1. ScotFarm A linear programming farm level model for Scottish farms
Shailesh Shrestha
Bouda Vosough Ahmadi
Steven Thomson
Andrew Barnes

2. Background FarmAdapt – 2000 FDLP – 2002 TeagascMod – 2004
CAP Agenda 2000, adaptations, market prices, nutrient balance
FDLP – 2002
Climate change impact on English dairy farms
TeagascMod – 2004
CAP MTR, Milk quota Removal, Climate change

3. Model characteristics
Linear programming – optimising profits
Farm system analysis
Replicates farm activities
Repetitive decision makings
Financial and physical parameters
All labour skilled
Farm level data
Pseudo-dynamic
timeframe can be set
yearly runs with month as a subset

4. Model characteristics
Linear programming – optimising profits
Farm system analysis
Replicates farm activities
Financial and physical parameters
Activities are interlinked
Repetitive decision makings
Farm level data
Pseudo-dynamic
timeframe can be set
yearly runs with month as a subset

5. Farm system analysis Livestock Land Milk Grass/forage Crops
Animal sell
Labour
Machinery
Feed
Replacement
Liv. variable costs
Output

6. Farm system analysis

7. Model characteristics
Linear programming – optimising profits
Farm system analysis
Replicates farm activities
Repetitive decision makings
Financial and physical parameters
All labour skilled
Farm level data
Pseudo-dynamic
timeframe can be set
yearly runs with month as a subset

8. Data input Farm data (physical): land, animals Prices/costs
Coefficients : LU/ha, feed contents, lab requirements, feed requirements
Production: milk, crop, grass yields
External factors: policies, market

9. Data input (FAS) Scottish Farm Accountancy Survey (FAS)
Around 480 farms
Contains physical/ financial data
Cluster analysis
System, production, size, milk yield, labour, farm margins, feed, costs
Farm groups identified
Representative farms

10. Data input (clusters)

11. Data input (farm data)

12. Data input (feed)

13. Model characteristics
Linear programming – optimising profits
Farm system analysis
Replicates farm activities
Repetitive decision makings
Financial and physical parameters
All labour skilled
Farm level data
Pseudo-dynamic
Runs over a number of years but results averaged out of middle years
timeframe can be set
yearly runs with month as a subset

14. Flow chart Farm data FAS2010 ScotFarm Cluster analysis
Farm types A,B...
Cluster analysis
ScotFarm Y 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

15. Flow chart Farm data FAS2010 ScotFarm Cluster analysis
Baseline
Scenarios
Farm data FAS2010
Farm types A,B...
Cluster analysis
ScotFarm Y 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

16. Dynamic model Farm adjustments – optimise Farm adaptations
Herd dynamics – dairy / replacement cycle
Crop rotation
Structural change
Price effect

17. Modules Livestock module Crop module Feed module Grass module
Crop yield model
Crop rotation module
Feed module
Feed requirement model
Grass module
Grass yield model

18. Livestock module Dairy: Beef: Sheep: calf, heifer and dairy
4 replacement cycle
Beef:
suckler, calf (0-6m), beef1 (7-12m), beef2 (13- 24m)
8 year replacement cycle
Sheep:
lamb and ewe
5 year replacement cycle

19. Livestock module Dairy Beef Sheep Replacement Labour (hired)
Labour (family)
Land
Feed
Milk
Animal
t+2...
Grass Yield Model
Feed Requirement Model
t+1 t t

20. Model code (dairy)
tani12(f,y)$(ord(y)>1 ).. totani(f,'ac',y) =e= totani(f,'ad',y)*calrate*0.5*survrate ; tani13(f,y)$(ord(y)>1 ).. totani(f,'ah',y) =e= totani(f,'ac',y-1) + buyheif(f,y); tani14(f,y)$(ord(y)>1 ).. totani(f,'ad',y) =e= totani(f,'ad',y-1) + totani(f,'ah',y-1) sellheif(f,y-1) - culldairy(f,y); tani15(f,y).. sellmcalf(f,y) =e= totani(f,'ad',y)*calrate*0.5; tani16(f,y).. culldairy(f,y) =e= totani(f,'ad',y) *0.25;

21. Crop module Basic Decision making - based on yield and GM
Most common crops are included
New crops can be introduced (data?)
Initial land use taken from farm data
Crop yields - farm data or biophysical model
Land reallocation
Crop rotation

22. Crop module Livestock module Labour (hired) Crop sell Labour (family)
Land
Crop Rotation Model
Feed
Livestock module
Crop sell
Crop Yield Model
Gross Margin

23. Model code (crop)
inicrp(f,c).. acrop(f,c,'y1') =e= CROPINI(f,c); inicrp2(f,y).. aland(f,y) =e= sum(c, acrop(f,c,y)) ; inicrp3(f,c,y)$(ord(y)>1 ).. acrop(f,c,y) =g= acrop(f,c, y-1)*0.5; crpland(f,y).. aland(f,y) =e= aland(f,'y1') - tranland(f,y) +tranland2(f,y);

24. Land Fixed total land Divided into arable, grassland, rough grazing,
Grassland – grazing, grass silage and hay land
Reallocation between activities
Capability to include land market
Rent/Let
Livestock constraint over stocking rate
Basic payment is linked

25. Model code (Land - grass)
land1(f,y).. gland(f,y) =e= G_LAND(f) + tranland(f,y)- tranland2(f,y); # +R_LAND(f,y) - L_LAND(f,y); land2(f,y).. gland(f,y) =e= gfland(f,y)+ gsland(f,y)+ ghland(f,y) ; land3(f,y).. sum(a, totani(f,a,y)*LU(a)) =l= gland(f,y)*STR(f) + RGRAZ(f)*STR2(f); land4(f,y).. tranland2(f,y) =l= G_LAND(f);

26. Feed module Feed considered Feed – produced on farm/bought in
Fresh grass, grass silage, hay, maize silage whole crop grain, concentrate
Feed – produced on farm/bought in
Energy and protein content required for each feed

27. Feed requirement model
Model is written in excel
Based on feed requirement criteria set by Alderman and Cottrill (1993)
Determines monthly requirement of energy, protein and feed intake per animal
Considers species, age, production level of an individual animal

28. Feed module

29. Data input (feed)

30. Model code (feed)
feeden(f,a,y,m).. totani(f,a,y)*ENREQ(a,m) =l= sum(b, mfeed(f,a,y,m,b)*ENFEED(b)); feedp(f,a,y,m).. totani(f,a,y)*PREQ(a,m)*0.001 =l= sum (b, mfeed(f,a,y,m,b)*PRFEED(b)) ; feedi(f,a,y,m).. totani(f,a,y)*DMI(a,m) =l= sum (b, mfeed(f,a,y,m,b)*DMFRAC(b)) ; feedgraz(f,y,m).. sum(a, mfeed(f,a,y,m,’fg’)) =l= gfland(f,y)*GRASS_YIELD(m)*1000*GRASS_SWT(m)+ gsland(f,y)*GRASS_YIELD(m)*1000*GSILAGE_SWT(m)+ RGRAZ(f)*GRASS_YIELD(m)*1000*GRASS_SWT(m)*0.5;

31. Model code (feed)
feedhay(f,y,m).. sum(a, mfeed(f,a,y,m,’hay’)) =l= ghland(f,y)*HAYYIELD; feedsil(f,y,m).. sum(a, mfeed(f,a,y,m,’gsil’)) =l= gsland(f,y)*SILAGE_YIELD(m)* buysil(f,y,m); feedc(f,a,y).. sum(m, mfeed(f,a,y,m,'conc')) =g= totani(f,a,y)*ConcUse(f,a)*CONC_LEV(f) feedgs(f,a,y).. sum(m, mfeed(f,a,y,m,'grain')) =l= sum(fc, (acrop(f,fc,y)*CROPYIELD(f,fc))) ;

32. Labour Constraint over requirement and availability
Uses family labour first >> if not sufficient paid labour
Assumes all labour as skilled and unlimited supply for paid labour
Labour requirements - farm management data

33. Model code (labour)
lab1(f,a,y).. livlab(f,a,y)=e= totani(f,a,y) * LAB(a); tlab2(f,y).. tlab(f,y) =e= sum(a,livlab(f,a,y)); tlab3(f,y).. tlab(f,y) =l= (flab(f)*2200) + hirelab(f,y); lcost(f,y).. tlabcost(f,y)=e= hirelab(f,y)*lab_cost;

34. Subsidy payments Included in the objective function
BPS is linked with the total farm land
LFAS is added as a parameter for each farm
Under CAP reform scenarios, different rates of payments can be linked to different land use

35. Objective function
FarmMargin(f,y).. tfgm(f,y) =e= e1gm(f,y) + e2gm(f,y) + e3gm(f,y) + e4gm(f,y) + (tland(f,y)+RGRAZ(f))*SFP(f)+ LFAS(f) - tlabcost(f,y); dairygm(f,y).. e1gm(f,y) =e= totmilk(f,y)*MILKPrice(f)*MI(y) + sellmcalf(f,y)* CALFSPrice(f)*BI(y)+culldairy(f,y)*DAIRYSPrice(f)*BI(y)- buyheif(f,y)*HEIFBPrice(f) *BI(y) - sum(ads, totani(f,ads,y)*LU(ads)*(VARCosts(f)+ OHCosts(f))*VarIndx1(ads,y)) - sum((ads,m), (mfeed(f,ads,y,m,"conc")*CONCPrice(f)*0.001));

36. Subsidy payments Included in the objective function
BPS is linked with the total farm land
LFAS is added as a parameter for each farm
Under CAP reform scenarios, different rates of payments can be linked to different land use

37. includes land under arable, temporary grass and permanent grass
Objective function
FarmMargin(f,y)..
tfgm(f,y) =e=
e1gm(f,y) + e2gm(f,y) + e3gm(f,y) + e4gm(f,y)
+ tland(f,y)*SFP90 + RGRAZ(f)* SFP10
+ (LFAS(f)+ SFP(f)*0.32)
- tlabcost(f,y);
includes land under arable, temporary grass and permanent grass

38. Outputs Farm margins Land use Animal numbers Feed use Production level
Costs of production
Marginal costs

39. Applications Policy impacts: CAP reforms and Milk quota removal
Climate change impacts
Farmers adaptations
Structural change
Cost analysis: GHG mitigation options
Capable of running individual farms or representative farms

40. CAP reform outputs

41. CAP reform outputs

42. Outputs
Percentage change on farm margin with greening and no-greening measures under SFP
Farm types Baseline farm Share of SFP % change in farm
margins inc in the farm margin under
SFP (£) margin (%) greening scenarios
Greening No Greening
Beef cereal finisher ,
Beef grass finisher ,
Beef rearer ,
Beef hill suckler ,
Beef upland sukler ,
Beef lowland suckler ,
Sheep lowland ,
Sheep hill ,
Sheep upland ,

43. CAP reform outputs Farm margins Structural change – system, land use
Counterfactual scenarios
Farm types – systems, regions, individual

44. Outputs
B Vosough Ahmadi, S Shrestha, S G Thomson, A P Barnes and A W Stott, Impact of greening the Common Agricultural Policy on Scottish beef and sheep farms. Paper accepted in International Food and Agribusiness Management Review
V Eory, M MacLeod, S Shrestha and D Roberts, Linking an economic and a biophysical model to support farm GHG mitigation policy. Paper submitted to German Journal of Agricultural Economics
S Shrestha, B Vosough Ahmadi, S Thomson and A Barnes, Scottish farms under post 2015 CAP reforms: winners and losers. Paper submitted to 88th AES conference, Paris, 9-11 April.
S Shrestha, B Vosough Ahmadi, S Thomson and A Barnes, Greening of the CAP – how will it affect Scottish beef and sheep farming? Policy Briefing, Rural Policy Centre, SRUC.

45. Limitations Large dataset
Optimiser: do not represent behaviour aspects
Initial and terminal effects of LP
Not recursive
Validation:
not important for counterfactual studies
If important >> PMP needs to be added