RELU FARM-level ModellingLand-use/ environmental interactionsU. Stirling 30-6-08

Biodiversity and Agricultural Production Planning by LPDaniel L. Sandars & E. Audsley

Structure

• Background

• Methodological challenges

• Results

• Summary & (Discussion)

Declining farmland birds

A political objective to

halt the decline

Arable farming

• Why a decline? – reduced winter food resources• Intensification leads large-scale

homogenisation in the landscape• Herbicides lead to few weeds surviving to

harvest• High capacity machinery leads to timely

harvest and the swift removal of residues and stubble

• Increased winter sown cropping leads to less over wintering stubbles

Decision variables

• Crop choice

• Rotational combination

• Operational choice

• Operational timing

• Input choice

• Input timing

Policy questions

• How would farmers react, in the long term, to change?• Climatic• Technical• Financial• Regulatory• Social

• How does the cropping, environmental emissions and biodiversity change?

• What would make a particular management action appealing to farmers?

• For example, how will farmers respond to increasing prices of biofuel crops. What will the unintended consequences be?

Model-based farm-level policy impactanalysis

• Linear Programming, such as Silsoe whole-FARM Model (SFARMMOD), is well established at predicting the optimizing behavioural response of farmers in response to choice and change in prices, technology and regulations.

• Recently extensions include environmental pollution, such as nitrate leaching as multiple objectives to be constrained or minimised

• We extend this modelling approach to predict the impact of biodiversity policy on farmers and the consequences of farming on biodiversity

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

Medium

Light

Workable hours - typical profile

Structure

• Background

• Methodological challenges

• Results

• Summary & (Discussion)

Key tasks

Three main types of model extension are envisaged

1) Quantified measures of biodiversity, which could include four mammal species, indicator bird species, and weed species.

2) Field boundary features and the effects of spatial geometry. These are habitats that support biodiversity.

3) Incorporate sets of criteria to explain and predict the decision behaviour of a population of land managers

Weeds, birds and mammals

• A wide varied of detailed ecological models• Habitat association models of birds• Difference equation and Markov chain models of

weed dynamics• Game theory models of bird populations and winter

feed availability• Development of a single metric ‘biodiversity units’?• Fitting these to an LP requires meta-modelling to

enable each to be quantified for the set of all farm plans

LP model of weeds, etc

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Field boundary featuresSpatial geometry effects

• The length and depth of field boundary per cropped hectare effects field shape which effects the efficiency of field work

• A model of field work efficiency is being developed to quantify the effects and determine significant non-linear behaviour

• At a larger scale the increase of contract farming operations can mean entire farms are in a single crop in a given year

Non linearity!

Can we maintain linearity and model the effects of promoting an increase in hedges and probable reduction in

field size

Decision Making Behaviour 1

• Profit maximising (long-term net farm profit) accounts well for the aggregate production behaviour of farmers, but what about conservation behaviour?

• At farm level decision making behaviour may differ due personal values, views on future prices, risk, and the information available

• Conservation behaviour may involve the understanding of objectives such as ‘stewardship of the land’, and ‘professional pride/identity’, etc

• Aggregate behaviour can be built up from a distribution of farmer values. Is this a better decision model?

Decision Making Behaviour 2

• Multiple Objective Decision Making (MODM) can be used. It is based on Multiple Attribute Value Theory (MAVT)

• The two common implementations are• Goal Programming (GP): Objectives are satisfied

by obtaining a series of hierarchical goals• Multiple Objective Programming (MOP):

Objectives are involved in a weighted trade-off• Which is better …both or ANP or Stated Choice or…?

Structure

• Background

• Methodological challenges

• Results

• Summary & (Discussion)

Comparison of cropping

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5

10

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45

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Modelled

Sensitivity to commodity prices

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50

100

150

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Change in oilseed commodity price

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Rotational setaside

Dried Peas

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Spring Barley

Winter Barley

Spring Wheat

Winter wheat

Stubble

Prices £/t: W Wheat £78, S Wheat £81, Barley £73, Peas £87, Rape seed £150Sandy clay loam with 595 mm annual rainfall

Promoting spring crops v. stubbles

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10

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Net farm profit, £/250 ha

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Clay 700mm rainfall, stubble area Sand 500mm rainfall, stubble area

Clay 700mm rainfall, risk Sand 500mm rainfall, risk

Structure

• Background

• Methodological challenges

• Results

• Summary & (Discussion)

Summary

• Farmers on lighter and dryer soils can increase the amount of stubble available more readily than those on heavier wetter soils.

• However, in doing so the risks rise sharply• Promoting spring crops does not in itself provide

more stubble.• Raise farm incomes do to higher prices tends to

reduce winter stubble availability because the benefits of timeliness progressively outweigh machinery costs

The ENDCollaborators

Discussion

• Can we maintain linearity and its high utility• Can we identify the ‘missing’ attributes? Do they

exist? Would we be better quantifying the farmers true full economic costs?

• Can we quantify and model them for all farm plans?• Can we elicit preferences and value functions?• Can we generalise for all farmers for some farmers?• Can readily evaluate future, as yet unspecified

choices by estimating their attributes only?