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small-holder timber production: tree inventories/biomass surveys international workshop

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small-holder timber production: tree inventories/biomass surveys international workshop

small-holder timber production: tree inventories/biomass surveys international workshop

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  • 1. SMALL-HOLDER TIMBER PRODUCTION: TREE INVENTORIES/BIOMASS SURVEYS INTERNATIONAL WORKSHOP ON SMALL-HOLDER TIMBER PRODUCTION ICRAF HOUSE 29 TH NOVEMBER 2004 PAUL M. NJUGUNA
  • 2.
    • PRESENTATION SUMMARY :
    • What is Farm Inventory/Biomass Survey?
    • Why conduct an inventory on farms?
    • What do you need for the survey?
    • Kenya: Biomass Surveys History and case studies.
  • 3.
    • What is farm Inventory/Biomass Survey:
    • Entails collection of tree data on farms including:
    • Tree Species
    • Size (DBH)
    • Age
    • Growing Place-Homestead, Homegarden, Cropland, Boundary, Grazing Area, Woodland, Woodlot.
    • Main use-Timber, fuelwood, herbs, fruit, amenity,
    • Germplasm Origin
    • -Aims at getting Densities, Distribution and Volumes
  • 4.
    • Why Conduct a farm Inventory/Biomass Survey :
    • Baseline Data For the Planning and later implementation of
    • farm Tree based planting projects
    • Status at Inception of a Tree Planting Project on Farms
    • Evaluation after a farm based Tree Planting programme
    • What is the Project impact?
    • Is there a need for another phase and the required emphasis?
    • Timber Marketing/tree sales
    • What tree stock is available, how is it distributed in terms of
    • geographical coverage species, age, Growing place.
    • How can sustainability be planned?
  • 5.
    • What do you need for a farm inventory:
    • Personnel, Equipment, Transport
    • Objective Sampling Frame-Remote sensing images,
    • photographs, vegetation Maps, topographical maps, GPS
    • Sample Farms/plots
    • Data Entry and Analysis facilities
    • Data Presentation
  • 6. History
    • History of farm inventories in Kenya
    • 1991 under the Intensified Forestry Project in Nakuru and
    • Nyandarua Districts-(“Miti Mingi Project”)
    • LANDSAT images were used,
    • Gave volume estimates on forested areas but no detailed species
    • differentiation,
    • Scale too small to give sufficient information to Project
    • management team especially on small holder farms.
    • Recommended another survey using Aerial photographs
  • 7.
    • January 1993: Aerial photographs were taken at a scale of
    • 1:10,000 using 8 flight lines at an interval of 1Km.
    • 600 photographs were taken
    • Enlargement to 1:2,500 done at centre of photographs:
    • All photographs falling on Forests, or without small holder farms
    • or without a complete small farm were rejected-200 photos
    • rejected.
    • Photo-interpretation had shortcoming due to shading
    • Round and Irregular crowns of indigenous species and old Eucalyptus
    • Young planted seedling could not be identified.
  • 8.
    • Project management decided to conduct ground inventory
    • on 20% of the farms
    • Farms systematically selected along the flight lines
    • (every 5 th photograph); 62 farms selected and 100% enumeration
    • was done-38 in Nakuru and 24 in Nyandarua
    • The Intensified Forestry project ended in 1995 (project duration
    • 1990-1995)
    • Kenya Forestry Masterplan came out in 1994-The Masterplan
    • indicated that 65% of all wood products in high and medium
    • potential areas were from farms and predicted that by 2015,
    • 85% would be from Farms;
    • Proposed farm based tree planting projects.
    • 1996: The Farm Forestry Project started in Nakuru and Nyandarua
    • 1998: Evaluation earlier Project done by re-inventorying
    • the 62 farms done in 1993
  • 9. Comparison between 1993 and 1998 inventories 17.07m 3 7.5m 3 Usable Vol/farm (30% of Vol/ha) Up 107% 19.9m 3 9.6m 3 Vol/Ha Up 128% 56.9m 3 25m 3 Vol/farm Up 59% 397 250 Trees/Ha Up 73% 1137 656 Trees per farm difference 1998 1993
  • 10.
    • Replicability of the methodology
    • 2000: Biomass surveys in Ukambani Districts of Machakos,
    • Makueni, Kitui and Mwingi
    • 180 sample plots taken-Biggest in Kenya-Covered farms,
    • ranches (sparsely tree populated areas) and also woodlands
    • (densely tree populated areas)
    • Sample plots randomly selected-used soil maps as indicator
    • of vegetation. Plots proportionally distributed depending
    • on geographical coverage of each soil type.
    • Formed Baseline Data for a 5 year Belgium Funded Programme
    • Report not yet published but being used by project
    • management
  • 11.
    • 2001: Detailed farm inventories done on 35 farms earlier
    • randomly selected by the Tree Diversity Trials Programme
    • in Nchoroiboro, Kigane and Egoji villages in Meru Central
    • 2002; Farm Inventories for the Timber MarketingProgramme
    • (ICRAF, FAN and GOK programme)
    • 35 farms within an area supplying tree products covering the
    • Coffee growing Zone and the Cotton/ tobacco Growing Zone
    • in Meru Central
  • 12. Tree shapes used for Farm based Surveys..(1,2, 3, 4) Computing usable volume (stem volume from stump to tip) ln(v)=a+b ln(d) V-usable volume (dm3) D-diameter at breast height (cm) a ad b are constants constants and are for different tree forms Tree type parameter (a) Parameter(b) Source 1 -2.2945 2.5703 Laasasenaho 1982 2 -1.7322 2.3992 Pukkala 1989 3 -1.6493 2.3567 Pukkala 1989 4 -1.6840 2.2406 Pukkala 1989 Equations used to calculate tree volumes and constant for the four form factors used .
  • 13.
    • Fuelwood is a
    • salvage operation after
    • Sawmillers remove
    • the logs
    • Most of the splitting
    • done on farm using
    • Tractor driven circular
    • Saws and Powersaws
    Tree Cutting on farms-Giaki Area in Meru Central
  • 14. Grevillea Boundary Planting in Giaki Area-Meru Central Silvicultural Operations such as thinning not done since the tree were not meantfor timber production at initiation .
  • 15. Splitting of Grevillea logs using Powersaws- Kigane Village, Meru Central
  • 16. Some findings from on farms biomass surveys-Timber Marketing Programme…
    • More woody biomass in the
    • Cotton zone than coffee zone
    • More stems in the cotton zone than coffee zone
    • More species composition in Cotton zone than coffee zone
    • Grevillea robusta, Vitex Keniensis and Cordia The most popular species
    • Total biomass count over 35 farms found ca 267 species per farm
  • 17. TREE USES Use No. of stems Volume (m3) Fuelwood 1448 298.651 Charcoal 15 8.641 Fruit tree 547 17.083 Herbal 45 197.751 Timber 3032 1,295.250 Honey 16 16.446 Fodder 13 11.767 Carving 93 47.949 Others 186 17.417 5395 1,910.955 Some findings from on farms biomass surveys- Timber Marketing Programme
  • 18. DISTRIBUTION OF DBH CLASSES DBH CLASS No. of stems Volume (m3) 1 - 5 1325 4.867 6 - 10 725 20.406 11 - 15 662 60.019 16 - 20 682 131.904 21 - 25 592 195.630 26 - 30 642 368.316 31 - 35 252 201.030 36 - 40 240 278.350 41 - 45 125 193.486 >45 150 456.947 5395 1,910,955 Some findings from on farms biomass surveys- Timber Marketing Programme
  • 19. Growing place No. of stems Volume (m3) Homestead 289 120.543 Border 2733 941.808 Grazing 212 90.816 Cropland 1939 698.951 Home garden 53 17.008 Woodlot 83 16.925 Woodland 86 24.905 5395 1,910.955 Some findings from on farms biomass surveys -Timber Marketing Programme
  • 20. DISTRIBUTION OF AGE CLASSES Age class No. of stems Volume (m3) < 6 2080 46.974 6 - 10 1081 213.202 11 - 15 1269 568.629 15 - 20 602 501.173 21 - 25 95 104.086 26 - 30 169 191.677 > 30 99 285.216 5395 1,910.955 Some findings from on farms biomass surveys -Timber Marketing Programme
  • 21.  
  • 22.
    • For the data/information from Farm the inventories
    • to be useful
    • It requires to be presented in a use friendly format,
    • It needs to be combined with socio-economic information,
    • It needs markets information
    • THANKYOU