Impacts of COVID-19 and desert locusts on smallholder farmers food systems and value chains in Kenya : reporting period : March - August 2020.
Date: 2021-03-16
Institutions: Joint Research Centre
Subjects: Kenya, agri-foodstuffs, agricultural region, coronavirus disease, financial loss, flood, food resources, food security, insect, report, value chain

Impacts of COVID-19 and Desert Locusts 
on Smallholder Farmers Food Systems 
and Value Chains in Kenya 

Reporting period: March - August 2020 

Odhiambo, K., Lewis, J., Tefera, N., Thomas, A., 
Meroni, M., Dimou, M., Rembold, F.  

2021 

EUR 30628 EN 

 
 
This publication is a Science for Policy report by the Joint Research Centre (JRC), the European Commission’s science and knowledge 
service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does 
not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the 
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concerning the delimitation of its frontiers or boundaries. 

Contact information  
Name: Felix Rembold 
Email: felix.rembold@ec.europa.eu 

EU Science Hub 
https://ec.europa.eu/jrc 

JRC123842 

EUR 30628 EN 

PDF 

ISBN 978-92-76-32073-9 

ISSN 1831-9424 

doi:10.2760/66626 

Luxembourg: Publications Office of the European Union, 2021 

© European Union, 2021 

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How to cite this report: Odhiambo, K., Lewis, J., Tefera, N., Thomas, A., Meroni, M., Dimou, M., Rembold, F., Impacts of COVID-19 and 
Desert Locusts on Smallholder Farmers Food Systems and Value Chains in Kenya , EUR 30628 EN, Publications Office of the European Union, 
Luxembourg, 2021, ISBN ISBN 978-92-76-32073-9, doi:10.2760/66626, JRC123842. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ...................................................................................................................................................................2 

Acknowledgements .................................................................................................................................................3 

Executive summary .................................................................................................................................................4 

1 

Introduction .......................................................................................................................................................8 

1.1  Survey Background ...................................................................................................................................8 

1.2  Purpose of the study .................................................................................................................................9 

1.3  Kenyan Context .......................................................................................................................................10 

2  Data Design and Methodology ........................................................................................................................11 

2.1  Consultation with key Informants ...........................................................................................................11 

2.2  Smallholder Farmers telephone Survey ..................................................................................................12 

3  Empirical Results .............................................................................................................................................15 

3.1  Main livelihoods characteristics and crops grown ..................................................................................15 

3.2 

Impact of COVID-19 on Input and Output Markets ................................................................................19 

3.3 

Impact of Floods and Desert Locusts ......................................................................................................25 

3.4 

Impacts of COVID-19 on Food Security and Diversity of the Respondents. ............................................29 

3.5  Satellite Imagery Analysis .......................................................................................................................30 

4  Conclusions ......................................................................................................................................................37 

5  Policy Recommendations ................................................................................................................................39 

References ............................................................................................................................................................41 

Glossary .................................................................................................................................................................42 

List of figures .........................................................................................................................................................43 

List of tables ..........................................................................................................................................................44 

Annex A .................................................................................................................................................................45 

Annex B – Survey methods ...................................................................................................................................48 

Annex C .................................................................................................................................................................49 

1 

 
 
 
 
 
 
 
 
 
 
 
 
Abstract 

The  study  analyses  impacts  of  multiple  stressors  including  floods,  COVID-19  and  desert  locusts  on  agri-food 
value-chains in Kenya’s main agricultural areas during the 2020 long rains season. While 76% of farmers reported 
negative impacts of COVID-19 on their primary income source, only 16% reported losses due to desert locusts.  

2 

 
 
 
 
 
Acknowledgements 

The  authors  would  like  to  acknowledge  the  numerous  farmers  and  interviewers  who  have  made  the  survey 
possible in Kenya. We would also like to thank the colleagues who helped reviewing or provided comments to 
the concept and to the draft report and in particular: Tharcisse Nkunzimana, Darrell Sexstone, Myra Bernardi 
and Stephen Wathome. 

The survey was funded under the Administrative Arrangement between DG DEVCO and the JRC: “Technical and 
scientific support to sustainable agriculture, food & nutrition security  and food systems (TS4FNS)”. 

Authors 

Odhiambo, K. and Lewis, J, agriBORA, Robert-Bosch Straße 7, Darmstadt Germany  

Tefera, N., Thomas, A., Meroni, M., Dimou, M., Rembold, F., Joint Research Centre of the European Commission 

3 

 
 
 
 
 
Executive summary 

Kenya has been hit by multiple shocks throughout 2020: the economy has been significantly affected by the 
COVID-19 pandemic and the containment measures introduced by the government, such as the restrictions on 
free  movement  of  people,  goods  and  services  that  have  had  a  great  impact  on  agricultural  and  food  supply 
chains.  Moreover,  desert  locust  invasions  have  occurred  since  December  2019,  followed  by  major  floods 
between May and June 2020. Understanding the impacts of these multiple stressors on agriculture, food systems 
and rural livelihoods is key to define adequate instruments to mitigate the effects on the economy and on food 
security. 

The  study  examines  the  impact  of  COVID-19  and  its  containment  measures,  as  well  as  of  floods  and  desert 
locusts,  on  the  smallholders  food  system  using  a  telephone  survey  conducted  in  August  2020  with  1,026 
smallholder  farmers  in  19  counties  of  the  main  crop  producing  regions  in  Southwestern  and  Central  Kenya, 
complemented  with  about  40  follow-up  field  visits  where  locust  damage  was  reported,  as  well  as  with 
consultations from 20 agro-dealers and 15 aggregators or agro-processors. The survey will be repeated during      
three consecutive cropping seasons: July-August 2020, November -December 2020 and July-August 2021.  This 
report is based on the first of this series of three surveys being conducted thanks to a research collaboration 
between the Kenyan-German digital service start-up agriBORA and the European Commission Joint  Research 
Centre (JRC). 

The  study  benefits  from  direct  contact  with  individual  farmers  and  other  value  chain  actors  to  obtain 
quantitative and qualitative insights. Each interview lasts for around 30 minutes to provide insights about farm 
activities and multiple challenges farmers faced during the pandemic period. The study focuses on the impact 
across the entire value chain for the inputs providers to the smallholders farmers and their (economic) welfare. 
The impact of the stressors on the food security of the smallholders farmers is part of the study. However, to 
keep the overall length of the questionnaire limited, the standard food security and nutritional indicators, such 
as the SDG2 indicators or the standard acute food security indicator used by the IPC or the UN agencies (FAO, 
WFP)  could  not  be  fully  included  in  the  phone  survey.  The  priority  was  put  on  collecting  indicators  on  the 
agricultural activity of households and impact of COVID-19 and desert locusts. In addition, the survey did not 
include  potential  differential  effects  by  gender.  Furthermore,  the  respondents  are  representative  only  for 
farmers in the agriBORA database representing selected value chains. However, the farmers network was spread 
across the  main cropping regions of the country and the value chains captured during the survey are varied 
enough to give a good picture of the situation in the main food production areas. Key informant interviews (i.e., 
with agro-dealers, aggregators and agro-processors) were  purely qualitative based on the selected  interview 
partners  and  their  experience  and  led  to  some  valuable  insights  that  helps  to  better  understand  challenges 
impacting farmers’ access to inputs. 

Policy context 

Food and nutrition security in the East African region continue to be challenged by multiple stressors including 
conflicts, economic downturn and climate extremes.  As shown by the 2020 Global Report on Food Crises, 3 out 
of 10 countries with the largest food crises are in this region (Ethiopia, South Sudan and Sudan). Since late 2019 
the pressure on these highly vulnerable food security and nutrition situations      further increased due to the 
extraordinary invasion and breeding of desert locusts. The COVID-19 health and economic crisis and the related 
containment measures are a further stressor. 

With  small  scale  farmers  being  an  important  target  of  the  EU’s  development  and  food  security  policies, 
understanding the impact of the exceptional combination of stressors on their livelihoods and the implications 
on agri-food value chains becomes more important than ever. The Green deal’s Farm to Fork strategy and the 
Comprehensive strategy for Africa aim at strengthening and making food systems in the continent more efficient 
and sustainable and increasing their resilience to multiple shocks is central. 

The EC also supports the role of STI (Science Technology and Innovation) in international cooperation and the 
important role of new technologies (i.a. Information and Communications Technology) in relation to achieving 
the Sustainable Development Goals is increasingly recognized. This survey provides an opportunity to use such 
innovative  technologies  in  collaboration  with  an  African-European  start-up  and  to  test  how  quickly  and 
efficiently information about the impact of multiple stressors on rural food systems can be retrieved and made 
available. A similar study has been launched in West Africa (Ivory Coast and Senegal) by JRC D4 and it will be 
interesting to have comparable results for an East African country. 

4 

 
 
 
 
Key conclusions 

The  survey  has  shown  impacts  of  COVID-19  on  smallholder  farmers  in  major  crop  growing  regions  in  Kenya 
spanning  the  entire  agricultural  sector.  The  associated  public  health  mitigation  measures  introduced  by  the 
government in mid-March 2020 to prevent further spread of the virus — particularly lockdowns, stay-at-home 
orders and the closing of borders and public spaces such as markets and schools  —have negatively impacted 
livelihoods of many smallholder farmers, and activities of agro-dealers, aggregators and agro-processors. 

The containment measures affected farmers due to lack of timely access to farm inputs, in particular to seeds 
and fertilizer, increased input costs and difficulties in accessing output markets for their harvest. While these 
challenges were most prominent during the March-June 2020 period, when the government measures had the 
maximum effect, they also persisted between July and August 2020, when farmers were harvesting and seeking 
markets for their crops. Decreasing household income due to job losses, reduced revenues from farming or other 
income generating activities as well as increase in price of food and low yields and/or grain quality experienced 
by farmers, contributed to reduced food access and reductions in both quantity and quality of food consumed 
by the smallholders farmers           during the pandemic period. The situation improved during the harvesting 
period as from July 2020. However, the food reserves of most households in August 2020 covered less than three 
months of consumption.  

The  above  average  rainfall  during  the  long  rains  season  in  most  areas  of  the  country  led  to  good  growing 
conditions but also resulted  in major floods on lake shores and riverine areas. The floods have affected crop 
fields of many farmers resulting in yield loss or low quality of produced commodities. Minimal flood impact on 
livestock was reported.  

While the Horn of Africa has suffered the worst desert locust invasion in decades, the survey has shown that the 
impact of the desert locusts on the investigated five crops in the sampled counties, which include Kenya’s most 
important production regions, was quite low. The findings confirm that the forecasted worst case scenario of 
desert locusts causing major crop losses during the long rain season, which had been of concern to the country 
in early 2020, has not materialized because the desert locust invasions did not reach the most productive areas. 
Also, in areas that have been affected by desert locusts it is assumed that the positive effect of the exceptional 
rainfall on both crop and rangelands in March-May 2020 has to a large extent prevailed on the negative impact 
of desert locusts. The hypothesis is corroborated by the results of the remote sensing analysis which has not 
found clear signals of biomass decrease that can be attributed to desert locusts. The survey managed to collect 
GPS points and photographs of affected fields including maize, sorghum, pigeon peas, okra, green grams and 
Napier grass fields. However, no clear traces of locust damages were found at the time of survey, as most fields 
were at maturity stage or had already been harvested. In addition, we were not able to confirm the severity of 
the infestation reported by a number of farmers with the remote sensing analysis. The survey also revealed that 
apart from desert locusts, the fall armyworm pest was affecting many maize farmers in the country.   

This farmers survey working with agricultural digitalization pioneers like agriBORA could be organized quickly 
and was flexible in terms of adapting questionnaires specific to survey needs.  Such surveys are a valid tool for 
COVID-19 impact assessment on food value chains and can be repeated easily in time (eg. for different waves). 
The survey has been successful in reaching and engaging farmers quickly and in representative numbers.  

Main findings 

The  findings  confirm  significant  drops  in  income  for  smallholder  farmers  as  well  as  disruptions  in  access  to 
agricultural inputs such as seeds, fertilisers and chemicals (herbicides, pesticides, fungicides) and output markets 
for many farmers, as also revealed by recent studies in Kenya and neighbouring East African countries1. About 
76% of farmers reported impacts of COVID-19 pandemic containment measures with direct consequences on 
their primary income source (crop farming). Lack of access to farm inputs, drastic reduction in sales, low market 
prices  for  harvested  crops,  high  cost  of  transport  and  difficulty  in  accessing  markets  are  among  the  major 
constraints linked to the COVID-19 containment measures and their consequences. Loss of secondary income 

1  FAO,  July  2020.  Impact  of  COVID-19  on  agriculture,  food  systems  and  rural  livelihoods  in  Eastern  Africa:  Policy  and 

programmatic options. https://reliefweb.int/sites/reliefweb.int/files/resources/CB0552EN.pdf 

5 

 
 
 
 
 
                                           
due to movement restrictions and business closure, as well as low yields resulting from lack of adequate inputs, 
floods  and  desert  locusts  have  also  jeopardized  both  quantity  and  quality  of  households’  food  consumption 
during the pandemic period. Those impacts are observed across all crops/values chains even if some of them 
could be more impacted. For example, the fertiliser shortage was especially deep for the tea value chain at the 
agro-dealer level. Smallholder farmers operating under contract were also affected by the drop in demand and 
prices changes. About ⅓ of them had to close their contracts or renegotiate the terms of the contracts because 
of the pandemic. 

The early onset of rains and above-average rainfall since February 2020 helped land preparation and planting 
activities for the long rain season across the country and were generally beneficial to agricultural production. 
On  the  other  side,  the  abundant  rains  also  caused  localized  flooding  and  river  overflows,  mainly  during  the 
months  of  March  -  May  2020,  causing  casualties,  infrastructure  damage,  and  crop  damage  in  parts  of  the 
country.  The  above  average  rainfall  also  created  favourable  conditions  for  further  spread  of  desert  locusts, 
providing  suitable  breeding  conditions  and  abundance  of  vegetation  growth  for  feeding  across  the  country. 
Contrarily however to major  fears about  major crop losses due to  Desert  Locusts, that had dominated early 
warning  information  in  early  2020,  the  survey  results  show  that  the  impact  of  the  desert  locusts  on  crop 
production during the 2020 long rains was low, as the main cropping regions of the country were eventually not 
infestation hot spots. 43% of the farmers concerned used some form of treatment of their fields against locusts 
and control measures by the Kenyan government and international organisations were generally perceived as 
adequate by the interviewed agro-dealers and agro-processors.  

More than 90% of the farmers surveyed have planted maize during the long rains season 2020. We also noticed 
a shift in the type of crop planted with a significant increase of maize planting. While growing maize is a typical 
activity for  most  smallholder farmers in Kenya, the surge in maize planting seems to be a  coping strategy in 
response to the difficulty to access inputs and markets, as maize is less demanding for inputs and other essential 
resources as compared with other crops. Availability of more labour force due to those who lost jobs in urban 
areas and return to rural areas also lead to increase maize farming.  

During  the  survey,  smallholder  farmers  as  well  as  agro  dealers  and  agro  processors  reported  shortage  of 
cash/credit and requested for cash support in the form of loans or subsidies to afford buying essential inputs for 
the next agricultural season (October to December 2020). At the time of the survey in August 2020, only 11% of 
the farmers could access farm loans and/or subsidy incentives.      

While we have limited information on the actual food security and diet quality of the smallholder farmers, food 
security is the main concern for most of the respondents. About 38% of the respondents perceived their status 
as food insecure households (do not have food stock for consumption). Furthermore, although about 62% of the 
respondents have food stock for consumption, only 28% of them have stock that can last for more than three 
months per a year. About a quarter of them have limited stock that can last within less than a month and about 
half of them within 2 or 3 months in a year. Majority of them were forced to reduce either quantity or quality of 
their food consumption during March to June 2020 to retain some of their stock for future consumption. During 
the June to August 2020 period, with the harvest, the food consumption resumed to its usual pattern for a bit 
less than 60% of the farmers while 40% of them still have a reduced quantity and quality of their diet compared 
to usual. The reduction of food consumption disproportionally affects the population with low incomes. 

Related and future JRC work 

The 2020 crop production was not as severely affected as expected by multiple stressors including COVID-19 
pandemic, floods and desert locusts and the abundant rainfall led to a close to average production in the main 
agricultural areas. However, in the case of low or irregular rainfall, the impact of COVID-19 on food production 
could  be  much  more  severe  than  during  the  2020  long  rains.  Moreover,  the  continuation  of  the  COVID-19 
pandemic with its detrimental effects on the economy is expected to have negative impacts on food production 
which go beyond the 2020 long rains season. The next two phases of the study will be key in understanding the 
impacts of the prolonged pandemic effects on small scale farmers. These studies will add information to the 
JRC’s  continuous  agricultural  hotspots  monitoring  which  is  carried  out  as  part  of  the  Anomaly  hotSpots  of 
Agricultural  Production  system  (https://mars.jrc.ec.europa.eu/asap/)  and  will  support  food  security 
assessments in the East Africa Region in general. 

The increased shift towards maize cultivation should be further investigated. The 2020 above average maize 
production to some extent might have been obtained at the expense of crop diversity and shifts from cash crops 

6 

 
 
 
 
to  staple  crops.  With  the  constantly  worrying  nutrition  situation  in  Kenya,  the  quantity  versus  quality 
aspect could  be  an  important  factor  to  look  at.  Planting  of  vegetables  and  diversified  food  crops  should  be 
further supported. 

It was difficult to trace desert locusts during the field surveys as most fields were at maturity stage or had just 
been harvested and the pest traces obtained could not be fully attributed to desert locusts. Better timing of field 
visits will be necessary during the next two phases of the survey to collect improved information about Desert 
Locusts impacts on green crops. We also recommend more efficient ground data collection from the early stages 
of the invasion and renewed strengthening of the DL early warning systems, which in the East African region 
have partially become obsolete and understaffed over time during the years with little invasions. Satellite data 
derived  information  has  not  yet  proven  to  make  the  difference  in  locusts  swarms  monitoring  nor  in  impact 
assessment, but this is to some extent due to lack of high-quality ground information. The JRC will continue to 
assess  the  potential  of  different  Earth  Observation  data  and  methods  for  improved  desert  locusts  impact 
monitoring. 

Since the economic effects of COVID-19 are not limited in time to a single crop season, the negative impacts for 
example on access to farming inputs are expected to extend to the next seasons, leading to a progressive erosion 
of crop diversity and product quality. With the repetition of the survey during the next two crop seasons the 
study will focus on those protracted effects of the pandemic as well as looking more in detail at some specific 
aspects. The questionnaires will be further improved in order to include some more food security information 
and in order to improve the quality and timing of the desert locust information. The latter will of course also 
depend on the dynamics of further desert locusts breeding in the region. 

The  results  integrate  the  earlier  JRC  macro-economic  modelling  analysis  by  Nechifor  et  al.,  2020  and  the 
questionnaire development has been aligned with a similar study carried out simultaneously by JRC D4 in Ivory 
coast (Tillie et al., 2020). 

7 

 
 
 
 
1 

Introduction 

1.1  Survey Background 

Agricultural communities in East Africa have suffered from a series of stressors since early 2020, including the 
outbreak  of  the  COVID-19  pandemic,  floods,  plagues  of  desert  locusts,  and  fears  about  increasing  food 
insecurity. Monitoring the impacts of these stressors on the smallholder farmers food system is challenging due 
to several constraints in accessing information, both from farmers and from all other actors engaging in the food 
system value chains. 

The outbreaks of COVID-19 pandemic and desert locust invasion are a very recent phenomenon. The pandemic 
first  appeared in China, in December 2019, and rapidly spread to Asia, Europa, America, and the rest  of the 
world. More than 61 million people were tested positive in more than 185 countries, including more than 1.4 
million deaths, when this report is organized. The outbreak of the virus attests to global health, supply, demand, 
and financial shocks at a time, and containing the transmission of the virus becomes a global challenge, requiring 
coordinated efforts among governments, nations, and businesses, as well as various stakeholders. 

The world had also experienced a limited number (8 events) of desert locust infestations in the last 50 years 
although there were exceptional events in 2018 and 2019 in the Arabic Peninsula and then in remote areas of 
the Africa Red Sea coast (Meynard et al., 2020). However, early in December 2019, FAO (2020) reported locust 
upsurges in East Africa that have been the worst in the last 70 years in Kenya, as well as in the last 25 years in 
Ethiopia and Somalia. During the early stages of the infestation, major cropping areas in the region had not been 
severely affected, as most of the crops had either already been harvested or were in the last stage of maturity 
(FSNWG, 2020). However, unexpected heavy rains, in Eastern Ethiopia  and Somalia in early December 2019, 
allowed swarms breeding conditions to remain favorable through June 2020. The swarms in Ethiopia, Kenya and 
Somalia bred, gave rise to substantial hopper bands in March and a new generation of swarms in April and then 
another new generation of immature swarms in about mid-June to July (near the end of cropping seasons in 
most areas in the region). In early 2020 there were widespread fears about a possible worst scenario desert 
locust invasion of the region’s main agricultural areas, including Kenya’s cereal baskets in Western and Central 
Kenya. In order to better understand the impact of desert locust on agricultural activities (cropping and livestock 
rearing),  the  Food  Security  and  Nutrition  Working  Group  (FSNWG,  2020)  conducted  a  telephone  survey  in 
June/July 2020, using a randomly selected 10,831 agricultural respondents across desert locust affected areas 
of Ethiopia, Kenya, Somalia, and Uganda. The results show roughly about a third of them experienced desert-
locust related pasture or crop losses. Furthermore, about half of those who experienced desert locust perceived 
losses  to  their  crops  and  rangeland  as  high  or  very  high.  Desert  locusts  were  also  causing  emotional  stress, 
environmental impacts, increased food insecurity or malnutrition, and animal health issues. 

The COVID-19 and desert locust crises are simultaneously complemented with floods, insecurity and economic 
related issues in most developing countries including Kenya. Moreover, the economy of the country has been 
adversely affected following the COVID-19 containment measures the government had implemented since mid-
March  2020.  In  the  effort  to  have  a  better  understanding  of  the  combined  impact  of  COVID-19  and  desert 
locusts, agriBORA, used its farmers database built in the past three years, and carried out a limited scope of 
smallholder farmers survey in May 2020. A team of 16 enumerators were deployed on 12th-13th May 2020, and 
a  digital survey was completed for 194 respondents across  10 counties in Kenya. The  findings provide  some 
insightful  outcomes  on  loss  of  farmers  crop  production  and  income  generating  activities,  adopted  coping 
strategies  and  impacts  on  the  staple  foods  system.  Desert  locusts  affected  maize,  sorghum,  vegetables  and 
cowpeas at either germinating, late stage of maturity or harvesting of the 2019/2020 short rains crop. While the 
majority of respondents experienced the invasion of desert locusts at a late stage of maturity or harvesting (of 
the 2019 short rains), thereby limiting losses, about 20% reported locust invasion at stages of germination (of 
the 2020 long rains) that led to almost a total production loss2. The COVID-19 containment measures adversely 
affected farming activities too. Besides lack of access to seed and fertilizer, land preparation was also delayed 
due to the self-distancing protocol imposed by the government.  

2 agriBORA (2020). Report on Impact of Desert Locust (and COVID-19) on Livelihoods in Selected Kenyan Counties. Initial 
draft B. 

8 

 
 
 
 
                                           
The  European  Commission  Joint  Research  Centre  (EC-JRC)  supported  the  extension  of  the  study  in  three 
consecutive crop growing seasons, (July-August 2020, November-December 2020 and July-August 2021) in the 
effort to better understand the impacts of COVID-19 and desert locust invasion through interviewing the same 
households over three crop growing seasons. This report is based on the first phone survey (July-August 2020). 
In addition to extending the survey to a larger number of farmers and to other value chain actors, the EC-JRC 
included desert locusts impact field data collection among the objectives of the survey. This was driven by the 
experience that until mid-2020 there was only limited evidence of deserts locust impacts on vegetation based 
on Earth Observation analysis and one of the main reasons for that gap was the lack of ground observations to 
validate  satellite  observations.  The  satellite  imagery,  commonly  used  for  monitoring  biomass,  could  not  be 
extensively used for desert locust emergency in 2020 due to biomass anomaly attribution problems, as detecting 
a decrease of biomass during an exceptionally green season and wet rainy season are difficult. In discussions 
with  remote  sensing  experts  of  UN  agencies  and  development  partners  throughout  summer  2020,  it  was 
confirmed that more and better field data on direct impacts on vegetation is crucial in order to quantify the 
impact on crops and rangeland with the use of remote sensing.  

1.2  Purpose of the study 

The  study  is  designed  to  assess  impacts  of  COVID-19  containment  measures  and  biophysical  factors  limiting 
agricultural production such as desert locusts and floods on food system value chains of smallholder farmers in 
Kenya. The analysis is based on quantitative information collected via phone interview from a randomly selected 
1,026 smallholder farmers in 19 counties supplemented with qualitative consultation of key informants from 20 
agro-dealers and 15 aggregators/agro-processors obtained from agriBORA’s networks and database (Table 1). 
While  agro-dealers  supply  farmers  with  seeds,  fertilisers  and  chemicals  such  as  herbicides,  pesticides,  and 
fungicides; aggregators and agro-processors engage in buying and processing harvested crops. The study focuses 
on five main crops and six value chains: maize (contracted and non-contracted), sorghum, Irish potatoes, tea 
and sunflower. These crops are the main sources of income and food security of smallholder farmers in Kenya. 
In the case of maize, there is a distinction between “contracted” (the farmers that have a contract to deliver 
harvested output) and “non-contracted” (where the farmers have no such contract). The telephone interviews 
were supplemented with about 39 field visits planned as follow-up in areas where locust damages are reported. 
This study extends the scope of similar studies on the impact of COVID-19, such as the “60_Decibels”3 and the 
“Precision  Agriculture  for  Development4  (PAD)”,  from  agro-dealers  and  farmers  perspective  in  Kenya  with 
inclusion of relevant consumer markets, as well as capturing information on impact of floods and desert locusts 
on smallholder farmers’ crop production. It was also launched simultaneously with a similar study organized by 
the  JRC  in  collaboration  with  partners  in  Ivory  Coast  and  there  has  been  close  coordination  for  to  the 
development of the questionnaire. 

Table 1. Summary of Key Points of the Survey 

Descriptions 
Consecutive growing seasons to be covered in the survey. 
Main crop types surveyed. 
Value chains (or strata) addressed. 
Number of counties surveyed. 
Agro-dealers, aggregators and agro-processors interviewed prior to the farmer survey. 
Number of field visits following telephone interviews. 
Sample smallholder farmers interviewed via telephone. 
Source: Authors’ computation from August phone survey 

N 
3 
5 
6 
19 
35 
39 
1026 

The survey also involved field visits to collect GPS positions of affected fields and photographs showing traces of 
the desert locust invasion. About 4% of farmers were revisited during the exercise. The study also aims to provide 

3 Source: https://app.60decibels.com/COVID-19/agriculture#explore  

4 Source: https://precisionag.org/COVID-19-dashboard-2-0/#more-1181   

9 

 
 
 
 
 
                                           
information supporting use of satellite data analysis in assessing damage caused by desert locusts. The collected 
information will be made available to the WFP-FAO’s Desert Locust Damage Assessment Working Group. 

1.3  Kenyan Context 
Agriculture contributes for 30% of Kenya’s GDP5 and is the main source of income for more than 75% of the 
rural population; over 18 million6 Kenyans earn income from agriculture. The importance of the sector in the 
country’s  economy  has  been  emphasized  through  Kenya  Vision  2030,  the  Medium-Term  Plan  III,  and  most 
recently through the President’s Big Four priority agenda for 2017-2022, which  emphasizes its dominance in 
food and nutrition security for all Kenyans.  

The outbreaks of the COVID-19 pandemic happened during a critical period in the agricultural cycle, the planting 
season, which coincides with the on-set of the March-April-May (MAM) 2020 long rains. The associated public 
health mitigation measures introduced by the Kenyan government in mid-March 2020 — particularly lockdowns, 
stay-at-home orders and closing of public spaces such as markets, schools, religious institutions, and borders — 
to prevent further spread of the virus, had a great impact on livelihoods and businesses in both government and 
private sectors. The nationwide overnight curfew between 7pm  – 5am introduced on 27th of March and the 
directive  to  public  transport  vehicles  to  operate  at  60%  capacity  are  among  the  measures  taken  by  the 
government that have had substantial impact on movement of people and goods within the country between 
April and June. Apart from the national government’s containment efforts of the COVID-19 pandemic, worldwide 
travel bans, and restrictions  have adversely affected  supplying inputs and exporting crop outputs and led to 
workforce  reduction7;8.  As  of  June  2020,  some  of  the  restrictions  put  in  place  were  eased  as  the  country’s 
infections  appeared  to  reach  a  manageable  level  and  containment  measures  focused  on  hotspots  such  as 
counties of Nairobi, Mombasa, Kilifi and Kwale. Curfew hours were adjusted to run between 9pm and 4am to 
enable businesses thrive for more hours. The restrictions on free movement of people, goods and services – a 
key enabler in any market – have had a great impact on businesses and huge ramifications on producers, buyers, 
sellers,  consumers  and  consequently  vulnerable  households  that  depend  on  markets  for  their  livelihoods. 
According to Nechifor et al., 2020, the April-June lockdowns in Kenya and abroad would lead to the country’s 
economic  slowdown  by  5.6%  and  employment  by  11.8%,  as  compared  to  the  projected  pre  COVID-19. 
Households welfare could also be expected to decrease by 7.9% and 6.8% in rural and urban areas, respectively. 

Furthermore, farmers have also faced locust invasion and extreme flooding in some regions. Abundant rains 
have promoted the breeding and development of desert locusts and protracted the locust outbreak across the 
region9,  which  continues to pose a  significant  threat to food security. The floods have  impeded farm inputs 
provision,  delayed  land  preparation  and  swamped  cropland,  and  then  expected  to  worsen  food  insecurity. 
Efforts  to  tackle  one  crisis  have  sometimes  been  hampered  by  measures  to  curtail  another.  The  business  of 
growing food and moving them to markets has become increasingly difficult and this has in turn posed a twin 
threat of exacerbating poverty and hunger in vulnerable populations. 

5  Kenya  Economic  Survey  2017, KNBS  ,  July  2017;  Quarterly  Gross  Domestic  Product  Report  2017  Q1  Statistical  Release, 
KNBS, July 2017. 

6  Modelled ILO estimate. Employment is defined as persons of working age, who are engaged in any activity to produce 
goods or provide services for pay or profit, ~28 million Kenyans are employed by this definition. 

7 Roussi, A. 2020. Kenya farmers face uncertain future as COVID-19 cuts exports to EU. Financial Times, June 4, 2020. (also 
available at https://www.ft.com/content/05284de8-c19f-46de-9fe7- 482689be364b). 

8  FAO,  July  2020.  Impact  of  COVID-19  on  agriculture,  food  systems  and  rural  livelihoods  in  Eastern  Africa:  Policy  and 
programmatic options. https://reliefweb.int/sites/reliefweb.int/files/resources/CB0552EN.pdf 

9  “Eastern  Africa  Region  (2020).  Floods  and  Locust  Outbreak  Snapshot  (May  2020).”  Reliefweb,  May  11,  2020. 
https://reliefweb.int/report/ethiopia/eastern-africa-region-floods-and-locust-outbreak-snapshot-may-2020 

10 

 
 
 
 
 
 
                                           
2  Data Design and Methodology 

2.1  Consultation with key Informants 

Agro-dealers,  aggregators  and  agro-processors  are  the  “key  informants”  consulted  to  better  understand  the 
distributional impacts of stressors along identified value chains with possible market disruptions. The informants 
were selected from the agriBORA database and represent input dealers (fertilizers, seeds and chemicals such as 
herbicide, pesticides, fungicide etc.,) and output aggregators (collecting products and delivering to the markets) 
of agricultural products. The selections were based on their knowledge of the Kenyan agri-value chain, existing 
engagements and relationships with smallholder farmers and their distribution across the country.  Figure 1 and 
Table  2  present  geographic  locations  and  number  of  selected  key  informants  consulted  respectively.  These 
include 20 agro-dealers and 15 aggregators or agro-processors. 

Table 2. Numbers of aggregators and agri- processors Consulted 

Crop 
Maize  (contracted  and  non-
contracted) 
Sunflower 
Sorghum 
Irish Potatoes 
Tea 
Total 

# of Agro-dealers 
7 

# of Aggregators/Agro-processors 
4 

3 
5 
3 
1 
20 

3 
3 
3 
2 
15 

The interview guidance for key informants of agro-dealers and aggregators/agro-processors were independently 
designed  in  the  form  of  questionnaires  and  the  responses  were  recorded  and  the  follow  up  questions  were 
documented during the consultation period. Since the purpose of consultation was to explore issues for better 
understanding on the impact of multiple stressors, the responses were used as  basis for additional unstructured 
questions aiming at obtaining general descriptive information about issues such as combined impact of COVID-
19 and desert  locusts, most  affected crops, and qualitative comparison with previous seasons/years and the 
effects  of  confinement  measures,  on  import  or  trade  slowdowns,  changes  in  input  prices  and  demand  for 
farmers inputs.  
For  the  selection,  the  target  was  to  reach  at  least  one  agro-dealer  and  agro-processor  per  county,  so  for 
examples for the agro-dealers we called around 230 in total, to achieve the 20 complete interviews. 

11 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 1. Counties of Agro-processors Interviewed (agro-dealers work also in: Kiambu, Kajado, Murang’a and 
Narok) 

2.2  Smallholder Farmers telephone Survey  

The smallholder farmers survey was designed to undertake quantitative assessment on the impact of the COVID-
19  pandemic  and  associated  restriction  measures,  floods  and  desert  locust  invasion  on  their  agricultural 
activities such as production and income sources, food value chains including input and output marketing, as 
well as on household food consumption and food availability. The survey also collected basic information on 
general characterstics of respondents such as age and gender of respondents, their livelihoods, and food security 
status  and  livestock  ownership,  as  well  as  main  sources  of  drinking  water.  agriBORA  designed  survey 
questionnaires in collaboration with experts from JRC with an effort to make the questionnaires complementary 
to and comparable with other surveys implemented in other African countries, for example the WFP’s mVAM 
surveys, or the JRC survey about COVID-19 impact on rural households in Côte d'Ivoire.  

The farmers database used, has been setup over the last three years by agriBORA. Farmers subscribe (via SMS) 
to services offered by agriBORA including access information to markets (inputs and outputs) and agro weather 
advisories.  Most  entries  in  the  database  have  been  a  result  of  radio/SMS  advertisements  promoting  the 
agroweather  advisory  of  both  agriBORA  and  KALRO’s  Kenya  Agricultural  Observatory  Platform  (KAOP).  In 
addition, agriBORA works with local producer organisations who represent large numbers of farmers and use 
the  digital  platform  to  manage  the  communication  process  with  individual  farmers.  The  database  can  be  
expected  to  represent  typical  smallscale  farmer  households  in  Kenya’s  main  crop  growing  areas,  who  have 
access to radio and own a mobile phone capable of basic SMS functionality. 

The survey uses a two-pronged approach. Firstly, sample farmers were randomly selected from the agriBORA 
farmers database, built in the past three years, and interviewed via phone. The phone survey was targeted to 
run for a maximum of 30 minutes to ensure higher completion rates within a short time.  Secondly, those farmers 
who experienced desert locust infestation on their fields and traced locust infestation, were requested to have 
appointment for field visits with enumerators to collect photographs and GPS data of affected fields, landscape 
photographs of the area, as well as close-up photos of the crops showing traces of desert locusts. Estimates of 
the extent of the field with the same crop type or grassland were made in the four cardinal directions (north, 
south, east and west) including recording of information on percentage damage (where crop was still present) 

12 

 
 
 
 
 
 
 
 
 
 
 
 
up to 50 meters from the recorded point in the four cardinal directions. A control farm within a 5 km radius with 
similar crop type or grassland but not affected by desert locusts was visited and data was collected, as much as 
possible.  The  farmers  affected  by  desert  locusts  according  to  phone  surveys  also  helped  to  identify  other 
affected farmers through suggestions made by interviewed farmers. This had a “snowball effect” for the data 
collected through 39 field visits. 

It  is  a  rapid  appraisal  survey  targeting  a  sample  of  1,000  randomly  selected  farmers  participating  in  6  value 
chains10 (maize (contracted and non-contracted), sorghum, sunflower, tea and Irish potatoes) from a total of 
about 43,000 farmers registered in agriBORA platform, with additional 29 farmers drawn through the “snowball 
effect” method, from 19 counties in Kenya11. The sample farmers are drawn in a two-stage sampling technique. 
In the first stage, spatially distributed total number of farmers participating in each value chain are identified 
from the list of agriBORA platform. The targeted sample size from each county for identified value chains are 
then computed using a proportion of farmers in each county for identified value chain to total number of farmers 
participating in the value chains. In the second stage, sample farmers for identified value chain are randomly 
drawn from wards (level 3 administrative units) of each county. Finally, a total of 1,026 sample farmers12 were 
interviewed via phone survey.  

Figure 2 presents selected crops in each county. 

The  study  plan  is  to  set  a  panel  data  analysis  through  repeatedly  surveying  sample  famers.    Thus,  a  list  of 
potentially  up  to  three  farmers  were  identified  for  replacement  in  the  case  of  failure  to  reach  the  previous 
farmers due to no longer register lines, unreachable farmers, or unwillingness to participate in the survey.  If 
selected farmers could not be reached, the enumerators could then contact one of the two remaining farmers 
in  the  same  “ward”  (possibly  with  identical  crops).  The  smallholder  farmers  can  be  attributed  to  level  3 
administrative units, also known as “wards” 

10 The crops value chains are yet to clearly identified in the survey and this reports, as some of the essential 
inputs and constraints are reported for all crops together.  

11 The  survey was  carried  out  using Computer Assisted  Telephone Interviewing (CATI) as  described briefly in 
Annex B. 

12  Although  a  survey  was  planned  to  interview  1,000  smallholder  farmers  via  telephone,  and  additional  29 
farmers  through  “snowball”  methods,  interviews  with  3  farmers  were  not  completed  due  to  bad  network 
connection for 1 farmer or unknown reasons for another two farmers.  

13 

 
 
 
 
 
 
 
                                           
Figure 2. Counties where selected crops are growing 

14 

 
 
 
 
 
 
 
3  Empirical Results 

3.1  Main livelihoods characteristics and crops grown 

The  phone  survey  was  completed  for  1,026  smallholder  farmers  from  19  counties  in  Kenya.  The  survey  was 
largely drawn from male respondents, 83.0% (852), as compared with 17% (174) female respondents (Table 3). 
The phone survey may have systematically excluded female farmers with no access to phones and hence not yet 
registered on the digital platform of the agriBORA database.  The majority of the respondents are household 
heads (80%) with male heads accounting for 81.8% of surveyed household heads. About three-fourths of the 
respondents are economically active (aged between 18 and 65) and have attended at least primary school. On 
average, the respondents are from big families comprising of six members albeit the average number of children 
(4 persons) in a family are slightly greater than the average number of adult family members (3 persons) (Table 
4). Own production of farms is the main source of food for the majority of them (72%) while about 28% rely on 
purchase. About 62% (635 farmers) reported having food in stock for consumption. While only 28% have stock 
that can serve for more than three months per year, about a quarter have stock that can serve for less than a 
month, about half of them have stock that can serve only for 2 to 3 months per year (Table 3). About 80% of 
respondents own livestock, as part of their livelihoods, with animal’s body conditions mostly ranked as hovering 
around average, and about 40% own pastureland to feed their cattle. Borehole and surface water are the major 
sources of drinking water for more than half (56%) of respondents. While less than 10% of the respondents use 
either protected wells or springs, public tap/standpipes, or pipe into dwellings as main sources of drinking water, 
about  12%  use  unprotected  wells  or  springs.  About  5%  of  respondents  also  consider  rainwater  as  the  main 
source of drinking (Table 3).  

Table 3.  Smallholder farmers basic characteristics (N=1026) 

 Characteristic 
Sex of respondents 

Are you head of households (HH)? 

Sex of household head (HH) 

Age of respondents 

Highest level of respondent’s education 

Main sources of food 

Do you have food stock for consumption? 

# of months food stock can serve per year 

Do you have livestock? 

If yes, body conditions of livestock 

 Categories 
Female 
Male 
Yes 
No 
Female 
Male 
NA (not available) 
18-35 
36-50 
51-65 
66 + 
No School (illiterate) 
Primary 
Secondary 
College/University 
Donors 
Farm (own production) 
Purchase 
No 
Yes 
Less than a month 
2 months 
3 months 
More than 3 months 
No 
Yes 
Don't Know 
Exceptionally poor 
Less than average 
On average 
More than average 

15 

N 
174 
852 
823 
203 
150 
673 
197 
238 
312 
194 
45 
21 
431 
415 
159 
2 
738 
286 
391 
635 
153 
198 
105 
179 
204 
822 
3 
15 
118 
427 
189 

% 
17.0 
83.0 
80.0 
19.7 
18.2 
81.8 
19.2 
23.2 
30.4 
18.9 
4.4 
2.0 
41.9 
40.3 
15.5 
0.2 
71.9 
27.9 
38.1 
61.9 
24.1 
31.2 
16.5 
28.2 
19.9 
80.1 
0.4 
1.8 
14.4 
52.0 
23.0 

 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
Do you have pastureland 

Main sources of drinking water 

Exceptionally good 
No 
Yes 
Bore hole 
Bottled water 
Piped into dwelling 
Protected well or spring 
Public tap/Standpipes 
Rainwater 
Surface water 
Tanker, truck or cart with small tank 
Unprotected well or spring 

70 
614 
412 
361 
1 
81 
91 
100 
49 
219 
1 
123 

8.6 
59.84 
40.16 
35.1 
0.1 
7.9 
8.8 
9.7 
4.8 
21.3 
0.1 
12.0 

Source: Authors computation from July-August telephone survey 

Table 4. Respondents family size. 

Total number of people in household 
Number of adult persons 
Number of children 

Mean 
6.5 
3.1 
3.5 

Median 
6 
2 
3 

Min 
1 
0 
0 

Max 
25 
15 
22 

While crop farming is the main sources of income for almost all smallholder farmers, about 38% (387 farmers) 
are also involved in own business (36%), informal but casual employment (25%), petty trade such as selling of 
cattle and animals (14%), and formal employment (12%), as secondary sources of income (Table 5). Most of the 
respondents (about 38%) earn less than KES 25,000 per annum from either primary or secondary sources. While 
barely less than a quarter of them earns between KES 25,000 and KES 50,000 and about 10% -12% earn between 
KES 50,000 and KES 100,000, a very limited number (less than 10%) of them earn more than KES 100,000 (Table 
6). 

Table 5. Primary and secondary Sources of income 

Main sources of income 
   Crop farming 
   Formal-Employment 
   Informal-casual-Employment 
   Pastoralism-Livestock-Sales-of-cattle. 
Total sample farmers 
Secondary Sources of income 
   Own business 
   Informal casual Employment 
   Pastoralism Sales of cattle and animals 
   Formal Employment 
   Crop farming 
   Employed Business 
   Donation 
   Honey production 
   Other: specify 
Total farmers reporting secondary sources of income 

N 

1,023 
1 
1 
1 
1,026 

141 
95 
55 
50 
23 
9 
1 
1 
12 
387 

% 

99.42 
0.1 
0.1 
0.1 
100 

36.43 
24.55 
14.21 
12.92 
5.94 
2.33 
0.26 
0.26 
3.1 
37.7* 

Source: Authors computation from July-August telephone survey; * represents the percentage of respondents 
reporting secondary sources of income in the total sample of 1,026 farmers. 

Table 6. Level of income generating from either primary or secondary sources per annum 

Don’t know 
Less than  KES 25,000 

Primary 
N 
181 
392 

% 
17.6 
38.2 

16 

Secondary  
N 
118 
149 

% 
30.5 
38.5 

 
 
 
 
 
 
  
  
  
  
  
  
  
  
 
  
 
 
  
  
  
 
 
 
 
 
  
  
  
  
KES 25,000- KES 50,000 
KES 50,000 - KES 100,000 
KES 100,000 + 
Total respondents 

237 
131 
85 
1026 

23.1 
12.8 
8.3 
100.0 

81 
39 
36 
387 

20.9 
10.1 
9.3 
37.7* 

Source: Authors computation from July-August telephone survey; * represents the percentage of respondents 
reporting secondary sources of income in the total sample of 1,026 farmers. 

During the Long Rain (LR) season, majority of farmers are growing maize (91%) and beans (60%)13along with 
other crops (Figure 3). While growing maize is a typical activity for most smallholder farmers who are growing 
both food and cash crops in Kenya, the high maize planted areas in 2020 could also be partially attributed to a 
surge in farming activities across the country. It was in fact reported that the pandemic restrictions lasted for a 
longer period than initially expected and many citizens who lost their jobs returned to run crop farming in rural 
areas of their county. Furthermore, as input availability was challenging, reducing crop diversity by moving from 
cash crops to maize is a coping strategy for most of them as it would not only be used to fetch income but also 
to sustain the needs for household consumption. Most students could also be potentially participating in crop 
farming, as schools were closed due to lockdown restrictions. More importantly, about 35% of them reported 
participating  in  these  activities  for  the  first  time,  in  2020,  due  to  the  COVID-19  pandemic  and  containment 
measures  imposed  by  the  government  (Figure  4).  The  findings  support  the  hypothesis  of  increasing  in  the 
number of agricultural workforces from March to June 2020 although it needs to be augmented with further 
research  findings.  Furthermore,  about  55%  of  smallholder  farmers  engage  in  either  cash  and  food  crops  or 
gardening,  as  an  alternative  source  of  income  (Table  7).  About  8%  of  them  also  consider  daily  laborers  as 
alternative  income  sources.  However,  more  than  a  quarter  of  respondents  do  not  have  alternative  income 
sources. 

Figure 3. Crops grown during (LR) season. 

Figure 4. Nature of main crops grown. 

0.0

20.0

40.0

60.0

80.0 100.0

90.7

60.2

3.6
3.8
1.1

17.1

10.8

0.5

Maize

Beans

Green grams

Soy beans

Cow peas

Sunflowers

Cassava

Wheat

Tea

Vegetables

21.9

28.2

22.2
19.8

Rice

2.8

Irish potatoes

Sorghum

Sweet Potatoes

Millets

Fruits

8.1

3.9
1.8

Napier grass (Pasture)

34.1

16%

7%

20%

57%

Off season crop not typical for LR Se..

Perennial crop

Seasonal crop not typical for LR season

Seasonal crop typical for LR season

13 a total of 341 (33% of panel) farmers participating in both contracted and non-contracted maize value chains 
were randomly selected from the agriBORA database. During the survey, farmers selected multiple main crops 
grown on their fields which increased the number of farmers participating in the maize value chain drastically 
to almost the entire panel. 

17 

 
 
 
 
  
  
  
  
 
 
 
 
  
 
 
 
 
 
                                           
Figure 3 shows crops grown by percentages of all farmers. While sampling design identified farmers based on 
specific  value  chains,  multiple  crops  could  be  indicated  during  the  survey,  revealing  that  most  farmers  were 
growing maize in the 2020 long rains season.  

Table 7. Alternative income sources 

Agriculture (cash/crop/gardening) 
Livestock 
Remittance 
Unskilled wage labourer 
Handicrafts/artisanal work 
Skilled labourer (construction/electrician etc.) 
Selling of natural resources (charcoal/grass/firewood/wild food) 
Transport/motorcycle business (operating taxi/keke/tuk-tuk) 
Daily labourer (agriculture) 
Salaries/wage employees 
Petty trade/street vending (including stall/booths 
Begging 
Gift/Aid/Assistance 
Trade/Commerce 
No other income generating activity 

Source: Authors computation from July-August telephone survey 

N 
566 
56 
2 
60 
7 
13 
13 
8 
31 
18 
39 
3 
10 
26 
281 

% 
55.2 
5.5 
0.2 
5.8 
0.7 
1.3 
1.3 
0.8 
3.0 
1.8 
3.8 
0.3 
1.0 
2.5 
27.4 

18 

 
 
 
 
 
 
 
 
3.2 

 Impact of COVID-19 on Input and Output Markets  

The  COVID-19  pandemic  and  its  containment  measures  imposed  by  the  government  have  affected  both  the 
demand  and  supply  side  of  the  food  system  value  chains.  On  the  supply  side,  key  agro-dealer  informants 
underlined that containment measures disrupted the supply of critical farming inputs such as seeds, fertilisers 
and chemicals (herbicides, pesticides, fungicides, etc...), due to the restrictions imposed on the international and 
domestic trades. According to the Kenya Tea Development Agency (KTDA)  – a local agro-dealer and a leading 
management  agency  for  small  scale  tea  farmers  in  Kenya  –  the  company  was  forced  to  suspend  distributing 
fertilizer  to  smallholder  tea  farmers  (for  2020  cropping  seasons)  following  disruptions  in  the  import  chain 
because of the pandemic and its containment measures. Another fertilizer provider also emphasized there were 
logistics  related  issues  at  the  beginning  of  the  pandemic  (around  March  2020),  although  the  government 
streamlined agriculture as essential  services that helped them to provide  enough fertilizers to be distributed 
across the country as of early April 2020. However, many transporters, as well as aggregators, reported their 
preference to stay at home fearing for their security, as well as to avoid any fine or arrest by overzealous police. 
The dealers also underlined difficulties of supplying agricultural inputs due to ‘stay at home measures’ and ‘night 
curfews’ that disrupted business working hours, besides logistic related issues such as lack of transport services.  

On the demand side, the outbreak of the pandemic adversely affects farmers' demand for farm inputs as well as 
their main and secondary sources of income. About 76.8% (788 out of 1026 farmers) surveyed via phone reported 
an impact of COVID-19 on their main sources of income (crop farming) and about 81.1% (314 out of 387 farmers) 
on their secondary sources of income (Table 8). Absence of economic activities due to lockdowns, unable to sale 
crop  outputs  due  to  movement  restrictions  or  drastic  reduction  in  sales  due  to  sharp  drop  in  demand  for 
products,  are  frequently  mentioned  as  the  major  channels  through  which  COVID-19  and  its  containments 
measures had affected their primary and secondary sources of income (Table 8). Lack of access to farm inputs 
due to movement restrictions is the second most frequently cited way through which the pandemic has affected 
the primary income generating activities (crop farming).  

19 

 
 
 
 
 
 
 
 
Table 8. Main channels of COVID-19 pandemic and containment measures have affected primary and secondary 
sources of income (Multiple responses). 

No sales due to movement restrictions 
No access to farm inputs due to movement restrictions 
Drastic reduction in sales due to lack of demand 
Activity stopped or Business closed due to COVID-19 
Daily wage is much lower 
Increase in prices of inputs 
Impossible to find work force 
Reduced working hours due to curfew restrictions 
No worth to cultivate due to no hope to sale 
Reallocation due to COVID-19 
Other: specify 
Total sample farmers reporting the impacts 

Primary income 

N 
221 
210 
200 
199 
127 
94 
79 
24 
5 
8 
51 
788 

% 
28.0 
26.6 
25.4 
25.3 
16.1 
11.9 
10.0 
3.0 
0.6 
1.0 
6.5 
76.8¥ 

Secondary income 
N 
66 
11 
79 
101 
38 
13 
11 
29 
1 
7 
23 
314 

% 
21.0 
3.5 
25.2 
32.2 
12.1 
4.1 
3.5 
9.2 
0.3 
2.2 
7.3 
81.1* 

Source:  Authors  computation  from  July-August  telephone  survey;  ¥  and  *  represent  the  percentage  of 
respondents reporting impact of COVID-19 on their primary and secondary sources of income in the total sample 
of 1,026 and 387 farmers, respectively. 

The COVID-19 pandemic containment measures have also negatively affected contract farming (maize or tea), a 
potential better income source. While about 17.5% (180 farmers) had contract farming before COVID-19, about 
one third of them retained their contract, which might be attributed to the fact that certain contracts had long 
standing enforcement such as contracts for perennial crops or were already signed before the outbreak of the 
pandemic. By contrast, about 15% were forced to stop the contract, more than a quarter to decrease buying 
prices, about 9.4% to decrease quantity of supply under contract, and about 10% forced to cancel their contract 
and sign a new one (Table 9). Key informant aggregators highlighted the decrease in buying prices led them to 
terminate a few contracts, as it could have an adverse impact or be uneconomical for some farmers. During July-
August, they also noticed farmers operating in parallel markets or ‘side selling’ but had been challenging to react, 
as it led to a substantial increase in the cost of managing the contracts (field extension activities) following travel 
restrictions and increased transportation costs. 

Table 9. Comparison of contract farming before and after COVID-19 (Multiple responses)  

Contract stopped 
Decrease quantity under contract 
Decrease buying prices 
Increase buying price 
Increase quantity under contract 
Remain the same both quantity and prices 
Signed a new contract 
Total farmers reporting having contract farming before COVID-19 

N 
27 
17 
50 
12 
5 
58 
18 
180 

% 
15.0 
9.4 
27.8 
6.7 
2.8 
32.2 
10.0 
17.5* 

Source: Authors computation from July-August telephone survey; * represent the percentage of respondents 
reported that had contract farming before the outbreak of COVID-19 in a total sample of 1,026 farmers. 

Furthermore, about 87% (891 farmers) interviewed via phone reported several constraints during planting and 
growing stages of the season (March-June 2020), that were directly or indirectly linked to supply disruptions or 
income losses due to government restrictions to combat spread of the virus, such as ‘night curfews between 7pm 
and 5am’ and ‘limiting number of passengers in public transport to a maximum of 60% of vehicles capacities’ 
that limit a number of economic activities. Farmers are constrained from lack of agricultural inputs and tools 
(seeds, fertiliser, pesticides, herbicides, fungicides, etc…) (29.4%), lack of capital and finance (23%) and high cost 
of agricultural inputs (15%) (Table 10). They also reported pests (fall armyworm) and diseases (32.2%) affecting 
maize production due to lack of chemicals. During the period, less than 2.5% of them reported the impact of 
desert locust and lack of workforce in the field, and only about 0.6% (5 farmers) expected lack of market for their 
products.  Notwithstanding,  during  July-August  2020  (harvesting  period),  about  20%  (198  farmers)  reported 
difficulties  of  selling  their  products  that  directly  linked  to  COVID-19  containment  measures  such  as  lack  of 

20 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
demand in the markets (no buyers) (47%), absence of transport services or poor yield quality (11% each) and lack 
of brokers/middleman or closed markets (9% each) (Table 12). Furthermore, during this period about 72% of 
sample farmers are yet to harvest their crops and will expect to harvest soon14(Table 11), but about 13.5% (101 
farmers) will expect challenges of selling outputs (harvested crops) due to poor quality of the products (41%), 
resulting from insufficient treatment during growing seasons; and lack of demand in the markets (21%) as a result 
of movement  restrictions, as  well as low quantity of the products (18%) (Table 12). Furthermore, about  60% 
reported constraints of harvesting at least some of their crops (Table 11).  

Table 10. Main constraints during March-June (Planting period) (Multiple responses) 
N 
236 
302 
153 
90 
10 
3 
30 
330 
6 
2 
82 
1 
183 
891 

Lack of capital and finance 
Lack of Agricultural inputs and tools (seeds/seedlings, fertilizer, pesticide etc.) 
High cost of agricultural inputs 
Lack of workforce at field preparation, planting or weeding stage 
Expected lack of market for produce sales 
No contract anymore (contract farming) 
Desert Locust 
Other Pest and Diseases 
Movement/Travel restrictions (COVID-19 related) 
Could not lease land because of travel restriction 
No rains 
Harvested enough last month 
Others 
Total farmers reporting mains constraints  

% 
23.0 
29.4 
14.9 
8.8 
1.0 
0.3 
2.9 
32.2 
0.6 
0.2 
8.0 
0.1 
17.8 
86.8* 

Source: Authors computation from July-August telephone survey; * represent the percentage of respondents 
reporting main constraints during July-August survey in a total sample of 1,026 farmers. 

Table 11. Respondents reporting crops yet to be harvested and will expect to be harvested 

Do you have crops still to 
harvest? 
N 
283 
743 
1026 

% 
27.58 
72.42 
100 

If  Yes,  do  you  plant  to 
harvest soon? 
N 
67 
676 
743 

% 
9.02 
90.98 
100 

If,  yes,  do  you  have  any 
constraints to harvest? 
N 
298 
445 
743 

% 
40.11 
59.89 
100 

No 
Yes 
Total 

Source: Authors computation from July-August telephone survey 

Table  12.  Farmers  experiencing  challenges  for  selling  harvested  crops  (July-August  2020)  and  will  expect 
challenges 

No buyers 
Closed shops 
Lack of brokers 
Lack of transport 
Poor yield quality  
Low quantity produced 
Lack of aggregators 
Other specify 
Total 

Constrained for selling crop 
harvested (July-August 2020) 
% 
N 
47 
93 
9.6 
19 
9.1 
18 
11.6 
23 
11.6 
23 
- 
- 
1.5 
3 
9.6 
19 
19.3¥ 
198 

Expecting challenges of selling 
harvested crops (yet harvested) 
% 
N 
20.8 
21 
3.0 
3 
8.9 
9 
5.9 
6 
40.6 
41 
18.8 
19 
1.0 
1 
1.0 
1 
13.6* 
101 

14 About 70% rated the current conditions of their crops yet to be harvested as either “less than average” (40%) 
and  “on  average”  (32%)  largely  due  to  either  above  average  rainfall  conditions  (25%)  or  flooding  (22%).  The 
impact of desert  locust, pest, inability to access due to COVID-19, lack of work  forces at  field operations are 
reported by less than 5% of farmers yet to harvest their crops (Annex A2; Annex A3) 

21 

 
 
 
 
 
  
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
                                           
Source:  Authors  computation  from  July-August  telephone  survey;  ¥  and  *  represent  the  percentage  of 
respondents  experienced  challenges  of  selling  harvested  crops  and  will  expect  challenges  of  selling  their 
harvested crops in a total sample of 1,026 and 743 farmers, respectively. 

Farmers  were  also  requested  to  compare  the  constraints  they  experienced  during  Mach-June  2020  and  July-
August 2020. Most of farmers (748 farmers or 73%) had experienced difficulties in accessing inputs such fertilizer 
(31%), fertilizer and seeds (27%) and fertilizer and pesticides (19.3%) during March-June 2020, at the early stage 
of restrictions to contains the spread of the virus (Table 13). During July-August 2020, only about half of sample 
farmers (533 farmers) had planted short rain crops due to lack of fertilizer (30%), fertilizer and seeds (28.3%) and 
seeds (20%). However, about 20% of them did not need such inputs during the short rain season, as compared 
with only 5% reporting the same reason during the long rain season (Annex A1).  

Table 13. Inputs with constrained access to farmers, March-June 2020 and July-August 2020 

Fertiliser 
Chemicals such as insecticide, herbicide, fungicide etc. 
fertilizer and chemicals 
Fertilizers and seeds 
Seeds for planting 
Land preparation or farming machine 
Others 
Total famers reporting input constraints 

March-June 2020 
N 
232 
47 
144 
200 
106 
10 
9 
748 

% 
31.0 
6.3 
19.3 
26.7 
14.2 
1.3 
1.2 
72.9* 

July-August 2020 

  N 

155 
24 
72 
145 
104 
8 
4 
512 

% 
30.3 
4.7 
14.1 
28.3 
20.3 
1.6 
0.8 
49.9* 

Source: Authors computation from July-August telephone survey; * represent the percentage of respondents 
reporting main constraints during March-June 2020 and July-August in a total sample of 1,026 farmers. 

Furthermore,  Agro-dealers  underlined,  farmers  spending  on  agricultural  inputs,  between  March  and  August 
2020, was on average about 50% higher than their spending in the same period in 2019, resulting from supply 
disruption caused by outbreaks of the pandemics and its containment measures imposed by the government. 
More than half of sample farmers (about 58%) also perceived farm input prices had increased after the outbreak 
of COVID-19 pandemic and its containment measures (Table 14). However, about 42% of perceived farm input 
prices remain the same as before or even decreased by less than 20%.  Agro-dealers had also confirmed an overall 
decrease in footfall (visits), resulting from lower sales of farm inputs to farmers, as compared with the same 
period in 2019.  Moreover, they also stated the challenges to condense their regular operations into fewer hours 
following the government 7pm-5am nationwide curfew in place since early August 2020. 

Table 14. Changes in inputs prices level after evolution of COVID-19 

Increase (indication: increase 5 to 20) 
Largely increase (indication: increase > 20%) 
Lower (indication: decrease 5 to 20) 
Much lower (indication: decrease >20%) 
Remain similar 
Total 

N 
510 
78 
48 
4 
386 
1026 

% 
49.71 
7.6 
4.68 
0.39 
37.62 
100.0 

Most  farmers  (about  79%)  usually  plant  during  the  short-rain  (SR)  season,  between  October  and  December. 
While about 61% of them had already planted at the time of interviews, about 78% had claimed lack of access to 
inputs such as seeds (39%) and fertilizers (40%), as inputs providers and distributors had challenges of restocking 
shops and the transport costs remained high following government restrictions. The fact that the rainfall in the 
months of August and September 2020 was also above average in many parts of the country could have been 
assumed to be an early onset of the SR season and led to many farmers choosing to plant earlier than usual. 
Others planted earlier to capitalize on the earlier rainfall availability as they expect less rainfall in the next season. 
According  to  the  Kenya  Meteorological  Department  (KMD),  the  short  rain  season  between  October  and 
December 2020 is expected to experience lower rains than usual and this is likely to affect yields and overall food 
security plans of smallholder farmers.  

22 

 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
Although the outbreaks of the pandemic and its containment measures affect major income sources of most 
smallholder farmers, appropriate measures to mitigate the shocks are not yet in place. For instance, farmers' 
access to farm loans and/or subsidy incentives are limited (less than 11% of them could access these services) 
(Table  15).  During  field  visits,  enumerators  recognized  a  substantial  number  of  farmers  requested  for  cash 
support in the form of loans or subsidies to afford buying essential inputs for August- September 2020 short rain 
(SR) planting season. During this period, about 19% (198 farmers) reported absence of essential farm inputs such 
as fertilizer (67%) and pesticides (14%) in their stocks (Figure 5). Most agro-dealers emphasized farmers must be 
supported to regain their financial standing (ability to invest in their farms) prior to even their own access to 
credit for stocking inputs in  shops. Moreover, they want  to assure demand for inputs will be in place before 
considering fresh stocking in shops.  

Table 15. Farmers access to farm loans and subsidy incentives (July-August 2020) 
Farm loans 
N 
914 
112 
1026 

Subsidy incentives 
N 
910 
116 
1026 

% 
88.69 
11.31 
100 

No 
Yes 
Total 

% 
89.08 
10.92 
100 

Source: Authors computation from July-August telephone survey 

Figure 5. Farmer’s input stocks in July-August 2020 

Furthermore,  although  the  LR  season  is  the  normal  time  for  input  companies  and  agro-dealers  to  undertake 
promotion and extension services to farmers through programs such as seed fairs, weekly village markets, and 
field days, the ban on congregation of people has hindered farmers to access farming information and thereby 
limited  the  pathways  through  which  some  inputs  companies  and  agro-dealers  advertising  their  products 
available  in  stocks.  While  some  agro-dealers  doubled  up  provision  of  the  services  through  field  inspection, 
extension  services  on  pest  and  diseases  and  post-harvest  handling  have  been  challenging  to  offer  farmers 
training and advice on their farms. 

The impact of the pandemic and its containment measures on the demand side were also felt by Agro-processors. 
According to key informants of agro-processor, lack of sufficient raw materials to meet that current demand led 
them  to  proactively  manage  their  inventory  and  stocking  up  only  whenever  they  could.  A  few  large  agro-
processors reported importing sufficient raw materials before the outbreak of the pandemic that allowed them 
to overcome major supply chain disruptions at least until March 2020. However, the COVID-19 regulations and 
cross border movement restriction results in delaying access to import raw materials and also increasing the cost 
of transport services, which in turn results in rapidly increasing, as well as fluctuating, prices of raw materials and 
commodities between March and August 2020.  

Aggregators  and  agro-processors  were  constrained  not  only  to  access  importing  raw  materials  but  also  from 
most farmers’ decision to withhold their production for their own consumption (Figure 6). During March-August 
2020, while 56% (576 farmers) were reporting usually selling their products, only about 40% of them were selling 
out  more  than  75%  of  the  total  products  in  the  markets,  either  to  aggregators  agro-processors,  retailers  or 

23 

 
 
 
 
 
 
 
  
  
  
  
  
 
 
 
 
 
 
directly to consumers. Most farmers' decision to withhold production is partly linked to the low margins for sales 
as confirmed by key informants, because of low market prices and high cost of transport services.  Moreover, 
fears of food insecurity due to poor yields and giving a maximum care for own consumption needs are the main 
drivers for holding their product for those who yet to sell their crops output but still to harvest (72%) at the time 
of telephone interviews, during July-August 2020. This partly implies the impacts of multiple stress imposed on 
food supply and security across the country. While the ministry of agriculture has assured the counties that the 
government has put in place all the necessary mechanisms to avert potential food shortages, farmers could be 
more  concerned  on  the  widespread  availability  of  products  in  the  market  cannot  be  taken  for  granted  as  it 
remains unclear when the country would return to normal activities. 

Figure 6. Proportion of Harvest Usually Sold 

24 

 
 
 
 
  
 
 
 
 
3.3 

Impact of Floods and Desert Locusts 

According to the Kenya Meteorological Department (KMD), March to May (MAM) rainfall period was one of the 
wettest  the  region  has  experienced  since  1981,  following  an  already  record  wet  2019  October  to  December 
(OND) rainfall period. The early onset of rains and above-average rainfall since February 2020 promoted land 
preparation and planting activities for the MAM period across the country. However, the abundant rains have 
also  caused  localized  flooding,  mudslides,  flash  floods,  and  river  overflows  over  the  past  months  causing 
casualties,  displacement,  infrastructure  damage,  and  crop  damage  in  parts  of  Kenya15.    The  geographic 
distribution of floods was reported mainly in Western Kenya and the Lake Victoria basin region in the counties 
of Homa Bay, Busia, Siaya and Kericho (Figure 7). Desert locusts were reported mainly in Eastern Kenya in the 
Arid and Semi-Arid Land (ASAL) counties of Meru, Embu and Nyeri.   

Figure 7. Counties Affected by Floods and Desert Locusts 

About half of farmers were drawn from wards experiencing flooding during LR season and about 44% of them 
experienced floods on their field (Table 16). Moreover, among farmers who experienced floods, about 78% own 
livestock, as part of their livelihoods.  The farmers were requested to rank their perceptions of floods effects on 
their  farmland,  crops  or  yields  and  livestock  as  very  little  or  none  (<  10%),  small  percentage  (10%  -  25%), 
moderately  affected  (25%  -  50%),  majority  affected  (50%-75%)  and  mostly  affected  (75%  -100%).  While  the 
impact  of  floods  on  their  livestock  are  perceived  as  very  limited  or  almost  none  for  more  than  64%  of 
respondents, its impacts on farmland and yields were ranked as moderately affected and/or majority affected, 
as  reported  by  about  60%  of  the  respondents.    More  importantly,  about  20%  and  25%  of  the  respondents 
perceived as mostly affected damage on their fields and yields, respectively (Table 17).  

Although heavy rainfall could create favorable conditions for further spread of desert locusts as it could provide 
suitable breeding conditions and promoted vegetation growth for feeding, only about 16% of the respondents 
experienced desert locusts (defined as “mostly flying” to distinguish them from hoppers) once or twice in their 
fields, during LR season (Table 16; Annex A4; Annex A5). Notwithstanding, between February and March 2020, 
agro-processors were worried about desert locust invasion as menace was spreading through the Horn of Africa. 
The peak infestation occurred in March (Figure 8) at a time when most farmers had already harvested for short 
rain season and yet to plant for the long rain season. The greatest fear was passed in December 2019, when fresh 
vegetation suitable for the spawn would help them to generate and wipe out the next harvest as of early in July 

15  “Eastern  Africa  Region:  Floods  and  Locust  Outbreak  Snapshot  (May  2020).”  Reliefweb,  May  11,  2020. 
https://reliefweb.int/report/ethiopia/eastern-africa-region-floods-and-locust-outbreak-snapshot-may-2020. 

25 

 
 
 
 
 
 
 
 
 
                                           
2020. However, there was a sigh of relief as the destruction to crops did not happen as it had been expected in 
worst case scenarios and the Kenyan government long rains assessment also confirmed that desert locusts have 
finally not had a significant negative impact on the long rains crop production16. FAO’s Desert Locusts warning 
team however warned that there will be a threat of possible re-infestation towards the end of the year.  

Table 16. Flooding and desert locust experienced during July-August 2020. 

Do you live in a ward 
experiencing 
flooding? 
N 
515 
511 
1026 

% 
50.19 
49.81 
100 

Do  you  experience 
flooding? 
N 
576 
450 
1026 

% 
56.14 
43.86 
100 

If 
experienced 
flooding,  do  you 
have Livestock? 
N 
100 
350 
450 

% 
22.22 
77.78 
100 

you 

Do 
experience 
desert locust  
% 
N 
84.11 
863 
15.89 
163 
100 
1026 

No 
Yes 
Total 

Source: Authors computation from July-August telephone survey. 

Table 17. If experienced flooding, extent of its impacts on: 

Very little to none <10% 
A small percentage (10-25%) 
A moderately affected (25-50%) 
Majority affected (50-75%) 
Mostly affected (75-100%) 
Total farmers experiencing flooding 

Farms land 
N 
25 
57 
138 
144 
86 
450 

% 
5.56 
12.67 
30.67 
32.00 
19.11 
43.86* 

  Crops/yields 
  N 
28 
41 
117 
153 
111 
450 

% 
6.22 
9.11 
26.00 
34.00 
24.67 
43.86* 

Livestock 
N 
224 
44 
41 
31 
10 
350 

% 
64.00 
12.57 
11.71 
8.86 
2.86 
77.78¥ 

Source: Authors computation from July-August telephone survey; * and ¥ represent the percentage of farmers 
experiencing flooding in a total sample of 1,026 and 450 who experienced flooding, respectively. 

Figure 8. Locust Presence peak months 

16 THE 2020 LONG RAINS SEASON ASSESSMENT REPORT, Collaborative report of the Kenya Food Security 
Steering Group (KFSSG): Ministries of Devolution and ASALs; Agriculture, Livestock and Fisheries; Water and 
Irrigation; Health; and Education, Science and Technology; Regional Pastoral Livelihoods Resilience Project 
(RPLRP), National Drought Management Authority (NDMA), WFP, FEWS NET, UNICEF, FAO, World Vision, ACF, 
and Arid and Semi-Arid Lands (ASAL) County Steering Groups (CSGs): with financial support from the 
Government of Kenya (NDMA), WFP, UNICEF and partners. August 2020 

26 

 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
 
 
      
Among farmers who faced desert locusts, about 43% (70 out of 163 farmers) applied farm treatments such as 
making noise (44%) and bio pesticides (34%) (Table 19) before or during locust infestations (Annex A6). Most of 
them (92%) underlined the treatments were either fully effective (43%) or effective to some extent (46%) (Annex 
A7).  

Table 18. Stages at which the desert locust had affected crops 

Flowering (the plant is fully grown)  
Maturity (flowers dried and developing) 
Vegetative (the plant is growing stalling) 
small plant growing its first pairs  
Harvest 
Total 

N 
38 
21 
45 
30 
3 
134 

% 
26.76 
14.79 
31.69 
21.13 
2.11 
82.2* 

Source:  Authors  computation  from  July-August  telephone  survey;  *  represents  the  percentage  of  farmers 
reporting the stage at which their crops experienced desert locust in a total of 163 farmers who reported desert 
locust infestation. 

Table 19. Type of treatment applied to control desert locust 

Bio-pesticides 
Noise 
Smoke 
Traditional pesticides 
Total 

N 
24 
31 
9 
6 
70 
Source: Authors computation from July-August telephone survey; * represents the percentage of farmers who 
applied treatment to control the locust in a total of 163 farmers who reported desert locust infestation.  

% 
34.29 
44.29 
12.86 
8.57 
42.94* 

Most farmers (about 60%) with desert locust damage perceived the impact on their farmland, crops or yields in 
the range between a moderate percentage (25%-50%) to large (75%-100%) (Table 20) and about 46% of them 
reported crop losses between KES 10,000  -  KES 30,000 (Table 21).  About  34% (55 out of 163 farmers)  were 
forced  to  replant,  on  average  around  25%-50%  of  their  affected  fields  in  a  bid  to  save  their  crop  (Table  22). 
Furthermore,  about  40%  of  the  respondents  had  pastureland17  and  less  than  10%  (33  out  of  412  farmers) 
reported the effects of desert  locust  on their pastureland (Table 23) but  21 farmers  were  forced to increase 
expenditure on animal feed (Annex A10). Another unexpected feedback received by many maize farmers is that 
the  perceived  impact  of  fall  armyworm  was  greater  than  the  impact  of  the  desert  locusts’  invasion.  It  was 
observed  that  some  farmers  were  referring  to  desert  locusts  during  phone  surveys  while  a  more  in-depth 
unstructured discussion with the enumerators in the field revealed that in various cases the actual pest was fall 
armyworm. The fact that fall armyworm is better known in Western Kenya than desert locusts confirms once 
again that desert locusts are not perceived as a major problem in the most productive agricultural areas. 

Table 20. Extent of farmland, crops and yields affected by desert locust 

Do not know 
very little to none <10% 
A small percentage (10-25%) 
A moderate percentage (25-50%) 
Majority of them (50-75%) 
Most to all them (75-100%) 

Farmland 
N 
13 
17 
20 
35 
37 
41 

% 
7.98 
10.43 
12.27 
21.47 
22.70 
25.15 

Crops 
N 
 - 
30 
18 
38 
42 
35 

% 
-  
18.4 
11.04 
23.31 
25.77 
21.47 

Yields 

  N 
-  
28 
18 
44 
35 
38 

% 
- 
17.18 
11.04 
26.99 
21.47 
23.31 

17 Most households perceived their pastureland as on average or less than average (Annex A8) due to above 
average and average rainfall (Annex A9). 

27 

 
 
 
 
 
  
 
 
  
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
Total 

163 

100 

163 

100 

163 

100 

Source: Authors computation from July-August telephone survey. 

Table 21. Estimated crop loss due to desert locust 

Can't /Don't want to say 
KES 10,000 - KES 30,000 
 KES 30,000 - KES 70,000 
>KES 70,000 
Total 

N 
33 
75 
32 
23 
140 

% 
20.25 
46.01 
19.63 
14.11 
100 

Source: Authors computation from July-August telephone survey.  

Table 22. Size of farm replanted due to desert locust infestation 

<25% of farm 
between 25% - 50% of farm  
between 50% - 75% of farm 
100% of farm 
Total 

N 
8 
22 
9 
16 
55 

% 
14.55 
40.00 
16.36 
29.09 
33.74* 

Source:  Authors  computation  from  July-August  telephone  survey;  *  represents  the  percentage  of  farmers 
replanted crops due to desert locust in a total of 163 farmers who reported desert locust infestations.  

Table 23. Extent of desert locust impacts on pastureland 

NA 
Very little to none <10% 
A small percentage (10-25%) 
A moderate percentage (25-50%) 
Majority of land (50-75%) 
Most to all pastureland (75-100%) 
Total farmers owned farmland 

N 
379 
1 
5 
10 
10 
7 
412 

% 
91.99 
0.24 
1.21 
2.43 
2.43 
1.70 
40.15* 

Source: Authors computation from July-August telephone survey; * represents the percentage of farmers owned 
pastureland in a total sample of 1,026 farmers.  

28 

 
 
 
 
 
 
 
 
 
  
  
 
 
3.4 

Impacts of COVID-19 on Food Security and Diversity of the Respondents. 

Food security is the main concern for most respondents. About 38% of the respondents perceived their status as 
food insecure households and do not have food in stock for consumption. The remaining  62% (635 farmers) have 
reported food remains in stock for family's consumption. Only about 28% of them have stocks that can last for 
more than three months, half of them have stock for two to three months and the remaining  a quarter of them 
have stock for less than a month (Table 3). Most sample households including those with an existing food stock      
(58%) earn less than KES 50,000 per annum (Annex A12).  

Households report reducing both the quantity and diversity of their food consumption to retain their stock for 
future consumption, as they: don’t  have enough money to restock their  food demand or they lost  their jobs 
(52%), their production are destroyed because of locust or pest (18%), food prices are too high (15%) and food 
is not available in the market (7.4%) and they could not able to access the markets (5.5%) (Table 24). During the 
June to August 2020 period, with the harvest, the food consumption resumed to its usual pattern for a bit less 
than 60% of the farmers while 40% of them still have a reduced quantity and quality of their diet compared to 
usual.  About 62% of these households consume grains, white roots, and tubers, about 36% of them consume 
meat, poultry, and fish and about 25% pulses (beans, peas and lentils) (Table 25). About a third of them were 
also forced to sell their property to purchase food for consumption and the number of households able to get 
assistance are limited to 15% of these households (Annex A11).  

Furthermore,  food insecurity is the major  future concern  for about  a  quarter of respondents in August 2020 
complemented with inability to get inputs for the next season. The same holds as concern over the next three 
months (September to December 2020) with disruption of livelihoods being another major concern highlighted 
by respondents. The low level of confidence amongst farmers may be one of the reasons, as they entered to a 
new planting season and lack of access to affordable inputs may be one of the top challenges anticipated by 
these farmers. The challenges associated with low revenue from the sale of their produce as well as the difficulty 
in accessing output markets during the Long rains (LR) season 2020, as compared to the previous LR season in 
2019, indicate wavering confidence in farmers’ future earning potential. 

Table 24. Reasons for reducing food quantity and diversity (N=635) 

NA 
Unable to access the market (no access, markets) 
No food available at the markets 
No money to buy food/ lost job or sources 
Own production destroyed (locust and pest) 
Too expensive/ price too high 
Total farmers reducing food quantity and diversity 

Source: Authors computation from July-August telephone survey 

N 
9 
35 
47 
333 
116 
95 
635 

% 
1.42 
5.51 
7.40 
52.44 
18.27 
14.96 
100 

29 

 
 
 
 
 
 
Table 25. If reduced quantity and diversity, changes in consumption July-August 2020 (Multiple responses) 

Grains, white roots and tubers, and plantains 
Pulses (beans, peas and lentils) 
Nuts and seeds 
Dairy 
Meat, poultry and fish 
Eggs 
Dark Green leafy vegetables 
Other vitamin-A reach fruits and vegetables 
Other vegetables 
Fruits 
Others 

Source: Authors computation from July-August telephone survey 

N 
414 
170 
14 
73 
241 
69 
30 
19 
21 
39 
28 

% 
61.5 
25.3 
2.1 
10.8 
35.8 
10.3 
4.5 
2.8 
3.1 
5.8 
4.2 

3.5 

 Satellite Imagery Analysis  

Remote sensing analysis has been used in the effort to map desert locust impacts on crops and rangelands in 
East  Africa  in  2020,  including,  for  example,  the  Kenya  Red  Cross18  and  the  Chinese  Academy  of  Science19. 
According to these reports about 350,000 ha of land have been lost due to desert locust in Meru country alone 
and  more  than  1  Mio.  ha  of  cropland  in  the  Central  Kenya  province.  The  reports,  however,  acknowledged 
limitations  in  ground  data  retrieval  due  to  COVID-19  driven  movement  restrictions.  Moreover,  based  on  JRC 
internal analysis, using mainly Sentinel 2 NDVI and Sentinel 1 data in 2020, we have not been able to associate 
any anomalies of the satellite indicators uniquely with desert locust damages. This survey is therefore perceived 
as an opportunity for additional field evidence for collection of desert locust damage at least for the counties 
with reported observed large swarms such as Meru, Embu and Nyeri. 

The  remote  sensing  (RS)  analysis  carried  out  for  this  study  specifically  aimed  at  exploring  the  possibility  of 
disentangling  desert  locust  damages  to  crops  from  natural  crop  senescence  and  harvesting  using  Sentinel-2 
imagery. Field visits were conducted to gather objective evidences about desert locust damage to support the 
analysis. The field visits were organised for those farmers  that acknowledged the presence of visible signs of 
locust damage during the phone interview. The visits allow us to get a precise geolocation of the affected fields 
(n=39), when possible together with the geolocation of a control farm (i.e. a nearby farm that was not affected 
by locusts; n=9); information about the damage severity and replanting activities; information about the extent 
of the crop damage in four cardinal directions from the GPS measurement; landscape, transect and close-up 
photographs of the damaged area.  

The first wave of the survey was deployed in August 2020, when crops had either already been harvested or were 
in  senescence  and  thus  did  not  meet  optimal  conditions  for  collecting  damage  evidence.  The  inspection  of 
photographs collected in August revealed the difficulties of identifying damage on plants that were reported to 
have been affected by locusts mostly from March to May. Nevertheless, it is useful to describe here the results 
of the RS analysis for a few selected farms to exemplify the challenges related to this analysis. 

18 Impact of Desert Locust Invasion in Kenya, Kenya Red Cross, June 2020. 

19  Report  of  Monitoring  and  Assessment  of  Desert  Locust  in  Africa  and  Asia  2020  (No.  1-12).  Aerospace 
Information Research Institute, Chinese Academy of Sciences 

30 

 
 
 
 
  
 
 
 
 
 
 
 
                                           
 
Sentinel-2  multispectral  data  at  10  m  spatial  resolution  is  used  to  provide  first  mapping  insights  through 
comparative  analysis  of  before-  vs.  after-infestation.  As  dense  infestations  of  locusts  are  expected  to  reduce 
standing  green  biomass,  the  analysis  focuses  on  Normalised  Difference  Vegetation  Index  (NDVI),  a  spectral 
vegetation index exploiting the differential absorption of green vegetation in the spectral band of the red (light 
strongly absorbed by the chlorophyll present in the green leaves) and near-infrared (light reflected by healthy 
green leaf). NDVI ranges roughly from 0 (bare soil) to 1 (dense healthy vegetation fully covering the ground). 

NDVI is commonly employed as a proxy of green standing biomass in RS analysis. Nevertheless, a decrease in 
NDVI  (and  hence  in  green  biomass)  can  be  due  to  different  causes,  including  natural  or  drought-induced 
senescence of vegetation (i.e. dry-up and yellowing of leaf tissues), complete or partial harvest of the plants, and 
biomass reduction due to locusts and other pests. Therefore, it is obvious that a decrease in NDVI at a time when 
natural senescence or harvesting operations are taking place should not be regarded as a univocal sign of locust 
infestation. 

Disentangling the latter (locust damage) from the former causes (senescence and harvest) thus requires a full 
understanding of the conditions in which the reduction of NDVI occurs. In order to gain some further in insights 
and develop a possible operational protocol to detect locust damages with RS, the NDVI variation before and 
after the reported infestation were considered under two types of controls: a temporal and a spatial one. The 
temporal control refers to the same location (i.e. the farm reported to be affected) observed in the same period 
of the previous year. The temporal control is useful to understand the seasonal dynamics of vegetation when not 
affected by locusts. The spatial control refers to the control farm, a farm located nearby (maximum 5 km away) 
that was not affected by locusts. The control farm is identified by the affected farmer and then visited by our 
surveyor that registered precise geolocation and collected photographs. Similarly, to the temporal control, the 
NDVI temporal trajectory of the control farm in the current and previous year were analysed and qualitatively 
compared  with  the  affected  farm.  The  processing  is  made  on-the-fly  using  Google  Earth  Engine  and  level  2 
Sentinel-2 data.    

We  analysed  all  39  visited  farms  to  conclude  that  we  are  not  able  attribute  NDVI  reductions  to  DL  damage 
unambiguously.  We  reported  here  the  detailed  results  for  four  farms  as  an  example.  Such  four  farms  were 
selected among those having an available control farm (n = 9) and  with farmers reporting damage during the 
phone interview. 

Here, we present examples from four western counties  —Vihiga, Uasin Gishu, West  Pokot and Trans Nzoia— 
among the visited farmlands affected by desert locusts and visited during the field survey (Figure 9). Table 26 
presents peak of DL infestation dates, dates of the selected Sentinel-2 imagery, estimation of DL damages and 
extent of crop damage (damaged area radius). The damaged area radius was derived from information about the 
extent of the crop damage in four cardinal directions from the GPS measurement. The selection of a specific date 
is  driven  by  the  availability  of  cloud-free  imagery.  Data  about  infestation  severity  were  also  reported  (from 
surveyors during field visits and from farmers during phone interviews). Approximate damage to crops refers to 
the % of plants showing some visible sign of damage. 

31 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 9. Location of selected affected farmlands: Vihiga, Uasin Gishu, West Pokot and Trans Nzoia 

Table 26. Peak interstation dates and images dates, ’before- and after-infestation’’ comparative analysis for the 
four farms. 

Data  and imagery for Vihiga farm (farm 1) are presented here while the same information for the remaining 
farms (farms 2 to 4) are reported in Annex C. Figure 10 and Annex (C1, C4, C7) shows the Sentinel-2 images for 
each farm before (left column) and after (centre column) the reported peak infestation time in 2020 (bottom 
row) and before and after the same time of the previous year (i.e. 2019, top row). The NDVI difference (i.e., NDVI 
after - NDVI before) is shown for 2020 (right column, bottom) and for 2019 (right column, top). The InfraRed 
False Colors RGB composites (R: Near Infrared, G: Red, B: Green) are shown for before and after images. This 
band combination is useful to detect healthy vegetation, shown in red color. The NDVI difference is expressed 
using a blue-yellow-red legend from 0.25 to -0.25. The affected farm is depicted as a green circle in all imagery 
while the control farm as a blue circle only in the before-after imagery of the current year. 

32 

NorthEastSouthWest1Vihiga03/05/2001/04/2021/05/2012/04/1917/05/1921-30%21-30%61-70%11-20%2Uasin Gishu28/03/2012/03/2006/04/2013/03/1902/04/1911-20%11-20%11-20%11-20%3West Pokot18/02/2017/01/2021/02/2022/01/1926/02/1911-20%41-50%11-20%11-20%4Trans Nzoia29/02/2011/02/2022/03/2022/01/1913/03/1911-20%11-20%11-20%11-20%Phone survey40>5030>50Proportion of land affected 25-50%75-100%50-75%50-75%Peak infestation dateCountyFarm IDBefore, previous yearAfter, previous yearField visit observationsApproximate damage to crops Date of S2 imageryBeforeAfterDamaged area radius (meters) 
 
 
 
 
 
 
 
 
 
 
 
Figure 10. Satellite images of Vihiga farm (farm 1) 

Note: Satellite images of Farm 1 for the previous year (2019, top row) and the current year (2020, bottom row) 
before (left column) and after infestation (middle column). In the third column we show the NDVI difference for 
the previous (top) and the current year (below). The green circle is the farm affected and the blue circle is the 
control farm. 

Vegetation conditions for the first three farms are not compromised after the peak infestation date for 2020 as 
it can be observed in the satellite imagery of Figure 10, C1, and C4 (Farms 1, 2 and 3), where bright red color 
points to healthy vegetation. This can be also observed in the NDVI difference images for 2020, where the blue 
color indicates a higher NDVI value in the “after’’ image for the affected farms. 

Comparing the NDVI difference images for 2020 and 2019 in all farms, the evolution of vegetation condition has 
seen from before to after the infestation peak is similar between the two years for Farm 1 (Figure 10) and Farm 
2 (Figure C1). For Farm 3 (Figure C4), conditions seem slightly better in 2020, whereas for Farm 4 (Figure C7), 
conditions seem to have worsened significantly in 2020, as compared to 2019. For farm 4, however, a reduction 
of NDVI over the period was experienced in both years were observed, possibly due to senescence or harvesting 
operations. The stronger reduction observed for the whole area in 2020 can be due to the fact that the overall 
season of 2020 was more productive and ended later than the previous one, resulting in the larger decrease in 
the investigated period observed for 2020. 

Figure 11, C2, C5 and C8 show the temporal NDVI profiles from March 2020 to August 2020 for the affected- and 
control-farms before and after the peak infestation period. DL damage can be very scattered in space, thus an 
area  of the field may be affected while another one may be left  untouched. To focus on damaged areas, we 
extracted profiles for a circular area centred on the collected GPS coordinates of the field centre. To determine 
the radius of such circular, we used information about the extent of the crop damage in four cardinal directions 
from the GPS measurement. In particular, the radius was set to the minimum of such reported lengths to ensure 
the coverage of the damaged area. 

It is noted that NDVI temporal trajectories are not continuous lines because several observations are missing due 
to  severe  cloud  cover.  In  addition,  NDVI  drops  in  one  or  two  consecutive  observations  are  likely  due  to 
undetected partial cloud cover as only a simplified cloud masking algorithm and no temporal smoothing are used 
for analysis. The actual trajectory of NDVI typically  follows the upper envelope (i.e. the local maxima) of the 
observed data points. Additionally, in Figure 11, C3, C6 and C9 the affected-farm 2020 profile are compared with 
that of the previous year. All temporal NDVI profiles represent the average NDVI value over a circular area of x 
meters radius; x being the damaged area radius observed in the field (Table 26).  

33 

 
 
 
 
 
 
 
 
Figure 11. Temporal NDVI profile of Vihiga (Farm 1) 

Note. Temporal NDVI profile for Farm 1 (above) and for the corresponding control farm (below). A 40-m buffer 
around farm GPS location is used to extract profiles. 

Figure 12. Average temporal NDVI profile of farm I 

Note. Farm 1, average temporal NDVI profile (current and previous year) for a circular area with radius 40 m, i.e. 
the report extent of the damaged area. Doy stands for day of year. 

For all four examples farms, the NDVI profiles have similar patterns for the affected farm and the control farm. 
In other words, a larger reduction of NDVI in the affected farm was not observed, as compared to the control 
one after the reported infestation time. 

34 

 
 
 
 
 
 
 
Furthermore, focusing on the affected farm only, the 2020 NDVI trajectory is rather similar to that of 2019, and 
in many cases, we observed increased NDVI values in 2020 compared to 2019, attributed to the above-average 
rains this year that supported more vigorous crop growth.  

These examples show that the impact of locust on crop conditions in the affected farms is not evident from RS 
imagery. Table 27 and Figure 13 summaries show NDVI variation from before to after infestation time is very 
similar between affected and control farm for each of example presented here.  

Table 27. Variation of NDVI values between two periods. 

Note. NDVI values variation for two periods, one month before and one month after the peak infestation date for 
affected farms and control farms. 

Figure 13. Average NDVI over a  period of month before and after peak infestation date for the affected and 
control farms (data from Table 27) 

We can therefore conclude that desert locust damage is not detectable with NDVI for the analysed farms. The 
RS analysis is not able to confirm the severity of the farmer’s reported damages. It is likely that desert locusts 
had actually visited the farms as reported by the farmers but the damage to standing green biomass has been 
limited  and  possibly  compensated  by  the  more  vigorous  than  average  growth  of  vegetation  in  2020,  due  to 
abundant  rainfalls.  That  is,  the  damage  might  not  have  been  severe  enough  to  be  ascertained  with  satellite 
imagery and attributed unambiguously to desert locusts. Unfortunately, this analysis is not conclusive because 
the timing of our field visits (occurred in August, some months later than the reported infestation) did not allow 
us to collect clear traces of DL invasions (whether there were actual damages or not). However, the exercise 
clearly confirms the attribution challenges of remote sensing detected NDVI anomalies to desert locusts impacts 
that had already emerged during preliminary analysis and that leads us to call for careful evaluation of remote 
sensing based area impact assessments published by several sources in 2020 and which are not supported by 
extensive calibration and validation of the remote sensing methodology applied. 

35 

 Farm(ID)AvgMin, date Max, date AvgMin, date Max, date AvgMin, date Max, date AvgMin, date Max, date 10.480.340.630.580.540.640.590.450.780.540.340.6201/04/2001/05/2001/04/2001/05/2016/05/2021/05/2016/05/2006/05/2020.330.220.460.40.230.580.420.370.470.50.470.5517/03/2022/03/2027/03/2022/03/2011/04/2001/04/2006/04/2016/04/2030.530.430.590.490.410.560.530.480.580.50.430.5327/01/2011/02/2027/01/2011/02/2026/02/2021/02/2026/02/2022/03/2040.520.330.640.570.340.660.390.350.440.450.420.4926/02/2011/02/2026/02/2011/02/2012/03/2007/03/2007/03/2012/03/20NDVI affected farm NDVI control farmNDVI affected farm NDVI control farm1 month before infestation1 month after infestation 
 
 
 
 
 
 
The second wave of the survey focusing on the Short Rain (SR) agricultural season will be executed in November 
closer to the expected vegetation peak, thus potentially allowing us to identify the impact of dessert locusts on 
vegetation in a more timely manner. In addition to that, the study plan to deploy a field survey independent from 
the farmers survey and aiming to maximize the utility of the field activity for desert locusts impacts only. With 
this new exercise the study will adapt to the current status of locust outbreak, expected to have a stronger impact 
on ASAL counties of Kenya. In addition to monitoring ASAL areas (agricultural and pastoral areas), the study will 
adopt a different procedure for the identification of the sites to be visited in the field. Instead of the selection 
based on the random sampling phone survey, the study will quickly react to the report of agriBORA agents in the 
ASAL area to promptly sample sites where DL infestation has just occurred. This new protocol is expected to 
improve the quality of the ground information as compared to the first wave of field visits. 

36 

 
 
 
 
4  Conclusions 

The study examines the impact of COVID-19 and its containment measures, as well as floods and desert locusts, 
on the small holder’s food system using a telephone survey from 1,026 smallholder farmers in 19 counties in 
Kenya,  complemented  with  consultations  from  20  agro-dealers  and  15  aggregators  or  agro-processors.  The 
survey  is  planned  to  be  conducted  in  three  consecutive  cropping  seasons:  July-August  2020,  November  -
December  2020  and  July-August  2021.    This  report  is  based  on  the  first  phase  of  the  survey  and  has  shown 
impacts  of  COVID-19  on  smallholder  farmers  in  major  crop  growing  regions  in  Kenya  spanning  the  entire 
agricultural  sector.  The  associated  public  health  mitigation  measures  introduced  by  the  government  in  mid-
March 2020 to prevent further spread of the virus — particularly lockdowns, stay-at-home orders and the closing 
of  borders  and  public  spaces  such  as  markets  and  schools  —have  negatively  impacted  livelihoods  of  many 
smallholder  farmers,  agro-dealers,  aggregators  and  agro-processors.  The  containment  measures  affected 
farmers due to lack of access to farm inputs on time, in particular seeds and fertilizer, increased input costs and 
difficulties in accessing output markets for their harvest. While these challenges were most prominent during 
the  March-June  2020  period,  when  the  government  measures  had  the  maximum  effect,  they  also  persisted 
between July and August 2020, when farmers were harvesting and seeking markets for their crops. Decreasing 
household income due to job losses, reduced revenues from farming or other income generating activities as 
well  as  increase  in  price  of  food  and  low  yields  and/or  grain  quality  experienced  by  farmers,  contributed  to 
reductions  in  both  quantity  and  quality  of  food  consumed  by  households  during  the  pandemic  period.  The 
situation  improved  during  the  harvesting  period  as  from  July  2020.  However,  the  food  reserves  of  most 
households in August 2020 covered less than three months of consumption.  

The study benefits from direct contact with individual farmers and other value chain actors to obtain quantitative 
and qualitative insights. Each interview lasts for around 30 minutes to provide insights about farm activities and 
multiple challenges farmers faced during the pandemic period. Because of the multiple stressors addressed and 
to keep the overall size limited, the standard food security and nutritional indicators could not be fully included 
in the phone survey. The priority was put on collecting indicators on the agricultural activity of households and 
impact of COVID-19 and desert locusts. In addition, the survey did not include potential differential effects by 
gender.  Furthermore,  the  respondents  are  representatives  only  for  farmers  in  the  agriBORA  database 
representing selected value chains. However, the farmers network was spread across the main cropping regions 
of the country and the value chains captured during the survey are varied enough to give a good picture of the 
situation in the food production areas. Key informant interviews (i.e., with agro-dealers, aggregators and agro-
processors) were purely qualitative based on the selected interview partners and their experience and led to 
some valuable insights that helps to better understand challenges impacting farmers’ access to inputs. 

The  above  average  rainfall  during  the  long  rain  season  in  most  areas  of  the  country  led  to  good  growing 
conditions but also resulted in floods in some parts of the country. The floods had affected crop fields of many 
farmers resulting in yield loss or low quality of produced commodities. Minimal impact on livestock was reported 
due to the floods.  

While the Horn of Africa has suffered the worst desert locust invasion in decades, the survey has shown that the 
impact of the desert locusts on the investigated five crops in the sampled counties, which include Kenya’s most 
important production regions, was quite low. The findings confirm that the forecasted worst case scenario of 
desert locusts causing major crop losses during the long rain season, which had been of concern to the country 
in early 2020, has not materialized because the desert locust invasions did not reach the most productive areas. 
Also, in areas that have been affected by desert locusts it is assumed the positive effect of the exceptional rainfall 
on both crop and rangelands in March-May 2020 has to a large extent prevailed on the negative impact of desert 
locusts. The hypothesis is corroborated by the results of the remote sensing analysis which has not found clear 
signals of biomass decrease that can be attributed to desert locusts. The survey managed to collect GPS points 
and photographs of affected fields including maize, sorghum, pigeon peas, okra, green grams and Napier grass 
fields.  However,  no  clear  traces  of  locust  damages  were  found  at  the  time  of  survey,  as  most  fields  were  at 
maturity  stage  or  had  already  been  harvested.  In  addition,  we  were  not  able  to  confirm  the  severity  of  the 
infestation reported by a number of farmers with the remote sensing analysis. The survey also revealed  that 
apart from desert locusts, the fall armyworm pest was affecting many maize farmers in the country.   

The  results  so  far  have  provided  some  interesting  indications  which  form  a  good  basis  for  the  following  two 
phases of the study. Since the economic effects of COVID-19 are not limited in time to a single crop season, the 

37 

 
 
 
 
negative impact for example on access to farming inputs are expected to extend to the next seasons, leading to 
a progressive erosion of crop diversity and products quality. With the repetition of the survey during the next 
two crop seasons the study will focus on those protracted effects of the pandemic as well as looking more in 
detail at some specific aspects. The questionnaires will be further improved in order to include some more food 
security information and in order to improve the quality and timing of the desert locust information. The latter 
will of course also depend on the dynamics of further desert locusts breeding in the region. 

38 

 
 
 
 
5  Policy Recommendations 

●  The study has  shown major  negative impact of the  COVID-19 containment measures on farm inputs 
availability and access as well as farmers’ access to output sales markets. It is recommended that, given 
the  critical  role  of  small  scale  farming  for  food  security,  farming  input  supply  chains  are  as  much  as 
possible excluded from COVID-19 import and transport restrictions. Farming inputs supply chains should 
be  monitored  and  agro-dealers  supported  towards  keeping  stocks  of  fertilizers  and  seeds.  It  is 
recommended to improve coordination efforts to ensure timely availability of inputs, support farmers 
access  to  inputs  (e.g.  strengthening  of  E-voucher  systems),  support  to  farm  extension  services, 
transportation and value addition across the sector.  In the longer term, decreasing the dependency of 
agriculture on inputs by developing low inputs agricultural practices or local procurement of inputs  may 
help mitigate shocks on the input supply chain. 

●  The survey confirms that credit constraints and higher than usual prices limit the access of smallholder 
farmers to inputs. Scale up of access to finance programs to reach smallholder farmers are likely to help 
mitigate that constraint and potentially avoid the shortages of cash and income due to COVID during 
the long rain season 2020 results in further impact on the next season input use, production and farmers 
income. Access to social protection or social safety net programmes can also be recommended for poor 
households to protect against long-term effects of post COVID-19 containment measures in the effort 
to enhance their resilience to future shocks.  

●  Digital financial services to smallholders have the potential to provide faster delivery of credit either for 
agricultural  inputs  or  of  income  support  to  farmers.  If  well  designed,  improved  access  to  credit    of 
farmers through digital solutions could be further supported.  

●  The  shift  of  cultivation  to  maize  should  be  further  investigated.  The  2020  above  average  maize 
production might to some extent have been obtained at the expense of crop diversity and shifts from 
cash crop to staple crops. With the constantly worrying nutrition situation in Kenya this quantity versus 
quality aspect could be an important factor to look. Planting of vegetables and diversified food crops 
should  be  further  supported.  Furthermore,  some  farmers  may  be  planting  maize  with  no  qualified 
advice about which would be the best crop. Looking at ways of deriving and supplying such advice would 
help the decision. 

●  The 2020 crop production was overall not severely affected by the multiple stressors including COVID-
19, floods and desert locusts and the abundant rainfall led to a close to average production in the main 
agricultural  areas.  However,  in  the  case  of  low  or  irregular  rainfall  the  impact  of  COVID  on  food 
production could be much more severe than during the 2020 long rains. Also it can be expected that the 
continuation of the COVID-19 pandemic with its detrimental effects on the economy will have negative 
impacts  on  food  production  which  go  beyond  the  2020  long  rains  season.  Further  monitoring  is 
recommended. 

● 

For Desert Locust impact assessment we recommend more efficient ground data collection from the 
early stages of the invasion and strengthening of the DL early warning systems. Although locusts control 
interventions have been scaled up in Kenya and are perceived to be adequate, monitoring and control 
systems have partially become obsolete and understaffed in neighbouring countries over time during 
the  years  with  little  invasions.  Satellite  data  derived  information  has  not  yet  proven  to  make  a  big 
difference  in  locusts  swarms  monitoring  nor  in  impact  assessment  in  this  study,  but  this  is  to  some 
extent due to lack of timely high-quality ground information and should be further investigated for areas 
with major impact (e.g. in Somalia and Ethiopia).  

●  Based on interviews done  for this study the impression  was gained (but  not  confirmed by a  specific 
question) that transport restrictions led to an increase in the number of unserious middlemen in the 
sales chain resulting in lower-than-expected prices for farmers desperate to sell their crops in order to 
have cash for life in general but also to buy inputs. To reduce this kind of risk it is recommended to 
strengthen the warehouse receipt system, whereby farmers can store their harvests against receipts, 
which can then be used to obtain credits. This would avoid the need for panic sales at low prices.  

39 

 
 
 
 
 
●  Extension  services:  Kenya  operates  a  scheme  whereby  government  employees  travel  the  country, 
providing  advice  to  farmers  through  their  contacts  at  county  and  ward  level.  During  COVID-19,  this 
scheme, which relies on ease of physical movement, was severely disrupted. An obvious solution here 
is to strengthen e-extension services using digital technology. In general, it is recommended to support 
the capacity building of farmers on the use and benefits of digital agricultural tools such as e-extension, 
digital  marketplace  and  digital  transactions  to  ensure  correct  and  timely  flow  of  information  and 
services  amidst  containment  regulations.  This  can  include  supporting  sensitization  campaigns  for 
Kenyans on the integral role of good nutrition in the fight against the pandemic. 

●  The survey shows that a computer assisted phone survey of farmers can be organized  quickly and is 
flexible in terms of adapting the questionnaires to specific survey needs.  Such surveys are a valid tool 
for  COVID-19  impact  assessment  on  food  value  chains  and  can  be  repeated  easily  in  time  (e.g.  for 
different waves). The survey has been successful in reaching and engaging smallscale farmers quickly, 
though it is representative only for farmers already registered in the agriBORA database. Similar surveys 
could be scaled up by including also farmers from other networks and extending  to other geographic 
areas.  

40 

 
 
 
 
 
 
 
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economic recovery in Kenya, EUR 30296 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 
978-92-76-19424-8, 

doi:10.2760/767447,  JRC121284Precision  Agriculture  for  Development  (website),  Introducing  our  Updated 
COVID-19 Dashboard, https://precisionag.org/COVID-19-dashboard-2-0/#more-1181  

Roussi, A. 2020. Kenya farmers face uncertain future as COVID-19 cuts exports to EU. Financial Times, June 4, 
2020. (also available at https://www.ft.com/content/05284de8-c19f-46de-9fe7- 482689be364b). 

Reliefweb, May 11, 2020.  “Eastern Africa Region (2020). Floods and Locust  Outbreak Snapshot (May 2020).”. 
https://reliefweb.int/report/ethiopia/eastern-africa-region-floods-and-locust-outbreak-snapshot-may-2020 

Tillie, P., Boti, D., Zeze, P. et Gomez-Y-Paloma, S. 2020  Impacts du COVID-19 sur les ménages ruraux en Côte 
d’Ivoire. Draft. 

41 

 
 
 
 
Glossary 

Term 

Meaning 

Aggregator 

A person or organization which buys crops direct from the farmers. 

Agro-processor 

Agro-dealer 

An  organisation  which  buys,  directly  or  indirectly  crops  and  processes 
them before selling on to consumers. 

A person or organisation which sells equipment, seeds, fertiliser, pesticide 
and other goods to farmers. 

Contracted (crops) 

Crops grown under an agreement with a buyer.  

COVID-19 

Corona virus disease 2019 

Non-contracted (crops) 

Crops grown without any pre-existing agreements between a farmer and 
a buyer. 

NDVI 

Panel 

Normalised  Difference  Vegetation  Index  -  a  spectral  vegetation  index 
commonly  employed  as  a  proxy  of  green  standing  biomass  in  Remote 
Sensing analysis. 

A list of farmers who have been selected to be contacted for the survey 

Remote Sensing 

Images taken of the earth from altitude. In this document Remote Sensing 
refers to images taken from satellites. 

Smallholder farmers 

Farmers cultivating farms under 2ha 

Strata 

Value Chain 

A partition of a large data sample that can be defined as homogeneous.  

Used interchangeably (1) to  describe the different  crops  covered in the 
survey  and  (2)  to  describe  the  different  actors  in  the  agricultural  chain 
from production to consumption. 

42 

 
 
 
 
List of figures 

Figure 1. Counties of Agro-processors Interviewed 

Figure 2. Counties where selected crops are growing 

Figure 3. Crops grown during (LR) season. 

Figure 4. Nature of main crops grown. 

Figure 5. Farmer’s input stocks in July-August 2020 

Figure 6. Proportion of Harvest Usually Sold 

Figure 7. Counties Affected by Floods and Desert Locusts 

Figure 8. Locust Presence peak months 

12 

14 

17 

17 

23 

24 

25 

28 

Figure 9. Location of selected affected farmlands: Vihiga, Uasin Gishu, West Pokot and 
Trans Nzoia 

32 

Figure 10. Satellite images of Vihiga farm (farm 1) 

Figure 11. Temporal NDVI profile of Vihiga (Farm 1) 

Figure 12. Average temporal NDVI profile of farm I 

33 

34 

34 

Figure 13. Average NDVI over a period of month before and after peak infestation date 
for the affected and control farms (data from Table 27) 

35 

43 

 
 
 
 
 
 
List of tables 

Table 1. Summary of Key Points of the Survey 

Table 2. Numbers of aggregators and agri- processors Consulted 

Table 3.  Smallholder farmers basic characteristics (N=1026) 

Table 4. Respondents family size. 

Table 5. Primary and secondary Sources of income 

Table 6. Level of income generating from either primary or secondary sources per 
annum 

Table 7. Alternative income sources 

9 

11 

15 

16 

16 

16 

18 

Table 8. Main channels of COVID-19 pandemic and containment measures have affected 
20 
primary and secondary sources of income 

Table 9. Comparison of contract farming before and after COVID-19 (July-August 2020)

Table 10. Main constraints during March-June (Planting period) 

Table 11. Respondents reporting crops yet to be harvested and will expect to be 
harvested 

Table 12. Farmers experiencing challenges for selling harvested crops (July -August 
2020) and will expect challenges 

Table 13. Inputs Farmers had constrained during March-June 2020 and July-August 
2020 

Table 14. changes in inputs prices level after evolution of COVID-19 

Table 15. Farmers access to farm loans and subsidy incentives (July-August 2020) 

Table 16. Flooding and desert locust experienced during July-August 2020. 

Table 17. If experienced flooding, extent of its impacts on: 

Table 18. Stages at which the desert locust had affected crops 

Table 19. Type of treatment applied to control desert locust 

Table 20. Extent of farmland, crops and yields affected by desert locust 

Table 21. Estimated crop loss due to desert locust 

Table 22. Size of farm replanted due to desert locust infestation 

Table 23. Extent of desert locust impacts on pastureland 

Table 24. Reasons for reducing food quantity and diversity (N=635) 

20 

21 

21 

21 

22 

22 

23 

26 

26 

27 

27 

27 

28 

28 

28 

29 

Table 25. If reduced quantity and diversity, changes in consumption July-August 2020 
(Multiple responses) 

30 

Table 26. Peak interstation dates and images dates, ’before- and after-infestation’’ 
comparative analysis for the four farms 

Table 27. Variation of NDVI values between two periods 

32 

35 

44 

 
 
 
 
 
 
 
 
 
Annex A  

Annex A1. Access to inputs during March-June 2020 and July-August 2020 

March-June 2020 
N 

% 

Missing 
Don't need inputs during the season 
No 
Yes 
Total 

50 
228 
748 
1026 

4.87 
22.22 
72.9 
100 

Annex A2. Last conditions of your crops yet to harvest 

Don't Know 
Exceptionally high 
Exceptionally low 
Less than average 
On average 
More than average 
Total 

N 
10 
21 
66 
302 
236 
108 
743 

% 
6.14 
19.30 
22.61 
51.95 
100.0 

July-August 2020 

  N 
63 
198 
232 
533 
1026 

% 
1.35 
2.83 
8.88 
40.65 
31.76 
14.54 
100 

Annex A3. What determines the current condition of your crops yet harvested? 

Crop Disease 
Desert Locusts 
Other pests 
Below average rainfall 
Average rainfall 
Above average rainfall 
Flooding 
Inability to access farm due to COVID-19 
Lack of inputs due to low availability 
Lack of working force at field preparation 
Other (Specify) 
Total 

N 
32 
29 
30 
30 
110 
187 
163 
4 
84 
5 
69 
674 

% 
4.31 
3.9 
4.04 
4.04 
14.8 
25.17 
21.94 
0.54 
11.31 
0.67 
9.29 
100 

Annex A4. Nature of desert locust 

Crawling only 
Flying 
Total 

N 
53 
110 
163 

% 
32.73 
67.27 
100 

Annex A5. Number of times farmers faced desert locust 

Once 
Twice 
Thrice 
>3 
Total 

% 
37.42 
38.04 
17.18 
7.36 
100 

N 
61 
62 
28 
12 
163 

45 

 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
  
 
  
  
  
  
  
Annex A6. Desert locust treatment period 

Before 
During 
Total 

Period 1  
N 
12 
58 
70 

% 
17.14 
82.86 
100 

Period 2 
N 
61 
9 
70 

% 
87.14 
12.86 
100 

Note: Period 1 refers to when the locust appeared for the first time and period 2 when happened for the second 
time etc. 

Annex A7. Treatment effectiveness for desert locust infestation on the farm 

% 
1.43 
8.57 
45.71 
1.43 
42.86 
100 

% 
0.24 
6.31 
2.67 
14.81 
56.07 
19.9 
40.16 

I cannot tell 
No 
To some extent 
Was too late 
Yes 
Total 

N 
1 
6 
32 
1 
30 
70 

Annex A8. Respondents perception about their pastureland conditions 

Don't Know 
Exceptionally good 
Exceptionally poor 
Less than average 
On average 
More than average 
Total 

Annex A9. Key drivers of pastureland 

Crop Disease 
Desert Locusts 
Other pests 
Above average rainfall 
Average rainfall 
Below average rainfall 
Flooding 
Lack of inputs due to low availability 
Lack of working force at field preparation 
Other (Specify) 
Total 

N 
1 
26 
11 
61 
231 
82 
412 

N 
4 
11 
3 
84 
190 
32 
14 
11 
1 
62 
412 

% 
0.97 
2.67 
0.73 
20.39 
46.12 
7.77 
3.4 
2.67 
0.24 
15.05 
100 

Annex A10. Changes in expenditure due to desert locust on pastureland 

Had to sale off animals 
deteriorated body conditions 
had to buy feed or had to buy more feed.  
Total 

N 
2 
9 
22 
33 

% 
5.41 
24.32 
56.76 
100 

Annex A11. Coping strategies to mitigate food insecurity and future major concerns 

N 

% 

46 

 
 
 
 
 
 
 
  
 
 
 
 
  
  
  
  
 
 
Selling property 
Receiving food assistance 
Food shortage is a main concern 

293 
101 
226 

46.14 
15.91 
35.59 

Note, percentage are computed from a total sample of 635 for insecure farmers. 

Annex A12. Household reporting food stock for consumption and their level of income 

Earnings label 
Don’t want to say 
< KES 25,000 
KES 25,000- KES 50,000 
KES 50,000  -KES 100,000 
> KES 100,000 
Total 

N 
112 
219 
146 
99 
59 
635 

% 
17.64 
34.49 
22.99 
15.59 
9.29 
100 

Source: Authors’ computation from survey report 

47 

 
 
 
 
 
Annex B – Survey methods 

The survey was carried out using Computer Assisted Telephone Interviewing (CATI). It is an interviewing mode 
in which an electronic device (computer/tablet/mobile) displays questions on its screen, the interviewer reads 
them to a respondent over a phone call and enters the respondent’s answers directly into the electronic 
device. While CATI will typically involve setting up call centers from where enumerators can make the phone 
calls to the respondents, due to the COVID-19 advisories on managing the spread of the virus by, among 
others, social distancing, we used the “one-person call centers” approach which basically meant that our 
enumerators were working from home to conduct telephone interviews with respondents. The prepared 
questionnaires were coded into a software platform (Survey CTO) which has both web and mobile applications 
for running the survey and submitting the forms after completion. The same tool was used for all phone 
surveys and field surveys as well. Enumerators were trained on the use of the platform. This was done 
remotely, keeping in mind the social distancing advisory. The data collected using the Survey CTO tool are in a 
.csv format and were further analyzed using python scripting language. 

48 

 
 
 
 
Annex C 

Figure C1. Satellite images of Farm 2 

Note: Satellite images of Farm 2 for the previous year (top row) and the current year (bottom row) before (left 
column) and after infestation (middle column). In the third column it is presented the NDVI difference for the 
previous (top) and the current year (below). The green circle is the farm affected and the blue circle is the 
control farm. 

49 

 
 
 
 
 
 
 
 
Figure C2. Temporal NDVI of farm 2 

Note: profile for Farm 2 (above) and for the corresponding control farm (below). A 50-m buffer around farm GPS 
location is used to extract profiles. 

Figure C3. Average temporal NDVI profile of Farm 2 

Note: average temporal NDVI profile of Farm 2 (current and previous year) for a circular area with radius 50 m, 
i.e. the report extent of the damaged area. doy stand for day of year. 

50 

 
 
 
 
 
 
 
 
 
 
 
 
Figure C4. Satellite images of Farm 3 

Note: Satellite images for Farm 3 for the previous year (top row) and the current year (bottom row) before (left 
column) and after infestation (middle  column). In the third column it is presented the NDVI difference for the 
previous (top) and the current year (below). The green circle is the farm affected and the blue circle is the control 
farm. 

Figure C5. Temporal NDVI profile of Farm 3 

Note. Temporal NDVI profile for Farm 3 (above) and for the corresponding control farm (below). A 30-m buffer 
around farm GPS location is used to extract profiles. 

51 

 
 
 
 
 
 
 
 
 
 
 
 
 
Figure C6. Average temporal NDVI of farm 3 

Note: average temporal NDVI profile of Farm 3 (current and previous year) for a circular area with radius 30 m, 
i.e. the report extent of the damaged area. doy stand for day of year. 

Figure C7. Satellite images of Farm 4 

Note: Satellite images for Farm 4 for the previous year (top row) and the current year (bottom row) before (left 
column) and after infestation (middle  column). In the third column it is presented the NDVI difference for the 
previous (top) and the current year (below). The green circle is the farm affected and the blue circle is the control 
farm. 

Figure C8. Temporal NDVI profile of Farm 4 

52 

 
 
 
 
 
 
 
 
 
Note: Temporal NDVI profile for Farm 4 (above) and for the corresponding control farm (below). A 50-m buffer 
around farm GPS location is used to extract profiles. 

Figure C9. Average temporal NDVI profile of farm 4 

Note: Farm 4, average temporal NDVI profile (current and previous year) for a circular area with radius 50 m, i.e. 
the report extent of the damaged area. doy stand for day of year 

53 

 
 
 
 
 
 
 
 
GETTING IN TOUCH WITH THE EU 

In person 

All over the European Union there are hundreds of Europe Direct information centres. You can find the address of the centre nearest 
you at: https://europa.eu/european-union/contact_en 

On the phone or by email 

Europe Direct is a service that answers your questions about the European Union. You can contact this service: 

- by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 

- at the following standard number: +32 22999696, or 

- by electronic mail via: https://europa.eu/european-union/contact_en 

FINDING INFORMATION ABOUT THE EU 

Online 

Information about the European Union in all the official languages of the EU is available on the Europa website at: 
https://europa.eu/european-union/index_en 

EU publications 
You can download or order free and priced EU publications from EU Bookshop at: https://publications.europa.eu/en/publications. 
Multiple copies of free publications may be obtained by contacting Europe Direct or your local information centre (see 
https://europa.eu/european-union/contact_en). 

 
 
 
 
 
 
 
 
N

N
A
-

X

X

-

X
-
E
N

X
X
X
X

-

K
J
-

N
A
-
3
0
6
2
8
-
E
N
N

-

doi:10.2760/66626 
ISBN 978-92-76-32073-9