DYNAMIC ECONOMIC MODEL OF ARABLE CROP ROTATION

School of Agriculture, Policy and Development DYNAMIC ECONOMIC MODEL OF ARABLE CROP ROTATION Ian McFarlane i.d.mcfarlane@reading.ac.uk 1 Copyright University of Reading

SHORT ABSTRACT An economic model has been developed to provide a decision support tool for assessing the return on investment in crops with novel traits, as part of a work package with the FP7 project Assessing and Monitoring the Impacts of Genetically modified plants on Agro-ecosystems (AMIGA) (www.amigaproject.eu). 2

SHORT ABSTRACT An economic model has been developed to provide a decision support tool for assessing the return on investment in crops with novel traits, as part of a work package with the FP7 project Assessing and Monitoring the Impacts of Genetically modified plants on Agro-ecosystems (AMIGA) (www.amigaproject.eu). The progress of a novel crop compared with an equivalent conventional crop is modelled in monthly time steps, with management decisions about application of controls (pesticide or herbicide for example) applied at each monthly step. The simulation extends over up to seven crop cycles, enabling simulation of the effect of crop rotation on soil condition, including decisions regarding use of tillage. 3

FEATURES Time period: Modelling of crop sequences over 1 to 7 crop cycles. Time step: one-month time steps, sufficient to model the management decisions made during a crop cycle. Plot area: user can specify plot size between 5 and 80 ha. Broad geographical region: choice of five biogeographic regions: Atlantic (Ireland, UK, Denmark, Netherlands, Belgium, Portugal, Luxembourg) Boreal (Finland, Sweden, Estonia, Latvia, Lithuania) Continental (Austria, Germany, Slovakia, Hungary, Czech Republic, Poland, Slovenia) Mediterranean (France, Italy, Spain, Cyprus, Greece, Malta) Balkans (Bulgaria, Romania) 4

Crops drill-mth end-mth seed- /ha yieldkg/ha harvest- /t min till- /ha tillage- /ha control- /ha irrigation- /ha fertiliser- /ha 1 winterwheat 1 6 8000 60 125 40 120 150 160 200 2 springwheat 2 8 6000 60 125 40 120 150 160 200 3 grain maize 3 10 5000 160 125 40 120 100 160 200 4 silage maize 3 8 36000 130 20 40 120 100 160 200 5 springbarley 3 8 5000 80 140 40 120 80 160 120 6 winterbarley 1 6 6000 75 110 40 120 100 160 160 7 rye 2 8 6000 100 100 40 120 100 160 100 8 winteroats 1 6 5500 50 110 40 120 50 160 50 9 rape 3 9 3000 50 300 40 120 150 160 250 10 soya 3 9 3000 140 250 40 120 100 160 150 11 potato 2 7 45000 1200 80 40 120 450 160 300 12 sugarbeet 2 7 60000 220 30 40 120 250 160 250 13 legume 2 8 3000 100 200 40 120 100 160 100 14 fallow 1 12 1 1 1 40 120 1 1 1 15 HT rape 3 9 3000 57.5 300 40 120 75 160 250 16 IR potato 2 7 45000 1380 80 40 120 225 160 300 17 PT potato 2 7 45000 1440 80 40 120 225 160 300 18 DTwinterwheat 1 6 8000 69 125 40 120 150 160 200 19 FTspringwheat 1 6 6000 69 125 40 120 150 160 200 20 FTspringbarley 3 8 5000 92 140 40 120 80 160 120 21 HT soya 3 9 3000 161 250 40 120 50 160 150 22 HT sugarbeet 3 10 60000 253 30 40 120 125 160 250 23 DT grain maize 3 10 5000 192 125 40 120 100 160 200 5 24 Bt grain maize LIMITLESS 2 POTENTIAL 10 5000 184 LIMITLESS 125 OPPORTUNITIES 40 120 LIMITLESS 50 160 IMPACT 200

Crops IR crit HT crit DT crit FT crit pest inc weed inc dr damage fr damage 1 winterwheat 60 50 75 75 1.01 1.005 0.95 0.8 2 springwheat 60 50 75 75 1.01 1.01 0.95 0.8 3 grain maize 60 50 75 75 1.02 1.01 0.95 0.8 4 silage maize 60 50 75 75 1.02 1.01 0.95 0.8 5 springbarley 60 50 75 75 1.01 1.002 0.95 0.8 6 winterbarley 60 50 75 75 1.01 1.002 0.95 0.8 7 rye 60 50 75 75 1.01 1.01 0.95 0.8 8 winteroats 60 50 75 75 1.01 1.01 0.95 0.8 9 rape 60 50 75 75 1.01 1.005 0.95 0.8 10 soya 60 50 75 75 1.01 1.02 0.95 0.8 11 potato 60 50 75 75 1.03 1.02 0.95 0.8 12 sugarbeet 60 50 75 75 1.03 1.005 0.95 0.8 13 legume 60 50 75 75 1.01 1.01 0.95 0.8 14 fallow 60 50 75 75 1.01 1.01 0.95 0.8 15 HT rape 60 50 75 75 1.01 0.92 0.95 0.8 16 IR potato 60 50 75 75 0.95 1.02 0.95 0.8 17 PT potato 60 50 75 75 0.96 1.02 0.95 0.8 18 DTwinterwheat 60 50 75 75 1.01 1.01 0.95 0.8 19 FTspringwheat 60 50 75 75 1.01 1.01 0.95 0.8 20 FTspringbarley 60 50 75 75 1.01 1.01 0.95 0.8 21 HT soya 60 50 75 75 0.95 1.01 0.95 0.8 22 HT sugarbeet 60 50 75 75 0.95 0.95 0.95 0.8 23 DT grain maize 60 50 75 75 1.02 1.01 0.95 0.8 6 24 Bt grain maize LIMITLESS 60 POTENTIAL 50 75 LIMITLESS 75 OPPORTUNITIES 0.95 1.01 0.95 LIMITLESS 0.8 IMPACT

ECONOMIC DATA from Khan et al (2009), Brookes (2012): yield per hectare of selected crops seed costs ex-farm value per tonne at harvest from Qaim & Traxler (2005), Fu et al (2006), Nix (2015): costs of tillage pesticide and herbicide costs cost of irrigation 7

[GRAPHIC LEGEND] 8

[GRAPHIC LEGEND] 9

'load variables from attached worksheets: 'read from Namelist "Crops" For i = 1 To 33 CropName(i) = Worksheets("econ").Cells(i + 1, 2) 'read the properties for each of the crops in tables of data For j = 1 To 10 econdata(i, j) = Worksheets("econ").Cells(i + 1, j + 2) Next j For j = 1 To 8 scidata(i, j) = Worksheets(SciShtName).Cells(i + 1, j + 2) Next j Next i 10

ASSUMPTIONS potential yield is recalculated at each monthly step using: an empirical function of pest pressure, taking account of past management policy and prior conditions an empirical function of weed pressure, taking account of tillage and weed management policy and prior conditions water-use management, taking account of simulated drought pressure 11

'assess this months effect on potential yield YieldThisCrop = ((1 - (CurrentPressure / 100) ^ 1.5) * _ econdata(thiscropid, 3)) 12

'assess this months effect on potential yield YieldThisCrop = ((1 - (CurrentPressure / 100) ^ 1.5) * _ econdata(thiscropid, 3)) Set trigger point(s) for control action (e.g. herbicide) 13

Wheat yield under water constraint 14

Predicted outcomes for conventional wheat under weed pressure and water constraint 15

Czech Rep CZ1 Dairy farm in west of Czech Republic CZ2 All-arable farm in west of Czech Republic Germany DE1 All-arable farm in Brandenburg DE2 Mixed farm in Saxony Slovakia SK1 Large arable farm complex close to Nove Zamky SK2 Arable farm at Vrable close to Nitra SK3 Cooperative farm complex at Hlohovec Spain ES1 Arable farm in Los Monegros, Aragon ES2 Mixed farm in la Hoya de Huesca, Aragon Sweden SW1 Small all-arable farm in province of Scania SW2 Large arable farm in Scania SW3 Medium size arable farm in Scania UK UK1 Mixed farm in SW of England growing continuous maize for on-farm use UK2 All-arable farm in the South-west of England UK3 Arable farm in the East of England growing all arable crops inc sugar beet, OSR UK4 Arable farm in the East of England growing all arable crops inc oilseed rape OUTPUT OBTAINED FOR 16 FARM CASE STUDIES 16

PER CENT CHANGE IN GROSS MARGIN PER HA 17

HT crop(s) following crop control cost saving ( /ha) CZ2 OSR winter wheat 19 DE1 OSR winter wheat 27 SK3 OSR winter wheat 19 SW2: HT OSR feed wheat 11 HT+HT sugarbeet, OSR feed wheat 23 SW3: HT OSR feed wheat 17 HT+HT sugarbeet, OSR feed wheat 30 UK3: HT OSR winter wheat 39 HT+HT sugarbeet, OSR winter wheat 65 BENEFIT FOR FOLLOWING CROP 18

SUMMARY Based on Model of Economic consequences of Transgenic crops in the EU (METE) (McFarlane, Park & Ceddia, 2014) 19

SUMMARY Based on Model of Economic consequences of Transgenic crops in the EU (METE) (McFarlane, Park & Ceddia, 2014) Two aspects of decision support: - monthly assessment as to intervention required to maintain yield - indicator of potential benefit of alternative crop rotations. 20

SUMMARY Based on Model of Economic consequences of Transgenic crops in the EU (METE) (McFarlane, Park & Ceddia, 2014) Two aspects of decision support: - monthly assessment as to intervention required to maintain yield - indicator of potential benefit of alternative crop rotations. Response adapted to biogeographic region 21

SUMMARY Based on Model of Economic consequences of Transgenic crops in the EU (METE) (McFarlane, Park & Ceddia, 2014) Two aspects of decision support: - monthly assessment as to intervention required to maintain yield - indicator of potential benefit of alternative crop rotations. Response adapted to biogeographic region Easily applied to case studies of particular farms 22

SUMMARY Based on Model of Economic consequences of Transgenic crops in the EU (METE) (McFarlane, Park & Ceddia, 2014) Two aspects of decision support: - monthly assessment as to intervention required to maintain yield - indicator of potential benefit of alternative crop rotations. Response adapted to biogeographic region Easily applied to case studies of particular farms Information about actual performance of novel crops can be incorporated as available 23

SUMMARY Based on Model of Economic consequences of Transgenic crops in the EU (METE) (McFarlane, Park & Ceddia, 2014) Two aspects of decision support: - monthly assessment as to intervention required to maintain yield - indicator of potential benefit of alternative crop rotations. Response adapted to biogeographic region Easily applied to case studies of particular farms Information about actual performance of novel crops can be incorporated as available 24

REFERENCES Brookes G. (2012) European arable crop profit margins 2010/2011. ISBN 0-954 2063-7-1 Fu, G., Chen, S., & McCool, D. K. (2006). Modeling the impacts of no-till practice on soil erosion and sediment yield with RUSLE, SEDD, and ArcView GIS. Soil and tillage research, 85(1), 38-49 Khan, S., Khan, M. A., Hanjra, M. A., & Mu, J. (2009). Pathways to reduce the environmental footprints of water and energy inputs in food production. Food policy, 34(2), 141-149. McFarlane I., Park J., Ceddia G. (2014) The Extent to which Potential Benefits to EU Farmers of Adopting Transgenic Crops are Reduced by Cost of Compliance with Coexistence Regulations. AgBioForum 17(1), 37-43 Nix (2015) Farm Management Pocketbook, 45th ed. ISBN 978-0-9576939-1-3 Qaim, M., & Traxler, G. (2005). Roundup Ready soybeans in Argentina: farm level and aggregate welfare effects. Agricultural economics, 32(1), 73-86. 25