DYNAMIC ECONOMIC MODEL OF ARABLE CROP ROTATION

Transcription

1 School of Agriculture, Policy and Development DYNAMIC ECONOMIC MODEL OF ARABLE CROP ROTATION Ian McFarlane 1 Copyright University of Reading

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) ( 2

3 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) ( 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

4 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

5 Crops drill-mth end-mth seed- /ha yieldkg/ha harvest- /t min till- /ha tillage- /ha control- /ha irrigation- /ha fertiliser- /ha 1 winterwheat springwheat grain maize silage maize springbarley winterbarley rye winteroats rape soya potato sugarbeet legume fallow HT rape IR potato PT potato DTwinterwheat FTspringwheat FTspringbarley HT soya HT sugarbeet DT grain maize Bt grain maize LIMITLESS 2 POTENTIAL LIMITLESS 125 OPPORTUNITIES LIMITLESS IMPACT 200

6 Crops IR crit HT crit DT crit FT crit pest inc weed inc dr damage fr damage 1 winterwheat springwheat grain maize silage maize springbarley winterbarley rye winteroats rape soya potato sugarbeet legume fallow HT rape IR potato PT potato DTwinterwheat FTspringwheat FTspringbarley HT soya HT sugarbeet DT grain maize Bt grain maize LIMITLESS 60 POTENTIAL LIMITLESS 75 OPPORTUNITIES LIMITLESS 0.8 IMPACT

7 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

8 [GRAPHIC LEGEND] 8

9 [GRAPHIC LEGEND] 9

10 '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

11 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

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

13 '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

14 Wheat yield under water constraint 14

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

16 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

17 PER CENT CHANGE IN GROSS MARGIN PER HA 17

18 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

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

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. 20

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 21

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 22

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 23

24 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

25 REFERENCES Brookes G. (2012) European arable crop profit margins 2010/2011. ISBN 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), 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), 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), Nix (2015) Farm Management Pocketbook, 45th ed. ISBN Qaim, M., & Traxler, G. (2005). Roundup Ready soybeans in Argentina: farm level and aggregate welfare effects. Agricultural economics, 32(1),