lack of fertilizer|6.8|55.6|9.7|0.0|
||(4.11)|(7.39)|(6.53)||
|Lack of other inputs|2.1|24.2|1.5|2.8|
||(2.12)|(8.19)|(1.16)|(1.66)|
|Unable to sell outputs|0.0|4.7|0.0|1.1|
|||(3.22)||(0.82)|
|Illness|0.9|1.4|24.8|12.1|
||(0.77)|(1.34)|(12.99)|(4.13)|
|Delayed planting|0.7|0.0|0.0|3.1|
||(0.74)|||(1.40)|
|Climate|40.2|66.9|48.3|84.2|
||(9.48)|(6.14)|(13.68)|(3.54)|
|Pests|1.5|1.4|5.9|5.5|
||(1.48)|(0.77)|(5.82)|(2.09)|
|Insecurity|66.7|7.2|0.0|2.0|
||(9.12)|(2.05)||(1.10)|
|_N_|55|186|24|132|

Note: Reasons for disruption to agricultural activity in most recent round. Standard error in parentheses. Survey timing: BFA (01/24), ETH (02/24), TZA (11/23), UGA (10/23).

25
# **Table A5: Share of crop-farming households that used any inorganic fertilizer.**

||Mean|Std. Err.<br>_N_|
|---|---|---|
|**Burkina Faso**|||
|pre-COVID|52.6|2.5<br>886|
|<br>2021/01|58.7|2.2<br>1,142|
|2023/01|45.8|<br>2.5<br>889|
|2024/01|44.9|2.6<br>840|
|**Ethiopia**|||
|pre-COVID|66.9|2.4<br>670|
|2021/10|66.3|2.9<br>672|
|2023/01|61.6|4.7<br>556|
|2024/02|63.0|3.4<br>903|
|**Malawi **|||
|pre-COVID|77.4|1.9<br>1,143|
|2021/06|83.5|1.5<br>1,253|
|2023/06|70.0|2.2<br>1,076|
|**Nigeria**|||
|2019/01|43.8|2.0<br>1,279|
|**Tanzania**|||
|2023/11|26.3|1.8<br>1,319|
|**Uganda**|||
|pre-COVID|4.7|0.6<br>1,627|
|2023/01|10.1|1.3<br>1,284|
|2023/10<br>Note: Share of crop-farming hou|14.3<br>seholds that used any|1.4<br>1,344<br>inorganic fertilizer. All means in percent.|

26
## **Table A6. Time trends in the incidence of inorganic fertilizer use**

# **<u>Used any inorganic fertilizer</u>**

||(1)<br>Burkina Faso|(2)<br>Ethiopia|(3)<br>Malawi|(4)<br>Uganda|
|---|---|---|---|---|
|Survey round|-0.0360***|-0.0151|-0.0394***|0.0477***|
||(0.0114)|(0.0137)|(0.0147)|(0.00783)|
|Constant|0.596***|0.683***|0.851***|0.00152|
||(0.0305)|(0.0336)|(0.0302)|(0.0138)|
|Observations|3,757|2,801|3,472|4,255|
|R-squared|0.006|0.001|0.006|0.017|

Note: Results from OLS regression of the outcome on a variable indicating the n'th data point over time for this variable. The variable is 1 for the first round in which data on the outcome was collected, 2 for the second instance, and so on. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

27
# **Table A7. Correlates of fertilizer use**

# **<u>Used any inorganic fertilizer</u>**

||(1)<br>Ethiopia|(2)<br>Ethiopia|(3)<br>Malawi|(4)<br>Malawi|(5)<br>Nigeria|(6)<br>Nigeria|(7)<br>Burkina<br>Faso|(8)<br>Uganda|
|---|---|---|---|---|---|---|---|---|
|Wealth quintile|0.0204*|0.0481***|0.0600***|0.0619***|-0.0250***|-0.0252***|0.0728***<br>|0.0138**<br>|
||(0.0120)|(0.0142)|(0.00808)|(0.00794)|(0.00904)|(0.00899)|(0.0101)|(0.00551)|
|Distance to nearest market|-0.00141***||-0.000967||-6.55e-05||||
||(0.000395)||(0.000975)||(0.000288)||||
|Distance to nearest road|0.000820||0.000785||-0.00346**||||
||(0.00102)||(0.00137)||(0.00148)||||
|Distance to nearest|||||||||
|population center|-0.00232***||-0.000171||-0.000992||||
||(0.000755)||(0.00115)||(0.000678)||||
|Constant|0.754***|0.531***|0.613***|0.586***|0.547***|0.508***|0.349***|0.00946|
||(0.0463)|(0.0480)|(0.0397)|(0.0332)|(0.0394)|(0.0335)|(0.0361)|(0.0154)|
|Observations|2,786|2,786|4,507|4,511|3,064|3,064|3,757|4,027|
|R-squared|0.105|0.017|0.081|0.080|0.069|0.062|0.042|0.019|
|Survey Wave FE|YES|YES|YES|YES|YES|YES|YES|YES|

Note: Results from OLS regression of extensive margin fertilizer use on household characteristics. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

28
**Table A8: Reasons for not applying fertilizer.**

|**Burkina Faso**|**Did**<br>|**not need**|**Too expensive**|**Not available**|**Unable to travel**|
|---|---|---|---|---|---|
|2021/01|Mean|27.8|76.1|0.0|0.0|
||Std. Err.|2.1|2.0|0.0|0.0|
||_N_|472|472|472|472|
|2023/01|Mean|17.0|76.5|6.4||
||Std. Err.|1.8|2.0|1.2||
||_N_|441|441|441||
|2024/01|Mean|13.9|76.3|7.3||
||Std. Err.|1.6|2.0|1.2||
|**Ethiopia**|_N_|448|448|448||
|2023/01|Mean|67.8|19.0|13.2||
||Std. Err.|3.4|2.9|2.5||
||_N_|187|187|187||
|2024/02|Mean|29.6|58.0|6.9|2.3|
||Std. Err.|2.5|2.7|1.4|0.8|
|**Malawi **|_N_|327|327|327|327|
|2021/06|Mean|37.9|63.9|0.0|0.2|
||Std. Err.|3.6|3.6|0.0|0.3|
||_N_|184|184|184|184|
|2023/04|Mean|31.6|59.7|6.4||
||Std. Err.|2.5|2.7|1.3||
||_N_|334|334|334||
|2023/06|Mean|17.4|68.6|11.8||
||Std. Err.|2.2|2.7|1.8||
|**Nigeria**|_N_|307|307|307||
|2023/06|Mean|46.8|47.9|3.4||
||Std. Err.|1.9|1.9|0.7||
|**Tanzania**|_N_|686|686|686||
|2023/11|Mean|45.1|45.5|7.3|0.0|
||Std. Err.|1.6|1.6|0.8|0.0|
|**Uganda**|_N_|951|951|951|951|
|2023/01|Mean|49.3|42.6|6.5||
||Std. Err.|1.5|1.5|0.7||
||_N_|1,144|1,144|1,144||
|2023/10|Mean|51.2|46.1|1.6||
||Std. Err.|1.5|1.5|0.4||
|Note: Reasons|_N_<br>for not applying fer|1,157<br>tilizer among HHs|1,157<br>not applying fertilizer. All m|1,157<br>eans in percent.||

Note: Reasons for not applying fertilizer among HHs not applying fertilizer. All means in percent.

29
# **Table A9: Share of households using fertilizer that could not access the desired amount.**

|**Burkina Fa**|Mean<br>**so**|Std. Err.|_N_|
|---|---|---|---|
|2023/01|45.7|3.8|397|
|2024/01|41.5|3.7|392|
|**Ethiopia**||||
|2023/01|49.8|3.4|369|
|2024/02|36.6|3.0|576|
|**Malawi **||||
|2023/04|68.8|2.7|705|
|2023/06<br>**Nigeria**|68.9|2.6|769|
|2023/06|47.8|2.3|1,096|
|**Tanzania**||||
|2023/11|47.3|3.9|367|
|**Uganda**||||
|2023/01|38.1|6.6|111|
|2023/10|14.2|3.3|184|

Note: Share of households using fertilizer that could not access the desired amount. All means in percent.

30
**<u>Table A10: Linear trend in share of fertilizer-using households that could not access desired amount</u>**

||(1)<br>Burkina Faso|(2)<br>Ethiopia|(3)<br>Malawi|(4)<br>Uganda|
|---|---|---|---|---|
|Survey round|-0.0411|-0.132***|0.000192|-0.238***|
||(0.0521)|(0.0417)|(0.0328)|(0.0712)|
|Constant|0.498***|0.630***|0.688***|0.619***|
||(0.0836)|(0.0696)|(0.0543)|(0.134)|
|Observations|789|945|1,474|295|

Note: Results from OLS regression of the outcome on a variable indicating the n'th data point over time for this variable. The variable is 1 for the first round in which data on the outcome was collected, 2 for the second instance, and so on. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

31
## **Table A11. Incidence of fertilizer shortfall**

# **<u>Any fertilizer shortfall (dummy)</u>**

||(1)<br>Ethiopia|(2)<br>Malawi|(3)<br>Nigeria|
|---|---|---|---|
|Distance to nearest market|-0.00157***|0.00357**|-0.00107**|
||(0.000506)|(0.00166)|(0.000520)|
|Distance to nearest road|0.00575***|0.00204|-0.00554|
||(0.00199)|(0.00285)|(0.00418)|
|Distance to nearest population center|-0.000795|-0.00181|0.00218*|
||(0.00106)|(0.00210)|(0.00124)|
|Wealth quintile|-0.0415**|-0.0186|-0.0384**|
||(0.0177)|(0.0153)|(0.0163)|
|Constant|0.611***|0.693***|0.624***|
||(0.0758)|(0.0714)|(0.0635)|
|Observations|945|1,472|1,096|
|R-squared|0.039|0.015|0.024|

Note: Results from OLS regression of extensive margin fertilizer shortfall on household characteristics. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

32
**Table A12: Fertilizer shortfalls (means)**

||Quantity<br>(kg<br>Mean|purchased<br>/ha)<br>StdErr|Short<br>(kg/<br>Mean|fall<br>ha)<br>StdErr|Shortfall as<br>desired<br>Mean|% of total<br>quantity<br>StdErr|N|
|---|---|---|---|---|---|---|---|
|Ethiopia|233.35|25.30|281.50|26.49|55.66|1.57|218|
|Tanzania|169.88|19.62|311.07|33.93|67.12|1.31|162|
|Uganda|23.70|5.46|47.93|9.27|65.77|3.39|33|

Note: Shortfall defined as the quantity of inorganic fertilizer a household desired but did not acquire.

33
## **Table A13. Depth of fertilizer shortfalls**

# **<u>Log fertilizer shortfall (kg/ha)</u>**

||(1)<br>Ethiopia|
|---|---|
|Distance to nearest market|-0.00144<br>(0.00225)|
|Distance to nearest road|-0.000776<br>(0.00846)|
|Distance to nearest population center|0.00144<br>(0.00690)|
|Wealth quintile|-0.137**<br>(0.0567)|
|Constant|5.761***|
||(0.240)|
|Observations|218|
|R-squared|0.044|

Note: Results from OLS regression of the amount of additional fertilizer that could not be acquired on household characteristics. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

34
# **Table A14: Reasons for not having been able to access desired amount of fertilizer.**

||BFA|ETH|TZA|UGA|
|---|---|---|---|---|
|Too|||||
|expensive|98.2|58.5|94.3|100.0|
||(0.96)|(4.68)|(2.12)||
|Not|||||
|available|0.9|78.9|5.4|0.0|
||(0.74)|(4.21)|(2.09)||
|_N_|189|234|162|33|

Note: Reasons for not having been able to access desired amount of fertilizer. Standard error in parentheses. Survey timing: BFA (01/24), ETH (02/24), TZA (11/23), UGA (10/23).

35
# **Table A15: Share of crop-farming households that reported increase in fertilizer price relative to the past agricultural season.**

|**Burkina Fa**|Mean<br>**so**|Std. Err.|_N_|
|---|---|---|---|
|2023/01|87.3|2.3|439|
|2024/01<br>**Ethiopia**|63.6|3.7|382|
|2023/01|96.6|1.0|368|
|2024/02<br>**Malawi **|80.6|2.5|572|
|2023/04|68.8|2.7|705|
|2023/06<br>**Nigeria**|68.9|2.6|769|
|2023/06<br>**Tanzania**|47.8|2.3|1,096|
|2023/11<br>**Uganda**|42.4|3.2|546|
|2023/08|35.1|6.3|109|
|2023/10|31.5|6.4|118|

Note: Share of crop-farming households that reported increase in fertilizer price relative to the last agricultural season. All means in percent.

36
# **Table A16: Self-reported fertilizer price change compared to last agricultural season.**

||BFA|ETH|TZA|UGA|
|---|---|---|---|---|
|Much higher|22.1|35.4|10.7|2.5|
||(3.14)|(3.09)|(1.84)|(1.27)|
|Higher|41.4|45.1|31.7|29.1|
||(3.84)|(3.25)|(3.04)|(6.43)|
|About the same|26.3|14.2|38.8|45.8|
||(3.43)|(2.24)|(3.07)|(6.53)|
|Lower|8.4|5.2|17.0|22.7|
||(2.06)|(1.39)|(2.35)|(5.62)|
|Much lower|1.7|0.0|1.8|0.0|
||(0.71)|(0.01)|(0.96)||
|_N_|382|572|546|118|

Note: Reported change in fertilizer price compared to last agricultural season. Standard error in parentheses. Survey timing: BFA (01/24), ETH (02/24), TZA (11/23), UGA (10/23).

37
# **Table A17: Mean fertilizer price (LCU/Kg).**

|**Burkina Faso**|Mean Price<br>(LCU/Kg)|Std. Err.|_N_|
|---|---|---|---|
|2022/11|550|18|184|
|2023/01|606|14|505|
|2023/08|511|9|511|
|2024/01|568|8|434|
|**Ethiopia**||||
|2023/01|45|1|366|
|2024/02|60|2|571|
|**Malawi **||||
|2022/12|1,337|20|776|
|2023/04|1,231|22|1,005|
|2023/06|1,258|17|883|
|**Nigeria**||||
|2023/06|441|48|122|
|**Tanzania**||||
|2023/11|1,696|38|541|
|**Uganda**||||
|2023/03|6,914|341|382|
|2023/08<br>Note: Fertilizer|9,478<br>price in LCU/Kg.|1,740|403|

38
**Table A18: Coping strategies for high fertilizer prices.**

||<br>r<br>f|Used lower<br>ate of<br>ertilizer|Cultivated a<br>smaller area|Borrowed<br>money|Sold<br>productive<br>assets|Assistance<br>from<br>family/friends|Sharecropped/<br>rented out land|
|---|---|---|---|---|---|---|---|
|**Burkina F**|**aso**|||||||
|2023/01|Mean|45.7|1.6|15.8|35.3|6.9|0.8|
||Std. Err.|2.5|0.6|1.8|2.4|1.3|0.4|
||_N_|397|397|397|397|397|397|
|2024/01|Mean|39.1|4.8|22.4|29.7|18.8|3.3|
||Std. Err.|3.3|1.4|2.8|3.0|2.6|1.2|
||_N_|226|226|226|226|226|226|
|**Ethiopia**||||||||
|2023/01|Mean|14.1|0.6|17.6|57.1|5.0|0.8|
||Std. Err.|1.9|0.4|2.0|2.6|1.2|0.5|
||_N_|350|350|350|350|350|350|
|2024/02|Mean|37.3|3.6|30.3|36.5|8.5|0.3|
||Std. Err.|2.3|0.9|2.2|2.3|1.3|0.3|
||_N_|452|452|452|452|452|452|
|**Tanzania**||||||||
|2023/11|Mean|97.6|0.0|0.0|0.0|0.0|0.0|
||Std. Err.|1.0|0.0|0.0|0.0|0.0|0.0|
|Note: Copi|_N_<br>ng strategies f|237<br>or high fertili|237<br>zer prices among t|237<br>hose reporting in|237<br>creased prices sinc|237<br>e last season. All m|237<br>eans in percent.|

39
# **Table A19: Coping strategies for not having been able to access the desired amount of fertilizer.**

||On<br>pa<br>are|ly fertilized<br>rt of cultivated<br>a|Used lower rate of<br>fertilizer|Cultivated a<br>smaller area|Supplemented<br>with organic<br>fertilizer|Practiced<br>legume<br>intercropping|
|---|---|---|---|---|---|---|
|**Burkina F**|**aso**||||||
|2024/01|Mean|27.8|50.1|10.3|47.0|10.3|
||Std. Err.|3.3|3.6|2.2|3.6|2.2|
||_N_|189|189|189|189|189|
|**Ethiopia**|||||||
|2024/02|Mean|23.3|78.3|5.6|40.9|4.6|
||Std. Err.|2.8|2.7|1.5|3.2|1.4|
||_N_|234|234|234|234|234|
|**Malawi **|||||||
|2023/06|Mean|25.4|61.8|3.9|8.9||
||Std. Err.|1.9|2.2|0.9|1.3||
||_N_|510|510|510|510||
|**Nigeria**|||||||
|2023/06|Mean|60.6|62.3|40.9|64.7||
||Std. Err.|2.2|2.1|2.2|2.1||
||_N_|516|516|516|515||
|**Tanzania**|||||||
|2023/11|Mean|37.4|49.0|13.6|8.2|2.9|
||Std. Err.|3.8|3.9|2.7|2.2|1.3|
||_N_|162|162|162|162|162|
|**Uganda**|||||||
|2023/10|Mean|10.4|43.1|0.0|23.5|23.9|
||Std. Err.|5.4|8.8|0.0|7.5|7.5|
|Note: Cop|_N_<br>ing strategies fo|33<br>r not having bee|33<br>n able to access the des|33<br>ired amount of fer|33<br>tilizer. All means in p|33<br>ercent.|

40
# **Table A20. Price elasticity of fertilizer use (incidence).**

|Variable|(1)<br>Burkina Faso|(2)<br>Malawi|(3)<br>Nigeria|(4)<br>Tanzania|
|---|---|---|---|---|
|Log fertilizer price, winsorized|0.1036*|-0.00850|-0.0108|-0.349***|
||(0.0607)|(0.0378)|(0.0709)|(0.0991)|
|Constant|0.663*|0.661**|0.472|3.246***|
||(0.359)|(0.266)|(0.414)|(0.729)|
|Observations|882|1,772|122|541|
|R-squared|0.019|0.031|0.001|0.054|
|Survey Wave FE|YES|YES|YES|YES|

Note: Results from OLS regression of extensive margin fertilizer use on log fertilizer price. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

41
# **Table A21. Price elasticity of fertilizer purchases.**

|(1)<br>Variable<br>Ethiopia|(2)<br>Tanzania|
|---|---|
|Log fertilizer price, winsorized<br>-0.106|-1.069***|
|(0.185)|(0.314)|
|Constant<br>5.531***|12.73***|
|(0.722)|(2.298)|
|Observations<br>546|335|
|R-squared<br>0.002|0.070|
|Note: Results from OLS regression of log total fertilizer purc<br>hectare on log fertilizer price. Robust standard errors in paren<br>p<0.01, ** p<0.05, * p<0.1.|hased per<br>theses. ***|

42
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