Modelling approaches to address crop-residue tradeoffs in mixed crop-livestock systems

Mark T. van Wijk, Mariana C. Rufino and Lieven Claessens

Wageningen University, Plant Production Systems email: mark.vanwijk@wur.nl

CIP, Nairobi

Presentation: CGIAR Systemwide Livestock Programme

Livestock Policy Group, 1 December 2009

Setup

• Intro: role of crop residues within farming system

• Something on trade offs

• Our past work

• Models and farm characterization

• Role of models within SLP

Aim of SLP project

Optimizing livelihood and environmental

benefits from crop residues in smallholder

crop-livestock systems in sub-Saharan

Africa and South Asia: regional case studies

Theory of intensification

(from a NRM perspective)

Manure

Feed

Food

Fertilisers

Food + services

Feed

Market

Food

Livestock

Grasslands

Cropland

Household

ManureFood

Fertilisers

Food + services

Feed

Market

Food

Livestock

Cropland

Household

Feed

Grasslands Crop residues Supplements, forages

Rela

tive u

se o

f soil

ferti

lity

pra

ctic

es

/ fe

eds

Degree of intensification / population pressure

Fallows Manure Fertilisers, legumes

After: McIntyre et al. 1992, Fernández-Rivera et al. 2002

Crop residues

Manure

Feed

Food

Fertilisers

Food + services

Feed

Market

Food

Livestock

Grasslands

Cropland

Household

Crop residues have different functions

• Fodder for livestock short term productivity livestock

• Input for soil long term productivity crops

These are just the first, direct effects. These,

in turn, will have cascading effects on

functioning of farming system and livelihood!

Trade off

• Limiting resources: changes in allocation of resources will positively affect one aspect of the system, and negatively another aspect

• E.g., more crop residues to livestock can affect soil fertility negatively in the longer term

Role of simulation models

Can help to analyse effects which are

difficult to measure– Long term effects– What-if questions– Risk analyses

Some analyses performed in AfricaNUANCES project

• Research project– Typology of farming systems– Data mining: e.g. experiments– Combined model of livestock, manure

management, crop and soil

• Used for– quantifying trade offs– identifying intensification strategies

NUANCES-FARMSIM

FIELD: dynamic, summary model of CROP and SOIL processes

LIVSIM: individual based dynamic, summary model of livestock

HEAPSIM: dynamic summary model of manure management and storage

LABOURSIM and CASHSIM: summary models of socio-economic components and their interactions with production comp.

0 1000 2000 3000 4000 5000 6000 7000 800080

100

120

140

160

180

200

Trade off analysisN

loss

es a

t fa

rm s

cale

[k

g se

ason

-1]

Farm scale maize yield [kg season-1]

Tittonell, Van Wijk et al, 2007, Agricultural Systems

• Looking for best possible trade off (pareto solutions) between indicators

• User should then make decision on what is preferable!

Results of trade off analysis

model input

(parameters, management

settings

model system

model output

(set of indicators)

Analysis with crop residues: results of a sensitivity analysis

0

500

1000

1500

2000

2500

3000

3500

0.5 0.6 0.7 0.8 0.9 1Fraction aboveground residue removed [-]

Mai

ze y

ield

in

las

t se

aso

n [

kg/f

arm

]

with Fert, no Lab restr.

no Fert, with Lab restr.

no Fert, no Lab restr.

Van Wijk et al, 2009, Agricultural Systems

0

1

2

3

4

5

0 30 60 90 120

Clay

Sand

Activity A

Activity B

Input use

Pro

duct

ivity

Efficiency

Degree of crop-livestock integration

Stocks,

flows a

nd assets

0

1

2

3

4

5

0 30 60 90 120

Clay

Sand

Activity A

Activity B

Input use

Pro

duct

ivity

Efficiency

0

1

2

3

4

5

0 30 60 90 120

Activity AActivity B

Input use

Pro

duct

ivity

Efficiency

System state II

+

+

Management intensit

y

+

+System state III

System state I

Hou

seho

ld “

Wel

l-bei

ng”

Stress

Alleviation

Tittonell, Van Wijk et al, 2009, Agricultural Systems

Models and Farm characterization

milk

feed

$

feed $

$

feed

crop residuesnutrients

$

fertilisers

On-farm

Rented land

manure Investment capacity

Labour availability

Access to credit

Access to information

milk

feed

$

feed $

$

feed

crop residuesnutrients

$

fertilisers

On-farm

feed

crop residuesnutrients

$

fertilisers

Where to invest

How many cows?

What type of feed?

How much fodder produced on-farm?

How long can this be sustained?

manure

Rented land

milk

feed

$

manure

feed

$

feed

crop residuesnutrients

ensiling

feed

$

fertilisers

crop residuesnutrients

Rented land

On-farm

$

fertilisers

milk

feed

$

manure

feed

$

feed

crop residuesnutrients

ensiling

feed

$

fertilisers

crop residuesnutrients

Rented land

On-farm

$

fertilisers

milk

feed

$

feed $

$

feed

crop residuesnutrients

$

fertilisers

On-farm

Rented land

manure

Differences in:

Resource endowment

Decision making

Farm household types

Dury, Rufino, Van Wijk, De Ridder, Zingore and Giller, 2009 AEE submitted

0%

25%

50%

75%

100%

RG1 RG2 RG3 RG4

% of

farm

ers w

ithin

grou

ps

Do not collect

Burn

Under plough

Collect

RG1

100

RG2 RG3 RG4

50

0

25

75

Far

mer

s (

%)

Do not collectBurnIncorporateCollect

(A)

Criteria used to define the crop residue management strategies, and resource groups (RG) that practiced each of the strategies

Households

(#) Crop residue management Main use

RGs that followed strategy

Strategy 1 13 Collect Stored and directly stored in the kraal.

Feed, bedding, and compost

RG1, RG2

Strategy 2 17 Collect Directly stored in the kraal Bedding and compost

RG1, RG2

Strategy 3 9 Collect Compost or mulching Mulching in the garden

RG3, RG4

Strategy 4 5 Other practices

Winter plough, burning Various RG2, RG3, RG4

Strategy 5 21 No practices

All

Analysis tools

Simulation modelsParameters

Input data

Biophysical worldExpert knowledge

Experimentation

Surveys

Analysis tools

Simulation modelsParameters

Input data

Biophysical worldExpert knowledge

Experimentation

Surveys

Decision world

Optimisation toolsPreferences

Opportunities+Interviews

Surveys

Analysis tools

Simulation modelsParameters

Input data

Biophysical worldExpert knowledge

Experimentation

Surveys

Decision world

Optimisation toolsPreferences

Opportunities+Interviews

Surveys

Preferences

One decision maker (DM)

Multiple DMs

Influenced by the environment

Opportunities

Resources available

Inputs prices

Outputs prices

Trade offs: multiple objectives!

1. Maximise gross margin

2. Maximise labour productivity

3. Minimise soil erosion

4. Minimise variability of production

5. Maximise social acceptability

6. Short term versus long term productivity

Where do models fit within SLP?

• Farm characterization: – Available resources– Farming strategies – Statistical and econometric analysis of current

strategies• Data mining:

– Data for parameterization & testing of models • Model analyses of existing and new farming

strategies:– Long term effects– What-if questions of key indicators!– Risk analyses