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OR50, York, 9-11 September 2008 Daniel Sandars & Eric Audsley
Predicting farmer decision behaviour, taking a planning model beyond profit maximisation!
Sub-structure
• Background & challenges• Method choices and issues• Eliciting objectives and preferences from farmers
• Modelling and solving
• Next to do
Biodiversity options for English lowland arable farming
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.
• 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!
Questions
• How would farmers react, in the long term, to change?• Climatic• Technical• Financial• Regulatory
• How does the cropping, environmental emissions and biodiversity change?
• For example, how will farmers respond to increasing prices of biofuel crops. What will the unintended consequences be?
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
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?
What is new
• Conservation policy requires an understanding of farmer behaviour and its variability including:
• Better prediction of production behaviour
• Better prediction of voluntary behaviour• …• Better prediction of collective behaviour?
Alternatives to profit maximisation
• Modified profit maximisation• Profit with risk• Multiple Criteria methods
• Socio-psychological methods
• Theory of planned behaviour, Personal construct theory
• Bounded v complete rationality
• Agent based modelling
Utility Theory
• Jeremy Bentham (15 February 1748–6 June 1832)• Auto-Icon University College London
Utility curves
0%10%20%30%40%50%60%70%80%90%
100%
0% 20% 40% 60% 80% 100%
Recreational potential of the farm
Sat
isfa
cti
on
of
you
r re
cre
atio
nal
inte
rest
s
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
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
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
Problem structuring method
• Unstructured interviews with a few farmers• Good-bad-interesting aspects of investment
decisions, production decisions and environmental-ecological decisions
• Means are readily identified but you have to fish for the ends that are being met
• Plant wild flower margins• Use minimum tillage
>>fuel use>>constrain costs>> Optimise profit• Plant game cover crops
Means into ends
• Maximisations of long-term profit• Maximisation of long-term asset values<< landscape
features
• Maximisation of non-traded benefits <<private shoot
Value tree & measurable attributes
1. Income2. Income risk3. Autonomy (No. of regulations)4. Complexity (No. crop types, No. subsidy schemes)5. On-farm recreational lifestyle (Free time, Rough
shooting, No birds species seen, No. Skylarks seen)6. Farm appearance (No. tall weeds, No. other weeds,
hedge length, woodland area, No. Skylark plots)7. Social status (commercial shoot)
Sub-structure
• Background & challenges• Method choices and issues• Eliciting objectives and preferences from farmers
• Modelling and solving
• Next to do
Survey sample• 45 farmers in
three counties• Grouped based
on voluntary membership of:
• progressive farming organisations and/ or
• environmental/ conservation organisations
Survey methods
Weak motivation of participants, high number attributes and small sample size meant that many methods based on pair-wise elicitation were intractable, e.g. choice experiments, etc
• Simplified Multi-Attribute Rating Technique (SMART)• Intra-Criterion
• Bisection method• Inter-Criterion
• Swing weight methods• Survey conducted by Liz Mattison (Reading) and Anil
Graves
Survey resultsdirection of preference
• Counter intuitive (on some farms)• More: tall weeds, skylark plots, regulations,
subsidy regimes, crops, risk
• Less: Free time, rough shooting
• There are also issues with some utility curves being non monotonic- suggesting that our goals confuse two or more fundamental goals.
Survey resultsweights
• Profit (10.9,24.3,50)
• Free-time (0,12,30.8)
• Risk (0,9,32.5)
• Complexity: crops (0,8.4,36.5)
• Bird species seen (0,6.9,15.2)
• Autonomy (0,6.3,16.9)
• Complexity: schemes (0,6.3,16.9)
• Hedge (0.4,5.4,10.1)
• Tall weeds (0,5,14.4)
• No Skylarks seen (0,4.3,11)
• Woodland (0,3.8,10.5)
• Rough shoot (0,2.8,14.5) • Other weeds (0,2.8,10.5)
• No skylark plots (0,1.2,5.4)
• Social-shoot (0,0.3,9,6)
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?
Survey resultsIssues
• Most measures are appalling ambiguous proxies for the concept contained in the goal that they are representing.
• Each new environmental or social goal gets a little bit of weight thus inflating the combined relative importance of these goals to financial ones.
• The swing weight method does not force sacrifice and thus over states the importance of non-primary goals.
Sub-structure
• Background & challenges• Method choices and issues• Eliciting objectives and preferences from farmers
• Modelling and solving
• Next to do
Solving from a linear programme
• Separable programming - lambda form (piece wise linear approximation)
• Additive utility
Program output screen
Sub-structure
• Background & challenges• Method choices and issues• Eliciting objectives and preferences from farmers
• Modelling and solving
• Next to do
To do
• Cross-check the preference elicitation in group and as a representation of the all farmers
• Telephone, mail, focus groups, or indirectly by fitting preference weights to observed behaviour
• Cluster analysis
• Improve the modelling of the objectives/ attributes in the MP
• More integer variables for counts of crops, crop types, schemes.
• Evaluation and application