Aplication of the CropSyst model to Mallee farming systems (Australia)

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We showed the simulate impact applying the CropSyst model (Cropping systems simulation model) to crop rotations and management practices on the water balance of farming systems in a semiarid region of south-eastern Australia, where drainage beyond the root zone and rising water tables contribute to salinisation of soils and water streams.

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Aplication of the CropSyst model to Mallee farming systems (Australia)

  1. 1. Application of the CropSyst model to Mallee farming systems Carlos G. Hernández Díaz-Ambrona Dpto. Producción Vegetal: Fitotecnia Universidad Politécnica de Madrid March 2001 Visiting Scientist Joint Center for Crop Improvement Mallee Research Station Crop Production ILFR The University of Melbourne 1
  2. 2. Summary  Overview Mallee farming systems      Problems Methodology Model performance Model application Future needs 2
  3. 3. Mallee farming systems 3
  4. 4. Mallee farming systems Environment  Geology Murray-darling basin. Tertiary marine limestone capped by Pliocene sands  Topography coastal plains with trend of sandridges, dunes 4
  5. 5. Mallee farming systems Environment Soil solonized brown Hill: sandy soil Valley: sandy-clay soil 5
  6. 6. Mallee farming systems Environment  Natural vegetation Relict: Mallee scrub (Eucalyptus dumosa) 6
  7. 7. Mallee farming systems Climate Semi-arid type Mediterranean Tmax: 22.9 ºC [46.6ºC] Tmin: 9.6 ºC [-4.1ºC] T med: 16.5 ºC Prec: 340 mm y-1 Daily Sol. Rad.: 17.8 MJ m-2 d-1 Wind: 3.14 m s-1 ETo: 1500 mm y-1 Walpeup, BMSM 76064, 1939-2000 7
  8. 8. Mallee farming systems Walpeup, BMSM 76064 8
  9. 9. Mallee farming systems  Cropping land: 6 Mha (10  Wheat-fallow rotation  Long fallow management Mha) No till Traditional till 9
  10. 10. Mallee farming systems  Farm size: > 2 kha  Paddock size 100-300 ha 10
  11. 11. Mallee farming systems Land uses •1.5 M Sheep •0.9 M Meat cattle •Pulses •Oilseeds •Other 7% 1% 7% Cereals 35 % Fallow 20 % Pastures 30 % 11
  12. 12. Problems  Problems Low water use Low crop diversification High risk of wind erosion  Consequences Soil salinity Soil erosion Low productivity Low farm income  Constrains Soil Weather Market Complexity 12
  13. 13. Problems Consequences Soil salinity Soil erosion Low productivity Low farm income 13
  14. 14. Objectives There is an urgent environmental need to reduce the dependence on fallows and find alternative cropping systems that minimise deep drainage Long term assessment of different crop management 14
  15. 15. Our framework «The key to success in farming is to be able to identify and tactically adjust major control loops. The decision process is not as complex as it might seem. Once the decision about what crop to grow is made, choices of cultivar, planting date, land preparation, spacing, and fertilisation follow in sequence» (Loomis and Connor, 1992 p 9) 15
  16. 16. Methodology  Crop system processes  Long term analyses  Model applications Which model? 16
  17. 17. Methodology Previous studies Drainage recharge modelling  O’Connell 1998: wheat crop and management tillage, stubble. Model fallow-wheat O’Leary-Connor (Vic)  Zhang et al, 1999: wheat, oat, mustard, field pea, lucerne and medic. Model WAVES (NSW & Vic)  Asseng et al., 2001: wheat crop and sowing dates, N fertiliser, residues and hypothetical cultivar. Model APSIM (WA) Crop model in the Mallee  Rimmington et al. 1987: wheat yield and long-term  O’Leary & Connor 1995: wheat, water and nitrogen 17
  18. 18. Methodology  Which studies do we want?  Long term analysis  Cropping system  Water balance  Farm or regional level When using simulation models, it is important to understand how the model represents the physical, chemical, and biological processes involved in cropping system response to the environment and management 18
  19. 19. Methodology Cropping System Simulation model (Stöckle and Nelson, 2001)       CropSyst on-line Free Software www.bsyse.wsu.edu/CropSyst/ Water balance Farm or regional level Previous work: USA, Europe, Middle Est... 19
  20. 20. Methodology  Observed data (O’Connell, 1998)  Field experiment carried out MRS Walpeup from 1993-1997  Rotations FW Fallow-wheat FWP WW MWP Fallow-wheat-pea Wheat-wheat Mustard-wheat-pea  Field data Soil water content evolution, phenology, LAI, crop coverage, biomass, yield ... 20
  21. 21. Model performance Steps for model applications 1. Verification 2. Calibration sensibility analysis 3. Validation model acceptability model consistency 4. Application results interpretation 21
  22. 22. How does CropSyst work? CropSyst model based on crop approach Daily accumulation of crop biomass Main process:  Solar radiation and temperature  Water availability  Nitrogen availability 22
  23. 23. What processes are simulated? Phenology Biomass partition (above, Water balance (2 models) Nitrogen balance Soil Soil Soil soil root, leaf) erosion USLE runoff (2 models) and water salinity freezing model (2 models) Lineal CO2 response Management: sowing, fertilisation, tillage, stubble, irrigation, clipping 23
  24. 24. What processes are NOT simulated? Yield components Partitioning (yield comp.) Grain quality (N or protein content, oil) All nutrients except Nitrogen Pest or diseases Weeds Other abiotic stress (hail, soil limitations as B, Al, Na, …) Polycrop as individual crops Wind erosion 24
  25. 25. CropSyst input 64 Crop parameters for each crop or varieties Soil parameters for each soil Minimum texture by layer Surface USLE, SCS Curve number 4 Nitrogen parameters Daily Weather data (Tmax, Tmin, Prec, Radsol, HRmax, HRmin –DEWPT–, Wind) Included ClimGen and works with Universal Environmental Data file format More CropSyst manual 25
  26. 26. CropSyst initial conditions For each soil layer: Soil water content Nitrogen soil content (nitrate & ammonium) Salinity and water table salinity Existing residues CO2 concentration 26
  27. 27. CropSyst output Daily (one day step or more) Crop data Water balance Nitrogen balance Salinity balance Harvest (crop data at harvest) Annual Also Schedule (management) Summary (harvest report) Output reports in format XLS, TXT, HTML, UED 27
  28. 28. CropSyst verification Does the model run well? 1. 2. 3. 4. 5. Last version 3.02.07 (16 Feb 2001) Run the examples Run our modified examples Display all outputs Some errors found in the outputs but were not relevant (columns position, no use routines) 6. Mass balances: water and N ok! 28
  29. 29. CropSyst calibration Calibration can fit the model close to 1:1 But calibration parameters must be explain the crop model physiology Abolish unrealistic coefficient values for parameters calibration Calibration starts with default parameters and it continues with well known parameters 29
  30. 30. CropSyst calibration Crop parameters (64) for Wheat, Mustard and Field pea Parameters for a Sandy soil Hydraulic properties (Permanent wilting point, field capacity, bulk density, and saturated hydraulic conductivity) Also soil surface (Universal soil Loss Equation) and SCS Curve number Nitrogen Weather data from the MRS Initial condition = field experiment 30
  31. 31. CropSyst calibration Summary of some key crop parameters Variable Units Thermal time Base temperature ºC Emergence ºC days Begin flowering ºC days Physiological maturity ºC days Photo-period Day length to inhibit flowering hours Day length for insensitivity hours Crop morphology Maximum expected LAI m²/m² Specific leaf area m²/kg Stem/leaf partition coefficient 1-10 Crop growth Above ground biomass-transpiration efficiency kPa kg/m³ Radiation use efficiency RUE g/MJ Optimum mean daily temperature for growth ºC Extinction coefficient for solar radiation k 0-1 Harvest index Unstressed HI 0-1 Nitrogen crop parameters Maximum N concentration during early growth kgN/kgDM Minimum N concentration at maturity kgN/kgDM Maximum N concentration at maturity kgN/kgDM Minimum N concentration of harvested material kgN/kgDM Wheat Mustard Field pea 0 130 750 1400 0 150 950 2000 0 150 1100 1950 16.5 8 ns ns ns ns 5 20 5 5 22 4 5 24 6 5.8 3 20 0.82 6 1.85 15 0.65 3.25 1.47 10 0.76 0.4 0.2 0.25 0.050 0.007 0.012 0.030 0.055 0.008 0.022 0.030 0.060 0.050 0.060 31 0.030
  32. 32. CropSyst validation Water balance for long fallow compared CropSyst vs. O’Leary-Connor wheat-fallow model And CropSyst vs. observed data (O’Connell, 1998) Crop performance Simulated individual crops: wheat, field pea, and mustard vs. observed data Crops in rotation FW, WW, FWP, MWP 32
  33. 33. CropSyst validation Soil water content 0-1m (mm) 280 Water soil content (mm) fallow phase 260 240 220 200 180 160 140 1993 1994 1995 1996 1997 1998 1999 33
  34. 34. CropSyst validation 280 Water soil content (mm) fallow phase 260 240 220 200 180 160 140 34
  35. 35. CropSyst validation Crop performance Yield Biomass 3 8 Wheat Mustard 6 Field pea Simulated 2 4 1 y = 0.73x + 0.76 2 y = 0.84x + 0.10 2 r = 0.79 2 r = 0.81 0 0 0 2 4 Observed 6 8 0 1 2 Observed 35 3
  36. 36. CropSyst validation Crop performance water use Wheat Mustard 400 400 y = 0.61x + 94.91 r2 = 0.50 350 Sim ulated (m m ) 300 250 FW 200 MW 150 100 y = 1.14x - 8.81 r2 = 0.76 50 300 250 200 150 100 y = 1.02x + 8.07 r 2 = 0.57 50 0 0 50 Field pea 100 150 200 250 300 350 400 Observed (m m ) 0 0 50 400 100 150 200 250 300 350 400 Observed (m m ) y = 1.60x - 79.86 r 2 = 0.78 350 Sim ulated (m m ) Sim ulated (m m ) 350 300 250 FWP 200 MWP 150 100 y = 1.64x - 74.66 r2 = 0.81 50 0 0 50 100 150 200 250 300 350 400 Observed (m m ) 36
  37. 37. CropSyst validation Continuos run Biomass Water use 8 400 Simulated (t ha -1) 300 200 100 y = 1.34x - 54.03 r2 = 0.84 100 200 300 Observed (mm) 5 4 3 2 y = 0.71x - 0.003 r2 = 0.71 0 0 0 6 1 Yield 0 400 4 Simulated (t ha -1) Simulated (mm) 7 1 2 3 4 5 6 7 8 Observed (t ha -1) 3 sim. Cont. sim. year by year 2 1 y = 0.83x - 0.07 r2 = 0.84 0 0 1 2 3 Observed (t ha -1) 4 37
  38. 38. CropSyst validation Mallee wheat performance (Sadras, 2001 -umpublished data-) 5 Simulated yield (t/ha) r2 = 0.72 (P < 0.0001) n = 55 4 3 2 1 intercept = 0.11 (s.e. = 0.177, P = 0.529) slope = 0.97 (s.e. = 0.082, P < 0.0001) 0 0 1 2 3 4 5 Monitored yield (t/ha) Data: wheat crops managed by growers, three seasons and sites in South Australia, New South Wales and Victoria Mallee 38
  39. 39. Model application  Analysis of some agronomic practices in the Victorian Mallee  In terms of:  Water balance  Estimating drainage under different crop management  Also runoff  Water use efficiency  Nitrogen uses  Comparing rotations: Wheat continuous Fallow-wheat Fallow-wheat-pea Mustard-wheat-pea  Crop management effects  Yield-profit efficiency 39
  40. 40. Model application  Environmental conditions of the Victorian Mallee  61 year of weather data from Walpeup (1939-1999) Included several dry-wet seasons  Representative Mallee plain soil type Sandy soil 40
  41. 41. Experimental design  3 Tillage CT Conventional till MT Minimum tillage ZT Zero till (4LF-3SF till) (2 till) (0 till)  3 Stubble management SR stubble retention (100 %) SG stubble grazing (65 %) SB stubble burning (10 %)  3 Fertilisation levels F1 No N applied to any crop (minimum yield) F2 Current N fertiliser (Wheat & Mustard) F3 Simulation without N routine (potential yield)  4 Rotations and 3 crops FW FWP WW MWP Fallow-wheat Fallow-wheat-pea Wheat continuous Mustard-wheat-pea (50 %) (66 %) (100%) (100%) 41 15 000 simulated years
  42. 42. Experimental design  Soil profile 150 cm  Soil drainage Measured at 150 cm Maximum root depth 100 cm  Soil water balance Finite diference Up-Down water flow  Evapotranspiration Priesley-Tailor 30 000 simulated years 42
  43. 43. Some results  Water drainage  Water runoff  Effect of stubble management in the water balance  Effect of fertilisation levels Yield potential on the Mallee (potential yield) Annual variability  Effect of crop diversification  Comments about not simulated effects 43
  44. 44. Model consistency Grain yield (kg ha -1) 6000 y = 13.09x - 1480.6 r2 = 0.43 5000 4000 3000 2000 1000 0 0 Grain yield (kg ha -1) 6000 100 200 400 Water use y = 15.82x R2 = 0.63 5000 300 4000 3000 2000 1000 0 0 50 100 Actual transpiration 150 200 44
  45. 45. Model consistency Grain yield (kg ha -1) 7000 6000 Line 2:5 y = 0.37x - 551.14 r2 = 0.90 5000 4000 3000 2000 1000 0 0 2000 4000 6000 8000 10000 12000 14000 Biomass (kg ha -1) 45
  46. 46. Sustainability approach  Agronomy sustainability  Yield productivity  Resources use efficiency  Stability and trends  Environmental sustainability  Minimize environmental impact Reduce water drainage Reduce water runoff Reduce nitrogen loss  Maximize environmental gain  Social sustainability  Gross margins and profit 46
  47. 47. Some results  Average of Water drainage mm y-1 STUBBLE ROTATION SB FW -2.4 76 FWP -8.6 12 MWP -9.8 WW -7.1 Total SB SG % TILLAGE % -7.4 FW 11.2 51 MWP -2.3 -5.4 13 ZT -3.4 37 -9.5 WW -6.1 218 -4.7 CT MT 27 FWP 76 FERTIL Total SG mm y-1 mm y-1 -2.5 F1 -3.3 54 F2 -4.3 40 F3 -7.2 47
  48. 48. Water drainage Probability of exceedence 1.0 0.8 FW 0.6 WW 0.4 FWP 0.2 MWP 0.0 -50 0 50 100 150 200 Drainage (mm) ZT F2 SR 48
  49. 49. Water drainage WW Accumulated deviation (mm) Drainage 100 Rainfall 600 50 0 -50 0 10 20 30 40 50 60 -100 -150 -200 -250 -300 ? 400 70 200 0 + Drainage -200 -400 -600 49
  50. 50. Water runoff  Runoff events Annual rainfall > 250 mm soil SCS curve number, slope < 1 % No differences among treatments FW: Probability of exceedence CTF2SG 1.0 MTF2SG 0.8 ZTF2SG 0.6 CTF2SB MTF2SB 0.4 ZTF2SB 0.2 CTF2SR MTF2SR 0.0 -50 0 50 100 Runoff (mm) 150 200 ZTF2SR 50
  51. 51. Crops and rotations Grain yield (kg ha-1) FW FWP WW MWP Fallow-wheat Fallow-wheat-pea Wheat continuous Mustard-wheat-pea (50 %) (66 %) (100%) (100%) 5000 4000 CT SB F1 WW wheat y = -1.0713x2 + 4223.3x - 4E+06 r 2 = 0.4775 3000 2000 1000 0 1938 1950 1962 1974 1986 1998 Grain yield (kg ha-1) Year 5000 4000 y = -1.24x2 + 4886.3x - 5E+06 r 2 = 0.1607 ZT SG F3 FW wheat 3000 2000 1000 0 1938 1950 1962 1974 1986 1998 Year 51
  52. 52. Crops and rotations Fallow-wheat Fallow-wheat-pea Wheat continuous Mustard-wheat-pea Stability yield index (50 %) (66 %) (100%) (100%) Stubble burnt + tillage +0N stubble grazed + no tillage+N CTF1SB CTF2SB CTF3SB MTF1SB MTF2SB MTF3SB ZTF1SB ZTF2SB ZTF3SB CTF1SG CTF2SG CTF3SG MTF1SG MTF2SG MTF3SG ZTF1SG ZTF2SG ZTF3SG Wheat FW FWP WW MWP 0.29 0.25 0.21 0.19 0.29 0.25 0.19 0.22 0.22 0.26 0.02 0.09 0.29 0.29 0.18 0.17 0.31 0.28 0.09 0.23 0.30 0.28 0.03 0.09 0.29 0.28 0.20 0.20 0.30 0.29 0.22 0.22 0.32 0.28 0.03 0.09 0.37 0.32 0.27 0.23 0.37 0.35 0.25 0.26 0.45 0.40 0.12 0.16 0.38 0.38 0.33 0.31 0.38 0.38 0.26 0.30 0.50 0.43 0.18 0.21 0.39 0.38 0.35 0.34 0.40 0.39 0.39 0.32 0.53 0.47 0.32 0.27 Pea FWP MWP 0.23 0.23 0.19 0.21 0.17 0.15 0.22 0.26 0.21 0.24 0.14 0.11 0.21 0.27 0.22 0.22 0.19 0.14 0.29 0.28 0.26 0.25 0.31 0.23 0.34 0.31 0.31 0.31 0.36 0.25 0.37 0.39 0.36 0.37 0.43 0.36 Mustard MWP 0.24 0.30 0.23 0.27 0.28 0.20 0.30 0.29 0.23 0.40 0.40 0.29 0.44 0.40 0.26 0.42 0.43 0.30 52
  53. 53. Farmer decision  profit profit Gross margins 60 Annualized gross margins 50 40 30 20 10 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 F1 F2 0 -10 CT MT ZT CT MT ZT CT MT ZT CT MT ZT CT MT ZT CT MT ZT -20 FW FWP WW FW FWP WW SB SG STUBBLE ROTATION TILLAGE FERTIL 53
  54. 54. Farmer decision Seasonal variation in the anualized yield and profitability of rotations in the Victorian Mallee (Australia) Lower (20%) Median Upper (80%) Rotation -1 FW 0N Yield kg ha 580 780 1046 -1 -1 Profit AUD ha y 21 45 77 -1 +N Yield kg ha 619 803 961 -1 -1 Profit AUD ha y 19 42 61 -1 WW 0N Yield kg ha 650 970 1391 -1 -1 Profit AUD ha y -19 19 69 -1 +N Yield kg ha 569 1026 1320 -1 -1 Profit AUD ha y -42 13 48 -1 FWP 0N Yield kg ha 566 862 1151 -1 -1 Profit AUD ha y -10 35 79 -1 +N Yield kg ha 555 867 1103 -1 -1 Profit AUD ha y -17 31 69 -1 MWP 0N Yield kg ha 487 773 1069 -1 -1 Profit AUD ha y -79 -32 17 -1 +N Yield kg ha 477 786 1046 -1 -1 Profit AUD ha y -97 -47 -2 54 Average of annualized yield
  55. 55. Farmer decision Seasonal variation in the anualized yield and profitability of rotations in the Victorian Mallee (Australia) Lower (20%) Median Upper (80%) Rotation -1 FW 0N Yield kg ha 580 780 1046 -1 -1 100 100 100 Profit AUD ha y 21 45 77 -1 +N Yield kg ha 619 803 961 -1 -1 92 79 Profit AUD ha y 19 42 92 61 -1 WW 0N Yield kg ha 650 970 1391 -1 -1 -91 90 Profit AUD ha y -19 19 42 69 -1 +N Yield kg ha 569 1026 1320 -1 -1 -197 62 Profit AUD ha y -42 13 28 48 -1 FWP 0N Yield kg ha 566 862 1151 -1 -1 -48 103 Profit AUD ha y -10 35 78 79 -1 +N Yield kg ha 555 867 1103 -1 -1 -78 89 Profit AUD ha y -17 31 69 69 -1 MWP 0N Yield kg ha 487 773 1069 -1 -1 -374 -71 22 Profit AUD ha y -79 -32 17 -1 +N Yield kg ha 477 786 1046 -1 -1 -457 -103 -3 Profit AUD ha y -97 -47 -2 55 Average of annualized yield
  56. 56. Farmer decision FW Wheat yields Probability of exceedence 1.0 CTF2SG 0.8 MTF2SG ZTF2SG 0.6 ZTF3SG CTF2SB 0.4 MTF2SB ZTF2SB 0.2 ZTF3SB 0.0 0 1000 2000 3000 4000 5000 6000 Grain yield (kg/ha) FWP Probability of exceedence 1.0 CTF2SG 0.8 MTF2SG ZTF2SG 0.6 ZTF3SG CTF2SB 0.4 MTF2SB ZTF2SB 0.2 ZTF3SB 0.0 0 1000 2000 3000 4000 5000 6000 Grain yield (kg/ha) 56
  57. 57. Farmer decision Pea Probability of exceedence 1.0 0.8 0.6 0.4 Field peas 0.2 0.0 0 1000 2000 3000 4000 5000 6000 Grain yield (kg/ha) FWP Probability of exceedence 1.0 CTF2SG 0.8 MTF2SG ZTF2SG 0.6 ZTF3SG CTF2SB 0.4 MTF2SB ZTF2SB 0.2 ZTF3SB 0.0 0 1000 2000 3000 4000 Grain yield (kg/ha) 5000 6000 Wheat yields 57
  58. 58. Some results  Stubble management: SR stubble retention SG stubble grazing SB stubble burning Maintenance of stubble increased the water retention It had a positive effect on yield but also on water drainage 58
  59. 59. Some results  Fertilisation levels F1 No N applied to any crop (minimum yield) F2 Current N fertiliser (Wheat & Mustard) There were little differences between F1 and F2 F3 without N simulation (potential yield) Showed that actual yield can be double with optimum N application Increased stability in low intensity rotations but did not occur in high intensive land uses, water was the limiting factor 59
  60. 60. Conclusions CropSyst showed a good performance compared with observed data and similar other models Long term application of CropSyst showed the effect of different management on drainage, runoff, crop yield and profitability CropSyst appears ideal to address some of the Mallee issues 60
  61. 61. Conclusions Also long term results obtained with CropSyst can explain some of the current farming systems of the Mallee, with advantages and limitations Further improvements in the model should widen its aplication 61
  62. 62. Are model assumptions valid for this environment? Mallee crops are crops? Continuous medium, were LAI and k represents these crops Low LAI and Low crop coverage do to think that crop are no continuos during long time of periods Need other models for dryland areas? 62
  63. 63. Future work Spatial application of crop model for Mallee region The drainage process is a biflow process in which some areas loss water and other gain water but which salt Dune-slawe systems Paddock diversity and farming practices diversity among farmers 63
  64. 64. Future work (continued) The cereals (wheat and barley) are the main crops Soils constrains and ‘low rainfall’ limit production Need for new models to understand the processes that limited yield Models versus long term experiment Model for a paddock and model for spatial analyses 64
  65. 65. Acknowledgments Thank you to Mallee Research Station The University of Melbourne The Joint Centre for Crop Improvement And special thanks to Prof. David Connor Dr. Garry O’Leary Mark O’Connell Universidad Politécnica de Madrid for my fellowship 65
  66. 66. Publish Environmental risk analysis of farming systems in a semiarid environment: effect of rotations and management practices on deep drainage Field Crops Research, Volume 94, Issue 2-3, November 2005, Pages 257-271 Diaz-Ambrona, C.G.H.; O'Leary, G.J.; Sadras, V.O.; O'Connell, M.G.; Connor, D.J. 66
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