Manual of FALLOW model - | World Agroforestry



Manual of FALLOW Model Version 2.1R. Mulia, B. Lusiana, and D. A. SuyamtoWorld Agroforestry Centre2013Disclaimer and CopyrightThis is a model on rural landscape dynamic. Although efforts have been made to incorporate relevant process knowledge on a range of interactions, the model is not more (and not less) than a research tool. Model prospective outputs may help in developing specific hypotheses for research, in exploring potential future options on development strategies, but they should not be used as authoritative statements per se.Copy right, but do not copy wrong. The FALLOW model was developed on the basis of publicly funded research at the World Agroforestry Centre (ICRAF) and may be used for non-commercial research purposes in the interest of research or governmental institution as well as farmers of the world. -------------------------------------------------------------------------------- For further information, consultation, and technical support please contact: Betha Lusiana (blusiana@),Rachmat Mulia (rmulia@), orMeine van Noordwijk (m.van-noordwijk@)World Agroforestry centre (ICRAF)Southeast Asia ProgramP.O. Box 161Bogor 16001 IndonesiaTable of contentsIntroductionWhat is it?1How it works?2How to use it?3How to get the model?3Examples of model applicationWhich development strategy is most appropriate: genuine or pseudo development?4S-curve story of subsidy for development5Description of FALLOW modeling conceptsLanduse option and change8Succession and growth14 Land productivity15Appendix 1. List of input maps and variables for FALLOW simulation18Appendix 2. Minimum inputs for FALLOW simulation26Appendix 3. Running FALLOW model with Nutshell28Appendix 4. Important notes in running FALLOW31References35IntroductionWhat is it?Figure 1 Prospective diagram depicting the impact of development strategies to economical (x axes) and ecological value (y axes) relative to the initial condition before implementing the strategies (baseline, central point of the diagram).The main issues in prospecting landuse strategies for rural landscapes are mostly related to: Non-linear baseline trajectories;Trade-offs between economical utilities and environmental services; andAdditionalityThe FALLOW Model has been developed as a tool to prospect the likely shifts of some scenarios on such strategies from the baseline. The strategies may imply to:Losses in both economical and ecological values (collapse);Gains in economical value but loss in ecological value (red development);Gain in ecological value but loss in economical value (conservation); orGains in both economical and ecological values (green development).How it works?-1905047625The FALLOW Model simulates land use/cover change dynamics due to local responses on external drivers with various feedback loops, and assess the consequences of the resulting land use mosaics on economical utilities (welfare and food security) and environmental services (carbon stocks, watershed functions and biodiversity). Local responses portrayed by the model comprise:How farmers adjust their expectation about economical utility of each available option on land-based and non-land-based investments through learning;How farmers allocate their capitals (labors, money and land) to each available option of investments;How farmers perceive about attractiveness of a plot to expand particular land use system, with regards to some spatial factors determining potential benefits (soil fertility, suitability and attainable yield) and potential costs (transportation, maintenance and land clearing);Succession, growth, fire and land conversion; andLaws of diminishing and increasing marginal utilities on soil fertility and land use productivity.The main external drivers incorporated in the model include:Market mechanisms and relevant regulation interventions, articulated through commodity prices, costs and harvesting labor productivities;Development programs, articulated through extension, subsidies, infrastructures (settlements, road, market, processing factories), and land use productivities; andConservation programs, articulated through forest reserves as prohibited zones for farmers.How to use it?The FALLOW Model is a raster-based spatially explicit model with default spatial resolution of 1 ha, temporal resolution of 1 year and socio-economical resolution of 1 community, applicable for rural landscapes. The model uses PCRaster () as the main platform. Your computer should have operating system of Microsoft Windows XP Professional, version 2002 or later, which processor having at least 3.2 GHz of speed and 496 MB of RAM, and which hard disks having at least 15 GB of free space.How to get the model?We provide you with free sources of the model, which are freely downloadable from: or upon request to fallow@. We also provide you with educational versions of the model, developed using STELLA and NetLogo. Examples of model applicationWhich development strategy is most appropriate: genuine or pseudo development?Figure SEQ Figure \* ARABIC 2. FALLOW model as applied in 4 regions in Indonesia: Muara Sungkai (MS) and Way Tenong (WT) in Lampung, Sumatra; Sindenreng Rappang (SR) in southern Sulawesi; dan Sebuku (SB) in western Kalimantan.Approach for stimulating the installation of tree-based landuse systems in developing countries is usually of two types: project or programmatic approach, and both involve some regulations and interventions applied to farmer society.Project-approach is usually characterized by top-down incentive, applied only in the previously-determined project area and duration without any substantial effort to solve the apparent problems experienced by the society in the observed area. Programmatic-approach has more intentions to overcome the apparent problems which relate to e.g. access to market, to land, or to source of knowledge or information. Model applications in four regions in Indonesia (Figure 2) show that programmatic-approach (blue circles) induced the development of tree-based landuse systems by farmers and can likely convert marginal lands with relatively low investment cost. This offers better economical (x axes) and ecological (y axes) merit than project-approach (red circle). Project-approach mostly induces ecological benefit, but inferior related economical value. Please see van Noordwijk et al. (2008) for a more detail description and explanation. S-curve story of subsidy for developmentThe model has also been applied to simulate the effect of subsidy expected to accelerate the conversion of pioneer forest (i.e. marginal lands with shrubs) to rubber-based landuse systems in Muara Sungkai, Sumatra. Without any subsidy, it is unlikely that this region could be converted into landscape with prevailing rubber-plantations. The results suggest, however, that prolonged subsidy is no longer efficient when land and labour availability are limiting (Figure 3-6) (Suyamto et al., 2005). For other examples of model application, please see Lusiana et al. (2012), Mulia et al. (2013), and Tata et al. (2013).Figure SEQ Figure \* ARABIC 3. Landcover dynamic without any subsidy for development of rubber-based landuse system.lefttopFigure SEQ Figure \* ARABIC 4. Limiting resources in the development of rubber-based landuse system without subsidy.Figure SEQ Figure \* ARABIC 5. Landcover dynamic with subsidy for the development of rubber-based landuse system.Figure SEQ Figure \* ARABIC 6. Limiting resources in the development of rubber-based landuse system with subsidy. Description of FALLOW modeling conceptsDescription of FALLOW model principles can also be seen in van Noordwijk (2002) and Suyamto et al. (2009). Below is a more detailed description with equations.Landuse option and changeIn the current version, 15 livelihood options can be considered by farmers: off-farm, NTFP, timber/logging, 4 types of agricultural crops, and 8 types of agroforesty/plantation. Profit from each option is calculated relative to land area or labor (i.e. payoff to land and to labor respectively):Piland=yi*hi-ciai(1)Pilabor=yi*hi-cili(2)Where Pi is profit in unit currency person day-1 related to labor and in unit currency unit area-1 related to land obtained with livelihood option i, yi is total attainable yield in the year (unit yield), hi is average yield price in the year (unit currency unit yield-1), ci is total cost (unit currency), ai is total harvesting area (unit area), and li is total labor involved (person day). Total cost c comprises of labor and non-labor cost. The first accounts for external labor cost, i.e. salary for labors coming from outside local community. Non-labor cost consists of establishment and maintenance cost that are not related to labor, e.g. cost for buying seeds and/or fertilization at planting time or during productive season in the year. Any subsidy from government for plot establishment or maintenance reduces the non-labor cost. Total labor l is the total of allocated ‘internal’ labors to livelihood option i (see equation 12 below) plus external labor involved in that option. Hereafter, unit currency is represented in Rp, unit area in ha, and unit yield in ton.Expected payoff to land and laborFarmers do have certain profit expectations for their livelihood options. Next year profit expectation is determined based on knowledge obtained in current year: Et+1,i=Et,i+∝(Pt,i-Et,i)(3)Where Et,i is current year profit expectation for livelihood option i and reflects farmer’s adjustment rate to that expectation after current year profit analysis. Equation 3 is used to calculate expected payoff to land and to labor. The same value is applied for the two calculations. Farmer’s profit expectation, however, might not be in accordance with that of outsiders, e.g. economists. ‘Non-steady’ farmers might consider suggestions from others for calculating their ‘final’ profit expectation: Et+1,ifinal=Et+1,i+β(St+1,i-Et+1,i)(4)Where St+1,I is profit expectation suggested by others and reflects farmer’s adjustment rate to their own expectation:β=Sav*Sexp*Scred(5)Sav is suggestion availability (0 or 1) e.g. through extension or group discussion; Sexp is farmer’s exposure to attend the extension (0-1); and Scred is farmer’s assessment to extension credibility (0-1). A similar value of is used to calculate final expected payoff to land and labor. Two types of farmer’s community can be simulated with different and/or value. Resource allocation based on profit expectationBased on profit expectations, farmers determine variety and intensity of their future livelihood options. In principle, productive plots will be maintained whereas unproductive ones are possible to change into another landuse type. Farmers might also open new plots for profitable livelihood options. In the model, 3 types of landuse are simulated: forest, agriculture, and agroforestry or plantation. Forest consists of 4 classes based on aboveground biomass: pioneer, young secondary, old secondary and primary forest. Agroforestry or plantation also consists of 4 classes based on productivity level: pioneer, early production, late production, and post production. Potential areas for conversion are those of 4 types of forest except protected forests, agroforestry/plantation plots that are at late or post production stage, and agricultural crop plots with production less than 0.5 ton ha-1. Suppose that Apot describes total area potential for conversion (ha) and Apot,i is total area potential for conversion into livelihood i:Apot,i=Apot*ft+1,iland(6) And:ft+1,iland=(wi*Et+1,i)ρi=1n(wi*Et+1,i)ρ(7)That basically describes relative expected payoff to land. The power reflects farmer’s degree of profit-orientation: >0 means livelihood options with higher profit expectations are prioritized, =0 means equal prioritization and <0 gives more prioritization to less profit options. It is also possible that farmers prefer certain livelihoods option due to a non-economic reason such as cultural reason. For this, different weighting values wi can be set between livelihood options. In the current model version, farmers might be different in value but not in weighting factors. Available labors should be distributed to existing productive plots for maintenance and/or to potential converted areas for land clearing. Total available labor (person day) is:L=H*flabor*ndays(8)Where H is total population (person), flabor is labor fraction (0-1), and ndays is annual working day (person day). Total labor for livelihood option i is:Li=L*ft+1,ilabor+Lext,i(9)Where Lext is total external labor required for livelihood option i (person day) and flabor is calculated in the same way as fland in equation 7 but with expected payoffs to labor instead to land. No differentiation in and w values are made between the two calculations. No change in demand for external labor is assumed throughout the years. If different types of farmer were simulated, a fraction will determine population of each farmer’s type and labor allocation to livelihood options (equation 9) will be carried out for each group.Total area potential for conversion into a specific livelihood option has been calculated in equation 6 but not their specific locations in the landscape. Probability that a certain plot within potential areas for conversion is reserved for livelihood option i is Apot,i/Apot. Suppose that Fi is cumulative probability for Apot,i/Apot then any plot is reserved for option i if Fi-1<r<Fi where r is a generated random number (0-1), F0 = 0 and F11 = 1. If size of a plot is m ha, there are Apot/m plots to process. Actual converted areasOpening new plots needs labors for land clearing and budget for covering establishment cost. All allocated labors calculated in equation 9 can be considered as available labors for land clearing by assuming they are firstly allocated to establish new plots and later to maintain all existing including new established plots. Budget for covering establishment cost can come from two sources: allocated budget by farmers for opening new plots (explained below) and subsidy from government. Total actual converted area (ha) is a function of labor and financial capacity: Aact,i=min?(Lidi,Mexp,icest,i,Apot,i) (10)Li is allocated labor (equation 9), di is labor requirement for plot establishment (person day ha-1), Mexp,i is available budget for land expansion (Rp, see equation 21 below) allocated to each livelihood type proportional to labor allocation fraction (flabor, equation 9) plus subsidy for establishment if any, and cest,i is establishment cost (Rp ha-1).Location of actual new plotsFor each livelihood option, plot selection should be done if total actual is less than total potential converted area (i.e. Aact,i < Apot,i). The selection is based on ‘attractiveness index’: Xi,j=wfert,i*fertj+wsuit,i*suitj+wpyield,i*pyieldj1+wtrans,i*transj+wmain,i*mainj+wslope,i*slopej+wfbiom,i*fbiomj(11)Xij is attractive index of plot j reserved for livelihood option i; wfert,i describes importance of soil fertility factor for option i (0-1), e.g. oil palm plantation that needs higher level of soil fertility than rubber plantation has a higher wfert value. Fertj is actual level of soil fertility represented qualitatively (1=very poor-5=very fertile); suitj is plot suitability (0 or 1); pyieldj is potential yield (ton ha-1) (see explanation below for potential and actual yield). Transj is distance (m) between plot j and the closest transportation network:transj=min?(roadj, riverj, marketj, industryj) (12) Roadj, riverj, marketj, and industryj are the closest distance (m) from plot j to road, river, market, and processing industry respectively. Mainj is the closest distance to maintenance centre (i.e. settlement or existing plots with the same livelihood option):mainj=min?(setj,existj)(13)Slopej and fbiomj measure plot steepness (%) and floor biomass (ton ha-1) respectively. Attractive index basically compares profit that can be derived from factors supporting yield (soil fertility, land suitability, and potential yield) and potential cost for accessing, clearing and maintaining plot (transportation, maintenance, land slope, and floor biomass). Based on their degree of attractiveness, potential new plots are classified into 5 categories: z < 0 = very unattractive plot, 0 z < 1 = less attractive plot, 1 z < 2 = quite attractive plot, 2 z < 3 = attractive plot and z 3 = very attractive plot where z is normally standardized attractive index. Now suppose ni,k is number of potential new plots reserved for option i that classified into attractiveness category k (k=1, 2, …, 5 for very attractive) and gi,k describes number of plots with higher categories than k. Thus, gi,5 = 0, gi,4 = ni,5 + gi,5, …, and gi,1 = ni,2 + gi,2. Probability that a plot (between potential new plots for livelihood option i) with attractiveness category k will be selected as actual new plot is:pi,k=min?(ni,k,Aact,im-gi,k)ni,k (14) If a generated random number is less than pi,k then plot with category k will be selected as actual new plot for livelihood option i. m is size of a plot (ha). Equation 14 basically ignores potential new plots with a lower attractiveness category if total number of plots with a higher category is still sufficient to satisfy Aact,i/m. Farmers might use fire for opening lands and fire can spread to adjacent plots if a generated random number is less than the probability that fire escapes from one plot to its adjacent plots. Succession and growth Initial plot age and aboveground biomass of each landcover system are estimated based on a specified mean and variation between plots. In the dynamic process, as long as plot is not converted into another landuse type or damage due to fire accident or disaster, plot age increases and plot can achieve a higher ecological maturity or production status. Otherwise, age will be reset to 0. For new logged forests, their ecological stage and age degrades depending on logging intensity. In case farmers do slash and burn activity and fire escapes to outside the opened plot then adjacent plots are converted into pioneer forests. The opened plot however will be directly used for a particular livelihood option. Plots affected by disaster and marginal plots of any agricultural crop system that are not converted into another livelihood option will also become pioneer forests; whereas non-converted forests, late or post productive agroforestry or plantation types will keep their status until selected as an actual new plot later. Human population grows according to an annual population growth rate but will decrease in case of a disaster. Settlement growth is not simulated in the current model version. Land productivitySoil fertilitySoil fertility is defined as the ability of soil to support plant growth and yield. In agricultural crop systems, biomass and yield are totally harvested and not returned to the soil. This induces soil fertility degradation since planting time. Soil recovery can occur due to e.g. fertilization:δrecov=(fertmax –fert)2(1+h1/2)*fertmax -fert(15)Where fertmax is maximum soil fertility reflecting inherent soil fertility based on geological class, fert is actual soil fertility (1-5), and h1/2 is half time recovery, i.e. time needed to achieve half of fertmax (year). No soil degradation is assumed in other landuse types (i.e. forest, agroforestry or plantation).Harvest and yieldIn agricultural crop systems, a unit decrease in soil fertility is equivalent to a certain production level (ton ha-1). Crop yield is therefore a function of soil depletion:yi,jpot=yref,i*δdep,j(16) Where yref,i is attainable yield (ton ha-1) in plot of livelihood option i with one unit decrease in soil fertility. dep,j is soil fertility depletion in plot j which is not constant throughout the years: δdep,t=fertt*εi(17)Where fertt is actual soil fertility and is a constant depletion rate (0-1). The soil fertility dynamic in agricultural crops is then:fertt+1=min?(fertmax, max?(0, fertt-δdep,t+δrecov,t))(18)The crop yield in equation 16 represents potential value whereas actual yield is limited by labor harvesting productivity and availability: yi,j=min?(yi,jpot,τi*Li)(19)Where yi,j is actual or attainable yield of livelihood option i in plot j (ton) and the total from all plots is used in equation 1 (see above), i is harvesting productivity for option i (ton person day-1), and Li is the available labor for option i calculated in equation 9. Potential yield of non-agricultural crop systems are estimated based on mean and variation between plots specified for each ecological stage of forest types and for each production level of agroforestry or plantation types. The actual or attainable yield depends on labor harvesting productivity and availability as in equation 19. The potential harvesting zones for NTFP and timber/logging are plots of any forest type but not protected forest. Those for agroforestry products are agroforestry plots at any production stage and for foods are plots of any agricultural crop. Food consumption and storageThe actual attainable yield and food storage will be used to satisfy domestic consumption. The latter is the product of total population (H, person) and food requirement per capita (, ton per capita year-1) calculated for each food type:Fcons,i=H* θi(20)In case of shortage, the community will buy the necessary product from the market and actual buying depends on current financial capital:Fbuy,i=min?(Fcons,i-Fstore,i*1-floss,Mcap,thi)(21)Where Fstore,i is current storage of food i (ton), Mcap,t is current financial capital (Rp), hi is product price (Rp ton-1) and floss is food loss fraction (0-1), e.g. due to pests. In case of surplus, a portion will be kept in the storehouse and the rest will be sold to the market. Fsell,i=Fstore,i*1-floss-Fcons,i*fsell,i(22)Where fsell is a fraction of surplus food will be sold to the market. Food security index (0-1) is measured as follows:Xfood,i=min?(1,1-Fcons,i-(Fstore,i+Fbuy,i-Fsell,i)Fcons,i)(24)Income from selling foods will be used to cover total cost and the rest will become part of financial capital after allocating for secondary consumption and land expansion: Mcap,t+1=(Mcap,t+Minc,t-Mbuy,t-ct)*(1-fsec)*(1-fexp)(25) Where Minc is income from selling foods to the market (Rp), Mbuy is expense for buying foods in the market (Rp), c is total cost (Rp) as the sum of non-labor and labor cost as explained before in equation 1 and 2, fsec is a fraction of income for secondary (i.e. non-food) consumption (0-1), and fexp is a fraction of income allocated for establishing new plots. All non-agricultural crop products will be sold to the market and thus Minc is total income from selling both agricultural and non-agricultural products. In case of a disaster, loss in financial capital is calculated according to a certain fraction. Appendix 1. List of input maps and variables for FALLOW simulationMapsNoNameTypeDescriptionName of input file1Land-cover and legendScalarTo produce a land-cover map for FALLOW, we need to reclassify the original land-cover types into those specified in FALLOW interface (menu Customize) registered in a scenario file. We need therefore a source legend and specify the scenario name containing the referenced land-cover names. We need also to specify if the land-cover types have been logged or not by checking the ‘logged’ check box. This will produce a logging map of Boolean type.Initlc.xxx2Soil and geology, and legend ScalarSoil and geological map and their legend are used to produce inherent soil fertility map. One or two assessors need to do a scoring (usually 1-5, 1= poor, 5=excellent) for the soil and geological types. An actual soil fertility map is produced through setting fractions from inherent soil fertility.Initsoil.xxx for inherent soil map and soil.xxx for actual soil map3Area boundaryScalarOption ‘fill no data’ is provided to set ‘no data’ in the map into a unit valueArea.xxx4Forest reserve boundaryBooleanOption ‘no forest reserve?’ is provided for a region without forest reserveReserve.xxx5Elevation m. aslWe need an elevation map to produce a slope map (in degree)Slope.xxx6Distance to roadmOption ‘No available objects to measure distance?’ should be checked when there is no referenced object from which distances are measured. In that case, the map will contain values of 1e10 which means every plot has a very far distance to the referenced object. For a dynamic simulation of road, we need to prepare 4 maps of distance to road, at maximum, for 4 different time intervals during simulation period.Droada.xxx, droadb.xxx, droadc.xxx, droadd.xxx for 4 different simulation periods7Distance to rivermIdem with no 6Drivera.xxx, driverb.xxx, driverc.xxx, driverd.xxx,8Distance to marketmIdem with no 6Dmarta.xxx,dmartb.xxx,dmartc.xxx,dmartd.xxx,9Distance to settlementmIdem with no 6Dseta.xxx,dsetb.xxx,dsetc.xxx,dsetd.xxx,10Distance to processing industriesmIdem with no 6Dindaf1a.xxx, …, dindaf5d.xxx, dindfd1a, …, dindfd5d.xxx,Dindloga.xxx, …, dindlogd.xxx,Dindnta.xxx, …, dindntd.xxx11Suitability ScalarWe should prepare a suitability map for each of livelihood option. For this, the spatial team can directly produce those suitability maps (0=no suitable, 1=suitable) for each of the livelihood options, or we can use a general suitability map containing information of available land-cover types and we decide whether each of the land-cover types is suitable or not for a certain livelihood option by selecting the check box ‘suitable?’ Staf1.xxx, …, staf5.xxx, stfood1.xxx, …, stfood4.xxx12Sub-catchment boundaryScalar (integer)Map describing the sub-catchment boundarySubcatch.xxx13DisasterBooleanMap describing the area of a disaster event where the affected areas will be directly converted to pioneer forestDisaster.xxxVariable inputs (biophysics)NoNameUnitDescriptionName of input fileSoil1Depletion rate% unit yield-1Soil fertility depletion rate to produce a unit yieldSoil.par2Half time for recoveryYearsPeriod needed to achieve half of inherent soil fertilitySoil.parEnvironmental service3Aboveground biomassTon ha-1Average aboveground biomass for a certain land-cover typeLces.par4Floor biomass%Fraction from aboveground biomassLces.par5Probability of fire escape(0-1)A probability fire spreads from adjacent plotsLces.parSuccession and growth6Time boundYearLower time bound for a certain land-cover typeLctime.par7Initial ageYearInitial age of land-cover typesInitlcage.parYield 8Yield agroforestryTon ha-1Yield in agroforestry plots at 4 successive stages (pioneer, early production, late production, post production)Yield.par9Yield NTFPTon ha-1NTFP Yield at 4 successive stages of forest (pioneer, young secondary, old secondary, and primary forest)Yield.par10Loggingm3 ha-1Logging yield at 4 successive stages of forestYield.par11AgricultureTon ha-1Agriculture yield for each type of agriculture Yield.parVariable inputs (Social economic)NoNameUnitDescriptionName of input fileHarvesting1Harvesting productivityUnit yield person day-1Harvesting productivity for each livelihood optionHarvest.parEstablishment2Establishment costRp ha-1Cost required to open the land minus the labor cost Estcost.par3Labor requirementPerson day ha-1Labor needed to clear land for each livelihood optionEstlab.parLearning4Population fraction100%Population fraction for two different types of farmers (agent 1 and 2). E.g. agent 1 = conservative farmers, agent 2 = modern farmersAgent.par5Alpha learning(0-1)Adjustment rate based on current year experience for subsequent year land-uses. 0 = farmers ignore current year experience, 1= farmers fully use current year experienceAgent.par6Beta learning(0-1)Adjustment rate related to suggestions from others for subsequent year land-uses. 0 = farmers ignore suggestions from others, 1= farmers fully use suggestions from othersAgent.par7Prioritization degree[]Describing preference in allocating financial or labor resource to available livelihood options for subsequent year land-uses. 0=available resources will be allocated uniformly among livelihood options. >1 = available resource will be mostly allocated to the most profitable livelihood option Agent.parExternal labor8External labor involvedPerson dayLabor from outside of simulated areaExtlab.parDemographic9Initial human populationPersonInitial human population in the simulated areaDemo.par10Annual population growth rate%Of population in the simulated areaDemo.par11Labor force fraction100%Fraction of labor from populationDemo.par12Annual working daysday person-1 year-1 Of labor in the simulated areaDemo.par13Initial financial capitalRpDemo.par14Secondary consumption fraction100%Fraction of saved money used for secondary consumptionDemo.par15In case of disaster, human population decrease%Decrease in human population due to a disasterSocdis.par16In case of disaster, financial capital decrease%Decrease in financial capital due to a disasterSocdis.par17In case of disaster, working day decrease%Decrease in working day due to a disasterSocdis.parInitial knowledge18Expected payoff to laborRp person day-1For each farmer typeIntknow.par19Expected payoff to landRp ha-1For each farmer typeIntknow.parFire use20Probability of using fire for land clearing(0-1)Slash and burn for clearing land?Fire.parYield storing21ConsumptionUnit per capitaCollected yield used for consumptionStore.par22Probability to sell(0-1)Probability that the unconsumed yield will be sold to the marketStore.par23Loss fraction100%Fraction of collected will be loss, e.g. due to pest problem Store.parCultural value24Cultural value(0-1)Non-economic consideration for subsequent year livelihood optionsCulture.parExtension25Extension availability?0 or 10=no extension available, 1= extension availableExtense.par26Extension credibility(0-1)Credibility assessed by farmers to the extensionExtense.par27Exposure fraction100%Farmer exposure for an extensionExtense.parExtension suggestion28Payoff to laborRp person day-1Suggested by the extensionExtknow.par29Payoff to landRp ha-1Suggested by the extensionExtknow.parSubsidy30For establishmentRpSubsidy for plot establishmentSubsidy.par31For maintenanceRpSubsidy for plot maintenanceSubsidy.parMarket32PriceRp unit-1Price for each harvested productsPrice.parNon-labor cost33Non-labor cost NTFP and loggingRp ha-1Non-labor cost for each of 4 successive stages of forest types (pioneer, young secondary, old secondary, and primary forest)Cost.par34Non-labor cost agricultureRp ha-1Non-labor cost for each of agricultural typeCost.par35Non-labor cost agroforestryRp ha-1Non-labor cost for each of 4 successive stages (pioneer, early production, late production, post production) of agroforestry plotsCost.parExpansion determinant36Expansion determinant(0-1)Fractions describing the importance of spatial aspects considered in land expansion (i.e. soil fertility, plot utility, suitability of land, transportation cost, maintenance cost, land clearing cost due to slope, land clearing cost due to floor biomass)Spatial.parTime series37PriceRp unit-1Dynamic price (4 time intervals during simulation period). Option ‘on’ or ‘off’ is available for input time series used38Extension availability0 or 1Dynamic extension availability (4 time intervals during simulation period). Option ‘on’ or ‘off’ is available for input time series used39Subsidy0 or 1Dynamic subsidy availability (4 time intervals during simulation period). Option ‘on’ or ‘off’ is available for input time series usedAppendix 2. Minimum inputs for FALLOW simulationMapsNoNameTypeDescription1Land-cover ScalarMap of land-cover type2Soil and geology ScalarThis to produce map of soil fertility index3Area boundaryScalarSimulation area4Forest reserve boundaryBooleanIf any protected forest5Elevation m. aslTo produce a slope map 6Distance to roadmDistance of each pixel to the closest road7Distance to rivermDistance of each pixel to the closest river8Distance to marketmDistance of each pixel to the closest market9Distance to settlementmDistance of each pixel to the closest settlement10Distance to processing industrymDistance of each pixel to the closest processing industry11Suitability ScalarSuitability map for each simulated livelihood options. (0=pixel not suitable, 1=suitable) Economic Input variables 1Establishment costUSD ha-12Labor requirement for establishmentPerson day ha-13Initial financial capitalUSDFinancial capital in the simulated landscape4Secondary consumption fraction100%Fraction of saved money used for secondary consumption5ConsumptionUnit per capitaCollected yield used for consumption6Return to laborUSD person day-17Return to landUSD ha-18Subsidy for establishmentUSD9Subsidy for maintenanceUSD10PriceUSD ton-111Non-labor costUSD ha-1Biophysical and demographic variablesNoNameUnitDescription1Aboveground biomassTon ha-1Average aboveground biomass for each land-cover type2Age range of growth stageYearThe simulated livelihood options consist of pioneer, early, mature, and post production. We need to specify the age range for these stages 3YieldTon ha-1Yield for each stage4Harvesting productivityTon person day-1Harvesting productivity for each livelihood option5Initial human populationPerson6Annual population growth rate%7Labor force fraction100%Fraction of labor from population8Annual working daysPerson day-1 year-1 Of labor in the simulated areaAppendix 3. Running FALLOW model with NutshellThere are some advantages of using Nutshell to run the FALLOW model:More options for model settingsMore options for displaying resultsPossibility to change the model codeRuns under different operating system versions, which may not be compatible with the FALLOW GUI (e.g. Windows Vista)Detailed report when the model crashesThe followings are steps in running FALLOW with Nutshell: The interface of Nutshell is as shown in Figure 7. It opens by executing the Nutshell.exe file. For the first time use, it usually asks the location of PC Raster/apps folder. So please set this path in the dialog box that appears when opening Nutshell for the first timeNext step is to select the model folder. If the FALLOW model is located in C:/ for example, then we need to select this folder and click the button for setting the working directory (no 4 in Figure 7) To run the model we need to open the Fallow8AF.mod file. Please find this in the model folder and open it by clicking the button ‘Edit a model’ above the explorer window. The contents of the file appear like shown in the right window of Nutshell in Figure 7. The file contains the code of FALLOW model written in PC Raster language The second line in the code specifies the length of simulation in year. Please modify accordingly and save the file before runningTo run the model, please select the green arrow head (no 6 in Figure 7). If the model runs well, it will appear ‘Executing time step 1’ in the above left window and all outputs will be stored in the same folder where we store all inputs (for example C:/Fallow like above) The arrow in the above left window indicates the place to write PC Raster commands (i.e. to do map operations). Please read the PC Raster manual for descriptions of each PC Raster command and example. Appendix 4 below also describes some important commands for map operationFigure 7 Nutshell interface as a user friendly way to run and edit the FALLOW modelAll files with *.xxx extension are input maps for FALLOW model. The extension .xxx indicates that it can be modified by users. But modification should be made consistently too in the FALLOW code (i.e. Fallow8AF.mod). All input data are in .par format. Please see again Appendix 1 for the list of input maps and data required by the FALLOW model. Please see Appendix 4 for instruction of how prepare the input maps and dataAll output variables are with *.out extension. For example lcarea.out-af1_pion is for time series output of area of agroforestry system type 1 at pioneer stage. The output landcover maps are with the landcovr.* nameTo extract important outputs, we provide an Excel file ‘output.xlsm’ that can display output files from the model folder. Please open this file, set the model folder and then activate the import button Appendix 4. Important notes in running FALLOWPreparing input dataCheck if all Ascii maps have the same attributesIf yes, please prepare the clone map (see below how to do this)Then convert all maps to .xxx format (see below how to do this). Please see the FALLOW code so we know what input maps that we need to prepareAfter preparing maps, please see the FALLOW code that lists each *.par file to input socio economic and biophysic parameter valuesMap operations in PCRaster Create a clone mapPlease find tmp.bat from the FALLOW directory that contains the following commands for creating a clone map:mapattr -s -R 1036 -C 840 -S --small -P yt2b -x 544247.37365491 -y 2411507.6846066 -l 100 clonemap2.tmpasc2map -a -m -9999 --clone clonemap2.tmp BK_border_1ha.asc area.rmpdisplay area.rmpPlease adjust the map attributes accordingly and type tmp in the Nutshell dialog box.Change from Ascii maps to FALLOW mapsFor example: asc2map --clone area.map -a -S lc_backan.asc lc_backan.xxxChange from FALLOW map to Ascii mapsFor example: map2asc -a -m 9999 backan.xxx backan.ascConditionalityFor example: pcrcalc test2.map = if(test1.map eq 1,2, if(test1.map eq 2 or test1.map eq 3 or test1.map eq 4,3, if(test1.map eq 6, 4, if(test1.map eq 7 or test1.map eq 8,5,0))))Change a missing value to a valueFor example: pcrcalc test.xxx=cover(test1.xxx, 0)Running the FALLOW modelThere are two ways:Open the FALLOW code and then run the model by clicking the ‘run’ buttonWith the PCRaster command in the Nutshell dialog box: pcrcalc -f fallow08.modError that comes out if format of the input maps are not similarFor example:C:\FALLOW>pcrcalc -f fallow08.modpcrcalc version: May 31 2001 (win32)fallow08.mod:530:9: ERROR: droadb(binding=droadb.xxx): location attributes of 'area.xxx' and 'droadb.xxx' are differentThe solution is that to make a new clone map that matches the other input maps, or in case that the attributes of the input maps are different each other, then try to resample the input maps according to the clone:C:\FALLOW>resample --clone area.xxx droada.xxx test.xxxresample version: May 7 2001 (win32)C:\FALLOW>copy test.xxx droada.xxxOverwrite droada.xxx? (Yes/No/All): y 1 file(s) copied.To clean the outputs of FALLOW running the type clean in the Nutshell dialog boxDisplay a map with its legenddisplay -p lc.pal BK_landcover.mapDisplay maps dynamically after long time simulationFor example to display the map along the year after 20 year simulation with the FALLOW model: display -p lc.pal landcovr.001+20To make a slope map from the elevation mappcrcalc slope.xxx=slope(elevation.xxx)To calculate distance from each pixel to settlement dset=cover(spread(lc eq lcid[set],0,1),1e11)*area;ReferencesLusiana, B., van Noordwijk, M., Cadisch, G. 2012. Land sparing or sharing? Exploring livestock fodder options in combination with land use zoning and consequences for livelihoods and net carbon stocks using the FALLOW model. Agriculture, Ecosystems and Environment 159: 145– 160Lusiana, B., van Noordwijk, M., Johana, F., Galudra, G., Suyanto, Cadisch, G. 2013. Implication of uncertainty and scale in carbon emission estimates on locally appropriate designs to reduce emissions from deforestation and degradation (REDD+). Mitig Adapt Strateg Glob Change. doi:10.1007/s11027-013-9501-zMulia, R., Widayati, A., Suyanto, Agung, P., Zulkarnain, M.T. 2013. Low carbon emission development strategies for Jambi, Indonesia: simulation and trade-off analysis using the FALLOW model. Mitig Adapt Strateg Glob Change. doi: 10.1007/s11027-013-9485-8van Noordwijk, M., 2002. Scaling trade-offs between crop productivity, carbon stocks and biodiversity in shifting cultivation landscape mosaics: the FALLOW model. Ecol. Model. 149, 113–126.van Noordwijk, M., Suyamto, D.A., Lusiana, B., Ekadinata, A. and Hairiah, K. 2008. Facilitating agroforestation of landscapes for sustainable benefits: tradeoffs between carbon stocks and local development benefits in Indonesia according to the FALLOW model. Agriculture Ecosystems and Environment Special Issue on Climate Change Research CGIAR.Suyamto, D.A., van Noordwijk, M., Lusiana, B., Ekadinata, A. and Khasanah, N. 2005. Prospects of adoption of tree-based systems in a rural landscape and its likely impacts on carbon stocks and farmers’ welfare: the FALLOW Model Application in Muara Sungkai, Lampung, Sumatra, in a “Clean Development Mechanism’ context. ICRAF Working Paper WP06034.Suyamto, D. A., Mulia, R., van Noordwijk, M., Lusiana, B. 2009. FALLOW 2.0. Manual and Software. World Agroforestry Centre, Bogor, IndonesiaTata, H.L., van Noordwijk, M., Ruysschaert, D., Mulia, R., Rahayu, S., Mulyoutami, E., Widayati, A., Ekadinata, A., Zen, R., Darsoyo, A., Oktaviani, R., and Dewi, S. 2013. Will REDD+ funding stop peat swamp conversion to oil palm in orangutan habitat in Tripa (Aceh, Sumatra, Indonesia)? Mitig Adapt Strateg Glob Change. doi: 10.1007/s11027-013-9524-5 ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download