MitigAdaptStratGlobChange(2007)12:1131–1152 DOI10.1007/s11027-006-9067-0 ORIGINAL PAPER Development of an agroforestry carbon sequestration project in Khammam district, India P. Sudha Æ V. Ramprasad Æ M. D. V. Nagendra Æ H. D. Kulkarni Æ N. H. Ravindranath Received:19April2006/Accepted:12August2006/Publishedonline:22March2007 (cid:1)SpringerScience+BusinessMediaB.V.2007 Abstract This paper addresses methodological issues in estimating carbon (C) sequestration potential, baseline determination, additionality and leakage in Khammamdistrict,AndhraPradesh,southernpartofIndia.Technicalpotentialfor afforestation on cultivable wastelands, fallow, and marginal croplands was consid- ered for Eucalyptus clonal plantations. Field studies for aboveground and below- ground biomass, woody litter, and soil organic carbon for baseline and project scenarios were conducted to estimate the carbon sequestration potential. The baseline carbon stock was estimated to be 45.3 t C/ha, predominately in soils. The additional carbon sequestration potential under the project scenario for 30 years is estimated to be 12.8 t C/ha/year inclusive of harvest regimes and carbon emissions due to biomass burning and fertilizer application. Considering carbon storage in harvested wood, an additional 45% carbon benefit can be accounted. The project scenariohasahigherbenefit/costratiocomparedtothebaselinescenario.Theinitial investment cost requirement, however, is high and lack of access to investment is a significant barrier for adoption of agroforestry in the district. Keywords Climate mitigation Æ Afforestation Æ Aboveground biomass Æ Soil organic carbon Æ Baselines Æ Leakage 1 Introduction Globally, forestry has taken center stage as one of the options to mitigate climate change.Totalglobaltechnicalpotentialforafforestationandreforestationactivities fortheperiod1995–2050isestimatedbetween1.1and1.6 GtC/year,ofwhich70% potentially would occur in the tropics (IPCC 2000). Agroforestry is an attractive P.SudhaÆV.RamprasadÆM.D.V.NagendraÆN.H.Ravindranath(&) CentreforEcologicalSciences,IndianInstituteofScience,Bangalore,India e-mail:[email protected] H.D.Kulkarni ITC,PaperboardsandSpecialtyPapersDivision,Bhadrachalam,India 123 1132 MitigAdaptStratGlobChange(2007)12:1131–1152 option for carbon (C) mitigation as (i) it sequesters carbon in vegetation and soil, depending on the pre-conversion vegetation and soil carbon; (ii) wood products produced serve as substitutes for similar products that are unsustainably harvested fromnaturalforests;and(iii)itincreasesincometofarmers(MakundiandSathaye 2004). Approximately 1.2 billion people, 20% of the world’s population, depend directlyonagroforestryproductsandservicesinruralandurbanareasofdeveloping countries (Leakey and Sanchez 1997). ThepotentiallandareasuitableforagroforestryinAfrica,AsiaandtheAmericas has been estimated at 585–1215 Mha (Dixon 1995). An additional 630 Mha of cur- rentcroplandsandgrasslandscouldbeconvertedintoagroforestry,primarilyinthe tropics, via of two types of agroforestry activities: converting fallow and marginal croplandstoagroforestry,andadoptingagroforestrypracticesintoexistingcropping systems. Alargepotentialforagroforestryhasgivenrisetoscientificandpolicyquestions from national governments, climate change negotiators, potential investors in greenhouse gas mitigation activities and local communities. The contentious issues are (1) additional carbon that could be created, (2) magnitude of emissions reduc- tionsthatcanbeachieved,(3)cost-effectivenessandtotalcostforimplementationof agroforestryprojects,and(4)institutionalarrangements.Toresearchsomeofthese issues, this paper considers agroforestry options in Khammam district, Andhra Pradesh, in southern India. The paper’s main objectives are to: – Estimate carbon sequestration potential of farm forestry plantations promoted by industry, – Develop baselines for farm forestry plantation projects, – Establish additionality of carbon sequestration for farm forestry activity, – Measure carbon stock changes through the stock change approach, and – Assess leakage and measures to address leakage. 2 Description of project location TheKhammamdistrictliesinthenortheasternpartofthestateofAndhraPradesh, located between 17(cid:2)40¢ N and 81(cid:2)00¢ E, and rises 100 m above mean sea level. Forest area constitutes 52% of the district geographic area and the forest types are tropical moist deciduous, tropical dry deciduous and tropical thorn. Soils in the region are of black cotton, red alluvial loam and red sandy type. The study was conducted in six mandals,1 in the Khammam district, namely Burgampahad,Kukunoor,Bhadrachalam,Kunavaram,CherlaandVelairpadu.The studyareacomprises13%ofthedistrictarea.Thelandusepatterninthemandalsis giveninTable 1.Forestsdominatelanduse,andaccountfor62%ofthegeographic area. Cultivated agricultural lands form the second most abundant landuse with a meancroppingareaof18%,followedbynon-agriculturallands(8%).Therestofthe area is covered by barren and uncultivated land (4%), fallow land (1%), and land under tree crops, pasturelands and cultivable waste each less than 1%. 1 Mandal: Administrative unit below the district consisting of a group of villages/panchayats. In Andhra Pradesh blocks were subdivided into mandals but retained the administrative and local governmentfunctionsofblocks(http://www.velugu.org/faq.html). 123 MitigAdaptStratGlobChange(2007)12:1131–1152 1133 Table1 LandusepatterninthesixselectedmandalsofKhammamdistrict,AndhraPradesh(ha) Landuses Burgampahad Kukunoor Bhadra- Kuna- Cherla Velair- Total chalam varam padu Geographicarea 27,390 28,681 37,669 20,382 54,337 41,544 210,003 Forestcover 14,609 17,067 25,514 5,435 37,478 29,471 129,574 Totalcroppedarea 7,596 4,835 9,770 6,089 6,715 3,184 38,189 Misc.treecrops 208 277 48 315 163 218 1,229 Non-agricultureland 1,311 1,627 2,015 4,364 5,446 2,694 17,457 Pasture&grazing 284 297 0 19 0 236 836 Barren&uncultivated 624 902 159 2,128 3,499 709 8,021 Cultivablewaste 28 166 0 139 63 500 896 Otherfallowland 101 1,056 54 0 264 612 2,087 Total 24,761 26,227 37,560 18,489 53,628 37,624 198,289 Croplands vary in the mandals, but have identical management practices with regard to application of chemical fertilizers and irrigation practices. The cropping pattern in the six selected mandals in Khammam district is dominated by rice (Oryza) (38%), except in Bhadrachalam and Kunnavaram mandals (Table 2). Cotton (Gossypium) is the second largest crop with 16% of the cropping area, followedbychilli(9%)jowar(9%)andredgram(8%).Therestofthecroppingarea (19%) includes green gram, sugarcane, maize (Zea), black gram, tobacco (Nicoti- ana),groundnutandsesamum.Farmforestryaccountsforonly10%ofthecultivated areainthesemandals.EucalyptusandLuceanaleucocephalaclonesform90and3% of the area, respectively, and the remaining plantations are raised by the Andhra Pradesh Forest Department (7%). 3 Afforestation rates—past and projected Past, current and projected rates of afforestation and reforestation (A&R) are considered in projecting the ‘‘business as usual’’ or baseline scenario, and the po- tential for farm forestry project activities in the selected mandals. In this region, agroforestry is being promoted largely by ITC (a large national paper products companyoperatingapapermillatBhadrachalam),andtherateofafforestationwas about54 ha/yearduringtheperiod1992–1999(Table 3). Theratesincreased5-fold Table2 Croppingpatternintheselectedmandals(ha) Crops Bhadrachalam Burgampahad Kunavaram Kukunoor Cherla Velairpadu Total Paddy 2,762 2,569 1,447 1,812 3,220 774 8,590 Jowar 996 53 1,942 56 0 0 3,047 Maize 121 60 14 72 0 0 267 Greengram 190 266 404 150 692 227 1,010 Blackgram 288 48 998 331 126 0 1,665 Sugarcane 0 2 0 54 54 46 56 Redgram 1,319 434 182 395 287 148 2,330 Cotton 1,157 3,046 97 1,008 0 67 5,308 Tobacco 517 115 492 81 0 0 1,205 Chillies 1,955 273 377 348 7 120 2,953 Groundnut 99 81 1 0 197 197 181 Sesamum 190 94 96 18 0 0 398 Total 9,594 7,041 6,050 4,325 4,683 1,578 27,010 123 1134 MitigAdaptStratGlobChange(2007)12:1131–1152 Table3 AreaafforestedunderfarmforestryinselectedmandalsofKhammamdistrict(ha) Mandal Bhadra- Burghampahad Kuna- Kukunoor Cherla Velair- Total chalam varam padu 1992–1999 312 60 49 9 2 4 429 2000 76 17 36 6 10 2 136 2001 101 14 33 30 0 1 179 2002 126 83 74 54 0 0 338 2003 143 36 63 31 ,7 9 273 2004 154 54 51.2 12 0.0 0 272 Total 912 265 308 142 19,5 9 1,627 Averageplanting 120 41 52 27 3 2 240 rates(2000–2004) Note:areasareroundedtonearestha to about 240 ha/year during 2000–2004 (Table 3). The company intends to plant 364 ha/year in the next 6 years (2005–2010) in these six mandals. 3.1 Technical potential of land for afforestation The farmers are currently converting land under crops such as chilli, cotton and redgram to plantations. Though the yearly land use change pattern is not avail- able, discussion with the farmers reveal the preference of farmers to shift from crop cultivation to Eucalyptus plantation. Just considering uncultivated lands such as pasture land, cultivable and fallow land, 9,658 ha are available after deducting the projected afforestation rates by the ITC company for the period 2005–2010 to estimate additional ha above the baseline. Thus the land potential for agrofor- estry is significant, considering conversion of marginal croplands, the current practice. 4 Additionality A project activity is additional if anthropogenic emissions of greenhouse gases by sources are reduced below those that would have occurred in the absence of the project activity—the baseline. Additionality tests vary by greenhouse gas (GHG) mitigation program (e.g., the UNFCCC’s Clean Development Mechanism (CDM), but conceptually a project needs to demonstrate environmental, technical and financialadditionality.Aproject’senvironmentaladditionalityisdeterminedbythe comparison of baseline and project GHG-benefits. The proposed project activities should:(a)resultinincreaseofnetcarbonstocks,(b)wouldnothavegoneahead(or not in their proposed form) in the absence of the project, and (c) do not result in increased deforestation (or decreased carbon removals) elsewhere (known as leak- age). Further, in the case of the CDM, the project should contribute to sustainable development,e.g.,via local socio-economic benefitssuchasincreased employment, incomeoraccesstonon-timberforestproducts.Financialadditionalityrequirements intheCDMincludebothamacroadditionalityfactor,i.e.,notfinancedwiththehelp of Official Development Assistance (ODA), and the micro additionality factor or investment additionality. 123 MitigAdaptStratGlobChange(2007)12:1131–1152 1135 One approach is use of the additionality tool developed by the UNFCCC (www.cdm.unfccc.int/methodologies/PAmethodologies), which requires demon- strating the following: – Identifylikelyalternativelanduseprojectactivities:Thealternativetotheproject isdrylandagricultureorstatusquo.Cropssuchascotton,chilliandtobaccocan be cultivated on these lands or can remain fallow. – Identify investment options: Clonal Eucalyptus plantations require a high establishment cost for the initial 3 years. Farmers have to invest Rs. 40,000/ha (about $890 US at exchange rate of $1 US = Rupees 45) for raising Eucalyptus plantations.Financialinstitutionsdonotextendloansforplantationsduetothe risk factor, andfunding frominternationalsources islacking. The alternativeto theproject,continuedagriculture,iseasiertoadoptduetoloanavailabilityfrom banks and other financial organizations. – Analysisofbarriers:Asmentionedabove,financeisabarriersinceloanscannot be secured from national and international markets for afforestation and the investment required is high. – Analysis of usual practice: This step is to identify similar projects in terms of geographicalarea,technology,sizeandaccesstofinancinganddifferentiatethem from the proposed project. In Khammam district, most of the rich farmers with large landholdings plant part of their agricultural land with the Bhadrachalam Eucalyptus clones, which should be incorporated into the baseline. The farmers pay upfront for the seedlings and other establishment costs. Under the project scenario,smallandmarginalfarmerswithsmalllandholdingscouldbeidentified and planting performed on their lands. The impacts of such a project beyond environmental benefits would be to attract new investors. 5 Baseline development Baseline estimation guidance is not yet standardized. Under the U.N. Framework Convention on Climate Change’s Clean Development Mechanism (CDM), for example,‘‘thebaselineforprojectactivityisthescenariothatreasonablyrepresents anthropogenicemissionsbysourcesofGHGsandremovalbysinksthatwouldoccur in the absence of the proposed project activity’’. In this case, a structured project- specific approach to baseline development was adopted, based on reliable, site- specific information and comprehensive analysis, with the potential to credibly quantify additionality. Site-specific data were used to calculate the initial stock of carbon as climatic and site conditions, species planted and site management all significantly can affect the carbon content of different management systems. Socio- economic indicators and land suitability were examined while assessing the most likely land use for a project site, as they are important determinants of land use, These factors canvary from site to site. The generalapproach used to estimate the baseline was to: 1. Identify current land use/land use trends and associated carbon stocks of the project site; 2. Assesslikelyfuturelandusewithoutinterventionplannedintheprojectcase;and 3. Quantify carbon uptake and emissions of likely land use over the project life. 123 1136 MitigAdaptStratGlobChange(2007)12:1131–1152 The following steps were undertaken to establish the baseline (Ravindranath et al. 2006): • Define land use systems and their tenurial status; • Define the project boundary and prepare a map; • Select carbon pools and define methods for measurement; • Develop sampling design and strategy for biomass and soil carbon estimation; • Lay plots in different land use systems and measure identified parameters; • Analyze data for aboveground biomass (AGB) carbon stock, belowground biomass, woody litter, dead wood and soil carbon; • Assess past and current A&R rates; • Projectfuturelanduseandestimatepotentialareafortheprojectactivities;and • Estimate carbon stocks using area and per ha carbon stock data, for the project area. 5.1 Project area and legal status Theprojectactivity—afforestation—isproposedoncultivablewastes,marginalcrop and fallow lands in the six mandals of Khammam district. These lands are legally under private ownership of individual farmers. Cultivable waste (long fallow) lands are available for cultivation, but not culti- vatedduringcurrentyearandlast5 yearsormoreinsuccession.Suchlandsmaybe either fallow or covered with shrub and jungle, and are not put to any use (NRSA 1995). Fallowlandsarelandstemporarilyoutofcultivationforaperiodofnotlessthan 1 year and not more than 5 years, due to: (i) poverty of cultivators, (ii) inadequate supply of water, (iii) silting of canal and rivers, and/or (iv) the non-remunerative nature of farming (NRSA 1995). 5.2 Project boundary Theprojectboundaryneedstoencompassallanthropogenicemissionsbysourcesof GHGs and removals by sinks under the control of the project participants that are significantandreasonablyattributabletotheprojectactivity,inthecaseoftheCDM rules. The project area consists of geographic domain with more than one discrete area of land, within which GHG emissions or removals and other attributes of a projectaretobeestimatedandmonitored.Thusthesixmandalboundariesarethe project areas. The project boundary includes discrete blocks of plantations on individual farmer’s lands in each of the mandals. 5.3 Sampling strategy for baseline The carbon pools selected for baseline development are aboveground biomass, belowgroundbiomass,andsoilorganiccarbon.Deadwoodwasnotincludedasthis wasnotamajorcarbonpoolunderfarmforestry.Thedefinitionsofcarbonpoolsare as defined by the IPCC (2003). 123 MitigAdaptStratGlobChange(2007)12:1131–1152 1137 5.3.1 Aboveground and belowground biomass This dominant carbon pool was estimated by the commonly used plot method. Sampling on farmlands involved enumeration of all trees on individual farms. Sampling strategy for farm forestry involved randomly selecting 10 farmers who wereopentofarmforestryactivitywithEucalyptusclones,ofwhichfiveweresmall farmers(<2 ha)andfivewerelarge(>2 ha).Atotalof40farmerswereselectedand interviewedtoassessthecostandbenefitsofthepresentcropandtheareaavailable forfarmforestry.Atotalof95 haoffallowandculturablewastelandownedbythem wassampled.Alltrees>1.5 minheightor>5 cmDBH(DiameteratBreastHeight) were enumerated. Species-specific or generic volumeequations fromForest Survey of India (FSI) reports (1996) were used to convert DBH and height into volume (m3/ha),andacoefficientof0.45ofbiomasswasusedtoestimatecarboncontent.A default conversionfactor of 0.26 of abovegroundbiomasswas used to calculate the belowground biomass (IPCC 2003). 5.3.2 Soil carbon To estimate soil organic carbon, soil samples at depths of 0–15 and 15–30 cm were collected.Bulkdensitywasmeasuredandsoilorganiccarboncontentwasestimated in the laboratory using the Walkley–Black method. Soil samples from tree plots in marginal agricultural lands and other fallow lands representing baseline scenario werecollected.Acompositesoilsamplefrommultiplesoilsampleswaspreparedfor different land categories. 5.4 Determination of the baseline The features of the project land area are: – Theselandshavenotbeenforestedsince1990andhaveeitherbeencroplandsor fallow since then; – Theidentifiedlandsintheprojectareaconsist ofcultivablewastes,fallowlands and marginal croplands; and – Thus the current land use is either agriculture or fallow lands. 5.4.1 Biomass stock under baseline scenario The aboveground biomass under baseline scenario is comprised of trees that are planted on bunds2 of agricultural lands. In the sampled area of 95 ha of farmlands, theabovegroundbiomassvariedfromnilto0.19 t/ha,withanaverageaboveground biomassof0.02 ± 0.05 t/ha.Considering0.26astheconversionfactorforestimating belowground biomass from aboveground biomass, 0.005 t//ha accounts for the belowground biomass. There was negligible woody litter. Thus the total biomass underbaselineis0.025 t/haintheprojectarea—anextremelylowbaselineCvalue. 2 Earthenembankmentconstructedtoretainwaterorforseparatingonefarmfromanother. 123 1138 MitigAdaptStratGlobChange(2007)12:1131–1152 5.4.2 Soil organic carbon (SOC) under baseline scenario Land use history has a strong impact on the SOC pool. Ecosystem studies of soil carbonindicatelargedifferencesinsoilcarbondependingonsoiltype,topography, land-use history, and current land use and land cover (Marland 2004). The SOC variesdependingonagriculturalsystemsandcropsandontheinputstoproduction (e.g., fertilizers, irrigation and soil tillage). Therefore the SOC content of marginal croplandandfallow landsweredeterminedin theproposedprojectareafordepths of 0–15 and 15–30 cm. In the proposed project area, the SOC for black and red soil under marginal croplands and fallow lands was determined. Black cotton soil was prevalent in the mandals of Bhadrachalam and Kunnavaram and red sandy and alluvial soil in Burgampahad and parts of Kukunoor mandal. The agricultural system and the inputstoproductionweresimilarwithfertilizerapplication,irrigationandsoiltillage by all the farmers. The average SOC at 0–30 cm depth of black soil was 47.0 ± 15.9 t/ha and for red soil 37.1 ± 16.9 t/ha. Further analyzing at different soil depths, the deviation was low at 0–15 cm layers than at 15–30 cm level (Table 4). Aggregation of homogeneous land use systems provides a regional baseline for Khammam district SOC of 45.3 ± 16.0 t/ha. 5.5 Carbon stock changes under baseline Carbon stocks in the baseline on fallow or marginal cropland of aboveground bio- mass is 0.02 t/ha due to a few big trees on the bunds, with an average DBH of >40 cm, but with negligible growth, since they have reached equilibrium. The soil C status under the pre-plantations land use is assumed to be in approximate equilibrium with inputs equals to outputs. If land has been cultivated for decades, the rapid soil C changes with initial cultivation would have ceased, and either soil C is changing very slowly or has stabilized. Thus, the carbon stock change under baseline can be considered static. The C-stock under baseline is 45.3 t/ha, which could continue to remain so under the baseline scenario. For a project area of 8,000 ha, the baseline C-stock would remain constant at 362,000 t C. Table4 Soilorganiccarbon(t/ha)bydepthincroppingsystemsandsoiltypesinKhammamdistrict, basedonfieldstudies Managementpractice Soiltype Soilorganiccarbon(t\ha)atdifferentdepths 0–15cm 15–30cm 0–30cm Chilli BS 26.8±2.7 20.7±9.4 47.5±11.2 Cotton BS 27.9±8.0 20.1±14.9 47.9±20.1 Miscellaneous BS 21.0±5.4 18.3±9.2 39.3±3.8 Fallow BS 31.6±10.0 18.6±13.8 50.2±23.8 Average BS 27.2±6.9 19.8±11.6 47.0±15.9 Miscellaneous RS 16.6±4.3 20.5±13.3 37.1±16.9 Average BS+RS 25.4±7.6 19.9±11.5 45.3±16.0 Notes:BS=BlackSoil(Vertisols);RS=RedSoil(Alfisols) 123 MitigAdaptStratGlobChange(2007)12:1131–1152 1139 6 Project activities 6.1 Area for project activities The land categories considered for afforestation are pasture and grassland, barren and uncultivated, cultivable waste and fallow land use in the six selected mandals, totaling 10,687 ha. Of them, 8,000 ha are identified for AR from cultivable waste, fallowandmarginalcroplandsonprivatefarmland,throughplantingBhadrachalam Eucalyptus clones at a rate of about 2,000 ha/year. 6.2 Lifetime of the project Thelifetimeoftheprojectisdefinedbytechnicaloreconomicconsiderationsandis generallylongerthantheperiodduringwhichthecarboncreditscanbelegitimately generated (FA 2005). The lifetime of the project is assumed to be seven or eight rotations or approximately 30 years, i.e., 2006–2035. The PROCOMAP model (Sathaye et al. 1995) is used to account for annual changes in carbon stock for the project period of 30 years. The accounting period is a determining factor for the volume of emission reductions that can be generated by a mitigation project. 6.3 Sampling strategy for project scenario The AGB, BGB, SOC and woody litter pool carbon stocks and growth rates were measured in the same or neighboring villages with plantations, and estimated as inputs to PROCOMAP to project likely C-stocks for the project activities over 30 years. 6.3.1 Aboveground biomass AGB was determined by two-pronged strategy that included (a) monitoring in permanentplots,and(b)directAGBmeasurementsbyharvest.Permanentplotsof Eucalyptus clones that represent the project scenario are being measured twice a year for their annual increments in AGB by the paper mill company, providing 9 years of annual data for Current Annual Increment (CAI), Mean Annual Incre- ment (MAI) and Eucalyptus-specific volume equations. The harvest method is the most accurate of all the biomass estimation methods since it involves direct mea- surement of methodically harvested tree components. This avoids the usual inade- quaciesofequation-basedmethods,viz.,unavailabilityofspecificequationsandthe validrangeoftheseequationsforaccurateresults,variabilityinareaofsamplingand area measured in the equations, and manual errors. The biomass expansion factor was calculated by the above procedure (excluding the BGB) at private farmlands where Eucalyptus was commercially harvested. 6.3.2 Belowground biomass The belowground biomass is by far the most uncertain of the carbon pool biomass estimates, even though IPCC (2003) provides a conversion factor of 0.26 of the aboveground biomass. In this study, the harvest method was used. 123 1140 MitigAdaptStratGlobChange(2007)12:1131–1152 6.3.3 Soil carbon Soil samples at depths of 0–15 and 15–30 cm were collected from ploughed and unploughed sites, and analyzed using the Walkley–Black method. SOC was deter- mined for 0–30 cm depth on a per-hectare basis. The soil type was stratified into red and black soil, and then further stratified by landuseorcroptypeforthebaselinescenario.Fortheprojectscenario,soilsamples werecollectedfromdifferentageclassesandfromadjacentland,whichservedasthe control. The difference of SOC was taken as the increment over the age class. For each of the age class, weighted average of SOC was calculated, for a composite of soils from tilled and untilled lands. Eucalyptus plantations are regularly tilled after the rains every year, and fertil- izers such as urea, MOP and DAP are applied annually. The weighted average for various plantation age classes was computed, and the difference in increment from the subsequent age class was considered the annual SOC increment, and projected out for the regional baseline scenario (Fig. 1). 6.4 Carbon accumulation in various pools The PROCOMAP model was used to analyze the mitigation potential as well as cost-effectiveness of mitigation activities. The model estimates change in C stock annually under the baseline and mitigation scenarios. Adopting the C stock change method to estimate the C pool increment mathematically, the change in carbon stocks attributable to a project (DCnet) at any given time can be expressed as: n X(cid:1) DCnet = ðDCproject(cid:1)DCbaselineÞ + ðDCproject(cid:1)DCbaselineÞ time1 time2 i¼1 (cid:2) + (cid:2)(cid:2)(cid:2)ðDCproject(cid:1)DCbaselineÞ timen where DCproject and DCbaseline are the measured changes in carbon stocks at periodic monitoring time over the period i, associated with the project and the respective baseline case. Fig.1 Carbonstockchangein 70 30 thebaselineandproject n 60 25 o n scenario rOagcinCarb/t(ha) 23450000 5112050 auccumatilort(/h/a)y 10 0 C Slio 0 -5 SO -10 1 2 3 4 5 9 -10 Age (yrs) Baseline Project SOC t/ha/yr 123