LBA 17-19.11.08, Manaus

Jan Börner (CIAT, Amazon Initiative)

Arisbe Mendoza (ECOSUR)

Secondary forest valuation on family farms in the Eastern

Brazilian Amazon

LBA 17-19.11.08, Manaus

Outline

• Why value secondary forests?

• Evidence on the fallow-yield relationship

• A two-step approach to value the role of capoeira in slash-and-burn farming

• Policy and technology scenarios

• Implications for designing conservation incentives

LBA 17-19.11.08, Manaus

Why value secondary forests?

• > 8.7 million ha on farms in the Brazilian Amazon are covered by secondary forests

• About a quarter is frequently converted for agricultural purposes (crops, pastures)

• Depending on agricultural practices and technology, farmers may perceive secondary forest growth as cost or benefit factors

• i.e., conservation incentives can only be effective if these factors are taken into account

LBA 17-19.11.08, Manaus

Secondary forests, pastures and staple crop fields

Secondary forest fallows supply nutrients and other ecosystem services in crop producing S&B systems

Secondary forest re-growth reduces the productivity of pastures

LBA 17-19.11.08, Manaus

Evidence on the fallow-yield relationship

Cited in Mertz (2002) Agroforestry S.

• Positive fallow-length/yield relation rarely empirically proven

• Breakdown points could not be empirically established

• Yield is only one of the factors influencing farming decisions

• Fallow/yield relationship is only half the story

LBA 17-19.11.08, Manaus

Key questions

• How will policy incentives affect secondary forest cover and related ES?– What value to farmers attach to fallows (their

length)?– What impact does access and use of different

technologies have on this value?– How do conservation incentives (e.g. PES)

affect secondary forest cover and value under different technology scenarios?

LBA 17-19.11.08, Manaus

An example from the Eastern Brazilian Amazon

ZEF Bonn

NAEA/UFPA

LBA 17-19.11.08, Manaus

Recently burned

Pasture

Cassava

Passion fruit

Fallow vegetation

LBA 17-19.11.08, Manaus

Approach

plot-level profit function estimation

farm-level bio-economic land use modeling

model calibration

plot-level annual crop production

data from 450 plots

value of fallowing in the

S&B system

value of secondary forest fallows at

farm-level

farm-level data from 270 farm-households

technical coefficient data from

30 representativefarm-households

classification(cluster analysis)

Result Method Analysis Data

Legend:

LBA 17-19.11.08, Manaus

Results of econometric model II

Fixed Factors (standard errors in parentheses)

Factor demand Land Family labour Fallow length Soil pH

Fertilizer 0.222 (0.048) -0.032 (0.007) -0.052 (0.014) -0.041 (0.008)

Hired Labour 1.713 (0.162) -0.000 (0.000) 0.014 (0.001) -0.175 (0.016)

Obs.: Mean and standard errors of farm elasticities are calculated using one observation for each farm evaluated at mean farm prices. The standard errors are calculated by bootstrapping on 500 data re-sampling.

LBA 17-19.11.08, Manaus

Results of econometric model (plot-level)

Variables Elasticities(standard errors in parentheses)

Land 0.955 (0.122)

Fallow 0.222 (0.020)

Hired labour 0.140 (0.034)

Fertilizer 0.099 (0.013)

Elasticity Estimates of Profit with Respect to Farm Resources

Obs.: Mean and standard errors of farm elasticities are calculated using one observation for each farm evaluated at mean farm prices. The standard errors are calculated by bootstrapping on 500 data re-sampling.

Equivalent to R$ 385 per ha of 6 year old fallow!

LBA 17-19.11.08, Manaus

Results of bio-economic model(farm-level)

Key indicators Units Results

Annual Net income R$ 4064

Area under annuals ha 2.79

Area under perennials ha 0.97

Fallow ha 9.2

Fallow age Years 4

Fallow value R$/ha 128

R$1 ≈ 0.28 Euro

LBA 17-19.11.08, Manaus

Model validation

• No statistically significant bias in predicted 1st-year land use across farm types

• Long-term trends in average fallow age, and fallow/cropland ratio correspond to farmers’ observations of the past

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

annuals perennials fallow pasture unused

land use type

% o

f to

tal

farm

modeled

observed

Model versus Reality

LBA 17-19.11.08, Manaus

A selection of available technologies?

Farm-household

choices

Technology / Land Use Options

Slash & Burn

Mechanical Plowing

Chop-and-mulch

Green Revolution

Agroforestry& Processing

LBA 17-19.11.08, Manaus

-40

-20

0

20

40

60

80

100

% c

ha

ng

e f

rom

ba

se

line

S&B + fertilizationS&B + fertilization + chop-and-mulchS&B + fertilization + chop-and-mulch + mechanized plowingS&B + agro-forestry + on-farm processing

What would happen if farmers adopted?

Income Secondary forest/fallows

Fallow age Sequestered carbon

Labor demand

Adoption of continuous cropping + mechanical land preparation reduces fallow valuation by 71% to R$/ha 38

LBA 17-19.11.08, Manaus

Payments for environmental services

0

100

200

300

400

500

600

0 50 100 150 200 250 300 350annual set aside payment (R$/ha)

aver

age

carb

on

leve

l (t/

farm

)

carbon (restricted technology access)

carbon (access to mechanical land preparation)

R$1 ≈ 0.28 Euro

LBA 17-19.11.08, Manaus

Competitiveness

$0.00

$2.00

$4.00

$6.00

$8.00

$10.00

$12.00

0 300 600 900 1200 1500 1800

emissoes evitadas (t CO2 por estabelecimento) in T=20

PS

A (

R$

/t C

O2

)

Avoided emissions (t CO2 per farm) in T=20

CER price at CCX

LBA 17-19.11.08, Manaus

Conclusions & Implications

• For representative family farms in the Northeastern Amazon annual payments of > R$/ha 100 are needed to induce carbon sequestration

• Technology access reduces absolute carbon content on farms, but allows capturing additional carbon at 50% lower costs

• Hence, PES needed to be differentiated to increase scheme efficiency and reduce rent capture by capitalized and well off farmers