1

for the design of sustainable and applicable cropping systems

Meylan, L., Sibelet, N., Gary, C., Rapidel, B.

Combining numerical modeling with a participative approach

Scientific context

• Objective: designing agroforestry systems with multiple performance requirements

• Challenge: interaction of several spp; complex and long-term effects to take into account

• Two common approaches – Modeling: useful for complex interactions often found in

agroforestry; many trials, low cost; but theoretical results and few developed modelsVereijken, 1997; Rapidel et al, 2009; Whitbread et al, 2009

– Participative: involving farmers, suitable solutions, but solutions not genericTixier et al, 2006; Malézieux et al, 2009

?

Case study: shaded coffee in Llano Bonito, Costa Rica• Coffea arabica (mostly Caturra) + shade trees (mostly

Erythrina and Musa spp)• Relatively small farms (1-2ha) supporting many families;

600 farmers in the Llano Bonito watershed (18km²)• Intensive production (400kg of N/ha/yr) sold at

premium price

Case study: shaded coffee in Llano Bonito, Costa Rica• High erosion rates due to steep

slopes and rainy season (+3000 mm of rain/year)

• Operation of downstream hydroelectric dam threatened by sedimentation of Pirris river

• Interest in PES (Payment for Environmental Service) scheme for coffee growers to reduce erosion

Case study: shaded coffee in Llano Bonito, Costa Rica

Coffee production shade trees erosion• Trees help prevent erosion • … but how many trees in coffee systems to

maximize performance in both areas?

Minimum shade level needed – what is the optimum?

?Actual shade tree densities vary between 100 and 400 trees/ha; most trees are pruned 2-3 times a year

Shade trees can have positive or negative effects on coffee production depending on climate, environment, management…

CAF2007: numerical model for coffee shaded systems

Van Oijen et al, 2010

CAF: numerical model for coffee shaded systems

Van Oijen et al, 2010

Can we combine the information from a numerical model with farmers’ knowledge to design sustainable AND practicable systems?

Research objectives

1. Involve coffee growers in the modeling process in order to identify potential systems that farmers would be willing put into place themselves

2. Evaluate usefulness of CAF2007 in generating improved agroforestry systems that are acceptable to farmers

Participative modeling sessions

• Preparation– Previous data and conceptual

model to introduce trees, coffee & erosion relationship

– Progressively introduce new concepts – graphs/charts, model

• Participants asked to think about what they would test “on the field”

• All questions and suggestions recorded verbatim– number of variables in each “what if” question– topics covered

Participative modeling sessions

• 19 farmers in 4 groups; 5 sessions of 2hrs each

S1 •Ideas for experimentation – no model

S2 •Conceptual model, check outcomes of S1

S3 •Numerical data •What does “model” mean?

S4 •Presentation of CAF2007, potential scenarios to be simulated

S5 •Evaluation of simulation outcomes

Participative modeling sessions

Increased participation, complexity of questions, and number of topics covered after introduction of CAF2007 in S4

0

1

2

3

4

5

6

S1 S2 S4 S5

Complexity (mean number of variables per

question)

Working session

Low intensive

Work intensive

Shaded systems

Inputs intensive

0

5

10

15

S1 S2 S4 S5

Diversity (mean number of variables per

farmer)

Working session

Low intensive

Work intensive

Shaded systems

Inputs intensive

Evaluation of CAF2007 by participants

• High interest in simulation of N cycling and fertilization

• Model inaccuracies: effect of shade tree on yield over several years

Mostly agreed with model output

• Fertilization• Mineral N pool• Coffee LAI and

vegetative growth

Mostly disagreed with model output• Shade tree density• Shade tree pruning• Coffee pruning

Missing• Pests & disease• Annual yield

variations (e.g. dieback effect)

Conclusions

• Coffee growers actively engaged with a numerical model to discuss changes in their management– “what if?” and simulation requests; increased

number, scope and complexity with CAF2007– both positive and negative feedback on model

performance– exploration of normally “invisible” processes shown in

model, e.g. erosion, mineral N pool• New systems (change in management) were

identified – to be tested by growers themselves

Contact info:bruno.rapidel@cirad.frlouise.meylan@cirad.fr

For more information…pcp-agroforestry.org

ADDITIONAL SLIDES

Typology of coffee plots1: low intensity 2: labor

intensive3: shaded systems

4: agrochemical intensive

Fertilizer (USD/ha/yr) 518 c 1395 a 909 b 1157 a,bFungicide (USD/ha/yr) 51 a,b 28 b 16 b 79 aManual weed control (hrs/ha/yr)

131 a 203 a 120 a 144 a

Shade tree density (#/ha) 288 a,b 332 a,b 539 a 235 b

Yield (t/ha/yr) 4.2 b 8.9 a 7.2 a 8.1 a

Erosion control 1.1 a,b 1.9 a 0.6 b 1.2 a,b

Effect of shade tree density on coffee yield

R² = 0.301

R² = 0.565

2

4

6

8

10

12

0 200 400 600 800 1000

Coffe

e yi

eld

(t/h

a/yr

)

Shade tree density

Group 1

Group 2

Group 3

Group 4

Conceptual model

Conceptual model

Effect of shade trees on coffee

01122334455

full sun Erythrina banana

kg o

f litt

er p

er m

²

shade treatment

0

1000

2000

3000

4000

5000

6000

0.40 0.50 0.60 0.70 0.80 0.90 1.00 1.10 1.20

g of d

ry v

eget

ation

/m²

Infiltration delay (h)

Full sun

Erythrina

Banana

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Full sun Erythrina Banana

Infil

trati

on d

elay

0-3

0cm

(h)

No significant effect of shade on production; but positive effect on soil litter and infiltration, used here as proxies for erosion (actual runoff and erosion data forthcoming)

Preparing the modelVariable Site 1 Site 2 Site 3

YieldInitial simulation 2.36 1.95 3.47

After calibration 1.08 1.22 1.68Coffee LAI

Initial simulation 1.69 2.01 1.2

After calibration 0.52 0.88 0.67Tree LAI

Initial simulation 1.22 1.48 1.63

After calibration 0.89 0.70 0.45Coffee wood biomass

Initial simulation 2.30 2.01 1.87

After calibration 0.34 0.46 0.33Soil water content

Initial simulation 0.22 - 0.14

After calibration 0.06 - 0.04

Left: RMSE for output values used in model calibration

Right: example of calibration effect on

simulated coffee wood biomass values

4000 4100 4200 4300 4400 4500 4600 4700 48000

0.10.20.30.40.50.60.70.80.9

Carbon in coffee wood

after cali-bration

measured data

before cal-ibration

days of simulation

kg o

f C /

m2