Diao, Xinshen; Pauw, Karl; Smart, Jenny; Ellis, Mia; and Thurlow, James. 2023.

1. Transformation of Kenya’s Agrifood System
Structure and Drivers
Xinshen Diao, Mia Ellis, Karl Pauw, Jenny Smart, and James Thurlow
International Food Policy Research Institute
This diagnostic analysis was conducted by IFPRI with financial support from USAID and funders of the CGIAR Research Initiative on Foresight and on National Policies and Strategies.
A comprehensive report can be downloaded here.
July 2023

2. Four Parts to the Diagnostic
• Current size & structure
What does Kenya’s agrifood system (AFS) look like today?
• Decomposing value chains
How are different products contributing to the broader AFS?
• Growth and market structure
How is Kenya’s AFS growing and transforming?
• Future drivers of inclusive agricultural transformation
Which value chains could be most effective at reducing poverty, improving diet quality, and contributing to
growth and employment within the AFS or at an economywide level?
2019
2009-2019
2019+

3. Summary
Kenya’s agrifood system (AFS) diagnostic results
Kenya’s AFS has been transforming
• Growth in the off-farm components of the AFS has been faster than in the primary agriculture
• However, in value terms, agricultural GDP is still twice as large as GDP in the off-farm components of the AFS
AFS growth has been driven mainly by value chains oriented toward the domestic market
• Less-traded value chains (e.g., maize, root crops, vegetables, or cattle & dairy), as opposed to export-oriented value chains, dominate the
AFS both in terms their size and contribution to GDP growth in the AFS
• Domestic consumption patterns (and changing diets) are therefore important drivers of agricultural transformation
Looking forward, the structure of AFS growth will be crucial in driving development outcomes…
(e.g., poverty, dietary improvements, employment creation, and growth)
… but no single value chain is the most effective at driving all these development outcomes
• Fish, pulses & oilseeds, and maize are most effective at reducing poverty; cattle & dairy and fruits & nuts are best for improving diet
quality; coffee & tea have strong employment effects; and cattle & milk have large growth multipliers
Jointly promoting pulses & oilseeds, fruits & nuts, and cattle & dairy would offer an effective way to achieve
multiple development outcomes

4. Framework | Measuring the Size of the AFS
Primary agriculture
Agroprocessing
Trade and transport
Food services
Trade and transport
Input supply Demand
Consumption of own-
produced goods
Purchase of primary
agricultural goods
Purchase of processed
agrifood goods
Purchase of ready-made
foods outside of home
Imports
A
C
B
D
E
AFS includes agriculture, plus all upstream & downstream sectors
• The AFS is a complex network of actors, connected by their differing roles in
supplying, consuming, and governing agrifood products and jobs
• We measure the size and performance of the AFS from the supply-side using standard
economywide datasets (i.e., national accounts and employment statistics)
Agrifood System GDP (AgGDP+)
Total value added generated by all agricultural
value chains (in constant dollars)
Agrifood System Employment (AgEMP+)
Total number of workers who are primarily
employed in an agricultural value chain

5. Structure2019 | Kenya’s Agrifood System Today
GDP and employment in Kenya’s agrifood system (2019)
• Part 1 focuses on the current size & structure
of the national AFS
• Latest AgGDP+ and AgEMP+ estimates
• Decomposed into five AFS components
• Situates AFS within the broader economy
• Uses official data sources
• GDP from national accounts
• Employment from various sources
(population census, labor force surveys, ILO, etc.)
• Kenya AFS estimates indicate that
• AFS makes up one-third of GDP
($31.1 billion AgGDP+) …
• … and half of total employment
(10.2 million AgEMP+)
• Primary agriculture (A) is large, but off-farm
components (B–E) are also important
(one-third of AgGDP+, one-fifth of AgEMP+)
GDP
($ billions)
Employment
(millions of workers)
Total economy 92.0 100% 18.7 100%
Agrifood system 31.1 33.8% 10.2 54.7%
Primary agric. (A) 20.9 22.7% 8.1 43.3%
Off-farm AFS 10.2 11.1% 2.1 11.4%
Processing (B) 4.7 5.1% 0.5 2.5%
Trade & transport (C) 3.6 3.9% 1.1 6.1%
Food services (D) 0.8 0.9% 0.4 2.2%
Input supply (E) 1.1 1.2% 0.1 0.6%
Rest of economy 60.9 66.2% 8.5 45.3%

6. Structure2019 | Comparing to Other Countries
• Importance and structure of the AFS varies at different stages of development
Kenya is a lower-middle-income country (LMIC)
• A: Kenya’s AgGDP+ share of total GDP lies between low-income countries (LICs) and LMICs
• B: Kenya’s primary agriculture is larger than in most LICs
• C: Kenya’s agroprocessing is larger than expected, and trade & transport services are smaller
Share of total GDP (%) Share of AgGDP+ (%) Share of off-farm AFS GDP (%)
LIC = low-income countries | LMIC = lower-middle income | UMIC = upper-middle-income | HIC = high-income Source: IFPRI Agri-Food System Database
A B C
4.2
26.4
16.9
7.1
1.2
22.7
8.2
13.4
11.9
10.6
6.6
11.1
All LIC LMIC UMIC HIC Kenya
Primary agriculture Off-farm AFS
34.0
66.2
58.6
40.2
15.6
67.3
66.0
33.8
41.4
59.8
84.4
32.7
All LIC LMIC UMIC HIC Kenya
Primary agriculture Off-farm AFS
33.7 37.8 38.4
46.9
26.1
45.9
31.7
42.8 38.6 21.4
35.9
35.1
23.1
13.7
11.2
18.2 27.8
7.9
11.4 5.8 11.8 13.5 10.3 11.1
All LIC LMIC UMIC HIC Kenya
Processing Trade and transport
Food services Input supply

7. Structure2019 | Supply vs. Demand Sides of the Agrifood System
Agrifood GDP vs. consumption
Primary, processed, and other product shares (%)
• AgGDP+ defines the AFS on the supply side
• Household demand and trade (imports) capture AFS structure on the demand side
• Processing agriculture is more important on the demand side than the supply side in the AFS
AgGDP+ Household demand
Agrifood exports vs. imports
Primary and processed product shares (%)
Exports ($4.53 bil.) Imports ($2.07 bil.)
67.3%
15.0%
17.7%
$2.11 bil.
46.5%
$2.42 bil.
53.5%
Primary agriculture
Agrifood processing
$1.55 bil.
74.7%
$0.52 bil.
25.3%
47.1%
42.0%
10.9%
Primary agriculture
Agroprocessing
Other off-farm

8. Value Chains2019 | Contributions & Trade Orientation
• Part 2 decomposes the AFS across broad value chain
groupings
• Classify value chains based on trade orientation
• Exportable value chains have above-average export-output
ratios ( > 8.9%)
• Importable value chains have above-average import-demand
ratios (> 4.3%)
• Less-traded value chains make up the rest
• Domestic market dominates AgGDP+ (65.2%) – nine less
traded value chains; relatively smaller off-farm share (56.7%)
and larger on-farm (primary) share (69.4% of total), with cattle
& dairy a significant exception
• Only two export-oriented value chain groups (export crops
include tea, coffee, cut flowers); relatively small share of
AgGDP+ (16.5%)
• Four import-substitutable value chains account for a
disproportionate share of off-farm AFS (24.5%); these value
chains compete against processed agrifood imports
 Promoting some importable value chains and cattle &
dairy (less traded) could be effective in driving agricultural
transformation by boosting value added and employment
in off-farm AFS
Share of total GDP (%) Exports /
output
(%)
Imports /
demand
(%)
Total
AFS
Primary
agric.
Off-farm
AFS
Total 100.0 100.0 100.0 8.9 4.3
Exportable 16.5 19.1 11.3 45.2 2.7
Fruits and nuts 7.8 9.2 4.9 11.6 3.2
Export crops 8.7 9.9 6.3 65.0 2.0
Importable 15.8 11.5 24.5 3.3 12.9
Rice and wheat 3.2 1.7 6.4 0.5 15.4
Oilseeds 3.3 2.1 5.7 5.8 21.9
Other crops 6.8 5.0 10.6 4.8 9.4
Poultry and eggs 2.4 2.7 1.9 0.3 5.7
Less traded 65.2 69.4 56.7 1.1 1.6
Maize 10.5 13.4 4.6 0.2 2.3
Sorghum & other cereals 1.9 1.2 3.2 2.2 1.6
Root crops 8.9 12.2 2.2 0.0 0.3
Pulses 4.5 5.7 1.9 0.9 3.7
Vegetables 9.9 12.3 5.2 2.7 0.4
Cattle & dairy 17.4 11.6 29.4 0.2 1.1
Other livestock 2.1 2.9 0.4 5.8 0.6
Fish 2.3 2.7 1.6 5.3 3.9
Forestry 7.6 7.3 8.1 0.9 3.6
Breakdown of Kenya’s agrifood system (2019)

9. Shares of AFS and agricultural GDP in total GDP
and off-farm share of AgGDP+ (2009–2019)
37.2
38.3 38.1 37.6 38.0
36.7 36.7
36.0
34.9 34.7
33.8
26.4
27.6
26.9 26.5 26.7
25.9 25.6
24.9
23.7 23.7
22.7
28.9
28.0
29.5 29.5 29.8 29.4
30.3
31.0
32.1 31.8
32.7
20
25
30
35
40
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Shares
(%)
AgGDP+ share of total GDP
Agriculture share of total GDP
Off-farm share of AgGDP+
• Part 3 analyzes structural change in the AFS
and the contribution of value chains to AFS
growth
• Kenya’s AFS has been transforming
• Agricultural share of total GDP fell over time
(26.4 to 22.7%)
• Share of off-farm components in total AgGDP+
rose (28.9% to 32.7%)
• Agricultural employment also fell (49.0% to
43.3%)
• … but primary agriculture still dominates
AgGDP+
• Agriculture GDP is still twice as large as GDP in
the off-farm components
Growth2009-2019 | Agrifood System Performance

10. Growth2009-2019 | Value Chain Performance
• Modest AgGDP+ growth (3.9% p.a.) during 2009–
2019 compared with national GDP growth of 5.5%
• Most value chains with above-average AgGDP+
growth rates (*) ( > 3.9%) are in importable and
less-traded groups
• Less-traded value chains grew fastest (4.5%),
accounting for three-quarters of total AFS growth
• Export-oriented value chains fared worst (1.6%
growth rate), but mainly because of the poor
performance of the fruits & nuts value chain
• AgGDP+ growth driven by strong growth in off-farm
AFS (5.2%), including processing (4.7%)
• Most value chains, not only fast-growing ones,
experienced faster off-farm growth
Indicative of increased market orientation of the
AFS; associated with increased demand for trade,
transport, and processing
Value chain growth in Kenya (2009-2019)
Average annual GDP growth rate (%)
Total
AFS
Primary
agric.
Off-farm
AFS
Process-
ing
Total AFS 3.9 3.4 5.2 4.7
Exportable 1.6 0.8 4.9 4.3
Fruits and nuts -0.8 -1.7 4.4 4.4
Export crops* 4.3 4.1 5.2 4.3
Importable 4.3 3.6 5.0 4.8
Rice and wheat* 4.0 3.8 4.1 3.6
Oilseeds* 4.2 1.9 6.5 10.1
Other crops 3.0 1.8 4.5 4.4
Poultry and eggs* 10.2 11.1 7.9 4.4
Less traded 4.5 4.1 5.5 4.7
Maize* 6.5 6.6 5.7 5.1
Sorghum & other cereals* 5.9 5.9 5.8 5.5
Root crops 3.3 3.0 8.2 5.6
Pulses* 8.0 8.0 7.9 4.6
Vegetables* 5.5 4.9 9.0 4.3
Cattle & dairy 3.1 1.6 4.5 4.6
Other livestock 2.5 2.4 3.7 4.0
Fish 3.3 2.4 7.0 4.4
Forestry* 4.8 4.3 6.0 4.7

11. Future Drivers2019+ | Modeling Faster Growth
• IFPRI’s RIAPA model is used to analyze different sources of agricultural growth
• Expand production in different value chains
• Increase on-farm productivity growth rates in targeted value chains
• Achieve same overall growth in agriculture GDP (e.g., 1.0%)
• Track linkage effect within value chain and spillover effects to other value chains
• Assess outcomes
• Poverty – Poverty-growth elasticity in percentage points based on $2.15-a-day
• Hunger – Hunger-growth elasticity in percentage points based on prevalence of undernourishment
• Diet – Diet quality to growth elasticity in % derived from Reference Diet Deprivation index (REDD)
• Jobs – Employment multiplier in thousand employed persons associated with US$1 million growth in targeted value chain
• GDP – GDP growth multiplier in US$ millions associated with US$1 million growth in targeted value chain
• Average across outcomes
• The value of outcome indicators (elasticity or multiplier) is expected to differ across value chain growth; not all value chains are
equally effective at achieving all outcomes
• Normalizing the individual outcome scores
• The values of each outcome indicator are scaled so that the most effective value chain is given a score of one and the leasteffective is given a
score of zero. A value chain with adverse impact is also given a score of zero.
• An average score with equal weights is used to measure the total impacts across all value chains

12. Individual outcomes
(per unit change in agriculture GDP, ordered by poverty outcome)
Future Drivers2019+ | Prioritizing Agricultural Growth
Average across outcomes
(averaged normalized scores, reordered)
0.40
0.50
0.11
0.44
0.84
0.12
0.06
-0.01
0.84
0.37
-0.17
-0.92
-1.40
-0.05
-0.11
-0.13
-0.20
-0.13
0.20
-0.05
-0.64
-0.59
-0.58
-0.54
-0.50
-0.42
-0.29
-0.13
-0.09
-0.04
Fish
Pulses & oilseeds
Maize
Poultry & eggs
Fruits & nuts
Coffee & tea
Rice & wheat
Root crops
Cattle & dairy
Vegetables
Poverty
(change in %-point)
Hunger
(change in %-point)
Diet quality
(change in %)
Jobs
(change in 1,000)
GDP
(change in mil. $)
1.15
1.55
1.43
0.96
0.89
0.89
1.10
1.18
1.81
1.23
0.05
0.10
0.06
0.06
0.07
0.19
0.06
0.01
0.18
0.00
0.75
0.68
0.53
0.50
0.48
0.45
0.41
0.25
0.20
0.13
Cattle & dairy
Pulses & oilseeds
Fruits & nuts
Fish
Maize
Coffee & tea
Poultry & eggs
Rice & wheat
Vegetables
Root crops
Total
Cattle & dairy
Pulses &
oilseeds
Fruits & nuts
Fish
Maize
Coffee & tea
Poultry & eggs
Rice & wheat
Vegetables
Root crops
Poverty Growth Jobs Diets

13. Future Drivers2019+ | Key Messages
• AFS growth is pro-poor
• All value chains associated with poverty reduction; fish, pulses & oilseeds, and maize are most effective
• AFS growth is effective in improving food security (hunger) and diet quality
• Most value chains reduce hunger; maize and pulses & oilseeds are most effective
• Most value chains improve diet quality; cattle & dairy and fruits & nuts are most effective
• Agricultural growth creates jobs, but not necessarily on-farm
• All value chains are associated with an increase in total employment, but most AFS jobs are created off-farm
• Coffee and tea are most effective value chain group in creating jobs in the overall economy and within the AFS
• Agricultural growth has strong growth multiplier effects, generating income beyond agriculture
• Cattle & dairy have strongest growth multiplier effects both for AFS income and total GDP; strong linkages with
food industries ensures benefits of on-farm productivity gains spill over to rest of the economy
• In conclusion, promoting multiple value chains can achieve broad impacts
• No single value chain is the most effective at achieving all the development outcomes we consider
• Cattle & dairy, pulses & oilseeds, and fruits & nuts rank highly in the combined outcome scores for poverty, diet,
jobs, and growth
• Promoting these value chains would offer an effective way to achieve broad-based outcomes

14. Note: Value Chain Groups and Agricultural Sectors in Individual VC Groups
Value chain group and their share
of AFS GDP
Individual products and their share of group’s agriculture GDP
Maize (10.5%) Maize 100%
Rice & wheat (3.2%) Rice 33.1% | Wheat & barley 66.9%
Sorghum & other cereals (1.9%) Sorghum & millet 94.0% | Other cereals 6.0%
Oilseeds (3.3%) Groundnuts 51.0% | Other oilseeds 49.0%
Pulses (4.5%) Pulses 100%
Roots (8.9%)
Cassava 10.1% | Irish potatoes 64.0% | Sweet potatoes 24.6% |Other roots 0.7% |
Plantains 0.7%
Vegetables (9.9%) Leafy green vegetables 55.0% | Other vegetables 45.0%
Fruits & nuts (7.8%) Nuts 6.0%| Bananas 39.9% | Other fruits 11.6%
Export crops (8.7%) Tea 71.4% | Coffee 17.0% | Cut flowers 38.2
Other crops (6.8%) Sugarcane 36.8% | Cotton & fibers 6.3% | Tobacco 2.7% | Other crops 54.2%
Cattle & dairy (17.4%) Cattle meat 38.1% | Raw milk 61.9%
Poultry & eggs (2.4%) Poultry meat 77.5% | Eggs 22.5%
Other livestock (2.1%) Small ruminants 46.7% | Other livestock 53.3%
Fish (2.3%) Aquaculture 12.9% | Captured fish 87.1%
Forestry (7.6%) Forestry 100%