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Whole farm modelling Farmer decision-behaviour14th November 2008, U. ReadingDaniel SandarsResearch OfficerNatural Resources Management Centre
Introduction
• 1) Background to the Silsoe Whole Farm Model and the policy challenge
• 2) Extension from linear profit maximisation to non linear utility maximisation
• 3) Progress towards implementing the RELU-Birds preference models.
• 4) Reflections on the scientific challenges ahead
Farm LPs
• Whole farm planning LPs have two subtly different roles; Prescriptive uses guide an individual farmer to better decisions whereas predictive uses help understand how farmers response to choice or change. For the policy maker we are still doing prescriptive OR!!
• Profit maximisation has been effective for predicting the aggregate response of farmers to change.
• …even though there might be evidence that this does not describe how individuals behave!
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0.1 1 10 100 1000 10000 100000 1000000 10000000
Arable area, ha
Per
cen
tag
e ab
s re
lati
ve e
rro
r
Soils and Weather
Workable hours
Profitability (or loss)
Crop and livestock outputs
Environmental Impacts
Possible crops, yields, maturity
dates, sowing dates
Silsoe Whole Farm ModelLinear programme, important features timeliness
penalties, rotational penalties, workability per task, uncertainty
Machines and
people
Constraints and
penalties
Heavy clay, 800 mm annual rainfall
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50
100
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250
Ho
urs
Sandy loam, 500 mm annual rainfall
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50
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urs
Workable hours v. tractor hours
Period, fortnights Period, fortnights
Introduction
• 1) Background to the Silsoe Whole Farm Model and the policy challenge
• 2) Extension from linear profit maximisation to non linear utility maximisation
• 3) Progress towards implementing the RELU-Birds preference models.
• 4) Reflections on the scientific challenges ahead
The standard LP model
• xij are what could be produced, such as different crops,
with profit cj and resource consumption aij per unit
• bi are resource constraints, such as land area
njx
mibxa
ts
xcZ
j
n
jijij
n
jjj
,...,2,1,0
,...,2,1,
.
max
1
1
Voluntary conservation behaviour
• How would free conservation education influence farmer behaviour?
• What types of policy intervention do farmers find unacceptable?
• Biodiversity arises from hotspots rather than the average?
Multi-criteria methods
Discrete choice problems Continuous choice problems
Methods Multi-criteria Decision Making, Analytic Hierarchy Process, Outranking methods, etc
Goal programming, Compromise programming, Multiple Objective programming
Features Elicits a rich picture of attributes. Formal problem structuring methods. Interactive with a few motivated decision makers
Simple view of attributes. Few examples of formal problem structuring methods. Examples of non-interactive uses
Role Mostly prescriptive solutions, but have seen AHP claim to predict the outcome of the US presidential election
Most examples prescriptive
Utility Theory
• Jeremy Bentham (15 February 1748–6 June 1832)
• Auto-Icon University College London
What objectives/ Goals?
• Ask farmers? Few examples of robust repeatable methodology!
• From the farm planning literature? Many examples of using attributes that other people used!
• From the psychological literature?
• We used a mixture of both
Multiple-object LP
• zk are component objectives, such as profit, risk, biodiversity
• wk are a set of weights used to form a single composite objective
qkw
njx
mibxa
ts
xcz
zwZ
k
j
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jijij
n
jjjqq
q
kkk
,...,2,1,0
,...,2,1,0
,...,2,1,
.
max
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rl
qkw
njx
mibxa
ts
qkxcz
qkzwZ
kl
j
n
jijij
n
jjjklkl
q
kkllkl
...2,1
,...,2,1,0
,...,2,1,0
,...,2,1,
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...2,1,
...2,1,max
1
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1
Suppose we have a set of r decision makers, one of which is our normative ideal, each with view on how an action will change an objective (biodiversity) cjkl and the extent to which they are prepared to trade that objective off against profit wkl
Decision makers!
Non linear preferences
Value function
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 2 4 6 8 10 12
No birds/ha
Val
ue
of
bir
ds
to a
dec
iso
n m
aker
Decision makers 2
rl
qkw
njx
mibxa
ts
qkxcz
qkzvwZ
k
j
n
jijij
n
jjjklkl
q
kklkllkl
...2,1
,...,2,1,0
,...,2,1,0
,...,2,1,
.
...2,1,
...2,1,)(max
1
1
1
Where vkl is a piecewise linear value function coefficient
Separable programming
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0 2 4 6 8 10 12z ki
vki
A(1,1)
B(4,16)
C(7, 49)
D (10,100)
1,,,0
333
513315
4321
4321
4321
ki
ki
Z
V
If any δi is >0 then all preceding =1and all following =0
Introduction
• 1) Background to the Silsoe Whole Farm Model and the policy challenge
• 2) Extension from linear profit maximisation to non linear utility maximisation
• 3) Progress towards implementing the RELU-Birds preference models.
• 4) Reflections on the scientific challenges ahead
Entering weights
• Separable programming - lambda form (piece wise linear approximation) - Additive utility
Program output screen
Weight distributionattributes (metrics)
0
0.05
0.1
0.15
0.2
0.25
0.3
0 5 10 15 20
Attribute
No
rma
lise
d w
eig
ht
centroidobserved
Introduction
• 1) Background to the Silsoe Whole Farm Model and the policy challenge
• 2) Extension from linear profit maximisation to non linear utility maximisation
• 3) Progress towards implementing the RELU-Birds preference models.
• 4) Reflections on the scientific challenges ahead
Ruth GassonFarmers Goals
• Instrumental• Growth, Income, working conditions, security
• Expressive• Pride, self respect, creativity, achievement,
aptitude• Social
• Prestige, belonging, tradition, family, community• Intrinsic
• Physical effort, sense of purpose, independence, control, the outdoors
Issues
• Most measures are appalling ambiguous proxies for the concept contained in the goal that they are representing.
• Redundancy amongst attributes.• The swing weight method does not force sacrifice
and thus over states the importance of non-primary goals.
• -indirect estimation methods do we have the data?• -orthogonal elicitation methods – do we have the
resources and the patience of farmers?
Survey resultstrade offs
• Extreme
• -£25,279 to see another bird species
• -£2 mean profit to reduce profit deviation by £1
• £55,000 to give up a day off
• £661,826 to give up a days rough shooting
• £771,000 to fill out another set of forms?
Conclusions
• We can optimise a richer utility based predictive model of farmer behaviour, but can we specify, model, parameterise, and validate it.
• Hard…there are many open questions
• It is worth doing scientifically and simply being able to offer better or different insights than the alternatives available to policy makers is reward enough.
Other events
• The OR Society Special Interest Group on Agriculture and Natural Resources (chair Prof. Tahir Rehman (U. Reading) and secretary Daniel Sandars (Cranfield))
• Relaunch 2nd April 2009 @ Reading University
• The EURO working group on OR in Agriculture and Forestry Management (Co-ordinator Dr Lluis Plà)
• 5th Meeting EURO XXIII July 5th-8th (Bonn)
• EURO Summer School July 25th-August 8th (Lleida)