This document discusses using a portfolio approach to match food tree and crop species with nutrient needs in Kenya. It finds gaps in fruit/vegetable consumption and production. Data from 4 Kenyan counties shows food insecurity, malnutrition, and low dietary diversity. Food trees and crops were analyzed from farms in 2 counties. Over 30 species were identified, with differences in diversity between counties. Nutritional data was collected for 90 food species and used to create portfolios targeting nutrients like iron, vitamin A, and folate. The portfolios aim to fill consumption gaps and support healthier diets through local food systems.

1. Transforming Lives and Landscapes with Trees
Filling harvest and nutrient ‘gaps’
through site-specific
food tree and crop portfolios
Stepha McMullin, Barbara Stadlmayr,
Roeland Kindt, Ramni Jamnadass
21st May 2019

2. Transforming Lives and Landscapes with Trees
Fruit and vegetable consumption gaps
Source: Data adapted from Micha et al. 2015
WHO Recommendation

3. Transforming Lives and Landscapes with Trees
Gaps in production: global fruit and
vegetable supply

4. Transforming Lives and Landscapes with Trees
Generating evidence for location-specific
interventions Project sites: Laikipia, Tharaka Nithi, Kitui & Kwale Counties, Kenya

5. Transforming Lives and Landscapes with Trees
Data - food security and nutritional
status
0%
20%
40%
60%
80%
100%
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Laikipia Tharaka Nithi Kitui Kwale
Months of food insecurity
16
7 7 5
53
57 62 68
32 36 31 27
0
20
40
60
80
100
Laikipia Tharaka Nithi Kwale Kitui
% % % %
Double Burden of Malnutrition - Women
Underweight (< 18.5) Normal (18.5 - 24.9) Overweight / Obese (25 - 29.9/ 30+)

6. Transforming Lives and Landscapes with Trees
Data - dietary diversity and food
consumption
64%
49% 47%
70%
36%
51% 53%
30%
0%
20%
40%
60%
80%
100%
Laikipia Tharaka Nithi Kitui Kwale
Minimum Dietary Diversity - Women
< 5 food groups ≥ 5 food groups
0
50
100
Food Groups Consumed - Women
Laikipia % Tharaka Nithi % Kitui % Kwale %

7. Transforming Lives and Landscapes with Trees
Data – food crop diversity on farms
In Laikipia:
• Staples (Max 5, Min 1, Mean 1.7) Pulses &
Nuts (Max 4, Min 1, Mean 1.4)
• Vegetables (Max 6, Min 1, Mean 2.6)
• In Tharaka Nithi:
• Staples (Max 8, Min 1, Mean 3.0)
• Pulses & Nuts (Max 4, Min 1, Mean 1.3),
• Vegetables (Max 9, Min 1, Mean 2.3)

8. Transforming Lives and Landscapes with Trees
Data - food tree diversity on farms
• Higher number of farms in Tharaka Nithi
had food trees 89% , 61% in Laikipia
• Tharaka Nithi: 32 food tree species, 15
indigenous (498 ind.), 17 exotic (2,807
ind.) → 124 hectares
• On-farm: Max 8, Min 0, Mean 2.4
• Laikipia: 34 food tree species, 12
indigenous (76 ind.), 22 exotic (1014
ind.)→ 133 hectares
• On-farm: Max 15, Min 0, Mean 1.3
• Relevance of collecting location-specific
information to inform integrated ag-
nutrition interventions
County Origin
No.of
farms
(n)
Frequency
(%)
Relative
frequency
(%)
Individual
trees (#)
Relative
abundance
(%)
Laikipia Botanical name Common name
Persea americana Avocado e 37 23 16 216 20
Musa x paradisiaca Banana e 27 17 11 147 14
Passiflora edulis Passion fruit e 21 13 9 117 11
Citrus sinensis Orange e 17 11 7 102 9
Eriobotrya japonica Loquat e 13 8 6 96 9
Mangifera indica Mango e 13 8 6 79 7
Solanum beacea Tree tomato e 12 8 5 79 7
Citrus limon Lemon e 11 7 5 37 3
Morus alba Mulberry e 11 7 5 28 3
Psidium guajava Guava e 11 7 5 22 2
Carissa spinarum Bush plum i 7 4 3 21 2
Tharaka Nithi
Carica papaya Papaya e 68 43 13 1659 50
Musa x paradisiaca Banana e 65 41 13 276 8
Mangifera indica Mango e 63 40 12 244 7
Persea americana Avocado e 59 37 12 194 6
Berchemia discolor Bird cherry i 42 26 8 147 4
Passiflora edulis Passion fruit e 39 25 8 135 4
Psidium guajava Guava e 25 16 5 134 4
Macadamia integrifolia Macadamia e 23 15 5 97 3
Tamarindus indica Tamarind i 21 13 4 96 3
Vangueria madagascariensis Wild medlar i 18 11 4 63 2
Balanites aegyptiaca Desert date i 13 8 3 42 1
Food Tree Species
Table. 11 most frequent and abundant food tree species grown on farms in four sites in Laikipia and Tharaka Nithi Counties,
Kenya

9. Transforming Lives and Landscapes with Trees
Food tree and crop
portfolios to target
harvest and nutrient gaps
Iron Vitamin
A*
Folate Vitamin
C
~ ++ ~ +++
~ ~
~ ++ ++
~ +++ ~ ++
++ +++
++
~ ~ ~
~ ~ +++
~ +++
+++ ~
++ +++
++ +++ ~ +++
++ ++ ~
++ +++ ++ ++
+++ +++ ~ ++
~ ++
~ ++
~ ++
+++ +++
~ ~
++
~
Food Type Food Name Food description Scientific Name Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Pawpaw/Papaya pulp, raw Carica papaya * 2
Banana pulp, raw Musa spp.
Passion fruit purple, raw Passiflora edulis
Mango pulp, ripe, raw Mangifera indica** 1
Bird cherry raw Berchemia discolor ** 2
Tamarind pulp, ripe, raw Tamarindus indica** 3
, * 1
Grewia/Mallow raisin raw Grewia villosa
Ntuuka raw Tennantia sennii
Guava pulp, raw Psidium guajava
Desert date fresh, raw Balanites aegyptiaca
Desert date dried, raw Balanites aegyptiaca
Common wild medlar raw Vangueria madagascariensis
Mobola plum raw Parinari curatellifolia
Moringa seeds, raw Moringa oleifera
Moringa leaves, boiled Moringa oleifera
Fruits
Pumpkin leaves, boiled Cucurbita maxima
Cowpea leaves, boiled Vigna unguiculata
Amaranth leaves, boiled Amaranthus spp.
Vegetables
Bean mature, whole, water-soaked, boiled Phaseolus vulgaris ** 2
Green gram/ Mung bean mature, whole, water-soaked, boiled Vigna radiata** 3
, * 1
Cowpea mature, whole, water-soaked, boiled Vigna unguiculata * 2
Groundnut/peanut raw Arachis hypogaea
Pulses&
Nuts
Maize sweet, yellow, boiled Zea mays ** 1
Millet/Pearl millet whole grain, boiled Pennisetum glaucum * 3
Sorghum whole grain, boiled Sorghum bicolor
Staples
Notes:* Vitamin A (calculations based on Vitamin A retinol equivalent = retinol + 1/6 beta-carotene + 1/12 alpha-carotene + 1/12 beta-cryptoxanthin),
Data are expressed per 100 g fresh weight of edible portion, ** = most consumed * = most sold

10. Transforming Lives and Landscapes with Trees
Matching food tree and crop species
with nutrient content data
Food
Item
ID
Food
grou
p
Food name in English Pro
ces
sing
of
Scientific name Fibre
(g)
Iron
(mg)
Zinc
(mg)
Vitamin A
(mcg)
Folate
(mcg)
Vitamin
C (mg)
Iron
(mg)
Zinc
(mg)
Vitamin A
(mcg)
Folate
(mcg)
Vitamin C
(mg)
F: 01
fruits,
V: 02
veget
ables,
P: 03
r:
fres
h
raw
food
, c:
(dietary and
crude)
% of RNI % of RNI % of RNI % of RNI % of RNI (calculations
based on
Vitamin A
retinol
equivalent =
retinol + 1/6F0001 F Baobab fruit, pulp, raw r Adansonia digitata 20.7 18.9 19.7 606.7 ++ ++ +++
V Amaranth leaves, boiled (without salt)c Amaranthus spp. 5.0 35.0 9.5 79.4 10.5 42.2 +++ ~ +++ ~ ++
F0002 F Cherimoya, pulp, raw r Annona cherimola 10.0 2.1 1.7 0.2 5.8 28.0 ~ ~
F0003 F Sour sop, fruit pulp, raw r Annona muricata 11.0 6.8 1.7 0.0 3.5 48.4 ~ ++
F0053 F Custard apple, raw r Annona reticulata 8.0 5.1 0.0 0.2 0.0 42.7 ~ ++
F0004 F African custard apple/wild soursop, pulp, rawr Annona senegalensis 15.3 12.2 4.3 3.5 26.1 ~ ~
F0005 F Sugar apple, pulp, raw r Annona squamosa 14.7 5.7 1.7 0.2 3.5 80.7 ~ ++
N Groundnuts/peanuts, raw r Arachis hypogea 28.3 32.7 48.0 0.0 43.6 0.0 +++ +++ +++
F0006 F Jackfruit, pulp, raw r Artocapus heterophyllus 7.2 3.0 10.0 1.4 6.0 30.4 ~ ~ ~
F0007 F Breadfruit pulp, raw r Artocarpus atilis 16.3 9.1 2.0 0.2 3.5 63.0 ~ ++
F Azanza, pulp, ripe,raw r Azanza garckeana 79.7 31.7 0.0 0.0 0.0 0.0 +++
F0008 F Desert date, fresh, raw r Balanites aegyptiaca 6.7 11.4 5.3 4.5 112.9 ~ ~ +++
F0009 F Desert date, dried, raw d Balanites aegyptiaca 17.7 31.4 14.8 0.0 12.5 0.0 +++ ~ ~
F Bird cherry, raw r Berchemia discolor 9.3 16.0 0.0 0.0 0.0 111.8 ++ +++
F0010 F Borassus, pulp, raw r Borassus aethiopum 6.7 12.4 0.8 6.1 229.7 ~ ~ +++
V Kale, boiled, drained without saltc Brassica oleracea 13.3 6.0 9.0 50.6 16.3 39.6 ~ ~ +++ ++ ~
V Cabbage, boiled (without salt)c Brassica oleracea var. Capitata 8.8 3.0 2.6 2.6 6.2 49.0 ~ ++
P Pigeon pea, mature, whole, water-soaked, boiled in different water, without salt, drainedc Cajanus cajan 30.4 17.5 37.2 1.6 18.6 0.8 ++ +++ ++
P Pigeon pea, mature, whole, water-soaked, boiled in different water, without salt (cooking water not discarded)c Cajanus cajan 21.8 14.8 29.7 1.1 14.7 0.6 ++ +++ ~
→Calculating and scoring nutritional value
of food tree and crop species
Database to support decision making
Calculations
Scoring

11. Transforming Lives and Landscapes with Trees
Nutritional value of indigenous & underutilised
food tree and crop species

12. Transforming Lives and Landscapes with Trees
Conclusions
➢The portfolio approach makes use of location-specific data to not only
capture the socio-ecological dynamics of smallholder food production
diversity but uniquely includes individual food consumption data to inform
knowledge on local dietary gaps.
➢16 location-specific food tree and crops portfolios in East Africa developed
→ flexibility of methodology + potential to scale geographically
➢Food composition data for 90+ food tree species & underutilised crops,
can inform prioritisation of crops for mainstreaming in local (and global)
food systems
➢The portfolios and the database are important tools to support decision-
making for recommending healthier food production in local food systems.

13. Transforming Lives and Landscapes with Trees
World Agroforestry (ICRAF),
United Nations Avenue, Gigiri,
P.O Box 30677-00100, Nairobi, Kenya
Phone: +254 20 722 4000
Fax: +254 20 722 4001
Email: icraf@cgiar.org
Website: www.worldagroforestry.org
Thank you!
Stepha McMullin
s.mcmullin@cgiar.org

14. Transforming Lives and Landscapes with Trees
Outreach, influence & impact: schools as key entry
points for nutrition-sensitive agroforestry
Food Tree and
Crop Portfolios
targeting food
production and
consumption
diversity for
healthier diets
scaled across 16
sites in East
Africa (Kenya,
Uganda,
Ethiopia)
McMullin et al.
2018