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Filling harvest and nutrient 'gaps' through site-specific food tree and crop portifolios

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Filling harvest and nutrient 'gaps' through site-specific food tree and crop portifolios

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Filling harvest and nutrient 'gaps' through site-specific food tree and crop portifolios

  1. 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. 2. Transforming Lives and Landscapes with Trees Fruit and vegetable consumption gaps Source: Data adapted from Micha et al. 2015 WHO Recommendation
  3. 3. Transforming Lives and Landscapes with Trees Gaps in production: global fruit and vegetable supply
  4. 4. Transforming Lives and Landscapes with Trees Generating evidence for location-specific interventions Project sites: Laikipia, Tharaka Nithi, Kitui & Kwale Counties, Kenya
  5. 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. 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. 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. 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. 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. 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. 11. Transforming Lives and Landscapes with Trees Nutritional value of indigenous & underutilised food tree and crop species
  12. 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. 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. 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

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