Mapping Socio-biodiversity: Do Old Modelling Tools Suit New

Challenges?

Sónia Carvalho Ribeiro, William Leles da Costa, Amanda Ribeiro de Oliveira,

Danilo da Silveira Figueira, Isabella Lorenzini da Silva Teixeira, Lilian Aline Machado,

Herman Rodrigues Oliveira and Britaldo Silveira Soares Filho

Centro Sensoriamento Remoto, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

Keywords: Extractivist Landscapes, Non Timber Forest Products, Weights of Evidence, Cost Friction.

Abstract: This work shows an original use of classical methods in land change modelling. The aim of this study is to

model yields (productivity) and economic importance (annual rents) of rubber and Brazil nut in the Brazilian

Amazon. Biophysical variables related to rubber and Brazil nut yields as well as market access

(commercialization) were used to model favorability of productivity using Weights of Evidence (WofE)

method. To favorability of productivity were assigned yields base on case study data. The economic model

then combines the map of yields with output prices and costs of collection, processing, and transport to

estimate annual rents per hectare for a specific forest plot. For estimating transport costs we used cost friction

surface modelling tools. Our results show that yields for Brazil nut averages 8.19±7.41 kg ha

-1

year

-1

and rent

averages US$ 5.05±7.49 ha

-1

year

-1

. Rubber average yields is of 3.53 kg/ha/year and rubber rents average US$

0.56±0.7ha

-1

year

-1

. Coupling biophysical and economic models allowed us to explore which environmental

and governance improvements are needed to avoid deforestation and forest degradation in the Brazilian

Amazon. Our results also show that despite some methodological issues and the recurrent call for “new”

modelling approaches for addressing the complexity of socio ecological systems, “old” modelling tools such

as Weight of Evidence and Cost Friction Surface, are still suited for addressing the challenge of mapping

socio-biodiversity.

1 MAPPING

SOCIO-BIODIVERSITY

Amazon forest biodiversity in Brazil lives together

with a variety of sociocultural groups. Traditional

communities in Amazon use and trade raw materials

of surrounding forests as part of their livelihoods.

There is a rich case study based research illustrating

complexity and diversity of extractivist landscapes

across the Brazilian Amazon (MMA, 2009).

Although rich in detail, local case studies provide a

fragmented view of extractivist landscapes in the

Brazilian Amazon and do not account for fluxes and

migration of people and products across the biome

(Hecht, 2013). Therefore, mapping and modelling

extractivist landscapes at the Amazon´s scale would

need to deal with a huge variability and complexity of

extractivist systems . Empirically informing spatially

explicit models with social survey data then becomes

a limiting factor. Thus, despite acknowledging the

importance of Non-Timber Forest Products (NTFP)

for securing local forest communities livelihoods,

there is little information on how productivity and

rents of different NTFP are geographically

differentiated across the Brazilian Amazon(Homma,

2008). In order to contribute to fill in this gap, we

developed a systematic approach to map yields and

annual rents of two famed NTFP namely rubber and

Brazil nut.

Brazil nut and rubber extraction are important

components of Amazon´s socio-biodiversity (MMA

2009). In order to overcome methodological issues

for mapping productivity and rents across the

Brazilian Amazon biome we couple biophysical and

economic modelling approaches. Biophysical

variables related to rubber and Brazil nut productivity

were used to model favorability of productivity using

Weights of Evidence (method. The favorability of

productivity was then transformed into yields based

on case study data. Then, the socio-economic model

combines the map of yields with output prices and

costs of collection, processing, and transport to

Ribeiro, S., Costa, W., Oliveira, A., Figueira, D., Teixeira, I., Machado, L., Oliveira, H. and Filho, B.

Mapping Socio-biodiversity: Do Old Modelling Tools Suit New Challenges?.

DOI: 10.5220/0006383903350340

In Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2017), pages 335-340

ISBN: 978-989-758-252-3

335

estimate annual rents per hectare for a specific forest

plot (equation 1).

Rent=(Qxy*Pn)-(Qxy*CTprdn)-(Qxy*Ctrn_dz)) (1)

Where Qxy is the simulated production for a cell

with coordinates (x,y) in kg-1ha-1; Pn and CTprdn

correspond to respectively, selling price and the cost

of production in US$/kg of product n and the cost of

secondary transportation (Ctrn) of the product n by

means (dz) from the location (x,y) to the nearest

cooperative.

Although this approach has limitations it is

suitable to explore the ways in which “old” modelling

tools such as the Weights of Evidence and Cost

Friction Surface are able for addressing the new

challenges of mapping socio-biodiversity.

2 NEW CHALLENGES, OLD

TOOLS?

There has been a call for “new” modelling approaches

to tackle de specificities of bonded socio-ecological

systems at broad spatial scales (Rounsevell and

Arneth, 2011, Rounsevell et al., 2012). Despite

acknowledging the need to develop novel modelling

approaches we assess whether or not “old” and well-

known modelling tools such as the Weights of

Evidence and Friction Surfaces, are suited for

mapping both ecology (yields) and economy (rents)

of extractivist landscapes. A flowchart of the

methodological framework is presented in appendix.

2.1 Weights of Evidence

The method of Weight of Evidence (WofE) has been

widely used for mapping prospective mineral areas

(Payne et al., 2015), risk of landslides (Poonam et al.),

fire risk and modelling habitat suitability (Fan et al.,

2011). We did a search in Science Direct database

using as keywords “Weight of Evidence” and

“mapping” and we found 2,396 results (in February

2017).

In this work, we applied the Continuous Weights

of Evidence method (Soares Filho et al., 2009) for

modeling favorability of productivity of rubber and

Brazil nut. The models begin by simulating the yields

of Brazil nut and rubber in the Brazilian Amazon. To

do so we integrated a set of biophysical variables by

using the Weights of Evidence method. We used

bioclimatic and biophysical variables (Nunes et al.,

2012, Jaramillo-Giraldo et al., 2017) as well as time-

series (1994-2013) of production data from Brazilian

Statistics office (IBGE). We used the IBGE

maximum production in each municipality as a

surrogate for production capability based on the

assumption that if a municipality was able to produce

and trade such a quantity in a particular year, over the

20-year period, it still holds that production potential.

Correlated variables were removed from the model.

Based on case studies, we selected 12 variables

(Figure 1) and then calculated their influences (W

)

to determine the spatial probability of productivity









 



 



     



 













  













(2)

Where P is the probability of productivity at

location x,y given a series of spatial variables and

is the weight of evidence of category n of

variable V

(Bonham-Carter, 1994). Favorability was

then transformed into yields by applying a PDF

transformation so that the new distribution matches

the PDF of yields from the case study areas in Acre

(CSR, 2011, Jaramillo-Giraldo et al., 2017). The

Figure 1: Result of the Continuos Weight of Evidence for

maping favorability of productivity for Brazil nut (top) and

Rubber (bottom).

GAMOLCS 2017 - International Workshop on Geomatic Approaches for Modelling Land Change Scenarios

336

study by Nunes et al. 2012 and Jaramillo et al 2017

used fieldwork data on tree occurrence and

productivity for estimating Brazil nut and rubber tree

density and yields. We used Nunes’ and Jaramillo

yield distribution function for extrapolating the yields

from Acre to the Brazilian amazon.

In order to better understand the results of this

approach we organize semi structured interviews with

a variety of stakeholders. In total, we interviewed 30

people in Acre including 6 extrativists, 10 NGOs, 10

governmental bodies as well as professors at the Acre

Federal University (UFAC, Universidade Federal do

Acre). In Pará, we interviewed 9 extractivists, 2

cooperatives, 5 governmental bodies, 2 researchers

and 1 Brazil nut exporting industry. In these contacts

and from partnerships with local institutons we

included in the analysis socio-economic data from

over 10, 500 extractivist families.

2.2 Cost Friction Surface

Similarly to the WoE the use of cost friction surfaces

is widespread in environmental sciences. A search in

the Science Direct using keywords as “friction

surface” and “mapping” delivered 680 results. We

used the location of communities in the Brazilian

Amazon for calculating the area of influence of each

community that gathers Brazil nut and rubber (we

selected the communities inside the municipalities

where production was recorded by IBGE). By doing

so, we estimated the transport costs from any point in

the forest to the nearest community (Figure 2).

The second stage consists in transporting NTFPs

from the storehouse in the communities to the nearest

cooperative. We used the cooperative location (also

built on the basis of field work) for calculating the

“area of influence” for each one of the cooperatives

that work with Brazil nut and Rubber.

In order to estimate transportation costs, the

model uses a map of roads and navigable rivers. First,

it calculates a cost friction surface (cost per kg and

km), and then produces an accumulated cost from

point of collection in the forest to the village and then

to the cooperative (final destination), according to the

type of road/waterway and mode of transport (boat,

truck, donkey/motorcycle).

Accumulated transport cost (including transport

across the forest as well as to the nearest cooperative)

ranges from 0 to US$ 3.80 per kg (Figure 2).

This means that places farther than 200 km from

cooperatives or point of sale, it is not worth collecting

nut.

Figure 2: Accumulated transport costs: from the forest to

the community and from community to cooperative.

3 YIELDS AND RENTS

Our Brazil nut yields in the vast majority of areas

(99%) of the Brazilian Amazon situate between 0 to

30 kg per ha-1year-1, although there are locations

where yields can reach 152 kg per ha (1% of the

biome) The annual rents of Brazil nut, presented as

the Equivalent Annual Annuity (EAA), range from

US$ 0 to 46 ha-1year-1 (Figure 4.17), with average

rents of US$ 5.05 ha-1year-1 (Table 1).

Table 1: Rents for Brazil nut.

Rent Brazil nut

(US$/ha)

Minimum

0.00

Maximum

46.00

Mean

5.05

Variance

56.24

Standard deviation

7.49

Rubber extraction in the Amazon is not profitable

in areas of low productivity even with subsidies to

guarantee a minimum price to rubber tappers. In the

presence of governmental subsidies, rents average

US$ 0.56 ha

-1

year

-1

, varying from 0 to US$ 6.13 ha

year

-1

(Table 2).

Table 2: Rents for rubber.

Rent Rubber

(US$/ha)

Minimum

0.00

Maximum

6.13

Mean

0.56

Variance

0.57

Standard deviation

0.76

Mapping Socio-biodiversity: Do Old Modelling Tools Suit New Challenges?

337

4 CONCLUSIONS

Although it is widely acknowledged that Non-Timber

Forest Products (NTFP) are central for securing forest

communities livelihoods, there is little information on

how important features of extractivist landscapes

such as yields and rents are geographically

differentiated across the Amazon Biome. In order to

fill this gap, we have developed a systematic

approach to monetize values for non-timber forest

products across the Brazilian Amazon using spatially

explicit assessments. The information that such

assessments provide enables comparisons between

the natural, physical and human capitals, and hence

their contributions to the society’s welfare.

This work shows an original use of classical

methods in land change modelling. Using WofE and

friction surfaces we mapped both yields and rents for

rubber and Brazil nut across the Brazilian Amazon.

With such a goal we collapsed the diversity and

complexity of extractivist landscapes into simplified,

but meaningful, approximations. Developing such

models to the point of being operational is a long

term objective and this analysis is still developing.

Despite some limitations namely in data availability,

these modelling tools such as Wof E and friction

surface tools revealed to be able for representing the

complex economy of extractivist landscapes at the

biome scale. While Weight of Evidence was useful

for geographically differentiating productivity, cost

friction surfaces allowed us at geographically

differentiating transport costs. However, although we

find these tools useful and well suited for the purpose

of our study we did not compare them with other

modelling tools in order to gauge their performance.

This needs further work.

Our modelling approach estimates yields and

annual rents from the extraction, of rubber and Brazil

nut collection. We found that the annual values for

rubber and Brazil nut are relatively low. Rents for

Brazil nut averages US$ 5.05 ha

-1

year

-1

while rubber

extraction in the Amazon is not profitable in areas of

low productiviy. In areas with yields above the mean

(yields ≥ 3.53 kg ha

-1

year

-1

), and in the presence of

governamental subsidies, rubber rents average US$

0.56±0.7 ha

-1

year

-1

Our results show that areas that systematically

presented higher annual rents are located nearby

villages/towns with better access and larger

population. These areas, by contrast, are also the areas

with higher rates of deforestation. Thus, it seems

likely that factors that locally influence the rents also

drive forest conversion. However, NTFP

development in the form of better markets, improved

infrastructure and higher product demand and/or

prices, could provide an alternative to forest

conversion to agriculture, and hence be an ally of

forest conservation. Unfortunately, this is not the

current situation.

The results of this study allow us to question the

effectiveness of “narrow” market chains based on

specific products. We need thus to explore possible

policy contexts for enhancing the value of the

Amazon forests within a more holistic approach that

focuses on the forest as an entity providing multiple

ecosystem services. So far, the narrow market chains

of sustainable timber and NTFPs were unable to do

the job they were meant for because they do not

aggregate values to the products that are of paramount

importance to sustain local livelihoods. As a result,

“new” solutions are required.

ACKNOWLEDGEMENTS

We are thankful to CNPQ (Conselho Nacional de

Desenvolvimento Científico e Tecnológico) in Brazil

for providing Post-doctoral young talent scholarship

(300013/2015-9) to the first author of this work.

Funding to support this research was also provided by

Gordon and Betty Foundation and NORAD via

World Bank.

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APPENDIX

Methodology: Flowchart

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