2. Introductio
n
• 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
3. 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!
5. Soils and Weather
Workable
hours
Profitability
(or loss)
Crop and livestock
outputs
Environmental
Impacts
Possible crops,
yields, maturity
dates, sowing
dates
Silsoe Whole Farm
Model
Linear programme, important
features timeliness penalties,
rotational penalties,
workability per task,
uncertainty
Machines
and
people
Constraints
and
penalties
6. Heavy clay, 800 mm annual rainfall
0
50
100
150
200
250
7
Jan
7
Feb
7
M
ar
7
Apr
7
M
ay
7
Jun
7
Jul
7
Aug
7
Sep
7
O
ct
7
Nov
7
Dec
Hours
Sandy loam, 500 mm annual rainfall
-
50
100
150
200
250
7
Jan
7
Feb
7
M
ar
7
Apr
7
M
ay
7
Jun
7
Jul
7
Aug
7
Sep
7
O
ct
7
N
ov
7
Dec
Hours
Workable
hours v.
tractor
hours
Period, fortnights Period, fortnights
7.
8.
9. Introductio
n
• 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
10. The
standard
LP model
• xijare 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
j
ijij
n
j
jj
,...,2,1,0
,...,2,1,
.
max
1
1
=≥
=≤
=
∑
∑
=
=
11. Voluntary
conservatio
n 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?
12. 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
14. 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
15. 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
n
j
ijij
n
j
jjqq
q
k
kk
,...,2,1,0
,...,2,1,0
,...,2,1,
.
max
1
1
1
=≥
=≥
=≤
=
=
∑
∑
∑
=
=
=
19. Separable
programmin
g
0
20
40
60
80
100
120
0 2 4 6 8 10 12
z ki
vki
A(1,1)
B(4,16)
C(7, 49)
D (10,100)
1,,,0
333
513315
4321
4321
4321
<=<=
+++=
+++=
δδδδ
δδδδ
δδδδ
ki
ki
Z
V
If any δi is >0 then all preceding =1
and all following =0
20. Introductio
n
• 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
24. Introductio
n
• 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
25. Ruth
Gasson
Farmers
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
26. 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?
27. Survey
results
trade 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?
28. 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.
29. 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)
