Professor of Economics & Head Department of Economics and
Development Studies, Covenant University, Ota, Nigeria
COVID-19 Pandemic, Agricultural Risks, and
Diversification Strategies of Smallholder Farmers in SSA
Evans Osabuohien, Alhassan Karakara & Abdul Malik Iddrisu

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Outline
 Background, Motivation & Objectives
 Conceptual Framework
 Data & Methods of Analysis
 Some Results
 Summary of Main Findings
 Conclusion
 Appreciation

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Background, Motivation &
Objectives

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Background & Motivation
 Agriculture plays a key developmental role in SSA countries
 Accounts for more than 52% of total employment in most economies
 Majority of farmers are smallholder farmers (Herrero et al., 2017; Osabuohien, 2020)
 Smallholder farmers face a myriad of risk factors (different sources).
 The COVID-19 pandemic could increase the risks faced by farmers
 Previous studies dwelled much on farmers’ risk perception & how
they respond to it (e.g. Dasmani et al., 2020)
These studies are limited as they are based on small sample surveys or single-
country case studies.
Our paper addresses the issue & also examine the effects of the
COVID-19 pandemic on smallholder farmers.

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Objectives of the Study
1.What factors determine farmers’ exposure to agricultural risks
and do they vary by the type of risk?
2.How does context mediate the effect of various individual-level
factors on agricultural risks?
3.How do the drivers of agricultural risks differ across African
countries?
4.What is the effect of COVID-19 pandemic on the welfare of farm
versus non-farm households?
5.What diversification strategies are adopted by farm households
amidst COVID-19 pandemic?

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Conceptual Framework

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Conceptual Framework for the Study
 Brahmbhatt & Dutta (2008) economic epidemiological approach
(highlighted the dynamics of SARS, behaviour responses, economic
impacts, ill effect & death of SARS).
 Barratt et al. (2019) examined the indirect cost implications of
outbreak & spread of animal diseases (highlighted the cost of such
outbreak to smallholders & agricultural value chain - livestock
production).

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Conceptual Framework for the Study

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Data and Method of Analysis

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The Data
 We engaged two micro datasets
1. A nationally representative smallholder household’s
survey datasets from 5 countries in Africa.
 Cote D’Ivoire, Mozambique, Nigeria, Tanzania & Uganda
 Instrument used in administering the surveys are similar
This allows us to analyse them together.
2. Six rounds of the World Bank’s assisted High-Frequency
Phone Survey (HFPS) on COVID-19 Dataset for Uganda

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Method of Analysis
 Analysing the determinants of agricultural risks & that of
COVID-induced income reduction, we employ binary probit
estimator.
𝐴𝑖
∗
= 𝛾 + 𝛼𝑖𝐼𝑛𝑑𝑖 + 𝛽𝑖𝑍𝑖 + 𝜀𝑖 … … … … … (1)
 Equation (1) is the index function model
 𝐴𝑖
∗
is the latent continuous response variable
 𝐼𝑛𝑑𝑖 and 𝑍𝑖 denotes vectors of household and
farm/contextual level factors, respectively.
 Covariates (HH edu, gender, age, sizeHh, farming as main
income, Rural/urban, asset-livestock own).

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Some Results

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Table 1: Summary statistics of main dependent variables (agricultural risk model)
Variable Mean Std. dev. Range
Agr_risk_b Binary: measures whether or not the individual faced any form of agricultural risk.
Assumes a value of 1 if ‘Yes’ and zero otherwise.
0.885 0.319 0–1
weather Binary: measures whether or not the individual faced weather-related agricultural
risk. Assumes a value of 1 if ‘Yes’ and zero otherwise.
0.678 0.467 0–1
pest Binary: measures whether or not the individual faced pest-related agricultural risk.
Assumes a value of 1 if ‘Yes’ and zero otherwise.
0.581 0.493 0–1
accident Binary: measures whether or not the individual faced accident-related agricultural
risk. Assumes a value of 1 if ‘Yes’ and zero otherwise.
0.145 0.352 0–1
price_change Binary: measures whether or not the individual faced an unexpected price change-
related agricultural risk. Assumes a value of 1 if ‘Yes’ and zero otherwise.
0.208 0.406 0–1
contract_disease Binary: measures whether or not the individual faced a crop disease-related
agricultural risk. Assumes a value of 1 if ‘Yes’ and zero otherwise.
0.020 0.138 0–1
mkt_downturn Binary: measures whether or not the individual faced a market downturn-related
agricultural risk. Assumes a value of 1 if ‘Yes’ and zero otherwise.
0.082 0.275 0–1
breakdown_equip
ment
Binary: measures whether or not the individual faced a breakdown of equipment-
related agricultural risk. Assumes a value of 1 if ‘Yes’ and zero otherwise.
0.077 0.266 0–1
p_unrest Binary: measures whether or not the individual faced a political unrest-related
agricultural risk. Assumes a value of 1 if ‘Yes’ and zero otherwise.
0.039 0.195 0–1

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Table 2: Summary statistics of key variables (COVID-related income loss model)
Variable Mean Std. dev Range
Inc_loss1 Binary: measures whether or not a household experienced a
reduction in income or a total loss in income due to COVID. Assumes
a value of 1 if ‘Yes’ and zero otherwise.
0.938 0.241 0–1
Inc_loss2 Binary: measures whether or not a household’s current monthly
income is below the pre-COVID level. Assumes a value of 1 if ‘Yes’
and zero otherwise.
0.671 0.470 0–1
Farm Binary: measures whether or not the household’s main economic
activity is farming. Assumes a value of 1 if ‘Yes’ and zero otherwise.
0.863 0.343 0–1
Educ Categorical: measures the highest level of educational attainment of
the head of the household. Assumes a value of 0 if the head has No
education, 1 if Primary, 2 if Secondary, and 3 if Tertiary.
1.401 0.802 0–3
Male Binary: measures the gender of the head of the household. Assumes
a value of 1 if male and 0 if female.
0.688 0.463 0–1
Age Continuous: measures the age of the head of the household. 48.341 15.257 18–96

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Descriptive Results
Over 88.5% of farmers have experienced one form of
agricultural risk
Over 93.8% of households experienced either a
reduction in income or a total loss in income during the
post COVID period;
 while close to 67.1% of households had monthly
incomes below the pre-COVID level.
About 86.3% of households are engaged in agricultural
activities as their main economic activity.

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Table 3: Determinants of agricultural risk (marginal effects); baseline & disaggregation by locality
Dependent var:
Agricultural risk (binary) Baseline Baseline+ Baseline++ Locality-disaggregated estimations
I II III Urban sub-sample Rural sub-sample
Educational attainment (Base: None)
Primary 0.019 0.013 -0.010 -0.007 -0.002
(0.014) (0.012) (0.011) (0.016) (0.022)
Secondary 0.018 0.039*** -0.038** -0.074** -0.003
(0.015) (0.013) (0.017) (0.036) (0.024)
Tertiary -0.018 0.057*** -0.135*** -0.160** -0.069
(0.025) (0.016) (0.050) (0.077) (0.062)
Male (Base: Female) 0.012* -0.011* -0.022*** -0.030*** -0.016**
(0.007) (0.006) (0.007) (0.012) (0.008)
Age 0.005*** 0.002* -0.001 0.003 -0.003**
(0.001) (0.001) (0.001) (0.002) (0.002)
Age2 -0.000*** -0.000 0.000 -0.000 0.000**
(0.000) (0.000) (0.000) (0.000) (0.000)
Livestock_own -0.006 -0.010 -0.023* 0.001
(0.006) (0.007) (0.012) (0.009)
Customary_landown -0.008 -0.002 0.007 -0.014*
(0.006) (0.007) (0.011) (0.008)
Rural (Base: Urban) 0.053*** 0.010 - -
(0.006) (0.009)
Farm_bus 0.017** 0.016 0.014*
(0.007) (0.012) (0.008)
Crop_maize 0.017* 0.016 0.015
(0.010) (0.015) (0.011)
Prob > chi2 0.000 0.000 0.000 0.000 0.000
Observations 9,102 8,562 6,841 3,109 3,732

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Table 4: Determinants of agricultural risk (marginal effects): specific risk elements
Dependent var: Weather Pest Accident Price change Crop disease Market
downturn
Equipment
breakdown
Political
unrest
Covariates I II III IV V VI VII VIII
Educational attainment (Base: None)
Primary -0.022 0.024 0.012 0.059*** 0.007 0.011 0.004 -0.080***
(0.019) (0.022) (0.015) (0.016) (0.005) (0.012) (0.012) (0.023)
Secondary -0.034 0.049* 0.050*** 0.058*** 0.007 0.031** 0.001 -0.080***
(0.023) (0.026) (0.018) (0.020) (0.006) (0.014) (0.014) (0.023)
Tertiary -0.008 0.054 0.047 0.084*** 0.019** 0.048** 0.008 -0.063**
(0.032) (0.036) (0.029) (0.031) (0.010) (0.022) (0.019) (0.025)
Male (Base: Female) 0.012 -0.008 -0.006 0.008 0.002 0.004 0.013** 0.008
(0.010) (0.011) (0.008) (0.010) (0.003) (0.007) (0.006) (0.006)
Age 0.000 0.001 0.001 0.002 0.000 0.001 0.000 -0.001
(0.002) (0.002) (0.001) (0.002) (0.001) (0.001) (0.001) (0.001)
Age2 -0.000 0.000 -0.000 -0.000* -0.000 -0.000 -0.000 0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Livestock_own 0.004 0.027** -0.005 0.013 -0.002 0.010 0.006 0.006
(0.010) (0.011) (0.009) (0.009) (0.003) (0.006) (0.006) (0.006)
Cust_landown 0.007 0.057*** 0.017** 0.005 -0.009*** -0.013** -0.007 0.009
(0.010) (0.011) (0.008) (0.010) (0.003) (0.006) (0.006) (0.006)
Rural (Base: Urban) -0.010 0.013 -0.005 -0.029** -0.006 0.013 -0.005 -0.008
(0.014) (0.012) (0.010) (0.012) (0.004) (0.009) (0.009) (0.007)
Farm_bus -0.032*** 0.039*** 0.006 0.088*** 0.014*** 0.034*** 0.009 0.023***
(0.011) (0.012) (0.009) (0.012) (0.005) (0.008) (0.007) (0.008)
Crop_maize 0.046*** 0.075*** 0.028*** 0.028** 0.006 0.029*** 0.013 -0.007
(0.012) (0.014) (0.010) (0.012) (0.004) (0.009) (0.009) (0.006)
Observations 7,702 7,702 7,702 7,702 7,702 7,702 7,702 4,426

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How do the Agricultural Risks
Factors Differ Across SSA
Countries?

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Panel B: Farm versus Non-Farm Households
Figure 3: Panel A (Full Sample)

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Figure 4: Strategies Adopted by farm & non-farm households

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Summary of Key Findings

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Summary on Agricultural Risks
Agricultural risk is determined by;
 Individual factors (education, gender, age, family bus.)
 Context factors (locality, type of crop cultivated, and
land tenure system)
Agricultural risks varies across countries
 Farmers in Mozambique & Uganda are less likely to
experience agricultural risk compared to those in Cote d’Ivoire.
Farmers in Tanzania are more likely to experience risks related
to weather & pest attacks compared to those in Cote d’Ivoire.

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Summary on Effects of COVID-19
Effect of COVID-19 on welfare of farm vs non-farm
households
 58% of the farm households reported their current income was
below the pre-COVID-19 level compared to the non-farm HH
with 45%.
Diversification Strategies
 Unlike non-farm households, farm households adopt a wide range
of strategies to mitigate the effects of risks on their welfare.
The main ones include: reliance on savings, additional income-
generating activities, assistance from friends, & reducing food
consumption.

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Concluding Remarks

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We observe the that the probability of experiencing risks related
to agriculture is significantly influenced by a range of individual &
farm level/contextual factors with considerable variations across
context and countries in Africa.
We show that farm households witness important
reductions in their incomes during the COVID period.
Farm households rely on savings, additional income
generating activities, sale of assets & credit purchases as
their top 5 risk coping strategies.

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Appreciation
We thank UNU-WIDER for supporting the Study under SOUTHMOD-
simulating tax and benefit policies for development Phase 2, which is
part of the Domestic Revenue Mobilisation programme.
The original research report is online as WIDER Working Paper
117/2022 (DOI: https://doi.org/10.35188/UNU-WIDER/2022/251-5).

THANK YOU
Evans S. Osabuohien (pecos4eva@gmail.com
evans.osabuohien@covenantuniversity.edu.ng)
https://evansosabuohien.com/