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Part I: Participatory Mapping at Municipal

Participatory mapping of economic activity is the first step in the analysis of vulnerability

proposed in this manual. The objective of the mapping is the identification and location

Space activities most economically important for key stakeholders (farmers, fishermen,

gatherers and pastoralists) in the city.

1.1 Potential and limitations of participatory mapping of land use at the municipal

level

Participatory mapping brings the ability to leverage the knowledge of local people and

supplement it with expertise, for example, representatives of local organizations, as rural

extension, unions and government agencies.

Although participatory mapping can result in a representation very close to realitylocation, it can not replace the work of Geocoding supported by measurement activities

field. However, as an instrument of initial planning, identifying problems and priorities

interventions, participatory mapping brings the features necessary for an analysis of

spatially explicit vulnerability.

1.2 Contextualization

Participatory mapping is a technique for obtaining and recording information based

spatial in knowledge and awareness of the population of a particular locality. As a

method scientific, participatory mapping has become increasingly important with the

emergence of participatory methods of field research in the late '80s (Chambers, 2006).

Map participatively represents a complement exact methods of scientific measurement,

allowing obtaining information directly from local actors. Although natural resource

management

represents one of the most common areas for its implementation, participatory mapping

also this being applied in studies of social relationships, transportation, education and

fighting crime.

In the context of risk analysis and vulnerability in rural areas, mapping aims

the identification of economic activities and characterization of its main features in space.

There are already methods and integrated tools for participatory mapping. A good

example can be accessed at:http://mapeoamano.org/es/

http://mapeoamano.org/es/http://mapeoamano.org/es/http://mapeoamano.org/es/

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BOX #: Participation

Participation is now considered an essential ingredient for the success of interventions in

the area of Rural Development and in many research approaches, particularly in studies

socioeconomic. Different degrees of intensity of participation may be necessary and

desirable depending on the objectives of each action. However, it is important toemphasize that the application of methods Participatory also comes with responsibilities.

Primarily in equity research using the information obtained must be clarified to the actors

involved and their participation should be subject a prior and informed consent.

1.3 Actors of participatory mapping

For best results, you must join a group of players with the highest level of

possible representation. Ideally includes key community leaders, leaders

unions and associations, representatives of government agencies that deal with issues of

land use and natural resources and especially the producers (large and small) as: farmers,

gatherers, fishermen, miners, pastoralists, etc..

You can perform separate workshops if there are conflicts between actors or when the

group would very large because of the diversity of realities in the city. The ideal group

size for Participatory mapping is between 10-15 people, thus allowing everyone to

simultaneously monitor the activities of the mapping.

Although it was ideal that participants had minimal knowledge of cartography, such

knowledge is not strictly necessary, since it will be accompanied by a technician

geoprocessing. Below is a series of tips to facilitate interaction with actors without

familiarity with mapping exercises.

1.3 Preconditions, material needed and approximate costs

The implementation of this module requires experience in hosting events and conducting

groups

(Participatory mapping) and personnel with basic skills in the use of analysis software

geographic (GIS). Approximate costs are given in Table #.

There is a range of programs geoprocessing able to perform basic operations needed

within this manual. Examples are:

- TerraView (free software: www.dpi.inpe.br/ TerraView)- GvSIG (free software: www.gvsig.org)

- QGIS (free software: www.qgis.org)

- ArcGIS 9.3 (commercial software, used here:www.arcgis.com)

It is recommended to work with the following cartographic databases:

- Major Bases: limit municipalities, headquarters of municipalities, towns, drainage

http://www.arcgis.com/http://www.arcgis.com/http://www.arcgis.com/http://www.arcgis.com/

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network or hydrography and the county road system

- Complementary Bases: protected areas, indigenous lands; settlement projects, relief and

deforestation,

- Support Bases: Satellite image updated and good resolution

BOX #: Some of the main bases ecomplementares can be accessed at the following

addresses Electronics:

http://mapas.mma.gov.br/i3geo/datadownload.htm

http://www.sie.pa.gov.br/i3geo/aplicmap/geral.htm?c110d60ba28691ce4c22ea365955c3

1e

http://www.fepam.rs.gov.br/biblioteca/geo/bases_geo.asp

http://monitoramento.sema.pa.gov.br/simlam/

http://www.metadados.inde.gov.br/geonetwork/srv/br/main.home

http://www2.sipam.gov.br/geonetwork/intermap/srv/br/map.setContext?id=6

Table #: Approximate costs of participatory mapping

Activity Cost items Approximate cost

Event Mapping(duration, a morning orafternoon)

- Transportation costs(participants)- Merenda- Material

600

Digitization of maps Service of escanerizaoLarge format (unit)

100

Printing maps Service plot (unit) 100

1.4 Step-by-step methodology with examples of participatory mapping conducted in

Swindon, Northern Corridor

BOX # (two images): Participatory Mapping in the city of Swindon

Swindon, a town located in the Lower Amazon region, stands out for its diversity of

productive activities in rural areas, including family production, traditional fishing,

extractive and commercial agriculture. Swindon has around 52 000 inhabitants of which

approximately half live in rural areas. Most of the county is covered by protected areas

http://mapas.mma.gov.br/i3geo/datadownload.htmhttp://mapas.mma.gov.br/i3geo/datadownload.htmhttp://www.sie.pa.gov.br/i3geo/aplicmap/geral.htm?c110d60ba28691ce4c22ea365955c31ehttp://www.sie.pa.gov.br/i3geo/aplicmap/geral.htm?c110d60ba28691ce4c22ea365955c31ehttp://www.sie.pa.gov.br/i3geo/aplicmap/geral.htm?c110d60ba28691ce4c22ea365955c31ehttp://www.fepam.rs.gov.br/biblioteca/geo/bases_geo.asphttp://www.fepam.rs.gov.br/biblioteca/geo/bases_geo.asphttp://monitoramento.sema.pa.gov.br/simlam/http://monitoramento.sema.pa.gov.br/simlam/http://www.metadados.inde.gov.br/geonetwork/srv/br/main.homehttp://www.metadados.inde.gov.br/geonetwork/srv/br/main.homehttp://www2.sipam.gov.br/geonetwork/intermap/srv/br/map.setContext?id=6http://www2.sipam.gov.br/geonetwork/intermap/srv/br/map.setContext?id=6http://www2.sipam.gov.br/geonetwork/intermap/srv/br/map.setContext?id=6http://www.metadados.inde.gov.br/geonetwork/srv/br/main.homehttp://monitoramento.sema.pa.gov.br/simlam/http://www.fepam.rs.gov.br/biblioteca/geo/bases_geo.asphttp://www.sie.pa.gov.br/i3geo/aplicmap/geral.htm?c110d60ba28691ce4c22ea365955c31ehttp://www.sie.pa.gov.br/i3geo/aplicmap/geral.htm?c110d60ba28691ce4c22ea365955c31ehttp://mapas.mma.gov.br/i3geo/datadownload.htm

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and land indigenous, but in their more rural areas used predominantly agricultural crops.

Since 2004 the IPAM has been working with participatory mapping activities in

Swindon. Analysis vulnerability presented in Module II was tested and implemented in

Swindon during 2009/10.

Figure #: Participatory Mapping in the city of Swindon

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Figure #: Map of the City of Swindon clipping, Par, Brazil

Step 1 - Preparation of material

The outcome of participatory mapping depends on the quality of cartographic materialused during the group dynamic. If participants are unable to locate on the map providedby the organizers, the results may not be satisfactory or even incorrect. It isrecommended, therefore, work with maps that have good visual documentation includingimportant points reference, such as roads and rivers, cities and communities, and forestareas, where you have access to this information (see map #). The ideal scale forparticipatory mapping is between 1:1500 and 1:5000 map of which must measure at least90 x 140 cm For activity mapping is required to reserve a space with a capacity for the

number of invited participants (ideally between 20 and 30 people), with chairs and a tableor two Large support, radio (optional). If there is work in separate groups space shouldallow such separation.For the mapping is recommended to provide crayons and erasers to facilitatemodifications. It may also be useful to fix a transparent paper on the map thus allowingits reuse.

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Step 2 - Structure of the mapping workshop:The participatory mapping workshop with local stakeholders is the most important part ofthe process. It is recommended to partner with local organizations with goodrepresentation in rural areas and start mobilization of participants with one to two monthsin advance.

The workshop must be conducted by a facilitator able to communicate the mainobjectives andrationale as the methodological steps of the mapping. Questions and doubts ofParticipants must be clarified before starting the work.A division into groups is recommended especially when there is a large diversity ofactivities to be mapped. In this case each group should have a facilitator capable ofdriving work according to defined objectives and methodology. Each group must havethe material required for mapping.A precondition for processing the result of participatory mapping in programs GIS is thedefinition of a legend documenting the items displayed on the map (eg areas grain

production, pastures, areas affected by floods or flood, etc.).. This caption can bepredefined or constructed participatory with participants. Construction participatorylegend is usually a good way to confirm prior knowledge.Once defined the legend represents the standards to be met in all exercises mappingregarding the use of colors and symbols by the participants. The mapping begins with theidentification of map elements. At this stage the participants guided by map usingreference points (eg points of localities / communities villages, roads / extensions, rivers /streams) and can even correct the names or locations of these and add other references inorder to enrich the map.Later the participants begin to locate items / polygons on the map legend. To add items to

the map there should be a consensus among the group on its size and location. OfAccording to the goals of participatory mapping should be documented relevantinformation on each item / polygon added to the map, for example, on a separateworksheet. In the context of vulnerability assessment, such information may includefactors such as the level of importance economic (eg contribution to family income as apercentage) of the main activity and activities each secondary polygon.It follows the mapping exercise with the presentation and discussion (s) result (s) inplenaryin order to validate the product (s) (s).

Step 3 - Processing and preparation of final mapsThe goal of this task is to scan the information obtained in the workshop participatorymapping to enable its processing in Geographic Information Systems (GIS) and crossingwith information generated in module II of this manual. There are at least two scanningoptions (see Figure #): One. Scan the map drawn in the field of import scanned materialto program GIS and digital draw polygons on the map displayed on the screen computer(recommended). 2nd. Draw polygons over the digital base map (already available in GISformat) using the outcome of participatory mapping as a model on paper.

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Figure #: Scanning spatial information about scanned map (above) with the programArcGIS 9.1 and using the paper map (below).

Scan the outcome of participatory mapping usually produces a digital representation withbetter than the second option. In the absence of a large-format scanner can scan the mapin pieces (careful to avoid distortions to join the individual parts in software imagemanipulation) or use a digital camera with good resolution photograph of the material an

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angle of 90 . Before scanning spatial information is necessary to georeference thescanned map using the intersections of grid coordinates. For that activates the tool andgeoreferencing added to the coordinates of the control points (Figure #).

Figure #: Add control points for georeferencing

Logo is identified on the map coordinates of the intersection, marked with a cross, whichuses as a reference to insert the control point by clicking the right mouse button andtyping coordinates for the intersection point (see Figure #).

Figure #: Enter coordinatesRepeating the previous step fits the maximum points possible to save the georeferencedmap. Soon it creates a new database in shape, indicating that feature type is used (pointline or polygon) and particu-up according to the theme (Figure #). In ArcGis software (orother) is added to the map georeferenced database created earlier.

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Figure #: Creating new database, type the name polygon with family farming, through thetool ArcCatalog ArcGIS software.

With the database in edit mode, it begins to scan the features corresponding to that basecreated and named earlier (Figure #). Each theme (legend item) working in the field musthave a proper basis, ie if this working with 10 songs you create 10 bases. The types offeature must be appropriate to the themes. For example, to rivers or roads it isrecommended line features. Equipment for infrastructure, schools, houses, city, village or

phones public is more appropriate to use with features bases points. For themes thatdefine areas such as land, islands, conservation areas, lakes or administrative boundariesis more appropriate to use bases with polygon feature.

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Figure #: Scanning the polygons in each area: Livestock, Agriculture, Family, etc.. overthe map georeferenced.Once scanned all legend items or themes you can proceed to create (s) map (s) end(Figure #). This process usually involves (1) choose the topics to be exposed (eg for aland use map would be the shapes of predominantly agro-livestock activities), (2) insertthemap elements: title, scale, north coordinate grids (if relevant), Legend and notes(Source, date, name of institution / performer), and (3) choose the symbols to the legend,colors and fonts.

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Figure #: Choosing colors and symbols to the legend# Figure below shows an example of the outcome of participatory mapping asdocumented in this module.

Figure #: Final result of participatory mapping

GLOSSARYShape Shapefile is a vector file of the software's

own ESRI manufacturer. This file isresponsible for communication betweenproducts from ESRI and other GIS(Information Systems Geographical). TheShape, which has extension. Shp and mustbe accompanied by at least two other files

that can be identified by the extensions:.dbf and. shx. For ex.: Pecuria.shp - VectorFile Pecuria.dbf - Contains information onall vectors contained in shapefile in theform of database. Pecuria.shx - File thatcreates the link between the shp file and thedbf.

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Database Or Database - We can say a dataset relatedto a particular theme

Cartographic An instrument that represents one throughgraphic symbols reality on the ground

Georeferencing It is an activity which consists in giving thereference data and / or objects based ontheir geographic locationhttp://www.infoescola.com/engenharia-de-software/banco-de-dados-georreferenciados/

1.5. Literature for consultation

Chambers, R. (2006): Participatory mapping and geographic information systems. Whosemap? Who is empowered and who disempowered? Who gains and who looses? TheElectronic Journal on Information Systems in Developing Countries. 25:2, 1-11.

http://www.iapad.org/publications/ppgis/robert.chambers_participatory_mapping_en.pdf

Diagnosis in Maps: Lower Amazon. Belem: IPAM, (2007) - CD-Rom.

Module II: Analysis of vulnerability to climate risks and climate not in the middlerural

2.1 Potential and LimitationsModule I resulted in the identification and spatial distribution of the main rural activitiesat the municipal level.These productive activities represent the basis for vulnerability analysis presented below.Module 2 presents a methodology for quantifying and analyzing climate risks and noclimate in the context of rural production, from the perception and experience of producergroups.As such methodology does not provide statistically representative information and localrealities can differ considerably from the inevitable generalizations made in itsimplementation. However, the interaction with farmers, comes with the opportunity tounderstand how important aclimate risk in all other risks that affect rural production. Based on this, it is possibleset priorities for intervention at the municipal level.

BOX #: RisksThe term "risk" can be defined as the probability of losing income expected. In this sense,risk is related to the vulnerability because it depends on the size of the exposure

http://www.infoescola.com/engenharia-de-software/banco-de-dados-georreferenciados/http://www.infoescola.com/engenharia-de-software/banco-de-dados-georreferenciados/http://www.infoescola.com/engenharia-de-software/banco-de-dados-georreferenciados/http://www.infoescola.com/engenharia-de-software/banco-de-dados-georreferenciados/http://www.iapad.org/publications/ppgis/robert.chambers_participatory_mapping_en.pdfhttp://www.iapad.org/publications/ppgis/robert.chambers_participatory_mapping_en.pdfhttp://www.iapad.org/publications/ppgis/robert.chambers_participatory_mapping_en.pdfhttp://www.infoescola.com/engenharia-de-software/banco-de-dados-georreferenciados/http://www.infoescola.com/engenharia-de-software/banco-de-dados-georreferenciados/http://www.infoescola.com/engenharia-de-software/banco-de-dados-georreferenciados/

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sensitivityand adaptability of whom faces. Excessive rainfall, for example, represent a climate riskto farmers who may suffer damage to their crops.

2.2 The actors of risk analysisThe main source of information risk analysis are themselves farmers. Informationneeded are generated in group interviews involving between 5-15 people for each activityproductive. Information can be verified and validated with local experts, for example,professionals in rural technical assistance.

2.3 Preconditions, material needed and approximate costsBasic preconditions for the implementation of Module 2 are access to a computer withMicrosoftOffice Excel and staff trained in their use and techniques to conduct interviews in the

field. Table# Summarizes the necessary materials and approximate costs based on experimentsconducted by the authors.

Activity Cost items Approximate Cost * (R $)

Survey data field Material (pencils, eraser,clipboard, questionnaires(see models in attachment#) - Daily for interviewers -Transportation

2.500

Analysis and interpretationof results

Weather analyst 2.000

TOTAL 4.500

2.4 Contextualization

Risk analysis is a key element of risk management, ie, the development andimplementation of strategies to minimize social and economic losses caused by eventssubject to uncertainties. Risk management techniques are widely applied by public andprivate, for example, to evaluate and to reduce the effects of uncontrollable factors, suchas economic or political changes in project performance and production activities. Thereis also an extensive literature on analysis and risk management in agriculture in manycountries, including Brazil, gave rise to systems of insurance against damage caused byscratches as diseases and pests in agricultural crops and animals or extreme weatherevents. The methodological steps of risk analysis described in this module baseam

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entirely on existing methods and performed in various contexts. We start with the notionthat production and income farmer and therefore their welfare are subject to multiplefactors that are outside the control thereof. Besides climatic factors, market risks (egfluctuations or price changes) and personal risks (eg illness in the family) often poseconsiderable risks. The Main aim of risk analysis presented here is therefore

characterizing the risk profile in certain localities of the municipality.

BOX #: Risk ProfileThe risk profile of a producer is a representation of the contribution of the individualrisksvariability of the total value of their production. Since the total value of productionincluding both Part marketed as the non-commercialized production. Thus, the riskprofile which informs the importance of a specific risk to a producer (or group ofproducers in a region). Therisk profile can be determined through simple techniques simulation and regression

documented in this module.

The prioritization of risks based on the interpretation of a risk profile (or any otherinstrumentrisk analysis) represents the first step in risk management. Along the forecast changesclimate, for example, assessing whether such changes may come to change the prioritiesfor thesupport for rural development and production. However, the choice and design ofarrangements actions risk management depend on the local situation and therefore are notcovered in this manual.

2.5 Risk Analysis: Step-by-Step

The following are documented steps required to perform risk analysisStep 1 - Definition of the information neededThe quantification of risks to productive activities requires information on IndicatorsPerformance. The main performance indicators used in the analysis of productionsystems are physical performance, cost and production value that are related as follows:

Yield (kg / ha) x Price (U.S. $ / kg) = Value of production ( / ha)

Using hard numbers, for example, the production of cassava flour, the main source ofincome and nutrition for most farmers in the Brazilian Amazon, we calculate the value ofproduction as follows:

3000 (kg/ha) x 0.8 (R$/kg) = 2400 (R$/ha)

Without additional information, calculate the value of production is usually done on thebasis of

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yields and expected prices, ie average. Farmers generally know which inform yieldtypical or normal (average) of their crops and at what price (average) products typicallyare marketed.Risks related to production activities usually manifest through impacts (changes) inperformance and price. Thus, these risks are also reflected in the value of production. In

this context it isimportant to distinguish between:

1) seasonal changes (eg, periods of rain and drought)(2) non-specific changes (small changes in income and prices, whose causes areunknown), and(3) specific variations caused by extreme events (clearly attributable to variationsextreme events).

Since seasonal variations are generally known and expected by farmers, only two types of

variations are considered risks relevant to risk analysis.Our risk analysis is then to quantify the variations specific and non - specific in yieldsand prices of rural productive activities.Specific variations arise from extreme events such as droughts and heavy rains. itsquantification requires the following information:

A) Event Type(B) Impact on income and price (eg, reduced by X%)(C) Probability (eg, frequency in 10 years)

Non-specific variations, however, can be characterized by the following information:(D) Income / expected price (average in the absence of extreme events)(E) Income / low price (at least in the absence of extreme events)(F) Income / high price (maximum, in the absence of extreme events)

Step 2 - Conducting interviews in field

The information presented AF in the previous step can be raised in group interviews withproducers involved in productive activities in question. A model questionnaire for thispurposethe CD-ROM (sections II and III).

It is recommended to work with groups of 5 to 15 producers following a script whichstarts with the definition of the production system in question. This first step is essentialto avoidalleged variations in income arising from different assumptions about the use offertilizers.When dealing with complex activities, such as fishing, it is necessary to limit the survey

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to keyaspects of these activities. For example, in Amazon riverine communities and consumemarket a wide variety of fish, but generally there are noble species (Marketed by Unit) orsets of species (marketed per kilogram) that represent the foundation of economicactivity. In such cases, it is necessary to define the group of species to which the lifting

information relates.

Stimulating the memory of farmers (preferably in group interviews, see step 2), iscan list the main extreme events (A) that has affected their incomes and sales prices,say, during the past 10 years. The complexity of the analysis increases with the numberof events ends that arises with the need to prioritize the events with utmost importance.

The importance of an event must be evaluated subjectively (perception of informants)andobjectively information (B and C) seeking consistency between common sense and

quantitative information. Always remembering the need to adapt the language of theinterview to the local reality.

Both impacts on yields and prices (B) and the probabilities (C) of extreme eventsmust be the result of discussion and consensus among informants. If the informantsvery varied experiences in relation to extreme events, it becomes necessary to document arange of values and possible reasons for the observed variations in the perceptions ofinformants (see Step 4).After the characterization of specific variations (extremes) starts the characterization ofNon-specific variations in performance and price, (usually) with the following question:

In a yearno extreme event, which would yield (price) expected (D) of flour per hectare ofcassava planted?The same question should be discussed in relation to an income / low price (E) and ayield / high price (F). When it comes to productive activities subject to the effects ofseasonality,example, the extraction of Brazil nuts (Brazil) or fishing, the need may arise to divideproductive periods in sub-periods to prevent average, minimum and maximum areinfluenced by natural variations (non-probabilistic) in income and prices.

For non-agricultural activities, such as extraction, it is recommended to quantify incomebyunit of time worked (day, week or month). The reason is that such activities are generallyperformed on common access lands, where the accounting of income per unit area israrely practiced.It is recommended to repeat the information collected at the end of the interview forinformantscan reflect and validate the information.

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Step 3 - Scan and construction of the simulation model

In addition to interviews in the field, this module uses an integrated simulation tool toMicrosoft Excel program. The tool SimulAr1 brings the necessary functions and can be

usedlegally as an alternative to commercial tools. The main contribution of the tool issimulation of specific variations and non-specific yields and prices based on values raisedin Step 2.

The principle of risk analysis using simulation techniques is relatively simple. requiresdigitization of information gathered in the field in a second Excel spreadsheet illustratedinFigure # (see also spreadsheet template on CD-ROM).

1Visit the following website for information on downloading and using the tool to SimulAr

http://www.simularsoft.com.ar/

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Figure #: MS Excel to simulate an example of field data scanned

With the scanned information starts building a simulation model based on the relationshipbetween yield and price as shown in Step 1 (see Figure #). To represent variationsNon-specific prices and yields proposes to select the distribution function "Triangular"

using the information about income / expected price (D), income / low price (E) andyield / high price (F) to its definition as illustrated in Figure #. During a simulation(Step 4), the simulating program generates random numbers according to a distributionfunction thus specified.

BOX #: Distribution FunctionA distribution function characterizes the behavior of a random variable, ie, whosemanifestations are probabilistic. That is, their values vary but follow a set pattern withmost frequent values (most likely) and less frequent (with low probability).For example, in most cases (yield expected median) one hectare of cassava yields to

around 3000 kg of cassava. But there are a few cases where a hectare yields only 1000kg (low income, minimum) or reaches up to 4000 kg (high yield, maximum). The programsimulating generates random values according to their distribution function [INCLUDEFILE].

The representation of specific variations (extremes) requires specifying a variable thataffectsperformance / price, or even gross according to the type of hazard and the informationavailable. The then work with two examples:

1. Excess rain:

Suppose the group interview revealed that the Cassava crops suffer from rot root functionespecially in years with excessive rainfall. The information could be raised to follows:

Risk - excessive rain Probability 0.1 impact on the average income % 50.00%

That is, it was reported that the yield could fall by up to 50% once in 10 years(Probability

0.1). With the help of the program SimulAr simulate an event with these characteristicsspecifying first a random number generator in a cell free worksheet (Figure #). To doso, pick up a distribution "Uniform" with Min = 0 and Max = 1. During a simulation inthis cell values vary between 0 and 1.

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Figure #: Representation of an extreme event (excessive rain) in Excel to simulate

In the cell next door, sets up a Condition impact that takes the value "1" whenever therandom number remains below or equal to 0.9 and the value "0.5" whenever the randomnumber is above 0.9, or 0.1 with a probability (or once every 10 seconds) during thesimulation.

2. Drought during plantingAnother frequent occurrence in the cultivation of Cassava is the lack of rain duringplanting. in this If it happens that a farmer needs to replant, or spend additional timewhich could haveinvested in other productive activity. We assume that the average time to plant a hectareof Cassava is three-man dias2. The daily value of $ 20, the damage caused by lack of rain$ 60.

Risk - dry season planting Probability 0.1 Cost R$/ha 60

For simulation purposes, as in the previous example, it will be necessary to specify anumber generator Random independent of other extreme events. As illustrated in Figure

# define thecondition under which the dry happens, or always when the random number is greaterthan 0.9 (one 10 instants of time in simulation) 60 or the impact is zero in all other cases.

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Figure #: Representation of an extreme event (excessive rain) in Excel to SimulAr

In this case, the injury is a monetary value and therefore must be subtracted from grossincomeas shown in Figure #.

Finally, simulate the program requires the definition of an output variable - the variableinterest for vulnerability analysis, here, the gross income.With variations specific and non-specific and output variable defined in this way canthrough the simulation and interpretation of results.

Step 4 - Analysis and interpretation of results

The risk analysis with the program is to simulate an iterative process (simulation) ofgenerating random values for all cells with distribution functions defined. Thus, in eachiteration, it generates a new value for the variable output (gross income) according to thevalues simulated for each input variable.Clicking on the "simulate" the toolbar simulate, opens the menu "Run Simulation, "inwhich you can specify the simulation patterns. It is recommended to keep the standards

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and / or use numbers above 500 iterations to obtain stable results. The simulation timeincreases with the number of iterations. At the end of the simulation, click OK to viewthe results.

Figure #: Simulation Options to SimulAr

The "Simulation Results" provides four options for presentation of results (Figure #).To view results you must select the output variable (gross income) on top of window. Asummary of the statistics of the output variable is below the left side of the windowResults. Clicking "Show Histogram of the selected variable" generates the histogramshown in# second part of Fig.

For our example the cultivation of Cassava early results indicate that the value ofproduction (or gross income) can range between / ha 974 and 4407 depending onspecific variations and Non-specific.

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Figure #: Overall results of the simulation to SimulAr

Our main interest is to find the relative importance of each risk. Simply choose theoption "Generate Report of the variable selected in Excel" - the program will add a newworksheet your Excel file with detailed results and graphics standards.

In the "Sensitivity Analysis" of the new worksheet are regression coefficients andcorrelation characterizing the influence of each factor of change in the variable output(gross income).

Figure #: Detailed results of the simulation to SimulAr: Sensitivity analysis withnumerical results and graphics (reformatted by the authors).

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On the same sheet of detailed results were generated diagrams that let you see theresults graphically. Interpreting the graph of Figure # regards the coefficientscorrelation table above, we note that the main source of variation in gross income is theNon-specific variation in the yield of cassava followed by the risk of losses due

of excess rain. Other risks such as drought at planting and during later stages ofcultivation appear less important than non-specific variations in the price of flour.

The graph in Figure # represents the risk profile of the activity of producing flouraccording to standards current. Considering the possibility of climate change increase theincidence of events extremes, we can now (try to) simulate scenarios, for example, morefrequentextremes (0.3 instead of 0.1).

Risk - excessive rain Probability 0.3

impact on the average income % 50%

Risk - dry season planting Probability 0.3Cost R$/ha 60

Risk - dry at planting advanced Probability 0.3 impact on the average income % 30%

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Figure #: risk profile in scenario 3 instead of 1 extreme event (drought and excessrain in 10 years) (reformatted by the authors).

The simulation result indicates that the risk profile of the activity of flour production is

sensitive to changes in weather patterns. Although still predominates the non-specificvariation of income,risks related to drought have surpassed the influence of price changes in gross income forhectare. In addition, statistics show that the generated average yield of flour fell byapproximately 20% compared to the scenario without climate change. Valued at the priceAverage flour this corresponds to a loss of R $ / ha 512, a significant value from the pointof view family production.

Finally, we can imagine a scenario in which measures to mitigate the effects of changeclimate are being developed. In fact, there are more varieties of cassava resistant

root rot that could be used by farmers to adapt to the effects of climate change. Figure #shows the results of a scenario in which the use of planting material Improved reducesthe losses caused by rot by 50%, displayed in histograms characterize the distribution ofgross income.

Risk - excessive rain Probability 0.3 impact on the average income % 25%

Risk - dry season planting Probability 0.3Cost R$/ha 60

Risk - dry at planting advanced Probability 0.3 impact on the average income % 30%

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Figure #: Histograms of distribution of gross income in climate change scenario withand without measure adaptation (reformatted by the authors).

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The histograms show that the measure of adaptation can significantly influence thedistributiongross income derived from the production of cassava flour. In fact, the adjustmentmeasure allowed redistribution of values of gross income below $ 2000 with relativelyhigh frequencies (part top of Figure #) to larger values. The result is that the average

gross income of simulationadjustment measure was higher, thus reducing the expected loss of climate change byhalf.

2.6. Final considerationThe methods presented here represent the basic principles of risk analysis and must beadapted to each case according to the local reality and activity analyzed. The simulationnon-agricultural productive activities comes with the challenge of identifying units ofanalysis (eg by income hectare, weeks or months worked) corresponding to logicalproduction while being

informative in terms of risk analysis.There is also the possibility of facing risk of extreme events which have correlations,especially when it comes to individuals or localities where various productive activitiescontribute significantly to household income. For example, riverine populations oftendepend on fishing and agriculture and years with extreme flooding events tend to bringexcess rain. In these cases, the analyst must use the advanced features of the programsimulating as the specification of correlation matrices relating the random numbergenerators for extreme events in question.

2.7. Literature for consultation

Barreto, P.N. 2011. Adaptation to climate variability and extreme precipitation events inthe middle rural Amazon: Swindon, State of Par Dissertation, Federal University ofPar,Brazil.JB Hardaker, RBM Huirne, JR Anderson and G. Lien 2004. Coping with Risk inAgriculture.CABI Publishing, Oxfordshire, UK.

Module III: Integration of participatory mapping and vulnerability analysisspatial risk assessment for

3.1 ObjectiveThis module documents methodological steps to integrate the results from Modules I andIIproposes options analysis and interpretation towards a spatial assessment of vulnerabilityproduction systems in rural Amazonia.3.2 Preconditions, material needed and approximate costs

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The preconditions techniques for implementing this module are the same as in theprevious modules which refers to computer hardware and software. The quality of theresult, however, depends on the time devoted to analysis and profile of the analyst. In thissense only show charge related to human resources dedicated to the analysis andinterpretation of information and its documentation.

3.3 Analysis of integrated spatial vulnerability: Step-by-StepStep 1 - Defining the extent of vulnerability (based on Module II)

The results of the vulnerability analysis consists of a series of measures that characterizethegross income distribution of rural productive activities (see Module II). The dialogue"ResultsSimulation la "or the report in Excel simulation program generated by simulatingcontains all

measures relevant to characterize the distribution of gross income of cassava flour.However,we need a Risk Level Benchmark for comparing the degree of vulnerability amongdifferent productive activities. Whereas this level tends to be subjective, it should be setindividually according to the objectives of the analysis.

We can, for example, compare two productive activities in terms of probabilityOccurrence of their incomes fall below 50%. In this case our benchmark level of risk is50% and our measure of vulnerability is the probability of occurrence.

In the report spreadsheet simulation simulate, below the graphs, there is a dialogue forcalculation of the probability of occurrence. The user can enter an absolute value andExcel calculates the its probability of occurrence. Can (a) manually calculate the absolutevalue according to the Our benchmark risk level (50%) or (b) automate such a calculationin Excel shown in seconds Figure #. For our example the production of cassava flour(Module II), 50% of gross income average are equivalent to R $ 1,501 and the probabilityof occurrence of values below this level benchmark risk is only 3.2% in the currentscenario and 8% in the scenario with climate change.

In other words, the climate change scenario makes producers more vulnerable to flourgross falls below 50% of the expected value, but the probability of such a fall is relativelylow (less than 10%), both without and with climate change.

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Figure #: Calculating the probability of second-level risk benchmark (cell B9 contains theaverage gross revenue as simulated by simulAr)

The simplest case is that of dealing with only one dominant productive activity, theproduction here manioc flour. However, usually dealing with multiple activities whichmakes this step needs to be repeated for each activity. Special cases of analysis arediscussed in Step 4 below.

Step 2 - Aggregation of the results of vulnerability assessment and spatialinformation

making the risk map (based on Module I)With our measure of vulnerability (probability of second-level risk referential) in handwe can start the integration of vulnerability assessment with information space obtainedduring the participatory mapping. Such integration involves insertion of probability ofeach production activity in the attribute table of shapes files generated in Module I.As Figure # selects the attribute table of the shape file in question (here Agriculturefamily based on the production of cassava flour) using the GIS program (here ArcGIS).Using the menu "Options Table", it creates a new column (here called vuln) in choosingthe attribute table data type "float". The tool "Editor" allows you to add Likelihood ofOccurrence manually, calculated above, the corresponding polygons.

When working with a large number of productive activities (ie, polygons) can be used tofunction "join" the program geoprocessing according to the instructions of the program.

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Figure #: Add information to the attribute table of a shapefile

For purposes of exposition we assume here that the lifting identified several types offarmingfamiliar with different levels of vulnerability (3.2, 10, 20, 50, 80). Using the new columnin tablecaption attribute based on the vulnerability map of the base production family flourcassava in Figure # can be generated using the same layout techniques described inModule II.

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Figure #: simple map with different levels of vulnerability Likelihood of Occurrence

Step 3 - Representing multiple activities per polygonIt is common for more than one productive activity contributes significantly to incomefarmers. In the case study conducted in the city of Swindon - PA, for example, manyfarmers practiced extraction Chestnut-do-Par and though the nut is are outside the areasof cassava production, extraction influences the farmers' income located in these areas.To consider this fact the model questionnaire (Appendix #) starts with the identificationandprioritization of productive activities and lists a percentage reflecting their importance toeachactivity. A common situation was, for example, that 80% of family income originated

from production flour and only 20% of extraction.To represent this fact in our analysis of vulnerability need to make adjustments to thestepspresented in Module II (see Excel file for SITE #).In the Excel file statement included two new worksheets. The first worksheet is called"Brazil Nut" and contains the information yield, price and gross income, as well asvariation specific and non-specific production and market for this activity (equivalent to

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Spreadsheet "Agriculture introduced in Module I"). The second new worksheet called"Income Analysisintegrated "and contains the results of spreadsheets" Brazil Nut "and" Agriculture "and avaluecalled "Integrated Income" represents the sum of the individual incomes of the two

activitiesweighted by importance factor obtained in the field (Figure #).

Figure #: Integrated Analysis of income

BOX #: Interpretation of integrated incomeThe integrated income does not represent an average income representative. The

variation, however, reflects the joint variation of individual incomes from productiveactivities (here Agriculture and Nut Par) according to their importance in family income.Therefore, the probability income is derived from the integrated vulnerability of a farmerwhose family income iscomposed of these activities independent of the scale of production.

Time to specify the range of productive activities specifies the analyst must considersome activities may be affected by these extreme events. For example, a dry Prolongedthat affects the development of an agricultural crop can also affect productivity ofCastanhais. In this case the "random number generator" (see Module II) should be the

same (As is the case in the Excel file statement) for the two productive activities.Spreadsheets BAU_cast, and BAU_agr BAU_renda_int contain the simulation resultswith simulated for the three output variables. And the spreadsheet BAU_renda we can seethat the integrated income production of cassava flour and extraction of Brazil nuts ismuch less vulnerable to falls more than 50% below the median income (1.7%) than theindividual income of the agricultural activity.The mapping of the probability of occurrence in base rents are integrated at the sameprocess, documented above, the mapping of individual incomes. Figure # shows a map

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hypothetical full of vulnerability based on several rural production activities identifiedduring the participatory mapping conducted by IPAM in Swindon.

Figure #: hypothetical vulnerability map for the city of Swindon PA

3.4 Interpretation, validation and use of the vulnerability mapAny quantitative analysis requires care when treating their interpretation anddissemination. inRegarding the use of maps like Figure # up in planning processes and decision making,the following observations are in order:

One. Although the mapped information arises participatory processes, can make itnecessaryvalidate the final result with representatives of the main actors involved in orderto avoid misinterpretations and correct possible errors of analysis.2nd. Since this is a simple method of survey and analysis is generally not possibleThis vulnerability assessment capture all sources of income and its relevant variations.In this sense it is recommended to supplement the quantitative analysis is qualitative

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information on the impacts and implications of extreme events in rural areas that usuallyarises in participatory mapping workshops and group interviews about the variability ofincome.3rd. Finally, there is the recognition that "vulnerability" is not a purely objective concept.For example, a probability of 10% of a fall below 50% income

the expected income can be assessed high or low depending on the individual perceptionof riskand / or depending on the income level of the actors in question. That is, it becomesnecessarycontextualize the quantitative levels of vulnerability according to the reality of eachgroup of relevant actor.