CAVES Grampian Modelling Report

1. CAVES Grampian Modelling Report Nick Gotts and Gary Polhill With thanks to Lee-Ann Sutherland and Dawn Parker

2. Overview • Use of FEARLUS 0-8-2-1 • Motivation • Method: FEARLUS and FEARLUS-W • Results • Some provisional conclusions • The next steps • Development and use of FEARLUS 1-0-1 • Data collection • Development of FEARLUS 1-1 • Next 6 months

3. Motivation:Diffuse agricultural pollution • Term generally applied to runoff from fields: • Nitrates • Phosphates • Pesticides • Faecal coliforms (potentially dangerous bacteria) • Could also be applied to airborne pollutants: • Pesticides • Greenhouse gases (methane, nitrous oxide)

4. Motivation: monitoring • Pollutants in runoff to rivers can be monitored downstream • Increasing ability to monitor airborne pollutants, but not at single-farm scale • Monitoring is less costly (in some cases only possible) at above farm scale – e.g. over a catchment or sub-catchment • Could social interactions between farmers be used to make such monitoring effective in reducing pollution?

5. Motivation:Farmers and their Neighbours • Farmers both compete and cooperate with peers and neighbours • Farmers learn from their peers and neighbours • Farmers are not straightforward profit-maximisers: they value the good opinion of peers and neighbours • In itself: to be seen as a “good farmer” • Because they may need neighbours’ help

6. FEARLUS-W • Framework for Evaluation and Assessment of Regional Land-Use Scenarios • FEARLUS-W originated in a project concerning the EU Water Framework Directive: interactions between environmental policy, rural social networks and pollution • Initial focus: diffuse pollution from phosphate fertilisers • Sufficiently abstract to apply to a wider range of cases • Refocusing: • Under what circumstances would collectively-earned payment for pollution reduction be a policy instrument worth considering? • How would this policy instrument interact with farmers’ social and informational networks?

7. FEARLUS0-8-2-1 £ Land use selection Calculation of Return Climate Land Uses Estimated Yield Market Conditions Land use Biophysical properties Yearly Cycle Estimated Social Acceptability Pollution Return Before Neighbours’ Approval/Disapproval After Social Interactions Land sales

8. FEARLUS0-8-2-1 Model of Farmer Decision-Making • Profit, Social Approval and Salience • FEARLUS-W Land Managers choose Land Uses on the basis of expected Profit and expected Approval from Neighbours • Relative importance of these varies between Land Managers and over time • Change over time due to salience-changing events, e.g. a Bad Harvest, a Neighbour’s Disapproval • Case-Based Reasoning • Land Managers maintain an episodic memory, or case base • Every Year they consider whether they are satisfied with how their Neighbours assess them, and for each Land Parcel, whether it is giving a satisfactory return. • If satisfied, they do not change Land Use for that Parcel. • Otherwise, they consider past experiences with each land use, selecting the case most similar to the current case. • If no suitable case is found, default values are used.

9. Land Uses Pareto Front Estimated Profit Estimated Social Acceptability FEARLUS0-8-2-1 Model of Farmer Decision-Making

10. Description of FEARLUS Runs Performed • Toroidal Environment of 20*20 Land Parcels • Spatially variable Biophysical Conditions and temporally variable (but auto-correlated) Climatic conditions determining Yield, temporally variable but auto-correlated Economic conditions then determining economic Return jointly with Yield. • Each Land Manager initially owning 1 parcel (unsuccessful Land Managers sell to clear debts) • Five Land Uses, with mean Yield varying linearly with Pollution generated (both expressed in arbitrary units) thus:

11. Description of FEARLUS Runs Performed • Six reward conditions: • Threshold 2000, reward per land parcel 50 • Threshold 2000, reward per land parcel 25 • Threshold 1750, reward per land parcel 50 • Threshold 1750, reward per land parcel 25 • Threshold 1500, reward per land parcel 50 • No reward • Six neighbour-(dis)approval and (dis)approval salience-increasing conditions • Base (dis)approval on absolute pollution levels, increase salience of neighbours’ opinion when disapproved of (Abs-D) • Base (dis)approval on absolute pollution levels, increase salience of neighbours’ opinion when reward not given (Abs-N) • Base (dis)approval on relative pollution levels, increase salience of neighbours’ opinion when disapproved of (Rel-D) • Base (dis)approval on relative pollution levels, increase salience of neighbours’ opinion when reward not given (Rel-N) • Base (dis)approval on relative pollution levels, disapprove more strongly than approve, increase salience of neighbours’ opinion when disapproved of (Dis-D) • No concern with neighbours, so no Social Approval Function (NoSAF) • Four runs for each of 34 combinations (it makes no sense to combine “No reward” with “Abs-N” or “Rel-N”)

12. Results Table 1. Annual total Pollution levels: grand means (upper row of each pair) and means of maximal values (lower row of each pair)

13. Results Table 2. Ordinal information concerning annual total Pollution levels, implicit in Table 1: lower numbers indicate lower grand means (upper row of each pair) and means of maximal values (lower row of each pair)

14. Pollution No Reward Dis-D No Reward NoSAF Reward 2000;50 NoSAF Reward 2000;50 Dis-D

15. Land Uses No Reward NoSAF No Reward Dis-D Reward 2000;50 NoSAF Reward 2000;50 Dis-D

16. Selection of Land Uses No Reward NoSAF No Reward Dis-D Reward 2000;50 NoSAF Reward 2000;50 Dis-D

17. Land Uses No Reward Dis-D No Reward NoSAF Reward 2000;50 NoSAF Reward 2000;50 Dis-D

18. Some Provisional Conclusions • Mean pollution levels were almost always above the reward threshold, but a range of thresholds nonetheless reduced this mean level, under a range of algorithms for Land Managers’ decision-making. • Overall means and means of maximum pollution levels showed very similar patterns (possible exception: the Dis-D class under less effective reward schemes). • As would be expected, increasing the collective reward increased the effect. • Varying relative profitability of Land Uses gave opportunities for Land Managers took longer to learn that cutting pollution can pay. When the pollution threshold was set too low (although at an achievable level), this took longer. • If Land Managers cared about their neighbours’ opinions (and disapproved of their neighbours polluting), pollution was lower. • The way social pressure to reduce pollution was implemented made some difference, but not much. • Effects 4 and 5 together had a larger effect than either acting separately. • The general pattern of a simulation run in which the reward had a marked effect was of considerable variation in pollution levels as exogenous factors made different land uses more or less profitable; but levels much above the threshold were seldom maintained for long periods.

19. Overview • Use of FEARLUS 0-8-2-1 • Development and use of FEARLUS 1-0-1 • Modifications to create 1-0-1 • ELMM • Data collection • Development of FEARLUS 1-1 • Next 6 months

20. 0-8-2-1 to 1-0-1 • Enhancements for 1-0 • Discussed last time: • Lookup Tables • ELMM • (Land Market model, in collaboration with Dawn Parker) • Economies of scale for Land Uses • Farm Scale Fixed Costs • Off-farm income • Imitative and Experimentation strategies in CBR • 1-0 to 1-0-1 • Farm scale profit and approval aspiration • Approval aspiration measured against approval received from those of whom LM does not disapprove • New rules for approving/disapproving • Disapprove of more polluting Land Uses of neighbours than the most polluting used on the farm • Otherwise approve • New rules for changing salience

21. Farm scale aspiration • Approval and profit aspirations affect actions as follows: • If happy with both approval and profit then make no changes to land use • Else (i.e. unhappy with either) use the case base / imitation / experimentation to decide the next land use for the parcel • Won’t necessarily change the land use

22. ELMM When to sell? Bankrupt Which to sell? All of them! How much money? Determined by interactions among buyers Classic When to sell? Another agent can make more money Which to sell? Any for which the above applies How much money? Enough to compensate for loss of parcel Vendor’s decisions: ELMM vs Classic Agricultural Economics

23. ELMM When to buy? Wealth > Land Offer Threshold Which to buy? Determined by a strategy (in current work, only contiguous land) How much money? Bid determined by another strategy (in current work, multiple of current wealth) Classic When to buy? I can make more money Which to sell? Any for which the above applies How much money? Less than the expected extra profit Purchaser’s decisions

24. Ongoing Work with FEARLUS1-0-1 • Current land use options and outcomes: • Livestock farming, with different levels of intensity, dependence on bought-in feed supplementation • Climate: good/fair/bad • Economy: input, output prices independently high/low • Both climate and economy variable but autocorrelated • Biophysical properties: • Good/fair/bad • Experiments so far with 3 spatial distributions: clumpy, blocks, based on Upper Deeside • Profit threshold is set at whole-farm level, proportional to farm size • Social interactions: • Farmers disapprove of those using land uses more polluting than any of their own. • Social approval threshold based only on opinion of those self does not disapprove of. • Net loss in a Year increases salience of financial return. • Social approval salience influenced by all neighbours • Hypotheses concerning model behaviour • For higher rewards, the most effective threshold should be lower – until the unachievable level is approached. • Those caring least for social approval will do best financially, hence pollution levels will tend to rise over time. • However two factors should reduce this tendency: • Low rates of land purchase by incomers (various ways this could occur) • Advantages of experience

25. Overview • Use of FEARLUS 0-8-2-1 • Development and use of FEARLUS 1-0-1 • Data collection • Development of FEARLUS 1-1 • Next 6 months

26. Grampian Region, location within Scotland • Named after Grampian Mountains. • Previously an administrative region; now divided into Aberdeenshire, Moray, and Aberdeen City. • Uplands inland, lowlands near the sea.

27. Grampian Region

28. Agricultural Census:map of Upper Deeside

29. Agricultural census: Upper Deeside land capability and a specific land use (beef cattle)

30. Overview • Use of FEARLUS 0-8-2-1 • Development and use of FEARLUS 1-0-1 • Data collection • Development of FEARLUS 1-1 • Next 6 months

31. Land Uses Pareto Front Estimated Profit Advice Estimated Social Acceptability FEARLUS1-1 • Main changes • ELMM: Parcels sold to social neighbours • SPOM • Case Base bounded • By time: maximum duration of a case before it is forgotten • By size: maximum number of cases • Land Managers can ask for advice • Advice = an Outcome for a State of the World and a Decision for which the LM has no experience • Advice only given if no disapproval (by either party) in the last Year

32. FEARLUS & Biodiversity • Policy concern with biodiversity in Scotland • Aimed at biodiversity • Natura 2000 site support • Deer management • Forest Grant Scheme • Good Agricultural and Environmental Condition • High Nature Value farmland and farm systems • Related to biodiversity • Less Favoured Areas Scotland Scheme • Energy crops • Changes to livestock numbers and mix in crofting areas • Nitrate Directives and Nitrate Vulnerable Zones • Water Framework Directive • Local and regional foods

33. Loose Coupling Close Coupling Coupling and integration Bankruptcies Land Use selection Exchange of Land Update economy FEARLUS Update climate Learning Approval/Disapproval Extinction Crop Yield Updatehabitats SPOM Harvest Government response Colonisation

34. Preliminary results • SPOM • 10 species (1 competitor), 2 habitats (5 spp each) • High/low dispersal rate • Fast/slow competition • FEARLUS • Case Based Reasoning for decision making in agents • Policy based on reward (or compensation) for biodiversity • Number of species on each field (>= 5, >=1) • Amount of reward per field (1000, 10000) • Economic return based on • Yield per unit area (0-100) • Price per unit yield (0-100) • 8 Land Uses with varying degrees of habitat availability • Loosely coupled FEARLUS+SPOM • Integrated version in preparation • Close coupling in near future

35. Species (Low disp, Slow comp) S F+S Reward 1000 for >= 5spp Reward 10000 for >= 1spp Policy

36. Land Use (Low disp, Slow comp) F F+S (10k;1)

37. Multi-layered networks in 1-1 • Various network layers available in 1-1 • Social neighourhood (own a neighbouring land parcel) • Affects imitation, approval, advice and land sales • Approval & Disapproval • Advice • Vendor network (who sold a land parcel to whom)

38. Network distributions

39. Possible investigations • How network communities/groups found by various physicists’ algorithms correspond to groupings of other land manager data • Age, Wealth, Estate Size • Links between changes in network structure over time and social-scale indicators • Mean Wealth/Age/Estate Size, Bankruptcies • Suggestions…? • Potential area to look at model coherence?

40. Overview • Use of FEARLUS 0-8-2-1 • Development and use of FEARLUS 1-0-1 • Data collection • Development of FEARLUS 1-1 • Next 6 months

41. Next 6 months • FEARLUS 1-2 • Biophysical similarity rather than proximity for comparing spatial aspects of cases • Farmers sell up and leave farming without necessarily being bankrupt • Tenanting (if sufficiently simple model can be found) • Final Grampian case study ontology • Calibrate and validate FEARLUS on AgCensus data • Modelling of land manager response to possible climate change mitigation policies • Contributions to generalisation and model coherence • Flagged so far: Aspiration, Approval, Land exchange • Publications and contributions to final report