Forest Carbon Assessment and Monitoring by Local Communities Experiences from Tanzania

Transcription

1 Forest Carbon Assessment and Monitoring by Local Communities Experiences from Tanzania Kyoto: Think Global Act Local Research Project 1

2 Introduction Key aspects of determining carbon benefits Net differences in carbon stocks between any two periods At national level: Need RS Ground forest inventory data At project level: Continuous assessment and monitoring 2

3 Growth Individual project With project case: to determine the rate of recovery Without project case: to determine the rate of degradation Normal growth pattern No deforestation & degradation Degradation Recover toward the threshold Present stock with project case Present stock without project case Baseline (without project case) Time (years) Start of mgt Start Measurements 3

4 Challenges At national level National inventories not carried out At project level Inventories are not carried out Reasons: Limited capacity Human & Financial resources 4

5 Involving Local Communities Strategy to reduce the transaction costs of measuring carbon and to overcome shortage of human resources Local communities in 4 locations were trained and tasked to conduct the measurements Techniques were developed to measure and monitor carbon stock based on IPCC good practice guideline etc. They are: User friendly to the users - i.e.to the communities reliable and presented in a format acceptable to the scientific community 5

6 The equipment Consists of: A handheld computer with ArcPad TM connected to GPS It is easy to use Is used to locate: forestry boundaries sample plots and 6.0 software and recording measurement data With a step-by-step guide to the procedures, local communities were trained in a short time and were able to use the system effectively 6

7 Steps in Carbon Assessment i. Forest mapping/stratification ii. Pilot survey to estimate variance and number of sample plots iii.locate the sample plots on the ground iv.measure the dbh of all trees and other variables v.set out the sub-plots for the grasses, herb and litter data vi.take soil samples randomly within the plot 7

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10 Locating sample plots on ground 10

11 Measurements taken from the plot 11

12 Other forest types 12

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14 Data analysis The following trees stand parameters were computed: Density i.e. the number of stems per ha (N) Basal area per hectare (Dominance)(G) Volume per ha (V) and Dry biomass / carbon (tones per ha) Trees volume and biomass were computed using tested local existing allometric functions for the areas. Computation were fitted on Ms Access database 14

15 With Project Case Vegetation type Location Results Average annual increment (t/ha/yr) CO2 sequestration (tco 2 /ha/yr) Forest Area (ha) Total sequestration (tco 2 /ha/yr) Woodlands Kitulangalo ,180 Ayasanda ,760 Lowland Ludewa Montane Mgambo ,760 Without Project case Vegetation type Location Average biomass loss (t/ha/yr) Average CO 2 Emissions (t/ha/yr) Forest Area (ha) Total CO 2 Emissions (tco2/ha/yr) Woodland Kitulangalo Montane Mgambo

16 Activity 1. Pilot Planning / Training If carried out only by Professionals No. of Days 2. Field Assessments Cost ( ) Cost per Year If Carried out by Local Communities with Little Assistance from Professionals No. of Days Cost ( ) 1 st Year 2 nd Year 3 rd Year 4 th Year , Univ. FR 10 1, , VFR Control Data anal. 10 1, Cons. Fees 34 4, Inst. Fees Total 34 10, ,681 2,716 1, Cost per ha

17 Conclusions Since forests under CFM are efficient in carbon storage and sequestration: governments are argued to consider CFM as part of their approach under climate mitigation policy Since there are no data on carbon stocks: Studies on forest inventories using methodology such as that developed by K:TGAL are recommended Already approached by the Norwegian Embassy in Dar on the possibility of nationally scaling up of the methodology for REDD demonstration activities 17

18 The End! Ahsanteni Sana 18