predicting farmer decision behaviour, taking a planning1

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

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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

The ENDCollaborators